CN111858704A - Data monitoring method and device, electronic equipment and storage medium - Google Patents

Data monitoring method and device, electronic equipment and storage medium Download PDF

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CN111858704A
CN111858704A CN202010608347.XA CN202010608347A CN111858704A CN 111858704 A CN111858704 A CN 111858704A CN 202010608347 A CN202010608347 A CN 202010608347A CN 111858704 A CN111858704 A CN 111858704A
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魏循
田正中
沈旭东
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Koubei Shanghai Information Technology Co Ltd
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Abstract

The application discloses a data monitoring method, a device, electronic equipment and a storage medium, wherein the data monitoring method comprises the following steps: obtaining target data to be analyzed of a target monitoring object; obtaining historical target data with time characteristics matched with the time characteristics of the target data to be analyzed; obtaining normal data corresponding to the time characteristics of the target data to be analyzed according to the historical target data; and determining whether the target data to be analyzed is abnormal data or not according to the target data to be analyzed and the normal data. By adopting the data monitoring method, the problems of high implementation threshold and high implementation complexity caused by setting a fixed monitoring threshold value for data monitoring can be avoided, and the accuracy of data monitoring is improved.

Description

Data monitoring method and device, electronic equipment and storage medium
Technical Field
The application relates to the technical field of computers, in particular to a data monitoring method. The application also relates to a data monitoring device, an electronic device and a storage medium.
Background
The data monitoring is a means for timely and accurately feeding back data abnormity by monitoring related data in real time, and is used for assisting related personnel to repair or perfect the operation of products, the operation of network platforms and the like in time. Taking the operation of the e-commerce platform as an example, an e-commerce platform operator often monitors data according to order quantity data of relevant stores in the platform and the like to determine whether the order quantity data of the stores are abnormal in some time periods, so as to determine whether abnormal behaviors such as order refreshing occur in the stores, and further facilitate the e-commerce platform operator to make adjustment of operation strategies in time according to the stores.
The existing data monitoring strategy has the general mode as follows: setting a fixed monitoring threshold value aiming at a specific data monitoring scene, and if the data in a certain time period is not matched with the fixed monitoring threshold value in the data monitoring process, such as: and if the fixed monitoring threshold is exceeded, judging that the data in the time period is abnormal data. The corresponding execution steps of the existing data monitoring strategy are relatively simple, however, the execution basis of the existing data monitoring strategy is a fixed monitoring threshold, and the setting of the fixed monitoring threshold often needs to know the background of a monitoring scene, so that the implementation threshold and the complexity of the data monitoring strategy are improved, and the specific analysis is as follows: firstly, different fixed monitoring thresholds are often required to be set for different data monitoring scenes, and in addition, in the process of setting a fixed monitoring threshold for a specific data monitoring scene, a relevant technician who has a deep understanding of the specific data monitoring scene is required to perform data statistics and analysis on historical data in the data monitoring scene, so that a fixed monitoring threshold which meets the specific data monitoring scene can be set. Then, in the same scene, different factors may affect data in the scene, and taking the order quantity data of the restaurant as an example, factors such as weather, holidays, time periods and the like may all affect the order quantity data of the restaurant, so when a fixed monitoring threshold is set for a specific data monitoring scene, the setting of the fixed monitoring threshold may be completed by considering multiple factors at the same time, and the more the factors considered, the more complicated the setting of the fixed monitoring threshold.
In summary, the implementation threshold and the implementation complexity of the existing data monitoring strategy are high, and in addition, data monitoring is implemented based on a fixed monitoring threshold, a scene of false alarm or false alarm of abnormal data often occurs, so that the accuracy of the existing data monitoring strategy is reduced, for example: when data fluctuation occurs to data in a certain time period, data monitoring is implemented based on a fixed monitoring threshold value, and false report or false report failure is often caused to data abnormity.
Disclosure of Invention
The embodiment of the application provides a data monitoring method and device, an electronic device and a storage medium, so as to avoid the problems of high implementation threshold and high implementation complexity brought to data monitoring by setting a fixed monitoring threshold and improve the accuracy of data monitoring.
An embodiment of the present application provides a data monitoring method, including: obtaining target data to be analyzed of a target monitoring object; obtaining historical target data with time characteristics matched with the time characteristics of the target data to be analyzed; obtaining normal data corresponding to the time characteristics of the target data to be analyzed according to the historical target data; and determining whether the target data to be analyzed is abnormal data or not according to the target data to be analyzed and the normal data.
Optionally, the historical target data is data corresponding to the target monitoring object.
Optionally, the obtaining of the historical target data with the time characteristic matched with the time characteristic of the target data to be analyzed includes: determining a target time window corresponding to the target data to be analyzed; and obtaining historical data of which the time window is matched with the target time window as the historical target data.
Optionally, the obtaining historical data of which the time window is matched with the target time window, as the historical target data, includes: determining a historical target time window with the same time interval as the target time window according to the target time window; and obtaining the historical data corresponding to the historical target time window as the historical target data.
Optionally, the determining, according to the target time window, a historical target time window having a time interval the same as that of the target time window includes: obtaining a starting time point corresponding to the target time window, and obtaining a specified time interval between the starting time point corresponding to the target time window and the starting time point corresponding to the historical target time window; determining the starting time point corresponding to the historical target time window according to the starting time point corresponding to the target time window and the designated time interval; and determining the historical target time window according to the starting time point corresponding to the historical target time window.
Optionally, the obtaining, according to the historical target data, normal data corresponding to the time characteristic of the target data to be analyzed includes: determining reference data corresponding to the normal data according to the historical target data; determining an endpoint value of a data interval corresponding to the normal data according to the historical target data and the reference data; and determining the data interval according to the endpoint value of the data interval, and taking the data in the range of the data interval as the normal data.
Optionally, the determining, according to the historical target data, reference data corresponding to the normal data includes: and obtaining an average value corresponding to the historical target data, and taking the average value as the reference data.
