Index monitoring method, electronic device and chip system

Abstract

The application is suitable for the terminal technical field, and provides an index monitoring method, an electronic device and a chip system. The index monitoring method comprises the following steps: a first electronic device generates a preset data generation model, generates theoretical monitoring data based on the preset data generation model, and acquires a first index value corresponding to a monitoring index; the first electronic device sends the theoretical monitoring data to a second electronic device; the second electronic device 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 device and / or the second electronic device monitor 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 itself is abnormal in the data processing process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of terminal, and in particular, to an index monitoring method, an electronic device and a chip system. BACKGROUND

[0002] Big data analysis is a technology of data analysis and data mining on massive text, image, audio and video data. ETL (Extraction-Transformation-Loading) is an important process in big data analysis, which is used to extract the required data from massive data for conversion. The converted data is the basis for further data analysis and data mining. Through the analysis of massive data, the behavior of users and the degree of dependence of users on products can be determined, and the products and operation strategies can be optimized based on the above results. For example, product functions or pages can be optimized, specific operation strategies can be developed for specific groups of people, and thus the ROI (Return On Investment) can be improved.

[0003] In order to provide accurate data support for the optimization of operation strategies, the data quality of the ETL data processing process needs to be monitored. For example, metadata information and task execution logs generated in the ETL process can be used to determine whether there is an abnormal data processing in the ETL data processing process. If no abnormal data processing in the ETL data processing process is detected, the data obtained from the ETL data processing process can be analyzed to obtain results that can optimize the products and operation strategies. However, the traditional method of monitoring the data quality of the ETL data processing process mostly focuses on monitoring the ETL data processing process from a macro perspective, and cannot guarantee that the monitoring indicators themselves may have abnormalities in the ETL data processing process. SUMMARY

[0004] The present application provides an index monitoring method, an electronic device and a chip system method, which solves the problem that the existing technology cannot monitor the possible abnormalities of the indicators in the data processing process.

[0005] To achieve the above purpose, the present application adopts the following technical solutions:

[0006] In a first aspect, embodiments of the present application provide a method for monitoring an index, the method comprising: generating, by a first electronic device, a preset data generation model, generating theoretical monitoring data based on the preset data generation model, and obtaining a first index value corresponding to a monitoring index; sending, by the first electronic device, the theoretical monitoring data to a second electronic device; processing, by the second electronic device, the theoretical monitoring data based on a data processing model, and determining a second index value corresponding to the monitoring index based on the processed theoretical monitoring data; and monitoring, by the first electronic device and / or the second electronic device, the monitoring index according to the first index value and the second index value of the monitoring index.

[0007] The data processing model is a data processing model actually used by the second electronic device in an ETL data processing process, and includes a plurality of data processing nodes. After a previous data processing node completes processing of the theoretical monitoring data, the processed theoretical monitoring data is transmitted to a next data processing node for processing. Therefore, during processing of the theoretical monitoring data by the second electronic device based on the data processing model, there can be abnormal situations such as loss of some data due to data transmission, processing logic errors of the second electronic device, and the like. By comparing the first index value and the second index value of the monitoring index, it can be determined whether the data processing model has the above abnormal situations according to the comparison result, and whether the monitoring index itself processed by the data processing model has an abnormality. If the monitoring index itself does not have an abnormality, the data generated by the data source can be processed according to the data processing model. Therefore, embodiments of the present application can improve the accuracy of the index value of the determined monitoring index, and can help business operators to monitor whether the monitoring index is abnormal in a timely manner.

[0008] It should be noted that the first electronic device and the second electronic device can be the same electronic device or different electronic devices, and are not limited in this regard.

[0009] The first electronic device can determine the first index value corresponding to the monitoring index according to the data generation rule information. Since the data generation rule information includes the monitoring index and related information thereof, the first electronic device can determine the first index value corresponding to the monitoring index according to the data generation rule information.

[0010] In combination with the first aspect, in some embodiments, monitoring the monitoring index according to the first index value and the second index value of the monitoring index comprises monitoring the monitoring index according to a difference between the first index value and the second index value.

[0011] If the first index value and the second index value are the same or close to each other, it indicates that the data processing model of the second electronic device will not cause the monitoring index to be abnormal after processing the theoretical monitoring data. If the first index value and the second index value are far apart, it indicates that the data processing model of the second electronic device will cause the monitoring index to be abnormal after processing the theoretical monitoring data.

[0012] In a scenario, a preset range can be set, and the monitoring index is monitored according to the preset range.

[0013] For example, monitoring the monitoring index according to the difference between the first index value and the second index value can include: if the difference between the first index value and the second index value is within a preset range, the monitoring index is normal; and if the difference between the first index value and the second index value is outside the preset range, the monitoring index is abnormal.

[0014] Specifically, if the difference between the first index value and the second index value is within the preset range, it indicates that the data processing model of the second electronic device will not cause the monitoring index to be abnormal after processing the theoretical monitoring data. If the difference between the first index value and the second index value is outside the preset range, it indicates that the data processing model of the second electronic device will cause the monitoring index to be abnormal after processing the theoretical monitoring data.

[0015] In combination with the first aspect, in some embodiments, monitoring the monitoring index according to the difference between the first index value and the second index value includes: the second electronic device displays the first index value, the second index value, and the difference between the first index value and the second index value; or the first electronic device compares the first index value and the second index value, and sends a reminder message to a system administrator terminal when the difference between the first index value and the second index value is outside a preset range.

[0016] In a scenario, the first index value and the second index value of the monitoring index can be displayed to a business operator through a UI. The business operator can log in to an index visualization system in the second electronic device through a business operator terminal, and the second electronic device displays the first index value and the second index value of the monitoring index to the business operator through the index visualization system.

[0017] In a scenario, the first electronic device can obtain the second index value, and then compare the first index value and the second index value, and send a reminder message to a system administrator terminal when the difference between the first index value and the second index value is outside a preset range.

[0018] In some embodiments of the first aspect, the first electronic device generates a preset data model, including: the first electronic device acquires a data generation rule, and establishes the preset data generation model according to the data generation rule; wherein the data generation rule includes at least one of the following: the number of new users and old users in each scene, the type and quantity of data required to be produced in each scene, the order of producing various data in each scene, and the monitoring indicators in each scene.

