CN114968696A

CN114968696A - Index monitoring method, electronic equipment and chip system

Info

Publication number: CN114968696A
Application number: CN202110202753.0A
Authority: CN
Inventors: 徐应明
Original assignee: Petal Cloud Technology Co Ltd
Current assignee: Petal Cloud Technology Co Ltd
Priority date: 2021-02-23
Filing date: 2021-02-23
Publication date: 2022-08-30

Abstract

The application is applicable to the technical field of terminals and provides an index monitoring method, electronic equipment and a chip system. The index monitoring method comprises the following steps: the method comprises the steps that a first electronic device generates a preset data generation model, theoretical monitoring data are generated based on the preset data generation model, and a first index value corresponding to a monitoring index is obtained; the first electronic equipment sends the theoretical monitoring data to second electronic equipment; the second electronic equipment processes the theoretical monitoring data through a data processing model, and determines a second index value corresponding to the monitoring index based on the processed theoretical monitoring data; and the first electronic equipment and/or the second electronic equipment monitors the monitoring index according to the first index value and the second index value of the monitoring index. The method can monitor whether the index is abnormal in the data processing process.

Description

Index monitoring method, electronic equipment and chip system

Technical Field

The present application relates to the field of terminal technologies, and in particular, to an index monitoring method, an electronic device, and a chip system.

Background

Big data analysis is a technology for carrying out data analysis and data mining on massive data such as texts, images, audio and video. The ETL (Extraction-Transformation-Loading) is an important process in big data analysis, and is used to extract needed data from mass data for Transformation, and the transformed data is the basis for further data analysis and data mining. The results such as the behavior of the user, the degree of dependence of the user on the product and the like can be determined through analysis of the mass data, and the product and the operation strategy can be optimized based on the results. For example, product functions or pages may be optimized, and specific operational strategies may be developed for specific groups of people, thereby increasing Return On Investment (ROI).

In order to provide accurate data support for optimization of the operation strategy, monitoring of data quality needs to be performed on the ETL data processing process. For example, whether the ETL data processing process has an exception of data processing may be determined according to the metadata information and the task execution log generated in the ETL process. If the abnormal data processing in the ETL data processing process is not detected, the data obtained in the ETL data processing process can be analyzed to obtain a result capable of optimizing the product and the operation strategy. However, most of the conventional methods for monitoring the data quality of the ETL data processing process focus on macroscopically monitoring the ETL data processing process, and cannot guarantee that monitoring indicates possible abnormalities of the specimen in the ETL data processing process.

Disclosure of Invention

The application provides an index monitoring method, electronic equipment and a chip system method, and solves the problem that the index itself may be abnormal in the data processing process in the prior art.

In order to achieve the purpose, the technical scheme is as follows:

in a first aspect, an embodiment of the present application provides an index monitoring method, where the method includes: the method comprises the steps that a first electronic device generates a preset data generation model, theoretical monitoring data are generated based on the preset data generation model, and a first index value corresponding to a monitoring index is obtained; the first electronic equipment sends the theoretical monitoring data to second electronic equipment; the second electronic equipment processes the theoretical monitoring data through a data processing model, and determines a second index value corresponding to the monitoring index based on the processed theoretical monitoring data; and the first electronic equipment and/or the second electronic equipment monitors the monitoring index according to the first index value and the second index value of the monitoring index.

The data processing model is actually used by the second electronic device in the ETL data processing process, and includes a plurality of data processing nodes. And after the previous data processing node finishes processing the theoretical monitoring data, transmitting the processed theoretical monitoring data to the next data processing node for processing. Therefore, in the process that the second electronic device processes the theoretical monitoring data through the data processing model, there may be some exceptions such as partial data loss and processing logic errors of the second electronic device due to data transmission. By comparing the first index value and the second index value of the monitoring index, whether the abnormal condition exists in the data processing model can be determined according to the comparison result, and whether the monitoring index obtained by the data processed by the data processing model is abnormal or not is further determined. If the monitoring index has no abnormality, the data generated by the data source can be processed according to the data processing model. Therefore, the method and the device for monitoring the index value of the monitoring index can improve the accuracy of the determined index value of the monitoring index, and can help business operators to monitor whether the monitoring index is abnormal or not in time.

The first electronic device and the second electronic device may be the same electronic device or different electronic devices, which is not limited to this.

The first electronic device may determine a first index value corresponding to the monitoring index according to the data generation rule information. Because the data generation rule information includes the monitoring index and the related information thereof, the first electronic device may determine the first index value corresponding to the monitoring index according to the data generation rule information.

With reference to the first aspect, in some embodiments, monitoring the monitoring indicator according to a first indicator value and a second indicator value of the monitoring indicator includes: and monitoring the monitoring index according to the difference value of the first index value and the second index value.

If the first index value and the second index value are the same or relatively close to each other, it is indicated that the monitoring index itself is not abnormal after the theoretical monitoring data is processed by the data processing model of the second electronic device. If the difference between the first index value and the second index value is increased, it is indicated that the monitoring index itself is abnormal after the theoretical monitoring data is processed by the data processing model of the second electronic device.

In one scenario, a preset range may be set, and the monitoring index may be monitored according to the preset range.

For example, the monitoring index according to the difference between the first index value and the second index value may include: if the difference value of the first index value and the second index value is within a preset range, the monitoring index is not abnormal; and if the difference value of the first index value and the second index value exceeds the preset range, the monitoring index is abnormal.

Specifically, if the difference between the first index value and the second index value is within the preset range, it indicates that the monitoring index itself is not abnormal after the theoretical monitoring data is processed by the data processing model of the second electronic device. If the difference value between the first index value and the second index value exceeds the preset range, it indicates that the monitoring index itself is abnormal after the theoretical monitoring data is processed by the data processing model of the second electronic device.

With reference to the first aspect, in some embodiments, monitoring the monitoring indicator according to a difference between the first indicator value and the second indicator value includes; the second electronic equipment displays the first index value, the second index value and the difference value of the first index value and the second index value; or the first electronic equipment compares the first index value with the second index value, and sends a reminding message to a system administrator terminal when the difference value between the first index value and the second index value exceeds a preset range.

