Network freight platform optimization system based on highway toll collection data

Abstract

The invention discloses a network freight platform optimization system based on highway toll collection data, relates to the technical field of platform optimization, and can comprehensively evaluate the traffic capacity and cost fluctuation condition of each path through a transport path section response vector calculation module. The path applicability evaluation module further judges whether the path is applicable or not according to the road section response vector and eliminates the path which does not meet the condition in time, and the path optimization module generates a path intensity diagram by fusing the traffic flow and the road section response vector, extracts a connected path area and eliminates the path area with overlarge resource consumption, and performs path optimization. And the main path selection and configuration module selects the path with the highest score as the main path by selecting the score sequence in the path set, so that the optimal configuration of the transportation path is ensured, the overall transportation efficiency is improved, and more scientific and flexible transportation scheduling is realized.

Description

Technical Field

[0001] The present invention relates to the field of platform optimization technology, and in particular to a network freight platform optimization system based on highway toll data. Background Art

[0002] The highway toll data online freight platform optimization system is an innovative solution designed to address issues such as inefficiency, information asymmetry, and excessive costs in the freight industry. With the increasing demand for e-commerce and logistics, traditional freight methods are facing tremendous pressure. In particular, how to rationally plan transportation routes, reduce transportation costs, and improve transportation efficiency have become prominent issues in the industry. Highway toll data plays an important role in this system. By acquiring and analyzing highway toll data, we can obtain real-time information on the toll standards, road conditions, traffic flow, and other information of each highway, providing the freight platform 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, loading status, etc.), we can effectively improve the scheduling efficiency of freight tasks, avoid unnecessary empty trips, and reduce transportation costs.

[0003] The existing technology has the following defects: Existing systems are often unable to accurately handle complex transportation tasks in terms of route suitability assessment, resulting in insufficient flexibility and adaptability in route selection results. When processing 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, making the final route selection often suboptimal.

[0004] Based on this, the present invention proposes a network freight platform optimization system based on highway toll data. By sorting the scores in the selected path set, the path with the highest score is selected as the main path to ensure the optimal configuration of the transportation route, improve the overall transportation efficiency, and achieve more scientific and flexible transportation scheduling. Summary of the Invention

[0005] The purpose of the present invention is to provide a network freight platform optimization system based on highway toll data to solve the shortcomings of the background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solutions: a network freight platform optimization system based on highway toll data, comprising a data acquisition module, a transport path segment response vector calculation module, a path suitability evaluation module, a path optimization module, and a main path selection and configuration module; The data acquisition module acquires raw data related to the transportation task in real time; The transport path section response vector calculation module calculates the capacity and cost fluctuation index of each transport path based on the collected raw data and generates the path section response vector; The path suitability evaluation module evaluates the suitability of each path based on the segment response vector. If the overall response difference of the path segments exceeds a threshold, the path will be marked as unsuitable and alternative paths will be re-evaluated. The path optimization module fuses the response vectors of different path segments with real-time traffic flow data to generate a path strength graph. By analyzing the path strength graph, it extracts a set of connected path regions. After removing the path regions that occupy too many resources from the connected path region set, the selected path set is formed. The main path selection and configuration module selects the path with the highest score from the selected path set as the main path and configures the main path as the main transportation route.

[0007] In a preferred embodiment, the path suitability evaluation module calculates the overall response difference of the path based on the response vectors of each road segment in the path, and the overall response difference is obtained by weighted calculation of the difference in the response vectors of each road segment; The calculated overall response difference is compared with the 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 means that the route cannot meet the requirements of the transportation task and is marked as not applicable, and enters the alternative route evaluation stage.

[0008] In a preferred embodiment, the path suitability evaluation module calculates the overall response difference of the path based on the response vectors of each segment in the path, and the calculation steps are as follows: For each segment in each path, the difference between the response vector of the segment and the response vector of the adjacent segment is calculated; Apply a weight value to the overall response difference of each road segment; The weighted differences of all road segments are summed up to obtain the overall response difference of the entire path.

[0009] In a preferred embodiment, the path optimization module combines the segment response vector of each path with real-time traffic flow data to form a strength value for each path and generates a path strength map; Connectivity algorithms from graph theory are used to identify connected regions in the path strength graph. Road sections with similar loads are grouped together based on the intensity distribution in the path strength graph to form connected path regions. Each path region is then screened based on preset resource consumption criteria to eliminate those that consume too many resources. Each path in the selected path set will be comprehensively scored based on its path strength, capacity, cost, and resource consumption; Sort the paths in the selected path set according to the comprehensive score.

[0010] In a preferred embodiment, the primary 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 primary path; Configure the main path, which includes calculating the path's center coordinates, estimated travel time, toll fees, and resource budget parameters; After completing the configuration of the main path, the center coordinates of the path, estimated travel time, toll fees and resource budget are output.

[0011] In a preferred embodiment, the main path selection and configuration module takes the average of the starting and ending points of all sections in the path as the center point of the main path. The estimated travel time refers to the estimated time required from the starting point to the end point of the path. The travel time of all sections is added together to obtain the estimated travel time of the entire path.

[0012] 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; The calculated capacity index, traffic flow coverage and toll fluctuation will be used as the three components of the road section response vector and normalized.

[0013] In a preferred embodiment, the transport path segment response vector calculation module calculates the capacity of each path, and the calculation logic is: Obtain the road capacity and dynamically adjust it based on real-time traffic flow data to determine the current road section capacity. If the traffic flow on a road section is greater than 80% of the road section capacity, the capacity indicator is reduced to 60% of the original capacity. If the traffic flow is less than 60%, the capacity indicator remains at 100% of the original value. Traffic flow coverage = current traffic flow / maximum traffic capacity, and toll fluctuation = fluctuation range of current toll standard / average toll standard.

[0014] In a preferred embodiment, the data acquisition module acquires the 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 section traffic conditions, real-time traffic flow, toll standards, and changes in road conditions; Structural processing of raw data, including data cleaning, data mapping and standardization, and data format conversion; The structured data will be transmitted to the transport route section response vector calculation module through the data interface or message queue system.

[0015] In a preferred embodiment, the transport route of the vehicle includes a starting point, a waypoint and an end point; the GPS positioning information includes latitude and longitude, vehicle speed, and driving direction; the traffic conditions of the road section include current traffic volume, road congestion, and traffic accident information; the real-time traffic flow includes obtaining the real-time traffic flow of the road section; the charging standards include tolls, time window fees at toll stations, and fees for different vehicle models; the road condition changes include weather changes, road closures, and construction information.

