A multi-traffic-entry online car-hailing aggregation platform intelligent scheduling and traffic distribution method and system

Abstract

This invention relates to the field of intelligent order scheduling technology for ride-hailing aggregation platforms. Specifically, it discloses a method and system for intelligent scheduling and traffic allocation on ride-hailing aggregation platforms with multiple traffic entry points. The system comprises a traffic channel access layer, a capacity port connection layer, an intelligent scheduling core layer, a rule configuration layer, and an operation monitoring layer. The traffic channel access layer connects to multiple upstream traffic channels, uniformly collecting and converting all order attributes before storing them in a global order pool. The capacity port connection layer connects to multiple downstream capacity ports, generating a real-time available capacity pool after compliance verification. This invention effectively solves the problems of order conflicts and poor adaptability caused by existing scheduling schemes that only match based on distance, significantly improving order matching efficiency and capacity resource utilization. It eliminates the need to customize scheduling logic for newly launched cities, significantly reducing the development and configuration costs for cross-city expansion. The scheduling rules can be flexibly adjusted to adapt to various operational needs.

Description

Technical Field

[0001] This invention relates to the field of intelligent order scheduling technology for ride-hailing aggregation platforms, and in particular to a method and system for intelligent scheduling and traffic allocation of ride-hailing aggregation platforms with multiple traffic entry points. Background Technology

[0002] Ride-hailing aggregation platforms represent the mainstream operating model in the current travel service sector. They acquire order resources by connecting to various upstream traffic channels such as Gaode Maps and Ctrip, while integrating multiple capacity pools including social ride-hailing services and traditional taxis to provide users with full-scenario travel services. With the industry's scaling up, aggregation platforms generally face core demands such as multi-channel order collaborative distribution, efficient utilization of multi-terminal capacity, and rapid cross-city deployment. The quality of the dispatch system directly determines the platform's operational efficiency, service quality, and profitability.

[0003] Current mainstream technical solutions fall into two categories. The first is a general proximity-based distribution solution. Its working principle involves unifying orders from all channels into a scheduling pool. Based on the real-time geographical location of the order's origin and the available transportation capacity, orders are dispatched to the nearest available capacity according to the shortest distance principle. This solution is simple to implement, requiring only a call to a geolocation service for deployment. It is currently used by most small and medium-sized aggregation platforms, and its advantages include low development costs, short deployment cycles, and rapid deployment without complex rule configurations. However, this solution does not consider the differences in order premium rules and timeliness requirements across different traffic channels, nor does it match the operational restrictions and individual order preferences of drivers' respective platforms. This easily leads to conflicts where orders from multiple channels simultaneously compete for the same transportation capacity, resulting in low efficiency in matching transportation capacity with traffic. Furthermore, it cannot accommodate the business requirements of multiple drivers operating independently, leading to significant waste of transportation resources.

[0004] The second type is a single-city customized dispatch solution. Its working principle involves the operations team manually customizing exclusive dispatch rules for a single city already in operation, based on local regulatory policies, capacity structure, and order characteristics. This allows for individual configuration of traffic allocation ratios across different channels. Its current advantage is strong adaptability to single-city operations, meeting local personalized operational needs, and performing well in stable, mature cities. However, this solution requires developing separate adaptation rules for each newly established city. The technical development and manual configuration costs for cross-city expansion are extremely high, resulting in a long expansion cycle. Furthermore, it lacks a full-chain channel operation performance monitoring mechanism, making it impossible to dynamically adjust traffic access weights based on actual channel operation data, thus hindering the optimization of the return on investment for channel resources.

[0005] As the scale of aggregation platforms continues to expand, access to multiple traffic channels, collaboration of multiple driver ports, and rapid expansion across cities have become common demands in the industry. Existing technologies can no longer meet the current needs of large-scale operations, and there is an urgent need to develop new intelligent dispatching solutions to address these pain points. Summary of the Invention

