Data monitoring method, electronic device, storage medium and program product

Abstract

The embodiment of the invention provides a data monitoring method, electronic equipment, a storage medium and a program product. The invention relates to the field of data monitoring. The method comprises the steps of obtaining multiple pieces of to-be-monitored data within a preset duration; determining a data category of each piece of to-be-monitored data, and monitoring each piece of to-be-monitored data according to a monitoring rule of the data category; monitoring results of all the to-be-monitored data are obtained; performing data processing on the to-be-monitored data corresponding to each abnormal monitoring result to obtain processed monitoring data; and sending all the abnormal monitoring results and all the processed monitoring data to a user side. The method is used for achieving the technical effect of perfecting the data monitoring process.

Description

Technical Field

[0001] The present application relates to the field of data monitoring technology, and in particular to a data monitoring method, electronic equipment, storage medium, and program product. Background Art

[0002] In the digital economy, data has become a core enterprise asset, and data monitoring technology has become critical infrastructure supporting digital transformation, improving decision-making efficiency, and achieving intelligent operations. Data accuracy, consistency, and integrity directly impact its reliability, making data monitoring a strategic priority for industry technology development.

[0003] Traditional data monitoring methods mainly focus on field-level data standardization processing, emphasizing the unification of data formats and data types.

[0004] However, as data complexity increases, field-level monitoring only focuses on the structure and format of a single piece of data, and fails to implement a multi-dimensional monitoring and evaluation mechanism for data. Therefore, existing technologies have the technical problem of incomplete data monitoring. Summary of the Invention

[0005] The embodiments of the present application provide a data monitoring method, an electronic device, a storage medium, and a program product to achieve the technical effect of improving the data monitoring process.

[0006] In a first aspect, an embodiment of the present application provides a data monitoring method, comprising:

[0007] Obtain multiple monitoring data within a preset time period;

[0008] Determine the data category of each data to be monitored, and monitor each data to be monitored according to the monitoring rules of the data category; obtain monitoring results for all data to be monitored; wherein each data category corresponds to a different monitoring rule; the monitoring results include normal monitoring results and abnormal monitoring results;

[0009] Perform data processing on the monitored data corresponding to each abnormal monitoring result to obtain processed monitoring data;

[0010] Send all abnormal monitoring results and all processed monitoring data to the user end.

[0011] In a possible implementation, the data categories include encryption package level, file level, table level, field level, and custom level.

[0012] In a possible implementation, monitoring each data to be monitored according to the monitoring rules of the data category includes:

[0013] If the data category is at the encrypted package level, the monitored data is monitored according to the encrypted package rules; wherein the encrypted package rules include one or more of the following: encrypted package arrival rules, encrypted package reception rules, encrypted package parsing rules, and encrypted package decompression rules;

[0014] If the data category is file level, the monitored data is monitored according to the file rules; wherein the file rules include one or more of the following: file receiving rules, file decompression rules, file loading rules and empty file verification rules;

[0015] If the data type is table level, the monitored data is monitored according to table rules. Table rules include one or more of the following: empty table check rule, historical threshold check rule, manually set threshold check rule, data volume comparison rule compared with the previous day, and data volume comparison check rule.

[0016] If the data category is field level, the monitored data is monitored according to the encryption package rules; wherein the encryption package rules include one or more of the following: null value check rule, value range check rule and non-negative check rule;

[0017] If the data category is a custom level, the data to be monitored is monitored according to the encryption package rules; wherein the encryption package rules include one or more of the following: date rationality verification rules and table association field rules.

[0018] In a possible implementation, before processing the monitored data corresponding to each abnormal monitoring result, the following steps are included:

[0019] The monitoring results obtained for each data to be monitored are stored in a data access table; wherein the data access table at least includes the data date, monitoring result, monitoring status, rule type and monitoring object of each data to be monitored.

