Airport bill processing method and device

Abstract

The invention discloses an airport bill processing method and device, and relates to the technical field of computers, and the method comprises the steps: receiving multiple pieces of airport flight business data; the airport flight business data comprises fixed cost type data and personalized type data; according to different calculation frequencies, the fixed cost type data and the personalized type data are respectively utilized to create calculation tasks, and the cost is calculated according to the priority of the calculation tasks; the priority of the calculation task is set according to one or any combination of calculation time, calculation data volume, airport identification and preset emergency degree; the calculation frequency corresponding to the fixed cost type data is higher than the calculation frequency corresponding to the personalized type data; and integrating the fixed cost and the personalized cost to generate a bill. The airport bill calculation resources can be saved, and the airport bill calculation efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to an airport billing method and apparatus. Background Technology

[0002] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section.

[0003] Airport billing operations involve complex scenarios such as airline agreement management, flight data matching, and dynamic adjustment of fee types. Traditional methods require parsing flight data (such as flight number, aircraft type, time, route type, etc.) line by line based on the charging agreements signed between airlines and airports, iteratively matching agreement terms, and finally generating settlement data. With the surge in flight volume and the diversification of agreement terms, the efficiency problems of existing solutions have become increasingly prominent.

[0004] In existing airport billing calculations, data is typically integrated into the system via an interface. Then, according to agreements between airports and airlines, the rules are entered into the system. After flight data is matched and linked, the system uniformly identifies the corresponding fee agreement rules based on the airline, aircraft type, and route of the flight data, calculates each flight's data entry, and generates various billing fees. In traditional systems, clients select the calculation range through the system and submit the calculation to the server. All airport fees are settled together, resulting in a massive workload. When multiple airports are involved, task scheduling is serialized, leading to rigid scheduling, low computational efficiency, and an inability to meet real-time or high-concurrency computing requirements. Summary of the Invention

[0005] This invention provides an airport billing processing method to save airport billing calculation resources and improve airport billing calculation efficiency. The method includes:

[0006] Receives flight service data from multiple airports; the airport flight service data includes fixed-fee type data and personalized type data; the fixed-fee type data represents the fixed airport charges generated with each flight, and the personalized type data represents the personalized consumption data of the flight at the airport;

[0007] Calculation tasks are created using fixed-fee type data and personalized type data at different calculation frequencies, and the fees are calculated according to the priority of the calculation tasks. The fees include fixed fees calculated using fixed-fee type data and personalized fees calculated using personalized type data. The priority of the calculation tasks is set according to one or any combination of calculation time, calculation data volume, airport identification, and preset urgency level. The calculation frequency corresponding to fixed-fee type data is higher than the calculation frequency corresponding to personalized type data.

[0008] Invoices are generated by combining fixed and personalized fees.

[0009] This invention also provides an airport billing processing device to save airport billing calculation resources and improve airport billing calculation efficiency. The device includes:

[0010] The data receiving module is used to receive flight service data from multiple airports. The airport flight service data includes fixed-fee type data and personalized type data. The fixed-fee type data represents the fixed airport charges generated with each flight, and the personalized type data represents the personalized consumption data of the flight at the airport.

[0011] The cost calculation module is used to create calculation tasks using fixed cost type data and personalized cost type data at different calculation frequencies, and calculate the cost according to the priority of the calculation tasks. The cost includes a fixed cost calculated using fixed cost type data and a personalized cost calculated using personalized cost type data. The priority of the calculation task is set according to one or any combination of calculation time, calculation data volume, airport identifier, and preset urgency level. The calculation frequency corresponding to fixed cost type data is higher than the calculation frequency corresponding to personalized cost type data.

[0012] The billing generation module is used to generate bills by integrating fixed fees and personalized fees.

[0013] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described airport billing method.

[0014] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described airport billing method.

[0015] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described airport billing method.

