Data service risk monitoring method, device, electronic device and storage medium

Abstract

The present disclosure provides a data service risk monitoring method, device, electronic device and storage medium, and relates to the field of computer technology. Among them, the data service risk monitoring method includes: detecting a calling business scenario, determining a data monitoring rule according to the business scenario; determining a monitoring parameter component according to the data monitoring rule, and the monitoring parameter component is used to perform risk control assessment on the interactive data in the business scenario; configuring sampling parameters of the data interface of the business scenario according to the monitoring parameter component, and obtaining the sampling results through the data interface; determining the performance indicators of the monitoring parameter component according to the sampling results of the data interface; and adjusting the data monitoring rules according to the performance indicators. Through the technical solution disclosed in the present disclosure, the monitoring efficiency and reliability of data services are improved, the response time of the processor is shortened, and the computing pressure is reduced.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to a data service risk monitoring method, device, electronic device and storage medium. Background Art

[0002] The risk control engine carries a number of business rules, which are used to monitor data services.

[0003] However, as the amount of data services increases and the types of data services increase, the engine will have to execute more and more business rules. When there are large concurrent requests, slow rules may cause the engine to run slower or even crash, which not only leads to a long response time for the risk control engine, but also affects the execution of data services.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The purpose of the present disclosure is to provide a data service risk monitoring method, device, electronic device and storage medium, which at least to a certain extent overcome the problem of long service response time caused by slow execution of business rules in related technologies.

[0006] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0007] According to one aspect of the present disclosure, a method for risk monitoring of data services is provided, including: detecting a calling business scenario, and determining a data monitoring rule according to the business scenario; determining a monitoring parameter component according to the data monitoring rule, the monitoring parameter component being used to perform risk control assessment on interactive data in the business scenario; configuring sampling parameters of a data interface of the business scenario according to the monitoring parameter component, and obtaining sampling results through the data interface; determining a performance indicator of the monitoring parameter component according to the sampling result of the data interface; and adjusting the data monitoring rule according to the performance indicator.

[0008] In one embodiment of the present disclosure, before detecting the calling of the business scenario, it also includes: logically encapsulating the business scenario, data monitoring rules and monitoring parameter components.

[0009] In one embodiment of the present disclosure, determining the performance indicator of the monitoring parameter component based on the sampling results of the data interface includes: adding and averaging the sampling array fed back by the data interface, and determining the calculation result as the sampling result; determining the time consumption and call volume of the monitoring parameter component based on the sampling result; and determining the response time of the monitoring parameter component based on the time consumption and call volume.

[0010] In one embodiment of the present disclosure, adjusting data monitoring rules according to performance indicators includes: obtaining a preset response time under a business scenario; determining whether the preset response time is less than or equal to the response time; if it is determined that the preset response time is less than or equal to the response time, determining the monitoring parameter component as a first-category monitoring parameter component; and adjusting the data monitoring rules according to the first-category monitoring parameter component.

[0011] In one embodiment of the present disclosure, adjusting the data monitoring rules according to the first category monitoring parameter component includes: adjusting the sampling parameters in the monitoring process according to the first category monitoring parameter component until the preset response time is less than or equal to the response time; or deleting the first category monitoring parameter components in the monitoring process in descending order of response time until the preset response time is less than or equal to the response time.

[0012] In one embodiment of the present disclosure, the sampling parameters include at least one of a time period of sampling data, a monitoring parameter component identifier, a monitoring performance dimension, a monitoring granularity, a data sampling indicator, and a data sampling type.

[0013] In one embodiment of the present disclosure, the business scenario includes at least one of a login scenario, a marketing scenario, and a financial transaction scenario.

[0014] According to another aspect of the present disclosure, there is provided a risk monitoring device for data services, comprising: a risk control module, for detecting a calling business scenario and determining a data monitoring rule according to the business scenario; a determination module, for determining a monitoring parameter component according to the data monitoring rule, the monitoring parameter component being used to perform a risk control assessment on interactive data in the business scenario; a configuration module, for performing sampling parameter configuration on a data interface of the business scenario according to the monitoring parameter component, and obtaining a sampling result through the data interface; the determination module is also used to determine a performance indicator of the monitoring parameter component according to the sampling result of the data interface; and an adjustment module, for adjusting the data monitoring rule according to the performance indicator.

