Invoice processing method and device, computer equipment and storage medium

Abstract

The invention relates to an invoice processing method, which comprises the steps of obtaining generated initial invoice data, and storing the initial invoice data into a storage table in a business processing layer based on a sub-table storage strategy; based on the data synchronization channel, reading the initial invoice data in the storage table from the storage table, and performing convergence operation and model transformation processing on the initial invoice data based on the query analysis layer to generate corresponding invoice processing data; storing the invoice processing data into a query library in a query analysis layer according to a preset table structure; performing conversion processing on the invoice processing data in the query library based on the conversion strategy to obtain corresponding target invoice data; determining a target distributed column storage database based on the demand information; and storing the target invoice data into the target distributed column storage database. The invention further provides an invoice processing device, computer equipment and a storage medium. According to the invention, the storage efficiency and the storage intelligence of the invoice data are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and particularly relates to an invoice processing method and device, computer equipment and a storage medium. BACKGROUND

[0002] With the rapid development of digital economy, as an indispensable key voucher of economic activities, the data volume of invoices is growing exponentially. Under the traditional invoice management mode, the storage and management of invoice data mainly rely on traditional databases. However, when dealing with massive invoice data, the traditional database exposes many defects that are difficult to overcome, of which the most prominent one is the problem of low data storage efficiency. Specifically, when dealing with large-scale invoice data, the storage capacity expansion of the architecture design and storage mechanism of the traditional database is limited, and the data writing and reading speed is slow, which cannot meet the demand for efficient storage of invoice data in a big data environment, thereby seriously restricting the subsequent analysis and utilization of invoice data, and it is difficult to adapt to the rapid, accurate and efficient requirements of invoice management in the digital economic era.

[0003] Therefore, there is an urgent need for a new technical solution to solve the problem of low data storage efficiency of the traditional invoice management mode in the big data environment. SUMMARY

[0004] The purpose of the embodiments of the present application is to provide an invoice processing method, device, computer equipment and storage medium to solve the technical problem of low storage efficiency of the existing invoice management mode.

[0005] In a first aspect, an invoice processing method is provided, comprising:

[0006] obtaining generated initial invoice data, and storing the initial invoice data into a storage table in a preset business processing layer based on a preset table storage strategy;

[0007] reading the initial invoice data in the storage table from the storage table based on a preset data synchronization channel, and performing convergence operation and model transformation processing on the initial invoice data based on a preset query analysis layer to generate corresponding invoice processing data;

[0008] storing the invoice processing data into a query library in the query analysis layer according to a preset table structure;

[0009] performing conversion processing on the invoice processing data in the query library based on a preset conversion strategy to obtain corresponding target invoice data;

[0010] determining a target distributed columnar storage database based on preset demand information;

[0011] store the target invoice data into the target distributed columnar storage database.

[0012] In a second aspect, an invoice processing apparatus is provided, comprising:

[0013] a first processing module configured to acquire generated initial invoice data and store the initial invoice data into a storage table in a preset business processing layer based on a preset table storage strategy;

[0014] a second processing module configured to read the initial invoice data in the storage table from the storage table based on a preset data synchronization channel and perform convergent operation and model transformation processing on the initial invoice data based on a preset query analysis layer to generate corresponding invoice processing data;

[0015] a first storage module configured to store the invoice processing data into a query library in the query analysis layer according to a preset table structure;

[0016] a conversion module configured to perform conversion processing on the invoice processing data in the query library based on a preset conversion strategy to obtain corresponding target invoice data;

[0017] a determination module configured to determine a target distributed columnar storage database based on preset demand information;

[0018] a second storage module configured to store the target invoice data into the target distributed columnar storage database.

[0019] In a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above invoice processing method when executing the computer program.

[0020] In a fourth aspect, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program, and the computer program implements the steps of the above invoice processing method when executed by a processor.

[0021] In the scheme implemented by the invoice processing method, the invoice processing device, the computer device, and the storage medium, first, the generated initial invoice data is acquired, and the initial invoice data is stored into a storage table in a preset business processing layer based on a preset table storage strategy; then, the initial invoice data in the storage table is read from the storage table based on a preset data synchronization channel, and the initial invoice data is subjected to aggregation operation and model transformation processing based on a preset query analysis layer, to generate corresponding invoice processing data; thereafter, the invoice processing data is stored into a query library in the query analysis layer according to a preset table structure; subsequently, the invoice processing data in the query library is subjected to conversion processing based on a preset conversion strategy, to obtain corresponding target invoice data; further, a target distributed columnar storage database is determined based on preset requirement information; finally, the target invoice data is stored into the target distributed columnar storage database. Based on the above automatic processing procedure, the initial invoice data is acquired, and the initial invoice data is stored into the storage table in the business processing layer based on the table storage strategy, then the initial invoice data in the storage table is read from the storage table based on the use of the data synchronization channel, and the initial invoice data is subjected to aggregation operation and model transformation processing based on the use of the query analysis layer, to generate the corresponding invoice processing data, then the invoice processing data is stored into the query library in the query analysis layer according to the table structure, thereafter the invoice processing data in the query library is subjected to conversion processing based on the use of the conversion strategy, to obtain the target invoice data, and further the target distributed columnar storage database is determined based on the use of the requirement information, finally the target invoice data is stored into the target distributed columnar storage database. Since the distributed architecture supports horizontal expansion, the storage and high-concurrency query requirements of invoice data of more than one million can be easily met, and the storage efficiency and storage intelligence of the invoice data are effectively improved. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the schemes in the present application, the drawings needed in the description of the embodiments of the present application will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0023] Figure 1 is an exemplary system architecture diagram to which the present application can be applied;

[0024] Figure 2 is a flowchart of one embodiment of the invoice processing method according to the present application;

[0025] Figure 3 is a structural schematic diagram of one embodiment of the invoice processing device according to the present application;

[0026] Figure 4 is a structural schematic diagram of one embodiment of the computer device according to the present application. DETAILED DESCRIPTION

[0027] 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 in the description herein is for describing particular embodiments only and is not intended to be limiting of the application; the use herein of terms such as "comprise" and "have" and any variations such as "comprises" and "has" is intended to cover the presence of successively stated integers or features but not preclude the presence of other integers or features; the use herein of terms such as "first", "second" and the like is intended to distinguish between different objects, not to describe a particular sequential order.