[0020] In a possible implementation, processing the monitored data corresponding to each abnormal monitoring result includes:

[0021] Determine a data monitoring anomaly category for each abnormal monitoring result; wherein the data monitoring anomaly category includes data missing and data anomaly;

[0022] If the data monitoring anomaly category of the data to be monitored is missing data, the missing data is interpolated;

[0023] If the data monitoring anomaly category of the data to be monitored is data anomaly, the abnormal data is removed and / or corrected.

[0024] In a possible implementation, after obtaining the processed monitoring data, the following steps are included:

[0025] Generate a monitoring report based on all abnormal monitoring results; the monitoring report includes monitoring warnings, monitoring situation summaries, and abnormal monitoring result handling strategies;

[0026] Send monitoring reports and processed monitoring data to the user end.

[0027] In a second aspect, an embodiment of the present application provides a data monitoring device, comprising:

[0028] An acquisition module is used to acquire multiple monitored data within a preset time period;

[0029] The monitoring module is used to determine the data category of each data to be monitored, monitor each data to be monitored according to the monitoring rules of the data category, and obtain monitoring results for all data to be monitored; wherein each data category corresponds to a different monitoring rule; the monitoring results include normal monitoring results and abnormal monitoring results;

[0030] A processing module is used to process the monitored data corresponding to each abnormal monitoring result to obtain processed monitoring data;

[0031] The sending module is used to send all abnormal monitoring results and all processed monitoring data to the user end.

[0032] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a memory, a processor;

[0033] The memory stores computer-executable instructions;

[0034] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0035] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementation methods of the first aspect.

[0036] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0037] The data monitoring method, electronic device, storage medium and program product provided in the embodiments of the present application obtain multiple data to be monitored within a preset time period; determine the data category of each data to be monitored, and monitor each data to be monitored according to the monitoring rules of the data category; obtain monitoring results of all data to be monitored; process the data to be monitored corresponding to each abnormal monitoring result to obtain processed monitoring data; and send all abnormal monitoring results and all processed monitoring data to the user end, thereby achieving the technical effect of improving the data monitoring process from multiple dimensions. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0039] Figure 1 Schematic diagram of the data monitoring method provided in this application Figure 1 ;

[0040] Figure 2 Schematic diagram of the data monitoring method provided in this application Figure 2 ;

[0041] Figure 3 A schematic diagram of the structure of the data monitoring device provided in this application;

[0042] Figure 4 This is a hardware diagram of the data monitoring device provided in this application.

[0043] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0044] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent like or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present application. Rather, they are merely examples of methods and approaches consistent with certain aspects of the present application, as detailed in the appended claims.

[0045] Against the backdrop of the booming digital economy, the critical position of data as a core enterprise asset has become increasingly prominent. It is not only the cornerstone for driving business innovation, but also the core production factor supporting the digital transformation of enterprises. In this context, data monitoring technology has become a key infrastructure for ensuring the credibility of enterprise data assets and improving decision-making efficiency. By dynamically monitoring the accuracy, consistency, and integrity of data, data monitoring technology provides a solid guarantee for the intelligent operation of enterprises in complex business scenarios. Currently, the technical frameworks commonly used in the industry mainly focus on field-level data standardization processing. By strictly verifying the format, type, and business rules of the data, high-quality output of single-source data is ensured. This technical approach has been very effective in early scenarios with small data volumes and relatively simple structures, and has become a fundamental means to promote the construction of data governance systems.

[0046] With the exponential growth of data volumes and the significant increase in business complexity, existing technical frameworks based on field-level monitoring have exposed significant limitations. Existing methods focus excessively on the structural consistency of individual pieces of data, lacking the ability to assess overall data quality across multiple dimensions. This includes the ability to detect underlying issues such as anomalies in data distribution, abnormal cross-system relationships, and deviations from business logic. This limitation makes it difficult for enterprises to identify systemic biases caused by data interaction, data fusion, or changes in the external environment, which in turn undermines the credibility of data-based decisions. In particular, in business scenarios involving complex requirements such as the fusion of multi-source heterogeneous data and dynamic business rule changes, the traditional "single-point defense" model of traditional technologies no longer meets the requirements of modern enterprises for refined data quality governance. Consequently, existing technologies suffer from incomplete data monitoring.