In one scenario, the first index value and the second index value of the monitoring index may be displayed to a service operator in a UI manner. The service operator can log in the index visualization system in the second electronic device through the service operator terminal, and the second electronic device displays the first index value and the second index value of the monitoring index to the service operator through the index visualization system.

In one scenario, the first electronic device may obtain the second index value, then compare the first index value with the second index value, and send a reminding message to the system administrator terminal when a difference between the first index value and the second index value exceeds a preset range.

With reference to the first aspect, in some embodiments, the first electronic device generates a preset data model, including: the first electronic equipment acquires a data generation rule and establishes the preset data generation model according to the data generation rule; wherein the data generation rule comprises at least one of: the number of new users and old users in each scene, the type and number of data to be produced in each scene, the sequence of producing various data in each scene, and the monitoring index in each scene.

With reference to the first aspect, in some embodiments, the establishing the preset data generation model according to the data generation rule includes: responding to a received monitoring scene establishing instruction, and establishing one or more monitoring scenes; setting names for the one or more monitoring scenes in response to the received monitoring scene name setting instruction; responding to a received priority setting instruction of each monitoring scene, and setting a priority sequence of each monitoring scene, wherein the priority sequence represents a sequence of generating each monitoring scene; responding to the received user rule setting instruction of each monitoring scene, and setting a user rule corresponding to each monitoring scene; wherein the user rules include: the method comprises the following steps of calculating the total user number, the new user proportion or number, the old user proportion or number and a user attribute resource pool, wherein the user attribute resource pool comprises a plurality of user attributes; responding to a received event rule setting instruction required by each monitoring scene, and setting required event rules for each monitoring scene; wherein the event rule comprises: the method comprises the steps of obtaining an event list comprising a plurality of events, a reporting sequence of the plurality of events and the number of each event in the plurality of events; responding to a received monitoring rule setting instruction of each monitoring scene, and setting a corresponding monitoring rule for each monitoring scene, wherein the monitoring rule comprises a monitoring index needing to be monitored and an action when the monitoring index does not meet a preset condition.

Wherein the first electronic device may include a data quality monitoring system and a data generation system. For example, the data quality monitoring system and the data generation system may be one processing unit in a processor of the first electronic device.

In one scenario, a system administrator may input data generation rule information to a system administrator terminal, and the system administrator terminal generates a monitoring scenario establishment instruction according to the data generation rule information. The monitoring scenario setup instruction may be used to instruct the data quality monitoring system to set up one monitoring scenario, or to set up multiple monitoring scenarios simultaneously. And then, the system administrator terminal sends the monitoring scene establishment instruction to the data quality monitoring system. And the data quality monitoring system responds to the received monitoring scene establishing instruction to establish the monitoring scene.

In addition, the system administrator terminal can also generate a monitoring scene name setting instruction, a priority setting instruction of each monitoring scene, a user rule setting instruction of each monitoring scene, an event rule setting instruction required by each monitoring scene, a monitoring rule setting instruction of each monitoring scene and the like according to the data generation rule information. The system administrator terminal can simultaneously send monitoring scene name setting instructions, priority setting instructions of all monitoring scenes, user rule setting instructions of all monitoring scenes, event rule setting instructions required by all monitoring scenes, monitoring rule setting instructions of all monitoring scenes and the like while sending monitoring scene establishing instructions to the data quality monitoring system. And the data quality monitoring system simultaneously receives the instruction sent by the system administrator terminal.

The monitoring scene establishing instruction may be used to instruct the data quality monitoring system to establish a plurality of monitoring scenes at the same time, where the monitoring scenes of the plurality of systems may have a simultaneous event, and at this time, the priority of each monitoring scene needs to be defined, so that the data generating system can generate data of each monitoring scene according to the set priority.

For example, the data quality monitoring system sets a priority level for a user funnel analysis scene to be 5, sets a priority level for an active user scene to be 4, sets a priority level for an application installation scene to be 3, sets a priority level for a new user scene to be 2, and generates data of each monitoring scene in sequence according to the sequence of the priority levels from high to low when the priority level number is larger and the priority level is higher.

Illustratively, the user attribute is a device characteristic of a terminal device used by the user, and the device characteristic includes at least one of the following: equipment manufacturer, equipment model, operating system of the equipment, physical address of the equipment, and IP address of the equipment.

For example, the event list may include a plurality of events, such as an open application event, a browse goods event, a join goods into a shopping cart event, and a place order event for goods in a shopping cart. The event rules for different monitoring scenarios are typically different. For example, the events in the event list of the monitoring scenario 1 may be different from the events in the event list of the monitoring scenario 2, the number of each event of the monitoring scenario 1 may be different from the number of each event of the monitoring scenario 2, and the reporting order of each event of the monitoring scenario 1 may be different from the reporting order of each event of the monitoring scenario 2.

The reporting sequence of the application event opening, the commodity browsing event, the commodity adding event in the shopping cart and the commodity ordering event in the shopping cart can be the application event opening, the commodity browsing event, the commodity adding event in the shopping cart and the commodity ordering event in the shopping cart in sequence.

For example, the indicators to be monitored may include one or more of the number of new users, the number of active users, the number of users installing a certain application, the number of users placing orders, and the like. If a certain index does not meet the preset condition, reminding information can be sent to a system administrator terminal and/or a service operator terminal, and the reminding information comprises index information which does not meet the preset condition. And the index value of the index which does not meet the preset condition index exceeds the preset range.

With reference to the first aspect, in some embodiments, the generating theoretical monitoring data based on the preset data generation model includes: analyzing the preset data generation model to obtain a data generation rule; generating a new user according to a user rule in the data generation rule; configuring user attributes for the new user according to a user attribute resource pool in the data generation rule; acquiring information of old users according to user rules in the data generation rules; according to an event rule in the data generation rule, events triggered by the new user and the old user are constructed; and sequentially sending each event to the second electronic equipment according to the reporting sequence of each event.

Wherein the data generation system may read the data generation rule information from the data quality monitoring system. The following information can be obtained by analyzing the data generation rule information: the name of each monitoring scene, the priority order of each monitoring scene, the user rule corresponding to each monitoring scene, the event rule required by each monitoring scene, and the monitoring rule corresponding to each monitoring scene.

Illustratively, the new users may be generated according to the number of new users in the user rules. Or, the new user can be generated according to the total number of users and the proportion of the new user in the user rule.

