A network freight platform optimization system based on highway toll data

Abstract

The application discloses a network freight transport platform optimization system based on expressway toll data, relates to the technical field of platform optimization, and can comprehensively evaluate the traffic capacity and cost fluctuation of each path through a transport path section response vector calculation module, a path applicability evaluation module further judges whether the path is applicable according to the section response vector, and timely eliminates the path that does not meet the condition, a path optimization module generates a path intensity map by fusing traffic flow and the section response vector, extracts a connected path region and eliminates the path region with excessive resource consumption, so that a more simplified and efficient path set is formed, a main path selection and configuration module selects the path with the highest score in the selected path set as the main path through score sorting, ensures the optimal configuration of the transport route, improves the overall transport efficiency, and realizes more scientific and flexible transport scheduling.

Description

Technical Field

[0001] This invention relates to the field of platform optimization technology, and specifically to a network freight platform optimization system based on highway toll data. Background Technology

[0002] The network freight platform optimization system for highway toll data is an innovative solution proposed to address the problems of inefficiency, information asymmetry, and high costs in the freight industry. With the continuous increase in e-commerce and logistics demands, traditional freight methods are facing enormous pressure, especially in terms of how to rationally plan transportation routes, reduce transportation costs, and improve transportation efficiency, which have become prominent issues in the industry.

[0003] Highway toll data plays a crucial role in this system. By acquiring and analyzing highway toll data, information such as toll standards, road conditions, and traffic flow for each highway can be monitored in real time. This provides freight platforms with more accurate route planning and cost optimization solutions. By combining this data with the freight platform's operational data (such as vehicle location, cargo type, and loading status), the scheduling efficiency of freight tasks can be effectively improved, unnecessary empty runs can be avoided, and transportation costs can be reduced.

[0004] The existing technology has the following drawbacks:

[0005] Existing systems often fail to accurately assess the applicability of routes for complex transportation tasks, resulting in route selection outcomes that lack sufficient flexibility and adaptability. When dealing with multiple routes, they fail to effectively combine traffic flow, road capacity, and toll standards, leading to resource waste and inefficiency in the route selection process, and ultimately, the final route selection is often not optimal.

[0006] Based on this, the present invention proposes a network freight platform optimization system based on highway toll data. By selecting the route with the highest score from the selected route set and ranking it, the system selects the route with the highest score as the main route, ensuring the optimal configuration of transportation routes, improving overall transportation efficiency, and achieving more scientific and flexible transportation scheduling. Summary of the Invention

[0007] The purpose of this invention is to provide a network freight platform optimization system based on highway toll data to address the shortcomings of the prior art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a network freight platform optimization system based on highway toll data, comprising a data acquisition module, a transportation route segment response vector calculation module, a route applicability assessment module, a route optimization module, and a main route selection and configuration module;

[0009] The data acquisition module acquires raw data related to transportation tasks in real time;

[0010] The transport route segment response vector calculation module calculates the capacity and cost fluctuation index of each transport route based on the collected raw data, and generates the route segment response vector.

[0011] The route suitability assessment module evaluates the suitability of each route based on the road segment response vector. If the overall road segment response difference of a route exceeds a threshold, the route will be marked as unsuitable and an alternative route will be re-evaluated.

[0012] The route optimization module integrates the road segment response vectors and real-time traffic flow data of different routes to generate a route intensity map. By analyzing the route intensity map, it extracts a set of connected route regions. After removing route regions that occupy too many resources from the set of connected route regions, a selected set of routes is formed.

[0013] The main path selection and configuration module selects the highest-scoring path from the selected path set as the main path and configures the main path as the primary transportation route.

[0014] In a preferred embodiment, the path suitability assessment module calculates the overall response difference of the path based on the response vectors of each road segment in the path. The overall response difference is obtained by weighting the differences in the response vectors of each road segment.

[0015] The calculated overall response difference is compared with a threshold. If the overall response difference of the path does not exceed the threshold, the path is considered applicable.

[0016] If the overall response difference of a route exceeds a threshold, it indicates that the route cannot meet the needs of the transportation task, is marked as unsuitable, and enters the alternative route evaluation stage.

[0017] In a preferred embodiment, the path suitability assessment module calculates the overall response difference of the path based on the response vectors of each road segment in the path. The calculation steps are as follows:

[0018] For each segment in each path, calculate the difference between the response vector of that segment and the response vector of its neighboring segments;

[0019] A weight value is applied to the overall response differences of each road segment;

[0020] The weighted differences of all road segments are summed to obtain the overall response difference for the entire path.

[0021] In a preferred embodiment, the path optimization module combines the segment response vector of each path with real-time traffic flow data to form the intensity value of each path and generate a path intensity map.

[0022] The connectivity algorithm in graph theory is used to identify connected regions in the path intensity graph. Based on the intensity distribution in the path intensity graph, road segments with similar loads are grouped together to form connected path regions. Each path region is then filtered based on a preset resource consumption standard to remove path regions that consume too many resources.

[0023] Each path in the selected path set will be scored based on its path strength, traffic capacity, cost, and resource consumption.

[0024] The paths in the selected path set are sorted based on the overall score.

[0025] In a preferred embodiment, the main path selection and configuration module traverses all optimized path sets, compares the comprehensive score of each path with other paths, and selects the path with the highest score as the main path.

[0026] Configure the main path, which includes calculating the path's center coordinates, estimated travel time, toll fees, and resource budget parameters;

[0027] After configuring the main path, the system outputs the path's center coordinates, estimated travel time, toll fees, and resource budget.

[0028] In a preferred embodiment, the main path selection and configuration module takes the average of the start and end points of all road segments in the path as the center point of the main path. The estimated travel time refers to the estimated time required from the start point to the end point of the path. The estimated travel time of the entire path is obtained by adding the travel times of all road segments.

[0029] In a preferred embodiment, the transport route segment response vector calculation module calculates the capacity of each route based on the traffic flow data and segment capacity data in the original data;

[0030] The calculated capacity index, traffic flow coverage, and toll fluctuation will be used as the three components of the road segment response vector and normalized.

[0031] In a preferred embodiment, the transport route segment response vector calculation module calculates the capacity of each route, and the calculation logic is as follows:

[0032] The system obtains the road's capacity and dynamically adjusts it based on real-time traffic flow data to determine the current road segment's capacity. If the traffic flow of a road segment exceeds 80% of its capacity, the capacity index is reduced to 60% of its original capacity; if the traffic flow is below 60%, the capacity index remains at 100% of its original value.

[0033] Traffic flow coverage = current traffic flow / maximum capacity; toll fluctuation = fluctuation range of current toll standard / average toll standard.

[0034] In a preferred embodiment, the data acquisition module acquires raw data required for the transportation task in real time through multiple sensors and data interfaces, including the vehicle's transportation route, GPS positioning information, road conditions, real-time traffic flow, toll standards, and road condition changes.

[0035] The raw data is structured, including data cleaning, data mapping and standardization, and data format conversion.

[0036] The structured data will be transmitted to the transportation route segment response vector calculation module via a data interface or message queue system.