[0016] In this embodiment of the invention, airport charges are divided into fixed charges and personalized charges when calculating airport billing fees. Fixed charges are fixed airport fees incurred with each flight, while personalized charges are dynamically changing. Current calculations calculate all charges simultaneously, resulting in wasted computational resources and low efficiency. This embodiment differentiates between fixed and personalized charges, calculating fixed charges at a higher frequency and personalized charges at a lower frequency. This significantly saves computational resources and improves the efficiency of airport billing calculation. According to experimental statistics, taking 1000 data entries as an example, this embodiment reduces repetitive logic execution by 60%, saving substantial resources and shortening the overall calculation time from 30 minutes to 5 minutes. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0018] Figure 1 This is a flowchart illustrating the airport billing processing method in an embodiment of the present invention;

[0019] Figure 2 This is a specific example diagram of the airport billing processing method in an embodiment of the present invention;

[0020] Figure 3 This is a specific example diagram of the airport billing processing method in an embodiment of the present invention;

[0021] Figure 4 This is a schematic diagram of an airport billing processing device in an embodiment of the present invention;

[0022] Figure 5 This is a schematic diagram of a computer device in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0024] To facilitate a clear description of the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish the same or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order.

[0025] The acquisition, transmission, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0026] In existing airport billing calculations, during the data matching phase, flight data is looped through one by one, and related agreement terms are queried from the database. This linear search or unoptimized database querying leads to response delays. During the billing fee calculation phase, agreement terms are matched one by one, with nested multi-layered conditional judgments (e.g., "If the flight is an international route and the maximum takeoff weight is greater than 51, then (1300 + 26 × (maximum takeoff weight - 50)) × 0.5"). However, the system queries all relevant agreements for that airline and then matches them one by one to calculate the fee, resulting in a large computational load and repetitive processing of a large amount of the same logic. This also leads to a poor user experience, long calculation times, and an inability to meet real-time requirements.

[0027] To address the shortcomings of existing technologies, this invention proposes a phased cost calculation method that considers the dynamic calculation of task priorities based on tasks submitted by multiple airports, as well as the extraction and structured storage of protocol feature values. This saves computing resources, improves protocol matching speed, and optimizes user experience. Figure 1 This is a flowchart illustrating the airport billing processing method in an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes:

[0028] Step 101: Receive multiple airport flight service data; the airport flight service data includes fixed fee type data and personalized type data; the fixed fee type data represents the airport fixed charge data generated with the flight, and the personalized type data represents the personalized consumption data of the flight at the airport;

[0029] Step 102: Create calculation tasks using fixed-fee type data and personalized type data respectively, according to different calculation frequencies, and calculate the fees according to the priority of the calculation tasks; the fees include fixed fees calculated using fixed-fee type data and personalized fees calculated using personalized type data; wherein, the priority of the calculation tasks is set according to one or any combination of calculation time, calculation data volume, airport identification, and preset urgency level; the calculation frequency corresponding to fixed-fee type data is higher than the calculation frequency corresponding to personalized type data;

[0030] Step 103: Generate an invoice by combining fixed fees and personalized fees.

[0031] The airport billing processing method in this embodiment of the invention is described in detail below.

[0032] Step 101 involves receiving data, step 102 involves calculating the expense details, and step 103 involves consolidating the bills. Figure 1 The steps constitute key steps in the embodiments of the present invention. Besides these, the embodiments may also include steps such as setting basic information like airport information, aircraft information, and customer information; protocol management; airport parameter configuration; and user terminal construction. The user terminal displays a user interface and provides permission management and task monitoring functions. Airport parameter configuration may include: setting the flight settlement caliber to the planned date or actual date; setting whether landing flights are charged together with departing flights; setting the range of months to trace back when pairing flights; and setting whether airport and ground handling fees are calculated separately. Overall, the embodiments of the present invention provide an airport billing processing system or platform. This system can adopt a microservice architecture to implement various functional modules. Figure 1 The Chinese method is based on the microservices model to implement an airport billing processing method.

[0033] In one embodiment, the airport flight service data has a data identifier;

[0034] Before calculating the cost according to the priority of the calculation tasks in step 102, by creating calculation tasks using fixed cost type data and personalized type data at different calculation frequencies, the method may further include:

[0035] By using a data type mapping table and data identifiers for airport flight business data, fixed-fee type data and personalized type data can be distinguished among multiple airport flight business data. The data type mapping table includes the mapping relationship between data types and data identifiers for airport flight business data. The data types include fixed-fee type and personalized type.