[0016] In the above technical solution, the technical effects and advantages provided by the present invention are: Through the transport path section response vector calculation module, the present invention enables the platform to comprehensively evaluate the capacity and cost fluctuations of each path, and convert this information into a structured section response vector, thereby enhancing the accuracy and adaptability of path evaluation. The path suitability evaluation module further determines whether the path is applicable based on the section response vector, and promptly eliminates paths that do not meet the conditions, avoiding the problem of inaccurate path selection in traditional methods. The path optimization module generates a path intensity map by integrating traffic flow and section response vectors, extracts connected path areas, and eliminates path areas with excessive resource consumption, thereby forming a more streamlined and efficient path set. Finally, the main path selection and configuration module selects the path with the highest score as the main path by sorting the scores in the selected path set, ensuring the optimal configuration of the transport route, improving overall transportation efficiency, reducing resource waste, and achieving more scientific and flexible transportation scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0018] Figure 1 This is an architectural diagram of the optimization system of the present invention. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0020] Example 1: Please refer to Figure 1As shown, the network freight platform optimization system based on highway toll data described in this embodiment includes a data acquisition module, a transportation path section response vector calculation module, a path suitability evaluation module, a path optimization module and a main path selection and configuration module.

[0021] The data acquisition module acquires raw data related to the transport task in real time. This data includes the vehicle's transport route, GPS location information, road section conditions, real-time traffic flow, toll rates, and changes in road conditions. This raw data is converted into structured information and sent to the transport route section response vector calculation module.

[0022] The transport path segment response vector calculation module calculates indicators such as the capacity and fee fluctuation of each transport path based on the collected raw data, and generates the segment response vector of the path. The segment response vector includes indicators such as segment capacity, traffic flow coverage, and fee fluctuation. By normalizing these indicators, detailed data support is provided for path optimization. The segment response vector is sent to the path suitability assessment module.

[0023] The path suitability assessment module evaluates the suitability of each path based on the segment response vector. If the overall response difference of the path segments exceeds a threshold (for example, large changes in traffic flow or sharp fluctuations in tolls), the path will be marked as unsuitable and alternative paths will be re-evaluated to ensure the efficiency and stability of the transportation task. The evaluation results are sent to the path optimization module.

[0024] The route optimization module fuses the response vectors of different route segments with real-time traffic flow data to generate a raw route intensity map. By analyzing this intensity map, it extracts a set of connected route regions. Based on a set resource consumption standard, it removes route regions that consume excessive resources. This ultimately forms a selected set of routes, which are then sent to the main route selection and configuration module.

[0025] The primary path selection and configuration module selects the highest-scoring path from the selected path set as the primary path and prioritizes it as the primary transport route. It configures the primary path based on factors such as road capacity, real-time traffic conditions, and toll rates. It also outputs the path's center coordinates, estimated travel time, toll fees, and resource budget for subsequent transport tasks.

[0026] Through the transport path section response vector calculation module, the platform of this application can comprehensively evaluate the capacity and cost fluctuations of each path, and convert this information into a structured section response vector, thereby enhancing the accuracy and adaptability of the path evaluation. The path suitability evaluation module further determines whether the path is applicable based on the section response vector, and promptly eliminates paths that do not meet the conditions, thereby avoiding the problem of inaccurate path selection in traditional methods. The path optimization module generates a path intensity map by integrating traffic flow and section response vectors, extracts connected path areas and eliminates path areas with excessive resource consumption, thereby forming a more streamlined and efficient path set. Finally, the main path selection and configuration module selects the path with the highest score as the main path by sorting the scores in the selected path set, ensuring the optimal configuration of the transport route, improving overall transportation efficiency, reducing resource waste, and achieving more scientific and flexible transportation scheduling.

[0027] The data acquisition module acquires raw data related to the transport task in real time. This data includes the vehicle's transport route, GPS location information, road section conditions, real-time traffic flow, toll rates, and changes in road conditions. This raw data is converted into structured information and sent to the transport route section response vector calculation module.

[0028] The main function of the data acquisition module is to obtain raw data related to the transportation task in real time and convert this data into structured information for subsequent calculation and analysis. The specific steps are as follows: The data acquisition module acquires the raw data required for transportation tasks in real time through multiple sensors and data interfaces. This data includes: Vehicle's transport route: This data is collected from the vehicle's GPS system and includes the vehicle's current route, including the starting point, waypoints, and destination. This data can be used to monitor the vehicle's location, route, and estimated arrival time.

[0029] GPS positioning information: The vehicle's GPS module obtains real-time location information, including latitude and longitude, speed, and direction of travel. This data is crucial for real-time scheduling and route planning.

[0030] Road traffic conditions: Through data interfaces with highway toll collection systems, traffic monitoring systems, or real-time road condition platforms, the traffic conditions of each road section can be obtained, including current traffic volume, road congestion, traffic accident information, etc.

[0031] Real-time traffic flow: Traffic flow sensors or network traffic data are used to obtain real-time traffic flow on specific road sections. This data helps the platform analyze the load conditions of road sections and avoid overly congested sections.

[0032] Toll rates: Access toll rates for different road sections through the interface with the highway toll system, including toll fees, time window fees at toll stations, and fees for different vehicle types. This information is an important basis for optimizing route selection.

[0033] Road condition changes: Collect real-time road condition change data on highways, including weather changes, road closures, construction information, etc., to ensure the real-time and accuracy of transportation routes.

[0034] Data structured processing The collected raw data is usually unstructured or partially structured (e.g. GPS data is latitude and longitude coordinates). To facilitate subsequent processing and calculation, these raw data need to be structured. The structured processing process includes: Data cleaning: Remove redundant information and invalid data, such as lost GPS data, invalid traffic flow data, incorrect road section information, etc. Standardized filtering algorithms are used during the cleaning process to exclude outliers and erroneous data.

[0035] Data mapping and standardization: Map data from different sources into a unified, standardized format. For example, traffic flow data may come from different monitoring devices, with varying collection times, units, and measurement accuracy. This data needs to be converted into a unified timestamp and numerical range. Furthermore, metrics such as routes, costs, and flow rates are standardized to ensure seamless integration into subsequent modules.

[0036] Data format conversion: Different data sources may use different storage formats. For example, GPS data and traffic flow data should be converted into structured data suitable for subsequent algorithm processing using a unified format (such as JSON, CSV, or database tables).

