Intelligent dispatching system and dispatching method of network freight platform based on big data analysis

Abstract

The present invention discloses an intelligent scheduling system and scheduling method for an online freight platform based on big data analysis. The system belongs to the field of online freight platform scheduling and is used to solve the problem of how to accurately predict the dynamic changes in transportation demand and vehicle resources, and improve the operational efficiency and service quality of the online freight platform. The system includes five basic modules: a data acquisition module, a data preprocessing module, a big data analysis module, an intelligent scheduling module, and a user interaction module. The intelligent scheduling module uses an intelligent algorithm to generate a recommended scheduling plan, monitors the vehicle transportation status in real time, and dynamically adjusts the recommended scheduling plan. The present invention realizes intelligent and precise vehicle scheduling by analyzing a large amount of transportation-related data, thereby improving transportation efficiency and reducing costs.

Description

Technical Field

[0001] The present invention belongs to the field of network freight platform scheduling, and specifically relates to an intelligent scheduling system and scheduling method for a network freight platform based on big data analysis. Background Art

[0002] With the rapid development of internet technology, online freight platforms have gained widespread adoption in the logistics industry. However, the current scheduling process within these platforms presents numerous challenges. Traditional scheduling methods often rely on manual experience and fail to fully account for numerous complex transportation factors, such as the vehicle's real-time location, route traffic conditions, cargo weight and volume, and customer delivery time requirements. This results in low scheduling efficiency, high idle vehicle rates, and increased transportation costs. Furthermore, while some existing scheduling systems incorporate information technology, they lack the in-depth analysis and utilization of big data, making it difficult to accurately predict dynamic changes in transportation demand and vehicle resources, making it difficult to achieve intelligent and precise scheduling.

[0003] Patent document: Urban Smart Traffic Safety Travel Monitoring and Analysis Method and Emergency Evacuation Decision-Making Platform, application number CN202210143402.1. Main classification number G06Q. This technical solution provides a decision-making platform. The urban smart traffic safety travel monitoring and analysis method and emergency evacuation decision-making platform includes: a traffic travel monitoring and analysis method, an emergency decision-making platform, a traffic data resource center, an integrated traffic operation status monitoring platform, a collaborative dispatching platform, a traffic safety management and operation platform, an operation analysis and decision support platform, and a public service platform.

[0004] This technical solution can improve the scientific management level of road traffic, the modern management of police personnel, and the pre-alarm and rapid response capabilities of traffic accidents, thereby enhancing decision-making capabilities.

[0005] Patent document: Capacity dispatching system and method for online freight car platform based on order distribution density, application number CN201910006238.8. Main classification number G06Q. This technical solution discloses a customer handheld terminal, which is interconnected with a cloud control platform through an external network, and the cloud control platform is interconnected with the owner's handheld terminal through an external network. The cloud control platform is composed of an order data collection unit, an order data integration unit, an order data statistics unit, an order density analysis unit, a centralized data collection unit, an online car viewing unit, and a vehicle dispatch control unit. The vehicle dispatch control unit is composed of a vehicle information analysis module, an online active statistics module, a dispatch time calculation module, and a dispatch completion feedback module.

[0006] This technical solution uses an algorithm to strictly control the scheduling time by setting up a scheduling time calculation module, thereby improving the overall user experience of ordering customers.

[0007] However, none of the aforementioned technical solutions address online freight platforms. Online freight platforms require comprehensive data collection from multiple sources, including vehicles, cargo, orders, and traffic, to accurately dispatch and effectively reduce idle vehicle rates. On the one hand, online freight platforms need to improve dispatch efficiency and significantly shorten dispatch times. On the other hand, online freight platforms need to control costs, reducing idle vehicle rates and mileage to effectively cut transportation costs.

[0008] Therefore, there is a need for a system that can use big data analysis to achieve intelligent scheduling in order to improve the operational efficiency and service quality of the online freight platform. Summary of the Invention

[0009] The purpose of this invention is to provide an intelligent scheduling system and scheduling method for an online freight platform based on big data analysis. By analyzing a large amount of transportation-related data, intelligent and precise vehicle scheduling can be achieved, transportation efficiency can be improved, and costs can be reduced.