[0036] After receiving airport flight data, it is necessary to distinguish between data related to fixed fee calculation and data related to personalized fee calculation. This allows us to calculate fixed fees using data of the fixed fee type and personalized fees using data of the personalized type.

[0037] Fixed fees may include one or any combination of the following:

[0038] Aircraft service fee, license plate fee, guide vehicle fee, passenger flow service fee, passenger bridge fee, and tool usage fee.

[0039] Personalized fees may include one or any combination of the following:

[0040] Landing and takeoff fees, parking fees, loading and unloading service fees, communication service fees, container equipment management service fees, passenger baggage service fees, loading and unloading ground transportation service fees, bridge-mounted power supply service fees, and bridge-mounted air conditioning fees.

[0041] In this process, a data type mapping table is pre-built to distinguish between fixed-fee data and personalized data.

[0042] In one embodiment, after receiving flight business data from multiple airports, the method may further include: performing data verification and auditing on the flight business data from multiple airports.

[0043] For example, the system can automatically perform data format verification, data content validity verification, flight matching, and data auditing on the received data.

[0044] Data Validation: The system incorporates machine learning models and anomaly detection algorithms (e.g., Isolation Forest, AutoEncoder) to validate data. Based on historical normal data patterns, the model detects anomalies (far exceeding the rule range) in the incoming data in real time. Examples include: significant differences in departure times for the same flight's load data or bridge-mounted data; values ​​outside the typical load range for a specific aircraft type at a specific airport; and misspelled aircraft numbers that conform to a certain pattern. The model continuously learns from human operation and can intelligently provide high-confidence correction suggestions for operator confirmation. For instance, graph neural network models automatically infer missing fields (e.g., predicting missing aircraft type, gate information, etc. based on historical models) from flight-related load, passenger bridge, bridge-mounted, and special vehicle data. This significantly improves data quality, reduces manual inspection workload, increases work efficiency, accelerates the data preparation process, and improves the accuracy of final revenue calculation.

[0045] Flight pairing: Normal data is integrated into the airport billing system. One flight represents a landing flight, and another flight represents a departure flight. These two flights are linked, which supports two methods: 1. The flight ID and paired flight ID provided by the upstream system; 2. The planned flight date or the actual flight date, plus fields such as aircraft number and takeoff / landing identifier, are used for pairing.

[0046] Data audit: This involves supplementing and verifying some of the entered data to prepare for the data used before calculation. For example, for flight transit data, the audit verifies the data against the transit configuration rules pre-defined in the system.

[0047] In data processing, a mark can be printed on the received airport flight business data. This mark is used to distinguish between fixed-fee type data and personalized type data. Then, in step 102, for example, fixed-fee type data is filtered from the airport flight business data according to the mark, and fixed fees are calculated using the fixed-fee type data. Similarly, personalized type data is filtered from the airport flight business data according to the mark, and personalized fees are calculated using the fixed-fee type data.

[0048] In this embodiment of the invention, airport flight business data may specifically include flight data, transport data, bridge data, passenger bridge data, special vehicle data, etc.

[0049] In step 101, flight service data from multiple airports is received.

[0050] In one embodiment, receiving multiple airport flight service data includes receiving multiple airport flight service data through one or any combination of the following methods:

[0051] File import, timed data retrieval via data interface, and user-entered data received on the client side.

[0052] File import includes flight data import, transport data import, bridge-mounted data import, and passenger bridge data import, primarily using CVS and EXCEL formats.

[0053] Data is retrieved on a scheduled basis via a data interface: Data interfaces are connected with upstream systems, and scheduled tasks retrieve data at night or business personnel retrieve data as needed.

[0054] The user-end receives user-entered data: Through the user interface, business personnel can manually enter data, providing add and edit function modules and buttons.

[0055] After receiving the data, it undergoes data cleaning, data verification and auditing, and finally inserts it into a database, which is called the business database.

