Reconciliation method, device and equipment for air ticket business data and medium

Abstract

The invention relates to an account checking method, device and equipment for airline ticket business data and a storage medium, and the method comprises the steps: obtaining first airline ticket business data of an airline company and second airline ticket business data of a third-party platform, and carrying out the preprocessing of the first airline ticket business data and the second airline ticket business data, and obtaining a first target data set corresponding to the first air ticket business data and a second target data set corresponding to the second air ticket business data, performing account checking operation based on the first target data set and the second target data set to obtain a target account checking result, and generating a target account checking report according to the type of the target account checking result. The account checking efficiency of the air ticket business data can be improved, errors caused by manual account checking are reduced, and the accuracy of the account checking data is improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, device, equipment and storage medium for reconciling air ticket business data. Background Art

[0002] At present, the reconciliation of air ticket sales between airlines and their third-party sales platforms is usually done manually offline. All parties need to summarize sales data regularly and fill in reconciliation forms or reports. After summarizing the data, when reconciling and checking, the air ticket business information between different data sources needs to be compared to ensure the consistency and accuracy of the data. However, the reconciliation process requires a lot of manual participation, takes a lot of time and is prone to errors, resulting in low efficiency in reconciliation of air ticket business data.

[0003] Therefore, how to improve the reconciliation efficiency of air ticket business data has become a technical problem that needs to be solved urgently by those skilled in the art. It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the invention

[0004] In view of the above, the present application provides a method, device, equipment and storage medium for reconciling air ticket business data, which aims to solve the above technical problems.

[0005] In a first aspect, the present application provides a method for reconciling air ticket business data, the method comprising:

[0006] Obtain the first ticket business data of the airline and the second ticket business data of the third-party platform;

[0007] Performing a preprocessing operation on the first air ticket business data and the second air ticket business data to obtain a first target data set corresponding to the first air ticket business data and a second target data set corresponding to the second air ticket business data;

[0008] Performing a reconciliation operation based on the first target data set and the second target data set to obtain a target reconciliation result;

[0009] Generate a target reconciliation report based on the type of the target reconciliation result.

[0010] In a second aspect, the present application provides a device for reconciling air ticket business data, the device comprising:

[0011] Acquisition module: used to acquire the first air ticket business data of the airline and the second air ticket business data of the third-party platform;

[0012] Preprocessing module: used to perform preprocessing operations on the first ticket business data and the second ticket business data, to obtain a first target data set corresponding to the first ticket business data and a second target data set corresponding to the second ticket business data;

[0013] Reconciliation module: used to perform a reconciliation operation based on the first target data set and the second target data set to obtain a target reconciliation result;

[0014] Generation module: used to generate a target reconciliation report according to the type of the target reconciliation result.

[0015] In a third aspect, the present application provides an electronic device, including a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus;

[0016] Memory: used to store a computer program;

[0017] Processor: when used to execute the program stored on the memory, implement the reconciliation method for ticket business data described in any embodiment of the first aspect.

[0018] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the reconciliation method for ticket business data described in any embodiment of the first aspect.

[0019] The above technical solutions provided by the embodiments of the present application have the following advantages compared with the prior art:

[0020] By obtaining the first ticket business data of the airline company and the second ticket business data of the third-party platform, performing preprocessing operations on the first ticket business data and the second ticket business data, obtaining a first target data set corresponding to the first ticket business data and a second target data set corresponding to the second ticket business data, performing a reconciliation operation based on the first target data set and the second target data set to obtain a target reconciliation result, and generating a target reconciliation report according to the type of the target reconciliation result, the present application can improve the reconciliation efficiency of ticket business data, reduce errors caused by manual reconciliation, and improve the consistency and accuracy of reconciliation data. Description of the Drawings

[0021] The drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0022] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0023] Figure 1 It is a schematic flowchart of an embodiment of the reconciliation method for ticket business data of the present application;

[0024] Figure 2 It is a schematic module diagram of an embodiment of the reconciliation device for ticket business data of the present application;