[0010] In order to achieve the above technical objectives, the technical solutions adopted by the present invention are as follows:

[0011] The present technical solution includes a data acquisition module for collecting vehicle information, cargo information, order information, historical transportation data and real-time traffic data on the online freight platform; a data preprocessing module for cleaning, converting and integrating the original data collected by the data acquisition module; a big data analysis module for using data mining to analyze the data processed by the data preprocessing module, establishing a transportation demand prediction model, analyzing road congestion, vehicle driving status and the matching degree between vehicles and cargo; an intelligent scheduling module for using an intelligent algorithm to generate a recommended scheduling plan based on the analysis results of the big data analysis module, combined with order information and vehicle resource conditions; a user interaction module for providing an interactive interface for cargo owners, drivers and platform managers, for cargo owners to publish transportation needs and query order status, for drivers to receive scheduling tasks and feedback on transportation status, and for platform managers to manage and monitor the system and view data reports.

[0012] Traditional dispatching methods fail to fully account for complex factors such as the vehicle's real-time location, cargo characteristics, order timing, and traffic conditions. This results in low dispatching efficiency, increased costs, and difficulty ensuring service quality. Therefore, this technical solution, through the design of an intelligent dispatching module, aims to address the multifaceted considerations of the dispatching process on online freight platforms and achieve dynamic vehicle dispatching.

[0013] The intelligent scheduling module uses an intelligent algorithm to generate a recommended scheduling plan, monitors the vehicle transportation status in real time, and dynamically adjusts the recommended scheduling plan. The intelligent scheduling module includes an initialization submodule, a multi-objective evaluation submodule, an intelligent algorithm optimization submodule, a real-time monitoring submodule, a dynamic adjustment submodule, and a plan output submodule.

[0014] The initialization submodule is used to initially construct a vehicle-order assignment combination based on the basic vehicle and order information provided by the big data analysis module. The multi-objective evaluation submodule is used to evaluate each vehicle-order assignment combination generated by the initialization submodule based on the three scheduling optimization objectives: transportation cost, delivery time, and vehicle utilization. The intelligent algorithm optimization submodule uses a genetic algorithm as an intelligent algorithm to optimize the results output by the multi-objective evaluation submodule. This submodule encodes each vehicle-order assignment combination as a chromosome and, through selection, crossover, and mutation genetic operations, it iteratively generates new combinations to generate an initial scheduling plan. The real-time monitoring submodule interacts in real time with the data source of the on-board equipment to obtain the vehicle's real-time location, driving speed, cargo status and transportation status, as well as real-time road traffic conditions, such as congestion and temporary traffic control information. The dynamic adjustment submodule dynamically adjusts the generated initial scheduling plan based on abnormal conditions reported by the real-time monitoring submodule. When a vehicle breaks down, this submodule reassesses remaining vehicle resources and order demand, selects a new vehicle from the optimized vehicle-order allocation set to take over the order, and replans the transport route. If traffic congestion occurs, the affected vehicles' routes or delivery sequence are adjusted based on the congestion level and estimated clearing time, minimizing the impact on overall transport efficiency and delivery time. The solution output submodule visually outputs the final recommended scheduling solution and dynamically adjusted solutions to relevant users, such as drivers, shippers, and platform administrators. Vehicle scheduling tasks are displayed through a user interface.

[0015] The intelligent dispatch module effectively implements dynamic vehicle dispatch through the coordinated operation of various submodules, thereby improving overall transportation efficiency. This is achieved through the coordinated work of the initialization submodule, the multi-objective evaluation submodule, and the intelligent algorithm optimization submodule.

[0016] First, based on the basic information of vehicles and orders, a feasible allocation combination is quickly constructed to narrow the search scope. Second, key indicators such as transportation costs, delivery time, and vehicle utilization are quantified to provide a clear basis for subsequent optimization and solve the problem of unclear target measurement. Then, intelligent algorithms such as genetic algorithms are used to iteratively optimize many combinations, seek a balance between multiple objectives, and form a recommended scheduling plan.