[0056] Step 102 may specifically include: creating a calculation task using fixed-fee type data at a first preset frequency, calculating the fixed fee according to the priority of the calculation task; and creating a calculation task using personalized type data at a second preset frequency, calculating the personalized fee according to the priority of the calculation task. The first preset frequency is higher than the second preset frequency.

[0057] For example, fixed costs are calculated every night, and personalized costs are calculated at the end of each month.

[0058] For fixed-fee calculation tasks, a first scheduled task is pre-set. Every evening, this first scheduled task will query the business database for flight business data that has been verified and approved, and filter out the fixed-fee type data. The first scheduled task will then create a fixed-fee type calculation task and submit it to the task queue.

[0059] For personalized calculation tasks, a second scheduled task can be pre-set. This second scheduled task retrieves personalized data at the frequency specified in the second task, and the personalized calculation task is then submitted to the task queue. Alternatively, at the beginning or end of each month, business personnel can initiate a cost calculation operation based on flight data. Depending on their needs, they can submit all data from a specific airport for a month for calculation, or, based on special requirements, calculate flight data for a specific customer within a specified start and end time range. Manually triggering the function will also submit the calculation task to the task queue.

[0060] In one embodiment, since fixed-cost data and personalized data have different data update frequencies, data volumes, and resource consumption, a first preset frequency and a second preset frequency can be determined based on the data update frequency, data volume, and resource consumption, respectively.

[0061] For example, the first preset frequency and the second preset frequency can be determined using the following formula:

[0062] f = Resource consumption coefficient / (Data update frequency × Buffer constant);

[0063] f is the first preset frequency or the second preset frequency. The data update frequency is the average update frequency of fixed-cost data or personalized data. The resource consumption coefficient is the quantified value of the resource cost required for each execution task of fixed-cost data or personalized data. The buffer constant is set by the user to avoid the frequency f being too high or too low.

[0064] Alternatively, design the following frequency calculation formula, and during implementation, select the appropriate frequency calculation formula to set the first preset frequency and the second preset frequency.

[0065] f1 = Updated and calculated every 6 hours; (f1: time frequency for periodic update and calculation);

[0066] f2 = Data size / 100 × Buffer constant; (f2: Data update frequency, calculated when the amount of data to be updated reaches a certain quantity);

[0067] f3 = Idle resources × Buffer constant; (f3: Resource frequency. If the current system resources are insufficient and the system is busy, the update and calculation tasks will be suspended until the system resources are idle before the update and calculation tasks are triggered again).

[0068] In one embodiment, computation tasks are created using fixed-fee type data and personalized type data according to different computation frequencies, and the cost is calculated according to the priority of the computation tasks. This may include caching the computation tasks in a Redis queue.

[0069] In this embodiment, a Redis queue is used to store the tasks that need to be computed, which enables high-speed read and write capabilities and improves computing speed.

[0070] In a preferred embodiment, the priority of the computation task can be determined as follows:

[0071] The priority of computation tasks is dynamically determined by using a string reduction algorithm, taking into account computation time point information, airport throughput, airport identification, and preset urgency level.

[0072] For example, a weighting function can be constructed based on airport throughput (which refers to the flight data calculated for each airport, and the number of flights per month is different for each airport; for example, one airport may have 9,000 flights per month, while another airport may have about 1,000 flights per month) and task urgency. For example, the throughput weight is 0.4 and the urgency weight is 0.6. The task queue can be adjusted in real time using a string reduction algorithm.

[0073] For example, airports begin processing data at the beginning of each month and start calculating fees before the 5th, which can lead to multiple airports performing calculations simultaneously. When Airport A submits a task, it is split into multiple tasks based on the set task splitting parameters (5 days) or data volume (500 records), and given priority scores. For example, Airport A's first task A1 might have a priority of 100 points, and the second task A2 might have a priority of 99 points. These tasks are then submitted to the task queue. The calculation thread first retrieves task A1 from the queue and begins calculating fees. If Airport B submits a task at this time, it will also split the task according to the configuration parameters, with the first split task being the most urgent and receiving a score of 100. Other tasks are scored sequentially. The task queue is then rearranged based on the scores. When the calculation thread completes the current task A1, it will retrieve task B1 submitted by Airport B from the queue as a new task.

