Overnight travel expense auto-generation method, apparatus, device, medium, and product

Abstract

The application relates to a flight overnight travel expense automatic generation method, device, equipment, medium and product. The method comprises the following steps: acquiring multi-source heterogeneous data; pre-processing the multi-source heterogeneous data to generate overnight task data set; performing multi-level rule matching on the overnight task data set according to a pre-constructed hierarchical rule engine to obtain candidate charging data; and generating overnight travel expense details according to the candidate charging data and preset overnight travel expense standards. The method can improve the accuracy of travel details by using multi-source data in a flight task, and a simple and efficient rule system is formulated so that the generated overnight travel expense details can cover all scenarios as much as possible and the proportion of manual intervention is reduced.

Description

Technical Field

[0001] This application relates to the field of aviation travel expense management technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for automatically generating overnight flight travel expenses. Background Technology

[0002] With the development of automated travel expense generation technology, a new technology has emerged that primarily generates travel expense data based on "route flights." Specifically, overnight flight travel expenses for flight crew refer to the expenses paid to flight crew members who stay overnight at domestic or international locations while performing flight missions (including route flights, flight backups, off-site simulator training flights, local training and domestic test flights, and domestic ferry flights). Unlike regular employee travel expenses, which are reimbursed voluntarily by the traveler, overnight flight travel expenses are paid monthly due to the large number of people involved and the extremely valuable time of flight crew members. However, current technologies have limited coverage of business scenarios, a high proportion of manual processing and intervention, and low efficiency. Furthermore, the high proportion of manual intervention makes the accuracy of the payments highly dependent on the experience and attitude of the personnel. Therefore, there is an urgent need for a concise and efficient rule system for overnight flight travel scenarios, so that the generated overnight travel expense details can cover as many scenarios as possible and reduce the proportion of manual intervention. Summary of the Invention

[0003] Therefore, it is necessary to address the aforementioned technical issues by providing a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for automatically generating flight overnight travel expenses. This method can utilize multi-source data from flight missions to improve the accuracy of travel details, while establishing a concise and efficient rule system to ensure that the generated overnight travel expense details cover all scenarios as much as possible, thereby reducing the proportion of manual intervention.

[0004] Firstly, this application provides a method for automatically generating overnight flight travel expenses, including:

[0005] Acquire multi-source heterogeneous data;

[0006] The multi-source heterogeneous data is preprocessed to generate an overnight task dataset;

[0007] The overnight task dataset is subjected to multi-level rule matching based on a pre-built hierarchical rule engine to obtain candidate firing data;

[0008] An overnight travel expense breakdown is generated based on the candidate expense data and the preset overnight travel expense standard.

[0009] In one embodiment, the multi-source heterogeneous data includes: flight mission data, personnel basic data, and dynamic authorization data; the preprocessing of the multi-source heterogeneous data to generate an overnight mission dataset includes:

[0010] The flight mission data of different types are concatenated according to the preset time and location association rules to obtain flight mission concatenation data;

[0011] The basic personnel data is parsed to obtain the field parsing results;

[0012] The timeliness of the dynamic authorization data is verified to obtain the timeliness verification result;

[0013] Based on the flight mission concatenation data, the field parsing results, and the timeliness verification results, the overnight mission dataset is generated.

[0014] In one embodiment, the construction step of the hierarchical rule engine step includes:

[0015] Obtain business element data and travel expense standard data;

[0016] Based on the travel expense standard data, the business element data is filtered to obtain the target business element;

[0017] The hierarchical rule engine is obtained by designing hierarchical rules based on the target business elements.

[0018] In one embodiment, the hierarchical rule engine includes a task-level sub-engine and a daily-level sub-engine; the step of performing multi-level rule matching on the overnight task dataset according to the pre-built hierarchical rule engine to obtain candidate execution data includes:

[0019] Based on the task granularity sub-engine, the overnight task dataset is matched with task granularity rules to obtain the task granularity candidate dispatch data;

[0020] After the candidate deployment data at the task granularity is split into daily segments, the daily granularity rules are matched using the daily granularity sub-engine to obtain the candidate deployment data.

