Automated dispensing method, system and apparatus based on air lane iron

Abstract

This application relates to an automated freight matching method, system, and equipment based on air-rail-road transportation. The method includes: acquiring order information of orders to be matched; filtering candidate transport capacity according to a preset configuration priority, wherein the candidate transport capacity includes at least air transport capacity, land transport capacity, and high-speed rail transport capacity; filtering the selected candidate transport capacity according to filtering rules to obtain available transport capacity, grouping them, and selecting the optimal solution for each group; selecting different decision branches to execute different final freight matching schemes based on the relationship between cargo weight and a preset threshold; and performing freight matching operations according to the final freight matching schemes. The solution provided by this application greatly improves freight matching efficiency, achieves fast and accurate warehouse outbound shipments, and reduces transportation costs.

Description

Technical Field

[0001] This application relates to the field of logistics and freight distribution technology, and in particular to an automated freight distribution method, system and equipment based on air, land and rail. Background Technology

[0002] Traditional logistics and freight matching systems rely heavily on manual experience or static schedules, and their capacity types are relatively limited. They typically optimize only for single-mode air or road transport, failing to dynamically combine and switch between air, high-speed rail, and road transport. When capacity is insufficient, manual intervention is required. Furthermore, they only compare flight departure and arrival times with customer requirements, ignoring multiple ground transportation links. This leads to many flights not actually connecting, resulting in low freight matching success rates. Additionally, they suffer from rigid rule configurations, with freight matching rules hard-coded and unable to be flexibly adjusted according to customers, regions, and service methods. Summary of the Invention

[0003] To address or partially address the problems existing in related technologies, this application provides an automated freight matching method, system, and equipment based on air, land, and rail transportation. Through data analysis and optimization algorithms, it analyzes customer demand, transportation routes, and transportation vehicles to achieve optimal freight matching, thereby reducing empty distances and transportation costs.

[0004] The first aspect of this application provides an automated freight distribution method based on air rail, including: Obtain the order information of the orders to be fulfilled, which includes at least the weight of the goods, the place of origin, the destination, the expected delivery time, and the time of order submission; Candidate transport capacity is selected according to a preset configuration priority, and the candidate transport capacity includes at least air transport capacity, land transport capacity and high-speed rail transport capacity; The selected candidate capacity is filtered according to the filtering rules to obtain the available capacity. The available capacity is divided into three groups: air, land and high-speed rail. Within each group, the optimal solution is selected according to the principle of lowest cost and earliest arrival. Different decision branches are selected based on the relationship between the weight of the goods and a preset threshold: If the weight of the goods exceeds the preset threshold, the optimal solution for each group is selected as the final allocation solution based on the highest comprehensive score using a three-dimensional optimization objective function. The three-dimensional optimization objective function is a weighted scoring function based on time, cost, and service quality. If the final scores are the same, the final allocation solution is selected in the order of air transport priority over land transport, and land transport priority over high-speed rail. If the weight of the goods is less than or equal to the preset threshold, the solution that meets the timeliness and has the lowest cost will be selected from each group as the final allocation solution. The order fulfillment operation will be carried out according to the final order fulfillment plan.

[0005] Furthermore, the three-dimensional optimization objective function includes: formula Where V represents the overall score of the optimal solution for each group, T represents the transportation time from pickup to delivery for the optimal solution for each group, C represents the total transportation cost, Q represents the historical service quality score, and α, β, and γ represent their respective coefficients, α+β+γ=1. The historical service quality score Q includes at least the weighted overall scores of historical on-time rate, integrity rate, response speed, and fulfillment stability. Where, P represents the historical on-time rate over a certain period, obtained as the percentage of on-time deliveries to the total number of deliveries over that period; I represents the historical integrity rate over a certain period, obtained as the percentage of undamaged waybills to the total number of waybills over that period; R represents the response speed over a certain period, obtained as the percentage of the difference between the standard response time and the average response time to the standard response time; S represents the fulfillment stability, obtained as the percentage of normal executions to the total planned executions. , , , For their respective coefficients, .

[0006] Furthermore, the historical service quality score also includes a score for the adaptability of transport capacity to special goods. The special goods include at least one of temperature-controlled goods, dangerous goods, and fresh goods. Based on the different types of special goods, the corresponding historical adaptability index values ​​are obtained, and the historical service quality score is obtained by combining the weighting coefficients.

[0007] Furthermore, the filtering rules include timeliness filtering, which includes using a route pickup time algorithm to determine whether the goods can be transported from the customer to the port for distribution on time and catch the scheduled transport schedule, and using a route dispatch time algorithm to determine whether the goods can be delivered to the customer on time after the scheduled transport schedule lands. Only transport schedules that pass both the route pickup time algorithm and the route dispatch time algorithm can become available transport capacity.

[0008] Furthermore, the line time acquisition algorithm includes: Obtain batch connection information of the route and travel time from the customer's location to the drop-off point; Calculate the earliest departure time from the point, where the earliest departure time from the point is the sum of the current time and the travel time. If there is a shared identifier for origin and destination, the travel time is set to 0. If the time from the customer's location to the point is not greater than the required arrival time, it is determined that the customer can make it; otherwise, the service is filtered out. If no shared identifier is specified, the first departure time of the selected batch is not less than the earliest departure time of the destination, and the arrival time of the batch is not greater than the required arrival time. If such a batch exists, it is determined that the batch can make it. If no matching batch exists, the site configuration is bound sequentially according to the preset priority, the first binding relationship that can match the route is selected, and the batch filtering is re-executed. If no matching route still exists, a special vehicle is used as a backup to determine whether the batch can make it. The dispatch line time algorithm includes: Get the arrival time, delivery time and pickup time of the transport capacity and schedule; Determine if there is a shared identifier for origin and destination: if so, set the travel time to 0. If the sum of the arrival time of the transport shift, the pickup time, and the time from the point to the customer is not greater than the delivery time, it is determined to meet the delivery time requirement; otherwise, the transport shift is filtered out. If there is no shared identifier, batches that meet the following conditions will be selected: the first departure time of the dispatch is not less than the sum of the arrival time of the transport shift, the pickup time, and the operation time, and the sum of the batch arrival time and the time from the point to the customer is not greater than the delivery time. If no matching batch is found, try binding the venue configuration in sequence according to the preset priority, take the first binding relationship that can match the route, and re-execute the batch filtering. If no matching route is found, the delivery time is filtered using the terminal reserved time of h hours. Retrieve all routes and corresponding batches that meet the time requirements for route retrieval and route dispatch, and filter out those that do not meet the time requirements.