[0017] After breaking through the dilemma of traditional methods that are difficult to take into account multiple objectives, the intelligent scheduling module ensures the stability and timeliness of the transportation process through real-time monitoring sub-modules, dynamic adjustment sub-modules, and solution output sub-modules, solving the problem of scheduling failure caused by unexpected situations during the transportation process.

[0018] On the one hand, it tracks vehicle transportation status and traffic information in real time, and adjusts the scheduling plan in time for emergencies based on monitoring feedback. On the other hand, it clearly presents the optimized scheduling plan to all users through the plan output sub-module to achieve effective information transmission.

[0019] The intelligent dispatching module of this invention has achieved remarkable results. It significantly shortens dispatching time and enables fast and accurate matching. In terms of cost control, it reduces vehicle idle rates and transport mileage, effectively cutting transportation costs. In terms of service quality, it ensures on-time delivery of goods and improves customer satisfaction. In terms of management decision-making, it provides platform managers with scientific data support, helps optimize operational strategies, enhances the platform's overall competitiveness, and promotes the intelligent and efficient development of online freight platforms.

[0020] Compared with the prior art, the present invention has the following four advantages.

[0021] First, efficient and accurate scheduling improves overall efficiency. The data acquisition module comprehensively collects multi-source data such as vehicles, goods, orders, and traffic. After pre-processing and in-depth analysis, the intelligent scheduling module can quickly and accurately match the optimal vehicle for each order based on the results of big data analysis and intelligent algorithms. For example, under traditional scheduling methods, matching an order to a remote area with a suitable vehicle may take several hours. However, this system uses intelligent algorithms to instantly generate scheduling solutions, greatly shortening scheduling time and average vehicle response time, greatly improving overall transportation efficiency.

[0022] Second, it reduces costs and improves economic efficiency. Precise scheduling effectively reduces the rate of empty vehicle runs. The system rationally arranges vehicles based on transportation demand forecasts, avoiding situations where vehicles run idle with no cargo to transport, thereby reducing the empty run rate. Furthermore, it optimizes transportation routes, reduces unnecessary mileage and fuel consumption, and rationally allocates cargo based on vehicle loads, fully utilizing vehicle capacity and reducing unit transportation costs. Overall, this reduces transportation costs, saving companies significant operating funds and improving economic efficiency.

[0023] Third, ensure on-time delivery and improve service quality. Real-time monitoring of vehicle transport status and road conditions allows for dynamic adjustments to scheduling based on delivery deadlines. In the event of sudden traffic congestion, the system promptly plans a new route for the vehicle, ensuring on-time delivery. Field verification shows that the implementation of this system has increased the on-time delivery rate for orders from 70%-80% with traditional methods to over 90%, significantly improving customer satisfaction, enhancing the competitiveness of the online freight platform in the market, and establishing a positive brand image.

[0024] Fourth, data-driven decision-making enhances scientific management. The big data analysis module provides a rich basis for decision-making for platform management. Through detailed data reports, managers gain an in-depth understanding of all aspects of the transportation business, such as changes in transportation demand by region and vehicle utilization efficiency. Based on this data, operational strategies can be scientifically adjusted, such as rationally increasing or decreasing vehicle numbers and optimizing service area layout, ensuring that platform operations are more aligned with market demand and achieving sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The present invention can be further illustrated by the non-limiting examples given in the accompanying drawings;

[0026] Figure 1 This is a schematic diagram of the scheduling system module of the present invention;

[0027] Figure 2 This is a schematic diagram of the intelligent scheduling module of the present invention;

[0028] Figure 3 This is a schematic diagram of the local flow of the intelligent algorithm optimization submodule of the present invention;

[0029] Figure 4 This is a flow chart of the scheduling method of the intelligent scheduling system of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the present invention, the technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0031] like Figure 1 , Figure 2 , Figure 3 , Figure 4 As shown, the intelligent dispatching system of the network freight platform based on big data analysis includes a data acquisition module for collecting vehicle information, cargo information, order information, historical transportation data and real-time traffic data on the network freight platform.