[0006] This invention proposes an intelligent scheduling and traffic allocation method and system for ride-hailing aggregation platforms with multiple traffic entry points, in order to solve the problems mentioned in the prior art, such as low scheduling and matching efficiency, poor capacity coordination, high cross-city landing costs, and insufficient extensive channel operation.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a method for intelligent order scheduling of a ride-hailing aggregation platform with multiple traffic entry points, comprising the following steps: S1. Through the pre-set multi-channel standardized adaptation interface, it synchronously accesses encrypted order messages from multiple upstream traffic channels, including Gaode, Ctrip, and Tongcheng. After format conversion and compliance verification of the messages, it extracts core order data, including order timeliness requirements, premium rules, origin and destination, and special passenger tags. At the same time, it synchronously collects real-time capacity distribution data of the target city, driver tag data including driver port, order preference, compliance qualifications, and daily order completion volume, dynamic premium calculation rules of each channel, and real-time traffic data including real-time congestion index and temporary traffic control points. S2. Construct a multi-channel traffic-capacity dynamic matching model. First, perform pre-compliance filtering on the capacity to remove those that are blocked, have expired qualifications, or do not meet the order acceptance conditions in the current service. Then, assign dynamic weights to the timeliness requirements, premium level, destination attributes, and special tags of the order. Assign dynamic weights to the current location and order origin distance, order acceptance preference matching degree, and daily order completion saturation of the capacity. The weighted sum is used to obtain the matching score of each order and each available capacity. Select the top 3 capacity with the highest matching scores as the initial distribution candidates and output the initial order distribution strategy. S3. Invoke the dual-version collaborative operation and scheduling mechanism. First, divide the transportation capacity of the aggregated version driver terminal, express version driver terminal, and cruising taxi driver terminal in the same city into dedicated transportation capacity pools for the corresponding ports and cross-port shared transportation capacity pools. Perform duplicate order verification on the candidate transportation capacity in the initial distribution strategy. If the same transportation capacity matches multiple orders at the same time, sort them according to the priority of special passenger orders > high premium orders > time-sensitive orders. Only push the order request with the highest priority to the transportation capacity. The remaining low priority orders are automatically transferred to the shared transportation capacity pool for rematching, so as to realize multi-port transportation capacity collaboration, order complementarity, and no duplicate order dispatch. S4. Call the city-localized city launch adaptation module, pre-store localized dispatch rule templates covering different city levels, regulatory requirements, and capacity structures, automatically match the corresponding template when launching in the target city, and automatically synchronize the latest regulatory rules through the public interface of the local transportation regulatory department. Support operators to customize and canary release dispatch weights, capacity pool allocation ratios, and traffic allocation rules, generate the final order distribution strategy adapted to the target city and push it to the corresponding driver end for execution. S5 calls the channel performance full-link monitoring module to perform data tracking and statistics on the entire chain of orders from each traffic channel, from access, distribution, order acceptance, order completion, and settlement. It collects operational data in real time, including order volume, order response rate, order completion rate, driver willingness to accept orders, and average revenue per order for each channel. Based on the return on investment calculation results, it smoothly adjusts the channel traffic access weight and identifies and automatically blocks abnormal traffic fraud.

[0008] Furthermore, the multi-channel traffic capacity dynamic matching model in S2 has a built-in new driver cold start adaptation unit. For new drivers who have been on the platform for less than 7 days and have no historical order preference data, the average order preference data of drivers of the same type and operating period in the same city is extracted as temporary preference data to participate in the matching score calculation. Once the number of orders completed by the new driver reaches the preset threshold, it will automatically switch to its own real preference data.

[0009] Furthermore, the dual-version collaborative operation scheduling mechanism in S3 sets a dispatch cooldown threshold, so that the same transportation capacity will only receive at most one dispatch request within 30 seconds after completing the previous order. At the same time, a special rule is set for the taxi driver's side, which prioritizes street-hailing orders over online-hailing orders, to protect the offline operation rights of taxi drivers.

[0010] Furthermore, the city-localized city opening adaptation module in S4 has a built-in time-segment rule switching unit, which can automatically switch the scheduling rules and traffic allocation weights for the corresponding time period according to the order density and capacity supply differences of different time periods such as morning and evening peak hours, off-peak hours, and nighttime in the target city, without the need for manual adjustment.

[0011] Furthermore, the channel performance monitoring module in S5 sets a weight adjustment threshold, and the daily adjustment range of the traffic access weight of a single channel does not exceed a preset upper limit of 20%, so as to avoid large fluctuations in order distribution caused by sudden changes in weight. At the same time, it automatically generates multi-dimensional channel operation reports and pushes them to the operation end.

