CN115292370A - Business document data processing method, device and medium - Google Patents
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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Abstract
The application relates to the field of databases, and discloses a method, a device and a medium for processing business document data, wherein the method comprises the following steps: a first retrieval instruction input by a user is acquired. And analyzing the first retrieval instruction to obtain retrieval keywords, obtaining attribute information of each retrieval keyword, and generating a second retrieval instruction according to each retrieval keyword and the attribute information, wherein the attribute information at least comprises a retrieval mode. And acquiring the target document corresponding to the second retrieval instruction from an Elasticissearch database, wherein the Elasticissearch cache level database is used for storing the document. According to the method and the device, the first retrieval instruction of the user is analyzed to obtain the retrieval key word, the second retrieval instruction is generated according to the retrieval key word and the attribute information of the retrieval key word, and the database is retrieved according to the second retrieval instruction, so that the user does not need to operate the database according to the inherent grammar, and the retrieval efficiency and the use experience of the user are improved.
Description
Technical Field
The present application relates to the field of databases, and in particular, to a method, an apparatus, and a medium for processing business document data.
Background
During the operation of internet service, a large amount of service documents related to service transaction are generated in the system. With the continuous increase of the data volume of the business documents, the difficulty of storing and searching the business documents by the user is increased, and the working efficiency and the use experience of the user are reduced. In order to solve the problem, different types of business documents are usually stored in different databases or data tables at present, so that the data volume in a single database is reduced, or the business documents with low use frequency are placed in a cache, thereby reducing the storage difficulty of the business documents. However, the operation of the database is too complex for the user, so that the user needs to spend a lot of time on learning the use mode of the database, and the use experience and the query efficiency of the user are reduced.
Therefore, it is an urgent need to solve the problem of providing a simple and efficient method for processing business form data for a user.
Disclosure of Invention
The method aims to provide a simple and efficient business form data processing method for a user so as to improve the efficiency and the use experience of the user for inquiring business documents from a system.
In order to solve the technical problem, the present application provides a method for processing business document data, including:
acquiring a first retrieval instruction input by a user;
analyzing the first retrieval instruction to obtain a retrieval keyword;
acquiring attribute information of each retrieval keyword, and generating a second retrieval instruction according to each retrieval keyword and the attribute information; wherein the attribute information at least comprises a retrieval mode;
and acquiring the target document corresponding to the second retrieval instruction from an Elasticissearch cache-level database, wherein the Elasticissearch cache-level database is used for storing documents.
Preferably, the method further comprises the following steps:
acquiring business document data sent by a business process;
and updating the Elasticissearch cache level database according to the business document data.
Preferably, the obtaining of the attribute information of each search keyword includes:
acquiring an index field table of the Elasticissearch cache level database;
and determining the attribute information of each retrieval keyword according to the index field table.
Preferably, after the step of obtaining the target document corresponding to the second retrieval instruction from the Elasticsearch cache-level database, the method further includes:
exporting the target document out of the Elasticissearch cache level database.
Preferably, before the step of updating the Elasticsearch cache-level database according to the business document data, the method further includes:
and classifying the business document data according to the flow type and the business type of the business document data so as to conveniently count the business document data of each type.
Preferably, the method further comprises the following steps:
creating and updating a database index and field structure at startup of the Elasticsearch cache level database.
Preferably, the acquiring the service document data sent by the service process includes:
carrying out integrity check on the obtained business document data to determine whether the business document data is damaged;
and if the business document data is damaged, sending early warning information to a manager so that the manager can maintain the business document data in time.
In order to solve the above technical problem, the present application further provides a business document data processing apparatus, including:
the instruction acquisition module is used for acquiring a first retrieval instruction input by a user;
the instruction analysis module is used for analyzing the first retrieval instruction to obtain a retrieval keyword;
the instruction generation module is used for acquiring the attribute information of each retrieval keyword and generating a second retrieval instruction according to each retrieval keyword and the attribute information;
and the target document acquisition module is used for acquiring the target document corresponding to the second retrieval instruction from an Elasticissearch cache-level database, wherein the Elasticissearch cache-level database is used for storing documents.
In order to solve the technical problem, the present application further provides another business document data processing apparatus, including a memory for storing a computer program;
and the processor is used for realizing the steps of the business bill data processing method when the computer program is executed.
In order to solve the technical problem, the present application further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the method for processing business document data are implemented.