[0025] Figure 3 It is a schematic diagram of an embodiment of an electronic device of the present application;

[0026] The realization of the purpose of the present application, functional features and advantages will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0027] To make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of them. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0028] The following disclosure provides many different embodiments or examples for implementing different structures of the present application. To simplify the disclosure of the present application, the components and settings of specific examples are described below. Of course, they are only examples and are not intended to limit the present application. In addition, the present application may repeat reference numerals and / or letters in different examples. This repetition is for the purpose of simplification and clarity, and does not itself indicate the relationship between the various embodiments and / or settings discussed.

[0029] The present application provides a reconciliation method for ticket business data. Referring to Figure 1 As shown, it is a schematic flowchart of the method of an embodiment of the reconciliation method for ticket business data of the present application. This method can be executed by an electronic device, which is implemented by software and / or hardware. The reconciliation method for ticket business data includes:

[0030] Step S10: Obtain the first ticket business data of the airline company and the second ticket business data of the third-party platform;

[0031] Step S20: Perform a preprocessing operation on the first ticket business data and the second ticket business data to obtain a first target data set corresponding to the first ticket business data and a second target data set corresponding to the second ticket business data;

[0032] Step S30: Perform a reconciliation operation based on the first target data set and the second target data set to obtain a target reconciliation result;

[0033] Step S40: Generate a target reconciliation report according to the type of the target reconciliation result.

[0034] The ticket business data includes data of various business types. For example, data such as the sales quantity, price, and commission of tickets. The ticket business data may also include flight information, customer information, payment information, etc. Among them, the flight information may be information such as flight number, departure and arrival times, seat number, and cabin class. The customer information may be personal information such as customer name, contact information, and ID number. The payment information may be information such as payment order number, payment method, payment status, and payment time.

[0035] The ticket business data of the airline is recorded as the first ticket business data, and the ticket business data of the third-party platform is recorded as the second ticket business data. The third-party platform may be a platform for ticket sales channels such as travel agencies.

[0036] After obtaining the first ticket business data of the airline and the second ticket business data of the third-party platform, since the acquired raw data may have duplicate data or missing data, it is necessary to preprocess the collected ticket business data. For example, perform a data cleaning operation. Use the target data set after the preprocessing as the data for reconciliation. Specifically, performing a preprocessing operation on the first ticket business data and the second ticket business data to obtain a first target data set corresponding to the first ticket business data and a second target data set corresponding to the second ticket business data includes:

[0037] Perform a data cleaning operation on the first ticket business data and the second ticket business data;

[0038] Perform a data format conversion operation on the first ticket business data after the data cleaning operation to obtain a first target data set corresponding to the first ticket business data;

[0039] Perform a data format conversion operation on the second ticket business data after the data cleaning operation to obtain a second target data set corresponding to the second ticket business data, where the data formats of the first target data set and the second target data set are the same.

[0040] Performing data cleaning operations on the first flight ticket business data and the second flight ticket business data can remove abnormal data and provide a correct data source for subsequent reconciliation operations. Since the channels for obtaining flight ticket business data are different, the first flight ticket business data and the second flight ticket business data may have inconsistent formats. To ensure the accuracy of the subsequent target reconciliation results, it is also necessary to perform data format conversion operations on the first flight ticket business data and the second flight ticket business data after the data cleaning operations. The data obtained by converting the format of the first flight ticket business data after the data cleaning operation is recorded as the first target data set, and the data obtained by converting the format of the second flight ticket business data after the data cleaning operation is recorded as the second target data set. This makes the data formats of the first target data set and the second target data set the same. For example, the flight ticket business data for which the data cleaning operation is performed needs to be converted to a unified field name and format, such as passenger name, flight number, date, time, fare, seat number, etc. For example, the date format is unified as YYYY-MM-DD, and the time format is unified as HH:MM:SS. If the flight ticket business data contains codes, such as currency codes and country codes, international standard codes can be used.