[0009] Furthermore, the filtering rules also include any one, two or more of the following: cargo volume filtering, single shipment weight filtering, single piece weight filtering, capacity / shift size filtering, capacity / shift user filtering, and capacity / shift service mode filtering, before timeliness filtering, to initially filter out unsuitable capacity based on the nature and configuration requirements of the goods.

[0010] Furthermore, the step of filtering candidate transport capacity according to a preset configuration priority includes: Based on the origin and destination of the waybill, and combined with the configuration rules of origin priority or destination priority, a priority list of capacity field pairs is generated. The origin priority rule is: the capacity field pair sequence is generated by fixing the origin capacity field and polling the destination capacity field. The destination priority rule is: the capacity field pair sequence is generated by fixing the destination capacity field and polling the origin capacity field. Capacities are matched sequentially according to the priority order of the capacity field pair list until a candidate capacity is found.

[0011] Furthermore, the step of filtering candidate transport capacity according to a preset configuration priority includes: Capacity allocation is matched according to a four-level priority order: customer configuration, charter configuration, special configuration, and general configuration. Customer configuration refers to configurations specific to the client's special requirements; charter configuration refers to capacity allocation for chartered flights by the carrier; special configuration refers to capacity allocation for specific service methods or special businesses; and general configuration refers to the regular capacity allocation maintained by the carrier. Within each configuration level, further filtering is performed according to the priority of available quotes on the current day, no quotes on the current day, quotes available the next day, and no quotes the next day. If no available capacity is matched in any of the four configuration levels, the process proceeds to manual cargo matching.

[0012] Secondly, this application also provides an automated freight distribution system based on air rail, comprising: The order information acquisition unit is used to acquire the order information of the orders to be fulfilled. The order information includes at least the weight of the goods, the place of origin, the destination, the expected delivery time, and the time of order submission. The candidate capacity screening unit is used to screen candidate capacity according to a preset configuration priority. The candidate capacity includes at least air capacity, land capacity and high-speed rail capacity. The optimal solution acquisition unit for each group is used to filter the candidate capacity according to the filtering rules to obtain the available capacity. The available capacity is divided into three groups: air, land and high-speed rail. Within each group, the optimal solution is selected according to the principle of lowest cost and earliest arrival. The final cargo allocation plan acquisition unit is used to select different decision branches based on the relationship between cargo weight and preset thresholds: If the weight of the goods exceeds the preset threshold, the optimal solution for each group is selected as the final allocation solution based on the highest comprehensive score using a three-dimensional optimization objective function. The three-dimensional optimization objective function is a weighted scoring function based on time, cost, and service quality. If the final scores are the same, the final allocation solution is selected in the order of air transport priority over land transport, and land transport priority over high-speed rail. If the weight of the goods is less than or equal to the preset threshold, the solution that meets the timeliness and has the lowest cost will be selected from each group as the final allocation solution. The order fulfillment unit is used to perform order fulfillment operations according to the final order fulfillment plan.

[0013] Thirdly, this application also provides an electronic device, including: Processor; and A memory that stores executable code, which, when executed by the processor, causes the processor to perform the method described above.

[0014] The technical solution provided in this application may include the following beneficial effects: This application obtains order information from pending orders, including at least cargo weight, origin, destination, expected delivery time, and order submission time. It then filters candidate transport capacity according to a preset priority, including at least air, land, and high-speed rail capacity. The selected candidate capacity is further filtered according to a filtering rule to obtain available capacity. This available capacity is divided into three groups: air, land, and high-speed rail. Within each group, the optimal solution is selected based on the principle of lowest cost and earliest arrival. Finally, different decision branches are chosen based on the relationship between cargo weight and a preset threshold: if the cargo weight exceeds the preset threshold, then... The optimal solution from each group is selected as the final allocation plan based on the highest comprehensive score using a three-dimensional optimization objective function, which is a weighted scoring function based on time, cost, and service quality. If the final scores are the same, the final allocation plan is selected in the order of air transport priority over land transport, and land transport priority over high-speed rail. If the weight of the goods is less than or equal to a preset threshold, the plan that meets the timeliness requirements and has the lowest cost is selected from each group as the final allocation plan. The allocation operation is carried out according to the final allocation plan, which greatly improves the allocation efficiency, achieves fast and accurate warehouse shipment, and reduces transportation costs.

[0015] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0016] The above and other objects, features and advantages of this application will become more apparent from the more detailed description of exemplary embodiments thereof in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments thereof.

[0017] Figure 1 This is a schematic flowchart illustrating an automated freight distribution method based on air rail, as shown in an embodiment of this application. Figure 2 This is a schematic diagram of the structure of an automated freight distribution system based on air rail, as shown in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device shown in an embodiment of this application. Detailed Implementation

[0018] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While embodiments of this application are shown in the drawings, it should be understood that this application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to make this application more thorough and complete, and to fully convey the scope of this application to those skilled in the art.

[0019] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0020] It should be understood that although the terms "first," "second," "third," etc., may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0021] The technical solutions of the embodiments of this application are described in detail below with reference to the accompanying drawings.

[0022] Figure 1 This is a schematic flowchart illustrating an automated freight distribution method based on air rail, as shown in an embodiment of this application. It includes: S101, Obtain the order information of the order to be fulfilled, wherein the order information includes at least the weight, volume, origin, destination, expected delivery time, order reporting time, and service method of the goods; In some embodiments, order data can be pulled in real time from an upstream order system (such as OMS) via an API interface, and can also be received asynchronously by subscribing to waybill creation events via a message queue (such as Kafka). For historical replenishment orders, they can be read in batches from the database.

[0023] In a preferred embodiment, obtaining order information using asynchronous Kafka message processing involves the system automatically sending a message containing order information to the order fulfillment system when a waybill is created or updated in the business system. Once the order fulfillment system consumes this message, the subsequent steps of this method are triggered. This approach enables real-time order fulfillment under high concurrency.

[0024] In some preferred embodiments, cargo weight data includes both actual weight and volumetric weight, which can be used to differentiate between large and small cargo, calculate costs, etc. Obtaining cargo weight data plays a crucial role in the accuracy of cargo allocation. Cargo volume is mainly used for cargo space matching and loading determination. The origin is the customer's shipping address, which is later used to map the dispatch point and the originating transportation facility, such as dispatch address and dispatch point. The destination is the customer's receiving address, which can be used to map the receiving point and the destination transportation facility, such as receiving address and receiving point. Expected delivery time represents the customer's required delivery time limit, specifically manifested as delivery efficiency and internal control efficiency. The order submission time is the time the waybill is entered into the system, used to calculate the time window. Service methods include next-day delivery, next-morning delivery, air freight, and special offer air freight.