The application provides a business document data processing method, which comprises the following steps: the method comprises the steps of obtaining a first retrieval instruction input by a user so as to provide retrieval service for the user according to the instruction of the user. And analyzing the first retrieval instruction to obtain retrieval keywords, obtaining attribute information of each retrieval keyword, and generating a second retrieval instruction according to each retrieval keyword and the attribute information, wherein the attribute information at least comprises a retrieval mode. And acquiring the target document corresponding to the second retrieval instruction from the Elasticissearch cache level database, wherein the Elasticissearch cache level database is used for storing the document. Therefore, according to the technical scheme provided by the application, the first retrieval instruction of the user is analyzed to obtain the retrieval key word, the second retrieval instruction which can be applied to the Elasticissearch cache level database is generated according to the retrieval key word and the attribute information of the retrieval key word, and the database is retrieved according to the second retrieval instruction to provide the target document data for the user. The user does not need to operate the database according to the inherent grammar, so that the retrieval efficiency and the use experience of the user are improved.
Drawings
In order to more clearly illustrate the embodiments of the present application, the drawings needed for the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.
Fig. 1 is a flowchart of a method for processing business document data according to an embodiment of the present application;
FIG. 2 is a schematic diagram of Kibana provided in an embodiment of the present application;
FIG. 3 is a diagram illustrating an index field table and a dictionary table according to an embodiment of the present disclosure;
FIG. 4 is a schematic diagram of a document export configuration table according to an embodiment of the present application;
FIG. 5 is a table field and index structure mapping relationship diagram provided by an embodiment of the present application;
fig. 6 is a structural diagram of a business document data processing apparatus according to an embodiment of the present application;
fig. 7 is a structural diagram of another business document data processing apparatus according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without any creative effort belong to the protection scope of the present application.
The core of the application is to provide a method, a device and a medium for processing the business form data so as to improve the efficiency and the use experience of a user for inquiring the business document from a system.
In the operation process of the internet business system, each business process can generate corresponding business document data, and the business document data needs to be put into a database for convenience of inquiry and use due to large quantity of the business document data. However, the database requires the user to operate according to a specific grammar, which brings inconvenience to the user to inquire the document. In order to solve the problem, the application provides a business document data processing method, which is used for acquiring a first retrieval instruction input by a user so as to provide retrieval service for the user according to the instruction of the user. And analyzing the first retrieval instruction to obtain retrieval keywords, obtaining attribute information of each retrieval keyword, and generating a second retrieval instruction according to each retrieval keyword and the attribute information, wherein the attribute information at least comprises a retrieval mode. And acquiring the target document corresponding to the second retrieval instruction from the Elasticissearch cache level database, wherein the Elasticissearch cache level database is used for storing the document. Therefore, according to the technical scheme provided by the application, the first retrieval instruction of the user is analyzed to obtain the retrieval key word, the second retrieval instruction which can be applied to the Elasticissearch cache level database is generated according to the retrieval key word and the attribute information of the retrieval key word, and the database is retrieved according to the second retrieval instruction to provide the target document data for the user. The user does not need to operate the database according to the inherent grammar, so that the retrieval efficiency and the use experience of the user are improved.
In order that those skilled in the art will better understand the disclosure, the following detailed description will be given with reference to the accompanying drawings.
Fig. 1 is a flowchart of a method for processing a business document according to an embodiment of the present application, and as shown in fig. 1, the method is applied to a cache database built based on an Elasticsearch, and includes:
s10: and acquiring a first retrieval instruction input by a user.
S11: and analyzing the first retrieval instruction to obtain a retrieval keyword.
In specific implementation, the system acquires a first retrieval instruction input by a user, and analyzes the first retrieval instruction so as to provide business document data for the user according to the retrieval instruction input by the user. It can be understood that, in order to improve the user experience and avoid that the user cannot acquire the business document data due to inputting an abnormal query syntax, in this embodiment, the first retrieval instruction input by the user is analyzed to acquire the retrieval key word, for example, if the user inputs "query the business document data with item amount on date a and item amount B being C", the key word includes "date", "item" and "amount", and the system generates the second retrieval instruction according to the key word input by the user to retrieve the business document data. Wherein the second retrieval instruction is a machine language recognizable by the database system.
It can be understood that, in addition to the above-mentioned generation of the second retrieval instruction capable of being identified by data according to the first retrieval instruction input by the user, the retrieval requirement of the user may also be obtained in the form of a table, specifically, all the retrieval items are configured in a table page, and the user selects corresponding attribute information for the input keyword in a manual selection manner to generate the second retrieval instruction, which is not limited herein.
S12: acquiring attribute information of each retrieval keyword, and generating a second retrieval instruction according to each retrieval keyword and the attribute information; wherein the attribute information at least comprises a retrieval mode.