[0021] In one embodiment, generating overnight travel expense details based on the candidate expense data and a preset overnight travel expense standard includes:

[0022] The candidate expense details are obtained by mapping the overnight travel expense standard to the candidate expense data.

[0023] Based on the candidate expense details, personnel expense statistics are performed to obtain overnight travel expense details.

[0024] In one embodiment, the method further includes:

[0025] The overnight travel expense details are aggregated and statistically analyzed according to a preset period to generate a per capita summary report;

[0026] The per capita summary report is visualized to obtain visualized travel expense data.

[0027] Secondly, this application also provides an automatic device for generating overnight flight travel expenses, comprising:

[0028] The acquisition module is used to acquire heterogeneous data from multiple sources.

[0029] The preprocessing module is used to preprocess the multi-source heterogeneous data to generate an overnight task dataset;

[0030] The rule matching module is used to perform multi-level rule matching on the overnight task dataset according to a pre-built hierarchical rule engine to obtain candidate firing data;

[0031] The generation module is used to generate overnight travel expense details based on the candidate expense data and the preset overnight travel expense standard.

[0032] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0033] Acquire multi-source heterogeneous data;

[0034] The multi-source heterogeneous data is preprocessed to generate an overnight task dataset;

[0035] The overnight task dataset is subjected to multi-level rule matching based on a pre-built hierarchical rule engine to obtain candidate firing data;

[0036] An overnight travel expense breakdown is generated based on the candidate expense data and the preset overnight travel expense standard.

[0037] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0038] Acquire multi-source heterogeneous data;

[0039] The multi-source heterogeneous data is preprocessed to generate an overnight task dataset;

[0040] The overnight task dataset is subjected to multi-level rule matching based on a pre-built hierarchical rule engine to obtain candidate firing data;

[0041] An overnight travel expense breakdown is generated based on the candidate expense data and the preset overnight travel expense standard.

[0042] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0043] Acquire multi-source heterogeneous data;

[0044] The multi-source heterogeneous data is preprocessed to generate an overnight task dataset;

[0045] The overnight task dataset is subjected to multi-level rule matching based on a pre-built hierarchical rule engine to obtain candidate firing data;

[0046] An overnight travel expense breakdown is generated based on the candidate expense data and the preset overnight travel expense standard.

[0047] The aforementioned method, apparatus, computer equipment, computer-readable storage medium, and computer program product for automatically generating overnight travel expenses acquire multi-source heterogeneous data; preprocess the multi-source heterogeneous data to generate an overnight task dataset; perform multi-level rule matching on the overnight task dataset according to a pre-built hierarchical rule engine to obtain candidate chargeback data; and generate overnight travel expense details based on the candidate chargeback data and preset overnight travel expense standards. Therefore, by preprocessing multi-source heterogeneous data, task data related to overnight travel expenses is efficiently integrated to obtain an overnight task dataset. Then, multi-level rule matching on the overnight task dataset based on the hierarchical rule engine allows for more efficient identification of candidate chargeback data that can be matched for determining chargeback and deduction. Finally, the overnight travel expense details generated based on the candidate chargeback data and preset overnight travel expense standards cover as many scenarios as possible, reducing the proportion of manual intervention. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a diagram illustrating the application environment of a method for automatically generating overnight flight travel expenses in one embodiment.

[0050] Figure 2 This is a flowchart illustrating a method for automatically generating overnight flight travel expenses in one embodiment;

[0051] Figure 3 This is a flowchart illustrating a method for automatically generating overnight flight travel expenses in one embodiment;

[0052] Figure 4 This is a schematic diagram of the preprocessing flow of an automatic overnight flight travel expense generation method in one embodiment;

[0053] Figure 5 This is a schematic representation of the task width of a method for automatically generating overnight flight travel expenses in one embodiment.

[0054] Figure 6 This is a task connection diagram unique to each flight crew member in one embodiment of the automatic generation method for overnight flight travel expenses.