[0037] The structured data is transmitted to the transport route segment response vector calculation module via a data interface or message queue system. The main steps in data transmission include packaging the structured data into an appropriate message format (such as JSON or XML) and transmitting it to the target module via the network. To improve transmission efficiency, the system may use batch or incremental transmission, transmitting only the modified data. During transmission, the platform checks the integrity and accuracy of the data to ensure that each data item conforms to the expected format and requirements. The system verifies the integrity of the received data and ensures that there are no data loss or misalignment. Because the freight platform's scheduling and optimization require high real-time performance, the data collection module uses different transmission strategies based on the real-time requirements of different data types. For example, GPS location information is typically updated once per second, while road condition information may be updated every minute. Therefore, during transmission, the module adjusts the transmission frequency and latency based on real-time requirements to ensure that the platform can quickly obtain the latest transportation information.

[0038] Imagine a truck traveling on a highway. The data acquisition module first uses GPS to obtain the vehicle's real-time location (e.g., longitude 113.2, latitude 22.3) and simultaneously records its speed (e.g., 60 km / h) and direction (e.g., southeast). Simultaneously, the system obtains the traffic volume for that section (e.g., 1,000 vehicles / hour) and the current toll rate (e.g., 10 yuan) from the traffic monitoring platform. Finally, weather sensors capture information about changing road conditions in the area (e.g., light rain, slippery roads). After structured processing, this raw data is sent to the transport route segment response vector calculation module for further route optimization and evaluation.

[0039] Through the above process, the data acquisition module can efficiently and accurately obtain and process the original data, providing accurate information support for subsequent path optimization.

[0040] The transport path segment response vector calculation module calculates indicators such as the capacity and fee fluctuation of each transport path based on the collected raw data, and generates the segment response vector of the path. The segment response vector includes indicators such as segment capacity, traffic flow coverage, and fee fluctuation. By normalizing these indicators, detailed data support is provided for path optimization. The segment response vector is sent to the path suitability assessment module.

[0041] The core task of the transport route segment response vector calculation module is to calculate the performance indicators of each transport 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 the subsequent route suitability assessment and optimization process. The specific steps are as follows: The module first receives the raw data transmitted from the data acquisition module, which usually includes: Road section capacity: This data is usually provided by a traffic monitoring system and indicates the maximum traffic volume or speed that a road section can carry within a specific time period.

[0042] Traffic flow data: Traffic flow information for each road section is obtained through traffic sensors or traffic monitoring systems, indicating the number of vehicles passing through the road section per unit time.

[0043] Toll standards and fluctuations: provided by the highway toll collection system, including toll standards, toll amounts for different vehicle types, toll fluctuations and other information.

[0044] 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 format and units across different data sources, making them comparable and consistent.

[0045] Based on traffic flow data and road capacity data, the system calculates the capacity of each path. The calculation logic is as follows: Section capacity (capacity index): This is usually determined by the physical conditions of the road (such as the number of lanes, road width, etc.) and the current traffic conditions. The system first obtains the basic capacity of the road (for example, the maximum number of vehicles or the maximum speed), and then dynamically adjusts it based on real-time traffic flow data to determine the current section capacity. The specific calculation process is as follows: If the traffic flow on a road segment is greater than 80% of its capacity, the capacity index is reduced to 60% of the original capacity. If the traffic flow is less than 60%, the capacity index remains at 100% of the original value.

[0046] Traffic flow coverage reflects the proportion of a particular road segment's traffic flow to its maximum carrying capacity. It's calculated as follows: Traffic flow coverage = current traffic flow / maximum capacity. For example, if a road segment's maximum capacity is 1,000 vehicles per hour and its current traffic flow is 800 vehicles per hour, its traffic flow coverage is 0.8, indicating that the segment is 80% loaded under current conditions.

[0047] Toll volatility indicates the degree of change in toll rates across different time periods or road sections. It's calculated using historical toll data and real-time toll rates using the following steps: Toll volatility = current toll rate fluctuation range / average toll rate. For example, if the toll rate for a particular road section changes from 10 yuan to 20 yuan in a single day, the toll volatility is 1 (indicating the maximum range). If the toll rate changes very little over a 24-hour period, the toll volatility is close to 0, indicating a relatively stable toll rate.

[0048] The capacity index, traffic flow coverage, and toll fluctuation calculated above will be used as the three components of the road segment response vector. To ensure that the different indicators have equal weight in affecting the route optimization, these indicators will be normalized. The normalization steps are as follows: Map the value of each indicator to between 0 and 1 to ensure that all indicators are compared within the same range. Usually, the maximum value is mapped to 1, the minimum value is mapped to 0, and other values ​​are scaled proportionally. The normalization formula is: Normalized value = (current value - minimum value) / (maximum value - minimum value), For example, if the capacity index ranges from 200 to 1000 and the current route has a capacity of 800, its normalized value is 0.75. Similarly, the other two indicators (flow coverage and toll volatility) are normalized in the same way. Ultimately, these normalized indicators form a segment response vector, which is used in subsequent route suitability assessments.

[0049] The calculated segment response vectors are then passed to the Route Suitability Assessment module. Each route's segment response vector serves as a crucial basis for evaluating its suitability, helping to assess its adaptability and reliability under varying conditions. If certain metrics in a route's response vector deviate significantly from the norm (e.g., low capacity or significant toll fluctuations), the route is marked as unsuitable, and the system automatically recommends an alternative route.

[0050] For example, in a transportation mission, the data acquisition module acquires the following information about a section of route A: capacity of 500 vehicles per hour, current traffic of 400 vehicles per hour, and a toll of 12 yuan. Calculated traffic flow coverage for route A is 0.8, and toll volatility is 0.1 (assuming that toll rates have changed little over the past day). After normalization, these metrics yield the following values: normalized capacity = 0.75, normalized traffic flow coverage = 0.8, and normalized toll volatility = 0.1. This data forms the section response vector for route A, which is then passed to the route suitability assessment module for further use in route optimization and selection.

[0051] Through the above steps, the transport 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 changing traffic environments.

[0052] The path suitability assessment module evaluates the suitability of each path based on the segment response vector. If the overall response difference of the path segments exceeds a threshold (for example, large changes in traffic flow or sharp fluctuations in tolls), the path will be marked as unsuitable and alternative paths will be re-evaluated to ensure the efficiency and stability of the transportation task. The evaluation results are sent to the path optimization module.