[0037] In a preferred embodiment, the vehicle's transportation route includes a starting point, a transit point, and a destination; the GPS positioning information includes latitude and longitude, vehicle speed, and direction of travel; the road traffic conditions include current traffic flow, road congestion, and traffic accident information; the real-time traffic flow includes the real-time traffic flow of the acquired road segment; the toll standards include toll fees, toll station time window fees, and fees for different vehicle types; and the road condition changes include weather changes, road closures, and construction information.

[0038] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0039] This invention utilizes a transportation route segment response vector calculation module. This module enables the platform to comprehensively assess the capacity and cost fluctuations of each route, transforming this information into structured segment response vectors. This enhances the accuracy and adaptability of route assessment. The route suitability assessment module further determines the suitability of a route based on the segment response vectors, promptly eliminating routes that do not meet the criteria. This avoids the inaccurate route selection problems of traditional methods. The route optimization module generates a route strength map by integrating traffic flow and segment response vectors, extracting connected route areas and eliminating resource-intensive route areas. This results in a more streamlined and efficient route set. Finally, the main route selection and configuration module selects the highest-scoring route from the selected route set as the main route, ensuring optimal configuration of transportation routes, improving overall transportation efficiency, reducing resource waste, and achieving more scientific and flexible transportation scheduling. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0041] Figure 1 The architecture diagram of the optimized system of this invention is shown below. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Example 1: Please refer to Figure 1 As shown in this embodiment, a network freight platform optimization system based on highway toll data includes a data acquisition module, a transportation route segment response vector calculation module, a route applicability assessment module, a route optimization module, and a main route selection and configuration module.

[0044] The data acquisition module acquires raw data related to the transportation task in real time, including vehicle transportation routes, GPS location information, road conditions, real-time traffic flow, toll rates, and road condition changes. This raw data is then converted into structured information and sent to the transportation route segment response vector calculation module.

[0045] The transportation route segment response vector calculation module calculates indicators such as traffic capacity and cost fluctuation for each transportation route based on the collected raw data, and generates a segment response vector for the route. The segment response vector includes indicators such as segment traffic capacity, traffic flow coverage, and toll fluctuation. Through normalization processing of these indicators, detailed data support is provided for route optimization. The segment response vector is then sent to the route suitability assessment module.

[0046] The route suitability assessment module evaluates the suitability of each route based on the segment response vector. If the overall segment response difference of a route exceeds a threshold (e.g., large changes in traffic flow or drastic fluctuations in tolls), the route will be marked as unsuitable and an alternative route will be reassessed to ensure the efficiency and stability of transportation tasks. The assessment results are then sent to the route optimization module.

[0047] The route optimization module fuses the segment response vectors of different routes with real-time traffic flow data to generate an original route intensity map. By analyzing the intensity map, a set of connected route regions is extracted. Based on a set resource consumption standard, route regions that consume excessive resources are eliminated, ultimately forming a selected route set. The optimized selected route set is then sent to the main route selection and configuration module.

[0048] The main route selection and configuration module selects the highest-scoring path from the chosen path set as the main route and prioritizes it as the primary transportation route. It configures the main route based on factors such as road capacity, real-time traffic conditions, and toll rates, and outputs the route's center coordinates, estimated travel time, toll fees, and resource budget for use in subsequent transportation tasks.

[0049] This application utilizes a transportation route segment response vector calculation module. This module enables the platform to comprehensively assess the capacity and cost fluctuations of each route, transforming this information into structured segment response vectors. This enhances the accuracy and adaptability of route assessment. The route suitability assessment module further determines the suitability of a route based on the segment response vectors, promptly eliminating routes that do not meet the criteria. This avoids the inaccurate route selection problems of traditional methods. The route optimization module generates a route strength map by integrating traffic flow and segment response vectors, extracting connected route areas and eliminating resource-intensive route areas. This results in a more streamlined and efficient route set. Finally, the main route selection and configuration module selects the highest-scoring route from the selected route set as the main route, ensuring optimal configuration of transportation routes, improving overall transportation efficiency, reducing resource waste, and achieving more scientific and flexible transportation scheduling.

[0050] The data acquisition module acquires raw data related to the transportation task in real time, including vehicle transportation routes, GPS location information, road conditions, real-time traffic flow, toll rates, and road condition changes. This raw data is then converted into structured information and sent to the transportation route segment response vector calculation module.

[0051] The main function of the data acquisition module is to acquire raw data related to transportation tasks in real time and transform this data into structured information for subsequent calculations and analysis. The specific steps are as follows:

[0052] The data acquisition module obtains raw data required for the transportation task in real time through multiple sensors and data interfaces. This data includes:

[0053] Vehicle transport route: Current driving route information obtained through the vehicle's GPS system, including origin, waypoints, and destination. This data can be used to monitor the vehicle's location, driving route, and estimated arrival time.

[0054] GPS location information: Real-time location information, including latitude and longitude, speed, and direction of travel, is obtained through the vehicle's GPS module. This data is crucial for real-time scheduling and route planning.

[0055] Road traffic conditions: By connecting with highway toll collection systems, traffic monitoring systems, or real-time traffic platforms, the system obtains information on the traffic conditions of various road sections, including current traffic flow, road congestion, and traffic accident information.

[0056] Real-time traffic flow: Real-time traffic flow for specific road segments is obtained through traffic flow sensors or network traffic data. This data helps the platform analyze the load situation of road segments and avoid excessive congestion.

[0057] Toll rates: By interfacing with the highway toll system, we obtain toll rates for different road sections, including toll fees, toll booth time window fees, and fees for different vehicle types. This information is crucial for optimizing route selection.

[0058] Traffic Changes: Collect real-time traffic change data for highways, including weather changes, road closures, and construction information, to ensure the real-time accuracy of transportation routes.

[0059] Data structuring

[0060] The collected raw data is usually unstructured or partially structured (e.g., GPS data consists of latitude and longitude coordinates). To facilitate subsequent processing and calculations, this raw data needs to be structured. The structured processing process includes:

[0061] Data cleaning: Removing redundant and invalid information, such as missing GPS data, invalid traffic flow data, and incorrect road segment information. Standardized filtering algorithms are used during the cleaning process to exclude outliers and erroneous data.

[0062] Data mapping and standardization: Mapping data from different sources to a unified standard format. For example, traffic flow data may come from different monitoring devices, and there may be differences in collection time, units, and measurement accuracy. It needs to be converted into a unified timestamp and numerical range. At the same time, indicators such as route, cost, and flow are standardized to ensure that the data can be seamlessly integrated into subsequent modules.

[0063] Data format conversion: Different data sources may use different storage formats. For data such as GPS data and traffic flow data, a unified format (such as JSON, CSV, or database tables) should be used to convert them into structured data suitable for subsequent algorithm processing.

[0064] The structured data will be transmitted to the transportation route segment response vector calculation module via a data interface or message queue system. The main steps of data transmission include: packaging the structured data into an appropriate message format (such as JSON, XML, etc.) and transmitting it to the target module over the network. To improve transmission efficiency, the system may use batch or incremental transmission methods, transmitting only the changed data portions. During transmission, the platform will check the integrity and accuracy of the data to ensure that each piece of data meets the expected format and requirements. The system will verify the integrity of the received data and ensure that there is no loss or misalignment. Because the scheduling and optimization of the freight platform have high real-time requirements, the data acquisition module will set different transmission strategies according to the real-time requirements of different types of data. For example, GPS positioning information is usually updated once per second, while road condition information may be updated once per minute. Therefore, during transmission, the module will adjust the transmission frequency and delay according to real-time requirements to ensure that the platform can quickly obtain the latest transportation information.