The old user information may include, among other things, an old user ID (identification) and user attributes of the old user. The old user ID may be a user name. It should be noted that, after a new user is generated, the new user may be changed to an old user after a certain period of time. After a new user changes to an old user, some user attributes of the user remain unchanged and some user attributes need to be changed. For example, the device characteristics, age, gender, etc. of the user may remain unchanged, and the user level, etc. of the user may change.

In some embodiments, user-triggered events may be constructed in terms of a list of events and the number of individual events. For example, one or more events may be triggered for each user according to the event list, and the number of the one or more events is set.

For example, the event list includes event 1, event 2, event 3, and event 4, where the number of events 1 is x ₁ The number of events 1 corresponds to x ₂ The number of events 1 corresponds to x ₃ The number of events 1 corresponds to x ₄ . Event 1, event 3 and event 4 in the event list may be triggered for user 1 and event 2, event 3 and event 4 in the event list may be triggered for user 2. The number of events 1 triggered for user 1 is x ₁ The number of triggered events 3 is x ₃ The number of triggered events 4 is x ₄ . The number of events 2 triggered for user 2 is x ₂ The number of triggered events 3 is x ₃ The number of triggered events 4 is x ₄ 。

Exemplarily, the sending of each event to the second electronic device in sequence according to the reporting sequence of each event may be: for the application event opening, the commodity event browsing, the commodity adding to shopping cart event and the commodity ordering event in the shopping cart, the reporting sequence can be the application event opening, the commodity event browsing, the commodity adding to shopping cart event and the commodity ordering event in the shopping cart in sequence. Correspondingly, the opening application event is firstly sent to the data processing system, then the browsing commodity event is sent to the data processing system, then the commodity adding shopping cart event is sent to the data processing system, and finally the commodity ordering event in the shopping cart is sent to the data processing system. And the data processing system processes each event sent by the data generation system in sequence.

In a second aspect, an embodiment of the present application provides an electronic device, including: one or more processors and memory; the memory coupled with the one or more processors, the memory to store computer program code, the computer program code comprising computer instructions; the computer instructions, when executed by the one or more processors, cause the electronic device to perform the method of any of the first aspects.

In a third aspect, an embodiment of the present application provides a chip system, where the chip system includes a processor, the processor is coupled with a memory, and the processor executes a computer program stored in the memory to implement the method according to any one of the first aspect. The chip system can be a single chip or a chip module formed by a plurality of chips.

In a fourth aspect, an embodiment of the present application provides a chip system, where the chip system includes a memory and a processor, and the processor executes a computer program stored in the memory to implement the method according to any one of the first aspect. The chip system can be a single chip or a chip module consisting of a plurality of chips.

In a fifth aspect, embodiments of the present application provide a computer program product, which, when run on an electronic device, causes the electronic device to perform the method of any one of the above first aspects.

In a sixth aspect, the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the method according to any one of the first aspect.

It is to be understood that the electronic device of the second aspect, the chip system of the third and fourth aspects, the computer program product of the fifth aspect, and the computer-readable storage medium of the sixth aspect, provided above, are all adapted to perform the method provided in the second aspect, or the method provided in the third aspect. Therefore, the beneficial effects achieved by the method can refer to the beneficial effects in the corresponding method, and are not described herein again.

Drawings

Fig. 1 is a schematic diagram of a system architecture applicable to a method for monitoring an ETL data processing process in the first related art;

FIG. 2 is a schematic flow chart illustrating a method for monitoring data processing based on a data processing process model in a second related art;

fig. 3 is a schematic diagram of an architecture of an index monitoring method according to an embodiment of the present application;

fig. 4 is a schematic flowchart of an index monitoring method according to an embodiment of the present application;

FIG. 5 is a schematic diagram of an index monitoring interface provided in an embodiment of the present application;

fig. 6 is a schematic flowchart of generating rule information for configuring data for a data quality monitoring system according to an embodiment of the present application;

fig. 7 is a schematic flowchart of generating theoretical monitoring data according to an embodiment of the present application;

fig. 8 is a schematic flowchart of monitoring a monitoring index according to an embodiment of the present application;

fig. 9 is a schematic flowchart of monitoring a monitoring index according to an embodiment of the present application;

fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It should also be understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items and includes such combinations.

As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".

Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.

Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.

In addition, the references to "a plurality" in the embodiments of the present application should be interpreted as two or more.

The steps involved in the index monitoring method provided in the embodiment of the present application are merely examples, and not all the steps are necessarily executed steps, or the content in each information or message is not always necessary, and may be increased or decreased as needed in the use process.

The same steps or messages with the same functions in the embodiments of the present application may be referred to with each other between different embodiments.

The service scenario described in the embodiment of the present application is for more clearly illustrating the technical solution of the embodiment of the present application, and does not form a limitation on the technical solution provided in the embodiment of the present application, and as a person having ordinary skill in the art knows that along with the evolution of a network architecture and the appearance of a new service scenario, the technical solution provided in the embodiment of the present application is also applicable to similar technical problems.

In the first related art, a method for monitoring an ETL data processing process is provided. Fig. 1 is a system architecture applicable to the method for monitoring the ETL data processing process. The system architecture includes: the monitoring system comprises a monitoring index configuration module and a monitoring processing module. The ETL processing units in fig. 1 are functional modules that perform ETL data processing, and typically ETL data processing procedures are performed by the corresponding ETL processing units, such as data extraction, conversion, or loading, respectively. The ETL processing unit may be a functional unit that performs a data processing procedure such as data extraction, conversion, or loading.

The method for monitoring the ETL data processing process determines the field type of output data in the data processing process according to the relevant information of the ETL data processing task, and generates the monitoring index of the ETL data processing process according to the field type of the output data. And then, monitoring the ETL data processing process according to the generated monitoring index, and detecting whether data processing abnormity exists or not. For example, statistics or calculation may be performed on corresponding fields of output data in the ETL data processing process to obtain a result value of the index, and whether a data processing exception exists in the ETL data processing process is determined according to the result value.