[0028] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase "in an embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another.

[0029] In order to make the technical personnel in the art better understand the scheme of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings.

[0030] As shown in Figure 1 , the system architecture 100 can include a terminal device 101, a network 102 and a server 103, the terminal device 101 can be a notebook computer 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is a medium for providing a communication link between the terminal device 101 and the server 103. The network 102 can include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0031] A user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0032] The terminal device 101 can be various electronic devices with a display screen and supporting web browsing, in addition to the notebook computer 1011, the tablet computer 1012 or the mobile phone 1013, the terminal device 101 can also be an electronic book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV), a laptop computer and a desktop computer, etc.

[0033] The server 103 can be a server providing various services, for example, a background server providing support for a page displayed on the terminal device 101.

[0034] It should be noted that the invoice processing method provided by the embodiments of the present application is generally executed by a server / terminal device, and accordingly, the invoice processing apparatus is generally arranged in a server / terminal device.

[0035] It should be understood that Figure 1 The number of terminal devices, networks and servers in

[0036] With reference to Figure 2 , a flow chart of one embodiment of the invoice processing method according to the present application is shown. The order of the steps in the flow chart can be changed according to different needs, and some steps can be omitted. The invoice processing method provided by the embodiments of the present application can be applied to any scenario requiring invoice processing, and then the invoice processing method can be applied to products in these scenarios. The invoice processing method includes the following steps:

[0037] In step S201, initial invoice data generated is acquired, and the initial invoice data is stored into a storage table in a preset business processing layer based on a preset storage strategy.

[0038] In the present embodiment, the electronic device (for example Figure 1The generated initial invoice data can be obtained by wired connection or wireless connection (e.g., by the server / terminal device shown). It should be noted that the wireless connection can include, but is not limited to, 3G / 4G / 5G connection, Wi-Fi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other now known or future developed wireless connection. The execution subject of the present application is an invoice processing system, which can be referred to as a system. Through the architecture design of the invoice processing system, the business processing layer and the query analysis layer are clearly divided. The business processing layer is mainly responsible for the invoice issuing process, including the core business logic such as the entry, review, and generation of invoice number of invoice information; the query analysis layer focuses on the subsequent processing of data, such as data synchronization, aggregation operation, model transformation, and query service provision. In addition, clear data interaction interfaces are defined between the business processing layer and the query analysis layer. After the business processing layer completes the invoice issuing operation, the invoice data is transmitted to the query analysis layer in a predetermined format and specification through these interfaces. For example, the transmitted data includes basic information, detailed information, and status information of the invoice, and the transmission mode and frequency of the data are specified. In addition, independent running environments are configured for the business processing layer and the query analysis layer, including server resources, network bandwidth, etc. The business processing layer is not affected by the large number of query operations of the query analysis layer when processing the invoice issuing business, ensuring the efficiency and stability of the invoice operation; at the same time, the query analysis layer can focus on data processing and query service in an independent environment, improving the query performance and user experience.

[0039] The initial storage is the starting point of the entire invoice data processing flow. By storing the massive invoice data in different tables according to the invoice month, the data volume of a single table is effectively reduced. This not only helps to improve the efficiency of subsequent data queries, as the relevant month table can be more accurately located for retrieval during the query, reducing unnecessary data scanning; it also greatly facilitates the archiving and management of historical data, and enterprises can easily backup, migrate, or clean up invoice data according to the month. At the same time, the dynamic expansion mechanism ensures that the system can flexibly cope with the growth of data volume, avoiding the impact of insufficient storage space on the normal operation of the invoice issuing business, providing a foundation for the long-term stable operation of the system.

[0040] In addition, the initial invoice data refers to the invoice data generated when the invoice issuing business occurs. The specific implementation process of storing the initial invoice data into the storage table in the preset business processing layer based on the preset table storage strategy will be described in further detail in the subsequent specific embodiments, and will not be described in detail here.

[0041] Step S202, reading the initial invoice data in the storage table based on a preset data synchronization channel, and performing aggregation operation and model transformation processing on the initial invoice data based on a preset query analysis layer to generate corresponding invoice processing data.

[0042] In the embodiment, a real-time data synchronization channel is established in advance between the business processing layer and the query library. The message queue technology can be used, and when the business processing layer completes the invoice issuing operation and generates invoice data, the data is immediately pushed into the message queue. The query analysis layer listens to the message queue in real time, and once it receives a new invoice data message, it immediately obtains the data from the message queue and performs subsequent processing. In addition, the specific implementation process of the above-mentioned aggregation operation and model transformation processing on the initial invoice data based on the preset query analysis layer to generate corresponding invoice processing data will be further described in detail in the subsequent specific embodiments, and will not be described in detail here.

[0043] Step S203, storing the invoice processing data into the query library in the query analysis layer according to a preset table structure.

[0044] In the embodiment, after the initial invoice data is converted into structured data (invoice processing data) suitable for query through aggregation operation and model transformation processing, a table structure corresponding to the structured data is established in the query library, such as a sales invoice detail query table, a statistical table according to invoice type, a statistical table according to tax rate dimension, etc. Then, the processed invoice processing data is stored into the query library according to the table structure, so as to facilitate subsequent quick query and analysis.

[0045] Step S204, performing conversion processing on the invoice processing data in the query library based on a preset conversion strategy to obtain corresponding target invoice data.

[0046] In the embodiment, the specific implementation process of the above-mentioned conversion processing on the invoice processing data in the query library based on a preset conversion strategy to obtain corresponding target invoice data will be further described in detail in the subsequent specific embodiments, and will not be described in detail here.

[0047] Step S205, determining a target distributed columnar storage database based on preset demand information.

[0048] In the embodiment, a suitable distributed columnar storage database such as ClickHouse, Apache Druid, etc. is selected as the target distributed columnar storage database according to actual system requirements and performance requirements. By deploying database nodes on multiple servers to build a distributed architecture and reasonably configuring network connections and data distribution strategies between nodes, it can be ensured that invoice data can be evenly stored on each node, thereby improving the overall storage capacity and query performance of the system.

