Abnormal data intelligent monitoring method, device, electronic device and storage medium

Abstract

The present invention relates to the field of data processing and discloses a method for intelligent monitoring of abnormal data, the method comprising: obtaining business data, converting the business data into a format and importing it into a preset data table to obtain a target data table; identifying whether the target data table has a dependency relationship; if the target data table has a dependency relationship, identifying the input table and output table in the target data table for consistency verification, and generating a first abnormal data monitoring result; if the target data table does not have a dependency relationship, identifying the basic data and indicator data in the target data table, and performing a consistency verification on the basic data according to a time attribute to generate a second abnormal data monitoring result, calculating the data ring value of the indicator data according to the time attribute, and judging whether it is within a preset range, and generating a third abnormal data monitoring result based on the judgment result. The present invention can improve the monitoring efficiency of abnormal data.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method, device, electronic device and computer-readable storage medium for intelligent monitoring of abnormal data. Background Art

[0002] As the trust industry continues to develop, the business data it generates also increases. Since most of the business data relies on manual collection and organization, many abnormal data will appear in the business data collected and organized manually. Therefore, it is increasingly important to ensure that abnormal data is monitored and processed in a timely manner.

[0003] At present, the monitoring of abnormal data is mainly done through simple manual verification and then reporting to the supervisory authorities. This may result in the omission of abnormal data, which will affect the accuracy of abnormal data monitoring. Even if the abnormal data is manually verified, a manual email needs to be sent to the owner of each abnormal data for confirmation, which will affect the efficiency of abnormal data monitoring. Summary of the Invention

[0004] The present invention provides a method, device, electronic device and computer-readable storage medium for intelligent monitoring of abnormal data, the main purpose of which is to improve the monitoring efficiency of abnormal data.

[0005] To achieve the above objectives, the present invention provides an abnormal data intelligent monitoring method, comprising:

[0006] Acquire business data, convert the format of the business data, and import the business data after format conversion into a preset data table to obtain a target data table;

[0007] Identify whether the target data table has a dependency relationship;

[0008] If the target data table has a dependency relationship, identifying an input table and an output table in the target data table, and performing a consistency check on the input table and the output table to generate a first abnormal data monitoring result in the target data table;

[0009] If the target data table does not have a dependency relationship, identifying the basic data and indicator data in the target data table, and performing a consistency check on the basic data according to a preset time attribute to generate a second abnormal data monitoring result in the target data table;

[0010] The data year-on-year value of the indicator data is calculated according to the preset time attribute, and it is determined whether the data year-on-year value is within a preset range. Based on the determination result, a third abnormal data monitoring result in the target data table is generated.

[0011] Optionally, configuring intelligent monitoring of the abnormal data according to the abnormal data includes:

[0012] determining a format of the business data;

[0013] Determining whether the format of the business data is a preset format;

[0014] If the format of the business data is not the preset format, a preset format conversion tool is called to convert the format of the business data into the preset format.

[0015] Optionally, importing the format-converted business data into a preset data table to obtain a target data table includes:

[0016] Selecting an import position of the business data after the format conversion in the preset data table;

[0017] and configuring a method for importing the business data after the format conversion into the preset data table;

[0018] According to the import position and the import method, the business data after the format conversion is imported into the preset data table.

[0019] Optionally, identifying whether the target data table has a dependency relationship includes:

[0020] Obtain the data source of each data table in the target data table;

[0021] Determine the data type of each data table in the target data table according to the data source;

[0022] If the data type is a preset type, identifying that a dependency relationship exists between the data tables;

[0023] If the data type is not a preset type, it is determined that no dependency exists between the data tables.

[0024] Optionally, if the target data table has a dependency relationship, identifying an input table and an output table in the target data table, performing a consistency check on the input table and the output table, and obtaining a first abnormal data monitoring result of the target data table includes:

[0025] Using a preset timed task to scan the contents of the input table and the output table respectively to obtain input content and output content;

[0026] Locating identical sequence positions of the input content and the output content;

[0027] Based on the identical sequence positions, an input table and an output table in the target data table are identified.

[0028] Optionally, the identifying of basic data and indicator data in the target data table includes:

[0029] Querying the data fields in the target data table and identifying the data structure of the data fields;

[0030] When the data structure is disordered, determining that the data corresponding to the disordered data structure in the target data table is basic data;

[0031] When the data structure is ordered, it is determined that the data corresponding to the ordered data structure in the target data table is indicator data.