[0055] Figure 7 This is a business element diagram of the hierarchical rule engine for constructing a method for automatically generating overnight flight travel expenses in one embodiment.

[0056] Figure 8 This is a specific rule matching example diagram for an embodiment of the automatic generation method for overnight flight travel expenses;

[0057] Figure 9 This is a schematic diagram of the overnight travel expense detail generation process in one embodiment of the automatic overnight travel expense generation method.

[0058] Figure 10 A hierarchical architecture diagram of the data management system for an automatic overnight flight travel expense generation method in another embodiment;

[0059] Figure 11 Here is a business process diagram for an automatic generation method of overnight flight travel expenses in another embodiment;

[0060] Figure 12 This is a structural block diagram of an automatic overnight flight travel expense generation device in one embodiment;

[0061] Figure 13 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0063] The automatic generation method for overnight flight travel expenses provided in this application can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed on the cloud or other network servers. Server 104 acquires multi-source heterogeneous data; preprocesses the multi-source heterogeneous data to generate an overnight task dataset; performs multi-level rule matching on the overnight task dataset according to a pre-built hierarchical rule engine to obtain candidate travel expense data; and generates overnight travel expense details based on the candidate travel expense data and preset overnight travel expense standards. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, projection devices, etc. Portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc. Head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. Server 104 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides cloud computing services.

[0064] In one exemplary embodiment, such as Figure 2 As shown, a method for automatically generating overnight flight travel expenses is provided, which can be applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 202 to 208. Wherein:

[0065] Step 202: Obtain multi-source heterogeneous data.

[0066] The sources of multi-source heterogeneous data mainly include: route mission data, simulator mission data, and flight backup information.

[0067] In some embodiments, multi-source heterogeneous data can be obtained from the flight mission database of flight crew members by introducing flight backup information on a monthly basis, simulator mission data by data source, and route missions on a weekly basis, and is not limited thereto.

[0068] Step 204: Preprocess the multi-source heterogeneous data to generate an overnight task dataset.

[0069] Preprocessing refers to data processing steps such as data integration and data synchronization. The overnight task dataset is a task connection table used to record the task process and task information of each aircrew member.

[0070] In some embodiments, such as Figure 4 As shown, Figure 4This document illustrates the process of acquiring heterogeneous data from multiple sources, building a task wide table, synchronizing the data, and finally completing data preprocessing to integrate the heterogeneous data into a task connection table. It should be noted that... Figure 4 The task integration and connection demonstrated involves preprocessing multi-source heterogeneous data, specifically connecting various tasks performed by each aircrew member in chronological order.

[0071] In some embodiments, the multi-source heterogeneous data includes: flight mission data, personnel basic data, and dynamic authorization data; preprocessing the multi-source heterogeneous data to generate an overnight mission dataset includes: concatenating different types of flight mission data according to preset time and location association rules to obtain concatenated flight mission data; parsing the fields of personnel basic data to obtain field parsing results; performing timeliness verification on the dynamic authorization data to obtain timeliness verification results; and generating an overnight mission dataset based on the concatenated flight mission data, field parsing results, and timeliness verification results.

[0072] In some embodiments, such as Figure 5 As shown, Figure 5 This document presents a flowchart illustrating the process of obtaining a task wide table through data synchronization after data preprocessing. The first step is to establish the data table and data synchronization mechanism. Based on preset time and location association rules, different types of flight mission data are linked together, and data is synchronously imported according to specific mission types. This integrates multi-source heterogeneous data into a unified task wide table, resulting in linked flight mission data. Next, a "mission association algorithm based on spatiotemporal rules" is established to parse the fields of personnel basic data, obtaining the parsing results and generating unique mission connection relationships for each aircrew member, such as... Figure 6 As shown, Figure 6 It displays a unique mission connection diagram for each aircrew member and judges whether the mission end point is the same as the next mission start point and whether the mission time interval is within a preset reasonable range, thereby verifying the timeliness of dynamic authorization data and obtaining timeliness verification results. Finally, based on flight mission connection data, field parsing results and timeliness verification results, an overnight mission dataset is generated to provide a data foundation for subsequent launch calculations.