Optionally, the determining, according to the historical target data and the reference data, an endpoint value of a data interval corresponding to the normal data includes: obtaining a squared difference between the historical target data and the mean; calculating a standard deviation corresponding to the historical target data according to the square difference and the data number of the historical target data; and determining an endpoint value of the data interval according to the standard deviation and the average value.
Optionally, the determining an endpoint value of the data interval according to the standard deviation and the average value includes: obtaining a designated reconciliation parameter, wherein the designated reconciliation parameter is used for determining an endpoint value of the data interval; and determining an endpoint value of the data interval according to the average value, the specified harmonic parameter and the standard deviation.
Optionally, the obtaining target data to be analyzed of the target monitoring object includes: respectively obtaining candidate data corresponding to a plurality of specified time windows; and selecting the candidate data corresponding to the specified time window meeting the condition to be analyzed from the candidate data corresponding to the specified time windows as the target data to be analyzed of the target monitoring object.
Optionally, the candidate data corresponding to the specified time window meeting the condition to be analyzed is the candidate data corresponding to the specified time window whose result of comparing with the data threshold meets the preset result.
Optionally, the determining whether the target data to be analyzed is abnormal data according to the target data to be analyzed and the normal data includes: judging whether the target data to be analyzed is in a data interval range corresponding to the normal data or not according to the target data to be analyzed and the normal data; if not, determining the target data to be analyzed as the abnormal data.
Optionally, the method further includes: and if the target data to be analyzed is in the data interval range corresponding to the normal data, determining that the target data to be analyzed is the normal data.
Optionally, the method further includes: if the target data to be analyzed is determined to be abnormal data, prompt information used for indicating that the target data to be analyzed is abnormal data is generated; and displaying the prompt information, or sending the prompt information to a specified client.
An embodiment of the present application further provides a data monitoring apparatus, including:
the target data to be analyzed obtaining unit is used for obtaining target data to be analyzed of the target monitoring object;
a historical target data obtaining unit, configured to obtain historical target data with time characteristics matched with the time characteristics of the target data to be analyzed;
the normal data obtaining unit is used for obtaining normal data corresponding to the time characteristics of the target data to be analyzed according to the historical target data;
and the abnormal data determining unit is used for determining whether the target data to be analyzed is abnormal data or not according to the target data to be analyzed and the normal data.
An embodiment of the present application further provides an electronic device, including: a processor; and the memory is used for storing a program of the data monitoring method, and the device executes the data monitoring method provided in the embodiment of the application after being powered on and running the program of the data monitoring method through the processor.
The embodiment of the present application further provides a storage medium, where the storage medium stores a computer program, and the computer program is run by a processor to execute the data monitoring method provided in the embodiment of the present application.
The embodiment of the present application further provides a method for monitoring order quantity data, including: obtaining target order quantity data to be analyzed of an online store of a target monitoring line; obtaining historical target order quantity data of which the time characteristics are matched with the time characteristics of the target order quantity data to be analyzed; acquiring normal order quantity data corresponding to the time characteristics of the target order quantity data to be analyzed according to the historical target order quantity data; and determining whether the target order quantity data to be analyzed is abnormal order quantity data or not according to the target order quantity data to be analyzed and the normal order quantity data.
According to the data monitoring method, the data monitoring device, the electronic equipment and the storage medium, after target data to be analyzed of a target monitoring object is obtained, historical target data with time characteristics matched with the time characteristics of the target data to be analyzed is obtained; then obtaining normal data corresponding to the time characteristics of the target data to be analyzed according to the historical target data; and further determining whether the target data to be analyzed is abnormal data or not according to the target data to be analyzed and the normal data. In the implementation process of data monitoring, normal data are determined according to historical target data matched with the time characteristics of the target data to be analyzed, the normal data are used as a basis for judging whether the target data to be analyzed is abnormal data, a fixed monitoring threshold value is not set for the target data to be analyzed, the basis for judging whether the target data to be analyzed is abnormal data, and the problems of high implementation threshold and high implementation complexity caused by the fact that the fixed monitoring threshold value is set for data monitoring can be avoided. In addition, the basis of abnormal data judgment is determined according to the historical target data matched with the time characteristics of the target data to be analyzed, the problem of abnormal data false alarm or abnormal data failure caused when the fixed monitoring threshold is used as the basis of abnormal data judgment can be solved, and the accuracy of data monitoring can be improved.
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Fig. 1 is a first schematic diagram of an embodiment corresponding to an application scenario of a data monitoring method provided in the present application.
Fig. 1A is a second schematic diagram of an embodiment corresponding to an application scenario of the data monitoring method provided in the present application.
Fig. 2 is a flowchart of a data monitoring method according to a first embodiment of the present application.
Fig. 3 is a flowchart of a method for calculating a historical target time window according to a first embodiment of the present disclosure.
Fig. 4 is a flowchart of an application access data monitoring method provided in the first embodiment of the present application.
Fig. 5 is a schematic diagram of a data monitoring apparatus provided in a second embodiment of the present application.
Fig. 6 is a schematic diagram of an electronic device provided in an embodiment of the present application.
Fig. 7 is a flowchart of a data monitoring method provided in a fifth embodiment of the present application.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is capable of implementation in many different ways than those herein set forth and of similar import by those skilled in the art without departing from the spirit of this application and is therefore not limited to the specific implementations disclosed below.