[0032] Optionally, performing consistency check on the basic data according to a preset time attribute to obtain a second abnormal data monitoring result in the target data table includes:

[0033] By comparing the first data and the second data of the basic data within the preset time attribute;

[0034] If inconsistent data occurs between the first data and the second data, the basic data corresponding to the inconsistent data is used as abnormal data, and the second abnormal data monitoring result is generated based on the abnormal data.

[0035] In order to solve the above problems, the present invention further provides an abnormal data intelligent monitoring device, the device comprising:

[0036] A business data import module is used to obtain business data, convert the business data into a format, and import the business data after format conversion into a preset data table to obtain a target data table;

[0037] A relationship identification module is used to identify whether the target data table has a dependency relationship;

[0038] a data table verification module, configured to, if a dependency relationship exists between the target data table, identify an input table and an output table in the target data table, and perform consistency verification on the input table and the output table to generate a first abnormal data monitoring result in the target data table;

[0039] a data verification module configured to, if no dependency exists in the target data table, identify basic data and indicator data in the target data table, and perform consistency verification on the basic data according to a preset time attribute to generate a second abnormal data monitoring result in the target data table;

[0040] The year-on-year value judgment module is used to calculate the year-on-year value of the indicator data according to the preset time attribute, and judge whether the year-on-year value is within the preset range, and generate the third abnormal data monitoring result in the target data table according to the judgment result.

[0041] In order to solve the above problem, the present invention further provides an electronic device, comprising:

[0042] at least one processor; and,

[0043] a memory communicatively connected to the at least one processor; wherein,

[0044] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to implement the above-mentioned abnormal data intelligent monitoring method.

[0045] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one computer program is stored. The at least one computer program is executed by a processor in an electronic device to implement the above-mentioned abnormal data intelligent monitoring method.

[0046] It can be seen that the embodiment of the present invention converts the format of the business data so that the messy data can be encapsulated in the same data format, ensuring the construction premise of importing the business data after subsequent format conversion into the preset data table, and importing the business data after format conversion into the preset data table to obtain the target data table. By identifying whether there is a dependency relationship between the target data tables, the embodiment of the present invention further classifies the target data tables so that the corresponding strategy can be selected to perform anomaly detection on the target data table. Furthermore, if the target data table has a dependency relationship, the embodiment of the present invention performs a consistency check on the input table and the output table to verify the data that is different from the input table and the output table, thereby determining the abnormal data in the target data table. If the target data table does not have a dependency relationship, the basic data and indicator data in the target data table are identified to monitor the future data in the target data table based on the basic data and indicator data in the existing target data table. Therefore, the intelligent monitoring method, device, electronic device and computer-readable storage medium of abnormal data proposed in the embodiment of the present invention can improve the monitoring efficiency of the abnormal data. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A flow chart of an abnormal data intelligent monitoring method provided by one embodiment of the present invention;

[0048] Figure 2 A schematic diagram of a module of an intelligent monitoring device for abnormal data according to an embodiment of the present invention;

[0049] Figure 3 A schematic diagram of the internal structure of an electronic device for implementing an intelligent monitoring method for abnormal data according to an embodiment of the present invention;

[0050] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0051] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0052] An embodiment of the present invention provides a method for intelligent monitoring of abnormal data. The execution subject of the method for intelligent monitoring of abnormal data includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present invention. In other words, the method for intelligent monitoring of abnormal data can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0053] Reference Figure 1 FIG. 1 is a flow chart of an abnormal data intelligent monitoring method according to an embodiment of the present invention. In an embodiment of the present invention, the abnormal data intelligent monitoring method includes the following steps S1-S5:

[0054] S1. Acquire business data, convert the format of the business data, and import the business data after format conversion into a preset data table to obtain a target data table;

[0055] In the embodiment of the present invention, the business data is format-converted so that the messy data can be encapsulated in the same data format, thereby ensuring that the business data after subsequent format conversion can be imported into a preset data table.

[0056] The data format is the format in which data is stored in a file or record, which may be in the form of numerical values, characters, or binary numbers, and is described by the data type and data length.