[0025] In some embodiments, order information may also include customer identifiers, special cargo identifiers, and operating modes that customers can select for "customer configuration." If the identifier is a special cargo identifier, it can affect the configuration matching priority. Special cargo identifiers include perishable goods, dangerous goods, and temperature-controlled goods, while operating modes include whether there is transshipment or whether a charter flight is used.

[0026] It should also be noted that after obtaining order information, preprocessing is required, including address resolution. For example, geocoding services can be used to convert the origin and destination into standard administrative division codes and latitude and longitude coordinates, facilitating subsequent mapping to transportation hubs (such as airports, high-speed rail stations, etc.). The expected delivery time can also be calculated based on the service method and order submission time (e.g., next-day delivery requires delivery before 18:00 the following day). During order information preprocessing, where weight is involved, the actual weight and volumetric weight are generally compared, and the larger value is used as the chargeable weight (i.e., loading weight) for cost calculation and cargo space determination. Additionally, large / small cargo can be determined based on preset weight thresholds (e.g., 100kg).

[0027] In some embodiments, the preprocessing stage can also perform information verification and anomaly handling. If the order information is incomplete (such as missing origin or destination), the anomaly handling process should be triggered, such as automatically filling in the missing fields through historical data or default rules. If it cannot be automatically filled in, the process should be switched to manual order picking and the reason for the missing information should be recorded.

[0028] By obtaining the aforementioned complete and multi-dimensional order information, an accurate data foundation can be provided for subsequent configuration matching, timeliness filtering, cost calculation, and decision-making. In particular, the introduction of order reporting time and service methods enables dynamic calculation of time windows and adaptation to different customer service methods. Furthermore, obtaining the cargo weight provides an important condition for realizing differentiated processing of large and small shipments.

[0029] S102, Select candidate transport capacity according to preset configuration priority, wherein the candidate transport capacity includes at least air transport capacity, land transport capacity and high-speed rail transport capacity; Before selecting candidate transport capacity, it's necessary to explain the advantages and disadvantages of the three modes of transport. Air transport boasts the fastest speed, but it also often comes with other drawbacks, such as the highest cost, low network coverage, punctuality significantly affected by weather and air traffic control, limited cargo space, strict size requirements, and stringent control over dangerous goods and temperature. High-speed rail, while slower than air transport and with limited carriages and restrictions on large items, offers extremely high punctuality, making it more suitable for general cargo. Land transport offers the lowest price, the widest network, less stringent size requirements, and flexibility, adapting to various types of goods. However, it is the slowest. No single transport capacity is optimal. This method aims to select the optimal configuration among the three modes based on the specific requirements of the order, while meeting customer requirements and carrier cost control. Air transport is chosen for high-time-delay needs, high-speed rail for medium-time-delay and high-cost-performance needs, and road transport for low-cost and flexible needs.

[0030] Air transport capacity data may include flight number, departure and arrival airports, departure / arrival time, remaining cargo space (weight / volume), price quote (including handling fees, standard pick-up rates, and expedited pick-up rates), and historical punctuality rates. Land transport capacity data includes routes (origin and destination distribution points), vehicle type, departure / arrival times, loading capacity, charter prices, or ton-kilometer rates. High-speed rail capacity data includes train number, origin and destination stations, departure / arrival times, number of available seats / carriages, section rates, and minimum fares.

[0031] In some embodiments, such as in air transport capacity, the step of filtering candidate capacity according to a preset configuration priority includes: Capacity configurations are matched according to a four-tier priority order: customer configuration, charter configuration, special configuration, and general configuration. Customer configuration refers to configurations tailored to specific customer requirements; charter configuration refers to capacity configurations for carrier-chartered flights; special configuration refers to capacity configurations for specific service methods or special businesses; and general configuration refers to the regular capacity configurations maintained by the carrier. Each configuration tier is further filtered according to the priority of available quotes for the current day, no quotes for the current day, quotes for the next day, and no quotes for the next day. If no available capacity is found at any of the four configuration tiers, manual cargo allocation is initiated. Capacity configurations are matched according to the four-tier priority order. Once an available capacity list is obtained at a certain configuration tier, it proceeds to the next filtering stage; if no available capacity is found at the current tier, it automatically downgrades to the next lower tier.

[0032] Some customers choose to configure their bookings, which allows them to specify specific flight numbers, airport pairs, airlines or agents, and service methods (such as next-day delivery or next-morning delivery). Within the same customer configuration, flights are filtered in the order of "available quotes on the same day > no quotes on the same day > available quotes for the next day > no quotes for the next day".

[0033] If the customer has selected all the designated flights for the same day, the system will then select the designated airport pair. If there are two designated flights, the system will prioritize flights arriving on the same day; if both are for the same day, the flight arriving at the receiving point earliest will be selected. For example, customer A has configured flight CA1234 (with a quote available for the same day), flight CA5678 (without a quote for the same day), and the airport pair PEK→SHA. The system will prioritize matching CA1234; if not available, it will try CA5678; if neither is available, it will match flights under the airport pair PEK→SHA.

[0034] Charter flight configuration is the second-tier option, and the configuration details include specifying the charter route, charter flight, and valid date range. Within the same customer configuration, flights are also filtered in the following order: "Available quote on the current day > No quote on the current day > Available quote for the next day > No quote for the next day".

[0035] Special configurations, as the third level of configuration, are matched based on special markings in the order (such as fresh produce, dangerous goods, temperature-controlled goods) or specific service methods (such as cold chain shipments). Dedicated transport capacity, priority airports, and special operational requirements are specified for special cargo. Within the same customer configuration, schedules are also selected in the order of "available quotes on the same day > no quotes on the same day > available quotes the next day > no quotes the next day".

[0036] All regular orders that do not meet the above three-level configuration will then undergo general configuration. Generally, the default airport priority, default time-of-flight level, and default flight filtering range (such as all partner airlines) will be used to filter flights in the order of "quotes available on the same day > no quotes available on the same day > quotes available the next day > no quotes the next day".

