Accounting methods, apparatus, computer equipment and storage media

Abstract

This application relates to artificial intelligence technology and provides an accounting processing method, apparatus, computer device, and storage medium, comprising: acquiring a first accounting dataset and a second accounting dataset; matching first accounting data in the first accounting dataset and second accounting data in the second accounting dataset to obtain a target accounting dataset; determining the target language corresponding to each target accounting data in the target accounting dataset and the accounting subject corresponding to the target language; determining the accounting behavior corresponding to the accounting subject; determining the part-of-speech information of the accounting subject and the accounting behavior; and combining the accounting subject and the accounting behavior according to the part-of-speech information in a preset part-of-speech concatenation format to obtain a target accounting language. This application can improve the speed of accounting language determination and promote the rapid development of smart cities.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to an accounting processing method, apparatus, computer equipment, and storage medium. Background Technology

[0002] Double-entry bookkeeping is an accounting method that uses the accounting equation as its principle and debits and credits as its accounting symbols to reflect changes in economic transactions. When a certain economic transaction occurs, three elements are extracted to complete the double-entry bookkeeping: "debit / credit direction, account, and amount".

[0003] In the process of implementing this application, the applicant discovered the following problems with the existing technology: accounting data is usually stored in the database according to the accounting rules of finance. In order for non-professional financial personnel to understand this accounting data, professional financial personnel need to convert this accounting data into a highly readable accounting language. The rate at which the accounting language is determined is extremely low.

[0004] Therefore, it is necessary to provide an accounting processing method that can improve the speed of accounting language determination. Summary of the Invention

[0005] In view of the above, it is necessary to propose an accounting processing method, accounting processing device, computer equipment and storage medium that can improve the speed of accounting language determination.

[0006] The first aspect of this application provides an accounting processing method, the accounting processing method comprising:

[0007] Obtain the first and second accounting datasets;

[0008] The first accounting data in the first accounting dataset and the second accounting data in the second accounting dataset are matched to obtain the target accounting dataset;

[0009] Determine the target language corresponding to each target accounting data in the target accounting dataset and the accounting subject corresponding to the target language;

[0010] Determine the accounting behavior corresponding to the accounting entity;

[0011] Determine the part-of-speech information of the accounting entity and the accounting behavior;

[0012] The accounting subject and the accounting behavior are combined according to the part-of-speech information in a preset part-of-speech concatenation format to obtain the target accounting language.

[0013] Furthermore, in the accounting processing method provided in the embodiments of this application, obtaining the first accounting dataset and the second accounting dataset includes:

[0014] Determine the first accounting characteristic corresponding to the first accounting data and the second accounting characteristic corresponding to the second accounting data;

[0015] Obtain the accounting dataset and the accounting characteristics corresponding to each accounting data in the preset database;

[0016] The accounting data that matches the accounting features with the first accounting features are used as the first accounting data to obtain the first accounting dataset;

[0017] The accounting data that matches the accounting features with the second accounting features are used as the second accounting data to obtain the second accounting dataset.

[0018] Further, in the accounting processing method provided in the embodiments of this application, the step of matching the first accounting data in the first accounting dataset and the second accounting data in the second accounting dataset to obtain the target accounting dataset includes:

[0019] Determine the first order code corresponding to each first accounting data in the first accounting dataset;

[0020] Determine the second order code corresponding to each second accounting data in the second accounting dataset;

[0021] Detect whether the first order code and the second order code are the same;

[0022] When the first order code is the same as the second order code, it is determined that the first accounting data matches the second accounting data;

[0023] The first and second accounting data that are determined to match are combined to form the target accounting data, thus obtaining the target accounting dataset.

[0024] Furthermore, in the accounting processing method provided in the embodiments of this application, determining the target language corresponding to each target accounting data in the target accounting dataset and the accounting subject corresponding to the target language includes:

[0025] Obtain the account information of the accounting data query object corresponding to each target accounting data;

[0026] Iterate through the pre-set mapping relationship between account information and language to obtain the target language corresponding to the account information;

[0027] Determine the preset accounting keywords in each target accounting data;

[0028] The accounting entity corresponding to the target accounting data is determined based on the preset accounting keywords.

[0029] Furthermore, in the accounting processing method provided in the embodiments of this application, determining the accounting behavior corresponding to the accounting entity includes:

[0030] Obtain a training sample set, which contains multiple loan training texts;

[0031] The training sample set is identified by calling a preset word vector algorithm to obtain multiple sets of first words corresponding to each accounting entity. The first word set contains multiple words with similar semantics.

[0032] Determine the target part of speech corresponding to the accounting behavior, and select several second vocabulary sets corresponding to the target part of speech from the multiple sets of first vocabulary sets;

[0033] Calculate the number of words in each second vocabulary set, and select the second vocabulary set whose number of words exceeds a preset vocabulary number threshold as the target vocabulary set, and use the target words in the target vocabulary set as accounting actions.