It is understood that each keyword in the business document database has corresponding attribute information, such as a field name and a field type of the keyword, and a retrieval mode corresponding to the keyword (e.g., fuzzy search, precise search, phrase search, etc.). In specific implementation, attribute information corresponding to each search keyword is obtained, and a second search instruction is generated according to each search keyword and the attribute information thereof, wherein the attribute information at least comprises a search mode corresponding to the search keyword. In addition, the attribute information also includes whether the search term can be used as a keyword for searching, a business document output mode (list output or file export) corresponding to the current search instruction, and the like.
S13: and acquiring the target document corresponding to the second retrieval instruction from the Elasticissearch cache level database, wherein the Elasticissearch cache level database is used for storing the document.
It will be appreciated that a database environment may also need to be built before document retrieval services are provided to users. Firstly, installing an Elasticisearch database on a server, and configuring parameters of the database, such as installation and selection of a word segmentation device; the word segmentation device is the key that the Elasticsearch can perform efficient query, is used for performing word segmentation storage on common data in the system so as to ensure that inverted index can be performed during query, and is used for performing quick and accurate query. In addition, in order to facilitate development and debugging and problem elimination of the elastic search cache level database, an elastic search client visualization management plug-in, such as Kibana software, can be built in the server. Fig. 2 is a schematic diagram of Kibana provided in the embodiment of the present application, and as shown in fig. 2, kibana provides a dashboard, a package development component, a log module, and the like for a user, and the user can view the working state of the Elasticsearch cache-level database on a visual management interface provided by Kibana, and perform development and debugging on the database.
The embodiment provides a business document data processing method, which comprises the following steps: the method comprises the steps of obtaining a first retrieval instruction input by a user so as to provide retrieval service for the user according to the instruction of the user. And analyzing the first retrieval instruction to obtain retrieval keywords, obtaining attribute information of each retrieval keyword, and generating a second retrieval instruction according to each retrieval keyword and the attribute information, wherein the attribute information at least comprises a retrieval mode. And acquiring the target document corresponding to the second retrieval instruction from the Elasticissearch cache level database, wherein the Elasticissearch cache level database is used for storing the document. Therefore, according to the technical scheme provided by the application, the first retrieval instruction of the user is analyzed to obtain the retrieval key word, the second retrieval instruction which can be applied to the Elasticissearch cache level database is generated according to the retrieval key word and the attribute information of the retrieval key word, and the database is retrieved according to the second retrieval instruction to provide the target document data for the user. The user does not need to operate the database according to the inherent grammar, so that the retrieval efficiency and the use experience of the user are improved.
In specific implementation, as other processes in the business system can generate new business document data continuously in the working process, in order to ensure that a user can search all business documents in the Elasticsearch cache-level database, the business document data in the database needs to be updated.
On the basis of the foregoing embodiment, the method for processing business document data provided by this embodiment further includes: acquiring business document data sent by a business process; and updating the Elasticissearch cache level database according to the business document data.
Specifically, each business process module sends business document data to the cache database at the initiation stage, the submission stage and the completion stage of each business link, so as to store newly generated business document data into the elastic search cache-level database, so that a user can conveniently check the newly generated business document data. It can be understood that data blocking may occur due to the large amount of data in the business documents. In order to solve the problem, when the service flow has no strict requirement on the sequence of the data, the service document data may be sent to the elastic search cache-level database in an asynchronous sending manner, so as to decouple the service document, and avoid data blocking of the service document to the service flow. When the business process requires the sequence of the data, an interface of the business process module is called to synchronously transmit the business document data to the database so as to realize the updating of the database.
As a preferred embodiment, the obtaining of the attribute information of each search keyword includes: acquiring an index field table of an Elasticissearch cache level database; and determining the attribute information of each retrieval keyword according to the index field table.
In this embodiment, the attribute information corresponding to each keyword is stored by a table structure. Specifically, the business document table mainly includes three tables: indexing a field table, a dictionary table and a document export configuration table; fig. 3 is a schematic diagram of an index field table and a dictionary table provided in an embodiment of the present application, and fig. 4 is a schematic diagram of a document derivation configuration table provided in an embodiment of the present application, as shown in fig. 3 or fig. 4, where the index field table is used to store an index field (a library approximate to a database) and to set a field type (an elastic search field type, such as a text type, may be used for performing word segmentation), a query type (such as an exact query, a fuzzy query, a range query, and the like), a tokenizer type, and the like. The document export configuration table is used for configuring header fields and field attributes of the document export table, such as field arrangement sequence and the like, provides key word searching and node searching functions, and can search document data according to supplier names, supplier types and node types input by users. The dictionary table comprises a common word segmenter (a self-defining word segmenter), an Elasticissearch field type and a query type (a self-defining query type), and the dictionary table is associated with the first two tables for query.