[0041] Among them, the data cleaning operation includes at least one of a missing value filling operation and a duplicate value removal operation. The missing value filling operation includes at least one of global constant automatic filling, central measure automatic filling, and within-group mean automatic filling.

[0042] The data cleaning operation includes a missing value filling operation and a duplicate value removal operation. The missing value filling operation can include at least one of global constant automatic filling, central measure automatic filling, and within-group mean automatic filling. Global constant automatic filling replaces all missing values with the same constant;

[0043] Central measure filling uses indicators such as the average, median, and mode of the attribute to fill in the missing values;

[0044] Within-group mean filling refers to referring to the other attribute values of the records with missing values, classifying the data according to other attributes for aggregation operations, calculating indicators such as the average or median of the column with missing values, and replacing the missing values with them. The data cleaning operation can also be operations such as data consistency detection and error data detection.

[0045] After obtaining the first target data set and the second target data set with the same data format, a reconciliation operation is performed based on the first target data set and the second target data set, and a target reconciliation result can be obtained. The reconciliation operation needs to select the item data for reconciliation from the target data set. The key to selecting the item data is to ensure the uniqueness and consistency of the data. Therefore, the paid order number can be selected as the item data. The order number can be directly associated with a specific transaction as the reference data, which can ensure the consistency and accuracy of the data.

[0046] Specifically, performing a reconciliation operation based on the first target dataset and the second target dataset to obtain a target reconciliation result, including:

[0047] Determining the key fields of each entry data in the first target dataset;

[0048] Determining the key fields of each entry data in the second target dataset;

[0049] Comparing the key fields of the entry data that are the same in the first target dataset and the second target dataset to obtain a first comparison result;

[0050] If there are different entry data in the first target dataset and the second target dataset, generating a second comparison result;

[0051] Generating a target reconciliation result based on the first comparison result and the second comparison result.

[0052] An order number is an entry data, and the key fields may include fields such as payment amount, payment status, flight number, cabin class (e.g., economy class, business class, first class), customer name, commission, etc. Determine the key fields of each entry data in the first target dataset and the second target dataset respectively, compare the key fields of the same order number in the first target dataset and the second target dataset to obtain a comparison result (denoted as the first comparison result), and determine whether there are different entry data in the first target dataset and the second target dataset. For example, if the first target dataset has an order number A23201, determine whether the second target dataset has an order number A23201. If there are different order numbers in the first target dataset and the second target dataset, use the different order numbers that exist in both as the second comparison result. Since the first comparison result represents the comparison result of the key fields of the same order number, and the second comparison result represents the different order numbers of the two, the target reconciliation result can be generated according to the first comparison result and the second comparison result.

[0053] Further, generating a target reconciliation result based on the first comparison result and the second comparison result includes:

[0054] Regarding the entry data with the same key fields in the first comparison result as normal entry data;

[0055] Regarding the entry data with different key fields in the first comparison result as abnormal entry data;

[0056] Generating a target reconciliation result according to the normal entry data, the abnormal entry data, and the second comparison result.

[0057] Order numbers with identical key fields in the first comparison result indicate that the reconciliation results of these orders are correct. Therefore, the entry data with identical key fields is regarded as normal entry data. Order numbers with different key fields in the first comparison result indicate that the reconciliation results of these orders are controversial. Therefore, the entry data with different key fields is regarded as abnormal entry data. Based on the correct reconciliation results, the controversial reconciliation results, and the second comparison result, a target reconciliation result can be generated.

[0058] Further, generating a target reconciliation report according to the type of the target reconciliation result includes:

[0059] Regarding the normal entry data as the target reconciliation result of the normal type;

[0060] Regarding the abnormal entry data and the second comparison result as the target reconciliation result of the abnormal type;

[0061] Populating the target reconciliation result of the normal type and the target reconciliation result of the abnormal type into a preset template to generate a target reconciliation report.