[0049] In step S206, the target invoice data is stored in the target distributed columnar storage database.

[0050] In the embodiment, after the process of storing the target invoice data in the target distributed columnar storage database is completed, the distributed columnar storage database can be optimized for query characteristics of the invoice data. Common query conditions and query patterns are analyzed, and appropriate indexes are established. For example, indexes are established for commonly used query fields such as invoice number and invoice date to speed up query. At the same time, the execution plan of the query statement is optimized to ensure that the database can efficiently handle various query requests. The query optimization and index establishment further improve the query performance, ensuring that the system can respond to user query requests in a short time and provide efficient and accurate query services to meet the needs of enterprises for fast analysis of invoice data.

[0051] The application first acquires generated initial invoice data, and stores the initial invoice data into a storage table in a preset business processing layer based on a preset table storage strategy; then reads the initial invoice data in the storage table from the storage table based on a preset data synchronization channel, and performs aggregation operation and model transformation processing on the initial invoice data based on a preset query analysis layer to generate corresponding invoice processing data; then stores the invoice processing data into a query library in the query analysis layer according to a preset table structure; subsequently, the invoice processing data in the query library is converted based on a preset conversion strategy to obtain corresponding target invoice data; further, a target distributed columnar storage database is determined based on preset requirement information; finally, the target invoice data is stored into the target distributed columnar storage database. Based on the above automatic processing process, the application acquires generated initial invoice data, and stores the initial invoice data into a storage table in a business processing layer based on a table storage strategy, then reads the initial invoice data in the storage table from the storage table based on the use of a data synchronization channel, and performs aggregation operation and model transformation processing on the initial invoice data based on the use of a query analysis layer to generate corresponding invoice processing data, then stores the invoice processing data into a query library in the query analysis layer according to a table structure, and subsequently converts the invoice processing data in the query library based on the use of a conversion strategy to obtain target invoice data, and further determines a target distributed columnar storage database based on the use of requirement information, and finally stores the target invoice data into the target distributed columnar storage database. Since the distributed architecture supports horizontal expansion, it can easily cope with the storage and high-concurrency query requirements of millions of invoices, effectively improving the storage efficiency and intelligence of invoice data.

[0052] In some optional implementations, the step of storing the initial invoice data into a storage table in a preset business processing layer based on a preset table storage strategy in step S201 includes the following steps:

[0053] The preset invoice month information is acquired, and a corresponding table splitting rule is constructed based on the invoice month information.

[0054] In this embodiment, the system pre-sets a table splitting rule taking invoice month as the basis for table splitting. Specifically, the rule content of the table splitting rule includes: it is specified that each month corresponds to an independent storage table group, including different types of tables such as sales invoice master table, sales invoice detail table, and sales invoice status table.

[0055] Based on the table splitting rule, a storage table corresponding to the invoice month information is constructed in the business processing layer.

[0056] In the embodiment, the execution processing can be performed based on the rule content of the above-mentioned table splitting rule to automatically construct the storage table corresponding to the invoice month information in the above-mentioned business processing layer.

[0057] A preset table splitting storage strategy is acquired.

[0058] In the embodiment, the strategy content of the above-mentioned table splitting storage strategy includes that when the invoice issuing business occurs, the invoice processing system automatically identifies the invoice month according to the invoice time information on the invoice data, and stores each type of information of the invoice data into each table of the corresponding month according to the preset table structure. For example, an invoice issued in May 2024 stores the main table information into the sales invoice main table corresponding to May 2024, the detail information into the sales invoice detail table of the same month, and the status information into the sales invoice status table.

[0059] Based on the table splitting storage strategy, the initial invoice data is correspondingly stored into the storage table in the business processing layer.

[0060] In the embodiment, the invoice storage processing of correspondingly storing the initial invoice data into the storage table in the business processing layer is completed by the strategy content of the above-mentioned table splitting storage strategy.

[0061] Among them, the invoice issuing and initial storage are the starting point of the entire invoice data processing flow. By storing the massive invoice data into different month tables in the manner of splitting tables according to the invoice month, the data amount of a single table is effectively reduced. This not only helps to improve the efficiency of subsequent data query, because when querying the invoice, the relevant month table can be more accurately located for retrieval, reducing unnecessary data scanning; also greatly facilitates the archiving and management of historical data, and the enterprise can easily backup, migrate or clean up the invoice data according to the month.

[0062] The application acquires a preset invoice month information, and constructs a corresponding table splitting rule based on the invoice month information; then constructs a storage table corresponding to the invoice month information in the business processing layer based on the table splitting rule; then acquires a preset table splitting storage strategy; and subsequently stores the initial invoice data into the storage table in the business processing layer based on the table splitting storage strategy. Based on the above processing flow, the application constructs a corresponding table splitting rule based on the acquired invoice month information, then constructs a storage table corresponding to the invoice month information in the business processing layer based on the use of the table splitting rule, and then stores the initial invoice data into the storage table in the business processing layer based on the use of the table splitting storage strategy, so as to realize the manner of splitting tables according to the invoice month to disperse the massive invoice data into tables of different months, effectively reducing the data amount of a single table. And it helps to improve the efficiency of subsequent invoice data query.

[0063] In some optional implementations of the embodiment, the preset query analysis layer in step S202 performs aggregation operation and model transformation processing on the initial invoice data to generate corresponding invoice processing data, including the following steps:

[0064] Based on the query analysis layer, a preset operation rule is obtained.

[0065] In the embodiment, after the query analysis layer obtains the invoice data, it performs aggregation operation according to the obtained preset operation rule. Specifically, the rule content of the operation rule includes: first, extract key fields such as invoice number, invoice time, amount, tax, etc., which are important basis for subsequent query and analysis; then perform dimension splitting, for example, classify the invoice data according to dimensions such as region, enterprise type, invoice type, etc.; finally, perform index calculation, such as calculating the total amount of invoices and total tax of different regions in each month, the number of different invoice types, etc.

[0066] Based on the operation rule, the initial invoice data is subjected to aggregation operation processing to obtain corresponding first processing data.

[0067] In the embodiment, the aggregation operation processing on the initial invoice data can be performed based on the rule content of the above-mentioned operation rule to generate corresponding first processing data.