[0057] Among them, the business data refers to the data generated in the business scenario, which is generated based on different business scenarios. For example, in the e-commerce industry scenario, the business data can be marketing data, traffic data, membership data, transaction and service data, etc.; in the banking business scenario, the business data can be customer transaction details, deposit and loan ratios, customer credential information, etc.

[0058] The data type generally refers to a data element, also known as a data element. This is a data unit whose definition, identification, representation, and allowed values ​​are described by a set of attributes. In a specific context, it is typically used to construct a semantically correct, independent, and unambiguous unit of information about a specific concept. A data element can be understood as the basic unit of data. Arranging several related data elements in a specific order to form an overall structure constitutes a data model.

[0059] As an embodiment of the present invention, the format conversion of the business data includes: determining the format of the business data, and judging whether the format of the business data is a preset format; if the format of the business data is not the preset format, calling a preset format conversion tool to convert the format of the business data into the preset format.

[0060] The format conversion tool is software that converts one format into a specific one. The format conversion tool can be compiled using a scripting language, such as JS. For example, some technology products only support software in a certain format. For example, if a mobile phone only supports MP4 video playback, and the video format downloaded from the computer is Rmvb, then the Rmvb video format needs to be converted to MP4 using the format conversion tool.

[0061] In another optional embodiment of the present invention, the format of the business data is determined by scanning the suffix of the data.

[0062] The suffixes include .pdf, .gif, .pngPNG, .swf, .mp3, etc.

[0063] In another optional embodiment of the present invention, whether the format of the business data is a preset format is determined by performing a consistency check between the scanned suffix name of the data and the preset suffix name. If the suffix name of the data is inconsistent with the preset suffix name, a preset format conversion tool is called to convert the format of the business data into the preset format. If the suffix name of the data is consistent with the preset suffix name, no format conversion is required.

[0064] In an embodiment of the present invention, the business data after format conversion is imported into a preset data table so that the data in the same format can be arranged neatly according to preset rules (such as sorting the data in the same format by date, length, etc.) to improve the efficiency of subsequent scanning work.

[0065] The preset data table consists of a table name, table fields and table records.

[0066] As an embodiment of the present invention, the business data after format conversion is imported into a preset data table to obtain a target data table, including: selecting an import position of the business data after format conversion in the preset data table; and configuring an import method of the business data after format conversion in the preset data table; and executing the import of the business data after format conversion in the preset data table according to the import position and the import method.

[0067] The import location refers to the storage sequence location of the format-converted business data in the preset data table, and the import method refers to the rules followed by the format-converted business data when imported into the preset data table. Furthermore, in an optional embodiment of the present invention, the import method is configured using the FolderBrowserDialog method.

[0068] S2. Identify whether the target data table has a dependency relationship;

[0069] In the embodiment of the present invention, by identifying whether there is a dependency relationship between the target data tables, the target data tables are further classified, so that corresponding strategies can be subsequently selected to perform anomaly detection on the target data tables.

[0070] In the dependency relationship of the target data table, the upstream is usually the party that generates data, and the downstream is usually the party that receives data. From the perspective of a river, the upstream is usually the upper part and source of the water flow, and the downstream is usually the lower part and end point of the water flow. Therefore, in the present invention, the dependency relationship of the target data table can be understood as follows: the upstream is the dependent party, and the downstream is the dependent party. For example, if system A depends on system B, and system B depends on system C, then system B is the upstream of system A, and system A is the downstream of system B; system C is the upstream of both systems A and B. If service A depends on service B, then service B is the upstream of service A, and service A is the downstream of service B.

[0071] As an embodiment of the present invention, identifying whether the target data table has a dependency relationship includes: obtaining the data source of each data table in the target data table; judging the data type of each data table in the target data table based on the data source; if the data type is a preset type, identifying that the data table has a dependency relationship; if the data type is not a preset type, identifying that the data table does not have a dependency relationship.

[0072] The data source refers to the production location of the data in the data table, which includes upstream systems, data warehouses, and regulatory marts. The data type refers to the dimensional features used to characterize each data table in the target data table, and the preset types include input types, output types, etc.

[0073] S3. If a dependency relationship exists between the target data table, identify an input table and an output table in the target data table, perform consistency check on the input table and the output table, and obtain a first abnormal data monitoring result of the target data table.