[0062] Regarding the normal entry data as the target reconciliation result of the normal type, regarding the abnormal entry data and the second comparison result as the target reconciliation result of the abnormal type, and populating the target reconciliation results of the two types into a preset template can generate and export a target reconciliation report.

[0063] In one embodiment, after generating the target reconciliation report, the method further includes:

[0064] Sending the target reconciliation report to a preset terminal.

[0065] The preset terminal can be the terminals corresponding to the airline and the third-party platform. The airline and the third-party platform can analyze, statistically process, and resolve the reconciliation results of the order numbers with anomalies according to the target reconciliation report. This can improve the reconciliation efficiency, reduce errors caused by manual reconciliation, and improve the accuracy of reconciliation data.

[0066] Refer to Figure 2 As shown, it is a schematic diagram of the functional modules of the reconciliation device 100 for ticket business data of the present application.

[0067] The reconciliation device 100 for ticket business data of the present application can be installed in an electronic device. According to the functions achieved, the reconciliation device 100 for ticket business data can include an acquisition module 110, a preprocessing module 120, a reconciliation module 130, and a generation module 140. The modules of the present application can also be referred to as units, which refer to a series of computer program segments that can be executed by the processor of the electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0068] In this embodiment, the functions of each module / unit are as follows:

[0069] Acquisition module 110: used to acquire the first ticket business data of the airline company and the second ticket business data of the third-party platform;

[0070] Preprocessing module 120: used to perform preprocessing operations on the first ticket business data and the second ticket business data to obtain a first target data set corresponding to the first ticket business data and a second target data set corresponding to the second ticket business data;

[0071] Reconciliation module 130: used to perform a reconciliation operation based on the first target data set and the second target data set to obtain a target reconciliation result;

[0072] Generation module 140: used to generate a target reconciliation report according to the type of the target reconciliation result.

[0073] In one embodiment, performing preprocessing operations on the first ticket business data and the second ticket business data to obtain a first target data set corresponding to the first ticket business data and a second target data set corresponding to the second ticket business data includes:

[0074] Performing a data cleaning operation on the first ticket business data and the second ticket business data;

[0075] Performing a data format conversion operation on the first ticket business data after the data cleaning operation to obtain a first target data set corresponding to the first ticket business data;

[0076] Performing a data format conversion operation on the second ticket business data after the data cleaning operation to obtain a first target data set corresponding to the second ticket business data, where the data formats of the first target data set and the second target data set are the same.

[0077] In one embodiment, the data cleaning operation includes at least one of a missing value filling operation and a duplicate value removal operation, and the missing value filling operation includes at least one of a global constant automatic filling, a central measure automatic filling, and a within-group mean automatic filling.

[0078] In one embodiment, performing a reconciliation operation based on the first target data set and the second target data set to obtain a target reconciliation result includes:

[0079] Determining the key fields of each entry data in the first target data set;

[0080] Determining the key fields of each entry data in the second target data set;

[0081] Compare the keyword fields of the same entry data in the first target data set and the second target data set to obtain a first comparison result;

[0082] If there are different entry data in the first target data set and the second target data set, generate a second comparison result;

[0083] Generate a target reconciliation result based on the first comparison result and the second comparison result.

[0084] In one embodiment, the generating a target reconciliation result based on the first comparison result and the second comparison result includes:

[0085] Use the entry data with the same keyword fields in the first comparison result as normal entry data;

[0086] Use the entry data with different keyword fields in the first comparison result as abnormal entry data;

[0087] Generate a target reconciliation result according to the normal entry data, the abnormal entry data and the second comparison result.

[0088] In one embodiment, the generating a target reconciliation report according to the type of the target reconciliation result includes:

[0089] Use the normal entry data as the target reconciliation result of the normal type;

[0090] Use the abnormal entry data and the second comparison result as the target reconciliation result of the abnormal type;

[0091] Fill the target reconciliation result of the normal type and the target reconciliation result of the abnormal type into a preset template to generate a target reconciliation report.