[0074] In one embodiment, artificial intelligence methods are used to determine the priority of computational tasks. In practice, historical data on billing processing tasks from various airports is collected. This data is then analyzed to understand the task processing habits and preferences of each airport, and priorities are determined based on these habits and preferences. Alternatively, historical data on billing processing tasks from various airports is labeled with priorities. A decision tree model is trained based on this labeled task dataset to predict task priorities. Input features may include computation time points, airport throughput, airport identification, and preset urgency levels. The output is the priority of the computational tasks. The number of trees can be set between 100 and 500, with a maximum depth limit for each tree, for example, a maximum depth of 10, to prevent overfitting.

[0075] In one embodiment, calculating the cost based on the priority of the computational tasks may include:

[0076] The task utilizes a Redis queue to compute specified key information from airport flight data. In parallel, it queries a hash table through a Redis cache to obtain protocol information that matches the specified key information. Feature data extracted from multiple protocol data is pre-stored in a hash table, and the hash table is cached in a Redis database when the computation task starts.

[0077] In this embodiment, airport fee agreement parsing is performed during the data preparation stage before receiving flight business data from multiple airports.

[0078] Feature dimension expansion: In addition to the basic fields (protocol type, validity period), aviation-specific features are added: airline code, flight weight, flight status, route type (international, domestic), take-off and landing identifiers, etc.

[0079] Index Construction: The signature is stored in a hash table, using MurmurHash3 as the hash function to reduce collisions. A two-level hash table design is employed: the main hash table stores the mapping between the protocol ID and the signature fingerprint (i.e., signature information), employing chaining for collision resolution, with a load factor ≤ 0.7; the sub-hash table stores the complete protocol parameters.

[0080] For example, during protocol cache initialization, the corresponding feature code is extracted based on the protocol data. The feature code includes (airline code, flight status, route type (international / domestic, take-off / landing identifier)). An index is built using the feature code, and the corresponding protocol content is stored in the cache.

[0081] For example, the system loads the protocol data initially entered into the database, based on the input original protocol such as {"protocol_id":"APT-159","airlines":["CA","MU"],"flightType":"ZB","routeType":"'DOM'","weight":"30","eq":"(maximum takeoff weight>50)AND(maximum takeoff weight<=100)AND(segment type='DOM')AND(flight nature classification IN('XL'))AND(flight abnormal status!='CAN')","formula":"1300+26×(maximum takeoff weight-50)"}.

[0082] Extract the following features: airline code [CA,MU], route type code DOM, flight type: ZB, flight abnormal status CAN, etc.

[0083] Step 102, cost calculation: When the computing service receives tasks to be processed in the task queue, it begins querying the corresponding airport database for flight data and data on passenger bridges, bridge-mounted vehicles, transport vehicles, and special vehicles attached to the flight, based on the time range, airline code, and airport personnel information in the task. Then, using the retrieved flight data and the airline code and flight segment type fields from the flight as signatures, it queries the hash table in parallel through the Redis cache to obtain the matching protocol ID. Finally, using the protocol ID, it locates the specified protocol. For example, if the flight data is CA and the route type code is DOM, then the protocol code APT-xxx is located based on the signature code CA and the route type code DOM. The protocol content is then retrieved by querying the hash table using the protocol code APT-xxx.

[0084] Step 102, cost calculation, involves differentiating flight data by airline code and starting in parallel using multiple threads to calculate various predefined costs, including aviation and non-aviation costs, based on the retrieved protocol rules. Non-aviation costs include, for example, utility bills, rental fees, flight delay fees, and insurance claims fees.

[0085] Step 103 uses fixed fees and personalized fees to generate an invoice.