[0015] According to another aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute any one of the above-mentioned data service risk monitoring methods by executing the executable instructions.

[0016] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, any of the above-mentioned data service risk monitoring methods is implemented.

[0017] The risk monitoring solution for data services provided by the embodiments of the present disclosure configures sampling parameters for the data interface of the business scenario according to the monitoring parameter component for the called business scenario, and determines the performance index of the monitoring parameter component according to the sampling result of the data interface. Based on this, at least one data monitoring rule with a slow response in the business scenario is extracted, and by adjusting the data monitoring rule, the risks and hidden dangers of the data service are reduced, and the security, reliability and response rate of the data service are improved.

[0018] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0020] Figure 1 A schematic diagram showing the architecture of a risk monitoring solution for data services in an embodiment of the present disclosure;

[0021] Figure 2 A flow chart showing a method for monitoring risk of data services in an embodiment of the present disclosure is shown;

[0022] Figure 3 A flow chart showing another data service risk monitoring method according to an embodiment of the present disclosure;

[0023] Figure 4 A flow chart showing another data service risk monitoring method according to an embodiment of the present disclosure;

[0024] Figure 5 A flow chart showing another data service risk monitoring method according to an embodiment of the present disclosure;

[0025] Figure 6 A flow chart showing another data service risk monitoring method according to an embodiment of the present disclosure;

[0026] Figure 7 A schematic diagram showing a risk monitoring device for data services in an embodiment of the present disclosure is shown;

[0027] Figure 8 A schematic diagram of an electronic device in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0028] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the disclosure will be more comprehensive and complete and to fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0029] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0030] The solution provided in the present application configures sampling parameters of the data interface of the business scenario according to the monitoring parameter component for the called business scenario, and determines the performance indicators of the monitoring parameter component according to the sampling results of the data interface. Based on this, at least one data monitoring rule with a slower response in the business scenario is extracted, and by adjusting the data monitoring rules, the risks and hidden dangers of the data business are reduced, the reliability and response rate of the risk control engine are improved, and it is also beneficial to improve the security, reliability and response rate of the data business.

[0031] The solution provided in the embodiments of the present application involves technologies such as monitoring and risk control, which are specifically explained through the following embodiments.

[0032] Figure 1 A schematic diagram showing a risk monitoring architecture for data services in an embodiment of the present disclosure is shown.

[0033] like Figure 1 As shown, the risk monitoring architecture of data business includes a slow rule positioning tool 102, a monitoring system 104 and a risk control business system 106, and is based on a combination of "monitoring system", "business scenario" and "rules" to quickly locate slow rules.

[0034] Among them, the risk control business system 106 includes multiple monitoring keys, such as login scenario monitoring key, marketing scenario monitoring key, communication scenario monitoring key and payment scenario monitoring key, but not limited to these.

[0035] Specifically, after accessing the risk control business system 106 in the login scenario, the user's login operation will be reported to the risk control business system 106, and the risk control engine will execute the corresponding login rules to determine whether there is a risk in this request.

[0036] The inventors have discovered that in some scenarios with large business demands, the response time of the risk control business system 106 is a challenge due to the sudden surge in traffic. Therefore, when the monitoring rules of the risk control business system 106 do not meet the expected requirements or do not meet the business requirements, it is necessary to optimize the rules in a targeted manner. It is particularly important to quickly locate slow rules, which is also one of the normalized tasks.

[0037] The technical solution disclosed in the present invention combines the ability of the monitoring system 104 to obtain performance data, obtains the rules in a certain scenario through the risk control business system 106, and processes the returned performance data after batch execution in the monitoring system 104 to obtain the execution results of the rules and display them on the front-end interface, thereby intuitively and quickly locating the slow rules, greatly improving work efficiency.

[0038] In addition, the present disclosure has made a layer of logic encapsulation outside the monitoring system 104 and the risk control business system 106. The encapsulated logic is: obtaining the preset relationship corresponding to the scene in the risk control business system 106 to obtain all monitoring key data; then obtaining the performance indicators of each monitoring key by calling the monitoring system 104, and returning them to the slow rule positioning tool 102. After the slow rule positioning tool 102 has processed it, a detailed list of slow rules is displayed on the front end.