[0012] Furthermore, this invention also discloses an intelligent order scheduling system for a ride-hailing aggregation platform with multiple traffic entry points, used to implement the aforementioned intelligent order scheduling method for a ride-hailing aggregation platform with multiple traffic entry points, including: The data access module is used to simultaneously access encrypted order messages from multiple upstream traffic channels through a pre-set multi-channel standardized adaptation interface. After format conversion and compliance verification of the messages, the core order data is extracted. At the same time, real-time capacity distribution data, driver tag data, channel dynamic premium rules, and real-time traffic data of the target city are collected simultaneously. The matching model module is used to build a dynamic matching model of multi-channel traffic and capacity. First, the capacity is filtered for compliance in advance. Then, dynamic weights are assigned to each indicator in the order dimension and the capacity dimension respectively. The weighted sum is used to obtain the matching score of each order and each available capacity. The top 3 capacity with the highest matching scores are selected as the initial distribution candidates, and the initial order distribution strategy is output. The collaborative scheduling module is used to call the dual-version collaborative operation scheduling mechanism to divide the transportation capacity of multiple driver terminals in the same city into a dedicated transportation capacity pool and a cross-port shared transportation capacity pool. It performs duplicate order verification on the candidate transportation capacity in the initial distribution strategy, pushes the highest priority order after sorting according to the preset priority, and transfers the remaining low priority orders to the shared transportation capacity pool for rematching. The local adaptation module is used to pre-store multiple sets of localized scheduling rule templates. When opening a city, it automatically matches the corresponding template, connects to the local regulatory interface to automatically synchronize the latest regulatory rules, supports operators to customize the configuration of scheduling rules and canary releases, generates the final order distribution strategy and pushes it for execution. The channel monitoring module is used to perform full-chain statistics on orders from each traffic channel, collect operational data from each channel in real time, smoothly adjust the channel traffic access weight based on the return on investment calculation results, and identify and block abnormal traffic fraud.

[0013] Furthermore, the matching model module has a built-in new driver cold start adaptation unit. For new drivers who have been on the platform for less than 7 days and have no historical order preference data, the average order preference data of drivers of the same type and operating period in the same city is extracted as temporary preference data to participate in the matching score calculation. Once the number of completed orders of the new driver reaches the preset threshold, it will automatically switch to its own real preference data.

[0014] Furthermore, the collaborative scheduling module has a built-in order dispatch cooling unit, which sets a 30-second order dispatch cooling threshold. During the cooling period after the same transportation capacity completes the previous order, it will only receive a maximum of 1 order dispatch request. At the same time, a special rule is set for the taxi driver's side, which prioritizes street-hailing orders over online-hailing orders.

[0015] Furthermore, the location adaptation module has a built-in time-segment rule switching unit, which can automatically switch the scheduling rules and traffic allocation weights for the corresponding time period according to the order density and capacity supply differences of the target city at different times.

[0016] Furthermore, the channel monitoring module has a built-in weight smoothing adjustment unit, which sets a preset upper limit of no more than 20% for the daily adjustment of the traffic access weight of a single channel, so as to avoid large fluctuations in order distribution caused by sudden changes in weight. At the same time, it automatically generates multi-dimensional channel operation reports and pushes them to the operation end.

[0017] Compared with existing technologies, the beneficial effects of this invention are: This invention achieves precise matching of orders and transportation capacity by integrating intelligent matching logic that combines order attributes from traffic channels, operational attributes of transportation capacity, and real-time supply and demand characteristics. This effectively avoids the conflict problem of multiple channels competing for the same transportation capacity, significantly improves order matching efficiency and transportation capacity utilization, and solves the defects of existing general proximity distribution schemes that fail to consider the differentiated attributes of multiple channels and multiple transportation capacities, resulting in insufficient matching accuracy and waste of transportation capacity resources.

[0018] This invention adopts a standardized and configurable scheduling rule template and a dynamic evaluation mechanism for the operation effect of the entire channel. It eliminates the need to develop customized scheduling logic for each new city, significantly reducing the technical development and manual configuration costs of cross-city expansion. At the same time, it can dynamically adjust the traffic allocation weight of each traffic channel based on real-time operation data, continuously optimizing the input-output ratio of channel resources. This solves the shortcomings of existing single-city customized scheduling solutions, such as long expansion cycle, delayed operation adjustment, and extensive channel operation management.

[0019] This invention is adaptable to various operating scenarios and cooperation models. It is compatible with the settlement rules and service timeliness requirements of different traffic channels, and can also adapt to the industry regulatory policies and local transportation capacity structure of different cities. At the same time, it supports the access and operation of multiple types of transportation capacity such as taxis and compliant ride-hailing vehicles. It is applicable to both the rapid launch and operation of small and medium-sized aggregation platforms and the large-scale expansion of leading aggregation platforms across regions, and has extremely high industry promotion value. Attached Figure Description

[0020] Figure 1 This is a schematic diagram illustrating the steps of an intelligent scheduling and traffic allocation method for a ride-hailing aggregation platform with multiple traffic entry points, as proposed in this invention. Figure 2 This is a schematic block diagram of an intelligent scheduling and traffic allocation system for a ride-hailing aggregation platform with multiple traffic entry points, as proposed in this invention. Figure 3 A bar chart comparing the efficiency of new city deployment of the intelligent scheduling and traffic allocation system for a multi-entry ride-hailing aggregation platform proposed in this invention. Figure 4 This is a line graph comparing the order matching effects of different scenario scheduling schemes of the intelligent scheduling and traffic allocation system for a multi-traffic entry point ride-hailing aggregation platform proposed in this invention. Detailed Implementation

[0021] 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, and 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.