[0037] A quote available on the same day indicates that the shipping provider has provided a valid quote for that day, and the available space is confirmed; this quote will be used first. No quote available on the same day means there is no confirmed quote for that day, but the shipping capacity itself can be booked (e.g., based on historical prices or estimates), and can be used as a backup when a quote is unavailable. A quote available the following day indicates that a quote will be available the next day; this is suitable for cargo allocation more than 12 hours in advance and is suitable for long-term cargo allocation scenarios. No quote available the following day indicates that there is still no quote available the following day; this has the lowest priority and is used as the last backup. This tiered design incentivizes shipping providers to provide quotes promptly while ensuring that the system can still allocate cargo based on available capacity when a quote is unavailable.

[0038] For example, consider a fresh produce order weighing 150kg (bulk cargo) expected to be delivered before 12:00 the next day, with the order placed at 09:00 on the same day. This customer may have a customer configuration. First, the system searches for a specified flight with a quote available that day (e.g., CA1234) in the customer configuration. If a flight is available and there is sufficient capacity, it is added to the candidate list. If not, it searches for a specified flight without a quote that day. If still not available, it searches for flights with quotes for the specified airport pair (e.g., PEK→SHA) in the customer configuration. If there is no available capacity in the customer configuration, it proceeds to charter configuration matching. If the order does not have a charter number, it is skipped. Next, special configuration matching is performed. If the order is tagged "fresh produce," a special fresh produce configuration is matched. This configuration prioritizes temperature-controlled flights with quotes available that day. Once a suitable flight is found, it is added to the candidate list. If there is also no capacity in the special configuration, it proceeds to the general configuration. The general configuration provides a default airport pair priority (originating priority, taking the first two airport pairs). It iterates through all flights with quotes for the day under these airport pairs and filters out flights that meet the time requirements. Through the above four-level progressive matching, candidate transportation capacity can be obtained from coarse to fine and from personalized to general, which not only meets the special needs of customers, but also ensures the bottom-line matching of ordinary orders.

[0039] In some embodiments, the step of filtering candidate capacity according to a preset configuration priority includes: Based on the origin and destination of the waybill, and combined with the configuration rules of origin priority or destination priority, a priority list of capacity field pairs is generated. The origin priority rule is: the capacity field pair sequence is generated by fixing the origin capacity field and polling the destination capacity field. The destination priority rule is: the capacity field pair sequence is generated by fixing the destination capacity field and polling the origin capacity field. Capacities are matched sequentially according to the priority order of the capacity field pair list until a candidate capacity is found.

[0040] For example, this describes the situation with air freight, but it also applies to station pairs on high-speed rail and distribution pairs on highways. In actual logistics operations, a city may have multiple airports (e.g., Shanghai has Hongqiao Airport (SHA) and Pudong Airport (PVG), while the origin and destination of waybills are usually only specified down to the city level. To determine specific airport pairs, the system needs to generate a priority list of airport pairs based on configuration rules. If destination priority is selected, the system tends to match the destination airport first, i.e., fix the destination airport first, and then poll the origin airport. If origin priority is selected, the system prioritizes the origin airport, i.e., fix the origin airport first, and then poll the destination airport. Additionally, the system can combine time periods to select either destination priority or origin priority, dynamically adjusting the matching strategy according to the business characteristics of different times. For example, waybills reported at night may focus more on the convenience of pickup at the destination airport (destination priority), while waybills reported during the day may focus more on the cut-off time at the origin airport (origin priority). For example, origin airports A and B, destinations C and D. According to the origin priority rule: AC, AD, BC, CD. If the number of origin priority items is maintained at 1, then only AC and AD are selected. According to the destination priority rule: AC, BC, AD, BD. If the number of destination priority items is maintained at 1, then only AC and BC are selected.

[0041] By prioritizing field pairs, high-probability, high-quality routes can be matched first (e.g., airport A has high cargo volume and many flights, so it is given priority), reducing unnecessary traversals. Furthermore, by configuring destination priority time periods, the business can flexibly adjust its cargo allocation preferences for morning and evening shifts to adapt to the operational characteristics of different time periods. Configurable limits on the number of field pairs can control the number of field pairs attempted in each cargo allocation attempt, preventing computational overload due to excessive field pairs, while ensuring that high-priority routes are used first.

[0042] S103, the selected candidate capacity is filtered according to the filtering rules to obtain the available capacity, and the available capacity is divided into three groups according to air, land and high-speed rail. Within each group, the optimal solution is selected according to the principle of lowest cost and earliest arrival. Air transport capacity, land transport capacity, and high-speed rail capacity differ fundamentally in cost structure, timeliness characteristics, and loading constraints. Grouping these three types of capacity not only significantly expands the scope of transport capacity but also avoids unfair evaluations caused by directly comparing all capacities due to differing units of measurement. Grouping ensures that each group contains only the same type of capacity (air transport vs. air transport), with identical units of measurement for cost and timeliness. This allows for direct selection using a simple rule of "lowest cost, earliest arrival," eliminating the need for complex normalization. The algorithm is highly efficient and yields fair results. Furthermore, it reduces decision-making complexity and increases computational speed. Within a group, selection only requires comparing cost and time, significantly reducing complexity. Subsequent cross-group comparisons only require processing three solutions (the optimal solution from each group), eliminating the need to process the entire pool. The grouping strategy also significantly reduces the computational load in subsequent weighted scoring stages, ensuring real-time response within minutes.

[0043] In some embodiments, the filtering rules include timeliness filtering, which includes determining whether the goods can be delivered from the customer to the port for distribution on time and catch the scheduled transport flight by using a route time algorithm, and also includes determining whether the goods can be delivered to the customer on time after the scheduled transport flight lands by using a route time algorithm. Only transport flights that pass both the route time algorithm and the route time algorithm can become available transport capacity.