[0073] In addition, multi-threaded parallel processing is used for time-consuming tasks. For example, there are more than 150,000 flight route tasks per month, which are divided into 4 threads per week. Also, for tasks that are independent between individuals, 50 threads are set up to connect and process each person's tasks.

[0074] Step 206: Perform multi-level rule matching on the overnight task dataset according to the pre-built hierarchical rule engine to obtain candidate firing data.

[0075] Among them, the hierarchical rule engine refers to the logical set built based on business elements to distinguish between billing scenarios and accounting standards, and the candidate billing data refers to the specific billing rule identifier determined after being filtered by the engine.

[0076] In some embodiments, the construction step of the hierarchical rule engine includes: acquiring business element data and travel expense standard data; filtering the business element data based on the travel expense standard data to obtain target business elements; and designing hierarchical rules based on the target business elements to obtain the hierarchical rule engine.

[0077] In some embodiments, such as Figure 7 As shown, Figure 7 The document showcases the business elements required to build a hierarchical rules engine. These elements include: personnel type, payout position, payout region, and expense name. Specifically, personnel types include pilots, flight attendants, and security officers; payout positions are differentiated based on mission nature, such as simulator training roles for pilots and the highest basic authorization obtained during mission execution for others; flight attendants and security officers are their onboard positions for the flights they perform; payout regions are divided into intercontinental, international, regional, and domestic; and expense names include overnight flight travel expenses, simulator transportation allowance, official overnight allowance, and accommodation allowance for transferring to Airbus simulator training.

[0078] In some embodiments, the hierarchical rule engine includes a task-level sub-engine and a daily-level sub-engine; performing multi-level rule matching on the overnight task dataset according to the pre-built hierarchical rule engine to obtain candidate delivery data includes: performing task-level rule matching on the overnight task dataset according to the task-level sub-engine to obtain task-level candidate delivery data; splitting the task-level candidate delivery data by day, and then performing daily-level rule matching through the daily-level sub-engine to obtain candidate delivery data.

[0079] In some embodiments, such as Figure 8 As shown, Figure 8 The diagram illustrates a specific rule matching example. When performing task-level rule matching, the overnight task dataset is first matched using a task-level sub-engine. Then, the task-level candidate deployment data is split by day and matched using a daily-level sub-engine to determine whether or not to deploy. The rule engine includes a self-developed rule parser covering 24 detailed items across three categories: "Airline," "Simulator," and "Backup." Rules can be flexibly configured and maintained at the system front-end, and special cases are handled through independent calculations of deployment and deduction rules. Subsequently, daily-level candidate deployment data is matched using standard cost data to determine the specific "deployment amount." The standard positioning for "deployment amount" can be set according to requirements. Finally, candidate deployment data containing both task-level and daily-level matching rules is output.

[0080] In this embodiment, the payout rules based on candidate payout data focus on calculating the "deserved" amount, while the deduction rules focus on handling "special cases." The two are calculated independently, and the results are then combined. This architecture allows modification of one type of rule without altering the logic of the other, improving system stability and scalability.

[0081] Step 208: Generate overnight travel expense details based on candidate expense data and preset overnight travel expense standards.

[0082] Among them, the overnight travel expense details refer to the final billing list compiled by personnel and date. The preset overnight travel expense standard is a digital mapping of financial compliance rules at the algorithm level. The overnight travel expense standard is stored in a standard database that supports dynamic configuration. The system realizes real-time retrieval of the amount through standard code index to ensure financial compliance.

[0083] In some embodiments, generating overnight travel expense details based on candidate expense data and preset overnight travel expense standards includes: mapping the candidate expense data to expense standards through overnight travel expense standards to obtain candidate expense details; and performing personnel expense statistics based on the candidate expense details to obtain overnight travel expense details.

[0084] In some embodiments, such as Figure 9 As shown, Figure 9 The diagram illustrates the process of generating overnight travel expense details. When the calculation engine breaks down the tasks that need to be calculated in the task granularity matching rules into daily amounts, it maps them to the corresponding candidate expense details through the applicable rules, and then locates the specific standard amount by combining personnel type, calculation position, and calculation area. This generates daily travel expense details for each aircrew member.