[0053] The Route Suitability Assessment module evaluates the suitability of each route based on its segment response vectors and, based on the evaluation results, decides whether to continue using that route. If the overall response variance of a route's segments exceeds a preset threshold (for example, due to significant traffic flow variations or toll fluctuations), the system marks the route as unsuitable and initiates an evaluation of alternative routes. The core function of this module is to ensure that transport missions remain efficient and stable under dynamically changing road conditions, avoiding the use of inappropriate routes. The specific steps are as follows: The Route Suitability Assessment Module receives segment response vector data from the Transport Route Segment Response Vector Calculation Module. This data includes information such as each route's capacity, traffic flow coverage, and toll fluctuation—key factors in route evaluation. During the input data preprocessing phase, the system first verifies the completeness and accuracy of the data, ensuring that the incoming data is free of missing or incorrect data. If any data anomalies (such as missing traffic flow or incorrect toll rates) are detected, the system automatically flags and excludes the route as ineligible.

[0054] For each transport route, the route suitability assessment module calculates the overall response difference of the route based on the response vectors of each route segment. The overall response difference is obtained by weighting the differences in the response vectors of each route segment. The calculation steps are as follows: For each road segment in each route, the difference between the response vector of that road segment and the response vector of the adjacent road segment is calculated. This difference can be obtained by calculating the absolute difference of each indicator (such as capacity, flow coverage, and toll fluctuation) in the two response vectors.

[0055] Because different road sections have different degrees of influence, the system will weight the differences based on factors such as the road section's geographical location, traffic conditions, time of day, etc. For example, a road section located in a peak traffic area may have a higher weight and a greater impact. The logic for calculating the weighted difference is as follows: A weight value is applied to the overall response difference of each road segment, and this weight value can be dynamically adjusted according to the traffic volume or other relevant indicators of the road segment.

[0056] The weighted differences of all road segments are summed up to obtain the overall response difference of the entire path.

[0057] The module compares the calculated overall response difference with a preset threshold. The threshold is set based on the carrying capacity of different road sections and routes and their impact on the transportation task. The threshold can be a fixed value or dynamically adjusted based on actual conditions (such as traffic flow and weather). Based on the evaluation results, the following judgments are made: Route suitability: If the overall response difference of a route does not exceed the preset threshold, the route is considered suitable and can continue to be used for the transportation task.

[0058] Unsuitable route: If the overall response difference of a route exceeds the 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 unsuitable and enter the alternative route evaluation stage.

[0059] When a route is marked as unsuitable, the Route Suitability Assessment module will initiate an assessment of alternative routes. This process includes: Based on the overall response difference assessment results, the system selects candidate routes that meet the requirements from the known alternative routes. Candidate routes must meet stability requirements for indicators such as traffic flow and cost fluctuations to ensure that they can provide similar or better transportation results.

[0060] For each candidate route, the system repeats the steps of route response vector calculation and difference evaluation to ensure that these routes have high applicability under current traffic and toll conditions.

[0061] Finally, the Route Suitability Assessment Module sends its evaluation results (including suitable and unsuitable routes) to the Route Optimization Module. When outputting the evaluation results, the system provides a detailed route suitability score and the specific reasons for the route discrepancies, such as excessive traffic volume or significant toll fluctuations. Based on these evaluation results, the Route Optimization Module further optimizes route selection to ensure that the most appropriate route is selected for the transport task.

[0062] Assume that the route A of a transportation task consists of three sections, namely R1, R2 and R3. Through calculation, the response vector of each section of route A is: R1: Capacity: 0.8, Traffic flow coverage: 0.75, Toll fluctuation: 0.2 R2: Capacity: 0.7, Traffic flow coverage: 0.85, Toll fluctuation: 0.3 R3: Capacity: 0.6, Traffic flow coverage: 0.9, Toll fluctuation: 0.5 Assume the preset threshold is 0.2 (tolerance for overall response variance). After weighted variance calculation, the overall response variance of route A is 0.25, exceeding the threshold. Therefore, route A is marked as unsuitable, and alternative routes are re-evaluated. The system then selects new candidate routes from the remaining available options and performs the same suitability evaluation to ensure that the selected route meets the transportation task requirements.

[0063] Through these detailed steps, the path suitability assessment module can effectively evaluate and optimize the transportation path in actual operations, ensuring the efficiency and stability of the transportation task.

[0064] The route optimization module fuses the response vectors of different route segments with real-time traffic flow data to generate a raw route intensity map. By analyzing this intensity map, it extracts a set of connected route regions. Based on a set resource consumption standard, it removes route regions that consume excessive resources. This ultimately forms a selected set of routes, which are then sent to the main route selection and configuration module.

[0065] The core task of the route optimization module is to fuse the response vectors of different route segments with real-time traffic flow data, analyze them to generate a route intensity map, and extract a set of connected route regions from it. Based on the set resource consumption criteria, 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: The route optimization module first receives input data from the route suitability assessment module and the data acquisition module, including: Path segment response vectors: These vectors include metrics such as each path's capacity, traffic flow coverage, and toll volatility, describing the path's performance under current road conditions.

[0066] Real-time traffic flow data: Data from traffic monitoring systems or flow sensors provides real-time traffic load information for each section of each route.

[0067] These data are first standardized to ensure consistency in numerical range and units. The standardized data provides accurate input data for the path optimization algorithm, making the subsequent path intensity map generation process more efficient and accurate.

[0068] The path optimization module fuses the processed data to generate a path strength map. The path strength map is a multi-dimensional graphical representation of the overall carrying capacity and real-time load of each path. The generation process is as follows: Each route's segment response vector is combined with real-time traffic flow data to generate a strength value for each route. This strength value is typically composed of a weighted average of the following factors: the degree to which the segment's capacity matches its traffic flow, the impact of segment toll fluctuations on the route's overall load, and traffic flow coverage and trends.

[0069] By combining these factors, the system calculates an overall "strength value" for each path, indicating how loaded the path is under the current conditions. A higher value indicates a path is more stressed under current traffic conditions, while a lower value indicates a stronger load-bearing capacity.

[0070] By analyzing the path strength graph, the path optimization module extracts a set of connected path regions. A connected path region consists of multiple consecutive road segments that operate under the same conditions and have similar flow and load characteristics. The extraction steps are as follows: The system uses connectivity algorithms from graph theory (for example, depth-first search or breadth-first search) to identify connected regions in the path graph. Each connected region represents a relatively stable path region with consistent load. Based on the intensity distribution in the path intensity graph, the system groups road sections with similar loads into a group to form connected path regions. For example, an area with high traffic flow may be divided into one connected region, while other areas with low traffic flow may be divided into another region. After obtaining the set of connected path regions, the path optimization module will screen each path region based on the preset resource consumption criteria and eliminate those path regions that occupy too many resources. The specific process is as follows: Resource consumption standards can be based on multiple factors, such as the toll of each path, resource occupancy ratio (such as the number of lanes, traffic limit during travel time, etc.), and traffic flow density. Based on the intensity value and resource occupancy of each path area, the system calculates the total resource consumption of the area. If the resource consumption of an area exceeds the set threshold (for example, the toll is too high or the traffic load is too heavy), the area will be deemed unsuitable for transportation tasks. For those path areas that exceed the resource consumption standard, the system will remove them from the candidate path set to ensure that the final path selection does not introduce excessive cost or traffic pressure.