[0019] In some embodiments of the first aspect, the establishment of the preset data generation model according to the data generation rule includes: in response to a received monitoring scene establishment instruction, establishing one or more monitoring scenes; in response to a received monitoring scene name setting instruction, setting a name for the one or more monitoring scenes; in response to a received priority setting instruction of each monitoring scene, setting a priority order of each monitoring scene, the priority order indicating the order of generating each monitoring scene; in response to a received user rule setting instruction of each monitoring scene, setting a corresponding user rule of each monitoring scene; wherein the user rule includes: total number of users, new user proportion or quantity, old user proportion or quantity, and a user attribute resource pool including a plurality of user attributes; in response to a received event rule setting instruction required by each monitoring scene, setting the required event rule for each monitoring scene; wherein the event rule includes: an event list including a plurality of events, a reporting order of the plurality of events, and a quantity of each event in the plurality of events; in response to a received monitoring rule setting instruction of each monitoring scene, setting a corresponding monitoring rule for each monitoring scene, the monitoring rule including a monitoring indicator to be monitored and an action when the monitoring indicator does not meet a preset condition.

[0020] In some embodiments of the first aspect, the first electronic device includes a data quality monitoring system and a data generation system. For example, the data quality monitoring system and the data generation system can be one processing unit in the processor of the first electronic device.

[0021] In one scenario, a system administrator can input data generation rule information to a system administrator terminal, and the system administrator terminal generates a monitoring scene establishment instruction according to the data generation rule information. The monitoring scene establishment instruction can be used to instruct the data quality monitoring system to establish one monitoring scene, or to simultaneously establish multiple monitoring scenes. Then, the system administrator terminal sends the monitoring scene establishment instruction to the data quality monitoring system. The data quality monitoring system establishes a monitoring scene in response to the received monitoring scene establishment instruction.

[0022] In addition, the system administrator terminal can generate, according to the data, monitoring scene name setting instructions, priority setting instructions of each monitoring scene, user rule setting instructions of each monitoring scene, event rule setting instructions required by each monitoring scene, and monitoring rule setting instructions of each monitoring scene, etc. The system administrator terminal can send the monitoring scene name setting instructions, the priority setting instructions of each monitoring scene, the user rule setting instructions of each monitoring scene, the event rule setting instructions required by each monitoring scene, and the monitoring rule setting instructions of each monitoring scene, etc. at the same time as sending the monitoring scene establishment instructions to the data quality monitoring system. The data quality monitoring system receives the above instructions sent by the system administrator terminal at the same time.

[0023] The monitoring scene establishment instructions can be used to instruct the data quality monitoring system to establish multiple monitoring scenes at the same time. When the multiple monitoring scenes may have events at the same time, the priority of each monitoring scene needs to be limited so that the data generation system can generate data of each monitoring scene according to the set priority.

[0024] For example, the priority level of the user funnel analysis scene set by the data quality monitoring system is 5, the priority level of the active user scene is 4, the priority level of the application installation scene is 3, and the priority level of the new user scene is 2. The larger the priority level number is, the higher the priority is. Therefore, the data generation system generates data of each monitoring scene in order from high to low priority.

[0025] For example, the user attribute is a device feature of a terminal device used by a user, and the device feature includes at least one of the following: device manufacturer, device model, device operating system, device physical address, and device IP address.

[0026] For example, the event list can include multiple events, such as an application opening event, a product browsing event, a product adding to a shopping cart event, and a product ordering event in the shopping cart. The event rules of different monitoring scenes are usually different. For example, the events in the event list of monitoring scene 1 can be different from the events in the event list of monitoring scene 2, the number of events of each monitoring scene 1 can be different from the number of events of each monitoring scene 2, and the reporting order of each event of monitoring scene 1 can be different from the reporting order of each event of monitoring scene 2.

[0027] For example, the reporting order can be, in sequence, the application opening event, the product browsing event, the product adding to the shopping cart event, and the product ordering event in the shopping cart.

[0028] Exemplarily, the monitoring indicators can 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 ordering users, and the like. If a certain indicator does not meet a preset condition, a reminder message containing the indicator information that does not meet the preset condition can be sent to the system administrator terminal and / or the business operator terminal. Wherein, the indicator value of the indicator that does not meet the preset condition exceeds the preset range.

[0029] In combination with the first aspect, in some embodiments, the generating theoretical monitoring data based on the preset data generation model comprises: parsing the preset data generation model to obtain a data generation rule; generating a new user according to the 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; obtaining information of an old user according to the user rule in the data generation rule; constructing events triggered by the new user and the old user according to the event rule in the data generation rule; and sequentially sending each event to the second electronic device according to the reporting order of each event.

[0030] The data generation system can read data generation rule information from the data quality monitoring system. Parsing the data generation rule information can obtain the following information: the name of each monitoring scenario, the priority order of each monitoring scenario, the user rule corresponding to each monitoring scenario, the event rule required by each monitoring scenario, and the monitoring rule corresponding to each monitoring scenario.

[0031] Exemplarily, the new user can be generated according to the number of new users in the user rule. Alternatively, the new user can be generated according to the total number of users and the proportion of new users in the user rule.

[0032] The old user information can include an old user ID (identity) and user attributes of the old user. The old user ID can be a user name. It should be noted that after a new user is generated, the new user can change to an old user after a 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, and the like of the user can remain unchanged, and the user level and the like of the user can be changed.

[0033] In some embodiments, the events triggered by the user can be constructed according to the event list and the number of each event. For example, one or more events can be triggered for each user according to the event list, and the number of the one or more events corresponding to each event is set.

[0034] For example, the event list includes event 1, event 2, event 3 and event 4, the quantity corresponding to event 1 is x1, the quantity corresponding to event 1 is x2, the quantity corresponding to event 1 is x3, and the quantity corresponding to event 1 is x4. User 1 can trigger event 1, event 3 and event 4 in the event list, and user 2 can trigger event 2, event 3 and event 4 in the event list. The quantity of event 1 triggered by user 1 is x1, the quantity of event 3 triggered is x3, and the quantity of event 4 triggered is x4. The quantity of event 2 triggered by user 2 is x2, the quantity of event 3 triggered is x3, and the quantity of event 4 triggered is x4.

[0035] For example, according to the reporting order of each event, each event is sent to the second electronic device in turn, which can be: for the application opening event, the commodity browsing event, the commodity adding to the shopping cart event and the commodity ordering event in the shopping cart, the reporting order can be in turn application opening event, commodity browsing event, commodity adding to the shopping cart event and commodity ordering event in the shopping cart. Correspondingly, the application opening event is first sent to the data processing system, then the commodity browsing event is sent to the data processing system, then the commodity adding to the 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. The data processing system processes each event sent by the data generation system in turn.