[0034] Furthermore, in the accounting processing method provided in the embodiments of this application, determining the part-of-speech information of the accounting subject and the accounting behavior includes:

[0035] Obtain the preset accounting keywords corresponding to the accounting entity;

[0036] By traversing the pre-set first correspondence between accounting keywords and parts of speech, the part of speech information corresponding to the accounting entity is obtained;

[0037] Obtain the pre-set second correspondence between accounting behavior and part of speech to obtain the part of speech information corresponding to the accounting behavior.

[0038] Furthermore, in the accounting processing method provided in the embodiments of this application, the step of combining the accounting subject and the accounting behavior according to the part-of-speech information according to a preset part-of-speech concatenation format to obtain the target accounting language includes:

[0039] Obtain the part-of-speech order and part-of-speech connection information in the preset part-of-speech splicing format;

[0040] The accounting entities and accounting behaviors are ordered according to the part-of-speech order;

[0041] The part-of-speech linking information is used to combine the accounting subject and the accounting behavior to obtain the target accounting language.

[0042] A second aspect of this application also provides an accounting processing apparatus, the accounting processing apparatus comprising:

[0043] The accounting data acquisition module is used to acquire the first accounting dataset and the second accounting dataset;

[0044] The accounting data matching module is used to match the first accounting data in the first accounting dataset and the second accounting data in the second accounting dataset to obtain the target accounting dataset;

[0045] The accounting entity determination module is used to determine the target language corresponding to each target accounting data in the target accounting dataset and the accounting entity corresponding to the target language;

[0046] The accounting behavior determination module is used to determine the accounting behavior corresponding to the accounting entity;

[0047] The part-of-speech tagging module is used to determine the part-of-speech tagging information of the accounting entity and the accounting behavior;

[0048] The accounting language acquisition module is used to combine the accounting subject and the accounting behavior according to the part-of-speech information in a preset part-of-speech concatenation format to obtain the target accounting language.

[0049] A third aspect of this application also provides a computer device, the computer device including a processor, the processor being configured to execute a computer program stored in a memory to implement the accounting processing method as described in any of the preceding claims.

[0050] A fourth aspect of this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the accounting processing method described in any one of the above embodiments.

[0051] The accounting processing method, apparatus, computer equipment, and computer-readable storage medium provided in this application's embodiments obtain a target accounting dataset by matching a first accounting dataset with a second accounting dataset. The accounting subject and accounting behavior of each target accounting data are determined. Then, the accounting subject and accounting behavior are combined according to a preset part-of-speech tagging format to obtain the target accounting language corresponding to the target language. Through natural language processing technology, accounting data can be identified quickly and accurately, improving the speed of accounting language determination. This application can be applied to various functional modules of smart cities, such as smart government affairs and smart transportation, including the accounting processing module of a smart city, thus promoting the rapid development of smart cities. Attached Figure Description

[0052] Figure 1 This is a flowchart of an accounting processing method provided in an embodiment of this application.

[0053] Figure 2 This is a flowchart illustrating the determination of a target accounting dataset provided in one embodiment of this application.

[0054] Figure 3 This is a flowchart illustrating the determination of the accounting entity according to an embodiment of this application.

[0055] Figure 4 This is a flowchart illustrating the determination of accounting transactions according to an embodiment of this application.

[0056] Figure 5 This is a flowchart illustrating the determination of part-of-speech information provided in an embodiment of this application.

[0057] Figure 6 This is a flowchart illustrating the determination of the target accounting language provided in one embodiment of this application.

[0058] Figure 7 This is a structural diagram of an accounting processing device provided in an embodiment of this application.

[0059] Figure 8 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application.

[0060] The following detailed description, in conjunction with the accompanying drawings, will further illustrate this application. Detailed Implementation

[0061] To better understand the above-mentioned objectives, features, and advantages of this application, the application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0062] Numerous specific details are set forth in the following description in order to provide a full understanding of this application. The described embodiments are only some, not all, of the embodiments of this application.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0064] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0065] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0066] The accounting processing method provided in this embodiment of the invention is executed by a computer device, and correspondingly, the accounting processing device runs in the computer device. Figure 1 This is a flowchart of an accounting processing method provided in an embodiment of this application. For example... Figure 1 As shown, the accounting processing method may include the following steps. The order of these steps in the flowchart may be changed, and some may be omitted, depending on different needs:

[0067] S11, obtain the first accounting dataset and the second accounting dataset.

[0068] In at least one embodiment of this application, the first accounting dataset contains multiple first accounting data sets, and the second accounting dataset contains multiple second accounting data sets. Both the first and second accounting datasets are stored in a preset database. Considering the reliability and privacy of data storage, the preset database can be a target node on a blockchain. Both the first and second accounting datasets are used to identify accounting activities. For example, taking debit and credit accounting as an example, the first accounting dataset is used to identify accounting data corresponding to lending activities, and the second accounting dataset is used to identify accounting data corresponding to loan activities.