[0047] The data monitoring method, electronic device, storage medium and program product provided in the embodiments of the present application obtain multiple data to be monitored within a preset time period; determine the data category of each data to be monitored, and monitor each data to be monitored according to the monitoring rules of the data category; obtain monitoring results of all data to be monitored; process the data to be monitored corresponding to each abnormal monitoring result to obtain processed monitoring data; and send all abnormal monitoring results and all processed monitoring data to the user end, thereby achieving the technical effect of improving the data monitoring process from multiple dimensions.

[0048] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0049] Figure 1 Schematic diagram of the data monitoring process provided for this application Figure 1 ,like Figure 1 As shown, the method includes:

[0050] S101: Acquire multiple pieces of data to be monitored within a preset time period.

[0051] In this embodiment, a data foundation for dynamic monitoring is established by limiting the time window to ensure that subsequent analysis can be carried out based on a timely and complete data set. Data records that meet the monitoring objectives are extracted from a variety of data sources such as databases, log systems, sensor networks or API interfaces at preset time intervals, and filtered according to timestamps or other identification fields to eliminate historical data that exceeds the preset time length. At the same time, in order to ensure the accuracy and efficiency of data collection, the process may involve technical means such as concurrent requests, incremental synchronization or cache optimization, and ultimately form a collection containing multiple data samples as the input basis for subsequent monitoring rule matching and anomaly detection. The design of this link needs to take into account the balance between data timeliness and system resource consumption, avoiding monitoring lags due to long collection cycles, and preventing frequent requests from causing performance pressure on data sources.

[0052] S102: Determine the data category of each data to be monitored, monitor each data to be monitored according to the monitoring rule of the data category, and obtain monitoring results of all data to be monitored.

[0053] In this embodiment, each data category corresponds to a different monitoring rule; the monitoring results include normal monitoring results and abnormal monitoring results. The data is divided into different categories according to predefined business logic or characteristic attributes, and differentiated monitoring rules are formulated according to the characteristics of each type of data, so as to ensure that the monitoring process is both targeted and flexible. The corresponding monitoring rules are dynamically loaded according to the data category, and the data is verified one by one through the rule engine; during the monitoring process, the verification status of each data is recorded, and a monitoring result containing a normal or abnormal identification is generated, which may be accompanied by detailed diagnostic information or anomaly description; the monitoring results of all data will be summarized into a unified output to provide a basis for subsequent exception processing and result feedback.

[0054] S103: Process the monitored data corresponding to each abnormal monitoring result to obtain processed monitoring data.

[0055] In this embodiment, to improve data quality and the reliability of the monitoring system, abnormal data discovered during the monitoring process is corrected, supplemented, or converted in a targeted manner to ensure that the data presented to users conforms to business logic and effectively supports decision-making analysis. This process enables rapid response to various abnormal situations while ensuring the accuracy of processing results and business consistency.

[0056] S104: Send all abnormal monitoring results and all processed monitoring data to the user end.

[0057] In this embodiment, by feeding back the abnormal monitoring results and processed monitoring data to the user end, efficient information interaction can be achieved between the monitoring system and the user, ensuring that the user can obtain data abnormality events and their processing status in a timely and accurate manner, thereby providing a reliable basis for business decision-making and supporting subsequent problem investigation and optimization and improvement.

[0058] The data monitoring method, electronic device, storage medium and program product provided in the embodiments of the present application obtain multiple data to be monitored within a preset time period; determine the data category of each data to be monitored, and monitor each data to be monitored according to the monitoring rules of the data category; obtain monitoring results of all data to be monitored; process the data to be monitored corresponding to each abnormal monitoring result to obtain processed monitoring data; and send all abnormal monitoring results and all processed monitoring data to the user end, thereby achieving the technical effect of improving the data monitoring process from multiple dimensions.

[0059] Figure 2 Schematic diagram of the data monitoring method provided in this application Figure 2 ,like Figure 2 As shown, this embodiment Figure 1 Based on the embodiment, the data monitoring method is described in detail, and the method includes:

[0060] S201: Acquire a plurality of monitored data within a preset time period.