[0036] In a second aspect, the embodiments of the present application provide an electronic device, comprising: one or more processors and a memory; the memory is coupled with the one or more processors, the memory is used to store computer program codes, the computer program codes comprise computer instructions; when the one or more processors execute the computer instructions, the electronic device executes the method in any one of the first aspect.

[0037] In a third aspect, the embodiments of the present application provide a chip system, the chip system comprises a processor, the processor is coupled with a memory, and the processor executes a computer program stored in the memory to realize the method in any one of the first aspect. Wherein, the chip system can be a single chip, or a chip module composed of multiple chips.

[0038] In a fourth aspect, the embodiments of the present application provide a chip system, the chip system comprises a memory and a processor, and the processor executes a computer program stored in the memory to realize the method in any one of the first aspect. Wherein, the chip system can be a single chip, or a chip module composed of multiple chips.

[0039] In a fifth aspect, an embodiment of the present application provides a computer program product, which, when executed on an electronic device, causes the electronic device to perform the method of any one of the first aspect.

[0040] In a sixth aspect, an embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the method of any one of the first aspect.

[0041] It can be understood that the electronic device of the second aspect, the chip system of the third aspect and the fourth aspect, the computer program product of the fifth aspect, and the computer readable storage medium of the sixth aspect are all used to execute the method provided in the second aspect or the method provided in the third aspect. Therefore, the beneficial effects achieved thereby can refer to the beneficial effects in the corresponding method, which will not be described here again. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 System architecture diagram applicable to the method for monitoring the ETL data processing process in the related art 1;

[0043] Figure 2 Flowchart of the method for monitoring data processing based on a data processing process model in the related art 2;

[0044] Figure 3 Architecture diagram of the index monitoring method provided by an embodiment of the present application;

[0045] Figure 4 Flowchart of the index monitoring method provided by an embodiment of the present application;

[0046] Figure 5 Interface diagram of the index monitoring provided by an embodiment of the present application;

[0047] Figure 6 Flowchart of the method for configuring data generation rule information for a data quality monitoring system provided by an embodiment of the present application;

[0048] Figure 7 Flowchart of the method for generating theoretical monitoring data provided by an embodiment of the present application;

[0049] Figure 8 Flowchart of the method for monitoring the monitoring index provided by an embodiment of the present application;

[0050] Figure 9 Flowchart of the method for monitoring the monitoring index provided by an embodiment of the present application;

[0051] Figure 10A structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0052] In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular architectures, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known methods, devices, circuits, and

[0053] It is to be understood that the terminology "includes", "has", "holds", "contains" or "comprises", "comprising", or "including" when used in this specification and in the following claims, specifies the presence of stated features, integers, steps, operations, elements, or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or groups thereof.

[0054] It is also to be understood that the terminology "and / or" when used in this specification and in the following claims, refers to at least one of the items, or any combination of the items, or all of the items included in the associated list.

[0055] As used in this specification and in the claims, the term "if" can be interpreted as meaning "when", or "once", or "in response to a determination", or "in response to detecting", as appropriate, depending on the context. Similarly, the phrase "if determined", or "if [the recited condition or event] is detected", can be interpreted as meaning "once determined", or "in response to a determination", or "once [the recited condition or event] is detected", or "in response to detecting [the recited condition or event]", as appropriate, depending on the context.

[0056] In addition, in the description of the specification and the appended claims, the terms "first", "second", "third", etc. are used only for distinguishing between similar objects, and cannot be understood as indicating or implying relative importance.

[0057] Reference throughout this specification to "one embodiment" or "an embodiment" or "some embodiments" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment", "in some embodiments", "in other embodiments", "in additional embodiments", etc. in various places throughout this specification are not necessarily all referring to the same embodiment, unless otherwise specifically stated. The terms "comprising", "including", "having" and their variants are meant to be construed as "including but not limited to", unless otherwise specifically stated.

[0058] In addition, the "multiple" mentioned in the embodiments of the present application should be interpreted as two or more than two.

[0059] The steps involved in the index monitoring method provided in the embodiments of the present application are only examples, and not all steps are necessarily performed, or the content in each information or message is optional. In use, it can be increased or reduced as needed.

[0060] The same step or the step or message with the same function in different embodiments can be mutually referenced and learned.

[0061] The business scenarios described in the embodiments of the present application are used to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, with the evolution of network architecture and the appearance of new business scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0062] A method for monitoring an ETL data processing process is provided in the related art. Figure 1 A system architecture applicable to the above method for monitoring the ETL data processing process. The system architecture includes a monitoring index configuration module and a monitoring processing module. Figure 1 The ETL processing unit in the above system architecture is a functional module for executing ETL data processing. Typically, the ETL data processing process is executed by a corresponding ETL processing unit, such as data extraction, conversion, or loading, which are executed by corresponding ETL processing units. The ETL processing unit can be a functional unit for executing data processing processes such as data extraction, conversion, or loading.

[0063] The above method for monitoring the ETL data processing process determines the field type of the output data in the data processing process according to the related information of the ETL data processing task, and generates monitoring indexes of the ETL data processing process according to the field type of the output data. Then, the ETL data processing process is monitored according to the generated monitoring indexes to detect whether there is a data processing exception. For example, the corresponding fields of the output data in the ETL data processing process can be counted or calculated to obtain the result value of the index, and the result value is used to determine whether the ETL data processing process has a data processing exception.

[0064] It should be noted that in the big data analysis, whether the index itself can accurately reflect the data processing abnormality that may exist in the ETL data processing process is also particularly important. Understandably, if the index cannot accurately reflect the data processing abnormality that may exist in the ETL data processing process, the related technology one cannot provide meaningful information for product optimization, and cannot develop targeted operation strategies based on big data analysis results. The related technology one can monitor the ETL data processing process in a macroscopic way, but cannot guarantee that the index itself can accurately reflect the data processing abnormality that may exist in the ETL data processing process. For example, there may be data processing process normal in the ETL data processing process, but the index is abnormal due to data loss in the transmission process.

[0065] The related technology two provides a method for monitoring data processing based on a data processing process model. Referring to Figure 2 , the method comprises: constructing a data processing process description model and a storage structure, collecting data processing process model information, collecting task execution state information, visualizing the data processing process, and using a visual interface to control the data full link. The above-mentioned model is a model for data processing process based on data processing process, which is used to describe various types of data and various data processing links. Then, the log generated by the execution state of each step in the data processing process is collected, the result of task execution is described, and the execution process is visualized.