[0032] Among them, the data collection module is used to collect various types of data on the online freight platform. The module interacts with the on-board terminal, platform database, and transportation department data interface to ensure the real-time and accuracy of the data.

[0033] The data preprocessing module cleans, converts and integrates the original data collected by the data acquisition module.

[0034] Among them, the data preprocessing module is used to remove duplicate, erroneous or incomplete data, convert data in different formats into a standard format that the system can recognize, and integrate data from multiple data sources for subsequent analysis.

[0035] The big data analysis module uses data mining to analyze the data processed by the data preprocessing module, establishes a transportation demand prediction model, and analyzes road congestion, vehicle driving status, and the matching degree between vehicles and goods.

[0036] Among them, the big data analysis module provides a reference for vehicle scheduling by establishing a transportation demand forecast model; at the same time, it analyzes the matching degree of vehicles and cargo to determine the optimal vehicle and cargo combination.

[0037] The intelligent scheduling module uses an intelligent algorithm to generate a recommended scheduling plan based on the analysis results of the big data analysis module, combined with order information and vehicle resource conditions.

[0038] The intelligent scheduling module comprehensively considers multiple factors, including transportation costs, delivery times, and vehicle utilization, to achieve precise matching of vehicles and cargo, and optimal scheduling. Specifically, in the present invention, the recommended scheduling plan is generated by the intelligent scheduling module encoding vehicles and orders, optimized through selection, crossover, and mutation genetic operations. Furthermore, the present invention utilizes the intelligent scheduling module to monitor vehicle transportation status in real time and dynamically adjust the recommended scheduling plan.

[0039] The user interaction module provides an interactive interface for shippers, drivers and platform managers, allowing shippers to publish transportation requirements and query order status, drivers to receive scheduling tasks and provide feedback on transportation status, and platform managers to manage and monitor the system and view data reports.

[0040] Among them, the user interaction module allows cargo owners, drivers and platform managers to track the implementation of the plan in a timely manner to ensure that the goods are delivered to the destination on time and safely.

[0041] In the intelligent scheduling module, the recommended scheduling plan uses a linear weighted method to integrate the following three objectives into a comprehensive objective function to generate a recommended scheduling plan. The three objectives are transportation cost target, delivery punctuality target, and vehicle utilization target; among them, the transportation cost minimization target, delivery punctuality maximization target, and vehicle utilization maximization target.

[0042] set up is a binary decision variable. If the vehicle Assigned to order ,but ,otherwise ,in , is the total number of vehicles, , is the total number of orders;

[0043] Shipping cost target for: ;

[0044] in, For vehicles Execute Order transportation costs; it should be noted that transportation costs are related to vehicle travel distance, fuel consumption, and driver fees.

[0045] On-time delivery target for: ;

[0046] Among them, For vehicles Execute Order Estimated time of arrival at the delivery location, For orders Delivery deadline. Definition For vehicles Execute Order Punctuality penalty factor, if , ;like , ;

[0047] Vehicle utilization target for: ;

[0048] in, For vehicles Rated cargo capacity, For orders The weight of the cargo; vehicle Utilization rate ;

[0049] Comprehensive objective function for: ;

[0050] Among them, for the three objectives, the linear weighted method is used to construct the comprehensive objective function , 、 、 is the weight coefficient, and ， The value of the weight coefficient is adjusted according to the actual business needs and focus. For example, if more emphasis is placed on transportation cost control, The value of is relatively large.

[0051] The intelligent scheduling module uses an intelligent algorithm to generate a recommended scheduling plan, monitors the vehicle transportation status in real time, and dynamically adjusts the recommended scheduling plan. The intelligent scheduling module includes an initialization submodule, a multi-objective evaluation submodule, an intelligent algorithm optimization submodule, a real-time monitoring submodule, a dynamic adjustment submodule, and a plan output submodule.