[0069] Optionally, obtaining the first accounting dataset and the second accounting dataset includes:

[0070] S111, determine the first accounting feature corresponding to the first accounting data and the second accounting feature corresponding to the second accounting data.

[0071] In one embodiment, the first accounting feature is used to uniquely identify the first accounting data, and the second accounting feature is used to uniquely identify the second accounting data. The first and second accounting features refer to the amount characteristics corresponding to accounting items. The accounting items can be items such as assets, equipment, furniture, liabilities, and bank inventory, and the amount characteristics can be greater than or equal to 0. Taking the first accounting data as accounting data corresponding to lending activities as an example, the first accounting feature can be the characteristic that the lending amount is greater than 0, while the loan amount is equal to 0. Similarly, taking the second accounting data as accounting data corresponding to loan activities as an example, the second accounting feature can be the characteristic that the lending amount is equal to 0, while the loan amount is greater than 0.

[0072] S112, Obtain the accounting dataset in the preset database and the accounting features corresponding to each accounting data.

[0073] In one embodiment, the accounting dataset refers to the set of the first accounting dataset and the second accounting dataset. The accounting feature refers to the monetary feature corresponding to the accounting items in the accounting data.

[0074] S113, the accounting data that matches the accounting feature with the first accounting feature is taken as the first accounting data to obtain the first accounting dataset.

[0075] For example, when the accounting feature is that the amount borrowed is greater than 0 and the amount of loan is equal to 0, the accounting data corresponding to the accounting feature is determined as the first accounting data, and multiple first accounting data are combined to obtain the first accounting dataset.

[0076] S114, the accounting data that matches the accounting feature with the second accounting feature is taken as the second accounting data, and the second accounting dataset is obtained.

[0077] For example, when the accounting feature is that the loan amount is equal to 0 and the loan amount is greater than 0, the accounting data corresponding to the accounting feature is determined as the second accounting data, and multiple second accounting data are combined to obtain the second accounting dataset.

[0078] S12, match the first accounting data in the first accounting dataset and the second accounting data in the second accounting dataset to obtain the target accounting dataset.

[0079] In at least one embodiment of this application, the double-entry bookkeeping method adheres to the principle of debit and credit identity, that is, for every debit, there must be a corresponding credit, and debits and credits must be balanced. According to the principle of debit and credit identity, the verification of debit and credit balance mainly involves two aspects: verification of behavior and verification of amount. Verification of behavior means verifying that for every debit, there must be a corresponding credit, and verification of amount means verifying that debits and credits must be balanced. Based on the above principle of debit and credit identity, first accounting data and second accounting data in a preset database can be matched to obtain target accounting data. In one embodiment, the first accounting data may correspond to only one second accounting data or multiple second accounting data. Similarly, the second accounting data may correspond to only one first accounting data or multiple first accounting data; no limitation is made here.

[0080] Combination Figure 2 This application describes the process for determining the target accounting dataset provided in its embodiments. Optionally, matching the first accounting data within the first accounting dataset and the second accounting data within the second accounting dataset to obtain the target accounting dataset includes:

[0081] S121, determine the first order code corresponding to each first accounting data in the first accounting dataset.

[0082] In one embodiment, the first order code is used to uniquely identify the first accounting data. A mapping relationship exists between the first order code and the first accounting data. By querying the mapping relationship, the first order code corresponding to the first accounting data is obtained. When there are multiple first accounting data sets within the first accounting dataset, the first order codes corresponding to each first accounting data set can be the same or different; this is not limited. When multiple first accounting data sets within the first accounting dataset share the same first order code, it indicates that the multiple first accounting data sets belong to the same order.

[0083] S122, determine the second order code corresponding to each second accounting data in the second accounting dataset.

[0084] In one embodiment, the second order code is used to uniquely identify the second accounting data. A mapping relationship exists between the second order code and the second accounting data. By querying the mapping relationship, the second order code corresponding to the second accounting data is obtained. When there are multiple second accounting data sets within the second accounting dataset, the second order codes corresponding to each second accounting data set can be the same or different; this is not limited. When multiple second accounting data sets within the second accounting dataset share the same second order code, it indicates that the multiple second accounting data sets belong to the same order.

[0085] S123, Detect whether the first order code and the second order code are the same.

[0086] In one embodiment, according to the borrowing identity principle, when a first accounting data exists in a preset database, a corresponding second accounting data should also exist. The first accounting data and the second accounting data are associated through an order code.

[0087] S124, when the first order code is the same as the second order code, it is determined that the first accounting data matches the second accounting data.

[0088] In one embodiment, when the first order code is the same as the second order code, it indicates that the first accounting data corresponding to the first order code and the second accounting data corresponding to the second order code belong to the same order. At this time, it is determined that the first accounting data and the second accounting data match.