[0068] Based on preset query scenario information, the first processing data is subjected to model transformation processing to obtain corresponding second processing data.

[0069] In the embodiment, the query scenario information refers to information obtained after query scenario analysis on the initial invoice data. Then, according to the obtained query scenario information, the first processing data after aggregation operation can be subjected to model transformation to convert it into structured data suitable for query, i.e., the above-mentioned second processing data, which serves as the final invoice processing data.

[0070] The second processing data is taken as the invoice processing data.

[0071] In the embodiment, real-time synchronization and data aggregation are key links connecting invoice issuing business and query analysis. Through the real-time synchronization mechanism, the data in the query library is ensured to be consistent with the data in the business processing layer, so that users can query the latest invoice data in time. Data aggregation operation processes and organizes the original invoice data in depth, extracts valuable information and classifies and statistics them, providing structured and easy-to-analyze data basis for subsequent query. Establishing a table structure suitable for query scenarios can further improve query efficiency and meet users' diversified query requirements, such as quickly obtaining detailed information of a certain type of invoice, statistical analysis according to different dimensions, etc., providing strong data support for enterprise decision-making.

[0072] The application obtains a preset operation rule based on the query analysis layer, then performs aggregation operation processing on the initial invoice data based on the operation rule to obtain corresponding first processing data, then performs model transformation processing on the first processing data based on preset query scene information to obtain corresponding second processing data, and subsequently takes the second processing data as the invoice processing data. Based on the above processing procedure, the application obtains the first processing data by performing aggregation operation processing on the initial invoice data based on the use of the operation rule, and then performs model transformation processing on the first processing data based on the use of the query scene information, so that the corresponding invoice processing data can be efficiently and accurately constructed, the accuracy of the obtained invoice processing data is ensured, and the structured and easily analyzed data basis is provided for subsequent invoice query.

[0073] In some optional implementations, step S204 includes the following steps:

[0074] The invoice processing data is organized and processed based on a columnar storage manner to obtain corresponding first invoice data.

[0075] In this embodiment, the invoice processing data is organized in the columnar storage manner, and data of the same type is stored together, such as storing all invoice numbers in a column and all invoice issuing times in a column. This manner can read only the required column data when querying, reduces the disk I / O operation, and improves the query speed.

[0076] A preset compression algorithm is called.

[0077] In this embodiment, the selection of the compression algorithm is not specifically limited, and can be determined according to actual business requirements, for example, an efficient compression algorithm (such as LZ4) can be used.

[0078] The first invoice data is compressed based on the compression algorithm to obtain corresponding second invoice data.

[0079] In this embodiment, the first invoice data is compressed according to the selected compression algorithm, and the obtained second invoice data is taken as the final target invoice data, so that the storage space can be further saved and the storage density of the invoice data can be improved.

[0080] The second invoice data is taken as the target invoice data.

[0081] In the embodiment, the distributed architecture enables the system to be horizontally expanded, and the storage capacity and processing capacity of the system are improved by adding server nodes, so that the storage and query of invoice data of more than one million can be easily handled. The columnar storage and compression technology reduces the data storage space and disk I / O operation, and improves the query efficiency, especially when detailed query and summary query are performed, the required data can be quickly located and read.

[0082] The application organizes and processes the invoice processing data in a columnar storage manner to obtain corresponding first invoice data, then calls a preset compression algorithm, then compresses the first invoice data based on the compression algorithm to obtain corresponding second invoice data, and subsequently takes the second invoice data as the target invoice data. Based on the above processing procedure, the application organizes and processes the invoice processing data in a columnar storage manner to obtain first invoice data, and then compresses the first invoice data based on the use of the compression algorithm to obtain the required target invoice data, so that the conversion processing of the invoice processing data can be efficiently and accurately completed, and the conversion intelligence of the invoice processing data is improved. Moreover, the columnar storage and compression technology reduces the data storage space and disk I / O operation, which is beneficial to improving the query efficiency of the invoice data.

[0083] In some optional implementations, after step S201, the electronic device can further perform the following steps:

[0084] A preset data monitoring tool is called.

[0085] In the embodiment, the data monitoring tool is an automatic tool with a data monitoring function which is constructed in advance.

[0086] The data volume of the invoice generation of each month is monitored based on the data monitoring tool, and it is determined whether there is a specified month in which the invoice data volume is greater than a preset capacity threshold.

[0087] In the embodiment, the growth of the invoice data volume of each month is continuously monitored based on the use of the data monitoring tool, and it is determined whether there is a specified month in which the invoice data volume is greater than a preset capacity threshold by analyzing the obtained monitoring data. The selection of the capacity threshold is not specifically limited, and can be set according to actual business requirements.

[0088] If yes, a specified storage table corresponding to the specified month is obtained from the storage table.

[0089] In the embodiment, the specified storage table corresponding to the specified month can be extracted from the storage table according to the one-to-one correspondence between the month and the storage table.

[0090] Capacity expansion processing is performed on the specified storage table based on a preset dynamic expansion mechanism.

[0091] In this embodiment, when it is detected that the data volume of a certain month approaches or reaches the upper limit of the capacity (capacity threshold) of the current storage table, or it is predicted that the future data volume will increase substantially, the system automatically triggers the dynamic expansion process. By generating a new storage table corresponding to the month according to the preset expansion rule, and updating the data storage routing information of the system, it is ensured that the invoice data of the month can be correctly stored in the newly generated table in the future.

[0092] The application calls a preset data monitoring tool, then monitors the data volume of the invoice generation of each month based on the data monitoring tool, and judges whether there is a specified month with invoice data volume greater than a preset capacity threshold, if so, obtains a specified storage table corresponding to the specified month from the storage table, and subsequently performs capacity expansion processing on the specified storage table based on a preset dynamic expansion mechanism. Based on the above processing flow, the application monitors the data volume of the invoice generation of each month based on the use of the data monitoring tool, and when it is detected that there is a specified month with invoice data volume greater than a preset capacity threshold, a specified storage table corresponding to the specified month is obtained from the storage table, and then capacity expansion processing is performed on the specified storage table based on the use of the dynamic expansion mechanism, so that the system can effectively cope with the growth of invoice data volume, avoid the impact of insufficient storage space on the normal operation of invoice issuance business, and provide a basic guarantee for the long-term stable operation of the system.