[0085] It should be further explained that, in accordance with the principle that "only one overnight travel expense can be calculated per calendar day," a strategy of "retaining only one record based on the highest daily amount" is adopted. The system first groups by "person" and "date," then within each group, it sorts the entries in descending order based on the "payment standard amount" field, and finally retains only the record ranked first (highest amount). This avoids multiple payments and ensures business compliance and security. In other words, it groups by person and payment date, marking the payment details for the day with the highest payment standard.

[0086] The aforementioned method for automatically generating overnight travel expenses involves acquiring multi-source heterogeneous data; preprocessing this data to generate an overnight task dataset; performing multi-level rule matching on the overnight task dataset using a pre-built hierarchical rule engine to obtain candidate expense calculation data; and generating overnight travel expense details based on the candidate expense calculation data and preset overnight travel expense standards. Therefore, by preprocessing multi-source heterogeneous data, task data related to overnight travel expenses is efficiently integrated to obtain an overnight task dataset. Then, multi-level rule matching on the overnight task dataset using a hierarchical rule engine allows for more efficient identification of candidate expense calculation data that can be matched for determining expense calculation and deduction. Finally, the overnight travel expense details generated based on the candidate expense calculation data and preset overnight travel expense standards cover as many scenarios as possible, reducing the proportion of manual intervention.

[0087] In one exemplary embodiment, such as Figure 3 As shown, the method includes visualization steps 302 to 304. Wherein:

[0088] Step 302: Aggregate and statistically analyze the details of overnight travel expenses according to a preset cycle to generate a per capita summary report.

[0089] Among them, the per capita summary report refers to the summary and statistical results of detailed data from multiple dimensions, which are used for financial auditing and macro analysis.

[0090] In some embodiments, the system automatically triggers a batch generation process at a preset cycle (e.g., monthly) to aggregate and statistically analyze the generated overnight travel expense details. This process includes: performing drill-down statistics by unit, department, and personnel to generate a per capita summary report; simultaneously extracting abnormal data (e.g., exceeding limits, rule conflicts) and generating an abnormal data list for business personnel to review.

[0091] Step 304: Visualize the per capita summary report to obtain visualized travel expense data.

[0092] Among them, travel expense visualization data refers to statistical analysis results displayed through a graphical interface.

[0093] In some embodiments, the system visualizes the per capita summary report through front-end components, providing progressive information query functions such as task, task issuance, and daily issuance.

[0094] In this embodiment, the above steps realize a visualized overnight travel expense generation algorithm. It provides progressively layered information query functions, including task-based, task-based, and daily-based payroll calculations. Complex calculation processes are recorded and output in a standardized manner. It implements three major functional modules: basic support, business processing, and query analysis, completing a closed-loop management of the entire process from data processing, payroll calculation, business application, financial review, and payroll push.

[0095] In some embodiments, to gain a better understanding of the solutions of the present application, the following is combined with Figure 10 and 11 to provide further description of the embodiments, as detailed below:

[0096] Figure 10 illustrates the layered architecture design of the data management system corresponding to the automatic generation method of flight overnight travel expenses. The system adopts the layered design concept of "business application layer - basic support layer", so as to realize the whole process management and support of data from collection and processing to business application. The business application layer is oriented to different business scenarios, and includes modules such as plan project management, mid-term management, budget preparation management, contract management, reimbursement management, physical asset management, procurement management, project process management, and archive contract management. Each module invokes lower-layer services through standardized interfaces to support the full online business process (such as project initiation, budget declaration, contract approval, reimbursement payment, and asset operation and maintenance), and realize linkage and closed-loop management of business data.

[0097] In addition, the basic support layer provides underlying technical services and data governance support, and includes modules such as data collection, data governance, basic data, and system management. The data collection module is responsible for docking with multi-source heterogeneous systems (such as business ledgers and external databases) to obtain original data; the data governance module improves data quality through means such as data cleaning, standardization, and lineage analysis; the basic data module includes master data such as maintenance expense systems, expense standards, flight natures, route information, and personnel.