[0089] In one embodiment, when the first order code and the second order code are different, it indicates that the first accounting data corresponding to the first order code and the second accounting data corresponding to the second order code do not belong to the same order. In this case, it is determined that the first accounting data and the second accounting data do not match.

[0090] S125, the first accounting data and the second accounting data that are determined to be matched are combined into target accounting data to obtain the target accounting dataset.

[0091] In one embodiment, the target accounting data refers to the combination of the matched first accounting data and the second accounting data. The combination method can be set according to needs and is not specifically limited. For example, the first accounting data and the second accounting data are stored in the same row of a table, with the first accounting data to the left of the second accounting data, to obtain the target accounting data. Multiple target accounting data constitute a target accounting dataset.

[0092] S13, determine the target language corresponding to each target accounting data in the target accounting dataset and the accounting subject corresponding to the target language.

[0093] In at least one embodiment of this application, the target language refers to the language of the target accounting language to be generated. For example, the target accounting language can be Chinese, English, Japanese, German, Korean, etc., and there is no limitation herein. The target language can be set according to actual needs. The accounting subject refers to the accounting elements corresponding to the target accounting data that reflect the increase or decrease of economic transactions. In one embodiment, the accounting subject may include accounting items and accounting amounts.

[0094] In one embodiment, for each target language, there is a corresponding accounting entity. For example, when the accounting item in the accounting entity is "asset" and the accounting amount is 8,000 yuan, when the target language is English, the accounting item is "asset" and the accounting amount is 8,000 yuan; when the target language is Chinese, the accounting item is "assets" and the accounting amount is 8,000 yuan.

[0095] Combination Figure 3 This application describes the process for determining the accounting entity provided in its embodiments. Optionally, determining the target language corresponding to each target accounting data in the target accounting dataset and the accounting entity corresponding to the target language includes:

[0096] S131, obtain the account information of the accounting data query object corresponding to each target accounting data.

[0097] In one embodiment, the target accounting dataset is stored in a preset database, the accounting data query object refers to the object that logs into the preset database to query the target accounting data, and the account information refers to the account that logs into the preset database. In one embodiment, a target tracking point is set on the login interface of the preset database, and the target tracking point is used to collect the account information of the accounting data query object.

[0098] S132, traverse the pre-set mapping relationship between account information and language to obtain the target language corresponding to the account information.

[0099] In one embodiment, when the accounting data query object logs into a preset database to query relevant target accounting data, different accounting data query objects may have different preferred languages ​​for the accounting data. For example, there are accounting data query objects A, B, and C. Accounting data query object A prefers Chinese, accounting data query object B prefers English, and accounting data query object C prefers Japanese. Therefore, a mapping relationship between account information and language is pre-established. By traversing this mapping relationship, the target language corresponding to the account information can be obtained. Since the account information is used to query the target accounting data, when the target language corresponding to the account information is Chinese, it indicates that the accounting data query object prefers the Chinese query method. In this case, the target accounting data is set to Chinese; when the target language corresponding to the account information is English, it indicates that the accounting data query object prefers the English query method. In this case, the target accounting data is set to English.

[0100] S133, determine the preset accounting keywords in each target accounting data.

[0101] In one embodiment, the preset accounting keywords are pre-set keywords used to uniquely identify the accounting subject in the target accounting data. Taking the accounting subject as including accounting items and accounting amounts as an example, the preset accounting keywords corresponding to the accounting items are "debitName" and "creditName", and the preset accounting keywords corresponding to the accounting amounts are "debitbalance" and "creditBalance". Here, "debitName" identifies the debit item, "creditName" identifies the credit item, "debitbalance" identifies the debit amount, and "creditBalance" identifies the credit amount.

[0102] S134, determine the accounting entity corresponding to the target accounting data according to the preset accounting keywords.

[0103] In one embodiment, by determining preset accounting keywords in the target accounting data, the accounting entity corresponding to the target accounting data can be obtained.

[0104] S14, determine the accounting behavior corresponding to the accounting entity.

[0105] In at least one embodiment of this application, taking the accounting entity including accounting subjects and accounting amounts as an example, the accounting behavior refers to the execution behavior of the accounting subject on the accounting amount. For example, the accounting behavior includes, but is not limited to, purchasing, buying, procuring, paying, spending, expenditure, and consumption. Purchasing, buying, procuring, and procuring are debit behaviors, while paying, spending, expenditure, and consumption are credit behaviors. In one embodiment, the accounting behavior can be obtained by processing training samples using natural language processing. Obtaining the accounting behavior through natural language processing can improve the efficiency of determining the accounting behavior, thereby improving the efficiency of determining the target accounting language.

[0106] Combination Figure 4 This application describes the process for determining accounting behavior provided in its embodiments. Optionally, determining the accounting behavior corresponding to the accounting entity includes:

[0107] S141, Obtain a training sample set, which contains multiple loan training texts.

[0108] In one embodiment, the training sample set contains multiple loan training texts, which can be downloaded from multiple financial websites using web scraping technology, and no limitation is imposed here.