[0093] In some optional implementation manners of this embodiment, after step S206, the electronic device can further perform the following steps:

[0094] Obtain index usage data corresponding to the target distributed columnar storage database.

[0095] In this embodiment, the index usage data including the usage frequency and query performance of the index can be obtained by periodically collecting data of the index in the target distributed columnar storage database.

[0096] Perform index optimization processing on the target distributed columnar storage database based on the index usage data.

[0097] In this embodiment, by analyzing the obtained index usage data (usage frequency and query performance of the index), unnecessary indexes are deleted, and frequently used indexes are reconstructed and adjusted, so as to complete the index optimization processing on the target distributed columnar storage database. Specifically, if it is found that the query efficiency of a certain index is reduced, it may be because the data distribution is uneven or the index fragmentation is too much, at this time the index can be reconstructed to improve the query performance of the index.

[0098] The data stored in the target distributed columnar storage database is sharded based on preset data query requirements.

[0099] In this embodiment, the sharding process includes sharding the data in the target distributed columnar storage database according to the characteristics and query requirements of the invoice data. Specifically, the data can be sharded according to dimensions such as region, time range, invoice type, and the like, and stored on different server nodes in a dispersed manner. In this way, when querying, data retrieval can be performed in parallel on multiple nodes, thereby improving the data query speed.

[0100] The data stored in the target distributed columnar storage database is partitioned based on a preset partitioning strategy.

[0101] In this embodiment, the data stored in the target distributed columnar storage database is managed by using a partitioning strategy, which divides the data into different partitions according to certain rules. For example, the data is partitioned according to the invoice month, and the data of each month is stored in a separate partition. This can reduce the amount of data that needs to be scanned when querying, improving the query efficiency. At the same time, the partitioning strategy also facilitates the management and maintenance of data, such as easily backing up, restoring, or cleaning up the data of a certain partition.

[0102] In addition, the load balancing and read-write separation strategy can also be applied to the target distributed columnar storage database. Specifically, in a distributed architecture, load balancing technology can be used to evenly distribute query requests to each server node, avoiding the performance degradation caused by excessive load on a certain node. At the same time, the read-write separation strategy is implemented to distribute query operations and write operations to different nodes. Write operations are mainly concentrated on the master node, while query operations can be performed in parallel on multiple slave nodes, improving the overall concurrent processing capacity and response speed of the system.

[0103] In addition, performance optimization and system stability guarantee are important measures to further improve system performance and reliability based on distributed columnar storage optimized query. Data sharding, index optimization, and partitioning strategy can improve query efficiency, reduce query time, and make the system respond to user query requests more quickly. Load balancing and read-write separation strategy can reasonably allocate system resources, avoid performance bottlenecks, and ensure that the system remains stable in high-concurrency scenarios. Through the comprehensive application of these measures, the system can provide efficient and reliable invoice data query services to meet the growing business needs of enterprises.

[0104] The application obtains index usage data corresponding to the target distributed columnar storage database, performs index optimization processing on the target distributed columnar storage database based on the index usage data, performs sharding processing on data stored in the target distributed columnar storage database based on a preset data query requirement, and performs partitioning processing on the data stored in the target distributed columnar storage database based on a preset partitioning strategy. Based on the above processing procedure, the application obtains index usage data corresponding to the target distributed columnar storage database, performs index optimization processing on the target distributed columnar storage database based on the index usage data, performs sharding processing on data stored in the target distributed columnar storage database based on a preset data query requirement, and performs partitioning processing on the data stored in the target distributed columnar storage database based on a preset partitioning strategy, so as to realize the combined use of data sharding, index optimization, and partitioning strategy, effectively improve query efficiency, reduce query time, enable the system to respond to user query requests more quickly, and further improve user experience.

[0105] In some optional implementations of the embodiment, after step S206, the electronic device can further perform the following steps:

[0106] It is judged whether a user-triggered invoice data query request is received; wherein the invoice data query request carries a query condition and an analysis dimension.

[0107] In the embodiment, the invoice data query request is triggered by the user according to actual requirements by customizing the query condition and the analysis dimension.

[0108] If yes, the query condition and the analysis dimension are extracted from the invoice data query request.

[0109] In the embodiment, the corresponding query condition and analysis dimension can be extracted by performing information analysis on the invoice data query request.

[0110] The target distributed columnar storage database is queried based on the query condition and the analysis dimension, and corresponding specified invoice data is obtained.

[0111] In the embodiment, the specified invoice data is obtained by querying the target distributed columnar storage database according to the received query condition and analysis dimension.

[0112] A corresponding display chart is generated based on the specified invoice data.

[0113] In this embodiment, the display chart can be a table and chart data corresponding to the specified invoice data generated based on a real-time data analysis and visualization technology platform (specifically RealInsight). Specifically, the generation process of the display chart includes: data source configuration: based on the target distributed columnar storage database, data source configuration is performed on the RealInsight platform. Connect the target distributed columnar storage database, specify the table and field to be queried, set the frequency and mode of data acquisition and other parameters, and ensure that the platform can correctly acquire the invoice data. Table analysis display: using the table analysis function provided by the RealInsight platform, the queried invoice data is displayed in a table according to the user's demand. The columns and rows of the table can be customized, and the data can be sorted, filtered, grouped and other operations, which facilitates the user to view and analyze the detailed information of the invoice data. For example, the user can filter and sort the invoice data according to the invoice type, invoice date and other conditions, and quickly obtain the required information. Graphical analysis display: through the graphical analysis function of the RealInsight platform, the queried invoice data is displayed in an intuitive chart form. Supports multiple chart types such as column chart, line chart, pie chart, etc. Users can select appropriate chart types according to data characteristics and analysis needs to convert invoice data into intuitive graphics to facilitate the discovery of data rules and trends. For example, use a column chart to display the number of invoices in different regions, and use a line chart to display the monthly invoice amount trend of a certain enterprise.

[0114] The specified invoice data and the display chart are displayed.

