Aviation logistics freight rate integration method, equipment and aviation logistics platform

Abstract

The invention provides an aviation logistics freight rate integration method and device and an aviation logistics platform, and the method comprises the steps: obtaining freight rate data from a freight rate data source, carrying out the fragmentation processing of the freight rate data according to a user identifier, and routing the freight rate data belonging to the same user to the same calculation node, each user being corresponding to a unique user identifier, and the computing node computes the freight rate data of the same user according to a preset computing rule to obtain the sales price of the user. Therefore, it can be ensured that the freight rate data of the same user can be processed by the same computing node, and the problems of computing errors possibly caused by data disorder when the freight rate data of the same user is processed by different computing nodes and partial data loss or repeated computing possibly caused by parallel computing are solved; and meanwhile, the time consumed by frequent cross-node communication in the calculation process is also reduced, so that the processing delay can be effectively reduced, and the system performance is improved.

Description

Technical Field

[0001] The present application relates to the field of information technology, and in particular to an aviation logistics freight rate integration method, equipment, and an aviation freight platform. Background Art

[0002] With the shift in China's economic growth model and the upgrading of its industrial structure, the aviation logistics industry has developed rapidly. Air logistics, a form of transportation using aircraft or other aircraft, is a crucial component of modern logistics operations, characterized by high speed, low damage rates, excellent safety, and wide-area coverage. Consequently, aviation logistics platforms have emerged that integrate the freight resources of various airlines and provide logistics services.

[0003] To determine the sales price of logistics services, each store on the aviation logistics platform needs to obtain freight rate data from various airlines, including home freight rates, air freight rates, manual import prices, cost prices, markups, and fixed fees. This data is then integrated and calculated to arrive at the final sales price, which is then published on the platform's stores. During this price integration process, when the amount of data to be processed is large, it can lead to significant processing delays from synchronizing freight rate data to generating sales prices, seriously affecting system responsiveness. Summary of the Invention

[0004] One purpose of this application is to provide an air logistics freight rate integration method, equipment and air logistics platform to solve the problems of large processing delay and poor system performance in high data volume scenarios in the existing technology.

[0005] To achieve the above objectives, the present application provides an aviation logistics freight rate integration method, which includes:

[0006] Obtain freight rate data from the freight rate data source;

[0007] The freight rate data is sharded according to the user ID, and the freight rate data belonging to the same user is routed to the same computing node, where each user has a unique user ID.

[0008] The calculation node calculates the freight rate data of the same user according to the preset calculation rules to obtain the sales price of the user.

[0009] Furthermore, freight rate data is obtained from the freight rate data source, including:

[0010] Obtain freight rate data in real time from the freight rate data source through the Kafka message queue.

[0011] Furthermore, the freight rate data is sharded according to the user identifier, and the freight rate data belonging to the same user is routed to the same computing node, including:

[0012] The Flink processing engine shards the freight rate data according to user IDs and routes the freight rate data belonging to the same user to the same computing node.

[0013] Furthermore, the freight rate data is segmented according to the user identifier, including:

[0014] Perform hash calculation on the user ID to determine the shard index;

[0015] According to the shard index, the fare data belonging to the same user is routed to the same computing node.

[0016] Furthermore, the method further comprises:

[0017] Monitor the amount of freight rate data to be processed in the computing nodes;

[0018] If the data volume exceeds the batch processing trigger threshold, the freight rate data to be processed is split into multiple batches;

[0019] The calculation node calculates the price data of the same user according to the preset calculation rules to obtain the sales price of the user, including:

[0020] The calculation node calculates the freight rate data of different batches of the same user according to the preset calculation rules to obtain the sales price of the user.

[0021] Furthermore, the method further comprises:

[0022] Monitor the processing load of computing nodes;

[0023] If the processing load exceeds the backpressure trigger threshold, the rate at which freight data is obtained from the freight data source is reduced.

[0024] Furthermore, the computing nodes calculate the fare data of different users in parallel.

[0025] Based on another aspect of the present application, an air logistics freight rate integration device is also provided, which includes a memory for storing computer program instructions and a processor for executing computer program instructions, wherein when the computer program instructions are executed by the processor, the device is triggered to execute the air logistics freight rate integration method.

[0026] An embodiment of the present application further provides a computer-readable medium having computer program instructions stored thereon, wherein the computer program instructions can be executed by a processor to implement the aviation logistics freight rate integration method.