[0044] Furthermore, the line time acquisition algorithm includes: Obtain batch connection information of the route and travel time from the customer's location to the drop-off point; Calculate the earliest departure time of the point, where the earliest departure time of the point is the sum of the current time and the travel time. If there is a shared identifier for origin and destination, the travel time is set to 0. If the time from the customer's location to the point is not greater than the required arrival time, it is determined that the customer can make it; otherwise, the flight is filtered out. If no shared identifier is specified, the first departure time of the selected batch is not less than the earliest departure time of the destination, and the arrival time of the batch is not greater than the required arrival time. If such a batch exists, it is determined that the batch can make it. If no matching batch exists, the site configuration is bound sequentially according to the preset priority, the first binding relationship that can match the route is selected, and the batch filtering is re-executed. If no matching route still exists, a special vehicle is used as a backup to determine whether the batch can make it. The dispatch line time algorithm includes: Obtain flight arrival time, delivery time, and pickup time; Determine if there is a shared identifier for origin and destination: if so, set the travel time to 0. If the sum of the arrival time of the scheduled flight, the pickup time, and the time from the point of departure to the customer is not greater than the delivery time, the flight is deemed to meet the delivery time requirement; otherwise, the flight is filtered out. If there is no shared identifier, batches that meet the following conditions will be selected: the first departure time of the dispatching truck is not less than the sum of the flight landing time, the pickup time and the operation time, and the sum of the batch arrival time and the time from the point to the customer is not greater than the delivery time. If no matching batch is found, try binding the venue configuration in sequence according to the preset priority, take the first binding relationship that can match the route, and re-execute the batch filtering. If no matching route is found, the delivery time is filtered using the terminal reserved time of h hours. Retrieve all routes and corresponding batches that meet the time requirements for route retrieval and route dispatch, and filter out those that do not meet the time requirements.

[0045] Specifically, the route time algorithm is used to determine whether goods can be transported from the customer to the port for distribution on time and catch the scheduled transport schedule, such as flights, high-speed trains, or highway buses. Its core is to verify that the connection between each segment of ground transportation from the customer's shipment to the cut-off time of the transport capacity can meet the time requirement.

[0046] By calling the routing backend interface, the batch sequence information of the route can be obtained using parameters such as order reporting time, delivery time, sender location identifier, and departure location identifier. The batch sequence information includes the departure and arrival times of multiple transport batches from the sender location to the departure distribution center, and then from the departure distribution center to the originating transport hub (airport / station).

[0047] The travel time from the customer's location to the drop-off point can be calculated by calling the map interface, based on the customer's address and the drop-off point address.

[0048] This involves calculating the earliest departure time from the dispatch point, which is calculated as: earliest departure time from the dispatch point = current time + travel time from the customer's location to the dispatch point. For example, if the current time is 09:00 on the same day and the travel time is 30 minutes, then the earliest departure time from the dispatch point is 09:30 on the same day.

[0049] Before calculating the delivery time, it is necessary to determine whether the origin and destination are shared. If the batch serialization information returned by the routing backend contains a special identifier indicating that the origin and destination are shared, it means that the parcel dispatch point and the outbound distribution center (or the outbound distribution center and the originating transportation site) are located in the same physical site. In this case, the ground transportation time can be regarded as 0.

[0050] Then, it can be directly determined whether the time from the customer's location to the destination is not greater than the required arrival time (flight / train cut-off time). If it meets the requirement, it is determined that the trip is "on time," the route is recorded, and subsequent batch matching is skipped; if it does not meet the requirement, the capacity is directly filtered out.

[0051] If there is no shared identifier, then the batch list is filtered to select batches that simultaneously meet the following two conditions: the first departure time of the batch is not less than the earliest departure time of the destination; the arrival time of the batch is not greater than the required arrival time. If at least one batch meets the conditions, then the capacity is determined to be "on time" and the batch information is recorded.

[0052] If the above scheme fails to match any batch, it checks whether the sending point is configured with a bound site, for example, temporarily binding the point to another distribution center. Bound sites are sorted by maintenance priority, such as priority 1: distribution center A, priority 2: distribution center B, ... Then, it tries in order of priority: using the bound site as a new outbound distribution center, re-calling the routing interface to obtain batch concatenation information, and judging whether it can catch up according to the above rules. Once a binding relationship successfully matches a batch, the attempts stop immediately, and the bound site used is recorded. If no batch is matched after trying all bound sites, it switches to the dedicated vehicle backup model. For example, first, the map interface is called to calculate the distance from the sending point to the outbound distribution center. Then, when the distance is no more than 30 kilometers, the catching time is obtained. Catching time = travel time from customer to point + travel time from point to distribution center + operation time. When the distance is greater than 30 kilometers, catching time = travel time from customer to point + travel time from point to distribution center + collection time + operation time. If the order submission time plus the delivery time does not exceed the flight's arrival time, the vehicle is considered "on time" and the special car option is recorded as a special route; otherwise, the vehicle is filtered out.

[0053] In some embodiments, the filtering rules further include any one, two or more of the following: cargo volume filtering, single shipment weight filtering, single piece weight filtering, capacity / shift size filtering, capacity / shift user filtering, and capacity / shift service mode filtering, before timeliness filtering, to initially filter out unsuitable capacity based on the nature and configuration requirements of the goods.

[0054] Specifically, cargo capacity filtering is used to determine whether the remaining capacity of candidate shipping routes is sufficient to handle the current order. It compares the remaining cargo capacity of a flight with the cargo weight of the waybill. If the remaining cargo capacity of the flight is not less than the cargo weight of the waybill, then the flight's cargo capacity is suitable for loading. Cargo capacity filtering prevents overbooking and ensures the feasibility of the loading plan. It also leverages Redis's high-performance read / write capabilities to support high-concurrency scenarios. Single-shipment weight filtering checks whether the total weight of the current waybill is within the carrier's allowed single-shipment weight limit. Single-shipment weight filtering avoids rejection due to overweight or additional charges. Single-piece weight filtering checks whether the maximum weight of each piece of cargo in the waybill exceeds the capacity's single-piece weight limit. Single-piece weight filtering can be used to connect with some shipping routes (such as passenger aircraft belly cargo) that have strict weight limits on single pieces (usually ≤70kg), preventing the loading of overweight cargo.

[0055] The capacity / schedule size filter is used to determine whether the outer packaging dimensions (sum of length, width, and height, or length of a single side) of the goods are within the allowable range of the capacity. This filter prevents goods from being too large to fit through the cargo door or occupying excessive cargo space. The capacity / schedule user filter filters capacity based on the access rules of the carrier (airline, agent, railway bureau, trucking company). The capacity / schedule service method filter filters capacity with corresponding service capabilities based on the service type of the order (e.g., next-day delivery, next-morning delivery, cold chain, dangerous goods), ensuring that special goods receive compliant and safe transportation services. The above filters are merely examples and not limitations. In some other embodiments, other filtering rules may be included and can be selected and enabled according to the actual configuration, such as requirements for wooden crating.

[0056] The above filtering methods can reduce the amount of computation, filter out a large number of unavailable transport capacity before timeliness filtering, avoid subsequent complex routing interface calls and map calculations, and ensure that the selected transport capacity can actually carry the order both physically and according to business rules. In addition, each filtering rule can be enabled / disabled independently and supports differentiated configuration by customer service department and service method to adapt to changing business scenarios.

