A service data processing method, device and equipment, and a storage medium

Abstract

Embodiments of the present application disclose a kind of processing method, device and equipment of service data and storage medium.The method comprises: obtaining service data in service data source, and the current data volume corresponding to service data is counted;If current data volume is less than or equal to preset data volume, then service data is compared with preset data range, if service data does not satisfy preset data range, then based on service data, execute warning operation;If current data volume is greater than preset data volume, then based on current data volume, execute warning operation.The embodiments of the present application are by counting the current data volume corresponding to service data, when current data volume is less than or equal to preset data volume, continue comparing service data with preset data range, execute warning operation from two dimensions of data volume dimension and data dimension, solve the problem that abnormal data in service data is not found in time, improve the timeliness and accuracy of checking service data.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method, apparatus, device and storage medium for processing business data. Background Technology

[0002] With the development of electronic technology, people can use the internet to view various business data of online products, such as website traffic and purchase volume. As the categories of online products continue to increase, the volume of business data becomes very large.

[0003] In the process of realizing this invention, at least the following technical problems were found in the prior art:

[0004] During data upload and processing, abnormal data can easily appear in business data. Currently, abnormalities are mainly identified manually, which is not only costly but also prone to delays and omissions in detecting abnormal data. Summary of the Invention

[0005] This invention provides a method, apparatus, device, and storage medium for processing business data to ensure the timeliness and accuracy of verifying business data.

[0006] In a first aspect, embodiments of the present invention provide a method for processing business data, the method comprising:

[0007] Obtain business data from the business data source and calculate the current data volume corresponding to the business data;

[0008] If the current data volume is less than or equal to the preset data volume, the business data is compared with the preset data range. If the business data does not meet the preset data range, a first warning operation is performed based on the business data.

[0009] If the current data volume is greater than the preset data volume, then a second warning operation is performed based on the current data volume.

[0010] Secondly, embodiments of the present invention also provide a business data processing apparatus, the apparatus comprising:

[0011] The business data acquisition module is used to acquire business data from the business data source and to count the current data volume corresponding to the business data.

[0012] The business data comparison module is used to compare the business data with a preset data range if the current data volume is less than or equal to a preset data volume, and to perform a first warning operation based on the business data if the business data does not meet the preset data range.

[0013] The data volume warning module is used to execute a second warning operation based on the current data volume if the current data volume is greater than the preset data volume.

[0014] Thirdly, embodiments of the present invention also provide an electronic device, the electronic device comprising:

[0015] One or more processors;

[0016] Memory, used to store one or more programs;

[0017] When the one or more programs are executed by the one or more processors, the one or more processors implement any of the above-described methods for processing business data.

[0018] Fourthly, embodiments of the present invention also provide a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform any of the above-mentioned methods for processing business data.

[0019] The embodiments of the above invention have the following advantages or beneficial effects:

[0020] This invention solves the problem of untimely detection of abnormal data in business data by statistically analyzing the current data volume corresponding to the business data and comparing the current data volume with the preset data volume. When the current data volume is less than or equal to the preset data volume, the business data is further compared with the preset data range. Alert operations are performed from two dimensions: data volume and data dimension. This improves the timeliness and accuracy of verifying business data. Attached Figure Description

[0021] Figure 1 This is a flowchart of a business data processing method provided in Embodiment 1 of the present invention;

[0022] Figure 2 This is a flowchart illustrating a specific example of a business data processing method provided in Embodiment 1 of the present invention;

[0023] Figure 3 This is a flowchart of a business data processing method provided in Embodiment 2 of the present invention;

[0024] Figure 4 This is a flowchart of a calculation method for a preset data range provided in Embodiment 2 of the present invention;

[0025] Figure 5 This is a flowchart of a page access method provided in Embodiment 2 of the present invention;

[0026] Figure 6This is a schematic diagram of a business data processing device provided in Embodiment 3 of the present invention;

[0027] Figure 7 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0028] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0029] Example 1

[0030] Figure 1 This is a flowchart of a business data processing method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where business data needs to be verified. The method can be executed by a business data processing device, which can be implemented in software and / or hardware. This device can be configured in a terminal device; for example, the terminal device can be a mobile terminal, laptop computer, desktop computer, server, tablet computer, or other smart terminal. Specifically, it includes the following steps:

[0031] S110. Obtain business data from the business data source and calculate the current data volume corresponding to the business data.