[0109] S142, invoke a preset word vector algorithm to identify the training sample set, and obtain multiple sets of first vocabulary corresponding to each accounting entity, wherein the first vocabulary set contains multiple words with similar semantics.

[0110] In one embodiment, taking the accounting entity as including accounting items and accounting amounts as an example, a preset word vector algorithm is used to identify the training sample set to obtain multiple sets of first vocabulary corresponding to each accounting item. The first vocabulary set contains multiple semantically similar words, such as "purchase," "buy," "purchase," and "procure" forming one set of words, and "pay," "spend," "expenditure," and "consume" forming another set of words.

[0111] S143, determine the target part of speech corresponding to the accounting behavior, and select several second vocabulary sets corresponding to the target part of speech from the multiple sets of first vocabulary sets.

[0112] In one embodiment, the part of speech may include subject, predicate, object, etc. The accounting behavior refers to the execution action of the accounting item on the accounting amount, that is, the target part of speech of the accounting behavior is predicate. For multiple sets of first vocabulary, the words in each set of first vocabulary have the same part of speech. A set of words with the part of speech of verbs is selected from the multiple sets of first vocabulary as a second vocabulary set. The number of words in the second vocabulary set can be one or more.

[0113] S144, calculate the number of words in each of the second vocabulary sets, and select the second vocabulary set whose number of words exceeds a preset vocabulary number threshold as the target vocabulary set, and use the target words in the target vocabulary set as accounting behavior.

[0114] In one embodiment, the second vocabulary set contains multiple words, which can be one or more. The preset vocabulary quantity threshold is a pre-set threshold used to identify words as high-frequency words. When the number of words exceeds the preset vocabulary quantity threshold, it indicates that words with similar meanings in the second vocabulary set are used more frequently, and the target vocabulary set is determined to be a high-frequency vocabulary set in the accounting subject. When the number of words is lower than the preset vocabulary quantity threshold, it indicates that words with similar meanings in the second vocabulary set are used less frequently, and the target vocabulary set is determined to be a low-frequency vocabulary set in the accounting subject. For example, when the preset vocabulary quantity threshold is 3, the second vocabulary set A contains the four words "purchase," "buy," "purchase," and "procure." Since the number of words in the second vocabulary set A exceeds the preset vocabulary quantity threshold 3, the second vocabulary set A can be used as the target vocabulary set, and all words in the second vocabulary set A are high-frequency words.

[0115] S15, determine the part-of-speech information of the accounting entity and the accounting behavior.

[0116] In at least one embodiment of this application, taking the accounting subject as an example, which includes accounting items and accounting amounts, the part-of-speech information of the accounting items, the accounting amounts, and the accounting parts of speech are all predetermined.

[0117] Combination Figure 5 This application describes the process for determining part-of-speech information provided in its embodiments. Optionally, determining the part-of-speech information of the accounting entity and the accounting behavior includes:

[0118] S151, Obtain the preset accounting keywords corresponding to the accounting entity.

[0119] In one embodiment, the preset accounting keywords are pre-set keywords used to uniquely identify the accounting subject in the target accounting data. Taking the accounting subject as including accounting items and accounting amounts as an example, the preset accounting keywords corresponding to the accounting items are "debitName" and "creditName", and the preset accounting keywords corresponding to the accounting amounts are "debitbalance" and "creditBalance". Here, "debitName" identifies the debit item, "creditName" identifies the credit item, "debitbalance" identifies the debit amount, and "creditBalance" identifies the credit amount.

[0120] S152, traverse the first correspondence between pre-set accounting keywords and parts of speech to obtain the part of speech information corresponding to the accounting subject.

[0121] In one embodiment, a first correspondence between accounting keywords and parts of speech is pre-defined. By querying the first correspondence, the part-of-speech information corresponding to the accounting subject can be obtained. For example, the accounting entries include debit entries and credit entries, where the part of speech corresponding to the debit entry is the object of the debit entry, and the part of speech corresponding to the credit entry is the subject of the credit entry. The accounting amounts include debit amounts and credit amounts, where the part of speech corresponding to the debit amount is the object of the debit amount, and the part of speech corresponding to the credit amount is the object of the credit amount.

[0122] S153, obtain the second correspondence between the pre-set accounting behavior and the part of speech, and obtain the part of speech information corresponding to the accounting behavior.

[0123] In one embodiment, a second correspondence between accounting actions and parts of speech is pre-defined. By querying the second correspondence, the part-of-speech information corresponding to the accounting action can be obtained. For example, the part of speech of the accounting action includes debit predicates and credit predicates.

[0124] S16, Based on the part-of-speech information, the accounting subject and the accounting behavior are combined according to a preset part-of-speech concatenation format to obtain the target accounting language.