[0066] Among them, the related technology two can monitor whether there is an abnormality in the data processing process, but cannot guarantee that the index itself can accurately reflect the data processing abnormality that may exist in the data processing process. Even if the index is abnormal is inferred from whether the data processing process is abnormal, the problem of whether the monitoring index itself is accurate cannot be solved. For example, there may be data processing process normal in the data processing process, but the index is abnormal due to data loss in the transmission process.

[0067] Based on the above problems, an index monitoring method is provided in the embodiments of the present application, which comprises: a first electronic device 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 contains the monitoring index of the theoretical monitoring data. Then, the first electronic device sends the theoretical monitoring data to a second electronic device. The second electronic device 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. The data processing model is a data processing model actually used by the second electronic device in the ETL data processing process, which comprises a plurality of data processing nodes. 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. Therefore, in the process of processing the theoretical monitoring data by the second electronic device through the data processing model, there may be abnormal situations such as data loss due to data transmission, processing logic error of the second electronic device, etc. By comparing the first index value and the second index value of the monitoring index, the presence of the above abnormal situations in the data processing model can be determined according to the comparison result, and then it can be determined whether the monitoring index itself processed by the data processing model is abnormal. If the monitoring index itself is not abnormal, the data generated by the data source can be processed according to the data processing model. Therefore, the embodiments of the present application can improve the accuracy of the index value of the determined monitoring index, and can help business operators to monitor whether the monitoring index is abnormal in time.

[0068] It should be noted that the first electronic device and the second electronic device described above can be the same electronic device, or can be different electronic devices, which are not limited. The hardware structure of the first electronic device and the second electronic device is described in detail in the following Figure 10 .

[0069] Figure 3 A system architecture to which the index monitoring method provided in the embodiments of the present application is applicable is shown. Referring to Figure 3 , the system architecture can comprise 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 business operator terminal.

[0070] The data quality monitoring system is used to manage the preset data generation model, the quality of the monitoring index, the abnormal notification of the monitoring index, and the query of the index value of the monitoring index. For example, the system administrator can input the preset data generation model through the system administrator terminal. Then, the data quality monitoring system receives the preset data generation model sent by the system administrator terminal. The specific information of the preset data generation model is described in the following, which is not described here.

[0071] The data generation system is configured to generate theoretical monitoring data according to the preset data generation model, and process the theoretical monitoring data based on a preset data processing model to determine 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.

[0072] The data processing system is configured to process the theoretical monitoring data according to a data processing model to determine a second index value corresponding to the monitoring index, and send the first index value and the second index value to the index visualization system. The data processing model includes a plurality of data processing nodes. After a previous data processing node completes processing of the theoretical monitoring data, the processed theoretical monitoring data is transmitted to a next data processing node for processing.

[0073] The index visualization system is configured to display the first index value and the second index value of the monitoring index to a user, for example, through a UI (User Interface). A business operator can access the index visualization system through a business operator terminal to view the first index value and the second index value of the monitoring index, and the difference between the first index value and the second index value.

[0074] The data processing system can also send the first index value and the second index value to the data quality monitoring system. A business operator can access the data quality monitoring system through a business operator terminal to query 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 query whether the calculation method of the monitoring index is accurate.

[0075] The data source is a system capable of providing source data. For example, the data source can be an APP (Application) provided by a developer or other system capable of generating data. The data source sends the generated source data to the data processing system. The data processing system processes the source data through an 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 two is within a preset range, the data processing system can process the source data through the actual data processing model. Alternatively, if the first index value and the second index value are the same, or the difference between the two is within a preset range, the index value of the monitoring index corresponding to the source data obtained by the data processing system is not abnormal and is more reliable.

[0076] Figure 4 A flowchart of an index monitoring method provided by an embodiment of the present application is shown in FIG. 1. Figure 4 The index monitoring method can include steps 101-104.

[0077] In step 101, the first electronic device generates a preset data generation model, generates theoretical monitoring data based on the preset data generation model, and obtains a first index value corresponding to a monitoring index.

[0078] For example, the system administrator can send data generation rule information to the first electronic device through an administrator terminal. The first electronic device receives the data generation rule information and configures according to the data generation rule to obtain the preset data generation model.

[0079] The preset data generation model can generate theoretical monitoring data for one or more application scenarios. For example, the application scenarios can include a new user scenario, an active user scenario, a user funnel analysis scenario, and an application installation scenario. The new user scenario can be a scenario for obtaining user information of a user who accesses an application such as a shopping website or an APP for the first time within a preset time period (for example, one day). The active user scenario can be a scenario for obtaining user information of a user who accesses an application such as a shopping website or an APP multiple times within a preset time period (for example, one week). The user funnel analysis scenario can be a scenario for analyzing user behavior states and user conversion rates at each stage from the starting point to the end point. The application installation scenario can be a scenario for obtaining the number of users who install a certain application within a preset time period.

[0080] In some embodiments, the preset data generation model can include one or more of the following data generation rule information: the number of new users and old users in each scenario, the type and number of data required in each scenario, the order of data generation in each scenario, and the monitoring index in each scenario.

[0081] The new user can be a user who logs in to an application such as a shopping website or an APP for the first time, and the old user can be a user who has logged in to an application such as a shopping website or an APP multiple times. The number of new users and old users can be set as needed. For example, the ratio of new users to old users can be 1:3, and the total number of new users and old users is c; or the number of new users is a, the number of old users is b, and the ratio of a to b is 1:3.

[0082] The type of data can be user behavior in a system such as a shopping website or an APP, such as logging in to an application, adding goods to a shopping cart, ordering goods in the shopping cart, and installing an application. The number of data can be the number of various user behaviors within a preset time, such as the number of users who log in to an application within a preset time, the number of users who add goods to a shopping cart within a preset time, the number of users who order goods in a shopping cart within a preset time, and the number of users who install a certain application within a preset time.

[0083] The sequence of production data in each scenario can be the sequence of user behaviors occurring in each scenario. For example, for user behaviors such as opening an application, adding a product to a shopping cart, and placing an order for a product in the shopping cart, the sequence of user behaviors occurring can be: first, the user behavior of opening the application, second, the user behavior of adding a product to the shopping cart, and last, the user behavior of placing an order for a product in the shopping cart.