[0039] The details list includes the identification, duration, and call volume of each monitoring key in the data service scenario.

[0040] The following will describe in more detail the steps of the data service risk monitoring method in this example implementation in conjunction with the accompanying drawings and embodiments.

[0041] Figure 2 A flow chart of a data service risk monitoring method in an embodiment of the present disclosure is shown. The method provided in the embodiment of the present disclosure can be executed by any electronic device with computing and processing capabilities, such as a server or a terminal, but is not limited thereto. In the following example description, the terminal is used as the execution subject for example description.

[0042] like Figure 2 As shown, the risk monitoring method for a terminal executing a data service comprises the following steps:

[0043] Step S202: Detecting a calling business scenario and determining a data monitoring rule according to the business scenario.

[0044] In the above embodiment, all data monitoring rules are determined by business scenarios, and it is further determined whether the data monitoring rules are slow rules. The slow rules are determined based on the total time of executing the data business.

[0045] Step S204, determining a monitoring parameter component according to the data monitoring rule, where the monitoring parameter component is used to perform risk control assessment on the interaction data in the business scenario.

[0046] Step S206: configure sampling parameters for the data interface of the business scenario according to the monitoring parameter component, and obtain sampling results through the data interface.

[0047] In the above embodiment, the above-mentioned parameters include monitoring key identifier, start time, end time, monitoring granularity and monitoring type, etc. The time period of the monitoring key is determined by the start time and end time. The monitoring granularity is the frequency of monitoring and analyzing the data according to the monitoring rules.

[0048] The monitoring type refers to the sampling type of the monitoring data, for example, extracting one data for every 9 monitoring data, or extracting one data for every 99 monitoring data, or extracting one data for every 999 monitoring data, but is not limited thereto.

[0049] Step S208: determining the performance index of the monitoring parameter component according to the sampling result of the data interface.

[0050] In the above embodiment, by calling the monitoring system, an array within the selected time is returned, and the array is summed and then divided by the length of the array to determine the average value of the array, which is used as an indicator for the slow rule.

[0051] Step S210: adjusting data monitoring rules according to performance indicators.

[0052] In the above example, after determining the slow rule according to the performance indicators, the monitoring key corresponding to the slow rule can be cancelled to monitor the data service, or the monitoring granularity of the monitoring key can be reduced, but not limited to this, so as to improve the monitoring efficiency of the data service, reduce the risks and hidden dangers of the data service, improve the reliability and response rate of the risk control engine, and also help to improve the response rate of the data service.

[0053] exist Figure 2 Based on the steps shown, Figure 3 As shown, before detecting the calling business scenario, it also includes:

[0054] Step S302: logically encapsulate the business scenarios, data monitoring rules and monitoring parameter components.

[0055] In the above embodiment, by logically encapsulating the business scenarios, data monitoring rules and monitoring parameter components, the monitoring keys are encapsulated for various business scenarios to improve the monitoring efficiency of data services.

[0056] exist Figure 2 Based on the steps shown, Figure 4 As shown, the performance indicators of the monitoring parameter component determined according to the sampling results of the data interface include:

[0057] Step S4082, performing summation and mean calculation on the sampling array fed back by the data interface, and determining the calculation result as the sampling result.

[0058] Step S4084, determining the time consumption and call amount of the monitoring parameter component according to the sampling result.

[0059] Step S4086, determine the response time of the monitoring parameter component according to the time consumption and the call amount.

[0060] In the above embodiment, the response time of the monitoring parameter component is determined by the time consumption and the call amount to reflect the time occupied by the monitoring key in the data service, or the pressure of the monitoring key on the server operation.

[0061] The time consumption is usually in milliseconds, and the time consumption of slow rules is generally greater than 120 milliseconds.

[0062] exist Figure 2 and Figure 3 Based on the steps shown, Figure 5 As shown in the figure, adjusting the data monitoring rules according to the performance indicators includes:

[0063] Step S5102, obtaining a preset response time in a business scenario.

[0064] Step S5104, determine whether the preset response time is less than or equal to the response time, if so, execute step S5106, if not, execute step S5102.

[0065] Step S5106: If it is determined that the preset response time is less than or equal to the response time, the monitoring parameter component is determined as a first-type monitoring parameter component.

[0066] Step S5108: adjust the data monitoring rules according to the first type of monitoring parameter components.