[0065] Suppose a truck is traveling on a highway. The data acquisition module first obtains the vehicle's real-time location (e.g., longitude 113.2, latitude 22.3) via GPS, and simultaneously records its speed (e.g., 60 km / h) and direction of travel (e.g., southeast). At the same time, the system obtains the traffic flow (e.g., 1000 vehicles / hour) and current toll rate (e.g., toll of 10 yuan) for that section of the road from a traffic monitoring platform. Finally, it obtains information on road condition changes in the area (e.g., light rain, slippery road surface) from weather sensors. This raw data, after being structured, is sent to the transport route segment response vector calculation module for further route optimization and evaluation.

[0066] Through the above process, the data acquisition module can efficiently and accurately acquire and process raw data, providing precise information support for subsequent path optimization.

[0067] The transportation route segment response vector calculation module calculates indicators such as traffic capacity and cost fluctuation for each transportation route based on the collected raw data, and generates a segment response vector for the route. The segment response vector includes indicators such as segment traffic capacity, traffic flow coverage, and toll fluctuation. Through normalization processing of these indicators, detailed data support is provided for route optimization. The segment response vector is then sent to the route suitability assessment module.

[0068] The core task of the transportation route segment response vector calculation module is to calculate the performance indicators of each transportation route based on the collected raw data (including road capacity, traffic flow, toll fluctuations, etc.) and generate corresponding segment response vectors. These vectors will be used in subsequent route suitability assessment and optimization processes. The specific steps are as follows:

[0069] The module first receives raw data transmitted from the data acquisition module, which typically includes:

[0070] Road segment capacity: This data is usually provided by traffic monitoring systems and represents the maximum traffic volume or speed that a road segment can handle within a specific time period.

[0071] Traffic flow data: Acquired through traffic sensors or traffic monitoring systems, this data represents the number of vehicles passing through that road segment per unit of time.

[0072] Toll rates and fluctuations: Provided by the highway toll system, including toll rates, toll amounts for different vehicle types, and toll fluctuations.

[0073] To ensure the accuracy of subsequent calculations, the system cleans and standardizes the raw data. Cleaning removes incomplete, duplicate, or erroneous data; standardization unifies the data formats and units from different data sources, making them comparable and consistent.

[0074] Based on traffic flow data and road segment capacity data, the system calculates the capacity of each route. The calculation logic is as follows:

[0075] Road segment capacity (capacity index): This is typically determined by the road's physical conditions (such as the number of lanes, road width, etc.) and current traffic conditions. The system first obtains the road's basic capacity (e.g., maximum number of vehicles or maximum speed), then dynamically adjusts it based on real-time traffic flow data to derive the current road segment capacity. The specific calculation process is as follows:

[0076] If the traffic flow on a road segment exceeds 80% of its capacity, the capacity index is reduced to 60% of its original capacity. If the traffic flow is below 60%, the capacity index remains at 100% of its original value.

[0077] Traffic flow coverage reflects the proportion of traffic flow on a specific road segment relative to its maximum capacity. The calculation is as follows: Traffic flow coverage = Current traffic flow / Maximum capacity. For example, if a road segment's maximum capacity is 1000 vehicles / hour and the current traffic flow is 800 vehicles / hour, then the traffic flow coverage of that segment is 0.8, indicating that the segment is at 80% capacity under current conditions.

[0078] Toll volatility indicates the degree of change in toll rates under different time periods or road conditions. It is calculated using historical toll data and real-time toll rates, following these steps: Toll volatility = Current toll rate fluctuation range / Average toll rate. For example, if the toll rate for a certain road segment changes from 10 yuan to 20 yuan within a day, the toll volatility is 1 (indicating that the range of change has reached its maximum). If the toll rate changes very little within 24 hours, the toll volatility is close to 0, indicating that the toll rate is relatively stable.

[0079] The capacity index, traffic flow coverage, and toll volatility calculated above will serve as the three components of the road segment response vector. To ensure that different indicators have equal weight in route optimization, these indicators will be normalized. The normalization steps are as follows:

[0080] Each metric's value is mapped to a range of 0 to 1, ensuring all metrics are compared within the same range. Typically, the maximum value is mapped to 1, the minimum value to 0, and other values ​​are scaled proportionally. The normalization formula is: Standardized value = (Current value - Minimum value) / (Maximum value - Minimum value).

[0081] For example, if the capacity index ranges from 200 to 1000, and the current route's capacity is 800, its normalized value is 0.75. Similarly, the other two indices (traffic coverage and toll volatility) will undergo the same normalization process. Ultimately, these normalized indices will form a road segment response vector, used for subsequent route suitability assessments.

[0082] The calculated road segment response vectors will be transmitted to the route suitability assessment module. The road segment response vector for each route will serve as a crucial basis for assessing its suitability, helping to evaluate its adaptability and reliability under different conditions. If certain indicators in a route's response vector deviate significantly from the normal range (e.g., excessively low capacity or large toll fluctuations), the route will be marked as unsuitable, and the system will automatically recommend alternative routes.

[0083] For example, in a transportation task, the data acquisition module obtains the following information for route A: capacity of 500 vehicles / hour, current traffic flow of 400 vehicles / hour, and toll of 12 yuan. Calculations show that route A has a traffic flow coverage of 0.8 and a toll fluctuation of 0.1 (assuming minimal toll changes over the past day). After normalization, these indicators are: normalized capacity = 0.75, normalized traffic flow coverage = 0.8, and normalized toll fluctuation = 0.1. This data forms the route A's segment response vector, which is then passed to the route suitability assessment module for further route optimization and selection.

[0084] Through the above steps, the transportation route segment response vector calculation module can provide accurate data support for route suitability assessment and subsequent optimization, ensuring that the freight platform can make efficient and stable route decisions in complex and ever-changing traffic environments.

[0085] The route suitability assessment module evaluates the suitability of each route based on the segment response vector. If the overall segment response difference of a route exceeds a threshold (e.g., large changes in traffic flow or drastic fluctuations in tolls), the route will be marked as unsuitable and an alternative route will be reassessed to ensure the efficiency and stability of transportation tasks. The assessment results are then sent to the route optimization module.

[0086] The main task of the route suitability assessment module is to evaluate the suitability of each route based on the road segment response vector and decide whether to continue using the route based on the assessment results. If the overall response difference of a route exceeds a preset threshold (e.g., large changes in traffic flow or drastic fluctuations in tolls), the system will mark the route as unsuitable and initiate an assessment of alternative routes. The core function of this module is to ensure that transportation tasks remain efficient and stable under dynamically changing road conditions, avoiding the use of unsuitable routes. The specific steps are as follows:

[0087] The route suitability assessment module receives route response vector data from the transportation route segment response vector calculation module. This data includes information such as the capacity of each route, traffic flow coverage, and toll fluctuation, which are key factors in route assessment. During the input data preprocessing stage, the system first verifies the completeness and accuracy of the data, ensuring that the input data is free of missing or incorrect information. If data anomalies are found (such as missing traffic flow data or incorrect toll rates), the system automatically marks and excludes routes that do not meet the criteria.