[0071] After removing high resource consumption areas, the system generates an optimized set of selected paths based on the remaining connected path areas. The optimization process includes: Each path in the selected path set is scored based on its path strength, capacity, cost, and resource consumption. Path scoring systems typically use a weighted average approach, assigning weights to different metrics based on their importance.

[0072] The paths in the selected path set are sorted based on the comprehensive scores. Paths with higher scores are prioritized and the system will prioritize these paths as the main paths for the transportation task.

[0073] Finally, the optimized set of selected routes is sent to the primary route selection and configuration module. At this point, the route optimization module has already optimized the set of routes based on multiple factors such as traffic volume, cost, and resource consumption. The primary route selection and configuration module then selects the most suitable primary transport route based on the priorities and comprehensive scores of these routes and configures it.

[0074] Assume that the platform receives transportation tasks for two routes: Path A: It passes through three sections, Section 1, Section 2, and Section 3, which have lower flow and cost fluctuations, but the overall intensity is higher due to the large changes in traffic flow.

[0075] Path B: It passes through five road sections, two of which have heavy traffic and large toll fluctuations, but the overall path intensity is low.

[0076] The path optimization module first generates a path strength graph. After calculating the strength values ​​for the two paths, the strength of path A is 0.85, while the strength of path B is 0.75. The module then identifies connected path regions. For example, if path A is divided into a high-traffic zone and a low-traffic zone, while path B has a more balanced traffic distribution, forming a stable path region, the module will remove the high-traffic zone of path A due to excessive resource consumption, leaving only path B as the selected path set after optimization.

[0077] Through this series of optimization steps, the path optimization module ensures the efficiency and stability of path selection, reducing the cost and resource consumption in transportation tasks.

[0078] The primary path selection and configuration module selects the highest-scoring path from the selected path set as the primary path and prioritizes it as the primary transport route. It configures the primary path based on factors such as road capacity, real-time traffic conditions, and toll rates. It also outputs the path's center coordinates, estimated travel time, toll fees, and resource budget for subsequent transport tasks.

[0079] The core task of the primary path selection and configuration module is to select the highest-scoring path from the optimized set of selected paths as the primary transport route and configure it in detail to ensure the efficiency and stability of the transport task. This module configures the primary path based on multiple factors, including the capacity of each road segment, real-time traffic conditions, and toll rates. It ultimately outputs the path's center coordinates, estimated travel time, toll fees, and resource budget, providing detailed path information for subsequent transport tasks. The specific steps are as follows: The primary route selection and configuration module first receives the set of selected routes from the route optimization module. Each route has been evaluated and optimized to ensure good traffic capacity, low resource consumption, and reasonable toll rates. Each route is also assigned a comprehensive score based on various metrics, such as route strength, capacity, traffic load, and toll fluctuation.

[0080] Scoring is typically calculated using a weighted average algorithm, combining multiple indicators (such as capacity, traffic flow, and toll fees) for a comprehensive score. Weights are set based on business needs and optimization goals to ensure that the most important indicators have a significant impact on the final selection.

[0081] For example, in some cases, capacity may be given a higher weight because it directly affects transportation time, while in other cases, toll rates may be more important and therefore their weight will be increased accordingly.

[0082] Based on the score of each route, the system selects the route with the highest score from the set of selected 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, cost-effective and feasible transportation.

[0083] The system iterates through the set of optimized routes, compares each route's overall score with the other routes, and selects the one with the highest score as the primary route. This step ensures that the primary route is selected based on multiple factors, such as capacity, traffic conditions, and toll charges, to best meet the actual needs of the transportation task.

[0084] After determining the primary route, the system will begin detailed configuration of the primary route. This includes calculating key parameters such as the route's center coordinates, estimated travel time, toll fees, and resource budget. These parameters will provide detailed guidance for subsequent transport tasks, ensuring controllability and transparency during the transport process. The specific configuration steps are as follows: The system calculates the "center coordinates" of the main path using the start and end coordinates of each section in the path. The center coordinates are the representative location of the path and are used for subsequent navigation and scheduling. The calculation logic of the center coordinates is: take the average of the start and end points of all sections in the path as the center point of the main path. The estimated travel time refers to the estimated time required from the start to the end of the path. When calculating, the system will take into account factors such as the traffic capacity of each section, current traffic flow, and vehicle speed. The calculation logic is as follows: For each road segment, the formula is used: Estimated travel time = segment length / current vehicle speed. The travel times for all segments are then added together to arrive at the estimated travel time for the entire route. The toll calculation for the main route involves the toll rates for each segment. The system calculates the total route toll based on the toll conditions for each segment (such as vehicle type and time of day). This is calculated by taking a weighted sum of the fees at each toll station along the route. For each toll segment, the system calculates the toll based on the vehicle type, toll rate, and travel time. The tolls for all segments are then summed to arrive at the total toll.

[0085] The resource budget calculation predicts the resource consumption of a route based on factors such as the total length of the route, traffic flow, capacity, and traffic density. The resource budget mainly considers resources such as transportation facilities, energy consumption, and time required for the route. The calculation logic is as follows: The system comprehensively calculates factors such as traffic flow, congestion, and capacity of each path to derive the resource consumption value of each path.

[0086] After completing the configuration of the main route, the system will output parameters such as the center coordinates of the route, estimated travel time, toll fees, and resource budget. This information will serve as input for subsequent transportation tasks to ensure efficient scheduling and resource optimization during the transportation process. Output content: Path center coordinates: The center position of the main path, usually the midpoint of the path or the most representative coordinates.

[0087] Estimated travel time: The estimated travel time from the starting point to the end point.

[0088] Toll Fee: The total toll fee for the entire main route, including the fees of all toll booths.

[0089] Resource budget: total resource consumption of the main path, including the use of transportation facilities, energy consumption and other resources.

[0090] Suppose the platform selects Route B as the primary route for a freight mission. Route B has an overall score of 90, making it the highest-scoring route. Based on the various sections of Route B, the system calculates the primary route's center coordinates to be (23.5, 45.8), with an estimated travel time of 4 hours, a toll of 50 yuan, and a resource budget of 200 units.

[0091] These configuration parameters will be passed to the transport scheduling system as the basis for truck route selection and scheduling. Transport tasks will be executed according to these configuration parameters to ensure the smooth progress of the transport process.