[0084] The monitoring indicators in each scenario can be indicators of interest in each scenario. For example, for the new user scenario, the monitoring indicator can be the number of new users generated within a preset time (e.g., one day). For the active user scenario, the monitoring indicator can be the number of active users within a preset time (e.g., one week). For the user funnel analysis scenario, the monitoring indicator can be the user conversion rate at each stage from the starting point to the end point. For the application installation scenario, the monitoring indicator can be the number of users installing a certain application.

[0085] It should be noted that the monitoring indicator can be one or multiple. For example, the monitoring indicator can be the number of new users, or the number of active users.

[0086] The first electronic device can determine the first indicator value corresponding to the monitoring indicator according to the data generation rule information. Since the data generation rule information contains the monitoring indicator and its related information, the first electronic device can determine the first indicator value corresponding to the monitoring indicator according to the data generation rule information.

[0087] For example, if the number of new users and the number of old users in the data generation rule information are: the number of new users is a, and the number of old users is b, then the number of new users in the theoretical monitoring data generated by the first electronic device is a, and the number of old users is b. Therefore, the first electronic device can determine that the indicator value of the monitoring indicator of the number of new users is a.

[0088] For example, if the number of new users and the number of old users in the data generation rule information are: the number of new users is a, and the number of old users is b, then the number of new users in the theoretical monitoring data generated by the first electronic device is a, and the number of old users is b. Therefore, the first electronic device can determine that the indicator value of the monitoring indicator of the number of new users is a.

[0089] For example, in the case of monitoring the number of users placing orders, the first electronic device can determine the number of users placing orders according to 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. 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, then in the theoretical monitoring data generated by the first electronic device, the number of users placing an order for the goods in the shopping cart within the preset time is m. Therefore, the first electronic device can determine that the index value of the monitoring index of the number of users placing orders is m.

[0090] In step 102, the first electronic device sends the theoretical monitoring data to the second electronic device.

[0091] In step 103, the second electronic device processes the theoretical monitoring data through the data processing model, and determines the second index value corresponding to the monitoring index based on the processed theoretical monitoring data.

[0092] The data processing model is the data processing model actually used by the second electronic device in the ETL data processing process. The data processing model can include multiple data processing nodes. 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 processing of the theoretical monitoring data by the second electronic device through the data processing model, there can be a situation where some data is lost due to data transmission between data processing nodes.

[0093] For example, during the processing of the theoretical monitoring data by the second electronic device, the information of some new users can be lost during data transmission between data processing nodes. After the second electronic device completes processing of the theoretical monitoring data, the second electronic device determines the second index value of the monitoring index of the number of new users based on the processed data. Due to the loss of information of some new users, the second index value obtained does not match the actual situation, i.e., the monitoring index of the number of new users itself can be abnormal.

[0094] In step 104, the first electronic device and / or the second electronic device monitor the monitoring index according to the first index value and the second index value of the monitoring index.

[0095] If the first index value and the second index value are the same or close to each other, it means that the data processing model of the second electronic device does not cause the monitoring index itself to be abnormal after processing the theoretical monitoring data. If the first index value and the second index value differ greatly, it means that the data processing model of the second electronic device causes the monitoring index itself to be abnormal after processing the theoretical monitoring data.

[0096] In a scene, a preset range can 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 above-mentioned preset range, it indicates that the data processing model of the second electronic device will not cause the monitoring index itself to be abnormal after processing the theoretical monitoring data. If the difference between the first index value and the second index value exceeds the above-mentioned preset range, it indicates that the data processing model of the second electronic device will cause the monitoring index itself to be abnormal after processing the theoretical monitoring data.

[0097] In some embodiments, the first index value and the second index value of the monitoring index can be displayed to the business operator through the UI.

[0098] Figure 5 The interface diagram of the index monitoring provided by the embodiments of the present application is shown in FIG. 1. Figure 5 The monitoring index in the embodiments includes multiple indexes such as the number of new users, the number of active users, and the number of installation events. Figure 5 The expected value in FIG. 1 is the first index value of the monitoring index, and the actual value is the second index value of the monitoring index. The expected value of the number of new users is 100, and the actual value is 70. The difference between the expected value and the actual value is 30, that is, the actual value decreases by 30% relative to the expected value. Similarly, 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, that is, the actual value does not change relative to the expected value. The expected value of the number of installation events is 100, and the actual value is 120. The difference between the expected value and the actual value is 20, that is, the actual value increases by 20% relative to the expected value.

[0099] In the embodiments, the business operator can know whether the monitoring index itself obtained by the data processing model of the second electronic device is abnormal through Figure 5 The expected value and the actual value of each index monitoring displayed in the embodiments can intuitively know whether the monitoring index itself obtained by the data processing model of the second electronic device is abnormal.

[0100] The index monitoring method compares the first index value and the second index value of the monitoring index, and determines whether the data processing model has the case of partial data loss caused by data transmission according to the comparison result, and further determines whether the monitoring index itself obtained by the data processing model is abnormal. If the monitoring index itself obtained by the data processing model is not abnormal, the data generated by the data source can be processed according to the data processing model. Therefore, the embodiments of the present application can improve the accuracy of the index value of the determined monitoring index, and can help the business operator to monitor whether the monitoring index is abnormal in time.

[0101] Figure 6 The flowchart of configuring the data generation rule information for the data quality monitoring system provided by the embodiments of the present application is shown in FIG. 2. Figure 6The process of configuring the data generation rule information for the data quality monitoring system includes steps 201-206.

[0102] In step 201, a monitoring scenario is established in response to the received monitoring scenario establishment instruction.

[0103] The first electronic device can include a data quality monitoring system and a data generation system. For example, the data quality monitoring system and the data generation system can be one processing unit in the processor of the first electronic device.

[0104] In one scenario, the system administrator can input data generation rule information into the system administrator terminal, and the system administrator terminal generates a monitoring scenario establishment instruction according to the data generation rule information. The monitoring scenario establishment instruction can be used to instruct the data quality monitoring system to establish one monitoring scenario, or to simultaneously establish multiple monitoring scenarios. Then, the system administrator terminal sends the monitoring scenario establishment instruction to the data quality monitoring system. The data quality monitoring system establishes a monitoring scenario in response to the received monitoring scenario establishment instruction.

[0105] For example, the monitoring scenario establishment instruction can be used to instruct the data quality monitoring system to establish one of scenario 1, scenario 2, scenario 3, and scenario 4. Alternatively, the monitoring scenario establishment instruction can be used to instruct the data quality monitoring system to establish multiple scenarios among scenario 1, scenario 2, scenario 3, and scenario 4.