[0052] It should be pointed out that when the intelligent algorithm optimizes the sub-module and adopts the genetic algorithm as the intelligent algorithm, in order to meet the conditions to achieve the balance and optimality among the three objectives, it is necessary to control the weight settings reasonably.

[0053] When the weight coefficient 、 、 When the settings are in line with the actual needs and focus of the current business, the genetic algorithm can optimize in the direction of meeting business expectations during the iteration process, making the comprehensive objective function To achieve the optimal balance between the three objectives. For example, in a freight scenario that is more sensitive to cost, if the reasonable setting 、 、 , the genetic algorithm will find a better balance solution that takes into account delivery punctuality and vehicle utilization while controlling costs.

[0054] When the control weights are set reasonably, when the genetic algorithm converges after a sufficient number of iterations, that is, the individuals in the population no longer change significantly, the optimal solution obtained at this time represents the balance and optimality among the three objectives under the current conditions.

[0055] Convergence means that the algorithm has found a relatively stable and optimal area in the search space, and the corresponding recommended scheduling solution has reached the best state when considering the three objectives comprehensively.

[0056] The initialization submodule is used to preliminarily build a vehicle-order allocation combination based on the basic information of vehicles and orders provided by the big data analysis module. The basic information includes the current location of the vehicle, cargo capacity, order type and quantity, shipping location and delivery location.

[0057] It's important to note that this submodule filters out obvious mismatches, such as when a vehicle's load doesn't meet the required order weight. It provides a viable starting point for subsequent optimization. By setting some basic rules and filtering criteria, it quickly narrows the search space and improves the efficiency of generating scheduling solutions.

[0058] The multi-objective evaluation submodule is used to evaluate each vehicle-order allocation combination generated by the initialization submodule based on the three optimization objectives of scheduling, namely transportation cost, delivery time, and vehicle utilization.

[0059] For transportation costs, each combination's cost is calculated by combining data such as vehicle mileage, fuel consumption, and driver compensation. For delivery time, each combination's ability to deliver on time, as well as potential advances or delays, is assessed based on real-time traffic data, vehicle speeds, and the order's delivery deadline. Vehicle utilization is measured by calculating the ratio of the total volume or weight of the ordered goods assigned to each vehicle to the vehicle's rated cargo capacity. This submodule quantifies these three objectives into specific numerical indicators, providing a clear evaluation basis for subsequent solution optimization.

[0060] The intelligent algorithm optimization submodule uses a genetic algorithm to optimize the results output by the multi-objective evaluation submodule. This submodule encodes each vehicle-order assignment combination as a chromosome and, through selection, crossover, and mutation genetic operations, iterates to generate new combinations and generate the initial scheduling solution.

[0061] In each iteration, based on the evaluation metrics provided by the multi-objective evaluation submodule, the combination with the highest fitness, which takes all objectives into consideration and is closer to the optimal solution, is selected as the parent for crossover and mutation, generating a new generation of combinations. Through repeated iterations, the scheduling plan is continuously optimized to achieve a balance and optimal solution among multiple objectives.

[0062] The real-time monitoring submodule interacts with the data source of the vehicle-mounted equipment in real time to obtain the vehicle's real-time location, driving speed, cargo status and transportation status information, as well as real-time road traffic conditions, such as congestion and temporary control information.

[0063] The real-time monitoring submodule continuously tracks each vehicle's progress in executing the dispatch plan, comparing real-time data with the expected data in the dispatch plan. This allows for timely identification of abnormalities that may require adjustments to the dispatch plan, such as sudden vehicle failures or temporary road closures. This provides real-time, accurate data support for the dynamic adjustment submodule, ensuring that the dispatch plan can respond promptly to actual transportation conditions.

[0064] The dynamic adjustment submodule dynamically adjusts the generated initial dispatch plan based on anomalies reported by the real-time monitoring submodule. When a vehicle breaks down, the submodule reassesses remaining vehicle resources and order demand, selects a new vehicle from the optimized vehicle-order allocation set to take over the order for the broken vehicle, and replans the transport route. In the event of road congestion, the submodule adjusts the routes or delivery order of affected vehicles based on the level of congestion and the estimated time it takes to clear the congestion, minimizing the impact on overall transport efficiency and delivery time.