[0022] Reference Figures 1 to 4 This invention discloses an intelligent order scheduling system for a ride-hailing aggregation platform with multiple traffic entry points, including a traffic channel access layer, a capacity port docking layer, an intelligent scheduling core layer, a rule configuration layer, and an operation monitoring layer.

[0023] The traffic channel access layer deploys a unified API gateway, compatible with three mainstream interaction protocols: HTTP, HTTPS, and WebSocket. All cooperating upstream traffic channels can submit order data according to the preset general message specifications. If a channel has a custom message format, the built-in message conversion engine in the access layer can complete the parsing through drag-and-drop configuration of field mapping rules, without the need to write custom code. The collected order attributes include origin and destination latitude and longitude, estimated mileage, estimated order amount, the maximum pick-up time required by the channel, the membership level priority of the channel user, the order premium ratio, user cancellation compensation rules, and capacity qualification requirements. All attributes are uniformly mapped to platform standard fields and then stored in the global order pool, completely solving the problem in existing technologies where separate parsing logic needs to be developed for each new traffic channel and channel attributes cannot be included in the scheduling reference.

[0024] The capacity interface layer connects to four types of downstream capacity carriers: the self-owned ride-hailing capacity management terminal, the open port of third-party ride-hailing platforms, the regulatory port of the taxi industry, and the compliant shared private car operation port. It adopts a long connection mechanism to synchronize the real-time latitude and longitude of the capacity, empty / passenger / return status, driver qualification type, operating area restrictions of the capacity port, driver's preset order preference, vehicle type, and number of orders completed that day. The interface layer has a built-in compliance verification engine that automatically matches the regulatory rules of the current operating city, filters out capacity that does not meet the qualification requirements or is outside the operating area, and generates a real-time available capacity pool. All capacity attributes are unified into platform standard fields, which solves the problem of high cost of accessing and adapting different types of capacity and the problem of illegal order dispatch caused by the lag in compliance verification in the existing technology.

[0025] The intelligent scheduling core layer receives pending orders from the global order pool and capacity data from the real-time available capacity pool. Based on the currently effective scheduling rules generated by the rule configuration layer, it calculates a matching score for each pending order with all available capacity within its service range. A capacity candidate queue is generated in descending order of matching scores, and a dispatch request is pushed to the capacity at the top of the queue. If the capacity accepts the order within a preset time, the pairing is completed and the status of both parties is locked. If the order is rejected, it is automatically pushed to the next available capacity. It also supports global conflict verification, ensuring that the same capacity receives only one dispatch request at a time, thus completely solving the problems of multiple orders competing for the same capacity and unreasonable matching priorities in existing technologies.

[0026] The rule configuration layer has built-in standardized dispatch rule templates covering all operational dimensions. The templates are divided into three categories of configurable items: city supervision rules, capacity structure adaptation rules, and channel cooperation rules. The city supervision rules item supports the configuration of the types of capacity qualifications allowed to operate, prohibited operating areas, peak hours, and the maximum daily order acceptance time limit for a single driver. The capacity structure adaptation rules item supports configuring order priority for different types of capacity, capacity weight adjustment for different time periods, and exclusive capacity matching rules for special orders such as carpooling / private car / airport transfer; The channel cooperation rules section supports configuring the order priority, maximum order radius, and channel-specific capacity qualification requirements for different channels. Operations personnel only need to enter the configuration parameters for the corresponding city in the visual backend, and the system will automatically generate compliant city-specific dispatch rules that take effect in real time without the need for code release. This solves the problem that in existing technologies, opening a new city requires customized development of dispatch logic, which is time-consuming and costly.

[0027] The operations monitoring layer collects real-time operational data across the entire order chain, including order volume, matching success rate, pick-up timeliness achievement rate, order cancellation rate, user complaint rate, and average revenue per order for each channel. It has a built-in multi-dimensional channel performance evaluation model that evaluates the return on investment for each channel on a daily, weekly, and monthly basis. It automatically adjusts the traffic allocation weight for each channel. If the complaint rate or cancellation rate of a certain channel continuously exceeds a preset threshold, it automatically reduces the order access priority of that channel and sends an alert to the operations personnel. This solves the problems of channel operations relying on manual experience, delayed adjustments, and resource waste in existing technologies.