[0032] Specifically, the business data source can be used to characterize the data storage system used to store business data. For example, the business types corresponding to the business data include, but are not limited to, visit volume, purchase volume, and remaining inventory. Specifically, when new business data appears in the business data source, the business data is retrieved from the business data source. Correspondingly, for each piece of business data retrieved, the current data volume corresponding to that business data is calculated. For example, when the first piece of business data is retrieved, the current data volume of that business data is 1; when the next piece of business data is retrieved, the current data volume of that business data is 2, and so on.

[0033] In one embodiment, optionally, acquiring business data from the business data source and calculating the current data volume corresponding to the business data includes: when the dynamic reading tool reads a new message generated by the business data source, sending the new message and the corresponding business data to the distributed publish-subscribe messaging system; consuming the new message and business data in the distributed publish-subscribe messaging system using a distributed data stream computing program, and calculating the current data volume corresponding to the business data based on the business data table name in the new message.

[0034] Specifically, the dynamic reading tool is used to monitor message changes in the business data source. A change message is generated when new business data appears in the business data source or when business data is modified. In this embodiment, the change message includes a new message. For example, the new message includes the name of the business data table where the business data resides. The business data table records business data of the same business type. For example, the business data table name could be visit volume, purchase volume, or remaining inventory.

[0035] Among them, the distributed publish-subscribe messaging system (Kafka) is a distributed, partitioned, multi-replica, and multi-subscriber distributed log system coordinated by ZooKeeper (a distributed application coordination service). For example, a distributed data stream computing program can be a Flink program.

[0036] S120. Determine whether the current data volume is less than or equal to the preset data volume. If yes, execute S130; otherwise, execute S140.

[0037] Specifically, the preset data volume can be used to represent the data volume threshold corresponding to the business type of the business data. For example, when the business type of the business data is access volume, the data volume for access volume can be 20 records; when the business type of the business data is purchase volume, the data volume for purchase volume can be 10 records. Specifically, users can pre-set the corresponding preset data volume according to the specific business type.

[0038] Specifically, the current data volume is compared with the preset data volume. If the current data volume is less than or equal to the preset data volume, it means that the business data is being pushed and subsequent operations can be performed on the acquired business data.

[0039] S130. Determine whether the business data meets the preset data range. If yes, execute S150; otherwise, execute S160.

[0040] Specifically, the preset data range can be used to characterize the range of parameter values ​​for business data. For example, the preset data range can be set by the user or calculated based on historical business datasets. The technical solution calculated based on historical business datasets will be explained in the following embodiments.

[0041] S140. Based on the current data volume, execute the second warning operation.

[0042] Specifically, the second alert operation includes, but is not limited to, at least one of the following: text alert, sound alert, indicator light alert, and email alert. For example, when the second alert operation includes a text alert, the text alert content could be "The amount of business data is 11, exceeding the preset data amount." When the second alert operation includes a sound alert, the sound alert content could be the spoken version of the text alert content, or other regular or irregular sounds; for example, regular sounds could be music, and irregular sounds could be white noise, etc. When the second alert operation includes an indicator light alert, the indicator light can flash at a preset frequency. When the second alert operation includes an email alert, the text alert content can be sent to the user as email content or email subject.

[0043] S150, End.

[0044] S160. Based on business data, execute the first alert operation.