[0065] During the adjustment process, the evaluation criteria set by the multi-objective evaluation submodule and the optimization logic of the intelligent algorithm optimization submodule will still be followed to ensure that the adjusted plan still maintains a good balance between multiple objectives and meets actual transportation needs.

[0066] The solution output submodule visually outputs the finalized recommended scheduling solution and dynamically adjusted solutions to relevant users, such as drivers, shippers, and platform administrators. This user interface displays vehicle scheduling tasks, including information such as loading and unloading locations, projected routes, and estimated arrival times. It provides shippers with the overall transportation schedule and estimated status of their orders, and provides platform administrators with a comprehensive summary of the scheduling solution for management and decision-making. This submodule also records scheduling solution data in the system database, providing data support for subsequent data analysis and performance evaluation.

[0067] The intelligent algorithm optimization submodule collects a large amount of historical transportation data obtained by the data acquisition module and analyzes the distribution of data errors. For vehicle speed data, the error range between it and the actual speed is calculated and the error tolerance range is set.

[0068] When the vehicle speed data error is within the set tolerance range of ±5%, the vehicle-order allocation combination code is considered to be in compliance with the scheduling plan;

[0069] When the vehicle speed data error is not within the set tolerance range of ±5%, the vehicle-order allocation combination coding is considered to be inconsistent with the scheduling plan, and a new combination is generated iteratively.

[0070] The data acquisition module collects data by interacting with the vehicle terminal, the platform database and the traffic department data interface.

[0071] The data preprocessing module removes duplicate, erroneous or incomplete data from the original data, and converts data in different formats into a standard format for integration.

[0072] The big data analysis module uses historical transportation data to train a transportation demand prediction model, combines real-time traffic data and vehicle location information to analyze road congestion and vehicle estimated arrival times, and calculates the matching degree between vehicles and cargo based on their attribute information.

[0073] The present invention also discloses a scheduling method of an intelligent scheduling system of a network freight platform based on big data analysis, which specifically includes the following steps:

[0074] S101 data collection step, using a data collection module to collect vehicle information, cargo information, order information, historical transportation data and real-time traffic data on the online freight platform;

[0075] S102 data preprocessing step, the collected raw data is transmitted to the data preprocessing module, and the data is cleaned, converted and integrated to make it into standard format data to be analyzed;

[0076] S103 big data analysis step, the big data analysis module conducts in-depth analysis of the pre-processed data, uses historical transportation data to train the transportation demand prediction model, and predicts the transportation demand in different regions in the future; combines real-time traffic data and vehicle location information to analyze the road congestion level and the estimated arrival time of the vehicle; calculates the matching degree of the vehicle and the cargo based on the attribute information of the vehicle and the cargo; the predicted future period is specifically 1 to 2 hours.

[0077] In step S104 of generating a scheduling plan, the intelligent scheduling module generates an initial scheduling plan using an intelligent algorithm based on the analysis results of the big data analysis module;

[0078] The initial dispatch plan aims to minimize transportation costs, meet delivery time requirements, and maximize vehicle utilization. During the plan generation process, factors such as the vehicle's current location, cargo capacity, cargo weight and volume, and order delivery time are fully considered to achieve the best match between vehicle and cargo.

[0079] S105 dispatch plan optimization and execution step, real-time monitoring of vehicle transportation status and road condition changes, sending the finalized dispatch plan to drivers and cargo owners through the user interaction module, and tracking the execution of the plan to ensure that the goods are delivered to the destination on time.

[0080] Among them, if an unexpected situation occurs, the intelligent scheduling module will optimize and adjust the scheduling plan in time.

[0081] Wherein, in the data collection step S101, the data collection module collects data in real time according to a set time interval or event triggering mechanism.

[0082] In the step S103 of big data analysis, the prediction model is trained using a time series analysis algorithm, and the road congestion status is determined by the road congestion index.