[0092] Through these detailed steps, the main path selection and configuration module not only selects the most suitable main path, but also provides comprehensive support for the transportation task by configuring its detailed parameters, ensuring the efficiency and predictability of the transportation process.

[0093] Example 2: In this embodiment, the application scenario examples of the present application solution are given as follows: A logistics company received an urgent transport mission to deliver a batch of urgently needed medical equipment from City A to City B. The mission required minimizing transportation time while keeping costs within reasonable limits. Because this mission involved traveling on highways, where toll rates and traffic conditions can fluctuate over time, the logistics company needed to optimize routes in a dynamic environment to ensure efficient and cost-effective transportation.

[0094] In this context, the online freight platform optimization system based on highway toll data will optimize the route according to real-time traffic flow, toll standards, road conditions and other information, so as to select the most suitable route for the transportation task.

[0095] S1: Data acquisition module The data acquisition module first collects raw data such as the vehicle's transportation route, GPS positioning information, road traffic conditions, real-time traffic flow, toll rates, and road condition changes through on-board equipment in real time. For example, the system will obtain the following information: Vehicle transportation path: The vehicle starts from city A, passes through highways X, Y and Z, and finally arrives at city B.

[0096] GPS positioning information: vehicle's real-time location and direction of travel.

[0097] Road traffic conditions: The system obtains information such as traffic density and traffic accidents on each section of the highway.

[0098] Real-time traffic flow: The traffic monitoring system provides real-time traffic flow data for each road section.

[0099] Charging standards: The tolls for each road section may be affected by factors such as time period and vehicle type.

[0100] Changes in road conditions: changes in road conditions caused by construction information, weather conditions, etc.

[0101] After these raw data are structured, the system will send them to the transportation route segment response vector calculation module for further processing.

[0102] S2: Transport route segment response vector calculation module Based on the collected raw data, this module calculates multiple indicators for each transport route and generates a segment response vector for the route. The segment response vector for each route contains the following: Road segment capacity: Evaluate the capacity of each road segment based on real-time traffic flow and road carrying capacity. For example, if the traffic flow on a road segment is too heavy, the capacity may be reduced.

[0103] Traffic flow coverage: This metric reflects the proportion of each route's traffic flow to the maximum carrying capacity of the road section. If the flow is high, it may cause congestion and reduce the effectiveness of the route.

[0104] Toll Fluctuation: This is done by analyzing the fluctuations in toll rates over time or between different road sections to assess the cost stability of a route. Large fluctuations in toll rates on certain sections of a route may impact transportation costs.

[0105] By normalizing these indicators, a standardized path response vector is generated and sent to the path suitability assessment module.

[0106] S3: Path suitability assessment module The Route Suitability Assessment Module evaluates the suitability of each route based on the route response vectors obtained from the Transport Route Segment Response Vector Calculation Module. If the overall response difference of a route segment exceeds a preset threshold (for example, due to excessive traffic flow changes or significant toll fluctuations), the route will be marked as unsuitable and alternative routes will be re-evaluated. The detailed steps of this process include: Evaluate route capacity and traffic flow variations: If a route experiences a significant change in traffic volume (for example, a sudden surge from normal traffic to overload), the route may not be suitable.

[0107] Evaluate the toll volatility of a route: If the toll volatility of a route is too high (for example, the tolls at some toll booths change erratically), the route will be marked as unsuitable.

[0108] After evaluation, the system removes unsuitable paths from the candidate paths and sends the evaluation results to the path optimization module.

[0109] S4: Path Optimization Module Based on the evaluation results, the route optimization module fuses the response vectors of different route segments with real-time traffic flow data to generate a raw route intensity map. The intensity map shows the comprehensive performance of each route, including factors such as capacity, flow, and toll fluctuations. The module's workflow includes: Generate a route strength map: The route strength map shows how different routes perform across various metrics. For example, some routes may have low route strength due to heavy traffic, while others may have higher tolls but higher capacity.

[0110] Extracting connected path area sets: By analyzing the path intensity graph, multiple connected areas with similar load conditions are extracted. These areas have relatively consistent performance in terms of traffic flow and charging standards.

[0111] Eliminate high-resource consumption paths: Based on defined resource consumption criteria (such as route fees and congestion), the system eliminates paths that consume excessive resources, ultimately selecting the optimal set of paths. The optimized set of paths is then sent to the main path selection and configuration module.

[0112] S5: Main path selection and configuration module The primary path selection and configuration module selects the path with the highest score from the optimized selected path set as the primary path. The configuration process includes: Select the highest-scoring path: From the optimized path set, the path with the highest score is selected. This is usually based on a comprehensive evaluation of multiple factors such as capacity, resource consumption, and toll rates.

[0113] Configure the details of the primary route: Configure the details of the primary route based on factors such as road capacity, real-time traffic conditions, and toll rates. For example, you may need to adjust the estimated travel time based on real-time traffic flow or adjust the budget based on toll fluctuations.

[0114] 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 provide detailed guidance for the execution of subsequent transport tasks. Ultimately, the output route information provides the optimal route configuration for the scheduling and execution of freight tasks.

[0115] Through these steps, the online freight platform optimizes transportation routes in real time, ensuring efficient and stable delivery. In this application scenario, the platform dynamically calculates the suitability of routes by collecting real-time information such as traffic flow, road conditions, and toll rates. Combining optimization algorithms with resource consumption standards, it selects the most suitable primary route and configures it in detail, ensuring that goods arrive at their destination on time, on budget, and safely.

[0116] Example 3: In an online freight platform optimization system based on highway toll data, users (such as shippers, transportation companies, or dispatchers) interact with the platform to optimize transportation routes and resource allocation, ensuring efficient execution of transportation tasks. The following is an example of a typical human-computer interaction scenario, involving the main operations and feedback between the user and the platform.

[0117] Suppose a shipper needs to transport goods through the platform, and the transport task requires efficient, low-cost, and fast transportation. The shipper enters relevant information about the transport task (such as the starting point, destination, type of cargo, and timeliness requirements) through the platform interface. The system selects and optimizes the route based on real-time highway toll data, traffic flow, toll rates, and other information, and outputs the final transport route and parameters. After logging into the platform, the shipper enters basic information about the transport task through the user interface. The interface requires the following information: Origin and destination: The shipper selects the origin and destination of the shipment (for example, City A to City B).

[0118] Cargo type: The shipper 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.

[0119] Time requirement: The shipper sets the expected transportation time (for example, requiring the transportation task to be completed within 3 hours).