[0045] Specifically, the first alert operation includes, but is not limited to, at least one of the following: text alert, sound alert, indicator light alert, and email alert. For example, when the first alert operation includes a text alert, the text alert content can be "Business data exceeds the preset maximum value within a preset data range" or "Business data is less than the preset minimum value within a preset data range." When the first alert operation includes a sound alert, the sound alert content can be the spoken version of the text alert content, or other regular or irregular sounds. For example, regular sounds can be music, and irregular sounds can be white noise, etc. Specifically, when the sound alert content is other regular or irregular sounds, the sound alert content in the first alert operation can be the same as or different from the sound alert content in the second alert operation. When the first alert operation includes an indicator light alert, the indicator light can flash at a preset frequency. Specifically, the preset frequency of the indicator light in the first alert operation can be the same as or different from the preset frequency of the indicator light in the second alert operation. When the first alert operation includes an email alert, the text alert content can be sent to the user as email content or email subject.

[0046] Based on the above embodiments, optionally, the method further includes: storing the business data corresponding to the execution of the first alert operation and the business code corresponding to the business data in the data storage space. Specifically, the business code is used to identify the business data. For example, the business code includes, but is not limited to, at least one of numbers, letters, and special characters. For instance, the business code may be the same as the current data volume corresponding to the business data. The specific encoding form of the business code is not limited here. For example, the data storage space may be a columnar database management system (ClickHouse).

[0047] Figure 2 This is a flowchart illustrating a specific example of a business data processing method provided in Embodiment 1 of the present invention. Specifically, a dynamic reading tool is used to monitor message changes in the business data source. New messages and corresponding business data are sent to Kafka. Flink consumes the new messages and business data from the storage space (topic) in Kafka. Flink uses the business data table name in the new message as the key to calculate the current data volume corresponding to the business data in the business data table name. If the current data volume is greater than a preset data volume, an alarm email is sent to indicate an abnormal data volume in the business data. If the current data volume is less than or equal to the preset data volume, Flink determines whether the business data meets the preset data range and stores the business data that does not meet the preset data range in ClickHouse.

[0048] The technical solution of this embodiment compares the current data volume corresponding to the statistical business data with the preset data volume. When the current data volume is less than or equal to the preset data volume, the business data is further compared with the preset data range. Alert operations are performed from two dimensions: data volume and data dimension. This solves the problem of untimely detection of abnormal data in business data and improves the timeliness and accuracy of verifying business data.

[0049] Example 2

[0050] Figure 3 This is a flowchart of a business data processing method provided in Embodiment 2 of the present invention. The technical solution of this embodiment is a further refinement based on the above embodiments. Optionally, the method further includes: acquiring historical business data corresponding to a preset statistical dimension; determining a preset data range based on the historical business data and a preset fluctuation ratio corresponding to the preset statistical dimension; wherein the preset data range includes at least one of a preset mean range, a preset maximum range, and a preset minimum range.

[0051] The specific implementation steps of this embodiment include:

[0052] S210. Obtain historical business data corresponding to the preset statistical dimensions.

[0053] Specifically, the preset statistical dimensions include, but are not limited to, at least one of the following: time, merchant's geographic location range, consumer's geographic location range, merchant's store name, consumer's account name, and item category. These preset statistical dimensions can be mapped to business types. For example, when the business type is website traffic, the preset statistical dimension could be time; when the business type is purchase volume, the preset statistical dimension could be item category. The mapping relationship between the preset statistical dimensions and business types can be pre-set; the specific mapping relationship is not limited here, and users can set it according to their actual application needs.

[0054] In one embodiment, optionally, the preset statistical dimensions include a daily dimension and / or a monthly dimension. In one embodiment, optionally, when the preset statistical dimensions include a daily dimension and / or a monthly dimension, the historical business data includes first historical business data corresponding to the daily dimension and / or second historical business data corresponding to the monthly dimension. Accordingly, obtaining the historical business data corresponding to the preset statistical dimensions includes: if the preset statistical dimensions include a daily dimension, obtaining the first historical business data within a first time period; and / or, if the preset statistical dimensions include a monthly dimension, obtaining the second historical business data within a second time period.

[0055] Specifically, the first time period can be the same as or different from the second time period. For example, the first time period can be 6 months and the second time period can be 12 months.

[0056] S220. Determine the preset data range based on historical business data and the preset fluctuation ratio corresponding to the preset statistical dimensions.