[0092] In one embodiment, the reconciliation device for ticket business data further includes a sending module, and the sending module is used to send the target reconciliation report to a preset terminal.

[0093] Refer to Figure 3 shown, which is a schematic diagram of a preferred embodiment of the electronic device of the present application.

[0094] The electronic device includes a processor 111, a communication interface 112, a memory 113, and a communication bus 114. Among them, the processor 111, the communication interface 112, and the memory 113 communicate with each other through the communication bus 114;

[0095] The memory 113 is used to store a computer program, for example, a reconciliation program for ticket business data;

[0096] Among them, in some embodiments, the processor 111 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips. The processor 111 is generally used to control the overall operation of the electronic device, such as performing control and processing related to data interaction or communication. In this embodiment, the processor 111 is used to run the program code stored in the memory 113 or process data, such as running the program code of the reconciliation program for air ticket business data, etc.

[0097] The communication interface 112 may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), and the communication interface 112 can also be used to establish a communication connection between the electronic device and other electronic devices.

[0098] The memory 113 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, a hard disk, a multimedia card, a card-type memory (such as an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 113 may be an internal storage unit of the electronic device, such as the hard disk or memory of the electronic device. In other embodiments, the memory 113 may also be an external storage device of the electronic device, such as a plug-in hard disk equipped with the electronic device, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Of course, the memory 113 may also include both the internal storage unit and the external storage device of the electronic device. In this embodiment, the memory 11 is generally used to store the operating system installed in the electronic device and various computer programs, such as the program code of the reconciliation program for air ticket business data, etc. In addition, the memory 113 can also be used to temporarily store various data that have been output or will be output.

[0099] In an embodiment of the present application, when the processor 111 is used to execute the program stored on the memory 113, it implements the reconciliation method for air ticket business data provided by any one of the foregoing method embodiments, including:

[0100] Obtain the first air ticket business data of the airline company and the second air ticket business data of the third-party platform;

[0101] Perform a preprocessing operation on the first ticket business data and the second ticket business data to obtain a first target data set corresponding to the first ticket business data and a second target data set corresponding to the second ticket business data;

[0102] Perform a reconciliation operation based on the first target data set and the second target data set to obtain a target reconciliation result;

[0103] Generate a target reconciliation report according to the type of the target reconciliation result.

[0104] For a detailed introduction to the above steps, please refer to the above Figure 1 Description of the flowchart of the embodiment of the reconciliation method for ticket business data.

[0105] In addition, an embodiment of the present application also proposes a computer-readable storage medium, which can be non-volatile or volatile. The computer-readable storage medium includes a storage data area and a storage program area. The storage program area stores a reconciliation program for ticket business data. When the reconciliation program for ticket business data is executed by a processor, the following operations are implemented:

[0106] Obtain the first ticket business data of the airline company and the second ticket business data of the third-party platform;

[0107] Perform a preprocessing operation on the first ticket business data and the second ticket business data to obtain a first target data set corresponding to the first ticket business data and a second target data set corresponding to the second ticket business data;

[0108] Perform a reconciliation operation based on the first target data set and the second target data set to obtain a target reconciliation result;

[0109] Generate a target reconciliation report according to the type of the target reconciliation result.

[0110] The specific implementation manner of the computer-readable storage medium of the present application is substantially the same as the specific implementation manner of the above reconciliation method for ticket business data, and will not be elaborated here.

[0111] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0112] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence, or the part that contributes to the related technologies, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0113] It should be noted that the descriptions involving "first", "second", etc. in this application are only for descriptive purposes, and cannot be understood as indicating or implying their relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between various embodiments can be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement them. When the combination of technical solutions appears to be contradictory or unable to be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0114] It should be understood that the terms used herein are only for the purpose of describing specific example embodiments and are not intended to be restrictive. Unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" as used herein may also include the plural forms. The terms "include", "comprise", "contain", and "have" are inclusive and thus specify the presence of the stated features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or their combinations. The method steps, processes, and operations described herein are not to be construed as necessarily requiring them to be executed in the particular order described or illustrated, unless the execution order is explicitly stated. It should also be understood that additional or alternative steps can be used.