[0057] S104, Select different decision branches based on the relationship between cargo weight and preset threshold: If the weight of the goods exceeds the preset threshold, the optimal solution for each group is selected as the final allocation solution based on the highest comprehensive score using a three-dimensional optimization objective function. The three-dimensional optimization objective function is a weighted scoring function based on time, cost, and service quality. If the final scores are the same, the final allocation solution is selected in the order of air transport priority over land transport, and land transport priority over high-speed rail. If the weight of the goods is less than or equal to the preset threshold, the solution that meets the timeliness and has the lowest cost will be selected from each group as the final allocation solution. This step, by employing differentiated optimization strategies for different cargo volume characteristics, both meets business needs and controls computational costs.

[0058] In some embodiments, the three-dimensional optimization objective function includes: formula Where V represents the overall score of the optimal solution for each group, T represents the transportation time from pickup to delivery for the optimal solution for each group, C represents the total transportation cost, Q represents the historical service quality score, and α, β, and γ represent their respective coefficients, α+β+γ=1. The historical service quality score Q includes at least the weighted overall scores of historical on-time rate, integrity rate, response speed, and fulfillment stability. Where, P represents the historical on-time rate over a certain period, obtained as the percentage of on-time deliveries to the total number of deliveries over that period; I represents the historical integrity rate over a certain period, obtained as the percentage of undamaged waybills to the total number of waybills over that period; R represents the response speed over a certain period, obtained as the percentage of the difference between the standard response time and the average response time to the standard response time; S represents the fulfillment stability, obtained as the percentage of normal executions to the total planned executions. , , , For their respective coefficients, .

[0059] Because large shipments have high per-shipment costs and significant transportation risks, such as greater losses due to delays or damage, this implementation method does not only consider cost or timeliness. By using a three-dimensional weighted scoring system that combines time, cost, and service quality, this method can flexibly balance multiple objectives based on business preferences. For example, it can increase the weight of time for time-sensitive customers and increase the weight of cost for cost-sensitive customers. This avoids falling into the trap of local optima based solely on cost or timeliness.

[0060] Furthermore, introducing service quality evaluation targets based on historical punctuality rate, availability rate, response speed, and fulfillment stability can proactively avoid capacity with unstable service. For example, a flight with a slightly higher price but a 99% punctuality rate is better than a flight with a lower price but a 70% punctuality rate, thereby reducing the delay risk and hidden costs for large cargo shipments.

[0061] For example, the optimal solution in each group is selected as air travel A, high-speed rail B, and highway C.

[0062] Airline A's transport time is 240 minutes, the cost is 1000 yuan, the on-time rate is 98%, the integrity rate is 99%, the response speed is 95%, and the performance stability is 99%. The high-speed rail B service takes 300 minutes, costs 800 yuan, has a punctuality rate of 95%, a good condition rate of 98%, a response speed of 90%, and a contract fulfillment stability of 97%. The transportation time for Highway C is 400 minutes, the cost is 600 yuan, the on-time rate is 90%, the integrity rate is 96%, the response speed is 85%, and the performance stability is 95%.

[0063] Assume α= 0.4, β= 0.4, γ= 0.2, =0.4, =0.2, =0.2, =0.2.

[0064] The service quality score is: Flight A: 0.4×98 + 0.2×99 + 0.2×95 + 0.2×99 = 39.2 + 19.8 + 19 + 19.8 = 97.8 High-speed rail B: 0.4×95 + 0.2×98 + 0.2×90 + 0.2×97 = 38 + 19.6 + 18 + 19.4 = 95.0 Highway C: 0.4×90 + 0.2×96 + 0.2×85 + 0.2×95 = 36 + 19.2 + 17 + 19 = 91.2.

[0065] The overall score of the optimal solution in each group: Flight A: 0.4 × 240 + 0.4 × 1000 + 0.2 × 97.8 = 515.56 High-speed rail B: 0.4 × 300 + 0.4 × 800 + 0.2 × 95.0 = 459 Highway C: 0.4 × 400 + 0.4 × 600 + 0.2 × 91.2 = 418.24 Result: Highway C had the highest overall score and was selected as the final solution.

[0066] It should be noted that when the overall scores are the same, the selection can be made in the preset order of air transport > land transport > high-speed rail. This is mainly based on the business priority design of air transport capacity maturity, standardized cabin management, and strong profitability, ensuring that the decision is unique and explainable.

[0067] In some embodiments, the historical service quality score also includes a rating of the capacity adaptability to special goods. The special goods include at least one of temperature-controlled goods, dangerous goods, and fresh goods. Based on the different types of special goods, the corresponding historical adaptability index values ​​are obtained, and the historical service quality score is obtained by combining the weighting coefficients.

[0068] In summary, large-scale freight shipments typically require advance booking of cargo space and may involve full truckloads or full containers, necessitating more refined evaluations in decision-making. The weighted scoring method provides multi-dimensional quantitative data and facilitates tracking and adjustments by operations personnel.

[0069] For small shipments, which are typically far more numerous than large shipments, applying the same weighted scoring to each small shipment would drastically increase the workload. Small shipments utilize a lightweight logic of "timeliness tier traversal + cost priority," significantly reducing computational complexity and ensuring high throughput. By progressively tiering timeliness (from strictest to most lenient), the system prioritizes meeting the most demanding customer timeliness requirements (e.g., within 12 hours of the same day). If this is not feasible, the shipment is automatically downgraded to the next most demanding tier (e.g., within 24 hours of the same day) instead of being immediately rejected. This "flexible degradation" mechanism significantly improves the success rate of shipment allocation and reduces manual intervention.

[0070] Prioritizing cost aligns with the economics of small-haul freight. Under the premise of meeting time requirements, the lowest-cost option is chosen first, thus reducing overall costs. For example, if delivery is required before 6 PM on the same day, and both air and high-speed rail are available, high-speed rail is chosen directly if it is cheaper and meets the time requirement, without requiring complex trade-offs.

[0071] In summary, by making differentiated decisions and adopting different strategies for different cargo branches, the maximum cost savings can be achieved while meeting timeliness requirements.

[0072] S105, Perform the order fulfillment operation according to the final order fulfillment plan.