[0028] This invention also discloses a new channel rapid access logic for the traffic channel access layer, which supports rapid connection to six mainstream traffic channels: OTA platforms, map service platforms, local life service platforms, government and public service platforms, and official transportation hub platforms. Operators only need to configure the message field mapping rules, interface authentication parameters, and channel-specific attribute rules of the channel in the access layer backend to complete the joint debugging and launch of the new channel. The average new channel access cycle is shortened from the original 72 hours to less than 4 hours, without the need for any adaptation adjustments to the scheduling core layer.

[0029] This invention also discloses a special capacity matching logic for the capacity port interface layer. For special demand orders such as accessible travel orders, minor escort orders, and large luggage transportation orders, the capacity interface layer automatically filters the capacity with corresponding service qualifications to enter a dedicated available pool. For example, accessible orders are only matched with vehicles equipped with accessible facilities, and minor escort orders are only matched with drivers with no bad operating records and safety scores higher than a preset threshold, ensuring the service safety of special orders.

[0030] This invention also discloses a time-based rule adaptation logic for the rule configuration layer, which supports setting independent scheduling rules for five special time periods: weekday morning peak, weekday evening peak, holidays, severe weather, and large-scale event periods. For example, during severe weather periods, the distance weight ratio can be adjusted and the order priority of cross-city orders can be reduced. The rules can be preset to take effect and will automatically switch after the preset time is reached, without the need for manual real-time operation.

[0031] This invention also discloses a capacity overload early warning logic for the operation monitoring layer. When the ratio of available capacity to pending orders in a certain area is lower than a preset threshold, the system automatically triggers capacity scheduling guidance, pushes regional order heat information to empty capacity within a 3-kilometer radius, guides capacity to flow to areas with supply and demand imbalance, and automatically adjusts the channel traffic access volume in that area to avoid the problem of non-delivery due to order backlog.

[0032] Scenario Example 1: Rapid City Launch and Implementation in New First-Tier Cities Application scenario description A ride-hailing aggregation platform plans to launch operations in Hangzhou. Local regulations require all operating vehicles to hold Hangzhou ride-hailing transport permits. Only new energy vehicles are allowed to enter the core area of ​​West Lake Scenic Area to pick up orders. The platform has signed traffic cooperation agreements with Alipay Local Life Channel and Hangzhou East Railway Station's official service platform. The agreements stipulate that orders from Alipay Local Life Channel will have first-level priority in order dispatching, and pick-up and drop-off orders from Hangzhou East Railway Station can only be dispatched to dedicated line vehicles connecting to the station. The original customized dispatching solution requires the development team to write adaptation code, which will take at least 10 days to go online, with development costs exceeding 100,000 yuan.

[0033] Technical adaptation details in different scenarios Operations personnel log into the rule configuration layer's visual backend and enter the following rules in the city supervision rules section: "Only vehicles holding Hangzhou ride-hailing transport permits are allowed to accept orders; only new energy vehicles are matched in the West Lake core scenic area." In the capacity structure adaptation rules section, they enter: "Pick-up and drop-off orders are only matched with dedicated pick-up and drop-off line vehicles; during peak hours, taxis have a higher priority than ride-hailing vehicles by one level." In the channel cooperation rules section, they enter: "Orders from Alipay Local Life Channel have a priority of Level 1; orders from Hangzhou East Railway Station have a priority of Level 2." The system automatically verifies the compliance of the parameters and generates Hangzhou-specific dispatch rules, which take effect immediately. The core configuration logic pseudocode is as follows: def generate_city_specific_rule(city_config): base_rule = load_standard_rule_template() # Configure regulatory rules base_rule.allowed_qualification = city_config.required_qualification base_rule.special_area_vehicle_limit["West Lake Core Area"] = "New Energy Vehicles" # Configure capacity rules base_rule.special_order_driver_limit["pickup and drop-off stations"] = "pickup and drop-off station dedicated line capacity" base_rule.peak_time_priority["cruising taxis"] = 1 # Configure channel rules base_rule.channel_priority["Alipay Local Life"] = 1 base_rule.channel_priority["Hangzhou East Railway Station"] = 2 return base_rule.

[0034] The original customized solution required a 10-day development cycle and the full involvement of two backend developers. This solution only requires operations staff to complete all configurations and go live in 3 hours, reducing the cost of city dispatch configuration by more than 95%. After going live, all order dispatches fully comply with the regulatory requirements of Hangzhou and the channel cooperation agreement, and there have been no issues with illegal order dispatches or channel priority not meeting the agreement.