In order to show the data monitoring method provided by the present application more clearly, an application scenario of the data monitoring method provided by the present application is first introduced. In practical application, the data monitoring method provided in the embodiment of the application is generally used for data monitoring of data of an online object provider in an e-commerce platform by an e-commerce platform operator, and at this time, a target monitoring object is the online object provider in the e-commerce platform. The online object provider is a virtual object provider which provides an object to a user based on an e-commerce platform, for example: an online store and an online merchant. The online stores are virtual stores which provide objects for the user based on the e-commerce platform, and the online merchants are a set of virtual stores which provide specific objects for the user based on the e-commerce platform. The data of the online object provider is data related to the objects provided by the online object provider to the user based on the e-commerce platform, such as: order volume data, order amount data, passenger flow volume data, and the like. Specifically, taking data monitoring for order quantity data of online stores as an example, an application scenario of the data monitoring method provided by the present application is described in detail below. It should be noted that an execution main body of the data monitoring method provided by the present application is generally a server installed with a program for executing the monitoring method provided by the embodiment of the present application, and the server is a server corresponding to an e-commerce platform where an online store is located. The server is a computing device or a computing device set providing services for the user and the online store, and can provide service request responses, service undertakes and service guarantees for the user and the online store in specific implementation.
As shown in fig. 1, the server 101 corresponding to the target online store acquires order amount data of the target online store. The order quantity data of the online store may be order quantity data of the online store installed on the mobile terminal in the e-commerce platform client, may also be order quantity data of the online store installed on the computer in the e-commerce platform client, and may also be order quantity data of the online store stored in a server for storing the data of the online store. After obtaining the order quantity data of the target online store, the server 101 corresponding to the target online store performs data monitoring on the order quantity data of the target online store, obtains a monitoring result, and feeds back the monitoring result. The data monitoring performed on the order quantity data of the target online store includes the following processes, specifically referring to fig. 1A:
the first process is as follows: and (5) preliminary screening of data. Firstly, respectively obtaining candidate order quantity data corresponding to a plurality of specified time windows; and then, selecting candidate order quantity data corresponding to the specified time window meeting the condition to be analyzed from the candidate order quantity data corresponding to the specified time windows as target order quantity data to be analyzed of the target monitoring object. The time window is a time interval divided into one or more time segments, and each time segment is called a time window. The order quantity data may be order quantity data in a data stream, and the candidate order quantity data corresponding to the specified time window satisfying the condition to be analyzed is candidate order quantity data corresponding to the specified time window whose result compared with the order quantity data threshold meets the preset result. The designated time window is a time period that divides a certain time interval into one or more time periods corresponding to the designated time interval, and each time period corresponding to the designated time interval is called a time window.
When the target monitoring object is a target monitoring online store and the order quantity data is order quantity data between 12:00 and 12:30 of the target monitoring online store, the time interval corresponding to 12:00 to 12:30 is a time interval to be divided. If the time interval corresponding to 12:00-12:30 is divided by taking each minute as the designated time interval, the time interval corresponding to 12:00-12:30 can be divided into 30 one-minute time periods, each one-minute time period being a designated time window, such as: the first designated time window is: 12:00-12:01, the second designated time window is: 12:01-12:02 … the thirtieth designated time window is: 12:29-12:30.
The second process is as follows: and (5) secondary screening of data. That is, the historical target order amount data whose time characteristics match those of the target order amount data to be analyzed is obtained. The historical target order quantity data with the time characteristics matched with the time characteristics of the target order quantity data to be analyzed generally includes: historical order amount data for which the time window matches the target time window. The time window matched with the target time window is a time window in which the starting time point and the starting time point corresponding to the target time window have a specified time interval, and the time interval is the same as the target time window, such as: time windows 12:01-12:02 at different dates from the target time windows 12:01-12:02 may be used as historical target time windows that match the target time windows 12:01-12: 02.
The third process is: and obtaining and feeding back a monitoring result. That is, abnormal order amount data existing in the target order amount data to be analyzed is obtained and fed back. First, normal order volume data corresponding to time characteristics of target order volume data to be analyzed is obtained from the historical target order volume data. And then, determining whether the target order quantity data to be analyzed is abnormal order quantity data or not according to the target order quantity data to be analyzed and the normal order quantity data. The normal order quantity data corresponding to the time characteristic of the target order quantity data to be analyzed is: in the target time window, the order quantity data in the order quantity data interval to which the target order quantity data to be analyzed belongs when the target order quantity data to be analyzed is in the normal state are collectively referred to as normal order quantity data corresponding to the time characteristic of the target order quantity data to be analyzed. The abnormal order quantity data is order quantity data outside the range of the order quantity data section corresponding to the normal order quantity data.
In the embodiment of the present application, an application scenario of the data monitoring method provided in the embodiment of the present application is not specifically limited, for example: the data monitoring method provided by the application can also be used for data monitoring of data of a target monitoring object in a network platform by an operator of the network platform except the e-commerce platform, or used for data monitoring of relevant data of relevant products and applications by a product operation and maintenance party and an application operation and maintenance party. When the data monitoring method provided by the application is used for data monitoring of relevant data of operation of relevant products and applications by a product operation and maintenance party and an application operation and maintenance party, the operation of the relevant products can be adjusted or maintained by the product operation and maintenance party and the application operation and maintenance party. The data monitoring method provided by the application can also be used in other application scenarios, and is not described in detail herein. The embodiment corresponding to the application scenario of the data monitoring method is provided for facilitating understanding of the data monitoring method provided by the present application, and is not used to limit the data monitoring method provided by the present application.
First embodiment
A first embodiment provides a data monitoring method, which is described below with reference to fig. 2 and 4.
Fig. 2 is a flowchart of a data monitoring method according to a first embodiment of the present application. The data monitoring method shown in fig. 2 includes: step S201 to step S204.
In step S201, target data to be analyzed of a target monitoring object is obtained.