[0074] In an embodiment of the present invention, when a dependency relationship exists between the target data tables, it means that the abnormal data monitoring system needs to detect whether the existing data contains abnormal data, rather than to perform preliminary prediction processing on future abnormal data. Therefore, the embodiment of the present invention identifies the input table and the output table in the target data table to ensure the premise of monitoring abnormal data in the subsequent target data table.

[0075] The input table refers to the target data table aggregated from data generated by the upstream system, and the output table refers to the output data table synchronized by the downstream system within a preset time. Furthermore, the upstream system includes a data production system, which is used to generate data sources, and the downstream system includes a data supervision system, which can integrate data sources from different systems to implement functions such as data governance, data mining, and data decision-making.

[0076] In an embodiment of the present invention, by performing a consistency check on the input table and the output table, data that differs between the input table and the output table can be verified, thereby determining abnormal data in the target data table. The purpose of generating the first abnormal data monitoring result in the target data table is to promptly issue an alarm or take corresponding measures when an abnormality occurs; subsequently, make predictions based on historical monitoring data; promptly locate the root cause of the problem based on monitoring alarms; and present information through visual charts to facilitate intuitive access to information.

[0077] The first abnormal data monitoring result includes the time, location, name, scope and category of the abnormal data.

[0078] In one embodiment of the present invention, the consistency check of the input table and the output table to obtain the first abnormal data monitoring result of the target data table includes: using a preset timer task to scan the contents of the input table and the output table respectively to obtain the input content and the output content; locating the same sequence position of the input content and the output content; based on the same sequence position, identifying whether the data of the input content and the output content are consistent; and when the data are inconsistent, generating the first abnormal data monitoring result of the target data table.

[0079] Among them, the preset scheduled task refers to the task of performing data consistency verification according to the preset time, and the identification of whether the data of the input content and the output content are consistent refers to whether the update date is consistent, whether the data quantity is consistent, whether the data storage address is consistent, and whether the data sorting is consistent.

[0080] Among them, the preset scheduled task can be compiled through a programming language, such as Java language; the contents of the input table and the output table are scanned through a preset script, and the preset script can be compiled through a scripting language, such as js scripting language; the same sequence position can be located through the Find method of the DataRowCollection object.

[0081] S4. If there is no dependency relationship in the target data table, identify the basic data and indicator data in the target data table, perform consistency check on the basic data according to a preset time attribute, and obtain a second abnormal data monitoring result in the target data table.

[0082] In an embodiment of the present invention, when there is no dependency relationship between the target data table, by identifying the basic data and indicator data in the target data table, abnormal monitoring of future data in the target data table is achieved based on the basic data and indicator data in the existing target data table.

[0083] Among them, the basic data refers to the original data obtained through direct investigation, which is generally called first-hand or direct statistical data; the indicator data refers to the data from third-party investigations, and the data published after processing and aggregation is usually called second-hand or indirect statistical data, such as KPI indicator data.

[0084] In one embodiment of the present invention, the identification of basic data and indicator data in the target data table includes: querying the data fields in the target data table; and identifying the data structure of the data fields; when the data structure is unordered, determining that the data in the target data table corresponding to the unordered data structure is basic data; when the data structure is ordered, determining that the data in the target data table corresponding to the ordered data structure is indicator data.

[0085] Each field in the data field is an attribute. For example, a Student table may contain a column of fields such as studentname and id. These columns of fields together form a set of records. A data structure refers to a collection of data elements that have one or more specific relationships with each other, including both logical and physical structures.

[0086] In an optional embodiment of the present invention, the data fields in the target data table are queried using a search algorithm.

[0087] Among them, the search algorithm only performs search operations without changing the data elements in the table. This type of search is called static search; on the contrary, it performs data insertion or deletion operations while performing search operations. This type is called dynamic search.

[0088] Furthermore, the embodiment of the present invention performs consistency check on the basic data according to a preset time attribute in order to determine whether the basic data is consistent, so as to detect abnormal data in the target data table.

[0089] The preset time attribute is used as a prerequisite for consistency verification of the basic data, such as setting a consistency verification between the data of the current day and the data of the previous day.

[0090] In one embodiment of the present invention, the consistency check of the basic data is performed according to a preset time attribute to obtain the second abnormal data monitoring result in the target data table, including: comparing the first data and the second data of the basic data within the preset time attribute, if the first data and the second data are inconsistent, the basic data corresponding to the inconsistent data is used as abnormal data, and the second abnormal data monitoring result is generated based on the abnormal data.