[0035] The system operation process is as follows: The traffic channel access layer connects with orders from four channels: Alipay Local Life, Hangzhou East Railway Station, Gaode Map, and Ctrip. After completing the standardized conversion of messages, the orders are stored in the global order pool. The capacity port connection layer connects with the capacity data of three local ride-hailing platforms and the Hangzhou Municipal Taxi Supervision Platform. After automatically filtering out capacity without Hangzhou operating qualifications or that does not meet the requirements of scenic spots, an available capacity pool is generated. The intelligent scheduling core layer calculates the matching degree according to the generated rules and completes order matching. The operation monitoring layer compiles the operation data of each channel in real time and dynamically adjusts the traffic allocation weight.

[0036] Scenario Example 2: Evening Rush Hour Dispatch Scenario during Heavy Rain Application scenario description A sudden rainstorm hit Guangzhou in the summer, causing 12 pending orders to flood into a 3-kilometer radius of the Tianhe business district during the evening rush hour. These included airport transfer orders from Ctrip, instant orders from Alipay's local services, train station transfer orders from Guangzhou South Railway Station, and carpooling orders from other channels. The cancellation compensation for airport transfer orders reached 80 yuan. There were only 8 available ride-hailing vehicles in the area. The original order dispatching scheme would prioritize sending all orders to the nearest available vehicle, resulting in the failure to match high-value, high-priority orders. The order cancellation rate exceeded 40%, and user complaints surged.

[0037] Technical adaptation details in different scenarios The core layer of intelligent dispatch first extracts all attributes of 12 orders, including channel priority, timeliness requirements, cancellation compensation amount, and order type for each order. It also extracts attributes of 8 idle transportation units, including whether they are airport transfer services, whether they can handle carpooling, and the estimated time from the current location to the origin of each order. The matching formula is then used to calculate the score between each order and its corresponding transportation unit. Matching is completed according to the score from highest to lowest, and a global conflict check is triggered to ensure that the same transportation unit only receives one dispatch request. The pseudocode for the core matching logic is as follows: def order_driver_matching(order_pool, available_drivers, current_rule): matched_pairs = [] used_drivers = set() # Sort by order priority sorted_orders = sorted(order_pool, key=lambda x: x.priority,reverse=True) for order in sorted_orders: candidate_drivers = [d for d in available_drivers if d.id notin used_drivers and d.in_service_range(order.start_loc)] if not candidate_drivers: continue # Calculate the matching scores for each candidate capacity score_list = [] for driver in candidate_drivers: time_score = 100 - min(abs(order.require_arrive_time -get_est_arrive(driver.loc, order.start_loc)) * 5, 100) channel_score = current_rule.channel_priority.get(order.channel, 0) * 20 adapt_score = 100 if driver.match_order_type(order.type)else 0 profit_score = min(order.estimate_profit * 0.5, 1, 100)<0000 I34>total_score = current_rule.weight_alpha * time_score +current_rule.weight_beta * channel_score + current_rule.weight_gamma * adapt_score + current_rule.weight_delta * profit_score score_list.append((driver, total_score)) # Obtain the capacity match with the highest score score_list.sort(key=lambda x:x[1], reverse=True) best_driver = score_list[0][0] It should be noted that there seems to be an error in the original text where "profit_score = min(order.estimate_profit * 0.5, I, 100)" has an incorrect "I". It is assumed to be a typo and is corrected to "1" in the translation. Also, the "<0000 I34>" in the original is likely a typo and should be " ". These corrections are made to ensure the integrity of the code logic in the translation. matched_pairs.append((order, best_driver)) used_drivers.add(best_driver.id) push_order(order, best_driver) return matched_pairs.

[0038] The original plan resulted in an order cancellation rate of over 40% in the region, with three high-compensation airport transfer orders being cancelled due to matching failures, incurring additional compensation losses. The new plan achieved a 92% order matching success rate in the region, with all high-priority and high-compensation orders successfully matched without incurring additional compensation. User complaints decreased by more than 80% compared to the original plan.

[0039] The multi-dimensional matching degree calculation formula of the intelligent scheduling core layer is as follows: S = α·A + β·B + γ·C + δ·D Where S is the total score of the matching degree between the target order and the corresponding matching capacity. The higher the score, the stronger the compatibility between the two and the higher the matching priority. A is the timeliness matching score, ranging from 0 to 100. It is calculated based on the difference between the longest pick-up time required by the order and the estimated time for the capacity to arrive at the order's origin. The smaller the difference, the higher the score. B is the channel priority score, ranging from 0 to 100. It is calculated based on the preset priority of the traffic channel to which the order belongs. The higher the priority, the higher the score. C is the capacity compatibility score, ranging from 0 to 100. It is calculated based on whether the capacity's qualifications, service type, and order acceptance preferences meet the order requirements. A perfect match gets full marks, and a non-compliance gets 0 marks. D is the revenue matching score, ranging from 0 to 100. It is calculated based on the order's estimated revenue, premium ratio, and cancellation compensation amount. The higher the revenue, the higher the score. α, β, γ, and δ are the dynamic weight coefficients of the four scoring items, ranging from 0 to 1. The sum of the four items is 1. They are automatically adjusted by the rule configuration layer according to the current city and current time period's operational strategy. This formula comprehensively considers four dimensions: timeliness, channels, transportation capacity, and revenue, and completely solves the problems of poor adaptability, resource waste, and order conflicts caused by existing technologies that only match based on the single dimension of distance.