In the first embodiment of the present application, the target monitoring object may be a related product or a subunit in the related product, which is controlled to operate by computer technology, such as: the intelligent power system, the intelligent distribution network in the intelligent power system, the intelligent mobile terminal and the like. The target monitoring object may also be an application or an applet in the application, such as: APP (Application), applets in APP, and the like. The target monitoring object may also be a network platform, or a relevant component in the network platform, such as: the e-commerce platform, a virtual object provider in the e-commerce platform, and the like. The target data to be analyzed is data satisfying the condition to be analyzed in the data of the target monitoring object. The data of the target monitoring object may be data related to the operation of the related product and the sub-unit in the related product, such as: the intelligent power distribution network power consumption management system comprises the following steps of generating capacity data of an intelligent power system, running long data of different devices in the intelligent power distribution network in the running process, electric quantity consumption data of the intelligent mobile terminal and the like. The data of the target monitoring object may also be data related to APP and applet running in APP, such as: access volume data of the APP and flow use data of small programs in the APP. The data of the target monitoring object may also be data generated in the operation process of the network platform and related components in the network platform, such as: total sales volume data of the e-commerce platform, order volume data of virtual object providers in the e-commerce platform, and the like.
In the first embodiment of the present application, a source of the data of the target monitoring object is not specifically limited, and taking the example that the target monitoring object is an online store in the e-commerce platform, and the data of the target monitoring object is order quantity data of the online store in the e-commerce platform, the order quantity data of the online store may be order quantity data of the online store installed in an e-commerce platform client on the mobile terminal, or order quantity data of the online store installed in the e-commerce platform client on the computer, or order quantity data of the online store stored in a server for storing the data of the online store.
The target data to be analyzed is data satisfying the condition to be analyzed in the data of the object to be monitored. The conditions to be analyzed are preset conditions for reducing the data volume of the data targeted for monitoring and optimizing the resource consumption of data monitoring. The condition to be analyzed is generally a fixed critical point corresponding to data in a specified time interval, or a critical interval, such as: the total data amount of the data in one day is 1000, the order amount data in each hour is 200, the vehicle speed value is 60-80 KM/h, and the like. The data meeting the condition to be analyzed is the data in the specified time interval matched with the condition to be analyzed, such as: the result of comparing the data in the designated time interval with the condition to be analyzed meets the preset result. In the first embodiment of the present application, a designated time window is a time period obtained by dividing a certain time interval into one or more time periods corresponding to the designated time interval, and each time period corresponding to the designated time interval is referred to as a time window. Such as: when the target monitoring object is a target monitoring line store and the data is order quantity data between 12:00 and 120:30 of the target monitoring line store, the time interval corresponding to 12:00 to 12:30 is a time interval to be divided. If the time interval corresponding to 12:00-12:30 is divided by taking each minute as the designated time interval, the time interval corresponding to 12:00-12:30 can be divided into 30 one-minute time periods, each one-minute time period being a designated time window, such as: the first designated time window is: 12:00-12:01, the second designated time window is: 12:01-12:02 … the thirtieth designated time window is: 12:29-12:30.
In the first embodiment of the present application, the data corresponding to the specified time window is the data threshold, and the result obtained by comparing the data corresponding to the specified time window and the data threshold satisfies the condition to be analyzed. Specifically, in the first embodiment of the present application, a process of obtaining target data to be analyzed of a target monitoring object is as follows: first, candidate data corresponding to a plurality of specified time windows are obtained, respectively. And then, selecting candidate data corresponding to the specified time window meeting the condition to be analyzed from the candidate data corresponding to the specified time windows as target data to be analyzed of the target monitoring object. And the candidate data corresponding to the specified time window meeting the condition to be analyzed is the candidate data corresponding to the specified time window, the result of which is compared with the data threshold value and accords with the preset result. Specifically, taking data as order quantity data as an example, if the condition to be analyzed is that order quantity data corresponding to the specified time window is 200 orders, comparing the order quantity data corresponding to the specified time window with 200, if the order quantity data corresponding to the specified time window exceeds 200, the preset result is met, otherwise, the preset result is not met. That is, the order quantity data corresponding to the designated time window exceeding 200 is the order quantity data to be analyzed. Such as: the order quantity data corresponding to the first designated time window 12:00-12:01 is 201, the order quantity data corresponding to the first designated time window 12:00-12:01 is target order quantity data to be analyzed, the order quantity data corresponding to the second designated time window 12:01-12:02 is 188, and the order quantity data corresponding to the first designated time window 12:01-12:02 is not the target order quantity data to be analyzed.
In the first embodiment of the application, the candidate data corresponding to the plurality of specified time windows are screened according to the condition to be analyzed, and then the candidate data corresponding to the specified time windows meeting the condition to be analyzed is used as the target data to be analyzed of the target monitoring object, so that the data volume of the target data to be analyzed can be reduced and optimized, and the resource consumption and the complexity during data monitoring are reduced.
In the first embodiment of the present application, when candidate data corresponding to a plurality of specified time windows are screened according to the conditions to be analyzed, data screening is generally initiated for the data volume. When the conditions to be analyzed are set, for example: when the data threshold value is used, the setting of the condition to be analyzed can be completed without depending on data statistics and analysis of related data by related technicians or considering multiple factors, and the appropriate condition to be analyzed can be simply set according to the data volume requirement.
In step S202, historical target data whose temporal features match those of target data to be analyzed is obtained.
In the first embodiment of the present application, the process of obtaining the historical target data with the time characteristics matched with the time characteristics of the target data to be analyzed is as follows: firstly, a target time window corresponding to target data to be analyzed is determined. Then, history data in which the time window matches the target time window is obtained as history target data. The history data is data related to the target monitoring object, and may be data corresponding to the target monitoring object in general, or data corresponding to a target similar object having the same object type as the target monitoring object. The data scale corresponding to the target similar object and the data scale corresponding to the target monitoring object are in the same specified data scale range.
In the first embodiment of the present application, before obtaining the historical data of the time window matching the target time window, the time window matching the target time window needs to be determined, specifically: according to the target time window, determining a historical target time window with the same time interval as the target time window, such as: time windows 12:01-12:02 at different dates from the target time windows 12:01-12:02 may be used as historical target time windows that match the target time windows 12:01-12: 02. After determining a historical target time window with the same time interval as the target time window, obtaining historical data corresponding to the historical target time window, and taking the historical data as the historical target data, namely, obtaining the historical target data with the time characteristics matched with the time characteristics of the target data to be analyzed. Fig. 3 shows a step of determining a historical target time window, which is a flowchart of a historical target time window calculation method provided in the first embodiment of the present application.