[0057] Specifically, the preset fluctuation ratios corresponding to different preset statistical dimensions can be the same or different. In one embodiment, when the preset statistical dimensions include a daily dimension and / or a monthly dimension, the preset fluctuation ratio includes a first preset fluctuation ratio corresponding to the daily dimension and / or a second preset fluctuation ratio corresponding to the monthly dimension. The first preset fluctuation ratio and the second preset fluctuation ratio can be the same or different. For example, the first preset fluctuation ratio is less than the second preset fluctuation ratio.

[0058] In this embodiment, the preset data range includes at least one of a preset average range, a preset maximum range, and a preset minimum range. Specifically, the preset fluctuation ratios corresponding to the preset average range, the preset maximum range, and the preset minimum range may be the same or different.

[0059] Based on the above embodiments, optionally, when the preset statistical dimension includes a daily dimension and / or a monthly dimension, the preset data range includes a first preset data range corresponding to the daily dimension and / or a second preset data range corresponding to the monthly dimension. Specifically, the first preset data range includes at least one of a first preset mean range, a first preset maximum range, and a first preset minimum range, and the second preset data range includes at least one of a second preset mean range, a second preset maximum range, and a second preset minimum range.

[0060] Taking the daily dimension as an example, the first preset average range is determined based on the daily average data determined by historical business data and the preset fluctuation ratio corresponding to the preset statistical dimension. For example, if the daily average data is 10 and the preset fluctuation ratio is 1, then the first preset average range is [9, 11]. The first preset maximum range is determined based on the daily maximum data determined by historical business data and the preset fluctuation ratio corresponding to the preset statistical dimension. For example, if the daily maximum data is 20 and the preset fluctuation ratio is 2, then the first preset maximum range is [18, 22]. The first preset minimum range is determined based on the daily minimum data determined by historical business data and the preset fluctuation ratio corresponding to the preset statistical dimension. For example, if the daily minimum data is 5 and the preset fluctuation ratio is 1, then the first preset minimum range is [4, 6].

[0061] Based on the above embodiments, optionally, the business type and the preset data range corresponding to the business type can be stored in a remote dictionary service. The remote dictionary service (Redis) is an open-source, network-enabled, in-memory or persistent log-structured key-value database written in ANSI C.

[0062] Figure 4 This is a flowchart of a calculation method for a preset data range provided in Embodiment 2 of the present invention. Specifically, Figure 4 This explanation uses an example where the preset statistical dimensions are either only monthly or daily. For the business type of the data to be analyzed, it determines whether the preset statistical dimension corresponding to the business type in the preset mapping relationship is a monthly dimension. If it is, 12 months of historical business data are retrieved using Spark; otherwise, 6 months of historical business data are retrieved using Spark. Based on the preset statistical dimension, the maximum, minimum, and average values ​​of the historical business data are determined. Based on the preset fluctuation ratio corresponding to the preset statistical dimension, a preset data range is calculated. Specifically, the preset data range includes a preset average range, a preset maximum range, and a preset minimum range. Spark is a fast and general-purpose computing engine for large-scale data processing.

[0063] S230. Obtain business data from the business data source and calculate the current data volume corresponding to the business data.

[0064] S240. Determine whether the current data volume is less than or equal to the preset data volume. If yes, execute S250; otherwise, execute S260.

[0065] S250. Determine whether the business data meets the preset data range. If yes, execute S270; otherwise, execute S280.

[0066] S260. Based on the current data volume, execute the second warning operation.

[0067] S270, End.

[0068] S280. Based on business data, execute the first alert operation.

[0069] In one embodiment, optionally, if the business data does not meet the preset data range, a first warning operation is performed based on the business data, including: if the business data does not meet the preset average range, an early warning operation is performed based on the business data; if the business data is greater than the preset maximum value in the preset maximum value range, or if the business data is less than the preset minimum value in the preset minimum value range, an alarm operation is performed based on the business data.