[0115] The above are only the specific embodiments of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application will not be limited to the embodiments shown herein, but rather will be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for reconciling ticket business data, characterized in that, The method includes: Obtaining first ticket business data of an airline company and second ticket business data of a third-party platform; Performing a preprocessing operation on the first ticket business data and the second ticket business data to obtain a first target data set corresponding to the first ticket business data and a second target data set corresponding to the second ticket business data; Performing a reconciliation operation based on the first target data set and the second target data set to obtain a target reconciliation result; Generating a target reconciliation report according to the type of the target reconciliation result.

2. The reconciliation method for ticket business data according to claim 1, characterized in that The performing a preprocessing operation on the first ticket business data and the second ticket business data to obtain a first target data set corresponding to the first ticket business data and a second target data set corresponding to the second ticket business data includes: Performing a data cleaning operation on the first ticket business data and the second ticket business data; Performing a data format conversion operation on the first ticket business data after the data cleaning operation to obtain a first target data set corresponding to the first ticket business data; Performing a data format conversion operation on the second ticket business data after the data cleaning operation to obtain a second target data set corresponding to the second ticket business data, wherein the data formats of the first target data set and the second target data set are the same.

3. The reconciliation method for ticket business data according to claim 2, wherein The data cleaning operation includes at least one of a missing value filling operation and a duplicate value removal operation, and the missing value filling operation includes at least one of global constant automatic filling, central measure automatic filling, and within-group mean automatic filling.

4. The reconciliation method for ticket business data according to claim 1, characterized in that, The performing a reconciliation operation based on the first target data set and the second target data set to obtain a target reconciliation result includes: Determining the key fields of each entry data in the first target data set; Determining the key fields of each entry data in the second target data set; Comparing the key fields of the same entry data in the first target data set and the second target data set to obtain a first comparison result; If there are different entry data in the first target data set and the second target data set, generating a second comparison result; Generating a target reconciliation result based on the first comparison result and the second comparison result.

5. The reconciliation method for ticket business data according to claim 4, wherein, The generating a target reconciliation result based on the first comparison result and the second comparison result includes: Regarding the entry data with the same key fields in the first comparison result as normal entry data; Regarding the entry data with different key fields in the first comparison result as abnormal entry data; Generating a target reconciliation result according to the normal entry data, the abnormal entry data, and the second comparison result.

6. The reconciliation method for ticket business data according to claim 1, wherein The generating a target reconciliation report according to the type of the target reconciliation result includes: Regarding the normal entry data as the target reconciliation result of the normal type; Regarding the abnormal entry data and the second comparison result as the target reconciliation result of the abnormal type; Filling the target reconciliation result of the normal type and the target reconciliation result of the abnormal type into a preset template to generate a target reconciliation report.

7. The reconciliation method for ticket business data according to claim 1, characterized in that After generating the target reconciliation report, the method further includes: Sending the target reconciliation report to a preset terminal.

8. A reconciliation device for ticket business data, characterized in that, The device includes: An acquisition module: configured to acquire first ticket business data of an airline company and second ticket business data of a third-party platform; A preprocessing module: configured to perform preprocessing operations on the first ticket business data and the second ticket business data to obtain a first target data set corresponding to the first ticket business data and a second target data set corresponding to the second ticket business data; A reconciliation module: configured to perform a reconciliation operation based on the first target data set and the second target data set to obtain a target reconciliation result; A generation module: configured to generate a target reconciliation report according to the type of the target reconciliation result.

9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the communication bus; The memory is used to store a computer program; The processor is configured to implement the reconciliation method for ticket business data according to any one of claims 1 to 7 when executing the program stored on the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program, when executed by the processor, implements the reconciliation method for ticket business data according to any one of claims 1 to 7.

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