Step S301: and acquiring a starting time point corresponding to the target time window, and acquiring a specified time interval between the starting time point corresponding to the target time window and the starting time point corresponding to the historical target time window.
Because the time window is a time period corresponding to a specified time interval, the time window has a start time point and an end time point, for example: the starting time point and the ending time point of the time window 12:00-12:01 are respectively assigned as 12:00 and 12: 01. After determining the starting time point corresponding to a certain time interval, further obtaining the ending time point corresponding to the time interval through the starting time point corresponding to the time interval and the time interval value corresponding to the time interval, thereby completing the determination of the time interval; in addition, after the ending time point corresponding to a certain time interval is determined, the starting time point corresponding to the time interval is further obtained through the ending time point corresponding to the time interval and the time interval value corresponding to the time interval, so that the determination of the time interval is completed. The method specifically adopts a mode of corresponding starting and stopping time points to complete the determination of the time interval.
The specified time interval between the starting time point corresponding to the target time window and the starting time point corresponding to the historical target time window is as follows: a specified time interval between the starting time point corresponding to the target time window and the starting time point corresponding to each different historical target time window.
Step S302: and determining the starting time point corresponding to the historical target time window according to the starting time point corresponding to the target time window and the designated time interval.
If the specified time interval between the starting time point corresponding to the target time window and the starting time point corresponding to the next first historical target time window is the same as the specified time interval between the starting time points corresponding to different historical target time windows, then the starting time point corresponding to the historical target time window can be determined in the following way:
firstly, defining the starting time point corresponding to the target time window as T0. Second, the number n of the history target time windows, that is, the number of data of the history target data is determined to be n. Thirdly, recording the starting time point corresponding to the target time window and the starting time point corresponding to the first historical target time window after the starting time point as T1Recording the starting time point corresponding to the target time window and the starting time point corresponding to the next second historical target time window as T2…, recording the starting time point corresponding to the target time window and the starting time point corresponding to the nth historical target time window as Tn. Finally, determining a specified time interval between the starting time point corresponding to the target time window and the starting time point corresponding to the first historical target time window, and calculating the starting time point corresponding to the historical target time window: t is i(i＝1...n)＝T0-i x g. And g is a specified time interval between starting time points corresponding to different historical target time windows.
Step S303: and determining the historical target time window according to the starting time point corresponding to the historical target time window.
In this step, the process of determining the historical target time window is as follows: and further obtaining a termination time point corresponding to the historical target time interval according to the starting time point corresponding to each historical target time interval and the time interval value corresponding to the historical target time interval.
Referring to fig. 2 again, in step S203, normal data corresponding to the time characteristic of the target data to be analyzed is obtained according to the historical target data.
In the first embodiment of the present application, the normal data corresponding to the time characteristic of the target data to be analyzed is: in the target time window, data in a data interval to which target data to be analyzed belongs in a normal state are collectively referred to as normal data corresponding to time characteristics of the target data to be analyzed. The abnormal data is data outside the range of the data section corresponding to the normal data.
In this step, the process of obtaining normal data is as follows: first, reference data corresponding to normal data is determined according to historical target data. And then, determining an endpoint value of a data interval corresponding to the normal data according to the historical target data and the reference data. And finally, determining a data interval according to the reference data and the endpoint value of the data interval, and taking the data in the data interval as normal data.
In general, the reference data is an average value corresponding to the historical target data, and at this time, the reference data corresponding to the normal data is determined according to the historical target data as follows: and obtaining an average value corresponding to the historical target data, and taking the average value as reference data. Specifically, taking data to be analyzed as order quantity data to be analyzed as an example, the calculation of the reference data is explained in detail: first, the number n of historical target time windows is obtained. That is, the number of data of the history target data is n. Secondly, all historical target data are determined, and the historical target data corresponding to the first historical target time window is recorded as S1Recording the historical target data corresponding to the second historical target time window as S2…, recording the history target data corresponding to the nth history target time window as Sn. Again, the total historical target data is summed:
Figure BDA0002559979980000111
finally, calculating the average value corresponding to the historical target data
Figure BDA0002559979980000121
When the reference data is an average value corresponding to the historical target data, in this step, the process of determining the reference data corresponding to the normal data according to the historical target data is as follows: first, the squared difference between the historical target data and the mean is obtained. And then, calculating the standard deviation corresponding to the historical target data according to the squared difference and the data number of the historical target data. And finally, determining the endpoint value of the data interval according to the standard deviation and the average value. In the first embodiment of the present application, the equation for calculating the squared difference is:
Figure BDA0002559979980000122
In the first embodiment of the present application, after the standard deviation is obtained, the specified harmonic parameters need to be further obtained, and the endpoint value of the data interval can be determined according to the average value, the specified harmonic parameters and the standard deviation. The so-called designated reconciliation parameter is used to determine the endpoint value of the data interval. The end point values of the data intervals are determined by adjusting the designated harmonic parameters and the standard deviation, and the range of the data intervals can be flexibly adjusted, so that normal data with different data intervals can be formulated according to the accuracy requirements of different data to be analyzed. If it is desired to have more precise requirements on the data to be analyzed, then a smaller specified reconciliation parameter may be selected, otherwise, a larger specified reconciliation parameter may be selected.
In the first embodiment of the present application, the specific implementation of determining the endpoint value of the data interval according to the average value, the specified harmonic parameter, and the standard deviation is as follows: and taking the difference between the average value and the product of the specified harmonic parameter and the standard deviation as the left end point value of the data interval, and taking the sum of the average value and the product of the specified harmonic parameter and the standard deviation as the right end point value of the data interval. That is to say that the first and second electrodes,
Figure BDA0002559979980000123
where r represents the data interval, α represents the specified blending parameter, and α generally belongs to [0,1 ] ]。
In step S204, it is determined whether the target data to be analyzed is abnormal data or not, based on the target data to be analyzed and the normal data.