[0040] refer to Figure 3This diagram visually demonstrates the core value of the city-localization adaptation module of this invention. The data comes from an implementation example in Hangzhou, a new first-tier city. Traditional customized solutions require separate development of scheduling logic, with an implementation cycle of up to 10 days and a cost of 100,000 yuan, resulting in extremely low efficiency for cross-city expansion. This invention relies on pre-set standardized rule templates, requiring only visual parameter configuration to quickly generate city-specific scheduling rules, compressing the implementation cycle to 3 hours (0.125 days) and reducing the configuration cost to 5,000 yuan, a reduction of over 95%. This design completely solves the pain points of long implementation cycles and high costs of traditional cross-city solutions, significantly improving the efficiency of large-scale expansion of the aggregation platform and meeting the platform's operational needs for rapid city expansion.

[0041] refer to Figure 4 This diagram highlights the advantages of the invention's multi-dimensional intelligent matching and conflict-free scheduling. The data comes from an example from a Guangzhou evening rush hour during a rainstorm. Traditional nearest-location dispatch only matches orders based on distance, resulting in a matching success rate of only 58% during severe weather like rainstorms, highlighting order conflicts and mismatches. This invention calculates matching scores based on four dimensions: timeliness, channel priority, capacity suitability, and revenue. Combined with a global conflict verification mechanism, the matching success rate exceeds 90% in normal, peak, and rainstorm scenarios, reaching 92% even during rainstorms. This design effectively avoids multiple orders competing for the same capacity, significantly improving the matching rate of high-value orders, reducing cancellation rates and compensation losses, and comprehensively optimizing platform operational efficiency.

[0042] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for intelligent order scheduling on a ride-hailing aggregation platform with multiple traffic entry points, characterized in that, Includes the following steps: S1. Through the preset multi-channel standardized adaptation interface, the encrypted order messages of the upstream traffic channels are accessed synchronously. After the message format is converted and compliance is verified, the order data is extracted. At the same time, the real-time capacity distribution data of the target city is collected simultaneously. S2. Construct a dynamic capacity matching model for multi-channel traffic. First, perform pre-compliance filtering on the capacity, then assign dynamic weights to the order dimension, and sum the weighted values ​​to obtain the matching score between each order and each available capacity. Select the top 3 capacity with the highest matching scores as initial distribution candidates and output the initial order distribution strategy. S3. Invoke the dual-version collaborative operation scheduling mechanism. First, divide the transportation capacity of the driver's end in the same city into the corresponding port's transportation capacity pool and the cross-port shared transportation capacity pool. Perform duplicate order verification on the candidate transportation capacity in the initial distribution strategy. If the same transportation capacity matches multiple orders at the same time, sort the order priorities and only push the order request with the highest priority to the transportation capacity. The remaining low-priority orders are automatically transferred to the shared transportation capacity pool for rematching. S4. Call the city-localized city opening adaptation module, pre-store localized dispatch rule templates covering different city levels, regulatory requirements, and transportation capacity structures, automatically match the corresponding template when opening in the target city, and automatically synchronize the latest regulatory rules through the public interface of the local transportation regulatory department, generate the final order distribution strategy adapted to the target city and push it to the corresponding driver end for execution. S5 invokes the channel performance full-link monitoring module to perform data tracking and statistics on orders from each traffic channel, collects operational data from each channel in real time, smoothly adjusts the channel traffic access weight based on the return on investment calculation results, and identifies and automatically intercepts abnormal traffic fraud.

2. The intelligent order scheduling method for a ride-hailing aggregation platform with multiple traffic entry points according to claim 1, characterized in that, The multi-channel traffic capacity dynamic matching model in S2 has a built-in new driver cold start adaptation unit. For new drivers who have been on the platform for less than 7 days and have no historical order preference data, the average order preference data of drivers of the same type and operating period in the same city is extracted as temporary preference data to participate in the matching score calculation. Once the number of completed orders of the new driver reaches the preset threshold, it will automatically switch to its own real preference data.