[0086] Figure 2 This is a specific example diagram of the airport billing processing method in an embodiment of the present invention, such as... Figure 2 As shown, after calculating the cost according to the priority of the computation tasks, the method may further include:

[0087] Step 201: Insert the calculated costs into the cost details table for each airport;

[0088] Step 103 generates a bill by integrating fixed and personalized fees, including:

[0089] Step 202: Based on the airline identifiers corresponding to each flight in the fee details table, integrate them to form the airport's bill to each airline.

[0090] During implementation, when the calculation service finishes executing the tasks in the task queue, it inserts the calculation results into the cost details table for each airport. Once the calculation is complete, it can use the calculated cost details data to generate the corresponding airline's invoice based on the airline code.

[0091] In one embodiment, after step 103, various reports can be generated based on the expense details table to provide customers with various report data as needed.

[0092] Figure 3 This is a specific example diagram of the airport billing processing method in an embodiment of the present invention, such as... Figure 3 As shown, the method includes:

[0093] Step 301: Basic data preparation stage. Basic data includes airport data, aircraft information, agreement data, etc.

[0094] For example, in the initial stage of system launch, basic data preparation requires business personnel to maintain airport data and aircraft information within the system. Agreement management involves entering agreement data and fee information, and personalized airport configuration involves setting system control parameters (as mentioned earlier) according to the different requirements of each airport.

[0095] Step 302: Receive a charge calculation request initiated by the user through the user terminal; the charge calculation request includes a first scheduled task, a second scheduled task, or a personalized task set by the user, and the personalized task includes charge calculation requirement information of the airport or airline.

[0096] Step 303: Calculate the fee according to the fee calculation request. Fixed fees are generated in batches according to the first scheduled task, and personalized fees are decomposed into parallel computing subtasks according to the second scheduled task or personalized task.

[0097] Subtasks include calculating the usage time of special vehicles and route types independently.

[0098] Step 304: Generate invoice.

[0099] The implementation is based on a server, which mainly consists of a middleware server (Redis, a distributed stream processing platform, Kibana data visualization and analysis platform, and Elasticsearch), a front-end server (nginx, a front-end service), an application server, and a database server.

[0100] In summary, in the embodiments of the present invention:

[0101] 1. Protocol Feature Extraction and Structured Storage: Key feature values ​​(such as protocol ID, type, and parameter thresholds) are extracted through protocol parsing. Efficient data structures such as hash tables or prefix trees are then constructed to reduce complexity and enable rapid protocol matching. Protocol matching speed is improved; in experiments with 1000 data entries, hash table indexing reduced protocol query time by 10 times.

[0102] 2. Phased Cost Calculation: Cost calculation is broken down into fixed cost pre-calculation (static rules) and personalized cost dynamic calculation (user variables), reducing redundant calculations. Furthermore, nighttime operations can prioritize pre-calculating a portion of the costs, shortening calculation time. Task priorities are dynamically calculated based on tasks submitted from multiple airports, and tasks are queued and adjusted, allowing tasks from multiple airports to be processed simultaneously. Computational resources are saved; after adopting phased calculation, fixed cost pre-calculation reduces redundant logic execution by 60%, saving significant resources, and reducing the overall calculation time from 30 minutes to 5 minutes.

[0103] 3. User experience optimization: The overall response time of user front-end operations is reduced by 80%, improving the user experience.

[0104] This invention also provides an airport billing processing device, as described in the following embodiments. Since the principle by which this device solves the problem is similar to that of the airport billing processing method, the implementation of this device can be referred to the implementation of the airport billing processing method, and repeated details will not be elaborated further.

[0105] Figure 4 This is a schematic diagram of an airport billing processing device in an embodiment of the present invention, as shown below. Figure 4 As shown, the device 400 includes:

[0106] The data receiving module 401 is used to receive multiple airport flight service data; the airport flight service data includes fixed fee type data and personalized type data; the fixed fee type data represents the airport fixed charge data generated with the flight, and the personalized type data represents the personalized consumption data of the flight at the airport;

[0107] The cost calculation module 402 is used to create calculation tasks using fixed cost type data and personalized cost type data at different calculation frequencies, and calculate the cost according to the priority of the calculation tasks. The cost includes a fixed cost calculated using fixed cost type data and a personalized cost calculated using personalized cost type data. The priority of the calculation task is set according to one or any combination of calculation time, calculation data volume, airport identifier, and preset urgency level. The calculation frequency corresponding to fixed cost type data is higher than the calculation frequency corresponding to personalized cost type data.