[0061] S202: Determine the data category of each data to be monitored, monitor each data to be monitored according to the monitoring rule of the data category, and obtain monitoring results of all data to be monitored.

[0062] In this embodiment, data categories include encryption package level, file level, table level, field level and custom level. Different data categories have different monitoring rules.

[0063] In one possible implementation, if the data category is at the encrypted packet level, the data to be monitored is monitored according to the encrypted packet rules; wherein the encrypted packet rules include one or more of the following: encrypted packet arrival rules, encrypted packet reception rules, encrypted packet parsing rules, and encrypted packet decompression rules. The monitoring rules can be shown in the following table:

[0064]

[0065] In one possible implementation, if the data category is file-level, the data to be monitored is monitored according to the file rules; wherein the file rules include one or more of the following: file receiving rules, file decompression rules, file loading rules, and empty file verification rules. The monitoring rules can be shown in the following table:

[0066]

[0067] In one possible implementation, if the data category is table-level, the data to be monitored is monitored according to table rules; table rules include one or more of the following: empty table check rule, historical threshold check rule, manually set threshold check rule, data volume comparison rule compared with the previous day, and data volume comparison check rule. The monitoring rules can be shown in the following table:

[0068]

[0069] In one possible implementation, if the data category is field level, the data to be monitored is monitored according to the encryption package rules; wherein the encryption package rules include one or more of the following: null value check rule, value range check rule, and non-negative check rule. The monitoring rules can be shown in the following table:

[0070]

[0071] In one possible implementation, if the data category is custom, the data to be monitored is monitored according to the encryption package rules; wherein the encryption package rules include one or more of the following: date rationality verification rules and table association field rules. The monitoring rules can be shown in the following table:

[0072]

[0073] S203: Store the monitoring results obtained for each data to be monitored in a data access table.

[0074] In this embodiment, the data access table at least includes the data date, monitoring result, monitoring status, rule type and monitoring object of each data to be monitored.

[0075] S204. Determine the data monitoring anomaly category of each abnormal monitoring result; if the data monitoring anomaly category of the data to be monitored is data missing, interpolate the missing data; if the data monitoring anomaly category of the data to be monitored is data abnormality, remove the abnormal data and / or correct the abnormal data.

[0076] In this embodiment, the data monitoring anomaly categories include data missing and data anomaly. Optionally, if the data monitoring anomaly category of the data to be monitored is data missing, the missing or incomplete data is reasonably copied or interpolated. First, the missing values ​​in the data are identified, and then a suitable interpolation method is selected based on the distribution characteristics of the data, such as the mean, median, and historical value to copy the missing data. If the data monitoring anomaly category of the data to be monitored is data anomaly, it is determined whether the data anomaly is an outlier or discrete point in the data set. If so, the outlier is identified through statistical analysis or visualization methods, and then the elimination criteria are determined based on the business logic and data characteristics, and the outliers that do not meet the criteria are deleted from the data set. If the data anomaly is determined to be erroneous or inconsistent data in the data set, corrections are performed to ensure the accuracy of the data. Determine the accuracy standards and rules for the data, identify the data items that violate these rules, and perform data corrections according to the preset rules.

[0077] S205: Generate a monitoring report based on all abnormal monitoring results; and send the monitoring report and processed monitoring data to the user end.

[0078] In this embodiment, the monitoring report reports monitoring warnings, monitoring situation summaries, and abnormal monitoring result handling strategies; the monitoring reports are classified and counted according to the preset classification standards to generate a monitoring warning section containing key information such as warning level, occurrence period, and related data items; on this basis, the overall data quality situation during this monitoring cycle is further summarized, including the normal data ratio, abnormal data trend, main problem distribution, etc., to form a monitoring situation summary with data support; for each type of abnormal event, the corresponding solution description is automatically generated according to the built-in handling strategy library, and traceable data comparison and verification results before and after processing are provided. This solution not only ensures the timely access to important information, but also realizes the full-link observability, auditability, and traceability of abnormal events through structured reporting, providing a solid decision-making support foundation for data governance, while meeting the company's stringent requirements for data security and compliance.