[0125] In at least one embodiment of this application, the preset part-of-speech tagging format is a pre-set data format for combining the accounting subject and the accounting behavior. For example, the preset part-of-speech tagging format can be {“Enterprise” + debit predicate + debit account object, “With” + credit account subject + credit predicate, debit amount object (or credit amount object)}. The target accounting language refers to the accounting-related language corresponding to the target language. When the target language is Chinese, English, or Japanese, the target accounting language can be a Chinese-type accounting-related language, an English-type accounting-related language, or a Japanese-type accounting-related language.

[0126] Combination Figure 6 This application describes the process for determining the target accounting language provided in its embodiments. Optionally, the step of combining the accounting subject and the accounting behavior according to the part-of-speech information in a preset part-of-speech concatenation format to obtain the target accounting language includes:

[0127] S161, Obtain the part-of-speech order and part-of-speech connection information in the preset part-of-speech splicing format.

[0128] In one embodiment, the preset part-of-speech (POS) concatenation format includes multiple POS. The POS order refers to the order of the POS in the preset POS concatenation format, and the POS connection information refers to the connection information between pairs of POS. Taking the preset POS concatenation format as {“Enterprise” + debit predicate + debit account object, “With” + credit account subject + credit predicate, debit amount object (or credit amount object)} as an example, the POS order is: debit predicate, debit account object, credit account subject, credit predicate, and debit amount object (or credit amount object). The POS connection information is a comma (,).

[0129] S162, Sort the accounting entity and the accounting behavior according to the part-of-speech order.

[0130] In one embodiment, as described above, the part-of-speech order is: debit predicate, debit account object, credit account subject, credit predicate, and debit amount object (or credit amount object). The accounting accounts, accounting amounts, and accounting transactions are arranged in the above order.

[0131] S163, the part-of-speech linking information is used to combine the accounting subject and the accounting behavior to obtain the target accounting language.

[0132] In one embodiment, the accounting subject, the accounting amount, and the accounting behavior are connected according to the above-mentioned词性连接信息“,”(it seems there is a mistake here, maybe it should be a specific connection information like a comma) to obtain the target accounting language. When the target language is Chinese, the target accounting language can be "The enterprise purchases assets and pays with bank inventory, with an amount of 8000 yuan"; when the target language is English, the target accounting language can be "The enterprise purchases the asset, pays with the bank inventory, the amount is 8000 yuan".

[0133] The above-mentioned accounting processing method provided by the embodiment of the present application matches the first accounting data set with the second accounting data set to obtain the target accounting data set, determines the accounting subject and accounting behavior of each target accounting data, and then combines the accounting subject and accounting behavior according to the preset词性拼接格式(again, seems there is an error here, should be a proper format) to obtain the target accounting language corresponding to the target language. Through natural language processing technology, it can quickly and accurately identify accounting data and improve the speed of determining the accounting language. This application can be applied to various functional modules of smart cities such as smart government affairs and smart transportation, such as the accounting processing module of smart cities, and can promote the rapid development of smart cities.

[0134] Please refer to Figure 7 , Figure 7 is the structural diagram of the accounting processing device provided by an embodiment of the present application.

[0135] In some embodiments, the accounting processing device 20 may include multiple functional modules composed of computer program segments. The computer programs of each program segment in the accounting processing device 20 can be stored in the memory of the computer device and executed by at least one processor to execute (see details in Figure 1 the description) the functions of accounting processing.

[0136] In this embodiment, the accounting processing device 20 can be divided into multiple functional modules according to the functions it executes. The functional modules may include: an accounting data acquisition module 201, an accounting data matching module 202, an accounting subject determination module 203, an accounting behavior determination module 204, a词性信息确定模块205(should be a proper name for the module, there is an error here), and an accounting language acquisition module 206. The module referred to in this application means a series of computer program segments that can be executed by at least one processor and can complete fixed functions, and are stored in the memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0137] The accounting data acquisition module 201 can be used to acquire the first accounting data set and the second accounting data set.

[0138] It should be noted that there seem to be some inaccuracies in the original text such as incorrect or incomplete descriptions of "词性连接信息" and "词性拼接格式", which are reflected in the translation as best as possible while keeping the overall context.The accounting data matching module 202 is used to match the first accounting data in the first accounting dataset and the second accounting data in the second accounting dataset to obtain the target accounting dataset.

[0139] The accounting entity determination module 203 is used to determine the target language corresponding to each target accounting data in the target accounting dataset and the accounting entity corresponding to the target language.

[0140] The accounting behavior determination module 204 is used to determine the accounting behavior corresponding to the accounting entity.

[0141] The part-of-speech information determination module 205 is used to determine the part-of-speech information of the accounting entity and the accounting behavior.

[0142] The accounting language acquisition module 206 is used to combine the accounting subject and the accounting behavior according to the part-of-speech information in a preset part-of-speech concatenation format to obtain the target accounting language.