[0067] In the above embodiment, if it is determined that the preset response time is less than or equal to the response time, the monitoring parameter component is determined as a first-class monitoring parameter component, and the data monitoring rules are adjusted according to the first-class monitoring parameter component to improve the monitoring efficiency of data services and reduce the operating pressure of the monitoring process.

[0068] exist Figure 2 , Figure 3 and Figure 5 Based on the steps shown, Figure 6 As shown, adjusting the data monitoring rules according to the first type of monitoring parameter components includes:

[0069] Step S61082, adjusting the sampling parameters in the monitoring process according to the first type of monitoring parameter component until the preset response time is less than or equal to the response time.

[0070] Or step S61084, delete the first type of monitoring parameter components in the monitoring process in order of response time from large to small, until the preset response time is less than or equal to the response time.

[0071] In the above embodiment, the first type of monitoring parameter components in the monitoring process are deleted in order of response time from large to small until the preset response time is less than or equal to the response time, so as to minimize the impact of the first type of monitoring parameter components (i.e., the monitoring key of the slow rule) on the response time of the data service.

[0072] According to an embodiment of the present disclosure, the sampling parameters include at least one of a time period of sampling data, a monitoring parameter component identifier, a monitoring performance dimension, a monitoring granularity, a data sampling indicator, and a data sampling type.

[0073] In the above embodiment, an embodiment of the configuration of the sampling parameters may be shown in Table 1 below:

[0074] Table 1 Big promotion - full scene monitoring performance data

[0075] Setting parameters Setting content Select app Sky Eye (risk-tianyan) Select scene (select sourcetype) Jeep Detection performance dimension Tp99 Monitoring granularity 1 minute Start time 2020 / 05 / 19 02:36 End Time 2020 / 05 / 19 02:38

[0076] The above Tp99 means taking one sample data for every 99 feedback data. After the user determines the setting contents of the setting parameters through the front-end interface, these parameters (setting contents) are passed into the corresponding system to obtain the corresponding rule monitoring key. The monitoring key obtains the performance through the monitoring system and finally returns to the front-end to display the monitoring result data, so that data risk monitoring can be realized during the execution of data business.

[0077] In addition, determining the setting content includes, but is not limited to, inputting the setting content, selecting the setting content, or inserting the setting content.

[0078] In one embodiment of the present disclosure, the business scenario includes at least one of a login scenario, a marketing scenario, and a financial transaction scenario.

[0079] Refer to the following Figure 7 The risk monitoring device 700 for data services according to this embodiment of the present invention is described below. Figure 7 The risk monitoring device 700 for data services shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0080] The risk monitoring device 700 for data services is in the form of a hardware module. The components of the risk monitoring device 700 for data services may include but are not limited to: a risk control module 702 , a determination module 704 , a configuration module 706 , and an adjustment module 708 .

[0081] The risk control module 702 is used to detect the calling business scenario and determine the data monitoring rules according to the business scenario.

[0082] The determination module 704 is used to determine a monitoring parameter component according to a data monitoring rule, and the monitoring parameter component is used to perform risk control assessment on the interaction data in a business scenario.

[0083] The configuration module 706 is used to configure sampling parameters of the data interface of the business scenario according to the monitoring parameter component, and obtain the sampling results through the data interface.

[0084] The determination module 704 is further configured to determine the performance index of the monitoring parameter component according to the sampling result of the data interface.

[0085] The adjustment module 708 is used to adjust the data monitoring rules according to the performance indicators.

[0086] Refer to the following Figure 8 An electronic device 800 according to this embodiment of the present invention will be described. Figure 8 The electronic device 800 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0087] like Figure 8 As shown, the electronic device 800 is in the form of a general computing device. The components of the electronic device 800 may include but are not limited to: at least one processing unit 810, at least one storage unit 820, and a bus 830 connecting different system components (including the storage unit 820 and the processing unit 810).

[0088] The storage unit stores program codes, which can be executed by the processing unit 810, so that the processing unit 810 performs the steps of various exemplary embodiments of the present invention described in the above “Exemplary Method” section of this specification. For example, the processing unit 810 can perform the following steps: Figure 1 The steps shown in, and other steps defined in the risk monitoring method for data services of the present disclosure.