It can be understood that, in order to facilitate the user to use the business document data, the business document processing method provided by this embodiment further supports the user to derive the business document from the database and to search for the document classification.
On the basis of the above embodiment, before the step of updating the Elasticsearch cache-level database according to the business document data, the method further includes: and classifying the business document data according to the flow type and the business type of the business document data so as to conveniently count the business document data of various types.
After the step of obtaining the target document corresponding to the second retrieval instruction from the Elasticsearch cache-level database, the method further includes: and exporting the target document from an Elasticissearch cache level database.
In a specific implementation, the target document may be exported from the database in a list form, or may be exported in a text document form, which is not limited herein.
It can be understood that, because the creating and updating of the Elasticsearch index and the field structure are complex, the Elasticsearch cannot create the index and the table structure through the script like the traditional databases such as mysql, and therefore, in the embodiment, the creating and the updating are selected when the Elasticsearch cache level database is started, and fig. 5 is a table field and index structure mapping relationship diagram provided in the embodiment of the present application.
It should be noted that, in order to ensure the accuracy of the data in the database, integrity check of the updated business document data is also required. In specific implementation, the obtaining of the service document data sent by the service process includes: integrity check is carried out on the obtained business document data to determine whether the business document data is damaged; and if the business document data is damaged, sending early warning information to a manager so as to facilitate the timely maintenance of the manager. Furthermore, after detecting that the business document data is damaged, the manager records and stores the process number of the business document data so as to maintain the database subsequently.
In the above embodiments, the method for processing the business document data is described in detail, and the application also provides embodiments corresponding to the business document data processing apparatus. It should be noted that the present application describes the embodiments of the apparatus portion from two perspectives, one from the perspective of the function module and the other from the perspective of the hardware.
Fig. 6 is a structural diagram of a business document data processing apparatus according to an embodiment of the present application, and as shown in fig. 6, the apparatus includes:
the instruction acquisition module 10 is configured to acquire a first retrieval instruction input by a user;
the instruction analysis module 11 is used for analyzing the first retrieval instruction to obtain a retrieval keyword;
the instruction generating module 12 is configured to obtain attribute information of each search keyword, and generate a second search instruction according to each search keyword and the attribute information;
and the target document obtaining module 13 is configured to obtain a target document corresponding to the second retrieval instruction from an Elasticsearch cache-level database, where the Elasticsearch cache-level database is a database used for storing documents.
Since the embodiment of the apparatus portion and the embodiment of the method portion correspond to each other, please refer to the description of the embodiment of the method portion for the embodiment of the apparatus portion, and details are not repeated here.
The embodiment provides a business document data processing device, which comprises: the method comprises the steps of obtaining a first retrieval instruction input by a user so as to provide retrieval service for the user according to the instruction of the user. And analyzing the first retrieval instruction to obtain retrieval keywords, obtaining attribute information of each retrieval keyword, and generating a second retrieval instruction according to each retrieval keyword and the attribute information, wherein the attribute information at least comprises a retrieval mode. And acquiring the target document corresponding to the second retrieval instruction from the Elasticissearch cache level database, wherein the Elasticissearch cache level database is used for storing the document. Therefore, in the technical scheme provided by the application, the first retrieval instruction of the user is analyzed to obtain the retrieval key word, the second retrieval instruction which can be applied to the Elasticissearch cache level database is generated according to the retrieval key word and the attribute information of the retrieval key word, and the database is retrieved according to the second retrieval instruction to provide the target document data for the user. The user does not need to operate the database according to the inherent grammar, so that the retrieval efficiency and the use experience of the user are improved.
Fig. 7 is a structural diagram of another business document data processing apparatus according to an embodiment of the present application, and as shown in fig. 7, the business document data processing apparatus includes: a memory 20 for storing a computer program;
and the processor 21 is used for implementing the steps of the business document data processing method in the embodiment when executing the computer program.
The terminal device provided by the embodiment may include, but is not limited to, a smart phone, a tablet computer, a notebook computer, or a desktop computer.
The processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the like. The Processor 21 may be implemented in at least one hardware form of a Digital Signal Processor (DSP), a Field-Programmable Gate Array (FPGA), and a Programmable Logic Array (PLA). The processor 21 may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in a wake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 21 may be integrated with a Graphics Processing Unit (GPU) which is responsible for rendering and drawing the content required to be displayed by the display screen. In some embodiments, the processor 21 may further include an Artificial Intelligence (AI) processor for processing computational operations related to machine learning.