[0070] Specifically, the warning operation includes, but is not limited to, at least one of text warning operation, sound prompt operation, indicator light prompt operation, and email warning operation. Specifically, the alarm operation includes, but is not limited to, at least one of text alarm operation, sound prompt operation, indicator light prompt operation, and email alarm operation. For example, the warning operation may be a text warning operation, and the alarm operation may be a sound prompt operation. Alternatively, the warning operation may be an indicator light operation with a first flashing frequency, and the alarm operation may be an indicator light operation with a second flashing frequency, wherein the first flashing frequency is less than the second flashing frequency. The specific warning operation and alarm operation are not limited here.

[0071] Based on the above embodiments, optionally, after performing the first warning operation based on business data, the method further includes: storing the business data corresponding to the execution of the first warning operation and the business code corresponding to the business data in the data storage space; when a business access instruction sent by the business front end is detected, determining whether the access business code in the business access instruction exists in the data storage space; if it exists, performing a masking operation on the business data corresponding to the access business code, and sending the access page data generated based on the masked business data to the business front end.

[0072] Specifically, business codes are used to identify business data. For example, business codes include, but are not limited to, at least one of numbers, letters, and special characters. For instance, the business code can be the same as the current data volume corresponding to the business data. The specific encoding format of the business code is not limited here. For example, the data storage space can be a columnar database management system (ClickHouse).

[0073] For example, the blocking operation can be a hiding operation, meaning the displayed content corresponding to the business data in the accessed page data is empty. The blocking operation can also be a replacement operation, meaning the displayed content corresponding to the business data in the accessed page data is replaced with replacement content. For example, the replacement content can be "--" or a status code; the specific replacement content is not limited here.

[0074] The advantage of this setup is that it prevents users from seeing incorrect business data, thereby improving the user experience.

[0075] Figure 5 This is a flowchart of a page access method provided in Embodiment 2 of the present invention. Specifically, when a business access instruction sent by the business frontend is received, it is determined whether the access business code in the business access instruction exists in ClickHouse. If it exists, it indicates that the business data is abnormal. The business data is replaced with a status code, access page data is generated based on the replaced status code, and the access page data is returned to the business frontend. If it does not exist, it indicates that the business data is normal. Access page data is directly generated based on the business data and returned to the business frontend.

[0076] The technical solution of this embodiment obtains historical business data corresponding to a preset statistical dimension, and determines a preset data range based on the historical business data and a preset floating ratio corresponding to the preset statistical dimension. This solves the problem that the preset data range needs to be set in advance, and allows the preset data range to change with the changes in actual business data, thereby improving the flexibility of the preset data range and further ensuring the accuracy of the verified business data.

[0077] Example 3

[0078] Figure 6 This is a schematic diagram of a business data processing device provided in Embodiment 3 of the present invention. This embodiment is applicable to situations where business data is verified. The device can be implemented using software and / or hardware and can be configured in the terminal settings. The business data processing device includes: a business data acquisition module 310, a business data comparison module 320, and a data volume warning module 330.

[0079] Among them, the business data acquisition module 310 is used to acquire business data from the business data source and to count the current data volume corresponding to the business data.

[0080] The business data comparison module 320 is used to compare the business data with the preset data range if the current data volume is less than or equal to the preset data volume, and to execute the first warning operation based on the business data if the business data does not meet the preset data range.

[0081] The data volume warning module 330 is used to execute a second warning operation based on the current data volume if the current data volume is greater than the preset data volume.

[0082] The technical solution of this embodiment compares the current data volume corresponding to the statistical business data with the preset data volume. When the current data volume is less than or equal to the preset data volume, the business data is further compared with the preset data range. Alert operations are performed from two dimensions: data volume and data dimension. This solves the problem of untimely detection of abnormal data in business data and improves the timeliness and accuracy of verifying business data.

[0083] Based on the above technical solution, optionally, the device further includes:

[0084] The historical business data acquisition module is used to acquire historical business data corresponding to preset statistical dimensions;

[0085] The preset data range determination module is used to determine the preset data range based on historical business data and a preset fluctuation ratio corresponding to a preset statistical dimension; wherein, the preset data range includes at least one of a preset mean range, a preset maximum range, and a preset minimum range.