[0143] In at least one embodiment of this application, the accounting data acquisition module 201 is further configured to determine a first accounting feature corresponding to the first accounting data and a second accounting feature corresponding to the second accounting data; acquire an accounting dataset in a preset database and an accounting feature corresponding to each accounting data; use the accounting data whose accounting feature matches the first accounting feature as the first accounting data to obtain a first accounting dataset; and use the accounting data whose accounting feature matches the second accounting feature as the second accounting data to obtain a second accounting dataset.

[0144] In at least one embodiment of this application, the accounting data matching module 202 is further configured to determine a first order code corresponding to each first accounting data in the first accounting dataset; determine a second order code corresponding to each second accounting data in the second accounting dataset; detect whether the first order code and the second order code are the same; when the first order code and the second order code are the same, determine that the first accounting data and the second accounting data are matched; combine the determined matched first accounting data and the second accounting data into target accounting data to obtain a target accounting dataset.

[0145] In at least one embodiment of this application, the accounting entity determination module 203 is further configured to obtain account information of the accounting data query object corresponding to each target accounting data; traverse the pre-set mapping relationship between account information and language to obtain the target language corresponding to the account information; determine the preset accounting keywords in each target accounting data; and determine the accounting entity corresponding to the target accounting data based on the preset accounting keywords.

[0146] In at least one embodiment of this application, the accounting behavior determination module 204 is further configured to acquire a training sample set, which contains multiple loan training texts; call a preset word vector algorithm to identify the training sample set to obtain multiple sets of first vocabulary corresponding to each accounting entity, wherein the first vocabulary set contains multiple words with similar semantics; determine the target part of speech corresponding to the accounting behavior, and select several sets of second vocabulary corresponding to the target part of speech from the multiple sets of first vocabulary; calculate the number of words in each set of second vocabulary, and select the set of second vocabulary with a number of words exceeding a preset number of words as the target vocabulary, and use the target words in the target vocabulary as the accounting behavior.

[0147] In at least one embodiment of this application, the part-of-speech information determination module 205 is further configured to obtain preset accounting keywords corresponding to the accounting entity; traverse the first correspondence between the preset accounting keywords and parts of speech to obtain the part-of-speech information corresponding to the accounting entity; and obtain the second correspondence between the preset accounting behavior and parts of speech to obtain the part-of-speech information corresponding to the accounting behavior.

[0148] In at least one embodiment of this application, the accounting language acquisition module 206 is further configured to acquire the part-of-speech order and part-of-speech connection information in the preset part-of-speech splicing format; sort the accounting subject and the accounting behavior according to the part-of-speech order; and call the part-of-speech connection information to combine the accounting subject and the accounting behavior to obtain the target accounting language.

[0149] See Figure 8 The diagram shown is a structural schematic of a computer device provided in an embodiment of this application. In a preferred embodiment of this application, the computer device 3 includes a memory 31, at least one processor 32, at least one communication bus 33, and a transceiver 34.

[0150] Those skilled in the art should understand that Figure 8 The structure of the computer device shown does not constitute a limitation of the embodiments of this application. It can be a bus structure or a star structure. The computer device 3 may also include more or fewer other hardware or software than shown, or different component arrangements.

[0151] In some embodiments, the computer device 3 is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits, programmable gate arrays, digital processors, and embedded devices. The computer device 3 may also include client devices, which include, but are not limited to, any electronic product that can interact with a client via a keyboard, mouse, remote control, touchpad, or voice control device, such as personal computers, tablet computers, smartphones, and digital cameras.

[0152] It should be noted that the computer device 3 described is merely an example. Other existing or future electronic products that are suitable for this application should also be included within the scope of protection of this application and are incorporated herein by reference.

[0153] In some embodiments, the memory 31 stores a computer program that, when executed by the at least one processor 32, implements all or part of the steps in the accounting processing method described above. The memory 31 includes read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0154] Furthermore, the computer-readable storage medium may primarily include a program storage area and a data storage area, wherein the program storage area may store the operating system, at least one application required for a function, etc.; and the data storage area may store data created based on the use of blockchain nodes, etc.

[0155] The blockchain referred to in this application is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.

[0156] In some embodiments, the at least one processor 32 is the control unit of the computer device 3, connecting various components of the computer device 3 via various interfaces and lines. It executes programs or modules stored in the memory 31 and calls data stored in the memory 31 to perform various functions and process data of the computer device 3. For example, when the at least one processor 32 executes the computer program stored in the memory, it implements all or part of the steps of the accounting processing method described in the embodiments of this application; or it implements all or part of the functions of the accounting processing device. The at least one processor 32 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips.

[0157] In some embodiments, the at least one communication bus 33 is configured to enable communication between the memory 31 and the at least one processor 32, etc.

[0158] Although not shown, the computer device 3 may also include a power supply (such as a battery) to power various components. Preferably, the power supply can be logically connected to the at least one processor 32 via a power management device, thereby enabling functions such as charging, discharging, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The computer device 3 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0159] The integrated unit implemented as a software functional module described above can be stored in a computer-readable storage medium. This software functional module, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, a computer device, or a network device, etc.) or a processor to execute portions of the methods described in the various embodiments of this application.