[0027] The present application also provides an aviation logistics platform, which includes:

[0028] The freight rate integration module is used to generate the user's sales price using the aforementioned air logistics freight rate integration method;

[0029] Order data synchronization is used to synchronize relevant data about the logistics order with the airline system through standardized interfaces and data interaction mechanisms after a logistics order is generated based on the sales price or the order is changed.

[0030] Compared with the existing technology, this application provides an aviation logistics freight rate integration solution. After obtaining freight rate data from the freight rate data source, this solution shards the freight rate data according to the user ID and routes the freight rate data belonging to the same user to the same computing node, wherein each user corresponds to a unique user ID. The computing node calculates the freight rate data of the same user according to the preset calculation rules to obtain the sales price of the user. In this way, it can be ensured that the freight rate data of the same user will be processed by the same computing node, avoiding the calculation errors that may be caused by data disorder when the freight rate data of the same user is processed by different computing nodes, and the problem of partial data loss or repeated calculation that may be caused by parallel computing. At the same time, it also reduces the time consumed by frequent cross-node communication during the calculation process, thereby effectively reducing processing delays and improving system performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Other features, objects and advantages of the present application will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0032] Figure 1 A processing flow chart of an air logistics freight rate integration method provided in an embodiment of the present application;

[0033] Figure 2 A schematic diagram of a process flow for implementing freight rate integration using the solution of an embodiment of the present application;

[0034] The same or similar reference numerals in the drawings represent the same or similar components. DETAILED DESCRIPTION

[0035] The present application is described in further detail below with reference to the accompanying drawings.

[0036] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0037] In a typical configuration of the present application, the terminal and the equipment of the service network each include one or more processors (CPUs), input / output interfaces, network interfaces and memories.

[0038] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0039] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology for information storage. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc-read only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0040] An embodiment of the present application provides an air logistics freight rate integration method. After obtaining freight rate data from a freight rate data source, the method shards the freight rate data according to user identifiers and routes the freight rate data belonging to the same user to the same computing node, wherein each user corresponds to a unique user identifier. The computing node calculates the freight rate data of the same user according to a preset calculation rule to obtain the sales price of the user. This ensures that the freight rate data of the same user will be processed by the same computing node, avoiding calculation errors that may result from data disorder when the freight rate data of the same user is processed by different computing nodes, as well as the problem of partial data loss or repeated calculation caused by parallel computing. At the same time, it also reduces the time consumed by frequent cross-node communication during the calculation process, thereby effectively reducing processing delays and improving system performance.

[0041] In practical scenarios, the execution subject of the method may include a user device, a network device, or a device formed by integrating a user device and a network device via a network, or an application running on the above-mentioned device. The user device includes but is not limited to various terminal devices such as computers, mobile phones, and tablet computers; the network device includes but is not limited to network hosts, single network servers, servers concentrated in a network of multiple servers, or servers in a distributed cloud network. The distributed cloud network herein is composed of a large number of hosts or network servers based on cloud computing.

[0042] Figure 1 The present invention provides an air logistics freight rate integration method. This method can be applied to an air logistics platform to integrate freight rate data from airline freight rate systems to ultimately obtain external sales prices. The method includes at least the following processing steps:

[0043] Step S101: Obtaining fare data from a fare data source. The fare data source may be the fare system of each airline. Once the airline's fare system is connected to the aviation logistics platform using the solution of the embodiment of the present application, the aviation logistics platform can obtain the airline's fare data from the connected fare system.

[0044] The freight rate data may include various data that may be involved in the air logistics business, including: HO freight rate, AQ freight rate, manual import price, cost price, markup and fixed miscellaneous fees, etc., so as to facilitate the integration of these freight rate data and generate the sales price provided to the outside in the store of the air logistics platform.

[0045] In some embodiments of the present application, when obtaining freight data from a freight data source, a Kafka message queue can be used, that is, the freight data can be obtained from the freight data source in real time through the Kafka message queue. For example, the freight data source, as a message producer (Producer), publishes messages about freight data to a designated topic (Topic), and the aviation logistics platform, as a message consumer (Consumer), can consume messages by subscribing to the topic, thereby obtaining the content of the message (i.e., freight data). Therefore, in the data access layer, this solution accesses freight data in real time through the Kafka message queue, ensuring high throughput and low latency for the overall solution.

[0046] Step S102 : Slice the freight rate data according to the user identifier, and route the freight rate data belonging to the same user to the same computing node.