[0086] Based on the above technical solution, optionally, the preset statistical dimensions include a daily dimension and / or a monthly dimension, and correspondingly, the historical business data includes first historical business data corresponding to the daily dimension and / or second historical business data corresponding to the monthly dimension, the preset fluctuation ratio includes a first preset fluctuation ratio corresponding to the daily dimension and / or a second preset fluctuation ratio corresponding to the monthly dimension, and the preset data range includes a first preset data range corresponding to the daily dimension and / or a second preset data range corresponding to the monthly dimension.

[0087] Based on the above technical solution, an optional historical business data acquisition module is specifically used for:

[0088] If the preset statistical dimensions include the daily dimension, then the first historical business data within the first time period will be obtained;

[0089] And / or,

[0090] If the preset statistical dimensions include a monthly dimension, then the second historical business data within the second time period will be retrieved.

[0091] Based on the above technical solution, optionally, the business data comparison module 320 is specifically used for:

[0092] If the business data does not meet the preset average range, then an early warning operation will be performed based on the business data.

[0093] If the business data is greater than the preset maximum value within the preset maximum value range, or if the business data is less than the preset minimum value within the preset minimum value range, then an alarm operation will be performed based on the business data.

[0094] Based on the above technical solution, optionally, the device further includes:

[0095] The page access module is used to store the business data corresponding to the execution of the first warning operation and the business code corresponding to the business data in the data storage space; when a business access instruction sent by the business front-end is detected, it determines whether the access business code in the business access instruction exists in the data storage space; if it exists, it performs a masking operation on the business data corresponding to the access business code, and sends the access page data generated based on the masked business data to the business front-end.

[0096] Based on the above technical solution, optionally, the business data acquisition module 310 is specifically used for:

[0097] When the dynamic reading tool reads a new message generated by the business data source, it sends the new message and the corresponding business data to the distributed publish-subscribe messaging system.

[0098] A distributed data stream computing program is used to consume new messages and business data from a distributed publish-subscribe messaging system. Based on the business data table name in the new message, the current data volume corresponding to the business data is calculated.

[0099] The business data processing apparatus provided in this embodiment of the invention can be used to execute the business data processing method provided in this embodiment of the invention, and has the corresponding functions and beneficial effects of executing the method.

[0100] It is worth noting that in the embodiments of the above-mentioned business data processing device, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0101] Example 4

[0102] Figure 7This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. The present invention provides services for the implementation of the business data processing method of the above embodiments of the present invention, and can be configured with the business data processing device in the above embodiments. Figure 7 A block diagram is shown of an exemplary electronic device 12 suitable for implementing embodiments of the present invention. Figure 7 The electronic device 12 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0103] like Figure 7 As shown, the electronic device 12 is represented in the form of a general-purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0104] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0105] Electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0106] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (… Figure 7 Not shown; usually referred to as a "hard drive"). Although Figure 7 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0107] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.

[0108] Electronic device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with the electronic device 12, and / or with any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through input / output (I / O) interface 22. Furthermore, electronic device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. Figure 7 As shown, network adapter 20 communicates with other modules of electronic device 12 via bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0109] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the business data processing method provided in the embodiments of the present invention.

[0110] The aforementioned electronic devices have solved the problem of untimely detection of abnormal data in business data, and improved the timeliness and accuracy of business data verification.

[0111] Example 5

[0112] Embodiment 5 of the present invention also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform a method for processing business data, the method comprising:

[0113] Retrieve business data from the business data source and calculate the current data volume corresponding to the business data;

[0114] If the current data volume is less than or equal to the preset data volume, the business data is compared with the preset data range. If the business data does not meet the preset data range, the first warning operation is executed based on the business data.

[0115] If the current data volume is greater than the preset data volume, then the second warning operation will be executed based on the current data volume.

[0116] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0117] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0118] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0119] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0120] Of course, the computer-executable instructions provided in the embodiments of the present invention are not limited to the above-described method operations, but can also perform related operations in the business data processing method provided in any embodiment of the present invention.