[0089] The storage unit 820 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 8201 and / or a cache memory unit 8202 , and may further include a read-only memory unit (ROM) 8203 .

[0090] The storage unit 820 may also include a program / utility 8204 having a set (at least one) of program modules 8205, such program modules 8205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0091] Bus 830 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0092] The electronic device 800 may also communicate with one or more external devices 840 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device, and / or may communicate with any device that enables the electronic device 800 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 850. Furthermore, the electronic device 800 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 860. As shown, the network adapter 860 communicates with other modules of the electronic device 800 via a bus 830. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0093] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0094] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present invention may also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present specification.

[0095] The program product for implementing the above method according to an embodiment of the present invention may adopt a portable compact disk read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus, or device.

[0096] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0097] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0098] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0099] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0100] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0101] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0102] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

Claims

1. A data service risk monitoring method, characterized in that: include: Detecting a calling business scenario, and determining a data monitoring rule according to the business scenario; Determine a monitoring parameter component according to the data monitoring rule, wherein the monitoring parameter component is used to perform risk control assessment on the interaction data in the business scenario; Performing sampling parameter configuration on the data interface of the business scenario according to the monitoring parameter component, and obtaining sampling results through the data interface; Determining the performance index of the monitoring parameter component according to the sampling result of the data interface; Adjusting the data monitoring rule according to the performance indicator includes: Obtaining a preset response time in the business scenario; Determining whether the preset response time is less than or equal to the response time; If it is determined that the preset response time is less than or equal to the response time, determining the monitoring parameter component as a first-category monitoring parameter component; Adjusting the data monitoring rule according to the first type of monitoring parameter component includes: Adjusting the sampling parameters in the monitoring process according to the first type of monitoring parameter component until the preset response time is less than or equal to the response time; Or the first type of monitoring parameter components in the monitoring process are deleted in descending order of response time until the preset response time is less than or equal to the response time.

2. The data service risk monitoring method according to claim 1, characterized in that: Before the calling business scenario is detected, it also includes: The business scenario, the data monitoring rules and the monitoring parameter components are logically encapsulated.

3. The data service risk monitoring method according to claim 1 or 2, characterized in that: Determining the performance index of the monitoring parameter component according to the sampling result of the data interface includes: Performing summation and mean calculation on the sampling array fed back by the data interface, and determining the calculation result as the sampling result; Determine the time consumption and call amount of the monitoring parameter component according to the sampling result; The response time of the monitoring parameter component is determined according to the time consumption and the call amount.

4. The data service risk monitoring method according to claim 1 or 2, characterized in that: The sampling parameters include at least one of a time period for sampling data, a monitoring parameter component identifier, a monitoring performance dimension, a monitoring granularity, a data sampling indicator, and a data sampling type.

5. The data service risk monitoring method according to claim 1 or 2, characterized in that: The business scenario includes at least one of a login scenario, a marketing scenario, and a financial transaction scenario.

6. A data service risk monitoring device, characterized in that: include: A risk control module is used to detect the calling business scenario and determine the data monitoring rules according to the business scenario; A determination module, used to determine a monitoring parameter component according to the data monitoring rule, wherein the monitoring parameter component is used to perform risk control assessment on the interaction data in the business scenario; A configuration module, used to configure sampling parameters of the data interface of the business scenario according to the monitoring parameter component, and obtain sampling results through the data interface; The determination module is further used to determine the performance index of the monitoring parameter component according to the sampling result of the data interface; An adjustment module, used to adjust the data monitoring rule according to the performance indicator, including: Obtaining a preset response time in the business scenario; Determining whether the preset response time is less than or equal to the response time; If it is determined that the preset response time is less than or equal to the response time, determining the monitoring parameter component as a first-category monitoring parameter component; Adjusting the data monitoring rule according to the first type of monitoring parameter component includes: Adjusting the sampling parameters in the monitoring process according to the first type of monitoring parameter component until the preset response time is less than or equal to the response time; Or the first type of monitoring parameter components in the monitoring process are deleted in descending order of response time until the preset response time is less than or equal to the response time.

7. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; The processor is configured to execute the data service risk monitoring method according to any one of claims 1 to 5 by executing the executable instructions.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the risk monitoring method for data services described in any one of claims 1 to 5 is implemented.

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