[0091] The first data may be the above-mentioned data of the day, and the second data may be the data of the previous day, which are determined based on the preset time attribute.

[0092] S5. Calculate the data year-on-year value of the indicator data according to the preset time attribute, and determine whether the data year-on-year value is within a preset range. Generate a third abnormal data monitoring result in the target data table based on the determination result.

[0093] The embodiment of the present invention can improve the efficiency of detecting abnormal data in the target data table by calculating the data year-on-year value of the indicator data according to the preset time attribute and judging whether the data year-on-year value is within a preset range.

[0094] The month-on-month value of the data refers to the month-on-month growth rate, which generally refers to the growth rate compared with the previous period.

[0095] In an optional embodiment of the present invention, the data ratio value of the indicator data is calculated using the following formula:

[0096] P=(MN ) / M*100%

[0097] Among them, P represents the data year-on-year value, M represents the number of generated data in this period in the preset time attribute, and N represents the number of generated data in the previous period in the preset time attribute.

[0098] Furthermore, in an optional embodiment of the present invention, the preset range can be set to be within the range of [a, b]. If the data ratio value is within the range of [a, b], it means that the data ratio value is within the preset range. If the data ratio value is not within the range of [a, b], it means that the data ratio value is not within the preset range.

[0099] The range [a, b] refers to the average of the year-on-year values ​​of historical normal data in the target data table ±2 times the standard deviation.

[0100] In one embodiment of the present invention, the third abnormal data monitoring result in the target data table is generated based on the judgment result, including: when the judgment result is that the data year-on-year value is not within the preset range, the data corresponding to the data year-on-year value in the target data table is taken as abnormal data, and the third abnormal data monitoring result is generated based on the abnormal data.

[0101] It can be seen that the embodiment of the present invention converts the format of the business data so that the messy data can be encapsulated in the same data format, ensuring the construction premise of importing the business data after subsequent format conversion into the preset data table, and importing the business data after format conversion into the preset data table to obtain the target data table. By identifying whether there is a dependency relationship between the target data tables, the embodiment of the present invention further classifies the target data tables so that the corresponding strategy can be selected to perform anomaly detection on the target data tables. Furthermore, if the target data tables have a dependency relationship, the embodiment of the present invention performs a consistency check on the input table and the output table to verify the data that is different from the input table and the output table, thereby determining the abnormal data in the target data table. If the target data table does not have a dependency relationship, the basic data and indicator data in the target data table are identified, so that the future data in the target data table can be monitored for anomalies based on the basic data and indicator data in the existing target data table. Therefore, the intelligent monitoring method for abnormal data proposed in the embodiment of the present invention can improve the monitoring efficiency of abnormal data.

[0102] like Figure 2 FIG. 1 is a functional module diagram of the abnormal data intelligent monitoring device of the present invention.

[0103] The intelligent abnormal data monitoring device 100 described in the present invention can be installed in an electronic device. Depending on the functionality implemented, the device may include a business data import module 101, a relationship identification module 102, a data table verification module 103, a data verification module 104, and a month-on-month value determination module 105. A module, also referred to as a unit, refers to a series of computer program segments that can be executed by a processor in an electronic device and perform a fixed function. These are stored in the memory of the electronic device.

[0104] In this embodiment, the functions of each module / unit are as follows:

[0105] The business data import module 101 is used to obtain business data, convert the business data into a format, and import the business data after the format conversion into a preset data table to obtain a target data table;

[0106] The relationship identification module 102 is used to identify whether the target data table has a dependency relationship;

[0107] The data table verification module 103 is configured to, if a dependency relationship exists between the target data table, identify an input table and an output table in the target data table, and perform a consistency check on the input table and the output table to generate a first abnormal data monitoring result in the target data table;

[0108] The data verification module 104 is configured to, if the target data table does not have a dependency relationship, identify the basic data and indicator data in the target data table, and perform consistency verification on the basic data according to a preset time attribute to generate a second abnormal data monitoring result in the target data table;

[0109] The year-on-year value judgment module 105 is used to calculate the year-on-year value of the indicator data according to the preset time attribute, and judge whether the year-on-year value is within a preset range, and generate the third abnormal data monitoring result in the target data table based on the judgment result.