[0108] Bill generation module 403 is used to generate bills by integrating fixed fees and personalized fees.

[0109] In one embodiment, the airport flight service data has a data identifier; the device may further include:

[0110] The data filtering module is used to create calculation tasks in the cost calculation module 402 according to different calculation frequencies, using fixed cost type data and personalized type data respectively. Before calculating the cost according to the priority of the calculation tasks, the module uses a data type mapping table and the data identifiers of airport flight business data to distinguish between fixed cost type data and personalized type data in multiple airport flight business data. The data type mapping table includes the mapping relationship between data types and data identifiers of airport flight business data. The data types include fixed cost type and personalized type.

[0111] In one embodiment, the priority of the computation task is determined as follows:

[0112] The priority of computation tasks is dynamically determined by using a string reduction algorithm, taking into account computation time point information, airport throughput, airport identification, and preset urgency level.

[0113] In one embodiment, the cost calculation module 402 is specifically used to: cache the calculation task in a Redis queue.

[0114] In one embodiment, the cost calculation module 402 is specifically used for:

[0115] The task utilizes a Redis queue to compute specified key information from airport flight data. In parallel, it queries a hash table through a Redis cache to obtain protocol information that matches the specified key information. Feature data extracted from multiple protocol data is pre-stored in a hash table, and the hash table is cached in a Redis database when the computation task starts.

[0116] In one embodiment, the device further includes: a cost details processing module, used to insert the calculated costs into the cost details table of each airport after the cost calculation module 402 calculates the costs according to the priority of the calculation tasks;

[0117] The billing generation module 403 is specifically used to: integrate the airline identifiers corresponding to each flight in the fee details table to form the airport's bills to each airline.

[0118] In one embodiment, the fixed cost includes one or any combination of the following:

[0119] Aircraft service fee, license plate fee, guide vehicle, passenger flow service fee, passenger bridge fee, and tool usage fee.

[0120] The personalized fees include one or any combination of the following:

[0121] Landing and takeoff fees, parking fees, loading and unloading service fees, communication service fees, container equipment management service fees, passenger baggage service fees, loading and unloading ground transportation service fees, bridge-mounted power supply service fees, and bridge-mounted air conditioning fees.

[0122] In one embodiment, the data receiving module 401 is specifically used for:

[0123] Receive flight data from multiple airports using one or any combination of the following methods:

[0124] File import, timed data retrieval via data interface, and user-entered data received on the client side.

[0125] In one embodiment, the device further includes a data preprocessing module, which performs data verification and auditing on the multiple airport flight business data after the data receiving module 401 receives multiple airport flight business data.

[0126] Figure 5 This is a schematic diagram of a computer device in an embodiment of the present invention, such as... Figure 5 As shown, this embodiment of the invention also provides a computer device 500, including a processor 501, a memory 502, and a computer program 503 stored in the memory 502 and executable on the processor 501. When the processor 501 executes the computer program 503, it implements the above-described airport billing method.

[0127] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described airport billing method.

[0128] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described airport billing method.

[0129] In this embodiment of the invention, airport fees are divided into fixed fees and personalized fees when charging airport fees. Fixed fees are fixed charges generated with each flight, while personalized fees are dynamically changing. Current calculations calculate all fees simultaneously, resulting in wasted computational resources and low efficiency. This embodiment differentiates between fixed and personalized fees, calculating fixed fees at a higher frequency and personalized fees at a lower frequency. This significantly saves airport fee calculation resources and improves calculation efficiency. According to experimental statistics, taking 1000 data points as an example, this embodiment reduces repetitive logic execution by 60%, saving substantial resources and shortening the overall calculation time from 30 minutes to 5 minutes.