The memory 20 may include one or more computer-readable storage media, which may be non-transitory. Memory 20 may also include high speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In this embodiment, the memory 20 is at least used for storing the following computer program 201, wherein after being loaded and executed by the processor 21, the computer program can implement the relevant steps of the business document data processing disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 20 may also include an operating system 202, data 203, and the like, and the storage manner may be a transient storage manner or a permanent storage manner. Operating system 202 may include, among other things, windows, unix, linux, etc. Data 203 may include, but is not limited to, search keywords, attribute information, and the like.
In some embodiments, the business document data processing device may further include a display 22, an input/output interface 23, a communication interface 24, a power supply 25, and a communication bus 26.
Those skilled in the art will appreciate that the architecture shown in FIG. 7 does not constitute a limitation on the processing of business document data and may include more or fewer components than those shown.
The business document data processing device provided by the embodiment of the application comprises a memory and a processor, and when the processor executes a program stored in the memory, the following method can be realized:
acquiring a first retrieval instruction input by a user;
analyzing the first retrieval instruction to obtain a retrieval keyword;
acquiring attribute information of each retrieval keyword, and generating a second retrieval instruction according to each retrieval keyword and the attribute information; wherein the attribute information at least comprises a retrieval mode;
and acquiring the target document corresponding to the second retrieval instruction from an Elasticissearch cache-level database, wherein the Elasticissearch cache-level database is used for storing documents.
Finally, the application also provides a corresponding embodiment of the computer readable storage medium. The computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps as set forth in the above-mentioned method embodiments.
It is to be understood that if the method in the above embodiments is implemented in the form of software functional units and sold or used as a stand-alone product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, which are essential or part of the prior art, or all or part of the technical solutions may be embodied in the form of a software product, which is stored in a storage medium and executes all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
The method, the device and the medium for processing the business document data provided by the application are described in detail above. The embodiments are described in a progressive mode in the specification, the emphasis of each embodiment is on the difference from the other embodiments, and the same and similar parts among the embodiments can be referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
It should also be noted that, in this specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A method for processing business document data is characterized by comprising the following steps:
acquiring a first retrieval instruction input by a user;
analyzing the first retrieval instruction to obtain a retrieval keyword;
acquiring attribute information of each retrieval keyword, and generating a second retrieval instruction according to each retrieval keyword and the attribute information; wherein the attribute information at least comprises a retrieval mode;
and acquiring the target document corresponding to the second retrieval instruction from an Elasticissearch cache-level database, wherein the Elasticissearch cache-level database is used for storing documents.
2. The business document data processing method of claim 1, further comprising:
acquiring business document data sent by a business process;
and updating the Elasticissearch cache level database according to the business document data.
3. The business document data processing method of claim 1, wherein the obtaining of the attribute information of each search keyword comprises:
acquiring an index field table of the Elasticissearch cache level database;
and determining the attribute information of each retrieval keyword according to the index field table.
4. The method for processing business document data according to claim 1, wherein after the step of obtaining the target document corresponding to the second retrieval instruction from the Elasticsearch cache-level database, the method further comprises:
exporting the target document out of the Elasticissearch cache level database.
5. The method for processing the service document data according to claim 2, wherein before the step of updating the Elasticsearch cache-level database according to the service document data, the method further comprises:
and classifying the business receipt data according to the flow type and the business type of the business receipt data so as to conveniently count the business receipt data of each type.
6. The business document data processing method of claim 1, further comprising:
creating and updating a database index and field structure at startup of the Elasticissearch cache level database.
7. The method for processing service document data according to claim 2, wherein the acquiring the service document data sent by the service process comprises:
integrity verification is carried out on the obtained business document data to determine whether the business document data are damaged or not;
and if the business document data is damaged, sending early warning information to a manager so that the manager can maintain the business document data in time.
8. A business document data processing apparatus, comprising:
the instruction acquisition module is used for acquiring a first retrieval instruction input by a user;
the instruction analysis module is used for analyzing the first retrieval instruction to obtain a retrieval keyword;
the instruction generation module is used for acquiring the attribute information of each retrieval keyword and generating a second retrieval instruction according to each retrieval keyword and the attribute information;
and the target document acquisition module is used for acquiring a target document corresponding to the second retrieval instruction from an Elasticissearch cache-level database, wherein the Elasticissearch cache-level database is used for storing documents.
9. A business document data processing apparatus comprising a memory for storing a computer program;
a processor for implementing the steps of the method of processing business document data according to any one of claims 1 to 7 when executing said computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the method for processing business document data according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210974061.2A CN115292370A (en) | 2022-08-15 | 2022-08-15 | Business document data processing method, device and medium |
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