[0088] For each transport route, the route suitability assessment module calculates the overall response difference of the route based on the response vectors of each segment. The overall response difference is obtained by weighting the differences in the response vectors of each segment, and the calculation steps are as follows:

[0089] For each segment in each path, calculate the difference between the response vector of that segment and the response vector of its adjacent segments. This difference can be obtained by calculating the absolute difference between each metric (such as capacity, traffic coverage, and toll volatility) in the two response vectors.

[0090] Because different road segments have varying degrees of impact, the system weights the differences based on factors such as geographical location, traffic conditions, and time of day. For example, road segments located in peak traffic areas may have a higher weight and thus a greater impact. The logic for calculating the weighted differences is as follows:

[0091] A weight value is applied to the overall response differences of each road segment, and this weight value can be dynamically adjusted based on the traffic flow or other relevant indicators of the road segment.

[0092] The weighted differences of all road segments are summed to obtain the overall response difference for the entire path.

[0093] The module compares the calculated overall response difference with a preset threshold. The threshold is set considering the carrying capacity of different road segments and routes, as well as their impact on transportation tasks. The threshold can be a fixed value or dynamically adjusted based on actual conditions (such as traffic flow, weather, etc.). Based on the evaluation results, the following judgments are made:

[0094] Path Applicability: If the overall response difference of a path does not exceed a preset threshold, the path is considered applicable and can continue to be used for transportation tasks.

[0095] Route not applicable: If the overall response difference of a route exceeds a preset threshold, it means that the route cannot meet the needs of the transportation task under the current traffic flow, road conditions or cost fluctuations. The system will mark it as unapplicable and enter the alternative route evaluation stage.

[0096] When a path is marked as unsuitable, the path suitability assessment module will initiate an evaluation of alternative paths. This process includes:

[0097] Based on the evaluation results of the overall response differences, the system selects candidate routes that meet the requirements from the known alternative routes. The candidate routes need to meet the stability requirements of indicators such as traffic flow and cost fluctuations to ensure that they can provide similar or better transportation results.

[0098] For each candidate path, the system repeats the steps of path response vector calculation and difference assessment to ensure that these paths are highly applicable under current traffic and toll conditions.

[0099] Finally, the route suitability assessment module sends the assessment results (including suitable and unsuitable routes) to the route optimization module. When outputting the assessment results, the system provides a detailed route suitability score and the specific reasons for route differences, such as excessive traffic flow or large toll fluctuations. Based on these assessment results, the route optimization module further optimizes route selection to ensure that the transportation task selects the most suitable route.

[0100] Suppose a transportation task has a route A consisting of three segments, R1, R2, and R3. The response vector for each segment of route A is calculated as follows:

[0101] R1: Capacity: 0.8, Traffic Flow Coverage: 0.75, Toll Fluctuation: 0.2

[0102] R2: Capacity: 0.7, Traffic Flow Coverage: 0.85, Toll Fluctuation: 0.3

[0103] R3: Capacity: 0.6, Traffic Flow Coverage: 0.9, Toll Fluctuation: 0.5

[0104] Assuming a preset threshold of 0.2 (tolerance for overall response difference), after weighted difference calculation, the overall response difference for path A is 0.25, exceeding the threshold. Therefore, path A will be marked as unsuitable, and alternative paths will be re-evaluated. The system will then select new candidate paths from other available options and perform the same suitability evaluation to ensure that the selected paths meet the transportation task requirements.

[0105] Through these detailed steps, the route suitability assessment module can effectively evaluate and optimize transportation routes in actual operation, ensuring the efficiency and stability of transportation tasks.

[0106] The route optimization module fuses the segment response vectors of different routes with real-time traffic flow data to generate an original route intensity map. By analyzing the intensity map, a set of connected route regions is extracted. Based on a set resource consumption standard, route regions that consume excessive resources are eliminated, ultimately forming a selected route set. The optimized selected route set is then sent to the main route selection and configuration module.

[0107] The core task of the route optimization module is to fuse the road segment response vectors and real-time traffic flow data of different routes, generate a route intensity map through analysis, and extract a set of connected route regions. Based on a set resource consumption standard, the module eliminates route regions that consume excessive resources, ultimately forming an optimized set of selected routes. This optimized set of routes is then sent to the main route selection and configuration module for final route configuration and selection. The specific steps are as follows:

[0108] The route optimization module first receives input data from the route suitability assessment module and the data acquisition module, including:

[0109] Path segment response vectors: These vectors include indicators such as the capacity of each path, traffic flow coverage, and toll fluctuation, describing the path's performance under current traffic conditions.

[0110] Real-time traffic flow data: Data from traffic monitoring systems or traffic sensors provides real-time traffic load information for each segment of each route.

[0111] These data were first standardized to ensure consistency in numerical range and units. The standardized data provided accurate input for the path optimization algorithm, making the subsequent path intensity map generation process more efficient and accurate.

[0112] The path optimization module fuses the processed data to generate a path strength map. The path strength map is a multi-dimensional graphical representation that reflects the overall carrying capacity and real-time load of each path. The generation process is as follows:

[0113] The segment response vector for each path is combined with real-time traffic flow data to form an intensity value for each path. The intensity value is typically composed of a weighted average of the following factors: the degree of matching between the segment's capacity and traffic flow, the impact of toll fluctuations on the overall load of the path, and the coverage and trend of traffic flow.

[0114] By considering these factors, the system calculates a total "intensity value" for each path, representing the load level of that path under the current conditions. The higher the value, the greater the pressure the path bears under the current traffic conditions, and vice versa.

[0115] By analyzing the path intensity map, the path optimization module extracts a set of connected path regions. This set consists of multiple consecutive road segments that operate under the same conditions and possess similar traffic and load characteristics. The extraction steps are as follows:

[0116] The system uses connectivity algorithms from graph theory (e.g., depth-first search or breadth-first search) to identify connected regions in the path graph. Each connected region represents a relatively stable path area with consistent load. Based on the intensity distribution in the path intensity graph, the system groups road segments with similar loads to form connected path regions. For example, areas with high traffic volume may be grouped into one connected region, while other areas with low traffic volume may be grouped into another. After obtaining the set of connected path regions, the path optimization module filters each path region based on preset resource consumption criteria, eliminating those path regions that consume excessive resources. The specific process is as follows:

[0117] Resource consumption standards can be based on multiple factors, such as toll fees for each route, resource occupancy ratios (e.g., number of lanes, traffic flow limits during peak hours), and traffic density. Based on the intensity value and resource occupancy of each route area, the system calculates the total resource consumption for that area. If the resource consumption of an area exceeds a set threshold (e.g., excessively high toll fees or excessive traffic load), that area will be considered unsuitable for transportation tasks. For route areas exceeding the resource consumption standards, the system will remove them from the candidate route set, ensuring that the final route selection does not introduce excessive cost or traffic pressure.

[0118] After removing high-resource-consumption regions, the system generates an optimized set of selected paths based on the remaining connected path regions. The optimization process includes:

[0119] Each path in the selected set of paths will be scored based on its path strength, accessibility, cost, and resource consumption. Path scoring systems typically use a weighted average method, assigning weights according to the importance of different indicators.

[0120] Based on the overall score, the paths in the selected path set are sorted. Paths with higher scores will be prioritized and selected by the system as the primary routes for transportation tasks.