3. The intelligent order scheduling method for a ride-hailing aggregation platform with multiple traffic entry points according to claim 1, characterized in that, The dual-version collaborative operation scheduling mechanism in S3 sets a dispatch cooldown threshold, so that the same transportation capacity will only receive a maximum of 1 dispatch request within 30 seconds after completing the previous order. At the same time, a special rule is set for the taxi driver's side that prioritizes street hailing orders over online order orders.

4. The intelligent order scheduling method for a ride-hailing aggregation platform with multiple traffic entry points according to claim 1, characterized in that, The city-localized city opening adaptation module in S4 has a built-in time-segment rule switching unit. Based on the differences in order density and transportation capacity supply during different time periods such as morning and evening peak hours, off-peak hours, and nighttime in the target city, it automatically switches the scheduling rules and traffic allocation weights for the corresponding time periods.

5. The intelligent order scheduling method for a ride-hailing aggregation platform with multiple traffic entry points according to claim 1, characterized in that, The channel performance monitoring module in S5 sets a weight adjustment threshold, and the daily adjustment range of the traffic access weight of a single channel shall not exceed a preset upper limit of 20%. At the same time, it automatically generates multi-dimensional channel operation reports and pushes them to the operation end.

6. A multi-traffic-entry ride-hailing aggregation platform order intelligent scheduling system, used to implement the multi-traffic-entry ride-hailing aggregation platform order intelligent scheduling method according to any one of claims 1-5, characterized in that, include: The data access module is used to simultaneously access encrypted order messages from multiple upstream traffic channels through a pre-set multi-channel standardized adaptation interface. After format conversion and compliance verification of the messages, the core order data is extracted. At the same time, real-time capacity distribution data, driver tag data, channel dynamic premium rules, and real-time traffic data of the target city are collected simultaneously. The matching model module is used to build a dynamic matching model of multi-channel traffic and capacity. First, the capacity is filtered for compliance in advance. Then, dynamic weights are assigned to each indicator in the order dimension and the capacity dimension respectively. The weighted sum is used to obtain the matching score of each order and each available capacity. The top 3 capacity with the highest matching scores are selected as the initial distribution candidates, and the initial order distribution strategy is output. The collaborative scheduling module is used to call the dual-version collaborative operation scheduling mechanism to divide the transportation capacity of multiple driver terminals in the same city into a dedicated transportation capacity pool and a cross-port shared transportation capacity pool. It performs duplicate order verification on the candidate transportation capacity in the initial distribution strategy, pushes the highest priority order after sorting according to the preset priority, and transfers the remaining low priority orders to the shared transportation capacity pool for rematching. The local adaptation module is used to pre-store multiple sets of localized scheduling rule templates. When opening a city, it automatically matches the corresponding template, connects to the local regulatory interface to automatically synchronize the latest regulatory rules, supports operators to customize the configuration of scheduling rules and canary releases, generates the final order distribution strategy and pushes it for execution. The channel monitoring module is used to perform full-chain statistics on orders from each traffic channel, collect operational data from each channel in real time, smoothly adjust the channel traffic access weight based on the return on investment calculation results, and identify and block abnormal traffic fraud.

7. The intelligent order dispatch system for a ride-hailing aggregation platform with multiple traffic entry points according to claim 6, characterized in that, The matching model module has a built-in new driver cold start adaptation unit. For new drivers who have been on the platform for less than 7 days and have no historical order preference data, the average order preference data of drivers of the same type and operating period in the same city is extracted as temporary preference data to participate in the matching score calculation. Once the number of orders completed by the new driver reaches the preset threshold, it will automatically switch to its own real preference data.

8. The intelligent order dispatch system for a ride-hailing aggregation platform with multiple traffic entry points according to claim 6, characterized in that, The collaborative scheduling module has a built-in order dispatch cooling unit, which sets a 30-second order dispatch cooling threshold. During the cooling period after the previous order is completed, the same transportation capacity will only receive a maximum of one order dispatch request. At the same time, a special rule is set for the taxi driver's side, which prioritizes street-hailing orders over online-hailing orders.

9. The intelligent order dispatch system for a ride-hailing aggregation platform with multiple traffic entry points according to claim 6, characterized in that, The local adaptation module has a built-in time-segment rule switching unit, which can automatically switch the scheduling rules and traffic allocation weights for the corresponding time period according to the order density and capacity supply differences of the target city at different times.

10. The intelligent order dispatching system for a ride-hailing aggregation platform with multiple traffic entry points according to claim 6, characterized in that, The channel monitoring module has a built-in weight smoothing adjustment unit, which sets a preset upper limit that the daily adjustment range of the traffic access weight of a single channel shall not exceed 20%.

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