It should be noted that, during big data analysis, it is also important that the index itself can accurately reflect the possible data processing abnormality in the ETL data processing process. It can be understood that, if the index cannot accurately reflect the data processing abnormality possibly existing in the ETL data processing process, the related art cannot provide meaningful information for product optimization, and cannot make a targeted operation strategy based on a big data analysis result. In the related art, the ETL data processing process can be macroscopically monitored, but it cannot be guaranteed that the indexes can accurately reflect the possible data processing exception in the ETL data processing process. For example, there may be index abnormality in the ETL data processing process due to data loss during transmission, although the data processing process is normal.

The second related art provides a method for monitoring data processing based on a data processing process model. Referring to fig. 2, the method includes: the method comprises the steps of constructing a data processing process description model and a storage structure, collecting data processing process model information, collecting task execution state information, performing visualization processing on the data processing process, and managing and controlling a data full link by using a visualization interface. The model is a model for constructing the data processing process based on the data processing process, and the model is used for describing various types of data and various data processing links. And then, collecting logs generated by the execution state of each step in the data processing process, describing the task execution result, and visually displaying the execution process.

The second related technology can monitor whether the data processing process is abnormal, but cannot guarantee that the indexes can accurately reflect the data processing abnormality possibly existing in the data processing process. Even if the data processing procedure is abnormal to determine whether the index is abnormal, the problem of monitoring whether the index itself is accurate cannot be solved. For example, there may be index abnormality in the data processing process due to data loss in the transmission process, although the data processing process is normal.

Based on the foregoing problems, an index monitoring method provided in an embodiment of the present application includes: the first electronic equipment acquires a preset data generation model, generates theoretical monitoring data based on the preset data generation model, and acquires a first index value of a monitoring index. The preset data generation model comprises monitoring indexes of theoretical monitoring data. And then, the first electronic equipment sends the theoretical monitoring data to the second electronic equipment. And the second electronic equipment processes the theoretical monitoring data through the data processing model, and determines a second index value corresponding to the monitoring index based on the processed theoretical monitoring data. The data processing model is actually used by the second electronic device in the ETL data processing process, and includes a plurality of data processing nodes. And after the previous data processing node finishes processing the theoretical monitoring data, transmitting the processed theoretical monitoring data to the next data processing node for processing. Therefore, in the process that the second electronic device processes the theoretical monitoring data through the data processing model, there may be some exceptions such as partial data loss and processing logic errors of the second electronic device due to data transmission. By comparing the first index value and the second index value of the monitoring index, whether the abnormal condition exists in the data processing model can be determined according to the comparison result, and whether the monitoring index obtained by the data processed by the data processing model is abnormal or not is further determined. If the monitoring index has no abnormality, the data generated by the data source can be processed according to the data processing model. Therefore, the method and the device for monitoring the index value of the monitoring index can improve the accuracy of the determined index value of the monitoring index, and can help business operators to monitor whether the monitoring index is abnormal or not in time.

The first electronic device and the second electronic device may be the same electronic device or different electronic devices, which is not limited to this. The hardware structure of the first electronic device and the second electronic device please refer to fig. 10.

Fig. 3 shows a system architecture to which the index monitoring method provided in the embodiment of the present application is applied. Referring to fig. 3, the system architecture may include a data quality monitoring system, a data generation system, a data processing system, a monitoring index visualization system, a data source, a system administrator terminal, and a service operator terminal.

The data quality monitoring system is used for managing a preset data generation model, monitoring index quality, monitoring index abnormity notification, index value query of a monitoring index and the like. For example, a system administrator may input a preset data generation model through a system administrator terminal. And then, the data quality monitoring system receives a preset data generation model sent by a system administrator terminal. For the specific information about the predetermined data generation model, please refer to the following contents, which are not repeated herein.

The data generation system is used for generating theoretical monitoring data according to the preset data generation model, processing the theoretical monitoring data based on the preset data processing model and determining a first index value corresponding to the monitoring index. The data generation system sends the theoretical monitoring data and the first index value to the data processing system, and sends the first index value to the data quality monitoring system.

The data processing system is used for processing the theoretical monitoring data according to the data processing model, determining a second index value corresponding to the monitoring index, and sending the first index value and the second index value to the index visualization system. The data processing model comprises a plurality of data processing nodes, and after the previous data processing node completes the processing of the theoretical monitoring data, the processed theoretical monitoring data is transmitted to the next data processing node for processing.

The index visualization system is configured to show the first index value and the second index value of the monitoring index to a User, for example, the first index value and the second index value of the monitoring index may be shown to the User through a User Interface (UI). The service operator can access the index visualization system through the service operator terminal to check information such as a first index value and a second index value of the monitoring index, and a difference between the first index value and the second index value.

The data processing system may also send the first indicator value and the second indicator value to a data quality monitoring system. The service operator can access the data quality monitoring system through the service operator terminal to inquire whether the calculation method of the monitoring index is accurate. A system administrator can access the data quality monitoring system through a system administrator terminal to inquire whether the calculation method of the monitoring index is accurate or not.

The data source is a system capable of providing source data. For example, the data source may be an APP (Application) provided by a developer or other system capable of generating data. The data source transmits the generated source data to the data processing system. And the data processing system processes the source data through the actual data processing model to obtain an index value of the monitoring index corresponding to the source data. For example, if the first index value and the second index value are the same or the difference between the first index value and the second index value is within a preset range, the data processing system may process the source data through the actual data processing model. Or if the first index value and the second index value are the same or the difference value of the first index value and the second index value is within the preset range, the index value of the monitoring index corresponding to the source data obtained by the data processing system is not abnormal, and the comparison is reliable.

Fig. 4 is a schematic flowchart of an index monitoring method according to an embodiment of the present application. Referring to fig. 4, the index monitoring method may include steps 101 to 104.

Step 101, generating a preset data generation model by first electronic equipment, generating theoretical monitoring data based on the preset data generation model, and acquiring a first index value corresponding to a monitoring index.

For example, the system administrator may send the data generation rule information to the first electronic device through the administrator terminal. And the first electronic equipment receives the data generation rule information and configures according to the data generation rule to obtain a preset data generation model.

The preset data generation model can be one or more application scenes to produce theoretical monitoring data. For example, the application scenario may include: new user scenarios, active user scenarios, user funnel analysis scenarios, application installation scenarios, etc. The new user scenario may be a scenario of acquiring user information of a user who accesses an application such as a mall website, APP, and the like for the first time within a preset time period (for example, one day). The active user scenario may be a scenario of acquiring user information of a user who accesses an application such as a mall website and an APP for multiple times within a preset time period (for example, one week). The user funnel analysis scene can be a scene for analyzing the user behavior state and the user conversion rate situation at each stage from the starting point to the end point. The application installation scenario may be a scenario in which the number of users who install a certain application in a preset time period is acquired.