[0121] Finally, the optimized set of selected routes will be sent to the main route selection and configuration module. At this point, the route optimization module has already optimized the route set based on multiple factors such as traffic, cost, and resource consumption. The main route selection and configuration module will then select the most suitable main transportation route based on the priority and comprehensive score of these routes and configure it accordingly.

[0122] Suppose the platform receives transportation tasks along two routes:

[0123] Route A: Passes through three road segments, segment 1, segment 2 and segment 3, each with relatively low traffic volume and cost fluctuations, but due to large changes in traffic volume, the overall intensity is relatively high.

[0124] Route B: Passes through five road segments, two of which have high traffic volume and large toll fluctuations, but the overall route intensity is low.

[0125] The path optimization module first generates a path strength map. After calculating the strength values ​​of the two paths, path A has a strength of 0.85, while path B has a strength of 0.75. The module then identifies connected path regions. For example, path A is divided into a high-flow and low-flow region, while path B has a more even flow distribution, forming a stable path region. Based on the set resource consumption criteria, the high-flow region of path A may be eliminated due to excessive resource consumption, ultimately retaining only path B as the optimized set of selected paths.

[0126] Through this series of optimization steps, the route optimization module ensures the efficiency and stability of route selection, reducing costs and resource consumption in transportation tasks.

[0127] The main route selection and configuration module selects the highest-scoring path from the chosen path set as the main route and prioritizes it as the primary transportation route. It configures the main route based on factors such as road capacity, real-time traffic conditions, and toll rates, and outputs the route's center coordinates, estimated travel time, toll fees, and resource budget for use in subsequent transportation tasks.

[0128] The core task of the main route selection and configuration module is to select the highest-scoring path from the optimized set of selected paths as the primary transportation route, and then configure this path in detail to ensure the efficiency and stability of the transportation task. This module configures the main route based on multiple factors such as the capacity of each road segment, real-time traffic conditions, and toll rates, and ultimately outputs the path's center coordinates, estimated travel time, toll fees, and resource budget, providing detailed route information for subsequent transportation tasks. The specific steps are as follows:

[0129] The main path selection and configuration module first receives a set of selected paths from the path optimization module. Each path has been evaluated and optimized, possessing good throughput, low resource consumption, and reasonable pricing. Each path also comes with a comprehensive score, calculated based on multiple indicators such as path strength, throughput, traffic load, and pricing fluctuations.

[0130] The scoring method typically employs a weighted average algorithm, which comprehensively evaluates multiple indicators (such as capacity, traffic flow, and toll fees). Weight values ​​are set based on business needs and optimization objectives to ensure that the most important indicators have a significant impact on the final selection result.

[0131] For example, in some cases, capacity may be given a higher weight because it directly affects transport time, while in other cases, toll rates may be more important and therefore their weight will be increased accordingly.

[0132] Based on the score of each route, the system selects the highest-scoring route from the chosen set of routes as the primary route. The main purpose of this step is to select the most suitable route for the transportation task from multiple alternative routes to ensure efficient, low-cost, and feasible transportation.

[0133] The system iterates through all optimized routes, compares the overall score of each route with other routes, and selects the route with the highest score as the primary route. This step ensures that the selection of the primary route takes into account multiple factors, such as traffic capacity, flow conditions, and toll fees, thus best meeting the actual needs of the transportation task.

[0134] Once the main route is determined, the system will begin detailed configuration of it. Route configuration includes calculating crucial parameters such as the route's center coordinates, estimated travel time, toll fees, and resource budget. These parameters will provide detailed guidance for subsequent transportation tasks, ensuring controllability and transparency during the transportation process. The specific configuration steps are as follows:

[0135] The system calculates the "center coordinates" of the main path using the start and end coordinates of each road segment. The center coordinates represent the path's central location and are used for subsequent navigation and scheduling. The calculation logic for the center coordinates is: take the average of the start and end points of all road segments as the center point of the main path. The estimated travel time refers to the anticipated time required to travel from the start to the end of the path. During calculation, the system considers factors such as the capacity of each road segment, current traffic flow, and vehicle speed. The calculation logic is as follows:

[0136] For each road segment, the formula is used: Estimated travel time = Segment length / Current speed. Then, the travel times of all road segments are summed to obtain the estimated travel time for the entire route. The toll calculation for the main route involves the toll standards for each road segment. The system calculates the total toll for the route based on the toll information for different road segments (such as vehicle type, time of day, etc.). The calculation method involves a weighted sum of the tolls at each toll station along the route. For each toll segment, the system calculates the toll based on vehicle type, toll standard, and travel time, and then sums the tolls of all road segments to obtain the total toll.

[0137] Resource budgeting predicts a route's resource consumption based on factors such as total route length, traffic flow, capacity, and traffic density. The resource budget primarily considers the route's required transportation infrastructure, energy consumption, and time. The calculation logic is as follows:

[0138] The system comprehensively calculates the resource consumption value of each route by taking into account factors such as traffic flow, congestion, and capacity.

[0139] After configuring the main route, the system will output parameters such as the route's center coordinates, estimated travel time, toll fees, and resource budget. This information will serve as input for subsequent transportation tasks, ensuring efficient scheduling and resource optimization during the transportation process. Output content:

[0140] Path center coordinates: The center position of the main path, usually the midpoint of the path or the most representative coordinates.

[0141] Estimated travel time: The estimated travel time from the origin to the destination.

[0142] Toll fees: The total toll fees for the entire main route, including fees at all toll booths.

[0143] Resource budget: Total resource consumption of the main route, including the consumption of resources such as transportation facilities and energy.

[0144] Suppose the platform selects route B as the main route for a certain freight task. Route B has a comprehensive score of 90 points, making it the highest-scoring route. Based on the various segments of route B, the system calculates the center coordinates of the main route as (23.5, 45.8), the estimated travel time as 4 hours, the toll as 50 yuan, and the resource budget as 200 units.

[0145] These configuration parameters will be transmitted to the transportation dispatching system as the basis for truck route selection and scheduling. The transportation task will be executed according to these configuration parameters to ensure the smooth operation of the transportation process.

[0146] Through these detailed steps, the main route selection and configuration module not only selects the most suitable main route, but also provides comprehensive support for transportation tasks by configuring its detailed parameters, ensuring the efficiency and predictability of the transportation process.

[0147] Example 2: In this embodiment, the application scenario of the solution of this application is shown as follows:

[0148] A logistics company received an urgent transportation task to move a batch of urgently needed medical equipment from city A to city B. The task required minimizing transportation time while ensuring costs remained within a reasonable range. Since the task involved highway travel, and highway toll rates and traffic conditions could change at different times, the logistics company needed to optimize routes dynamically to ensure the most efficient and cost-effective transportation.

[0149] Against this backdrop, the network freight platform optimization system based on highway toll data will optimize routes according to real-time traffic flow, toll standards, road conditions and other information, thereby selecting the most suitable route for transportation tasks.

[0150] S1: Data Acquisition Module

[0151] The data acquisition module first collects raw data in real time, including the vehicle's transport route, GPS location information, road conditions, real-time traffic flow, toll rates, and road condition changes, through onboard equipment. For example, the system will acquire the following information:

[0152] The vehicle's transportation route: The vehicle departs from city A, travels via highways X, Y, and Z, and finally arrives at city B.