[0083] The scheduling method of the intelligent scheduling system of the network freight platform based on big data analysis provided by the present invention, first, improves scheduling efficiency. Through big data analysis and intelligent algorithms, the most suitable vehicle is matched for each order, the scheduling time is reduced, and the overall transportation efficiency is improved. Second, it reduces transportation costs. Accurate scheduling plans can effectively reduce the empty driving rate of vehicles, reasonably plan transportation routes, reduce fuel consumption and transportation mileage, and thus reduce transportation costs. Third, it improves service quality. The scheduling plan is dynamically adjusted according to delivery time requirements and real-time road conditions to ensure that the goods are delivered on time. Fourth, it enhances the scientific nature of decision-making. Big data analysis provides a rich basis for decision-making, helping platform managers to better understand the transportation business situation, make scientific and reasonable decisions, and optimize platform operation strategies.

[0084] The above is a detailed introduction to the intelligent scheduling system and scheduling method of the network freight platform based on big data analysis provided by the present invention. The description of the specific embodiments is only used to help understand the method of the present invention and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. The intelligent dispatching system of the network freight platform based on big data analysis is characterized by: include: Data collection module, used to collect vehicle information, cargo information, order information, historical transportation data and real-time traffic data on the online freight platform; A data preprocessing module cleans, converts and integrates the raw data collected by the data acquisition module; A big data analysis module uses data mining to analyze the data processed by the data preprocessing module, establish a transportation demand forecast model, and analyze road congestion, vehicle driving status, and the matching degree between vehicles and cargo; An intelligent scheduling module, which uses an intelligent algorithm to generate a recommended scheduling plan based on the analysis results of the big data analysis module, combined with order information and vehicle resource conditions; The user interaction module provides an interactive interface for shippers, drivers, and platform managers. Shippers can publish transportation requirements and check order status, drivers can receive dispatch tasks and provide feedback on transportation status, and platform managers can manage and monitor the system and view data reports. The intelligent scheduling module uses an intelligent algorithm to generate a recommended scheduling plan, monitors the vehicle transportation status in real time, and dynamically adjusts the recommended scheduling plan. The intelligent scheduling module includes an initialization submodule, a multi-objective evaluation submodule, an intelligent algorithm optimization submodule, a real-time monitoring submodule, a dynamic adjustment submodule, and a plan output submodule. The initialization submodule is used to preliminarily construct a vehicle-order allocation combination based on the basic information of vehicles and orders provided by the big data analysis module; The multi-objective evaluation submodule is used to evaluate each vehicle-order assignment combination generated by the initialization submodule based on the three optimization objectives of scheduling: transportation cost, delivery time, and vehicle utilization; The intelligent algorithm optimization submodule uses a genetic algorithm as an intelligent algorithm to optimize the results output by the multi-objective evaluation submodule, encodes each vehicle-order allocation combination as a chromosome, and continuously iterates to generate new combinations through selection, crossover, and mutation genetic operations to generate an initial scheduling plan; The real-time monitoring submodule interacts with the data source of the vehicle-mounted device in real time to obtain the vehicle's real-time location, driving speed, cargo status and transportation status information, as well as real-time road traffic conditions; The dynamic adjustment submodule dynamically adjusts the generated initial dispatch plan based on abnormal conditions reported by the real-time monitoring submodule. When a vehicle failure occurs, it re-evaluates the remaining vehicle resources and order requirements, selects a new vehicle from the optimized vehicle-order allocation combination set to take over the order task of the failed vehicle, and re-plans the transportation route. In the event of road congestion, it adjusts the driving route or delivery order of the affected vehicles based on the congestion level and the estimated time for clearing. The solution output submodule outputs the final recommended scheduling solution and the dynamically adjusted solution to the driver, cargo owner and platform manager in a visual form, and displays the vehicle scheduling task through the user interaction interface.