In the first embodiment of the present application, the idea of determining whether target data to be analyzed is abnormal data is as follows: and judging whether the target data to be analyzed is in a data interval range corresponding to the normal data or not according to the target data to be analyzed and the normal data, and if not, determining that the target data to be analyzed is abnormal data. And if the target data to be analyzed is in the data interval range corresponding to the normal data, determining that the target data to be analyzed is the normal data.
In the data monitoring method provided in the first embodiment of the present application, after target data to be analyzed of a target monitoring object is obtained, historical target data whose time characteristics are matched with those of the target data to be analyzed is obtained first; then obtaining normal data corresponding to the time characteristics of the target data to be analyzed according to the historical target data; and further determining whether the target data to be analyzed is abnormal data or not according to the target data to be analyzed and the normal data. In the implementation process of data monitoring, normal data are determined according to historical target data matched with the time characteristics of the target data to be analyzed, the normal data are used as a basis for judging whether the target data to be analyzed is abnormal data, a fixed monitoring threshold value is not set for the target data to be analyzed, the basis for judging whether the target data to be analyzed is abnormal data, and the problems of high implementation threshold and high implementation complexity caused by the fact that the fixed monitoring threshold value is set for data monitoring can be avoided. In addition, the basis of abnormal data judgment is determined according to the historical target data matched with the time characteristics of the target data to be analyzed, the problem of abnormal data false alarm or abnormal data failure caused when the fixed monitoring threshold is used as the basis of abnormal data judgment can be solved, and the accuracy of data monitoring can be improved.
In the first embodiment of the present application, if it is determined that the target data to be analyzed is abnormal data, prompt information for indicating that the target data to be analyzed is abnormal data may be further generated, and the prompt information is displayed, or the prompt information is sent to a specified client. The prompt message may be a mail prompt message, a pop-up window prompt message, or the like, and specifically, a shock or an alarm may be issued at the same time when the prompt message is displayed or sent to a specific client. The client is a client corresponding to the target monitoring object, such as: and the mobile phone client or the computer client corresponding to the e-commerce platform where the online store is located.
In the first embodiment of the present application, a flow of monitoring data for access data of an application program is further provided, and specifically refer to fig. 4.
Step S401: and (6) reading data. That is, initial access volume data of the application is obtained.
Step S402: a time window is determined. Namely, the time interval corresponding to the initial access amount data of the application program is divided into a plurality of designated time windows, and then the initial access amount data corresponding to each designated time window is determined according to the initial access amount data of the application program.
Step S403: and judging whether the data is larger than the threshold data. That is, it is determined whether the initial access amount data corresponding to each of the designated time windows is greater than the threshold data G.
Step S403-1: if not, the operation is terminated. That is, further operations on the initial access amount data corresponding to the specified time window of the unsatisfied condition are terminated. That is, no further operation is performed on the initial access amount data corresponding to the specified time window in which the access amount data is not greater than the threshold data G.
Step S403-2: and if so, acquiring target access quantity data to be analyzed of the target monitoring application program. That is, the initial access amount data corresponding to the specified time window with the access amount data larger than the threshold data G is used as the target access amount data to be analyzed of the target monitoring object.
Step S403-21: and calculating the average value and the variance of the historical target visit volume data. Specifically, firstly, historical target visit volume data with time characteristics matched with the time characteristics of the target visit volume data to be analyzed are obtained; secondly, calculating an average value corresponding to the historical target visit volume data as reference data corresponding to normal visit volume data, wherein the normal visit volume data are data corresponding to time characteristics of the target visit volume data to be analyzed; then, a standard deviation corresponding to the historical target visit amount data is calculated according to the square difference and the data number of the historical target visit amount data.
Step S403-22: and determining normal access amount data. First, the end point values of the data interval are determined based on the standard deviation and the mean value. And then, determining a data interval according to the endpoint value of the data interval, and taking the data in the data interval as normal access data. Step S403-23: and (6) judging the abnormality. Namely, whether the target access volume data to be analyzed is abnormal access volume data or not is determined according to the target access volume data to be analyzed and the normal access volume data.
Step S403-23: and (6) warning of the abnormity. And if the target access volume data to be analyzed is determined to be abnormal access volume data, generating prompt information for indicating that the target access volume data to be analyzed is the abnormal access volume data, and displaying the prompt information.
Second embodiment
Corresponding to the embodiments corresponding to the application scenarios of the data monitoring method provided by the present application and the data monitoring method provided by the first embodiment, a second embodiment of the present application further provides a data monitoring device. Since the embodiment of the apparatus is basically similar to the embodiment corresponding to the application scenario and the first embodiment, the description is relatively simple, and for relevant points, reference may be made to the embodiment corresponding to the application scenario and part of the description of the first embodiment. The device embodiments described below are merely illustrative.
Fig. 5 is a schematic diagram of a data monitoring apparatus according to a second embodiment of the present application.
The data monitoring device provided in the second embodiment of the present application includes:
a target data to be analyzed obtaining unit 501, configured to obtain target data to be analyzed of a target monitoring object;
a historical target data obtaining unit 502, configured to obtain historical target data with a time characteristic matched with a time characteristic of the target data to be analyzed;
a normal data obtaining unit 503, configured to obtain, according to the historical target data, normal data corresponding to a time characteristic of the target data to be analyzed;
an abnormal data determining unit 504, configured to determine whether the target data to be analyzed is abnormal data according to the target data to be analyzed and the normal data.
Optionally, the historical target data is data corresponding to the target monitoring object.
Optionally, the historical target data obtaining unit 502 is specifically configured to determine a target time window corresponding to the target data to be analyzed; and obtaining historical data of which the time window is matched with the target time window as the historical target data.