In some embodiments, the preset data generation model may include one or more of the following data generation rule information: the number of new users and old users in each scene, the type and the number of production data required in each scene, the sequence of the production data in each scene, and the monitoring index in each scene.

The new user can be a user who logs in the applications such as the mall website and the APP for the first time, and the old user can be a user who logs in the applications such as the mall website and the APP for multiple times. The number of new users and old users can be set as desired. For example, the ratio of new users to old users may be 1:3, with the total number of new users and old users being c; or the number of the new users is a, the number of the old users is b, and the ratio of a to b is 1: 3.

The type of data can be user behaviors of a user in a shopping mall website, an APP and other systems, such as logging in an application, adding commodities to a shopping cart, ordering commodities in the shopping cart, installing the application and the like. The amount of data may be the amount of various user actions occurring within a preset time, such as the number of users logging into an application within a preset time, the number of users adding a commodity into a shopping cart within a preset time, the number of users placing an order for a commodity in a shopping cart within a preset time, and the number of users installing a certain application within a preset time.

The order of producing data in each scene may be the order in which user actions occur in each scene. For example, for user behaviors such as opening an application, adding a commodity into a shopping cart, ordering commodities in the shopping cart, and the like, the sequence of occurrence of the user behaviors may be: what happens first is a user action to open an application, what happens second is a user action to add a good to the shopping cart, and what happens last is a user action to place an order for the good in the shopping cart.

The monitoring index under each scene may be an index of interest in various scenes. For example, for a new user scenario, the monitoring indicator may be the number of new users generated within a preset time (e.g., one day). For an active user scenario, the monitoring indicator may be the number of active users within a preset time (e.g., one week). For a user funnel analysis scenario, the monitoring indicator may be user conversion rates at various stages from the start point to the end point. For an application installation scenario, the monitoring indicator may be the number of users installing a certain application.

It should be noted that one or more monitoring indexes may be used. For example, the monitoring indicator may be the number of new users, or the number of active users.

For example, in a case where the monitoring index is the number of new users, the first electronic device may determine the number of new users according to the number of new users and the number of old users in the data generation rule information. For example, if the number of new users and old users in the data generation rule information is: the number of the new users is a, the number of the old users is b, the number of the new users and the number of the old users in the theoretical monitoring data generated by the first electronic device are a and b, respectively. Therefore, the first electronic device may determine that the index value of the monitoring index, which is the number of new users, is a.

For example, the monitoring index is a number of users installing the application, and the first electronic device may determine the number of users installing the application according to the number of users corresponding to a user behavior of installing a certain application within a preset time in the data generation rule information. For example, if the number of users corresponding to the user behavior of installing a certain application in the preset time in the data generation rule information is l, the number of users installing a certain application in the preset time in the theoretical monitoring data generated by the first electronic device is l. Therefore, the first electronic device may determine that the index value of the monitoring index, which is the number of installed users, is/.

For example, when the monitoring index is the number of ordering users, the first electronic device may determine the number of ordering users according to the number of users corresponding to a user action of ordering goods in the shopping cart within a preset time in the data generation rule information. For example, if the number of users corresponding to the user behavior of placing an order for the goods in the shopping cart within the preset time in the data generation rule information is m, the number of users placing an order for the goods in the shopping cart within the preset time in the theoretical monitoring data generated by the first electronic device is m. Therefore, the first electronic device may determine that the index value of the monitoring index, which is the number of the order-placing users, is m.

And 102, the first electronic device sends theoretical monitoring data to the second electronic device.

And 103, the second electronic device processes the theoretical monitoring data through the data processing model, and determines a second index value corresponding to the monitoring index based on the processed theoretical monitoring data.

The data processing model is actually used by the second electronic device in the ETL data processing process. The data processing model may include a plurality of data processing nodes, and after the previous data processing node completes processing of the theoretical monitoring data, the processed theoretical monitoring data is transmitted to the next data processing node for processing. During the process of processing the theoretical monitoring data by the second electronic device through the data processing model, there may be a case where part of the data is lost due to data transmission between the data processing nodes.

For example, some new user information may be lost in data transmission between data processing nodes during the process of monitoring data processing by the second electronic device. After the second electronic device finishes processing the theoretical monitoring data, the second electronic device determines a second index value of the monitoring index, namely the number of new users according to the processed data. Because the information of part of new users is lost, the obtained second index value is not consistent with the actual situation, namely the monitoring index of the number of the new users is likely to have abnormality.

And 104, monitoring the monitoring index by the first electronic device and/or the second electronic device according to the first index value and the second index value of the monitoring index.

In one scenario, a preset range may be set, and the monitoring index is monitored according to the preset range. For example, if the difference between the first index value and the second index value is within the preset range, it indicates that the monitoring index itself is not abnormal after the theoretical monitoring data is processed by the data processing model of the second electronic device. If the difference value between the first index value and the second index value exceeds the preset range, it indicates that the monitoring index itself is abnormal after the theoretical monitoring data is processed by the data processing model of the second electronic device.

In some embodiments, the first index value and the second index value of the monitoring index may be displayed to the service operator by means of a UI.

Fig. 5 is a schematic interface diagram of index monitoring provided in the embodiment of the present application. Referring to fig. 5, the monitoring index in the present embodiment includes a plurality of new user numbers, active user numbers, installation event numbers, and the like. The expected value in fig. 5 is a first index value of the monitoring index, and the actual value is a second index value of the monitoring index. The expected value of the number of new users is 100, the actual value is 70, the difference between the expected value and the actual value is 30, that is, the actual value is reduced by 30% relative to the expected value. Similarly, if the expected value of the number of active users is 100 and the actual value is 100, the difference between the expected value and the actual value is 0, i.e. the actual value is unchanged from the expected value. The expected value of the number of mounting events is 100 and the actual value is 120, the difference between the expected value and the actual value is 20, i.e., the actual value rises by 20% from the expected value.