[0079] Figure 3 A schematic diagram of the structure of the data monitoring device provided in this application, such as Figure 3 As shown, the data monitoring device 30 provided in this embodiment includes:

[0080] An acquisition module 301 is used to acquire a plurality of monitored data within a preset time period;

[0081] Monitoring module 302 is used to determine the data category of each data to be monitored, monitor each data to be monitored according to the monitoring rules of the data category, and obtain monitoring results for all data to be monitored; wherein each data category corresponds to a different monitoring rule; the monitoring results include normal monitoring results and abnormal monitoring results;

[0082] The processing module 303 is used to process the monitored data corresponding to each abnormal monitoring result to obtain processed monitoring data;

[0083] The sending module 304 is used to send all abnormal monitoring results and all processed monitoring data to the user end.

[0084] In a possible implementation, the data categories include encryption package level, file level, table level, field level, and custom level.

[0085] In a possible implementation, the monitoring module 302 is further configured to:

[0086] If the data category is at the encrypted package level, the monitored data is monitored according to the encrypted package rules; wherein the encrypted package rules include one or more of the following: encrypted package arrival rules, encrypted package reception rules, encrypted package parsing rules, and encrypted package decompression rules;

[0087] If the data category is file level, the monitored data is monitored according to the file rules; wherein the file rules include one or more of the following: file receiving rules, file decompression rules, file loading rules and empty file verification rules;

[0088] If the data type is table level, the monitored data is monitored according to table rules. Table rules include one or more of the following: empty table check rule, historical threshold check rule, manually set threshold check rule, data volume comparison rule compared with the previous day, and data volume comparison check rule.

[0089] If the data category is field level, the monitored data is monitored according to the encryption package rules; wherein the encryption package rules include one or more of the following: null value check rule, value range check rule and non-negative check rule;

[0090] If the data category is a custom level, the data to be monitored is monitored according to the encryption package rules; wherein the encryption package rules include one or more of the following: date rationality verification rules and table association field rules.

[0091] In a possible implementation, the processing module 303 is further configured to:

[0092] The monitoring results obtained for each data to be monitored are stored in a data access table; wherein the data access table at least includes the data date, monitoring result, monitoring status, rule type and monitoring object of each data to be monitored.

[0093] In a possible implementation, the processing module 303 is further configured to:

[0094] Determine a data monitoring anomaly category for each abnormal monitoring result; wherein the data monitoring anomaly category includes data missing and data anomaly;

[0095] If the data monitoring anomaly category of the data to be monitored is missing data, the missing data is interpolated;

[0096] If the data monitoring anomaly category of the data to be monitored is data anomaly, the abnormal data is removed and / or corrected.

[0097] In a possible implementation, the sending module 304:

[0098] Generate a monitoring report based on all abnormal monitoring results; the monitoring report includes monitoring warnings, monitoring situation summaries, and abnormal monitoring result handling strategies;

[0099] Send monitoring reports and processed monitoring data to the user end.

[0100] The data monitoring device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effects are similar and will not be described in detail in this embodiment.

[0101] Figure 4 This is a hardware diagram of the data monitoring device provided in this application. Figure 4 As shown, the electronic device 40 provided in this embodiment includes: at least one processor 401 and a memory 402. Optionally, the device 40 further includes a communication component 403. The processor 401, the memory 402 and the communication component 403 are connected via a bus 404.

[0102] In a specific implementation process, at least one processor 401 executes the computer-executable instructions stored in the memory 402, so that the at least one processor 401 performs the above method.

[0103] The specific implementation process of the processor 401 can be found in the above method embodiment. Its implementation principle and technical effects are similar and will not be repeated here in this embodiment.

[0104] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASICs), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly executed by a hardware processor or by a combination of hardware and software modules within the processor.

[0105] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage.