[0106] In addition, the system administrator terminal can also generate a monitoring scenario name setting instruction, a priority setting instruction for each monitoring scenario, a user rule setting instruction for each monitoring scenario, an event rule setting instruction required for each monitoring scenario, and a monitoring rule setting instruction for each monitoring scenario, etc. according to the data generation rule information. The system administrator terminal can also send the monitoring scenario name setting instruction, the priority setting instruction for each monitoring scenario, the user rule setting instruction for each monitoring scenario, the event rule setting instruction required for each monitoring scenario, and the monitoring rule setting instruction for each monitoring scenario, etc. at the same time as sending the monitoring scenario establishment instruction to the data quality monitoring system. The data quality monitoring system simultaneously receives the above instructions sent by the system administrator terminal.

[0107] Alternatively, the system administrator terminal can send the above instructions to the data quality monitoring system respectively. Correspondingly, the data quality monitoring system receives the above instructions sent by the system administrator terminal respectively. For example, after step 201 is completed, the system administrator terminal sends the monitoring scene name setting instruction to the data quality monitoring system; after step 202 is completed, the system administrator terminal sends the priority setting instruction of each monitoring scene to the data quality monitoring system; after step 203 is completed, the system administrator terminal sends the user rule setting instruction of each monitoring scene to the data quality monitoring system; after step 204 is completed, the system administrator terminal sends the event rule setting instruction of each monitoring scene to the data quality monitoring system; and after step 205 is completed, the system administrator terminal sends the monitoring rule setting instruction of each monitoring scene to the data quality monitoring system.

[0108] In step 202, a monitoring scene name is set for the established monitoring scene in response to the received monitoring scene name setting instruction.

[0109] The monitoring scene name is used by a user to distinguish each scene, and each monitoring scene corresponds to a monitoring scene name. The monitoring scene name setting instruction is used to instruct the data quality monitoring system to set a corresponding name for each monitoring scene established.

[0110] For example, the system administrator can send the names of scenes 1 to 4 to the data quality monitoring system through the system administrator terminal. The name of scene 1 can be a new user scene, the name of scene 2 can be an active user scene, the name of scene 3 can be a user funnel analysis scene, and the name of scene 4 can be an application installation scene.

[0111] In step 203, the priority order of each monitoring scene is set in response to the received priority setting instruction of each monitoring scene.

[0112] The monitoring scene establishment instruction can be used to instruct the data quality monitoring system to establish multiple monitoring scenes at the same time, and the above multiple system monitoring scenes can occur at the same time. At this time, the priority of each monitoring scene needs to be limited, so that the data generation system can generate data of each monitoring scene according to the set priority.

[0113] In this step, the priority setting instruction of each monitoring scene is used to instruct the data quality monitoring system to set the order of generating each monitoring scene.

[0114] For example, the data quality monitoring system sets the priority level of the user funnel analysis scene to 5, the priority level of the active user scene to 4, the priority level of the application installation scene to 3, and the priority level of the new user scene to 2. The larger the priority level number is, the higher the priority is. Then, the data generation system generates the data of each monitoring scene in the order from high to low priority.

[0115] In step 204, the user rules corresponding to each monitoring scene are set in response to the received user rule setting instructions of each monitoring scene.

[0116] In this step, the user rule setting instruction is used to instruct the data quality monitoring system to set the user attributes for each user generated in each monitoring scene.

[0117] The user rules can include the total number of users, the proportion or number of new users, the proportion or number of old users, and a user attribute resource pool including a plurality of user attributes. For example, the user attributes can be the device characteristics of the terminal device used by the user. For example, the device characteristics can include the device manufacturer, the device model, the device operating system, the device MAC (Medium Access Control, physical address) address, or the IP address.

[0118] For example, after the data quality monitoring system receives the user rule information of each monitoring scene sent by the system administrator terminal, the user rules for each monitoring scene are set.

[0119] For example, the program code for generating the user attribute resource pool is as follows:

[0120]

[0121] In step 205, the required event rules for each monitoring scene are set in response to the received event rule setting instructions of each monitoring scene.

[0122] The event rules can include an event list, the reporting order of each event, and the number of each event. The event rule setting instruction is used to instruct the data quality monitoring system to set the corresponding event rules for each monitoring scene.

[0123] For example, the event list can include multiple events, such as an application opening event, a commodity browsing event, a commodity adding to cart event, and an order placing event for commodities in the cart. The event rules of different monitoring scenarios are usually different. For example, the events in the event list of monitoring scenario 1 can be different from the events in the event list of monitoring scenario 2, the number of events of each event in monitoring scenario 1 can be different from the number of events of each event in monitoring scenario 2, and the reporting order of each event in monitoring scenario 1 can be different from the reporting order of each event in monitoring scenario 2.

[0124] For example, the reporting order can be in the order of the application opening event, the commodity browsing event, the commodity adding to cart event, and the order placing event for commodities in the cart.

[0125] In addition, the number of events of each event can be set as needed. For example, the number of application opening events is 1000, the number of commodity adding to cart events is 100, and the number of order placing events for commodities in the cart is 20.

[0126] In one scenario, the data quality monitoring system sets the required event rules for each monitoring scenario after receiving the event rule setting instructions required by each monitoring scenario sent by the system administrator terminal.

[0127] For example, the program code corresponding to the event rules required by each monitoring scenario is as follows:

[0128]

[0129]

[0130]

[0131] Step 206, in response to the received monitoring rule setting instructions of each monitoring scenario, set the corresponding monitoring rules for each monitoring scenario.

[0132] The monitoring rules can include the indicators that need to be monitored and the actions when the indicators do not meet the preset conditions.

[0133] For example, the indicators that need to be monitored can 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 order placing users, etc. If a certain indicator does not meet the preset condition, an alert message can be sent to the system administrator terminal and / or the business operator terminal, which contains the indicator information that does not meet the preset condition. The indicator value of the indicator that does not meet the preset condition exceeds the preset range.

[0134] In one scenario, the data quality monitoring system sets the monitoring rules for each monitoring scenario after receiving the monitoring rules for each monitoring scenario sent by the system administrator terminal.

[0135] Figure 7 A flowchart of generating theoretical monitoring data is provided for the embodiments of the present application. Referring to Figure 7 , the process of generating theoretical monitoring data includes steps 301 to 307.

[0136] Step 301, read the data generation rule information and parse the data generation rule information.