[0115] In this embodiment, the specified invoice data and the display chart can be displayed by using the system interface to complete the feedback processing of the invoice data query request triggered by the user. The system also supports exporting the specified invoice data and the display chart to PDF or Excel format to facilitate further analysis and sharing by the user.

[0116] Among them, data query and display are the final link of invoice data processing, and the purpose is to present the invoice data processed by the previous series of processes to the user in an intuitive and easy-to-understand way, to meet the user's diversified query and analysis needs. Through the RealInsight real-time data analysis and visualization technology platform, users can easily configure data sources, perform table analysis and graphical analysis display, and quickly obtain the required information. Personalized query display service can better adapt to the business needs of different users and improve user experience.

[0117] The application determines whether a user triggered invoice data query request is received; wherein the invoice data query request carries a query condition and an analysis dimension; if so, the query condition and the analysis dimension are extracted from the invoice data query request; then the target distributed columnar storage database is queried based on the query condition and the analysis dimension to obtain corresponding specified invoice data; then the corresponding display chart is generated based on the specified invoice data; and the specified invoice data and the display chart are subsequently displayed. Based on the above processing flow, the application extracts information from the user triggered invoice data query request, and obtains specified invoice data by querying the target distributed columnar storage database according to the extracted query condition and analysis dimension, and generates a corresponding display chart based on the specified invoice data, and then displays the specified invoice data and the display chart, so as to provide personalized query display services for users, to better adapt to the business needs of different users and improve user experience.

[0118] In some optional implementations, the system also has a function of guaranteeing data consistency and availability, and the specific implementation process includes: distributed transaction management mechanism implementation: using distributed transaction management technology to ensure the consistency of invoice data in the storage, synchronization and query processes. For example, using two-phase commit (2PC) or three-phase commit (3PC) protocol, in the transaction operation involving multiple nodes, all nodes are ensured to either successfully execute the transaction or roll back the transaction, avoiding the situation of inconsistent data. When the business processing layer performs invoice issuing operation and needs to be synchronized to the query library, the distributed transaction management mechanism is used to ensure the synchronous update of data in the two libraries.

[0119] Data redundancy design: in the distributed architecture, the invoice data is redundantly stored. The data is replicated to multiple nodes, and when a node fails, the data can be obtained from other normal nodes to ensure the availability of the data. For example, using the master-slave replication method, the data on the master node is replicated to multiple slave nodes in real time, and when the master node fails, a slave node can be quickly promoted to the master node to continue providing data services.

[0120] High availability design: build a high-availability system architecture, use cluster technology, automatic fault transfer and other means to ensure that the system can still run normally when a node fails. For example, using a load balancer to monitor the running state of each node, when a node failure is detected, automatically transfer the request to other normal nodes to ensure that the system service does not interrupt. At the same time, regular backups and recovery drills are performed to ensure that data and system operation can be quickly recovered in the event of a serious failure.

[0121] Among them, data consistency and availability guarantee is a crucial link in the invoice processing system. Invoice data contains sensitive information and important business data of enterprises, and must ensure the consistency of data at each link to avoid business errors or decision-making mistakes caused by inconsistent data. Through the distributed transaction management mechanism, the accuracy of data in cross-node operation can be guaranteed. Data redundancy and high availability design improves the reliability of the system, and when some nodes fail, the system can still run normally, ensuring the reliability and availability of invoice data. These measures provide a stable and secure data environment for enterprises and ensure the normal development of business.

[0122] In some optional implementations of the embodiment, the system further includes the following functional modules: a data storage module: supports distributed storage and uses big data storage technology (such as Hadoop, HBase) to process massive invoice data. Provides data compression and indexing functions to improve storage efficiency and query speed. A data preprocessing module: cleans, standardizes, and unifies the data format of invoice data. Extract key fields (such as invoice number, invoice time, amount, tax, etc.) and store them in a classified manner. A data analysis module: provides real-time query function, supports multi-dimensional analysis (such as query by time, region, enterprise classification). Integrates machine learning algorithms to support trend prediction, anomaly detection, and other advanced analysis functions. A data security and privacy protection module: uses data encryption technology (such as AES, RSA) to protect invoice data. Implement strict access control policies to ensure that only authorized users can access sensitive data. A data visualization module: provides a variety of visualization charts (such as bar charts, line charts, pie charts, etc.) to visually display analysis results. Supports generating statistical reports and exporting them in PDF or Excel format.

[0123] In some optional implementations, the obtained user information seeks user consent and complies with relevant laws and relevant policies.

[0124] In addition, the non-company software tools or components appearing in the embodiments of the present application are only examples and do not represent actual use.

[0125] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0126] It should be emphasized that, in order to further ensure the privacy and security of the above-mentioned target invoice data, the above-mentioned target invoice data can also be stored in a node of a blockchain.

[0127] The blockchain referred to in the present application is a new application mode of distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm and other computer technologies. The blockchain is essentially a decentralized database, which is a series of data blocks associated using cryptographic methods, each data block containing information of a batch of network transactions, used to verify the validity of the information (anti-fake) and generate the next block. The blockchain can include a blockchain underlying platform, a platform product service layer, and an application service layer.

[0128] Embodiments of the present application can acquire and process related data based on artificial intelligence technology. Artificial intelligence (AI) is the use of digital computers or computer-controlled machines to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0129] Artificial intelligence basic technologies generally include technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction system, mechatronics, etc. Artificial intelligence software technology mainly includes computer vision technology, robot technology, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning, etc.

[0130] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through computer readable instructions, which can be stored in a computer readable storage medium. The program can include the processes of the above-mentioned embodiments when executed, wherein the storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM) or other non-volatile storage medium, or a random access memory (RAM) or the like.

[0131] It should be understood that although each step in the flowchart of the accompanying drawings is shown in sequence according to the direction of the arrow, these steps are not necessarily executed in sequence according to the direction of the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and they can be executed in other orders. Moreover, at least part of the steps in the flowchart of the accompanying drawings can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence is not necessarily sequential, but can be executed alternately or alternately with at least part of other steps or sub-steps or stages of other steps.

[0132] Further referenceFigure 3 As the implementation of the method shown in the above Figure 2 , the present application provides an embodiment of an invoice processing device, which corresponds to the method embodiment shown in the above Figure 2 . The device can be applied in various electronic devices.