[0073] In some embodiments, this step can generate structured cargo allocation instructions to guide operators in cargo allocation operations. This may include generating unique identifiers for the selected capacity (flight number / vehicle number / flight number), time information for the capacity / flight schedule (departure / departure time, arrival / departure time), origin and destination capacity locations (airport / station / distribution center), detailed transportation costs (unit price, handling fee, total cost), estimated arrival time at the customer, and route batch information for pick-up and delivery routes (including location or dedicated vehicle markings).

[0074] In addition, the loading log can be updated to record information such as waybill number, loading time, loading result (success / failure), selected capacity information (flight number, agent, departure / landing time, etc.), transportation cost, route (batch details of pick-up and delivery routes), and if loading fails, record the reason for failure (no configuration, no flight, missed the deadline, insufficient space, etc.). The loading log can be used for subsequent auditing, anomaly tracing, and algorithm optimization.

[0075] Additionally, the cargo allocation results can be synchronized to the waybill management system. For example, the cargo allocation results (capacity information, estimated delivery time, cost) can be written back to the waybill record, updating the waybill status to "cargo allocated" or "pending dispatch". At the same time, if the waybill was previously in the "pending cargo allocation" status, the status will be triggered to move to the next stage (such as waiting for pickup or waiting for loading). At the same time, updated flight cabin information will be obtained.

[0076] In some embodiments, if a change in the selected capacity status is detected during execution (such as a sudden flight cancellation or cabins being filled by other systems), an alarm and rollback mechanism is also included.

[0077] In summary, this method improves cargo distribution efficiency, reduces transportation costs, and enhances service quality.

[0078] Corresponding to the aforementioned application function implementation method embodiments, this application also provides an automated freight distribution system, electronic equipment, and corresponding embodiments based on air rail.

[0079] Figure 2 This is a schematic diagram of the structure of an automated freight distribution system based on air rail, as shown in an embodiment of this application.

[0080] See Figure 2 An automated freight distribution system based on air rail includes: The order information acquisition unit 201 is used to acquire the order information of the orders to be picked up. The order information includes at least the weight of the goods, the place of origin, the destination, the expected delivery time, and the order reporting time. The candidate capacity screening unit 202 is used to screen candidate capacity according to a preset configuration priority, wherein the candidate capacity includes at least air capacity, land capacity and high-speed rail capacity; The optimal solution acquisition unit 203 is used to filter the candidate capacity according to the filtering rules to obtain the available capacity. The available capacity is divided into three groups: air, land and high-speed rail. Within each group, the optimal solution is selected according to the principle of lowest cost and earliest arrival. The final cargo allocation plan acquisition unit 204 is used to select different decision branches based on the relationship between cargo weight and preset thresholds: If the weight of the goods exceeds the preset threshold, the optimal solution for each group is selected as the final allocation solution based on the highest comprehensive score using a three-dimensional optimization objective function. The three-dimensional optimization objective function is a weighted scoring function based on time, cost, and service quality. If the final scores are the same, the final allocation solution is selected in the order of air transport priority over land transport, and land transport priority over high-speed rail. If the weight of the goods is less than or equal to the preset threshold, the solution that meets the timeliness and has the lowest cost will be selected from each group as the final allocation solution. The order fulfillment execution unit 205 is used to perform order fulfillment operations according to the final order fulfillment plan.

[0081] Regarding the system in the above embodiments, the specific ways in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated further here.

[0082] This application also provides an electronic device. Figure 3This is a schematic diagram of the hardware structure of an embodiment of the electronic device of this application. The electronic device includes a memory 320 and at least one processor 310. The memory 320 is electrically connected to the at least one processor 310. The memory 320 stores instructions. The at least one processor 310 calls the instructions in the memory 320 to cause the electronic device to execute the automated freight distribution method based on air rail according to any of the foregoing embodiments of this application.

[0083] Specifically, the processor 310 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0084] Memory 320 may include a mass storage device for data or instructions. For example, and not limitingly, memory 320 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 320 may include removable or non-removable (or fixed) media. Where appropriate, memory 320 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 320 is non-volatile solid-state memory. In a particular embodiment, memory 320 includes read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0085] In one example, the control device may also include a communication interface 330 and a bus 340. The processor 310, memory 320, communication interface 330, and bus 340 are connected and communicate with each other.

[0086] The communication interface 330 is mainly used to realize communication between various modules, units and / or devices in the embodiments of this application.

[0087] Bus 340 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a Memory 320 bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 340 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0088] Furthermore, in conjunction with the automated freight matching method based on air rail in the above embodiments, this application embodiment can provide a computer-readable storage medium for implementation. This computer-readable storage medium stores instructions that, when executed by a processor, implement any of the single-shipment freight trajectory processing methods based on multi-source heterogeneous data in the above embodiments.

[0089] This application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0090] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0091] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0092] Alternatively, this application also provides a computer program product capable of implementing some or all of the steps of the methods in the above embodiments. The computer program product includes a computer program / instruction that, when executed by a processor, implements some or all of the steps of the methods in the above embodiments.

[0093] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. An automated freight distribution method based on air rail, characterized in that, include: Obtain the order information of the orders to be fulfilled, which includes at least the weight of the goods, the place of origin, the destination, the expected delivery time, and the time of order submission; Candidate transport capacity is selected according to a preset configuration priority, and the candidate transport capacity includes at least air transport capacity, land transport capacity and high-speed rail transport capacity; The selected candidate capacity is filtered according to the filtering rules to obtain the available capacity. The available capacity is divided into three groups: air, land and high-speed rail. Within each group, the optimal solution is selected according to the principle of lowest cost and earliest arrival. Different decision branches are selected based on the relationship between the weight of the goods and a preset threshold: If the weight of the goods exceeds the preset threshold, the optimal solution for each group is selected as the final allocation solution based on the highest comprehensive score using a three-dimensional optimization objective function. The three-dimensional optimization objective function is a weighted scoring function based on time, cost, and service quality. If the final scores are the same, the final allocation solution is selected in the order of air transport priority over land transport, and land transport priority over high-speed rail. If the weight of the goods is less than or equal to the preset threshold, the solution that meets the timeliness and has the lowest cost will be selected from each group as the final allocation solution. Execute the order fulfillment operation according to the final order fulfillment plan; The three-dimensional optimization objective function includes: formula Where V represents the overall score of the optimal solution for each group, T represents the transportation time from pickup to delivery for the optimal solution for each group, C represents the total transportation cost, Q represents the historical service quality score, and α, β, and γ represent their respective coefficients, α+β+γ=1. The historical service quality score Q includes at least the weighted overall scores of historical on-time rate, integrity rate, response speed, and fulfillment stability. Where, P represents the historical on-time rate over a certain period, obtained as the percentage of on-time deliveries to the total number of deliveries over that period; I represents the historical integrity rate over a certain period, obtained as the percentage of undamaged waybills to the total number of waybills over that period; R represents the response speed over a certain period, obtained as the percentage of the difference between the standard response time and the average response time to the standard response time; S represents the fulfillment stability, obtained as the percentage of normal executions to the total planned executions. , , , For their respective coefficients, .