[0160] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0161] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0162] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0163] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that it can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within this application. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other elements or, and the singular does not exclude the plural. Multiple elements or devices recited in the specification may also be implemented by a single element or device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any particular order.

[0164] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application.

Claims

1. An accounting processing method, characterized in that, The accounting methods include: Obtain the first and second accounting datasets; The process of matching first accounting data within the first accounting dataset and second accounting data within the second accounting dataset to obtain a target accounting dataset includes: determining a first order code corresponding to each first accounting data in the first accounting dataset; determining a second order code corresponding to each second accounting data in the second accounting dataset; detecting whether the first order code and the second order code are the same; determining that the first accounting data and the second accounting data are matched when the first order code and the second order code are the same; and combining the determined matched first accounting data and the second accounting data into target accounting data to obtain the target accounting dataset. Determine the target language corresponding to each target accounting data in the target accounting dataset and the accounting subject corresponding to the target language; Determine the accounting behavior corresponding to the accounting entity; Determine the part-of-speech information of the accounting entity and the accounting behavior; The accounting subject and the accounting behavior are combined according to the part-of-speech information in a preset part-of-speech concatenation format to obtain the target accounting language.

2. The accounting processing method according to claim 1, characterized in that, The acquisition of the first accounting dataset and the second accounting dataset includes: Determine the first accounting characteristic corresponding to the first accounting data and the second accounting characteristic corresponding to the second accounting data; Obtain the accounting dataset and the accounting characteristics corresponding to each accounting data in the preset database; The accounting data that matches the accounting features with the first accounting features are used as the first accounting data to obtain the first accounting dataset; The accounting data that matches the accounting features with the second accounting features are used as the second accounting data to obtain the second accounting dataset.

3. The accounting processing method according to claim 1, characterized in that, The step of determining the target language corresponding to each target accounting data in the target accounting dataset and the accounting subject corresponding to the target language includes: Obtain the account information of the accounting data query object corresponding to each target accounting data; Iterate through the pre-set mapping relationship between account information and language to obtain the target language corresponding to the account information; Determine the preset accounting keywords in each target accounting data; The accounting entity corresponding to the target accounting data is determined based on the preset accounting keywords.

4. The accounting processing method according to claim 1, characterized in that, The step of determining the accounting behavior corresponding to the accounting entity includes: Obtain a training sample set, which contains multiple loan training texts; The training sample set is identified by calling a preset word vector algorithm to obtain multiple sets of first words corresponding to each accounting entity. The first word set contains multiple words with similar semantics. Determine the target part of speech corresponding to the accounting behavior, and select several second vocabulary sets corresponding to the target part of speech from the multiple sets of first vocabulary sets; Calculate the number of words in each second vocabulary set, and select the second vocabulary set whose number of words exceeds a preset vocabulary number threshold as the target vocabulary set, and use the target words in the target vocabulary set as accounting actions.

5. The accounting processing method according to claim 3, characterized in that, The part-of-speech information for determining the accounting entity and the accounting behavior includes: Obtain the preset accounting keywords corresponding to the accounting entity; By traversing the pre-set first correspondence between accounting keywords and parts of speech, the part of speech information corresponding to the accounting entity is obtained; Obtain the pre-set second correspondence between accounting behavior and part of speech to obtain the part of speech information corresponding to the accounting behavior.

6. The accounting processing method according to claim 1, characterized in that, The step of combining the accounting subject and the accounting behavior according to the part-of-speech information in a preset part-of-speech concatenation format to obtain the target accounting language includes: Obtain the part-of-speech order and part-of-speech connection information in the preset part-of-speech splicing format; The accounting entities and accounting behaviors are ordered according to the part-of-speech order; The part-of-speech linking information is used to combine the accounting subject and the accounting behavior to obtain the target accounting language.

7. An accounting processing apparatus for performing the accounting processing method as described in any one of claims 1 to 6, characterized in that, The accounting processing device includes: The accounting data acquisition module is used to acquire the first accounting dataset and the second accounting dataset; The accounting data matching module is used to match the first accounting data in the first accounting dataset and the second accounting data in the second accounting dataset to obtain the target accounting dataset; The accounting entity determination module is used to determine the target language corresponding to each target accounting data in the target accounting dataset and the accounting entity corresponding to the target language; The accounting behavior determination module is used to determine the accounting behavior corresponding to the accounting entity; The part-of-speech tagging module is used to determine the part-of-speech tagging information of the accounting entity and the accounting behavior; The accounting language acquisition module is used to combine the accounting subject and the accounting behavior according to the part-of-speech information in a preset part-of-speech concatenation format to obtain the target accounting language.

8. A computer device, characterized in that, The computer device includes a processor that executes a computer program stored in a memory to implement the accounting method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is executed by a processor, it implements the accounting processing method as described in any one of claims 1 to 6.

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