[0133] As shown in the above Figure 3 , the invoice processing device 300 comprises a first processing module 301, a second processing module 302, a first storage module 303, a conversion module 304, a determination module 305, and a second storage module 306. Wherein:

[0134] The first processing module 301 is configured to obtain the generated initial invoice data, and store the initial invoice data into a storage table in a preset business processing layer based on a preset table storage strategy;

[0135] The second processing module 302 is configured to read the initial invoice data in the storage table from the storage table based on a preset data synchronization channel, and perform convergence operation and model transformation processing on the initial invoice data based on a preset query analysis layer to generate corresponding invoice processing data;

[0136] The first storage module 303 is configured to store the invoice processing data into a query library in the query analysis layer according to a preset table structure;

[0137] The conversion module 304 is configured to perform conversion processing on the invoice processing data in the query library based on a preset conversion strategy to obtain corresponding target invoice data;

[0138] The determination module 305 is configured to determine a target distributed columnar storage database based on preset requirement information;

[0139] The second storage module 306 is configured to store the target invoice data into the target distributed columnar storage database.

[0140] In the embodiment, the above modules or units are respectively used to perform operations corresponding to the steps of the invoice processing method of the foregoing embodiments, which will not be described here.

[0141] In some optional implementations of the embodiment, the first processing module 301 comprises:

[0142] A processing sub-module is configured to obtain preset invoice month information, and construct a corresponding table division rule based on the invoice month information;

[0143] A construction sub-module is configured to construct a storage table corresponding to the invoice month information in the business processing layer based on the table division rule;

[0144] a first obtaining sub-module, configured to obtain a preset table storage strategy;

[0145] a storage sub-module, configured to store the initial invoice data in the storage table in the business processing layer based on the table storage strategy.

[0146] In the embodiment, the above modules or units are respectively used for performing operations corresponding to the steps of the invoice processing method of the foregoing embodiments, and thus no further description is given herein.

[0147] In some optional implementations of the embodiment, the second processing module 302 includes:

[0148] a second obtaining sub-module, configured to obtain a preset operation rule based on the query analysis layer;

[0149] an operation sub-module, configured to perform convergent operation processing on the initial invoice data based on the operation rule to obtain corresponding first processing data;

[0150] a transformation sub-module, configured to perform model transformation processing on the first processing data based on preset query scene information to obtain corresponding second processing data;

[0151] a first determining sub-module, configured to take the second processing data as the invoice processing data.

[0152] In the embodiment, the above modules or units are respectively used for performing operations corresponding to the steps of the invoice processing method of the foregoing embodiments, and thus no further description is given herein.

[0153] In some optional implementations of the embodiment, the conversion module 304 includes:

[0154] an organization sub-module, configured to perform organization processing on the invoice processing data based on a columnar storage manner to obtain corresponding first invoice data;

[0155] a calling sub-module, configured to call a preset compression algorithm;

[0156] a compression sub-module, configured to perform compression processing on the first invoice data based on the compression algorithm to obtain corresponding second invoice data;

[0157] a second determining sub-module, configured to take the second invoice data as the target invoice data.

[0158] In the embodiment, the above modules or units are respectively used for performing operations corresponding to the steps of the invoice processing method of the foregoing embodiments, and thus no further description is given herein.

[0159] In some optional implementation manners of the embodiment, the invoice processing apparatus further includes:

[0160] The calling module is configured to call the preset data monitoring tool.

[0161] The first judging module is configured to perform data volume monitoring on the invoice generation situation of each month based on the data monitoring tool, and determine whether there is a specified month in which the invoice data volume is greater than the preset capacity threshold.

[0162] The first obtaining module is configured to, if so, obtain a specified storage table corresponding to the specified month from the storage table.

[0163] The expansion module is configured to perform capacity expansion processing on the specified storage table based on a preset dynamic expansion mechanism.

[0164] In the embodiment, the above modules or units are respectively used for operations corresponding to the steps of the invoice processing method of the foregoing embodiments, and thus will not be described here.

[0165] In some optional implementation manners of the embodiment, the invoice processing apparatus further includes:

[0166] The second obtaining module is configured to obtain index usage data corresponding to the target distributed columnar storage database.

[0167] The optimization module is configured to perform index optimization processing on the target distributed columnar storage database based on the index usage data.

[0168] The sharding module is configured to perform sharding processing on data stored in the target distributed columnar storage database based on a preset data query requirement.

[0169] The partitioning module is configured to perform partitioning processing on data stored in the target distributed columnar storage database based on a preset partitioning strategy.

[0170] In the embodiment, the above modules or units are respectively used for operations corresponding to the steps of the invoice processing method of the foregoing embodiments, and thus will not be described here.

[0171] In some optional implementation manners of the embodiment, the invoice processing apparatus further includes:

[0172] The second judging module is configured to determine whether a user-triggered invoice data query request is received, wherein the invoice data query request carries a query condition and an analysis dimension.

[0173] The extraction module is configured to, if so, extract the query condition and the analysis dimension from the invoice data query request.

[0174] The query module is configured to perform query processing on the target distributed columnar storage database based on the query condition and the analysis dimension, and obtain corresponding specified invoice data.

[0175] The generation module is configured to generate a corresponding display chart based on the specified invoice data.

[0176] The display module is configured to perform display processing on the specified invoice data and the display chart.

[0177] In the embodiment, the modules or units described above are respectively used to perform operations corresponding to the steps of the invoice processing method of the foregoing embodiments, and thus will not be described here.

[0178] To solve the above technical problems, the embodiments of the present application further provide a computer device. For details, please refer to Figure 4 , Figure 4 The basic structure block diagram of the computer device in the embodiment is shown in the figure.

[0179] The computer device 4 includes a memory 41, a processor 42, and a network interface 43, which are connected to each other through a system bus. It should be noted that only the computer device 4 with components 41-43 is shown in the figure, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be alternatively implemented. Among them, those skilled in the art can understand that the computer device here is a device capable of automatically performing numerical calculation and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0180] The computer device can be a desktop computer, a notebook computer, a palm computer, and a cloud server, etc. The computer device can perform human-computer interaction with a user through a keyboard, a mouse, a remote controller, a touchpad, a voice control device, etc.