[0047] Among them, each user corresponds to a unique user identifier. For example, the user in this embodiment can be a store on the air logistics platform, and each store has a corresponding user identifier (user_id). After the freight data is obtained from the freight data source, the freight data can be sharded according to the user identifier so that the freight data belonging to the same user will be routed to the same computing node. For example, the freight data obtained at a certain time includes the freight data of three stores, and their corresponding user_ids are 1001, 1002 and 1003 respectively. After these freight data are sharded, the freight data belonging to 1001 will be routed to the same computing node, such as computing node node1, the freight data belonging to 1002 will be routed to the same computing node, such as computing node node2, and all freight data belonging to 1003 will be routed to the same computing node, such as computing node node3.

[0048] When sharding fare data by user ID, a hash calculation can be performed on the user ID to determine a shard index. Based on this shard index, fare data belonging to the same user is then routed to the same computing node. Because the same user ID yields the same result after being hashed using the same hash algorithm, calculating the shard index used as the basis for sharding in this manner ensures accurate sharding.

[0049] In addition, in actual scenarios, the selection of computing nodes can be set according to the requirements of the scenario. For example, a certain load balancing strategy can be adopted to evenly route to different nodes to avoid unbalanced loads between computing nodes, which may lead to performance bottlenecks.

[0050] In some embodiments of this application, the Flink processing engine can shard freight rate data by user ID, routing freight rate data belonging to the same user to the same compute node. For example, in this embodiment, the Flink processing engine performs KeyBy sharding based on user_id of the freight rate data, ensuring that the freight rate data of the same user is processed by the same compute node and is not routed to different compute nodes for processing.

[0051] In step S103, the computing node calculates the fare data of the same user according to a preset computing rule to obtain the sales price of the user.

[0052] Once the freight rate data is routed to the various computing nodes, they can calculate the freight rate data for the same user according to pre-set calculation rules, thereby calculating the sales price for that user. These calculation rules can be set based on the actual scenario requirements, such as pre-setting freight rate priorities and markup strategies to generate a final sales price that meets the scenario requirements.

[0053] For example, if a store with user_id 1001 on the aviation logistics platform has three sales agents (agent1, agent2, and agent3) that need to calculate sales prices, the freight rate data from Flink processing engine 1001 will be routed to the same computing node, node1. This computing node will then perform processing in sequence according to the preset calculation rules: obtaining the HO freight rate, AQ freight rate, and manually imported price, calculating the markup, fixed miscellaneous fees, etc., and generating the final sales price version, thereby achieving freight rate integration.

[0054] If the sharding mechanism is not adopted, the freight data of the same user may be processed by different computing nodes, resulting in errors in the calculation of the final sales price. In addition, when calculating the sales price, since different parts of the freight data of the same user need to be obtained from different computing nodes, frequent cross-node communication will be required, which will increase processing time and reduce processing efficiency. In addition, parallel calculation of the freight data of the same user by different computing nodes is also likely to cause partial data loss or repeated calculation, increasing the risk of data consistency. The processing method in the embodiment of the present application can ensure that the freight data of the same user will be processed by the same computing node, avoiding the calculation errors that may be caused by data disorder when the freight data of the same user is processed by different computing nodes, as well as the problem of partial data loss or repeated calculation caused by parallel calculation. At the same time, it also reduces the time consumed by frequent cross-node communication during the calculation process, thereby effectively reducing processing delays and improving system performance.

[0055] In addition, the air logistics freight rate integration method provided in the embodiment of the present application can also monitor the data volume of the freight rate data to be processed in the computing node, and implement dynamic batch processing operations based on the data volume. Specifically, if the data volume exceeds the batch processing trigger threshold, the freight rate data to be processed can be split into multiple batches, wherein the trigger threshold can be set according to the needs of the actual scenario, for example, it can be set to a suitable value based on the computing power of the hardware. When the amount of freight rate data exceeds this value, the batch processing operation is triggered. At this time, the computing node can calculate the freight rate data of different batches of the same user according to the preset calculation rules to obtain the sales price of the user.

[0056] In other embodiments of the present application, the method can also monitor the processing load of the computing node and implement backpressure control based on the processing load of the computing node. Specifically, if the processing load exceeds the backpressure trigger threshold, the rate of obtaining freight data from the freight data source is reduced. The trigger threshold can also be set according to the needs of the actual scenario. For example, if the processing of a batch of freight data is slow, the computing node can automatically reduce the rate of obtaining freight data through the Kafka message queue according to the backpressure control strategy when the processing load exceeds the backpressure trigger threshold to avoid memory overflow.