The service operator can intuitively know whether the monitored index obtained by the data processing model of the second electronic device is abnormal or not through the expected value and the actual value of each index monitoring shown in fig. 5.

According to the index monitoring method, through comparison of the first index value and the second index value of the monitoring index, whether the data processing model has the condition that partial data are lost due to data transmission can be determined according to the comparison result, and whether the monitoring index obtained by the data processing model is abnormal or not is further determined. If the monitoring index obtained by the data processing model has no abnormality, the data generated by the data source can be processed according to the data processing model. Therefore, the method and the device for monitoring the index value of the monitoring index can improve the accuracy of the determined index value of the monitoring index, and can help service operators to monitor whether the monitoring index is abnormal or not in time.

Fig. 6 is a schematic flowchart of configuring data generation rule information for a data quality monitoring system according to an embodiment of the present application. Referring to fig. 6, the process of configuring data generation rule information for the data quality monitoring system includes steps 201 to 206.

Step 201, responding to the received monitoring scene establishing instruction, and establishing a monitoring scene.

For example, the monitoring scenario setup instructions may be used to instruct the data quality monitoring system to setup one of scenario 1, scenario 2, scenario 3, and scenario 4. Alternatively, the monitoring scenario setup instructions may be used to instruct the data quality monitoring system to setup multiple ones of scenario 1, scenario 2, scenario 3, and scenario 4.

Alternatively, the system administrator terminal may send the above-mentioned instructions to the data quality monitoring system, respectively. Correspondingly, the data quality monitoring system respectively receives the instructions sent by the system administrator terminal. For example, after step 201 is completed, the system administrator terminal sends a monitoring scene name setting instruction to the data quality monitoring system; after step 202 is completed, the system administrator terminal sends a priority setting instruction of each monitoring scene to the data quality monitoring system; after step 203, the system administrator terminal sends a user rule setting instruction of each monitoring scene to the data quality monitoring system; after step 204 is completed, the system administrator terminal sends an event rule setting instruction of each monitoring scene to the data quality monitoring system; after step 205 is completed, the system administrator terminal sends a monitoring rule setting instruction of each monitoring scenario to the data quality monitoring system.

Step 202, responding to the received monitoring scene name setting instruction, and setting a name for the established monitoring scene.

The monitoring scene names are used for distinguishing each scene by a user, and each monitoring scene corresponds to one monitoring scene name. The monitoring scene name setting instruction is used for indicating the data quality monitoring system to set a corresponding name for each established monitoring scene.

For example, a system administrator may send the names of scene 1 to scene 4 to the data quality monitoring system through a system administrator terminal. The name of the scene 1 may be a new user scene, the name of the scene 2 may be an active user scene, the name of the scene 3 may be a user funnel analysis scene, and the name of the scene 4 may be an application installation scene.

Step 203, responding to the received priority setting instruction of each monitoring scene, and setting the priority order of each monitoring scene.

The monitoring scene establishing instruction may be used to instruct the data quality monitoring system to establish a plurality of monitoring scenes, where the plurality of monitoring scenes may have a simultaneous event, and at this time, the priority of each monitoring scene needs to be defined, so that the data generating system can generate data of each monitoring scene according to the set priority.

In this step, the priority setting instruction of each monitoring scene is used to instruct the data quality monitoring system to set the sequence of generating each monitoring scene.

And 204, responding to the received user rule setting instruction of each monitoring scene, and setting the user rule corresponding to each monitoring scene.

In this step, the user rule setting instruction is used to instruct the data quality monitoring system to set user attributes for each user generated in each monitoring scenario.

The user rules may include a total number of users, a new user ratio or number, an old user ratio or number, and a user attribute resource pool, where the user attribute resource pool includes a plurality of user attributes. Illustratively, the user attribute may be a device characteristic of the terminal device used by the user. For example, device characteristics may include device vendor, device model, operating system of the device, device MAC (physical address) address or IP address, etc.

For example, after receiving the user rule information of each monitoring scenario sent by the system administrator terminal, the data quality monitoring system sets a user rule for each monitoring scenario.

Illustratively, the program code for generating the user attribute resource pool is as follows:

step 205, responding to the received event rule setting instruction required by each monitoring scene, and setting required event rules for each monitoring scene.

The event rule may include an event list, a reporting order of each event, and a number of each event. The event rule setting instruction is used for instructing the data quality monitoring system to set corresponding event rules for each monitoring scene.

In addition, the number of events can be set as desired. For example, the number of open application events is 1000, the number of add items to a shopping cart event is 100, and the number of place orders for items in the shopping cart is 20.

In one scenario, after receiving an event rule setting instruction required by each monitoring scenario sent by a system administrator terminal, a data quality monitoring system sets required event rules for each monitoring scenario.

Illustratively, the program code corresponding to the event rule required for generating each monitoring scenario is as follows:

step 206, responding to the received monitoring rule setting instruction of each monitoring scene, and setting a corresponding monitoring rule for each monitoring scene.

The monitoring rule may include an index to be monitored and an action when the index does not satisfy a preset condition.

In one scenario, after receiving a monitoring rule of each monitoring scenario sent by a system administrator terminal, a data quality monitoring system sets the monitoring rule for each monitoring scenario.

Fig. 7 is a schematic flowchart of generating theoretical monitoring data according to an embodiment of the present application. Referring to fig. 7, the process of generating theoretical monitoring data includes steps 301 to 307.

Step 301, reading the data generation rule information and analyzing the data generation rule information.

Wherein the data generation system may read the data generation rule information from the data quality monitoring system. The following information can be obtained by analyzing the data generation rule information: the name of each monitoring scene, the priority order of each monitoring scene, the user rule corresponding to each monitoring scene, the event rule required by each monitoring scene, and the monitoring rule corresponding to each monitoring scene. For specific information, please refer to the contents in the embodiment of fig. 6, which is not repeated herein.

Step 302, according to the user rule in the data generation rule information, generating a new user.

Referring to the relevant information of the user rule in step 204, a new user may be generated according to the number of new users in the user rule. For example, if the number of new users in the user rule is a, a new users are generated, and a is a positive integer. Or, the new user can be generated according to the total number of users and the proportion of the new user in the user rule. For example, if the total number of users in the user rule is c and the new user proportion is n%, c · n% new users are generated, where c is a positive integer and n is a positive number.