[0153] GPS location information: the vehicle's real-time location and direction of travel.

[0154] Road traffic conditions: The system obtains information such as traffic density and traffic accidents on various sections of the highway.

[0155] Real-time traffic flow: Provides real-time traffic flow data for each road segment through a traffic monitoring system.

[0156] Toll rates: Tolls for different road sections may vary depending on factors such as time of day and vehicle type.

[0157] Changes in road conditions: such as changes in road conditions caused by construction information, weather conditions, etc.

[0158] After structuring this raw data, the system will send it to the transportation route segment response vector calculation module for further processing.

[0159] S2: Transportation Route Segment Response Vector Calculation Module

[0160] Based on the collected raw data, this module calculates multiple indicators for each transportation route and generates a segment response vector for each route. The segment response vector for each route includes the following:

[0161] Road segment capacity: Assess the capacity of each road segment based on real-time traffic flow and road capacity. For example, if traffic flow is too high on a particular road segment, its capacity may be reduced.

[0162] Traffic flow coverage: This indicator reflects the proportion of traffic flow on each route to the maximum capacity of that route segment. High traffic volume may lead to congestion and reduce route effectiveness.

[0163] Toll volatility: By analyzing the fluctuations in toll rates across different time periods or road segments, the stability of the route's costs is assessed. Significant fluctuations in toll rates on certain road segments may impact transportation costs.

[0164] By normalizing these indicators, a standardized path response vector is generated and sent to the path suitability assessment module.

[0165] S3: Path Suitability Assessment Module

[0166] The route suitability assessment module evaluates the suitability of each route based on the route response vectors obtained from the transportation route segment response vector calculation module. If the overall segment response variation of a route exceeds a preset threshold (e.g., excessive traffic flow changes or drastic toll fluctuations), the route will be marked as unsuitable, and alternative routes will be reassessed. The detailed steps of this process include:

[0167] Assess route capacity and traffic flow differences: If traffic flow on a route changes dramatically (e.g., from normal flow to overloaded flow), the route may not be suitable.

[0168] Assess the toll volatility of a route: If the toll volatility of a route is too high (for example, the tolls at some toll stations are not stable), the route will be marked as unsuitable.

[0169] After evaluation, the system will remove unsuitable paths from the candidate paths and send the evaluation results to the path optimization module.

[0170] S4: Path Optimization Module

[0171] The route optimization module fuses the segment response vectors of different routes with real-time traffic flow data based on the evaluation results to generate an original route intensity map. The intensity map displays the overall performance of each route, including factors such as capacity, traffic flow, and toll fluctuations. The workflow of this module includes:

[0172] Generate a route strength map: The route strength map shows the performance of different routes across various metrics. For example, some routes may have low route strength due to excessive traffic volume, while others may have high tolls but strong capacity.

[0173] Extracting a set of connected path regions: By analyzing the path intensity map, multiple connected regions with similar load conditions are extracted. These regions show relatively consistent performance in terms of traffic flow and toll standards.

[0174] Eliminating high-resource-consuming path areas: Based on set resource consumption criteria (such as path fees, congestion levels, etc.), the system eliminates path areas that consume excessive resources, ultimately selecting the optimal set of paths. The optimized set of selected paths will then be sent to the main path selection and configuration module.

[0175] S5: Main Path Selection and Configuration Module

[0176] The primary path selection and configuration module selects the path with the highest score from the optimized set of selected paths as the primary path. The configuration process includes:

[0177] Select the highest-scoring path: From the optimized path set, select the path with the highest score, usually based on a comprehensive evaluation of multiple factors such as traffic capacity, resource consumption, and toll standards.

[0178] Configure main route details: Based on factors such as road capacity, real-time traffic conditions, and toll rates, configure detailed information for the main route. For example, it may be necessary to adjust the estimated travel time based on real-time traffic flow or adjust the budget based on toll fluctuations.

[0179] Output route parameters: The system calculates and outputs parameters such as the center coordinates of the main route, estimated travel time, toll fees, and resource budget. These parameters will provide detailed guidance for the execution of subsequent transportation tasks. Ultimately, the output route information will provide the optimal route configuration for the scheduling and execution of freight tasks.

[0180] Through the above steps, the online freight platform can optimize transportation routes in real time, ensuring that transportation tasks can be completed efficiently and stably. In this application scenario, the platform collects information such as traffic flow, road conditions, and toll standards in real time, dynamically calculates the applicability of the route, combines optimization algorithms and resource consumption standards, selects the most suitable main route, and performs detailed configuration to ensure that goods can arrive at their destination on time, within budget, and safely.

[0181] Example 3: In a network freight platform optimization system based on highway toll data, users (such as cargo owners, transportation companies, or dispatchers) interact with the platform to optimize transportation route selection and resource allocation, ensuring efficient execution of transportation tasks. The following is a typical human-computer interaction scenario example, involving the main operations and feedback between the user and the platform.

[0182] Suppose a shipper needs to transport goods through the platform, and the transportation task requires efficient, low-cost, and fast delivery. The shipper inputs relevant transportation task information (such as origin, destination, type of goods, and time requirements) through the platform interface. The system selects and optimizes the route based on real-time highway toll data, traffic flow, and toll standards, and outputs the final transportation route and parameters. After logging into the platform, the shipper inputs basic transportation task information through the user interface. The interface requires the following information:

[0183] Origin and Destination: The shipper selects the origin and destination of the transport (e.g., from city A to city B).

[0184] Cargo type: The cargo owner selects the cargo type (e.g., medical equipment, food, etc.), and the system recommends a suitable transportation route based on the characteristics of the cargo.

[0185] Time requirements: The shipper sets the expected delivery time (e.g., requiring the delivery task to be completed within 3 hours).

[0186] The platform receives information entered by cargo owners through a form interface and automatically displays the recommended route analysis function below.

[0187] The platform's data acquisition module automatically begins collecting raw data related to the transportation task, while the backend obtains real-time data from highway toll collection systems, traffic monitoring systems, and GPS devices. The platform will display the following information:

[0188] Real-time traffic flow: The platform displays the real-time traffic density of each major road segment through visual charts, making it easier for cargo owners to determine which road segments may cause traffic congestion.

[0189] Road segment capacity: The road segment capacity of each recommended route, as well as the possible toll rates, will be presented in the form of charts and tables.

[0190] Charging standards: The platform will list the charging details for different toll stations along each route (such as toll fees, time-based charges, etc.). For example, the platform displays the charges as shown in Table 1:

[0191] Table 1: Platform Display

[0192] Section Traffic capacity (vehicles / hour) Current traffic flow (vehicles / hour) Toll fee (yuan) Real-time traffic conditions Section A of the expressway 1500 800 30 normal Highway Section B 1200 1000 20 Congestion Section C of the expressway 1800 1200 25 Smooth

[0193] After data collection is complete, the transportation route segment response vector calculation module begins calculating the response vector for each route. The system generates the route's segment response vector based on indicators such as segment capacity, traffic flow coverage, and toll fluctuation, and displays this data to the user. Users can view detailed response vector data for each route on the interface, as shown in Table 2.