[0098] Figure 11 illustrates the business flowchart of "business application - financial review - fund disbursement" of the present application. The process takes the "daily disbursement details" as the data starting point, and realizes the accurate disbursement of compliant funds through collaborative verification between the business end and the financial end. Through the above multiple embodiments, the present solution can finally achieve the following specific application effects, which are mainly reflected in two aspects: promoting business by data, and improving quality and efficiency. By eliminating data breakpoints, fully integrating and utilizing upstream business data, the accuracy and automation level of disbursement data are improved. In the direction of promoting business by data, for a large number of aircrew flight overnight travel expenses, multi-source data integration and rule matching algorithms are adopted to reduce the risk of wrong or missed disbursement caused by insufficient manual experience or negligence. In addition, the proportion of scenarios covered by the automatically generated disbursement details has been greatly increased, totaling 30 scenarios, among which the number of scenarios requiring pure manual disbursement has been reduced from 15 to 0, the number of scenarios of pure system generation has been increased from 4 to 19, and the remaining 11 scenarios require manual verification on the basis of system-generated details. It is expected to reduce 85% of the manual workload (91.8 person-days per year).

[0099] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0100] Based on the same inventive concept, this application also provides an automatic overnight travel expense generation device for implementing the above-mentioned automatic overnight travel expense generation method. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more embodiments of the automatic overnight travel expense generation device provided below can be found in the limitations of the automatic overnight travel expense generation method described above, and will not be repeated here.

[0101] In one exemplary embodiment, such as Figure 12 As shown, an automatic overnight flight travel expense generation device is provided, comprising: an acquisition module 1201, a preprocessing module 1202, a rule matching module 1203, and a generation module 1204, wherein:

[0102] Module 1201 is used to acquire multi-source heterogeneous data;

[0103] Preprocessing module 1202 is used to preprocess multi-source heterogeneous data to generate overnight task dataset;

[0104] The rule matching module 1203 is used to perform multi-level rule matching on the overnight task dataset according to the pre-built hierarchical rule engine to obtain candidate firing data;

[0105] The generation module 1204 is used to generate overnight travel expense details based on candidate expense data and preset overnight travel expense standards.

[0106] In some embodiments, the multi-source heterogeneous data includes: flight mission data, personnel basic data, and dynamic authorization data; the preprocessing module 1202 is further configured to concatenate different types of flight mission data according to preset time and location association rules to obtain flight mission concatenation data; perform field parsing on the personnel basic data to obtain field parsing results; perform timeliness verification on the dynamic authorization data to obtain timeliness verification results; and generate an overnight mission dataset based on the flight mission concatenation data, field parsing results, and timeliness verification results.

[0107] In some embodiments, the rule matching module 1203 is further configured to acquire business element data and travel expense standard data; filter the business element data based on the travel expense standard data to obtain target business elements; and design hierarchical rules based on the target business elements to obtain a hierarchical rule engine.

[0108] In some embodiments, the hierarchical rule engine includes a task-level sub-engine and a daily-level sub-engine; the rule matching module 1203 is further configured to perform task-level rule matching on the overnight task dataset according to the task-level sub-engine to obtain task-level candidate delivery data; after splitting the task-level candidate delivery data by day, perform daily-level rule matching through the daily-level sub-engine to obtain candidate delivery data.

[0109] In some embodiments, the generation module 1204 is further configured to map the candidate expense data to the expense standard through the overnight travel expense standard to obtain the candidate expense details; and to perform personnel expense statistics based on the candidate expense details to obtain the overnight travel expense details.

[0110] In some embodiments, the device further includes: a visualization module, used to aggregate and statistically analyze overnight travel expense details according to a preset period to generate a per capita summary report; and to perform visualization design on the per capita summary report to obtain visualized travel expense data.