[0110] In detail, each module in the abnormal data intelligent monitoring device 100 in the embodiment of the present invention adopts the same method as above when in use. Figure 1 The technical means are the same as the abnormal data intelligent monitoring method described in and can produce the same technical effects, so they will not be repeated here.

[0111] like Figure 3 FIG. 1 is a schematic diagram of the structure of an electronic device 1 for implementing an intelligent monitoring method for abnormal data according to the present invention.

[0112] The electronic device 1 may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13. It may also include a computer program stored in the memory 11 and executable on the processor 10, such as an abnormal data intelligent monitoring program.

[0113] In some embodiments, the processor 10 may be comprised of an integrated circuit, such as a single packaged integrated circuit or a plurality of packaged integrated circuits with the same or different functions, including a combination of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control core (Control Unit) of the electronic device 1, connecting the various components of the electronic device 1 via various interfaces and circuits. It executes or runs programs or modules stored in the memory 11 (e.g., executing an intelligent abnormal data monitoring program) and accesses data stored in the memory 11 to execute various functions and process data of the electronic device 1.

[0114] The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 1. Furthermore, the memory 11 may include both an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can be used not only to store application software installed in the electronic device 1 and various types of data, such as the code of the abnormal data intelligent monitoring program, but also to temporarily store data that has been output or is about to be output.

[0115] The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. This bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0116] The communication interface 13 is used for communication between the electronic device 1 and other devices, and includes a network interface and an employee interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices 1. The employee interface may be a display, an input unit (such as a keyboard), or optionally, a standard wired interface or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual employee interface.

[0117] Figure 3 Only the electronic device 1 having components is shown, and it can be understood by those skilled in the art that Figure 3The structure shown does not constitute a limitation on the electronic device 1 , and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0118] For example, although not shown, the electronic device 1 may further include a power supply (e.g., a battery) to power various components. Preferably, the power supply may be logically connected to the at least one processor 10 via a power management device, thereby enabling functions such as charge management, discharge management, and power consumption management via the power management device. The power supply may further include any components such as one or more DC or AC power supplies, a recharging device, a power failure detection circuit, a power converter or inverter, and a power status indicator. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which are not further described here.

[0119] It should be understood that the embodiment is for illustrative purposes only and the scope of the patent invention is not limited to this structure.

[0120] The abnormal data intelligent monitoring program stored in the memory 11 of the electronic device 1 is a combination of multiple computer programs. When running in the processor 10, it can achieve the following:

[0121] Acquire business data, convert the format of the business data, and import the business data after format conversion into a preset data table to obtain a target data table;

[0122] Identify whether the target data table has a dependency relationship;

[0123] If the target data table has a dependency relationship, identifying an input table and an output table in the target data table, and performing a consistency check on the input table and the output table to generate a first abnormal data monitoring result in the target data table;

[0124] If the target data table does not have a dependency relationship, identifying the basic data and indicator data in the target data table, and performing a consistency check on the basic data according to a preset time attribute to generate a second abnormal data monitoring result in the target data table;

[0125] The data year-on-year value of the indicator data is calculated according to the preset time attribute, and it is determined whether the data year-on-year value is within a preset range. Based on the determination result, a third abnormal data monitoring result in the target data table is generated.

[0126] Specifically, the specific implementation method of the processor 10 for the above computer program can refer to Figure 1 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0127] Furthermore, if the modules / units integrated into the electronic device 1 are implemented as software functional units and sold or used as independent products, they may be stored in a non-volatile computer-readable storage medium. The computer-readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0128] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by the processor of the electronic device 1, the computer program can achieve:

[0129] Acquire business data, convert the format of the business data, and import the business data after format conversion into a preset data table to obtain a target data table;

[0130] Identify whether the target data table has a dependency relationship;

[0131] If the target data table has a dependency relationship, identifying an input table and an output table in the target data table, and performing a consistency check on the input table and the output table to generate a first abnormal data monitoring result in the target data table;

[0132] If the target data table does not have a dependency relationship, identifying the basic data and indicator data in the target data table, and performing a consistency check on the basic data according to a preset time attribute to generate a second abnormal data monitoring result in the target data table;

[0133] The data year-on-year value of the indicator data is calculated according to the preset time attribute, and it is determined whether the data year-on-year value is within a preset range. Based on the determination result, a third abnormal data monitoring result in the target data table is generated.

[0134] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the module division is merely a logical function division, and other division methods may be used in actual implementation.