[0194] Table 2: Response Vector Data

[0195] Section Traffic Capacity Response Traffic coverage response Fee volatility response Road segment response vector Section A of the expressway 0.85 0.75 0.2 0.74 Highway Section B 0.7 0.83 0.5 0.67 Section C of the expressway 0.9 0.67 0.3 0.75

[0196] The system will display the road segment response vector (overall score) to the user so that they can understand the advantages and disadvantages of each path. Path A has a higher score, indicating that its overall performance is better.

[0197] The platform's path suitability assessment module evaluates the path based on the differences in the response vectors of the aforementioned road segments and a set threshold. Assuming a threshold of 0.2, the system calculates the overall response difference for each path. If the overall response difference for a particular path is too large (e.g., path B has high toll fluctuations or uneven traffic coverage), the path will be marked as unsuitable. The interface will display the path assessment results, as shown in Table 3.

[0198] Table 3: Path Evaluation Results

[0199] path Overall score Applicability assessment results Section A of the expressway 0.74 Applicable Highway Section B 0.67 not applicable Section C of the expressway 0.75 Applicable

[0200] The evaluation result for path B shows "not applicable", so it will be excluded, and the system will automatically start looking for an alternative path.

[0201] After the route suitability assessment, the route optimization module fuses the segment response vectors of each route with real-time traffic flow data to generate a route intensity map and extracts a set of connected route regions. Based on predefined resource consumption standards, the system eliminates route regions that consume excessive resources. Users can then view the optimized route recommendations.

[0202] The optimized route results will be presented to the user in graphs and tables, such as Table 4:

[0203] Table 4: Path Optimization Results

[0204] path Optimized overall score Resource consumption (units) Reasons for recommendation Section A of the expressway 0.8 50 Suitable for transportation tasks with low traffic volume Section C of the expressway 0.78 60 Strong traffic capacity and high traffic volume

[0205] After obtaining the optimized set of selected routes, the main route selection and configuration module will choose the route with the highest score as the main route. For example, route A has the highest overall score, so it is selected as the main route. The system will configure the route based on factors such as road capacity, real-time traffic conditions, and toll rates, and output the configuration parameters. The output includes:

[0206] Path center coordinates: The center coordinates of path A (e.g., latitude and longitude), for subsequent scheduling.

[0207] Estimated travel time: The estimated travel time based on traffic flow and road capacity (e.g., 3 hours).

[0208] Toll: Total toll cost for the main route (e.g., 50 yuan).

[0209] Resource budget: The resources expected to be used during transportation (e.g., 100 units). The user interface will display the configuration results as shown in Table 5.

[0210] Table 5: Configuration Results

[0211] path center coordinates Expected travel time Fees resource budget Section A of the expressway (23.5，45.8) 3 hours 50 yuan 100 units

[0212] Through this complete human-computer interaction scenario, users (shippers or dispatchers) can input basic information about their transportation tasks via the platform. The system will automatically collect data, evaluate routes, optimize routes, and ultimately select the most suitable main route. Throughout the process, the platform provides clear route recommendations, toll estimates, travel time estimates, and resource budgets through real-time data and optimization algorithms, ensuring efficient, stable, and cost-controlled transportation tasks. This interactive mode not only improves transportation efficiency but also enhances the scientific basis of transportation decision-making.

[0213] Example 4: To better demonstrate the practical application of the network freight platform optimization system based on highway toll data, a set of simulated and experimental data will be used to illustrate the functions and effects of each step. This data will help demonstrate how the system achieves efficient execution of transportation tasks through data collection, route evaluation, optimization, and route selection processes.

[0214] 1. Data Acquisition Module

[0215] The goal of the simulated data acquisition is to collect raw data related to the transportation task in real time, including vehicle transportation routes, GPS location information, road conditions, real-time traffic flow, toll rates, and road condition changes. Table 6 shows the raw data from the simulation:

[0216] Table 6: Simulation Raw Data

[0217] Data types Example data Vehicle transport route City A -> Highway X -> Highway Y -> City B GPS location information (23.5，45.8)，(23.7，45.9)，(24.0，46.0) Road traffic conditions Highway X: Smooth traffic; Highway Y: Slight congestion Real-time traffic flow Highway X: 800 vehicles / hour; Highway Y: 1200 vehicles / hour Fee Standard Highway X: 10 yuan, Highway Y: 15 yuan Road condition changes Highway Y: Minor accident, estimated delay of 20 minutes.

[0218] This data will be collected in real time and transmitted to subsequent modules.

[0219] 2. Transportation Route Segment Response Vector Calculation Module

[0220] Based on the raw data collected above, the system calculates the response vector for each transportation route. The simulated road segment response vectors, covering indicators such as capacity, traffic flow coverage, and toll volatility, are shown in Table 7.

[0221] Table 7: Simulated road segment response vectors

[0222] Section capacity Traffic coverage Fee fluctuation Road segment response vector (overall score) Highway X 0.85 0.75 0.2 0.74 Highway Y 0.7 0.8 0.4 0.66

[0223] Traffic capacity: Calculated based on real-time traffic flow and the carrying capacity of a road segment, representing the traffic efficiency of that road segment.

[0224] Traffic coverage: Reflects the current traffic load of a road segment; a value close to 1 indicates that the road segment is close to full load.

[0225] Toll volatility: Calculated based on the fluctuation range of real-time toll standards. The greater the volatility, the worse the toll stability of the route. These response vectors will be sent to the route suitability assessment module for further evaluation.

[0226] 3. Path Applicability Assessment Module

[0227] The path suitability assessment module evaluates the overall response differences across all road segments. If the difference for a particular path exceeds a preset threshold, the system marks it as unsuitable. The threshold is set at 0.2. The simulation results are shown in Table 8.

[0228] Table 8: Simulation Results

[0229] path Overall score Applicability assessment results Highway X -> Highway Y 0.7 not applicable Highway X -> Highway Z 0.8 Applicable

[0230] Evaluation Logic: Highway X -> Highway Y: Due to the high traffic volume coverage (0.8) and toll volatility (0.4) of Highway Y, the overall score for this route is low, and it is ultimately evaluated as unsuitable. Highway X -> Highway Z: Traffic volume and tolls are relatively stable, therefore this route is evaluated as suitable.

[0231] 4. Path optimization module

[0232] The path optimization module generates a path intensity map based on the path response vector and real-time traffic data. It then analyzes the intensity map to extract a set of connected path regions and eliminates high-resource-consuming path regions. The following are the path selection results before and after optimization.

[0233] Before optimization: Route A (Highway X -> Highway Y): Score 0.74, high traffic volume, slightly poor capacity. Route B (Highway X -> Highway Z): Score 0.8, moderate traffic volume, strong capacity.

[0234] After optimization: After filtering based on resource consumption criteria, path B was retained, while path A was removed due to high traffic and low throughput, as shown in Table 9.

[0235] Table 9: Optimization results.

[0236] path Optimized overall score Resource consumption Reasons for recommendation Highway X -> Highway Z 0.8 50 Moderate traffic flow and strong traffic capacity Highway X -> Highway Y 0.74 70 High traffic volume and poor traffic capacity

[0237] 5. Main Path Selection and Configuration Module

[0238] Based on the optimized path set, the main path selection and configuration module selects the highest-scoring path from the selected path set. The following are the path configuration results:

[0239] Path selection: Path B (Highway X -> Highway Z) was selected as the primary path due to its highest score and lowest resource consumption. The configuration results are shown in Table 10.