[0130] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0131] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0132] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0133] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0134] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An airport billing processing method, characterized in that, include: Receives flight service data from multiple airports; the airport flight service data includes fixed-fee type data and personalized type data. The fixed fee type data represents the airport fixed charges generated with each flight, and the personalized type data represents the personalized consumption data of the flight at the airport. Calculation tasks are created using fixed-fee type data and personalized type data at different calculation frequencies, and the fees are calculated according to the priority of the calculation tasks. The fees include fixed fees calculated using fixed-fee type data and personalized fees calculated using personalized type data. The priority of the calculation tasks is set according to one or any combination of calculation time, calculation data volume, airport identification, and preset urgency level. The calculation frequency corresponding to fixed-fee type data is higher than the calculation frequency corresponding to personalized type data. Invoices are generated by combining fixed and personalized fees.

2. The method as described in claim 1, characterized in that, The airport flight service data has a data identifier; Based on different calculation frequencies, calculation tasks are created using fixed-cost data and personalized data, respectively. Before calculating the cost according to the priority of the calculation tasks, the following steps are also included: By using a data type mapping table and data identifiers for airport flight business data, fixed-fee type data and personalized type data can be distinguished among multiple airport flight business data. The data type mapping table includes the mapping relationship between data types and data identifiers for airport flight business data. The data types include fixed-fee type and personalized type.

3. The method as described in claim 1, characterized in that, The priority of the computation tasks is determined as follows: The priority of computation tasks is dynamically determined by using a string reduction algorithm, taking into account computation time point information, airport throughput, airport identification, and preset urgency level.

4. The method as described in claim 1, characterized in that, Based on different calculation frequencies, calculation tasks are created using fixed-cost data and personalized data, respectively. Costs are then calculated according to the priority of each calculation task, including: The computation tasks are cached in a Redis queue.

5. The method as described in claim 4, characterized in that, The cost is calculated based on the priority of the computation task, including: The task utilizes a Redis queue to compute specified key information from airport flight data. In parallel, it queries a hash table through a Redis cache to obtain protocol information that matches the specified key information. Feature data extracted from multiple protocol data is pre-stored in a hash table, and the hash table is cached in a Redis database when the computation task starts.

6. The method as described in claim 1, characterized in that, After calculating the costs according to the priority of the calculation tasks, the process also includes: inserting the calculated costs into the cost details table for each airport. Bills are generated by combining fixed and personalized fees, including: The airport's bill to each airline is generated by combining the airline identifiers corresponding to each flight in the fee details table.

7. The method as described in claim 1, characterized in that, The fixed fee includes one or any combination of the following: Aircraft service fee, license plate fee, guide vehicle, passenger flow service fee, passenger bridge fee, and tool usage fee; The personalized fees include one or any combination of the following: Landing and takeoff fees, parking fees, loading and unloading service fees, communication service fees, container equipment management service fees, passenger baggage service fees, loading and unloading ground transportation service fees, bridge-mounted power supply service fees, and bridge-mounted air conditioning fees.

8. The method as described in claim 1, characterized in that, Receives flight operation data from multiple airports, including: Receive flight data from multiple airports using one or any combination of the following methods: File import, timed data retrieval via data interface, and user-entered data received on the client side.

9. The method as described in claim 1, characterized in that, After receiving flight data from multiple airports, it also includes: Data verification and auditing were conducted on flight operation data from multiple airports.

10. An airport billing processing device, characterized in that, include: The data receiving module is used to receive flight service data from multiple airports; the airport flight service data includes fixed-fee type data and personalized type data. The fixed fee type data represents the airport fixed charges generated with each flight, and the personalized type data represents the personalized consumption data of the flight at the airport. The cost calculation module is used to create calculation tasks using fixed cost type data and personalized cost type data at different calculation frequencies, and calculate the cost according to the priority of the calculation tasks. The cost includes a fixed cost calculated using fixed cost type data and a personalized cost calculated using personalized cost type data. The priority of the calculation task is set according to one or any combination of calculation time, calculation data volume, airport identifier, and preset urgency level. The calculation frequency corresponding to fixed cost type data is higher than the calculation frequency corresponding to personalized cost type data. The billing generation module is used to generate bills by integrating fixed fees and personalized fees.

11. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 9.

13. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 9.

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