Step 303, according to the user attribute resource pool in the data generation rule information, configuring the user attribute for the generated new user.

For example, the user attribute may be a device feature of a terminal device used by the user, and a corresponding device feature, that is, a device manufacturer, a device model, an operating system of the device, a device MAC address or an IP address, may be configured for each new user. In addition, the user attribute may include information such as the user's age, sex, and user rating. And the user attributes corresponding to the new users are different.

And step 304, acquiring old user information according to the user rules in the data generation rule information.

The old user information may include, among other things, an old user ID (identification) and user attributes of the old user. The old user ID may be a user name, and the user attribute of the old user refers to the user attribute described in step 303, which is not described herein again.

It should be noted that, after a new user is generated, the new user may be changed to an old user after a certain period of time. After a new user changes to an old user, some user attributes of the user remain unchanged and some user attributes need to be changed. For example, the device characteristics, age, gender, etc. of the user may remain unchanged, and the user level, etc. of the user may change.

Step 305, according to the event rule in the data generation rule information, constructing the event triggered by each user.

The event rule may include an event list, a reporting order of each event, and a number of each event. Correspondingly, the constructed user-triggered events may include: one or more events triggered by each user, a number of individual events in the one or more events. Wherein, the events triggered by each user comprise the events triggered by the new user and the events triggered by the old user.

For example, the event list includes event 1, event 2, event 3, and event 4, where the number of events 1 is x ₁ The number of events 1 corresponds to x ₂ The number of events 1 corresponds to x ₃ The number of events 1 corresponds to x ₄ . Event 1, event 3 and event 4 in the event list may be triggered for user 1 and event 2, event 3 and event 4 in the event list may be triggered for user 2. The number of events 1 triggered for user 1 is x ₁ The number of triggered events 3 is x ₃ The number of triggered events 4 is x ₄ . Event 2 triggered for user 2Is x ₂ The number of triggered events 3 is x ₃ The number of triggered events 4 is x ₄ 。

And step 306, sending each event to the data processing system in sequence according to the reporting sequence of the events triggered by each user.

Wherein the data processing system may be one of the processing units of the processor of the second electronic device.

For example, for an application event, a commodity browsing event, a commodity adding event and a commodity ordering event in a shopping cart, the reporting sequence may be the application event, the commodity browsing event, the commodity adding event and the commodity ordering event in the shopping cart in turn. Correspondingly, the opening application event is firstly sent to the data processing system, then the browsing commodity event is sent to the data processing system, then the commodity adding shopping cart event is sent to the data processing system, and finally the commodity ordering event in the shopping cart is sent to the data processing system. And the data processing system processes each event sent by the data generation system in sequence.

Step 307, determining a first index value of the monitoring index according to the monitoring rule.

The monitoring index can be one or more. For example, the monitoring index may be one or more of a number of new users, a number of active users, a number of users installing a certain application, a number of orders placed, and the like. For how to determine the first index value of each monitoring index, please refer to the content in step 102, which is not described herein again.

Fig. 8 is a schematic flowchart of monitoring a monitoring index according to an embodiment of the present application. Referring to fig. 8, the process of monitoring the monitoring index includes steps 401 to 404.

Step 401, the index visualization system logs in the index visualization system in response to the received login information.

Step 402, the index visualization system obtains a second index value of the monitoring index obtained by the data processing system.

In step 403, the index visualization system obtains a first index value of the monitoring index obtained by the data generation system.

In step 404, the indicator visualization system presents the first indicator value and the second indicator value to the user.

The service operator can compare the first index value with the second index value, and determine whether the monitoring index is abnormal according to the comparison result. Please refer to step 104 for the specific comparison process, which is not described herein again.

In this embodiment, the service operator compares the first index value with the second index value, and may determine that the monitoring index is possibly abnormal when the comparison result of the first index and the second index does not satisfy the preset condition.

Fig. 9 is a schematic flowchart of monitoring a monitoring index according to an embodiment of the present application. Referring to fig. 9, the process of monitoring the monitoring index includes steps 501 to 504.

Step 501, configuring data generation rules and monitoring rules for a data quality monitoring system.

Please refer to steps 201-206, which are not described herein.

Step 502, the data generation system generates theoretical monitoring data based on the data generation rule, obtains a first index value of the monitoring index, and sends the first index value to the data quality monitoring system.

Step 503, the data processing system processes the theoretical monitoring index, determines a second index value of the monitoring index based on the processed theoretical monitoring data, and sends the second index value to the data quality monitoring system.

And step 504, the data quality monitoring system compares the first index value with the second index value, and sends a reminding message to the system administrator terminal under the condition that the comparison result does not meet the preset condition.

In this embodiment, when the comparison result of the first index and the second index does not satisfy the preset condition, the data quality detection system can automatically send a reminding message to the system administrator terminal according to the configured monitoring rule, so as to remind the administrator that the monitoring index may be abnormal.

Fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 10, the electronic apparatus 600 of this embodiment includes: one or more processors 610, a memory 620, and a computer program 621 stored in the memory 620 and operable on the processors 610. The processor 610, when executing the computer program 621, implements the steps in the above-described method embodiments, such as the steps 101 to 104 shown in fig. 4.

Illustratively, the computer program 621 may be divided into one or more modules/units, which are stored in the memory 620 and executed by the processor 610 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 621 in the electronic device 600.

The computer program 621 includes computer program code, which may be in a source code form, an object code form, an executable file or some intermediate form, and so on. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media which may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.

The electronic device 600 includes, but is not limited to, a processor 610, a memory 620. Those skilled in the art will appreciate that fig. 10 is merely an example of an electronic device 600 and is not intended to limit the electronic device 600 and that it may include more or less components than those shown, or some of the components may be combined, or different components, e.g., the electronic device 600 may also include input devices, output devices, network access devices, buses, etc.

The processor 610 may include one or more processing units. For example, the Processor 610 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The storage 620 may be an internal storage unit of the electronic device 600, such as a hard disk or a memory of the electronic device 600. The memory 620 may also be an external storage device of the electronic device 600, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device 600. Further, the memory 620 may also include both an internal storage unit and an external storage device of the electronic device 600. The memory 620 is used for storing the computer program and other programs and data required by the electronic device 600. The memory 620 may also be used to temporarily store data that has been output or is to be output.