[0137] The data generation system can read the data generation rule information from the data quality monitoring system. Parsing the above data generation rule information can obtain the following information: the name of each monitoring scenario, the priority order of each monitoring scenario, the user rules corresponding to each monitoring scenario, the event rules required by each monitoring scenario, and the monitoring rules corresponding to each monitoring scenario. For specific information, please refer to Figure 6 the contents in the embodiments, which will not be repeated here.

[0138] Step 302, generate new users according to the user rules in the data generation rule information.

[0139] According to the related information of the user rules in step 204, new users can be generated according to the number of new users in the user rules. For example, if the number of new users in the user rules is a, then a new users are generated, and a is a positive integer. Alternatively, new users can be generated according to the total number of users and the proportion of new users in the user rules. For example, if the total number of users in the user rules is c and the proportion of new users is n%, then c*n% new users are generated, c is a positive integer, and n is a positive number.

[0140] Step 303, configure user attributes for the generated new users according to the user attribute resource pool in the data generation rule information.

[0141] For example, the user attributes can be the device characteristics of the terminal devices used by the users, and the corresponding device characteristics, i.e., device manufacturer, device model, device operating system, device MAC address or IP address, etc., can be configured for each new user. In addition, the user attributes can also include user age, gender, user level, etc. The user attributes corresponding to each new user are different.

[0142] Step 304, obtain old user information according to the user rules in the data generation rule information.

[0143] The old user information can include old user ID (identity) and old user attributes. The old user ID can be the user name, and the old user attributes are described in step 303 and will not be repeated here.

[0144] It should be noted that after a new user is generated, the new user can change to an old user after a 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, and other information of the user can remain unchanged, and the user level and other information of the user can be changed.

[0145] Step 305, constructing the events triggered by each user according to the event rules in the data generation rule information.

[0146] Among them, the event rules can include an event list, a reporting order of each event, and a quantity of each event. Correspondingly, the constructed events triggered by the user can include one or more events triggered by each user and a quantity of each event in the one or more events. Among them, the events triggered by each user include events triggered by a new user and events triggered by an old user.

[0147] In some embodiments, the events triggered by the user can be constructed according to the event list and the quantity of each event. For example, one or more events can be triggered for each user according to the event list, and the quantity of the one or more events corresponding to each event is set.

[0148] For example, the event list includes event 1, event 2, event 3, and event 4, the quantity of event 1 is x1, the quantity of event 1 is x2, the quantity of event 1 is x3, and the quantity of event 1 is x4. User 1 can trigger event 1, event 3, and event 4 in the event list, and user 2 can trigger event 2, event 3, and event 4 in the event list. The quantity of event 1 triggered by user 1 is x1, the quantity of event 3 triggered is x3, and the quantity of event 4 triggered is x4. The quantity of event 2 triggered by user 2 is x2, the quantity of event 3 triggered is x3, and the quantity of event 4 triggered is x4.

[0149] Step 306, sequentially sending each event to the data processing system according to the reporting order of the events triggered by each user.

[0150] Among them, the data processing system can be one processing unit in the processor of the second electronic device.

[0151] For example, for the opening application event, the browsing commodity event, the adding commodity to the shopping cart event and the ordering commodity in the shopping cart event, the reporting sequence can be the opening application event, the browsing commodity event, the adding commodity to the shopping cart event and the ordering commodity in the shopping cart event in turn. Correspondingly, the opening application event is sent to the data processing system first, then the browsing commodity event is sent to the data processing system, then the adding commodity to the shopping cart event is sent to the data processing system, and finally the ordering commodity in the shopping cart event is sent to the data processing system. The data processing system processes each event sent by the data generation system in turn.

[0152] In step 307, according to the monitoring rule, the first index value of the monitoring index is determined.

[0153] The monitoring index can be one or more. For example, the monitoring index can be 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 ordering users, etc. How to determine the first index value of each monitoring index, please refer to the content in step 102, which will not be repeated here.

[0154] Figure 8 The flowchart for monitoring the monitoring index provided by the embodiment of the application is shown in FIG. 4. Figure 8 The process of monitoring the monitoring index includes steps 401 to 404.

[0155] In step 401, the index visualization system logs in the index visualization system in response to the received login information.

[0156] In step 402, the index visualization system obtains the second index value of the monitoring index obtained by the data processing system.

[0157] In step 403, the index visualization system obtains the first index value of the monitoring index obtained by the data generation system.

[0158] In step 404, the index visualization system shows the first index value and the second index value to the user.

[0159] The business operator can compare the first index value and the second index value to determine whether the monitoring index itself is abnormal according to the comparison result. The specific comparison process is described in step 104, which will not be repeated here.

[0160] In this embodiment, the business operator compares the first index value and the second index value, and when the comparison result of the first index and the second index does not meet the preset condition, it can be judged that the monitoring index may be abnormal.

[0161] Figure 9 The flowchart for monitoring the monitoring index provided by the embodiment of the application is shown in FIG. 4. Figure 9The process of monitoring the monitoring index includes steps 501 to 504.

[0162] Step 501, configure data generation rules and monitoring rules for the data quality monitoring system.

[0163] Please refer to steps 201 to 206, which will not be repeated here.

[0164] Step 502, the data generation system generates theoretical monitoring data based on the data generation rules, obtains the first index value of the monitoring index, and sends the first index value to the data quality monitoring system.

[0165] Step 503, the data processing system processes the theoretical monitoring index, determines the 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.

[0166] Step 504, the data quality monitoring system compares the first index value and the second index value, and sends a reminder information to the system administrator terminal in the case that the comparison result does not meet the preset condition.

[0167] In the embodiment, when the comparison result of the first index and the second index does not meet the preset condition, the data quality detection system can automatically send a reminder information to the system administrator terminal according to the configured monitoring rules, so as to remind the administrator that the monitoring index may be abnormal.

[0168] Figure 10 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. As shown in Figure 10 The electronic device 600 of the embodiment includes one or more processors 610, a memory 620, and a computer program 621 stored in the memory 620 and executable on the processor 610. The processor 610 implements the steps in each of the above method embodiments when executing the computer program 621, for example Figure 4 Steps 101 to 104 shown in the figure.

[0169] For example, the computer program 621 can be divided into one or more modules / units, which are stored in the memory 620 and executed by the processor 610 to complete the present application. The one or more modules / units can be a series of computer program instruction segments that can complete a specific function, which are used to describe the execution process of the computer program 621 in the electronic device 600.