[0240] Table 10: Configuration Results

[0241] path center coordinates Expected travel time Fees resource budget Highway X -> Highway Z (23.5，45.8) 3 hours 50 yuan 100 units

[0242] Path center coordinates: The location of the center point of the path (e.g., the latitude and longitude coordinates of the midpoint of the path) is (23.5, 45.8).

[0243] Estimated travel time: Based on traffic flow and road capacity, the system estimates the estimated travel time for route B to be 3 hours.

[0244] Fees: According to the fee schedule, the total fee for route B is 50 yuan.

[0245] Resource budget: The estimated resources used during transportation are 100 units, taking into account transportation facilities and energy consumption.

[0246] To verify the system's effectiveness, a simulation experiment was conducted to test the optimization effects of different paths. The experimental data is shown in Table 11.

[0247] Table 11: Experimental Data

[0248] path Initial rating Optimized rating Resource consumption (units) Applicability assessment results Highway A -> Highway B 0.75 0.72 60 not applicable Highway A -> Highway C 0.80 0.85 50 Applicable Highway A -> Highway D 0.70 0.74 80 not applicable

[0249] Initial score: The score calculated based on preliminary assessments of factors such as traffic flow and toll rates.

[0250] Optimized score: The score after path optimization takes into account factors such as resource consumption and path load.

[0251] Resource consumption: Calculate the resource consumption of each path and remove paths with excessive resource consumption.

[0252] The experimental data shows that path A->path B was still marked as unsuitable after optimization, while path A->path C received a higher score after optimization and was selected as a suitable path, with lower resource consumption.

[0253] The simulation and experimental data presented above demonstrate how a network freight platform dynamically optimizes transportation routes through a multi-stage route optimization process, from data collection to main route selection, ensuring the efficiency and stability of transportation tasks. By comprehensively analyzing reasonable resource consumption standards and real-time traffic flow, toll fluctuations, and other data, the system can make optimal route selections and provide detailed configurations for subsequent transportation tasks, helping to reduce transportation costs, improve efficiency, and minimize unnecessary resource waste.

[0254] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0255] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A network freight platform optimization system based on highway toll data, characterized in that: It includes a data acquisition module, a transportation route segment response vector calculation module, a route suitability assessment module, a route optimization module, and a main route selection and configuration module; The data acquisition module acquires raw data related to transportation tasks in real time; The transportation route segment response vector calculation module calculates the capacity and cost fluctuation index of each transportation route based on the collected raw data, and generates the route segment response vector. The route suitability assessment module evaluates the suitability of each route based on the road segment response vector. If the overall road segment response difference of a route exceeds a threshold, the route will be marked as unsuitable and an alternative route will be re-evaluated. The overall response difference calculation logic is as follows: for each road segment in each path, calculate the difference between the response vector of that road segment and the response vector of the adjacent road segments; A weight value is applied to the overall response differences of each road segment; The weighted differences of all road segments are summed to obtain the overall response difference for the entire path; The route optimization module integrates the road segment response vectors and real-time traffic flow data of different routes to generate a route intensity map. By analyzing the route intensity map, it extracts a set of connected route regions. After removing route regions that occupy too many resources from the set of connected route regions, a selected set of routes is formed. The main path selection and configuration module selects the highest-scoring path from the selected path set as the main path and configures the main path as the primary transportation route.

2. The network freight platform optimization system based on highway toll data according to claim 1, characterized in that: The path suitability assessment module calculates the overall response difference of the path based on the response vectors of each road segment in the path. The overall response difference is obtained by weighting the differences in the response vectors of each road segment. The calculated overall response difference is compared with a threshold. If the overall response difference of the path does not exceed the threshold, the path is considered applicable. If the overall response difference of a route exceeds a threshold, it indicates that the route cannot meet the needs of the transportation task, is marked as unsuitable, and enters the alternative route evaluation stage.

3. The network freight platform optimization system based on highway toll data according to claim 2, characterized in that: The path optimization module combines the segment response vector of each path with real-time traffic flow data to form the intensity value of each path and generate a path intensity map. The connectivity algorithm in graph theory is used to identify connected regions in the path intensity graph. Based on the intensity distribution in the path intensity graph, road segments with similar loads are grouped together to form connected path regions. Each path region is then filtered based on a preset resource consumption standard to remove path regions that consume too many resources.

4. The network freight platform optimization system based on highway toll data according to claim 3, characterized in that: The route optimization module selects each path in the route set and scores it comprehensively based on its route strength, traffic capacity, cost, and resource consumption. The paths in the selected path set are sorted based on the overall score.

5. The network freight platform optimization system based on highway toll data according to claim 4, characterized in that: The main path selection and configuration module traverses all optimized path sets, compares the comprehensive score of each path with other paths, and selects the path with the highest score as the main path. Configure the main path, which includes calculating the path's center coordinates, estimated travel time, toll fees, and resource budget parameters; After configuring the main path, the system outputs the path's center coordinates, estimated travel time, toll fees, and resource budget.

6. The network freight platform optimization system based on highway toll data according to claim 5, characterized in that: The main path selection and configuration module takes the average of the start and end points of all road segments in the path as the center point of the main path. The estimated travel time refers to the estimated time required from the start point to the end point of the path. The estimated travel time of the entire path is obtained by adding the travel times of all road segments.

7. The network freight platform optimization system based on highway toll data according to claim 1, characterized in that: The transportation route segment response vector calculation module calculates the capacity of each route based on the traffic flow data and segment capacity data in the original data. The calculated capacity index, traffic flow coverage, and toll fluctuation will be used as the three components of the road segment response vector and normalized.

8. The network freight platform optimization system based on highway toll data according to claim 7, characterized in that: The transport route segment response vector calculation module calculates the capacity of each route, and the calculation logic is as follows: The system obtains the road's capacity and dynamically adjusts it based on real-time traffic flow data to determine the current road segment's capacity. If the traffic flow of a road segment exceeds 80% of its capacity, the capacity index is reduced to 60% of its original capacity; if the traffic flow is below 60%, the capacity index remains at 100% of its original value. Traffic flow coverage = current traffic flow / maximum capacity; toll fluctuation = fluctuation range of current toll standard / average toll standard.

9. The network freight platform optimization system based on highway toll data according to claim 1, characterized in that: The data acquisition module acquires raw data required for transportation tasks in real time through multiple sensors and data interfaces, including vehicle transportation routes, GPS positioning information, road conditions, real-time traffic flow, toll standards, and road condition changes. The raw data is structured, including data cleaning, data mapping and standardization, and data format conversion. The structured data will be transmitted to the transportation route segment response vector calculation module via a data interface or message queue system.

10. The network freight platform optimization system based on highway toll data according to claim 9, characterized in that: The vehicle's transportation route includes the origin, transit points, and destination; the GPS positioning information includes latitude and longitude, vehicle speed, and direction of travel; the road conditions include current traffic flow, road congestion, and traffic accident information; the real-time traffic flow includes the real-time traffic flow of the acquired road segment; the toll standards include toll fees, toll station time window fees, and fees for different vehicle types; and the road condition changes include weather changes, road closures, and construction information.

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