2. The intelligent dispatching system for online freight platform based on big data analysis according to claim 1 is characterized in that: In the intelligent scheduling module, the recommended scheduling plan uses a linear weighted method to integrate the following three objectives into a comprehensive objective function to generate a recommended scheduling plan. The three objectives are transportation cost target, delivery punctuality target, and vehicle utilization rate target; set up is a binary decision variable. If the vehicle Assigned to order ,but ,otherwise ,in , is the total number of vehicles, , is the total number of orders; Shipping cost target for: ; in, For vehicles Execute Order transportation costs; On-time delivery target for: ; Among them, For vehicles Execute Order Estimated time of arrival at the delivery location, For orders Delivery deadline; definition For vehicles Execute Order Punctuality penalty factor, if , ;like , ; Vehicle utilization target for: ; in, For vehicles Rated cargo capacity, For orders The weight of the cargo; vehicle Utilization rate ; Comprehensive objective function for: ; Among them, for the three objectives, the linear weighted method is used to construct the comprehensive objective function , 、 、 is the weight coefficient, and ， 。 3. The intelligent dispatching system for online freight platform based on big data analysis according to claim 2 is characterized in that: The intelligent algorithm optimization submodule collects a large amount of historical transportation data obtained by the data acquisition module and analyzes the distribution of data errors. For vehicle speed data, the error range between the data and the actual speed is calculated and the error tolerance range is set. When the vehicle speed data error is within the set tolerance range of ±5%, the vehicle-order allocation combination code is considered to be in compliance with the scheduling plan; When the vehicle speed data error is not within the set tolerance range of ±5%, the vehicle-order allocation combination coding is considered to be inconsistent with the scheduling plan, and a new combination is generated iteratively.

4. The intelligent dispatching system for online freight platform based on big data analysis according to claim 3 is characterized in that: The data acquisition module collects data by interacting with the vehicle terminal, the platform database and the traffic department data interface.

5. The intelligent dispatching system for online freight platform based on big data analysis according to claim 4 is characterized in that: The data preprocessing module removes duplicate, erroneous or incomplete data from the original data, and converts data in different formats into a standard format for integration.

6. The intelligent dispatching system for online freight platform based on big data analysis according to claim 5 is characterized in that: The big data analysis module uses historical transportation data to train a transportation demand prediction model, combines real-time traffic data and vehicle location information to analyze road congestion and vehicle estimated arrival times, and calculates the matching degree between vehicles and cargo based on their attribute information.

7. The scheduling method of the network freight platform intelligent scheduling system based on big data analysis according to claim 6 is characterized in that: The specific steps include: S101 data collection step, using a data collection module to collect vehicle information, cargo information, order information, historical transportation data and real-time traffic data on the online freight platform; S102 data preprocessing step, the collected raw data is transmitted to the data preprocessing module, and the data is cleaned, converted and integrated to make it into standard format data to be analyzed; In step S103, the big data analysis module conducts in-depth analysis of the pre-processed data, uses historical transportation data to train a transportation demand forecasting model, and predicts transportation demand in different regions within the next 1 to 2 hours. It also analyzes road congestion and estimated arrival times of vehicles by combining real-time traffic data and vehicle location information. It also calculates the matching degree between vehicles and cargo based on their attribute information. In step S104 of generating a scheduling plan, the intelligent scheduling module generates an initial scheduling plan using an intelligent algorithm based on the analysis results of the big data analysis module; S105 dispatch plan optimization and execution step, real-time monitoring of vehicle transportation status and road condition changes, sending the finalized dispatch plan to drivers and cargo owners through the user interaction module, and tracking the execution of the plan to ensure that the goods are delivered to the destination on time.

8. The scheduling method of the intelligent scheduling system of the network freight platform based on big data analysis according to claim 7 is characterized in that: in, In the data collection step S101, the data collection module collects data in real time according to a set time interval or event triggering mechanism.

9. The scheduling method of the intelligent scheduling system of the network freight platform based on big data analysis according to claim 8 is characterized in that: in, In the big data analysis step S103, the prediction model is trained using a time series analysis algorithm, and the road congestion status is determined by the road congestion index.

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