[0057] In addition, the computing nodes can also calculate the fare data of different users in parallel during calculation to further improve processing efficiency.

[0058] Figure 2 The processing flow for implementing freight rate integration using the solution of the embodiment of the present application is shown. First, at the data access layer, freight rate data is accessed in real time through the Kafka message queue to ensure high throughput and low latency. Among them, the freight rate data source serves as the message producer of Kafka Topic, and the aviation logistics platform subscribes to Kafka Topic through the Flink data engine, so that the Flink job (Flink task) can obtain the required freight rate data from Kafka Topic. After obtaining the freight rate data, at the data sharding layer, these freight rate data are verified. If the verification fails, they are added to the dead letter queue. If the verification passes, they are sharded according to user_id, and the freight rate data belonging to the same user are routed to the same computing node. Then, at the batch processing layer, the computing nodes process in sequence: obtain HO freight rates, AQ freight rates, manually imported prices, calculate markups, fixed miscellaneous fees, etc., and generate the final sales price version. Finally, at the result output layer: write the calculated sales price into the database and push it to the aviation logistics platform, thereby realizing freight rate integration.

[0059] In addition, embodiments of the present application also provide an aviation logistics platform comprising at least a freight rate integration module and an order data synchronization mechanism. The freight rate integration module is configured to generate a user's sales price using the aforementioned aviation logistics freight rate integration method. The order data synchronization mechanism is configured to synchronize relevant data regarding logistics orders with the airline system via a standardized interface and data exchange mechanism after a logistics order is generated based on the sales price or after the order is modified.

[0060] After the freight rate data of different airlines are connected, the aviation logistics platform can distinguish the freight rates of different airlines according to flight numbers, automatically associate them to different store backends according to route attributes, and automatically generate freight rates applicable to all users or designated freight rates for specific agents according to the recipients of the freight rates.

[0061] When the user completes the agent binding in the store background, the system will give him / her an agent identification, and he / she can view the freight system operation page of each airline on the aviation logistics platform. After placing an order on the aviation logistics platform, the system will identify his / her sales agency order, automatically connect to the corresponding airline production system data, and execute the order flow of each airline's belly cargo order.

[0062] When an agent places an order in an airline store, the aviation logistics platform will identify the flight numbers of different products, exchange data with the production systems of different airlines, and automatically reserve space with the corresponding airline's production system.

[0063] Before the implementation of the solution of this application, the function of the store in the aviation logistics platform was a closed system. Sales personnel needed to maintain freight rates separately in the airline freight rate system and the aviation logistics platform store. The original order flow of the individual customer model was not suitable for the sales agency scenario. After placing an order on the aviation logistics platform, the agent was unable to pass the order information to the respective airline system to realize the booking operation. It was necessary to repeatedly operate in each system, which was a very cumbersome process.

[0064] The aviation logistics platform provided in the embodiments of this application has established a data integration strategy and synchronization mechanism that is compatible with various airlines. Starting from the underlying basic data configuration, it achieves the universalization of different airlines' data. This can greatly expand the functionality of the platform and reduce users' repeated operations. However, incompatible data formats will result in high integration costs. It also effectively solves the following problems:

[0065] 1. Connect the freight rate systems of various airlines, accept freight rates from different airlines, and integrate them into the backend of each store to generate the store's freight rates.

[0066] 2. Once an order is placed on the aviation logistics platform, a booking application can be submitted to the airline system.

[0067] 3. After the agent creates the order in the airline's production system, the actual billing weight of the order on the aviation logistics platform will be automatically corrected.

[0068] 4. Automatically adjust the actual billing weight of the air logistics platform order when the goods are pulled.

[0069] 5. Enable agents to directly access the airline’s production system on the aviation logistics platform.

[0070] Based on the same inventive concept, embodiments of the present application also provide an air logistics freight rate integration device. The corresponding method of this device is the air logistics freight rate integration method of the aforementioned embodiment, and its principle of solving the problem is similar to that of the aforementioned embodiment. The air logistics freight rate integration device provided in embodiments of the present application includes a memory for storing computer program instructions and a processor for executing the computer program instructions. When the computer program instructions are executed by the processor, the device is triggered to implement the methods and / or technical solutions of the aforementioned multiple embodiments of the present application.

[0071] Specific implementations of the device may include user devices, network devices, or devices formed by integrating user devices and network devices via a network, or may also be applications running on the above devices. The user devices include, but are not limited to, various terminal devices such as computers, mobile phones, and tablet computers; the network devices include, but are not limited to, network hosts, single network servers, servers concentrated in a network of multiple servers, or servers in a distributed cloud network. The distributed cloud network herein is composed of a large number of hosts or network servers based on cloud computing.