[0170] The computer program 621 includes computer program code, which can be in the form of source code, object code, executable code, or any some intermediate form. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the computer readable medium can include appropriate contents according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0171] The electronic device 600 includes but is not limited to a processor 610 and a memory 620. Those skilled in the art can understand that the electronic device 600 can further include other components, for example, the electronic device 600 can further include an input device, an output device, a network access device, a bus, etc. Figure 10 The electronic device 600 is only an example and does not constitute a limitation on the electronic device 600, and can include more or fewer components than the diagram, or combine some components, or different components, for example, the electronic device 600 can further include an input device, an output device, a network access device, a bus, etc.

[0172] The processor 610 can include one or more processing units. For example, the processor 610 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0173] The memory 620 can be an internal storage unit of the electronic device 600, for example, a hard disk or a memory of the electronic device 600. The memory 620 can also be an external storage device of the electronic device 600, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 600. Further, the memory 620 can include both the internal storage unit and the external storage device of the electronic device 600. The memory 620 is used to store the computer program and other programs and data required by the electronic device 600. The memory 620 can also be used to temporarily store data that has been output or will be output.

[0174] Optionally, the embodiment of the present application further provides an electronic device, including one or more processors, a memory and a display screen. The memory, the display and the one or more processors are coupled, and the memory is used to store computer program codes including computer instructions; when the one or more processors execute the computer instructions, the electronic device executes one or more steps in any one of the above methods.

[0175] Optionally, the embodiment of the present application further provides an electronic device, including one or more processors and a memory. The memory is coupled with the one or more processors, and the memory is used to store computer program codes including computer instructions; when the one or more processors execute the computer instructions, the electronic device executes one or more steps in any one of the above methods.

[0176] Optionally, the embodiment of the present application further provides a computer readable storage medium, which stores instructions, and when the instructions are run on a computer or a processor, the computer or the processor executes one or more steps in any one of the above methods.

[0177] Optionally, the embodiment of the present application further provides a computer program product including instructions, and when the computer program product is run on a computer or a processor, the computer or the processor executes one or more steps in any one of the above methods.

[0178] Optionally, the embodiment of the present application further provides a chip system, which can include a memory and a processor, and the processor executes a computer program stored in the memory to realize one or more steps in any one of the above methods. Wherein, the chip system can be a single chip, or a chip module composed of multiple chips.

[0179] Optionally, the embodiments of the present application further provide a chip system, which can include a processor coupled with a memory, and the processor executes a computer program stored in the memory to implement one or more steps in any of the above methods. The chip system can be a single chip or a chip module composed of multiple chips.

[0180] In the above embodiments, all or part of the processes or functions can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the processes or functions can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in or transmitted by a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line) or wireless (such as infrared, wireless, microwave, etc.) manner. 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, data center, etc. that includes one or more available media sets. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media (such as solid state disks (SSD)), etc.

[0181] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by a computer program to instruct the relevant hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. The storage medium includes ROM or random access memory (RAM), magnetic disks or optical disks, and various media that can store program codes.

[0182] Finally, it should be noted that the above description is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or replacements within the technical scope disclosed in the present application should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method of monitoring an indicator, characterized by, The method comprises: The first electronic device generates a preset data generation model, generates theoretical monitoring data based on the preset data generation model, and acquires a first index value corresponding to a monitoring index; The first electronic device sends the theoretical monitoring data to a second electronic device; The second electronic device 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; wherein the data processing model is a data processing model actually used by the second electronic device in an ETL data processing process; the ETL data processing process is an extraction, transformation, and loading data processing process; The first electronic device and / or the second electronic device monitor 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, The monitoring of the monitoring index according to the first index value and the second index value of the monitoring index comprises: Monitoring the monitoring index according to a difference between the first index value and the second index value.

3. The method of claim 2, wherein, The monitoring of the monitoring index according to the difference between the first index value and the second index value comprises: If the difference between the first index value and the second index value is within a preset range, the monitoring index is normal; If the difference between 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, The monitoring of the monitoring index according to the difference between the first index value and the second index value comprises: The second electronic device displays the first index value, the second index value, and the difference between the first index value and the second index value. Alternatively, The first electronic device compares the first index value and the second index value, and sends a reminder information to a system administrator terminal when the difference between 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 device 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 following: the number of new users and old users in each scene, the type and number of data to be generated in each scene, the order of generating various data in each scene, and the monitoring index in each scene.

6. The method of claim 1, wherein, Establishing the preset data generation model according to the data generation rule comprises: In response to a received monitoring scene establishment instruction, establishing one or more monitoring scenes; In response to a received monitoring scene name setting instruction, setting a name for the one or more monitoring scenes; In response to a received priority setting instruction of each monitoring scene, setting a priority order of each monitoring scene, wherein the priority order represents the generation order of each monitoring scene; In response to the received user rule setting instructions of each monitoring scenario, user rules corresponding to each monitoring scenario are set, wherein the user rules include: total number of users, new user proportion or quantity, old user proportion or quantity, and a user attribute resource pool, the user attribute resource pool including a plurality of user attributes; In response to the received event rule setting instructions required by each monitoring scenario, event rules required by each monitoring scenario are set, wherein the event rules include: an event list including a plurality of events, a reporting order of the plurality of events, and a quantity of each event in the plurality of events; In response to the received monitoring rule setting instructions of each monitoring scenario, corresponding monitoring rules of each monitoring scenario are set, the monitoring rules including monitoring indicators that need to be monitored and actions when the monitoring indicators do not meet preset conditions.

7. The method of claim 6, wherein, The user attribute is a device feature of a terminal device used by a user, and the device feature includes at least one of the following: device manufacturer, device model, device operating system, device physical address, and device IP address.

8. The method of claim 6, wherein, 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 the user rule in the data generation rule; Configuring user attributes for the new user according to the user attribute resource pool in the data generation rule; Obtaining information of an old user according to the user rule in the data generation rule; Constructing events triggered by the new user and the old user according to the event rule in the data generation rule; According to the reporting order of each event, each event is sent to the second electronic device in turn.

9. An electronic device, comprising: It includes: One or more processors and a memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, the computer program codes including computer instructions; When the one or more processors execute the computer instructions, the electronic device executes the method in any one of claims 1 to 8.

10. A chip system, characterized by The chip system includes a processor coupled to a memory, and the processor executes a computer program stored in the memory to implement the method in any one of claims 1 to 8. The chip system includes a processor coupled to a memory, and the processor executes a computer program stored in the memory to implement the method in any one of claims 1 to 8.

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