[0135] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0136] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0137] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0138] Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be embraced therein. Any reference to a figure in a claim should not be construed as limiting the claim to which it relates.

[0139] Embodiments of the present invention can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) refers to the theories, methods, techniques, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results.

[0140] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. Second-order terms are used to indicate names and do not imply any particular order.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for intelligent monitoring of abnormal data, characterized in that: The method comprises: Acquire business data, convert the format of the business data, and import the business data after format conversion into a preset data table to obtain a target data table; Determining the data type of each data table in the target data table based on the data source of each data table in the target data table, and identifying whether there is a dependency relationship between the data tables in the target data table based on whether the data type is a preset type; If a dependency exists, identifying the input table and output table in the target data table, performing a consistency check on the input table and the output table, verifying the data that is different between the input table and the output table, thereby identifying abnormal data in the target data table to generate a first abnormal data monitoring result in the target data table. The input table refers to the target data table aggregated from the data generated by the data production system, and the output table refers to an output data table synchronized once by the data supervision system within a preset time. If no dependency exists, querying the data fields in the target data table, identifying the data structure of the data fields, identifying the basic data and indicator data in the target data table according to whether the data structure is an unordered data structure or an ordered data structure, performing a consistency check on the first data and the second data corresponding to the first preset time and the second preset time of the basic data, thereby identifying inconsistent data between the first data and the second data to generate a second abnormal data monitoring result in the target data table; Calculate the data year-on-year value of the indicator data according to the first preset time and the second preset time, and determine whether the data year-on-year value is within a preset range. When it is determined that the data year-on-year value is not within the preset range, use the data corresponding to the data year-on-year value in the target data table as abnormal data, and generate a third abnormal data monitoring result in the target data table based on the abnormal data.

2. The method for intelligent monitoring of abnormal data according to claim 1, characterized in that: The format conversion of the business data includes: Determining the format of the business data, and judging whether the format of the business data is a preset format; If the format of the business data is not the preset format, a preset format conversion tool is called to convert the format of the business data into the preset format.

3. The method for intelligent monitoring of abnormal data according to claim 1, wherein: The step of importing the format-converted business data into a preset data table to obtain a target data table includes: Selecting an import position of the business data after the format conversion in the preset data table; and configuring a method for importing the business data after the format conversion into the preset data table; According to the import position and the import method, the business data after the format conversion is imported into the preset data table.

4. The method for intelligent monitoring of abnormal data according to claim 1, wherein: The identifying an input table and an output table in the target data table, and performing a consistency check on the input table and the output table, includes: Using a preset timed task to scan the contents of the input table and the output table respectively to obtain input content and output content; Locating identical sequence positions of the input content and the output content; According to the identical sequence position, it is identified whether the data of the input content and the output content are consistent.

5. The abnormal data intelligent monitoring method according to any one of claims 1 to 4, characterized in that: The step of identifying the basic data and indicator data in the target data table according to whether the data structure is an unordered data structure or an ordered data structure includes: When the data structure is an unordered data structure, determining that the data corresponding to the unordered data structure in the target data table is basic data; When the data structure is an ordered data structure, it is determined that the data corresponding to the ordered data structure in the target data table is indicator data.

6. An intelligent monitoring device for abnormal data, characterized in that: For implementing the method for intelligently monitoring abnormal data according to any one of claims 1 to 5, the device comprises: A business data import module is used to obtain business data, convert the business data into a format, and import the business data after format conversion into a preset data table to obtain a target data table; A relationship identification module is used to identify whether the target data table has a dependency relationship; a data table verification module, configured to, if a dependency relationship exists between the target data table, identify an input table and an output table in the target data table, and perform consistency verification on the input table and the output table to generate a first abnormal data monitoring result in the target data table; a data verification module configured to, if no dependency exists in the target data table, identify basic data and indicator data in the target data table, and perform consistency verification on the basic data according to a preset time attribute to generate a second abnormal data monitoring result in the target data table; The year-on-year value judgment module is used to calculate the year-on-year value of the indicator data according to the preset time attribute, and judge whether the year-on-year value is within the preset range, and generate the third abnormal data monitoring result in the target data table according to the judgment result.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the abnormal data intelligent monitoring method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the abnormal data intelligent monitoring method according to any one of claims 1 to 5 is implemented.

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