[0120] The platform receives information entered by the shipper through a form interface and automatically displays the recommended route analysis function below.

[0121] The platform's data acquisition module automatically collects raw data related to transportation tasks, and the backend obtains real-time data from highway toll systems, traffic monitoring systems, and GPS devices. The platform displays the following information: Real-time traffic flow: The platform uses visual charts to display the real-time traffic density of each major road section, making it easier for shippers to determine which sections of the road may cause traffic congestion.

[0122] Road Capacity: The road capacity of each recommended route, along with possible toll rates, is presented in charts and tables.

[0123] Charging standards: The platform will list the charging conditions of different toll stations on each route (such as tolls, time-based charges, etc.). For example, the platform displays the following as shown in Table 1: Table 1: Platform display road section Traffic capacity (vehicles / hour) Current traffic volume (vehicles / hour) Toll (yuan) Real-time traffic conditions Highway Section A 1500 800 30 normal Highway Section B 1200 1000 20 congestion Highway Section C 1800 1200 25 Smooth After data collection is complete, the transport route segment response vector calculation module begins calculating the response vector for each route. Based on indicators such as each route's segment capacity, traffic flow coverage, and toll fluctuation, the system generates a segment response vector for each route and displays it to the user. The user can view detailed response vector data for each route on the interface, as shown in Table 2: Table 2: Response vector data road section Capacity response Traffic coverage response Charge volatility response Segment Response Vector Highway Section A 0.85 0.75 0.2 0.74 Highway Section B 0.7 0.83 0.5 0.67 Highway Section C 0.9 0.67 0.3 0.75 The system displays the segment response vector (comprehensive score) to the user so they can understand the strengths and weaknesses of each route. Route A has a higher score, indicating that it performs better overall.

[0124] The platform's path suitability assessment module evaluates the differences in the response vectors of the aforementioned 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 between segments on a particular path is too large (e.g., high toll volatility and uneven traffic coverage on Path B), the path will be marked as unsuitable. The interface displays the path evaluation results, as shown in Table 3. Table 3: Path evaluation results path Overall score Applicability assessment results Highway Section A 0.74 Applicable Highway Section B 0.67 not applicable Expressway Section C 0.75 Applicable Path B is evaluated as "Not Applicable" and is therefore eliminated, and the system automatically begins searching for an alternative path.

[0125] After evaluating route suitability, the route optimization module integrates the response vectors for each route segment with real-time traffic flow data to generate a route strength map and extract a set of connected route regions. Based on predefined resource consumption criteria, the system removes route regions that consume excessive resources. Users can review the optimized route recommendations.

[0126] The results of the path optimization will be presented to the user through graphics and tables, as shown in Table 4: Table 4: Path optimization results path Optimized comprehensive score Resource consumption (units) Reasons for recommendation Highway Section A 0.8 50 Suitable for transport tasks with low flow rates Highway Section C 0.78 60 Strong traffic capacity and high flow rate After obtaining the optimized set of selected paths, the primary path selection and configuration module selects the path with the highest score as the primary path. For example, path A has the highest overall score, so it is selected as the primary path. The system configures this path based on factors such as road capacity, real-time traffic conditions, and toll rates, and outputs the configuration parameters. The output includes: Path center coordinates: The center coordinates of path A (for example, longitude and latitude), which are used for subsequent scheduling.

[0127] Estimated travel time: The system estimates the travel time (for example: 3 hours) based on traffic flow and road capacity.

[0128] Toll Fee: The total toll for the main route (e.g. 50 yuan).

[0129] Resource budget: The estimated resources used during transportation (e.g. 100 units). The user interface will display the configuration results as shown in Table 5: Table 5: Configuration results path Center coordinates Estimated travel time Fees Resource Budget Highway Section A (23.5，45.8) 3 hours 50 yuan 100 units Through this complete human-computer interaction scenario, users (shippers or dispatchers) can input basic information about the transport task through the platform. The system will automatically collect data, evaluate and optimize the route, and ultimately select the most appropriate primary route. Throughout the entire process, the platform provides clear route recommendations, toll estimates, travel time, and resource budgets through real-time data and optimization algorithms, ensuring efficient, stable, and cost-effective transport tasks. This interactive model not only improves transport efficiency but also enhances the scientific nature of transport decisions. Example 4: To better demonstrate the practical application of a network freight platform optimization system based on highway toll data, we will use a set of simulation and experimental data to demonstrate 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.

[0130] 1. Data acquisition module The goal of the simulated data collection is to collect raw data related to the transportation task in real time, including the vehicle's transportation route, GPS positioning information, road section traffic conditions, real-time traffic flow, toll standards, and road condition changes. Table 6 shows the raw data of the simulation: Table 6: Simulation raw data Data Type Sample 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, Highway Y: Slightly Congested Real-time traffic flow Highway X: 800 vehicles / hour, Highway Y: 1,200 vehicles / hour Fee Standard Highway X: 10 yuan, Highway Y: 15 yuan Changes in road conditions Highway Y: Minor accident, estimated delay of 20 minutes These data will be collected in real time and transmitted to subsequent modules.

[0131] 2. Transport route section response vector calculation module Based on the raw data collected above, the system calculates the response vector for each transport route. The following is the simulated road section response vector, covering indicators such as capacity, traffic flow coverage, and toll fluctuation, as shown in Table 7: Table 7: Simulated road segment response vectors road section capacity Traffic coverage Fee volatility Segment Response Vector (Comprehensive Score) Highway X 0.85 0.75 0.2 0.74 Highway Y 0.7 0.8 0.4 0.66 Traffic capacity: Calculated based on real-time traffic and the carrying capacity of the road section, indicating the traffic efficiency of the road section.

[0132] Traffic coverage: reflects the current traffic load of the road section. A value close to 1 indicates that the road section is close to full load.

[0133] Toll Fluctuation: This is calculated based on the fluctuation of the real-time toll standard. The greater the fluctuation, the less stable the route's toll. These response vectors are then sent to the Route Suitability Assessment Module for further evaluation.

[0134] 3. Path suitability assessment module The path suitability assessment module evaluates the overall response differences of the path segments. If the difference of a path exceeds a preset threshold, the system will mark it as unsuitable. The threshold is set to 0.2. The simulation results are shown in Table 8: Table 8: Simulation results path Overall score Applicability assessment results Highway X -> Highway Y 0.7 not applicable Highway X -> Highway Z 0.8 Applicable Evaluation Logic: Highway X -> Highway Y: Highway Y's traffic coverage is 0.8, and its toll volatility is high (0.4), resulting in a low overall score for this route, ultimately deemed unsuitable. Highway X -> Highway Z: Traffic and tolls are relatively stable, so this route is considered suitable.