[0181] The memory 41 includes at least one type of readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 4. Of course, the memory 41 can also include both an internal storage unit and an external storage device of the computer device 4. In this embodiment, the memory 41 is generally used to store an operating system and various application software installed on the computer device 4, such as computer readable instructions of the invoice processing method, etc. In addition, the memory 41 can also be used to temporarily store various data that have been output or will be output.

[0182] The processor 42 can be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to execute computer readable instructions or process data stored in the memory 41, such as computer readable instructions of the invoice processing method.

[0183] The network interface 43 can include a wireless network interface or a wired network interface, and is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0184] The present application also provides another embodiment, i.e., to provide a computer readable storage medium storing computer readable instructions, which can be executed by at least one processor to enable the at least one processor to perform the steps of the invoice processing method as described above.

[0185] Those skilled in the art can clearly understand the above-mentioned embodiment method can be realized by means of software and the necessary general hardware platform, of course, it can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes a plurality of instructions for making a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) execute the method described in each embodiment of the present application.

[0186] Obviously, the above-described embodiments are only some of the embodiments of the present application, not all the embodiments, and the drawings show the preferred embodiments of the present application, but do not limit the patent scope of the present application. The present application can be implemented in many different forms, and conversely, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing specific embodiments, or make equivalent replacements to some of the technical features. Any equivalent structure made by using the content of the specification and drawings, directly or indirectly applied to other related technical fields, is also within the scope of the patent protection of the present application.

Claims

1. An invoice processing method, characterized by, The method comprises the following steps: acquiring generated initial invoice data, and storing the initial invoice data into a preset storage table in a preset business processing layer based on a preset table storage strategy; reading the initial invoice data in the storage table from the storage table based on a preset data synchronization channel, and performing convergent operation and model transformation processing on the initial invoice data based on a preset query analysis layer to generate corresponding invoice processing data; storing the invoice processing data into a query library in the query analysis layer according to a preset table structure; performing conversion processing on the invoice processing data in the query library based on a preset conversion strategy to obtain corresponding target invoice data; determining a target distributed columnar storage database based on preset requirement information; storing the target invoice data into the target distributed columnar storage database.

2. The invoice processing method of claim 1, wherein, The step of storing the initial invoice data into a preset storage table in a preset business processing layer based on a preset table storage strategy comprises the following steps: acquiring preset invoice month information, and constructing a corresponding table division rule based on the invoice month information; constructing a storage table corresponding to the invoice month information in the business processing layer based on the table division rule; acquiring a preset table storage strategy; storing the initial invoice data into the storage table in the business processing layer based on the table storage strategy.

3. The invoice processing method of claim 1, wherein, The step of performing convergent operation and model transformation processing on the initial invoice data based on a preset query analysis layer to generate corresponding invoice processing data comprises the following steps: acquiring a preset operation rule based on the query analysis layer; performing convergent operation processing on the initial invoice data based on the operation rule to obtain corresponding first processing data; performing model transformation processing on the first processing data based on preset query scenario information to obtain corresponding second processing data; taking the second processing data as the invoice processing data.

4. The invoice processing method of claim 1, wherein, The step of performing conversion processing on the invoice processing data in the query library based on a preset conversion strategy to obtain corresponding target invoice data comprises the following steps: organizing the invoice processing data in a columnar storage manner to obtain corresponding first invoice data; calling a preset compression algorithm; performing compression processing on the first invoice data based on the compression algorithm to obtain corresponding second invoice data; taking the second invoice data as the target invoice data.

5. The invoice processing method of claim 1, wherein, After the step of storing the initial invoice data into a preset storage table in a preset business processing layer based on a preset table storage strategy, the method further comprises the following steps: calling a preset data monitoring tool; performing data volume monitoring on invoice generation in each month based on the data monitoring tool, and determining whether there is a specified month with invoice data volume greater than a preset capacity threshold; if so, acquiring a specified storage table corresponding to the specified month from the storage table; performing capacity expansion processing on the specified storage table based on a preset dynamic expansion mechanism.

6. The invoice processing method of claim 1, wherein, After the step of storing the target invoice data into the target distributed columnar storage database, the method further comprises the following steps: acquire index usage data corresponding to the target distributed columnar storage database; perform index optimization processing on the target distributed columnar storage database based on the index usage data; perform sharding processing on data stored in the target distributed columnar storage database based on a preset data query requirement; perform partitioning processing on data stored in the target distributed columnar storage database based on a preset partitioning strategy.

7. The invoice processing method of claim 1, wherein, After the step of storing the target invoice data into the target distributed columnar storage database, further comprising: determining whether a user-triggered invoice data query request is received; wherein the invoice data query request carries a query condition and an analysis dimension; if yes, extracting the query condition and the analysis dimension from the invoice data query request; performing query processing on the target distributed columnar storage database based on the query condition and the analysis dimension, to obtain corresponding specified invoice data; generating a corresponding display chart based on the specified invoice data; displaying the specified invoice data and the display chart.

8. An invoice processing apparatus characterized by comprising: comprising: a first processing module configured to acquire generated initial invoice data and store the initial invoice data into a storage table in a preset business processing layer based on a preset table storage strategy; a second processing module configured to read the initial invoice data in the storage table from the storage table based on a preset data synchronization channel, and perform aggregation operation and model transformation processing on the initial invoice data based on a preset query analysis layer, to generate corresponding invoice processing data; a first storage module configured to store the invoice processing data into a query library in the query analysis layer according to a preset table structure; a conversion module configured to perform conversion processing on the invoice processing data in the query library based on a preset conversion strategy, to obtain corresponding target invoice data; a determination module configured to determine a target distributed columnar storage database based on preset requirement information; a second storage module configured to store the target invoice data into the target distributed columnar storage database.

9. A computer device, comprising: comprising a memory and a processor, the memory storing computer readable instructions, and the processor executing the computer readable instructions to implement the steps of the invoice processing method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer readable instructions, and the computer readable instructions are executed by the processor to implement the steps of the invoice processing method of any one of claims 1 to 7.

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