Optionally, an embodiment of the present application further provides an electronic device, including: one or more processors, memory, and a display screen. A memory, the display coupled with the one or more processors, the memory to store computer program code, the computer program code comprising computer instructions; the computer instructions, when executed by the one or more processors, cause the electronic device to perform one or more steps of any of the methods described above.

Optionally, an embodiment of the present application further provides an electronic device, including: one or more processors and memory. A memory coupled to the one or more processors, the memory for storing computer program code, the computer program code comprising computer instructions; the computer instructions, when executed by the one or more processors, cause the electronic device to perform one or more steps of any of the methods described above.

Optionally, the present application further provides a computer-readable storage medium, which stores instructions that, when executed on a computer or a processor, cause the computer or the processor to execute one or more steps of any one of the methods described above.

Optionally, the present application further provides a computer program product containing instructions, which when run on a computer or a processor, causes the computer or the processor to perform one or more steps of any of the methods described above.

Optionally, an embodiment of the present application further provides a chip system, where the chip system may include a memory and a processor, and the processor executes a computer program stored in the memory to implement one or more steps of any of the methods described above. The chip system can be a single chip or a chip module consisting of a plurality of chips.

Optionally, an embodiment of the present application further provides a chip system, where the chip system may include a processor, the processor is coupled with a memory, and the processor executes a computer program stored in the memory to implement one or more steps of any of the above methods. The chip system can be a single chip or a chip module consisting of a plurality of chips.

In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The procedures or functions according to the embodiments of the present application are all or partially generated when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on or transmitted over a computer-readable storage medium. The computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., coaxial cable, fiber optics, digital subscriber line) or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), among others.

One of ordinary skill in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by hardware related to instructions of a computer program, which may be stored in a computer-readable storage medium, and when executed, may include the processes of the above method embodiments. And the aforementioned storage medium includes: various media capable of storing program codes, such as ROM or RAM, magnetic or optical disks, etc.

Finally, it should be noted that: the above description is only an embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions within the technical scope of the present disclosure should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. An index monitoring method, characterized in that the method comprises:

the method comprises the steps that a first electronic device generates a preset data generation model, theoretical monitoring data are generated based on the preset data generation model, and a first index value corresponding to a monitoring index is obtained;

the first electronic equipment sends the theoretical monitoring data to second electronic equipment;

the second electronic equipment processes the theoretical monitoring data through a data processing model, and determines a second index value corresponding to the monitoring index based on the processed theoretical monitoring data;

and the first electronic equipment and/or the second electronic equipment monitors the monitoring index according to the first index value and the second index value of the monitoring index.

2. The method of claim 1, wherein monitoring the monitoring indicator according to the first indicator value and the second indicator value of the monitoring indicator comprises:

and monitoring the monitoring index according to the difference value of the first index value and the second index value.

3. The method of claim 2, wherein monitoring the monitoring indicator based on the difference between the first indicator value and the second indicator value comprises:

if the difference value of the first index value and the second index value is within a preset range, the monitoring index is not abnormal;

and if the difference value of the first index value and the second index value exceeds the preset range, the monitoring index is abnormal.

4. The method of claim 2, wherein said monitoring the monitoring indicator based on a difference between the first indicator value and the second indicator value comprises;

the second electronic equipment displays the first index value, the second index value and the difference value of the first index value and the second index value; or,

and the first electronic equipment compares the first index value with the second index value, and sends a reminding message to a system administrator terminal when the difference value of the first index value and the second index value exceeds a preset range.

5. The method of claim 1, wherein the first electronic device generates a preset data model comprising:

the first electronic equipment acquires a data generation rule and establishes the preset data generation model according to the data generation rule; wherein the data generation rule comprises at least one of: the number of new users and old users in each scene, the type and number of data to be produced in each scene, the sequence of producing various data in each scene, and the monitoring index in each scene.

6. The method according to claim 1, wherein the establishing the preset data generation model according to the data generation rule comprises:

responding to a received monitoring scene establishing instruction, and establishing one or more monitoring scenes;

setting names for the one or more monitoring scenes in response to the received monitoring scene name setting instruction;

responding to a received priority setting instruction of each monitoring scene, and setting a priority sequence of each monitoring scene, wherein the priority sequence represents a sequence of generating each monitoring scene;

responding to the received user rule setting instruction of each monitoring scene, and setting a user rule corresponding to each monitoring scene; wherein the user rules include: the method comprises the following steps of calculating the total user number, the new user proportion or number, the old user proportion or number and a user attribute resource pool, wherein the user attribute resource pool comprises a plurality of user attributes;

responding to a received event rule setting instruction required by each monitoring scene, and setting required event rules for each monitoring scene; wherein the event rule comprises: the method comprises the steps of obtaining an event list comprising a plurality of events, a reporting sequence of the plurality of events and the number of each event in the plurality of events;

responding to a received monitoring rule setting instruction of each monitoring scene, and setting a corresponding monitoring rule for each monitoring scene, wherein the monitoring rule comprises a monitoring index needing to be monitored and an action when the monitoring index does not meet a preset condition.

7. The method according to claim 6, wherein the user attribute is a device characteristic of a terminal device used by the user, and wherein the device characteristic comprises at least one of: equipment manufacturer, equipment model, operating system of the equipment, physical address of the equipment, and IP address of the equipment.

8. The method of claim 6, wherein generating theoretical monitoring data based on the predetermined data generation model comprises:

analyzing the preset data generation model to obtain a data generation rule;

generating a new user according to a user rule in the data generation rule;

configuring user attributes for the new user according to a user attribute resource pool in the data generation rule;

acquiring information of old users according to user rules in the data generation rules;

according to an event rule in the data generation rule, events triggered by the new user and the old user are constructed;

and sequentially sending each event to the second electronic equipment according to the reporting sequence of each event.

9. An electronic device, comprising: one or more processors and memory;

the memory coupled with the one or more processors, the memory to store computer program code, the computer program code comprising computer instructions;

the computer instructions, when executed by the one or more processors, cause the electronic device to perform the method of any of claims 1-8.

10. A chip system, characterized in that the chip system comprises a processor coupled with a memory, the processor executing a computer program stored in the memory to implement the method according to any of claims 1 to 8.