[0111] The aforementioned automatic overnight travel expense generation device acquires multi-source heterogeneous data; preprocesses the multi-source heterogeneous data to generate an overnight task dataset; performs multi-level rule matching on the overnight task dataset according to a pre-built hierarchical rule engine to obtain candidate chargeback data; and generates overnight travel expense details based on the candidate chargeback data and preset overnight travel expense standards. Therefore, by preprocessing multi-source heterogeneous data, task data related to overnight travel expenses is efficiently integrated to obtain an overnight task dataset. Then, multi-level rule matching on the overnight task dataset based on the hierarchical rule engine allows for more efficient identification of candidate chargeback data that can be matched for determining chargeback and deduction. Finally, the overnight travel expense details generated based on the candidate chargeback data and preset overnight travel expense standards cover as many scenarios as possible, reducing the proportion of manual intervention.

[0112] The modules in the aforementioned automatic overnight travel expense generation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0113] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 13 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores heterogeneous data from multiple sources. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When executed by the processor, the computer program implements a method for automatically generating overnight flight travel expenses.

[0114] Those skilled in the art will understand that Figure 13 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0115] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method for automatically generating overnight travel expenses.

[0116] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method for automatically generating overnight travel expenses.

[0117] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the above-described method for automatically generating overnight travel expenses.

[0118] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0119] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0120] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0121] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for automatically generating overnight flight travel expenses, characterized in that, The method includes: Acquire multi-source heterogeneous data; The multi-source heterogeneous data is preprocessed to generate an overnight task dataset; The overnight task dataset is subjected to multi-level rule matching based on a pre-built hierarchical rule engine to obtain candidate firing data; An overnight travel expense breakdown is generated based on the candidate expense data and the preset overnight travel expense standard.

2. The method according to claim 1, characterized in that, The multi-source heterogeneous data includes: flight mission data, personnel basic data, and dynamic authorization data; the preprocessing of the multi-source heterogeneous data to generate an overnight mission dataset includes: The flight mission data of different types are concatenated according to the preset time and location association rules to obtain flight mission concatenation data; The basic personnel data is parsed to obtain the field parsing results; The timeliness of the dynamic authorization data is verified to obtain the timeliness verification result; Based on the flight mission concatenation data, the field parsing results, and the timeliness verification results, the overnight mission dataset is generated.

3. The method according to claim 1, characterized in that, The construction steps of the hierarchical rule engine include: Obtain business element data and travel expense standard data; Based on the travel expense standard data, the business element data is filtered to obtain the target business element; The hierarchical rule engine is obtained by designing hierarchical rules based on the target business elements.

4. The method according to claim 1, characterized in that, The hierarchical rule engine includes a task-level sub-engine and a daily-level sub-engine; the process of performing multi-level rule matching on the overnight task dataset based on the pre-built hierarchical rule engine to obtain candidate dispatch data includes: Based on the task granularity sub-engine, the overnight task dataset is matched with task granularity rules to obtain the task granularity candidate dispatch data; After the candidate deployment data at the task granularity is split into daily segments, the daily granularity rules are matched using the daily granularity sub-engine to obtain the candidate deployment data.

5. The method according to claim 1, characterized in that, The step of generating overnight travel expense details based on the candidate expense data and the preset overnight travel expense standard includes: The candidate expense details are obtained by mapping the overnight travel expense standard to the candidate expense data. Based on the candidate expense details, personnel expense statistics are performed to obtain overnight travel expense details.

6. The method according to claim 1, characterized in that, The method further includes: The overnight travel expense details are aggregated and statistically analyzed according to a preset period to generate a per capita summary report; The per capita summary report is visualized to obtain visualized travel expense data.

7. An automatic device for generating overnight flight travel expenses, characterized in that, The device includes: The acquisition module is used to acquire heterogeneous data from multiple sources. The preprocessing module is used to preprocess the multi-source heterogeneous data to generate an overnight task dataset; The rule matching module is used to perform multi-level rule matching on the overnight task dataset according to a pre-built hierarchical rule engine to obtain candidate firing data; The generation module is used to generate overnight travel expense details based on the candidate expense data and the preset overnight travel expense standard.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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