[0121] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for processing business data, characterized in that, include: Retrieve business data of the same business type from the business data source. For each piece of business data retrieved, count the number of retrieved business data and use it as the current data volume corresponding to the business data. The business data source is a data storage system that stores business data. If the current data volume is less than or equal to the preset data volume, the business data is compared with the preset data range. If the business data does not meet the preset data range, a first warning operation is performed based on the business data. The preset data volume represents the data volume threshold corresponding to the business type, and the preset data range represents the parameter value range of the business data. If the current data volume is greater than the preset data volume, then a second warning operation is performed based on the current data volume.

2. The method according to claim 1, characterized in that, The method further includes: Retrieve historical business data corresponding to preset statistical dimensions; Based on the historical business data and the preset fluctuation ratio corresponding to the preset statistical dimension, a preset data range is determined; wherein, the preset data range includes at least one of a preset mean range, a preset maximum range, and a preset minimum range.

3. The method according to claim 2, characterized in that, The preset statistical dimensions include a daily dimension and / or a monthly dimension. Correspondingly, the historical business data includes a first historical business data corresponding to the daily dimension and / or a second historical business data corresponding to the monthly dimension. The preset fluctuation ratio includes a first preset fluctuation ratio corresponding to the daily dimension and / or a second preset fluctuation ratio corresponding to the monthly dimension. The preset data range includes a first preset data range corresponding to the daily dimension and / or a second preset data range corresponding to the monthly dimension.

4. The method according to claim 3, characterized in that, The acquisition of historical business data corresponding to the preset statistical dimensions includes: If the preset statistical dimensions include the daily dimension, then the first historical business data within the first time period will be obtained; And / or, If the preset statistical dimensions include a monthly dimension, then the second historical business data within the second time period will be retrieved.

5. The method according to claim 2, characterized in that, If the business data does not meet the preset data range, then based on the business data, a first warning operation is performed, including: If the business data does not meet the preset average range, an early warning operation will be performed based on the business data. If the business data is greater than the preset maximum value in the preset maximum value range, or if the business data is less than the preset minimum value in the preset minimum value range, then an alarm operation is performed based on the business data.

6. The method according to any one of claims 1-5, characterized in that, After performing the first alert operation based on the business data, the method further includes: The business data corresponding to the execution of the first warning operation and the business code corresponding to the business data are stored in the data storage space; When a business access instruction sent by the business front end is detected, it is determined whether the access business code in the business access instruction exists in the data storage space; If it exists, a masking operation is performed on the business data corresponding to the access business code, and the access page data generated based on the masked business data is sent to the business front end.

7. The method according to any one of claims 1-5, characterized in that, The process of acquiring business data of the same business type from the business data source, and counting the number of acquired business data for each acquired business data, which is used as the current data volume corresponding to the business data, includes: When the dynamic reading tool reads a new message generated by the business data source, it sends the new message and the corresponding business data to the distributed publish-subscribe messaging system. A distributed data stream computing program is used to consume new messages and business data in the distributed publish-subscribe messaging system. Based on the business data table name in the new message, the number of business data in the business data table is counted as the current data volume corresponding to the business data. The business data table contains business data of the same business type.

8. A business data processing apparatus, characterized in that, include: The business data acquisition module is used to retrieve business data of the same business type from the business data source. For each piece of business data retrieved, the number of retrieved business data is counted and used as the current data volume corresponding to the business data. The business data source is a data storage system that stores business data. The business data comparison module is used to compare the business data with a preset data range if the current data volume is less than or equal to a preset data volume, and to perform a first warning operation based on the business data if the business data does not meet the preset data range. The preset data volume represents the data volume threshold of the business type, and the preset data range represents the parameter value range of the business data. The data volume warning module is used to execute a second warning operation based on the current data volume if the current data volume is greater than the preset data volume.

9. An electronic device, characterized in that, The electronic device includes: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the business data processing method as described in any one of claims 1-7.

10. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the business data processing method as described in any one of claims 1-7.

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