[0072] In particular, the methods and / or embodiments of the present application can be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for executing the method shown in the flowchart. When the computer program is executed by a processing unit, the above-mentioned functions defined in the method of the present application are performed.

[0073] It should be noted that the computer-readable medium described in this application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. Computer-readable media may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0074] In the present application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0075] Computer program code for performing the operations of the present application can be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0076] The flow chart or block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the equipment, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code include one or more executable instructions for realizing the logical function of the specification. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated system for hardware that performs the function or operation of the specification, or can be implemented with a combination of dedicated hardware and computer instructions.

[0077] As another aspect, the present application further provides a computer-readable medium, which may be included in the device described in the above embodiments, or may exist independently without being incorporated into the device. The computer-readable medium carries one or more computer program instructions, which can be executed by a processor to implement the methods and / or technical solutions of the above embodiments of the present application.

[0078] It should be noted that the present application can be implemented in a combination of software and / or software and hardware, for example, can be implemented using an application specific integrated circuit (ASIC), a general purpose computer or any other similar hardware device. In certain embodiments, the software program of the present application can be executed by a processor to implement the above steps or functions. Similarly, the software program of the present application (including related data structures) can be stored in a computer-readable recording medium, for example, a RAM memory, a magnetic or optical drive or a floppy disk and similar devices. In addition, some steps or functions of the present application can be implemented using hardware, for example, as a circuit that cooperates with a processor to perform each step or function.

[0079] It is obvious to those skilled in the art that the present application is not limited to the details of the above-mentioned exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or basic characteristics of the present application. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present application. Any figure marks in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device claim can also be implemented by one unit or device through software or hardware. Words such as first and second are used to indicate names and do not indicate any specific order. The numerical order of the serial numbers corresponding to the steps does not indicate any specific execution order. The steps can be executed in any order combination under the premise of conforming to the execution logic.

Claims

1. A method for integrating air logistics freight rates, characterized in that: The method comprises: Obtain freight rate data from the freight rate data source; The freight rate data is sharded according to the user ID, and the freight rate data belonging to the same user is routed to the same computing node, where each user has a unique user ID. The calculation node calculates the freight rate data of the same user according to the preset calculation rules to obtain the sales price of the user.

2. The method according to claim 1, characterized in that Obtain freight rate data from the freight rate data source, including: Obtain freight rate data in real time from the freight rate data source through the Kafka message queue.

3. The method according to claim 1, characterized in that The freight rate data is sharded according to the user ID, and the freight rate data belonging to the same user is routed to the same computing node, including: The Flink processing engine shards the freight rate data according to user IDs and routes the freight rate data belonging to the same user to the same computing node.

4. The method according to claim 1, wherein The freight rate data is segmented according to the user ID, including: Perform hash calculation on the user ID to determine the shard index; According to the shard index, the fare data belonging to the same user is routed to the same computing node.

5. The method according to claim 1, characterized in that The method further comprises: Monitor the amount of freight rate data to be processed in the computing nodes; If the data volume exceeds the batch processing trigger threshold, the freight rate data to be processed is split into multiple batches; The calculation node calculates the price data of the same user according to the preset calculation rules to obtain the sales price of the user, including: The calculation node calculates the freight rate data of different batches of the same user according to the preset calculation rules to obtain the sales price of the user.

6. The method according to claim 1, characterized in that The method further comprises: Monitor the processing load of computing nodes; If the processing load exceeds the backpressure trigger threshold, the rate at which freight data is obtained from the freight data source is reduced.

7. The method according to claim 1, characterized in that The computing nodes calculate the fare data of different users in parallel.

8. An aviation logistics freight rate integration device, the device comprising a memory for storing computer program instructions and a processor for executing computer program instructions, wherein: When the computer program instructions are executed by the processor, the device is triggered to execute the method according to any one of claims 1 to 7.

9. A computer-readable medium having computer program instructions stored thereon, wherein the computer program instructions can be executed by a processor to implement the method according to any one of claims 1 to 7.

10. An aviation logistics platform, characterized in that: The platform includes: A freight rate integration module, configured to generate a user's sales price using the method according to any one of claims 1 to 7; Order data synchronization is used to synchronize relevant data about the logistics order with the airline system through standardized interfaces and data interaction mechanisms after a logistics order is generated based on the sales price or the order is changed.

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