Optionally, the obtaining historical data of which the time window is matched with the target time window, as the historical target data, includes: determining a historical target time window with the same time interval as the target time window according to the target time window; and obtaining the historical data corresponding to the historical target time window as the historical target data.
Optionally, the determining, according to the target time window, a historical target time window having a time interval the same as that of the target time window includes: obtaining a starting time point corresponding to the target time window, and obtaining a specified time interval between the starting time point corresponding to the target time window and the starting time point corresponding to the historical target time window; determining the starting time point corresponding to the historical target time window according to the starting time point corresponding to the target time window and the designated time interval; and determining the historical target time window according to the starting time point corresponding to the historical target time window.
Optionally, the normal data obtaining unit 503 is specifically configured to determine, according to the historical target data, reference data corresponding to the normal data; determining an endpoint value of a data interval corresponding to the normal data according to the historical target data and the reference data; and determining the data interval according to the endpoint value of the data interval, and taking the data in the range of the data interval as the normal data.
Optionally, the determining, according to the historical target data, reference data corresponding to the normal data includes: and obtaining an average value corresponding to the historical target data, and taking the average value as the reference data.
Optionally, the determining, according to the historical target data and the reference data, an endpoint value of a data interval corresponding to the normal data includes: obtaining a squared difference between the historical target data and the mean; calculating a standard deviation corresponding to the historical target data according to the square difference and the data number of the historical target data; and determining an endpoint value of the data interval according to the standard deviation and the average value.
Optionally, the determining an endpoint value of the data interval according to the standard deviation and the average value includes: obtaining a designated reconciliation parameter, wherein the designated reconciliation parameter is used for determining an endpoint value of the data interval; and determining an endpoint value of the data interval according to the average value, the specified harmonic parameter and the standard deviation.
Optionally, the target data to be analyzed obtaining unit 501 is specifically configured to obtain candidate data corresponding to a plurality of specified time windows respectively; and selecting the candidate data corresponding to the specified time window meeting the condition to be analyzed from the candidate data corresponding to the specified time windows as the target data to be analyzed of the target monitoring object.
Optionally, the candidate data corresponding to the specified time window meeting the condition to be analyzed is the candidate data corresponding to the specified time window whose result of comparing with the data threshold meets the preset result.
Optionally, the abnormal data determining unit 504 is specifically configured to determine, according to the target data to be analyzed and the normal data, whether the target data to be analyzed is within a data interval range corresponding to the normal data; if not, determining the target data to be analyzed as the abnormal data.
Optionally, the method further includes: and if the target data to be analyzed is in the data interval range corresponding to the normal data, determining that the target data to be analyzed is the normal data.
Optionally, the data monitoring apparatus provided in the second embodiment of the present application further includes:
a prompt information generating unit, configured to generate prompt information indicating that the target data to be analyzed is abnormal data if it is determined that the target data to be analyzed is abnormal data;
and the prompt information processing unit is used for displaying the prompt information or sending the prompt information to a specified client.
Third embodiment
Corresponding to the embodiments corresponding to the application scenarios of the data monitoring method provided by the present application and the data monitoring method provided by the first embodiment, a third embodiment of the present application further provides an electronic device. Since the third embodiment is basically similar to the embodiment corresponding to the application scenario and the first embodiment, the description is relatively simple, and for relevant points, reference may be made to the embodiment corresponding to the application scenario and part of the description of the first embodiment. The device embodiments described below are merely illustrative.
Fig. 6 is a schematic view of an electronic device provided in an embodiment of the present application.
The electronic device includes: a processor 601;
and a memory 602, configured to store a program of the data monitoring method, where after the device is powered on and runs the program of the data monitoring method through the processor, the data monitoring method provided in the foregoing embodiment of the present application is executed.
It should be noted that, for the detailed description of the electronic device provided in the third embodiment of the present application, reference may be made to the embodiments corresponding to the application scenarios of the data monitoring method provided in the present application and the related descriptions of the data monitoring method provided in the first embodiment, and details are not repeated here.
Fourth embodiment
Corresponding to the embodiment corresponding to the application scenario of the data monitoring method provided by the present application and the data monitoring method provided by the first embodiment, a fourth embodiment of the present application further provides a storage medium. Since the fourth embodiment is basically similar to the embodiment corresponding to the application scenario and the first embodiment, the description is relatively simple, and for relevant points, reference may be made to the embodiment corresponding to the application scenario and part of the description of the first embodiment. The device embodiments described below are merely illustrative.
The storage medium stores a computer program, which is executed by a processor to perform the data monitoring method provided in the above-described embodiments of the present application.
It should be noted that, for the detailed description of the storage medium provided in the fourth embodiment of the present application, reference may be made to the embodiments corresponding to the application scenarios of the data monitoring method provided in the present application and the related descriptions of the data monitoring method provided in the first embodiment, and details are not repeated here.
Fifth embodiment
Corresponding to the embodiment corresponding to the application scenario of the data monitoring method provided by the present application and the data monitoring method provided by the first embodiment, a fifth embodiment of the present application further provides another data monitoring method. Since the fifth embodiment is basically similar to the embodiment corresponding to the application scenario and the first embodiment, the description is relatively simple, and for relevant points, reference may be made to the embodiment corresponding to the application scenario and part of the description of the first embodiment. The device embodiments described below are merely illustrative.
Please refer to fig. 7, which is a flowchart illustrating a data monitoring method according to a fifth embodiment of the present application.
Step S701: and acquiring target order quantity data to be analyzed of the store on the target monitoring line.
In the fifth embodiment of the present application, the online store is a virtual store that provides an object to a user based on an e-commerce platform. The order quantity data of the online stores may be order quantity data of the online stores installed in an e-commerce platform client on the mobile terminal, may also be order quantity data of the online stores installed in an e-commerce platform client on the computer, and may also be order quantity data of the online stores stored in a server for storing the data of the online stores.