2. The automated freight distribution method based on air rail as described in claim 1, characterized in that, The historical service quality score also includes a score for the adaptability of transport capacity to special goods. The special goods include at least one of temperature-controlled goods, dangerous goods, and fresh goods. Based on the different types of special goods, the corresponding historical adaptability index values ​​are obtained, and the historical service quality score is obtained by combining the weighting coefficients.

3. The automated freight distribution method based on air rail as described in claim 1, characterized in that, The filtering rules include timeliness filtering, which includes using a route time algorithm to determine whether the goods can be transported from the customer to the port for distribution on time and catch the scheduled transport schedule, and using a route time algorithm to determine whether the goods can be delivered to the customer on time after the scheduled transport schedule lands. Only transport schedules that pass both the route time algorithm and the route time algorithm can become available transport capacity.

4. The automated freight distribution method based on air rail as described in claim 3, characterized in that, The algorithm for obtaining the line time includes: Obtain batch connection information of the route and travel time from the customer's location to the drop-off point; Calculate the earliest departure time from the point, where the earliest departure time from the point is the sum of the current time and the travel time. If there is a shared identifier for origin and destination, the travel time is set to 0. If the time from the customer's location to the point is not greater than the required arrival time, it is determined that the customer can make it; otherwise, the service is filtered out. If no shared identifier is specified, the first departure time of the selected batch is not less than the earliest departure time of the destination, and the arrival time of the batch is not greater than the required arrival time. If such a batch exists, it is determined that the batch can make it. If no matching batch exists, the site configuration is bound sequentially according to the preset priority, the first binding relationship that can match the route is selected, and the batch filtering is re-executed. If no matching route still exists, a special vehicle is used as a backup to determine whether the batch can make it. The dispatch line time algorithm includes: Get the arrival time, delivery time and pickup time of the transport capacity and schedule; Determine if there is a shared identifier for origin and destination: if so, set the travel time to 0. If the sum of the arrival time of the transport shift, the pickup time, and the time from the point to the customer is not greater than the delivery time, it is determined to meet the delivery time requirement; otherwise, the transport shift is filtered out. If there is no shared identifier, batches that meet the following conditions will be selected: the first departure time of the dispatch is not less than the sum of the arrival time of the transport shift, the pickup time, and the operation time, and the sum of the batch arrival time and the time from the point to the customer is not greater than the delivery time. If no matching batch is found, try binding the venue configuration in sequence according to the preset priority, take the first binding relationship that can match the route, and re-execute the batch filtering. If no matching route is found, the delivery time is filtered using the terminal reserved time of h hours. Retrieve all routes and corresponding batches that meet the time requirements for route retrieval and route dispatch, and filter out those that do not meet the time requirements.

5. The automated freight distribution method based on air rail as described in claim 3, characterized in that, The filtering rules also include any one, two or more of the following: cargo volume filtering, single shipment weight filtering, single piece weight filtering, capacity / shift size filtering, capacity / shift user filtering, and capacity / shift service mode filtering, before timeliness filtering, to initially filter out unsuitable capacity based on the nature and configuration requirements of the goods.

6. The automated freight distribution method based on air rail as described in claim 1, characterized in that, The process of filtering candidate transport capacity according to preset configuration priorities includes: Based on the origin and destination of the waybill, and combined with the configuration rules of origin priority or destination priority, a priority list of capacity field pairs is generated. The origin priority rule is: the capacity field pair sequence is generated by fixing the origin capacity field and polling the destination capacity field. The destination priority rule is: the capacity field pair sequence is generated by fixing the destination capacity field and polling the origin capacity field. Capacities are matched sequentially according to the priority order of the capacity field pair list until a candidate capacity is found.

7. The automated freight distribution method based on air rail as described in claim 1, characterized in that, The process of filtering candidate transport capacity according to preset configuration priorities includes: Capacity allocation is matched according to a four-level priority order: customer configuration, charter configuration, special configuration, and general configuration. Customer configuration refers to configurations specific to the client's special requirements; charter configuration refers to capacity allocation for chartered flights by the carrier; special configuration refers to capacity allocation for specific service methods or special businesses; and general configuration refers to the regular capacity allocation maintained by the carrier. Within each configuration level, further filtering is performed according to the priority of available quotes on the current day, no quotes on the current day, quotes available the next day, and no quotes the next day. If no available capacity is matched in any of the four configuration levels, the process proceeds to manual cargo matching.

8. An automated freight distribution system based on air rail, characterized in that, include: The order information acquisition unit is used to acquire the order information of the orders to be fulfilled. The order information includes at least the weight of the goods, the place of origin, the destination, the expected delivery time, and the time of order submission. The candidate capacity screening unit is used to screen candidate capacity according to a preset configuration priority. The candidate capacity includes at least air capacity, land capacity and high-speed rail capacity. The optimal solution acquisition unit for each group is used to filter the candidate capacity according to the filtering rules to obtain the available capacity. The available capacity is divided into three groups: air, land and high-speed rail. Within each group, the optimal solution is selected according to the principle of lowest cost and earliest arrival. The final cargo allocation plan acquisition unit is used to select different decision branches based on the relationship between cargo weight and preset thresholds: If the weight of the goods exceeds the preset threshold, the optimal solution for each group is selected as the final allocation solution based on the highest comprehensive score using a three-dimensional optimization objective function. The three-dimensional optimization objective function is a weighted scoring function based on time, cost, and service quality. If the final scores are the same, the final allocation solution is selected in the order of air transport priority over land transport, and land transport priority over high-speed rail. If the weight of the goods is less than or equal to the preset threshold, the solution that meets the timeliness and has the lowest cost will be selected from each group as the final allocation solution. The order fulfillment unit is used to perform order fulfillment operations according to the final order fulfillment plan.

9. An electronic device, characterized in that, include: processor; as well as A memory having executable code stored thereon, which, when executed by the processor, causes the processor to perform the method as described in any one of claims 1-7.

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