[0106] A bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be categorized as address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0107] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0108] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0109] The readable storage medium may be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0110] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0111] The division of units is merely a logical functional division; actual implementations may employ alternative divisions, such as combining or integrating multiple units or components into another system, or omitting or disabling certain features. Furthermore, any direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via an interface, method, or unit, and may be electrical, mechanical, or otherwise.

[0112] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0113] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0114] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0115] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0116] Finally, it should be noted that those skilled in the art will readily identify other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. The present invention is not limited to the precise structure described above and illustrated in the accompanying drawings, and various modifications and variations may be made without departing from the scope thereof. The scope of the present invention is limited solely by the appended claims.

Claims

1. A data monitoring method, characterized in that: include: Obtain multiple monitoring data within a preset time period; Determine the data category of each data to be monitored, and monitor each data to be monitored according to the monitoring rules of the data category; Obtain monitoring results for all data to be monitored; wherein each data category corresponds to a different monitoring rule; the monitoring results include normal monitoring results and abnormal monitoring results; Perform data processing on the monitored data corresponding to each abnormal monitoring result to obtain processed monitoring data; Send all abnormal monitoring results and all processed monitoring data to the user end.

2. The method according to claim 1, characterized in that The data categories include encryption package level, file level, table level, field level and custom level.

3. The method according to claim 2, characterized in that The step of monitoring each data to be monitored according to the monitoring rules of the data category includes: If the data category is an encrypted package level, the monitored data is monitored according to the encrypted package rules; wherein the encrypted package rules include one or more of the following: encrypted package arrival rules, encrypted package reception rules, encrypted package parsing rules, and encrypted package decompression rules; If the data category is file level, the data to be monitored is monitored according to the file rules; wherein the file rules include one or more of the following: file receiving rules, file decompression rules, file loading rules and empty file verification rules; If the data category is table level, the monitored data is monitored according to table rules; wherein the table rules include one or more of the following: empty table check rule, historical threshold check rule, manually set threshold check rule, data volume comparison rule compared with the previous day, and data volume comparison check rule; If the data category is field level, the data to be monitored is monitored according to the encryption package rules; wherein the encryption package rules include one or more of the following: null value check rule, value range check rule and non-negative check rule; If the data category is a custom level, the data to be monitored is monitored according to encryption package rules; wherein the encryption package rules include one or more of the following: date rationality verification rules and table association field rules.

4. The method according to claim 1, wherein Before processing the data to be monitored corresponding to each abnormal monitoring result, the following steps are included: The monitoring results obtained for each data to be monitored are stored in a data access table; wherein the data access table at least includes the data date, monitoring result, monitoring status, rule type and monitoring object of each data to be monitored.

5. The method according to claim 1, wherein The data processing of the monitored data corresponding to each abnormal monitoring result includes: Determining a data monitoring anomaly category for each abnormal monitoring result; wherein the data monitoring anomaly category includes data missing and data anomaly; If the data monitoring anomaly category of the data to be monitored is missing data, interpolating the missing data; If the data monitoring anomaly category of the data to be monitored is data anomaly, the abnormal data is eliminated and / or the abnormal data is corrected.

6. The method according to claim 1, characterized in that After obtaining the processed monitoring data, the method further includes: Generate a monitoring report based on all abnormal monitoring results; wherein the monitoring report reports monitoring warnings, monitoring situation summaries and abnormal monitoring result handling strategies; The monitoring report and the processed monitoring data are sent to the user end.

7. A data monitoring device, characterized in that: include: An acquisition module is used to acquire multiple monitored data within a preset time period; A monitoring module is configured to determine the data category of each data to be monitored, monitor each data to be monitored according to the monitoring rules for the data category, and obtain monitoring results for all data to be monitored; wherein each data category corresponds to a different monitoring rule; and the monitoring results include normal monitoring results and abnormal monitoring results; A processing module is used to process the monitored data corresponding to each abnormal monitoring result to obtain processed monitoring data; The sending module is used to send all abnormal monitoring results and all processed monitoring data to the user end.

8. An electronic device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 6 when the computer program is executed by a processor.

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