The target order amount data to be analyzed is data satisfying a condition to be analyzed among data of the online stores to be monitored, wherein the condition to be analyzed is a condition preset for reducing the data amount of the data targeted for monitoring and optimizing the resource consumption of data monitoring.
Step S702: and obtaining historical target order quantity data of which the time characteristics are matched with the time characteristics of the target order quantity data to be analyzed.
The time window is a time interval divided into one or more time segments, and each time segment is called a time window.
In the fifth embodiment of the present application, the historical target order quantity data whose time characteristics are matched with the time characteristics of the target order quantity data to be analyzed generally includes: historical order amount data for which the time window matches the target time window. The time window matched with the target time window is a time window in which the starting time point and the starting time point corresponding to the target time window have a specified time interval and the time interval is the same as the target time window.
Step S703: and obtaining normal order quantity data corresponding to the time characteristics of the target order quantity data to be analyzed according to the historical target order quantity data.
The normal order quantity data corresponding to the time characteristic of the target order quantity data to be analyzed is: in the target time window, the order quantity data in the order quantity data interval to which the target order quantity data to be analyzed belongs when the target order quantity data to be analyzed is in the normal state are collectively referred to as normal order quantity data corresponding to the time characteristic of the target order quantity data to be analyzed. The abnormal order quantity data is order quantity data outside the range of the order quantity data section corresponding to the normal order quantity data.
Step S704: and determining whether the target order quantity data to be analyzed is abnormal order quantity data or not according to the target order quantity data to be analyzed and the normal order quantity data.
In the fifth embodiment of the present application, the idea of determining whether target order quantity data to be analyzed is abnormal order quantity data is as follows: and judging whether the target order quantity data to be analyzed is in an order quantity data interval range corresponding to the normal order quantity data or not according to the target order quantity data to be analyzed and the normal order quantity data, and if not, determining that the target order quantity data to be analyzed is abnormal order quantity data. And if the target order quantity data to be analyzed is within the range of the order quantity data interval corresponding to the normal order quantity data, determining that the target order quantity data to be analyzed is the normal order quantity data. It should be noted that, for detailed description of another data monitoring method provided in the fifth embodiment of the present application, reference may be made to the embodiments corresponding to the application scenarios of the data monitoring method provided in the present application and related descriptions of the data monitoring method provided in the first embodiment, which are not described herein again.
Although the present invention has been described with reference to the preferred embodiments, it should be understood that the scope of the present invention is not limited to the embodiments described above, and that various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the present invention.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or Flash memory (Flash RAM). Memory is an example of a computer-readable medium.
1. Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable Media does not include non-Transitory computer readable Media (transient Media), such as modulated data signals and carrier waves.
2. As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

Claims (10)

1. A method for monitoring data, comprising:
obtaining target data to be analyzed of a target monitoring object;
obtaining historical target data with time characteristics matched with the time characteristics of the target data to be analyzed;
obtaining normal data corresponding to the time characteristics of the target data to be analyzed according to the historical target data;
and determining whether the target data to be analyzed is abnormal data or not according to the target data to be analyzed and the normal data.
2. The data monitoring method according to claim 1, wherein the historical target data is data corresponding to the target monitoring object.
3. The data monitoring method according to claim 1, wherein the obtaining of the historical target data with the time characteristics matched with the time characteristics of the target data to be analyzed comprises:
determining a target time window corresponding to the target data to be analyzed;
and obtaining historical data of which the time window is matched with the target time window as the historical target data.
4. The data monitoring method according to claim 3, wherein the obtaining historical data with a time window matching the target time window as the historical target data comprises:
determining a historical target time window with the same time interval as the target time window according to the target time window;
and obtaining the historical data corresponding to the historical target time window as the historical target data.
5. The data monitoring method of claim 4, wherein the determining a historical target time window having a time interval that is the same as the target time window according to the target time window comprises:
obtaining a starting time point corresponding to the target time window, and obtaining a specified time interval between the starting time point corresponding to the target time window and the starting time point corresponding to the historical target time window;
Determining the starting time point corresponding to the historical target time window according to the starting time point corresponding to the target time window and the designated time interval;
and determining the historical target time window according to the starting time point corresponding to the historical target time window.
6. The data monitoring method according to claim 1, wherein the obtaining normal data corresponding to the time characteristic of the target data to be analyzed according to the historical target data comprises:
determining reference data corresponding to the normal data according to the historical target data;
determining an endpoint value of a data interval corresponding to the normal data according to the historical target data and the reference data;
and determining the data interval according to the endpoint value of the data interval, and taking the data in the range of the data interval as the normal data.
7. A data monitoring device, comprising:
the target data to be analyzed obtaining unit is used for obtaining target data to be analyzed of the target monitoring object;
a historical target data obtaining unit, configured to obtain historical target data with time characteristics matched with the time characteristics of the target data to be analyzed;
The normal data obtaining unit is used for obtaining normal data corresponding to the time characteristics of the target data to be analyzed according to the historical target data;
and the abnormal data determining unit is used for determining whether the target data to be analyzed is abnormal data or not according to the target data to be analyzed and the normal data.
8. An electronic device, comprising:
a processor; and
a memory for storing a program of a data monitoring method, the apparatus performing the data monitoring method of any one of claims 1 to 6 after being powered on and running the program of the data monitoring method through the processor.
9. A storage medium, characterized in that the storage medium stores a computer program, which is executed by a processor, for performing the data monitoring method of any one of claims 1-6.
10. A method for monitoring order quantity data is characterized by comprising the following steps:
obtaining target order quantity data to be analyzed of an online store of a target monitoring line;
obtaining historical target order quantity data of which the time characteristics are matched with the time characteristics of the target order quantity data to be analyzed;
acquiring normal order quantity data corresponding to the time characteristics of the target order quantity data to be analyzed according to the historical target order quantity data;
And determining whether the target order quantity data to be analyzed is abnormal order quantity data or not according to the target order quantity data to be analyzed and the normal order quantity data.
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