[0135] 4. Path optimization module The route optimization module generates a route strength graph based on the route response vector and real-time traffic data. It then analyzes the strength graph to extract a set of connected route regions and eliminate regions with high resource consumption. Below are the route selection results before and after optimization.

[0136] Before optimization: Path A (Highway X -> Highway Y): Score 0.74, high traffic volume, slightly poor capacity. Path B (Highway X -> Highway Z): Score 0.8, moderate traffic volume, strong capacity.

[0137] After optimization: After filtering by resource consumption criteria, path B is retained, and path A is eliminated due to high traffic volume and low capacity, as shown in Table 9: Table 9: Optimization results.

[0138] path Optimized comprehensive score Resource consumption Reasons for recommendation Highway X -> Highway Z 0.8 50 Moderate flow and strong traffic capacity. Highway X -> Highway Y 0.74 70 Too high traffic volume and poor traffic capacity 5. Main path selection and configuration module Based on the optimized path set, the main path selection and configuration module selects the path with the highest score from the selected path set. The following is the path configuration result: Path selection: Path B (Highway X -> Highway Z) is selected as the primary path because it has the highest score and low resource consumption. The configuration results are shown in Table 10: Table 10: Configuration results path Center coordinates Estimated travel time Fees Resource Budget Highway X -> Highway Z (23.5，45.8) 3 hours 50 yuan 100 units Path center coordinates: The center point of the path (for example, the latitude and longitude coordinates of the midpoint of the path) is (23.5, 45.8).

[0139] Estimated travel time: Based on traffic flow and road capacity, the system estimates that the estimated travel time for route B is 3 hours.

[0140] Charges: According to the charging standards, the total charge for route B is 50 yuan.

[0141] Resource budget: The estimated resources used during transportation are 100 units, taking into account transportation facilities and energy consumption.

[0142] In order to verify the effectiveness of the system, a simulation experiment was conducted to test the optimization effects of different paths. The experimental data is shown in Table 11: Table 11: Experimental data path Initial score 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 Initial score: A score calculated based on a preliminary assessment of factors such as traffic volume and toll rates.

[0143] Optimized score: The score after path optimization, taking into account factors such as resource consumption and path load.

[0144] Resource consumption: Calculate the resource consumption of each path and eliminate paths that consume too much resources.

[0145] The experimental data shows that path A->path B is still marked as inapplicable after optimization, while path A->path C has a higher score after optimization and is selected as an applicable path with lower resource consumption.

[0146] The simulation and experimental data above demonstrate how the online freight platform dynamically optimizes transportation routes through a multi-stage route optimization process, from data collection to primary route selection, ensuring efficient and stable 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.

[0147] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these 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 any one or more embodiments or examples.

[0148] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present 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 by: It includes data acquisition module, transport route section response vector calculation module, route suitability evaluation module, route optimization module and main route selection and configuration module; The data acquisition module acquires raw data related to the transportation task in real time; The transport path section response vector calculation module calculates the capacity and cost fluctuation index of each transport path based on the collected raw data and generates the path section response vector; The path suitability evaluation module evaluates the suitability of each path based on the segment response vector. If the overall response difference of the path segments exceeds a threshold, the path will be marked as unsuitable and alternative paths will be re-evaluated. The overall response difference calculation logic is as follows: for each segment in each path, the difference between the response vector of the segment and the response vector of the adjacent segment is calculated; Apply a weight value to the overall response difference of each road segment; The weighted differences of all road segments are summed up to obtain the overall response difference of the entire path; The path optimization module fuses the response vectors of different path segments with real-time traffic flow data to generate a path strength graph. By analyzing the path strength graph, it extracts a set of connected path regions. After removing the path regions that occupy too many resources from the connected path region set, the selected path set is formed. The main path selection and configuration module selects the path with the highest score from the selected path set as the main path and configures the main path as the main transportation route.

2. The network freight platform optimization system based on highway toll data according to claim 1, characterized in that: The path suitability evaluation module calculates the overall response difference of the path based on the response vectors of each section in the path, and the overall response difference is obtained by weighted calculation of the difference in the response vectors of each section; The calculated overall response difference is compared with the 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 means that the route cannot meet the requirements of the transportation task and is marked as not applicable, 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 the real-time traffic flow data to form a strength value for each path and generates a path strength map; Connectivity algorithms from graph theory are used to identify connected regions in the path strength graph. Road sections with similar loads are grouped together based on the intensity distribution in the path strength graph to form connected path regions. Each path region is then screened based on preset resource consumption criteria to eliminate those that occupy too many resources.

4. The network freight platform optimization system based on highway toll data according to claim 3 is characterized by: Each path in the path set selected by the path optimization module will be comprehensively scored based on its path strength, capacity, cost and resource consumption; Sort the paths in the selected path set according to the comprehensive score.

5. The network freight platform optimization system based on highway toll data according to claim 4 is characterized by: 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 completing the configuration of the main path, the center coordinates of the path, estimated travel time, toll fees and resource budget are output.

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 starting and ending points of all sections in the path as the center point of the main path. The estimated travel time refers to the estimated time required to travel from the starting point to the end point of the path. The travel time of all sections is added together to obtain the estimated travel time of the entire path.

7. The network freight platform optimization system based on highway toll data according to claim 1, characterized in that: The transport path section response vector calculation module calculates the capacity of each path based on the traffic flow data and the section 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 section 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 path segment response vector calculation module calculates the capacity of each path, and the calculation logic is: Obtain the road capacity and dynamically adjust it based on real-time traffic flow data to determine the current road section capacity. If the traffic flow on a road section is greater than 80% of the road section capacity, the capacity indicator is reduced to 60% of the original capacity. If the traffic flow is less than 60%, the capacity indicator remains at 100% of the original value. Traffic flow coverage = current traffic flow / maximum traffic capacity, and 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 the 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 section traffic conditions, real-time traffic flow, toll standards, and changes in road conditions; Structural processing of raw data, including data cleaning, data mapping and standardization, and data format conversion; The structured data will be transmitted to the transport route section response vector calculation module through the 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 a starting point, a waypoint, and an end point; the GPS positioning information includes latitude and longitude, vehicle speed, and driving direction; the road section traffic conditions include current traffic volume, road congestion, and traffic accident information; the real-time traffic flow includes obtaining the real-time traffic flow of the road section; the charging standards include tolls, time window fees at toll stations, and fees for different vehicle models; the road condition changes include weather changes, road closures, and construction information.

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