CN109885467B - Data fluctuation alarming method and device, storage medium and electronic equipment - Google Patents
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Abstract
The embodiment of the invention discloses a data fluctuation alarm method, a data fluctuation alarm device, a storage medium and electronic equipment, wherein the method comprises the following steps: responding to the operation of completing the input task by a user, and splitting the input task into a plurality of subtasks according to a splitting rule; detecting whether the execution result data of each subtask is out of a fluctuation range corresponding to the historical execution result data of the subtask; and when a subtask with execution result data outside the fluctuation range exists, sending alarm information according to a notification mode corresponding to the subtask. According to the embodiment of the invention, the time of the report leaving of each subtask statistical report is determined according to the execution ending time and the data volume of each subtask, the statistical report of the whole task can be obtained in a short time after the last subtask is executed, the time of the report leaving of the statistical report is greatly improved, the execution result data of each subtask can be compared with the historical execution result data, and the data with problems can be known earlier.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a method and an apparatus for alarming data fluctuation, a storage medium, and an electronic device.
Background
For the existing production operation data, such as daily activity, playing times and the like, a business side knows which data have problems after seeing the statistical report.
However, the time of the statistical form being presented is delayed by a relatively large time compared with the time of the task being executed, and the business party can know which data have problems after being delayed for a long time, and then, the reason why the data having problems are analyzed is solved.
Disclosure of Invention
In view of this, embodiments of the present invention provide a data fluctuation alarm method, apparatus, storage medium, and electronic device, so as to solve the following problems in the prior art: the time of the statistical report is delayed greatly relative to the time of completing the task execution, so that the problem analysis cannot be performed in time, and the user experience is poor.
On one hand, the embodiment of the invention provides an alarm method for data fluctuation, which comprises the following steps: responding to the operation of completing the input task by a user, and splitting the input task into a plurality of subtasks according to a splitting rule; detecting whether the execution result data of each subtask is out of a fluctuation range corresponding to the historical execution result data of the subtask; and when the subtask with the execution result data outside the fluctuation range exists, sending alarm information according to a notification mode corresponding to the subtask.
In some embodiments, detecting whether the execution result data of each subtask is out of a fluctuation range corresponding to the historical execution result data of the subtask further includes: and acquiring execution result data and historical execution result data of one or more dimensions of the subtasks.
In some embodiments, after obtaining the execution result data of one or more dimensions of the subtask and the historical execution result data, the method further includes: aggregating the execution result data of each dimension of the subtasks to obtain the execution result data of the subtasks; and aggregating the historical execution result data of each dimension of the subtasks to obtain the historical execution result data of the subtasks.
In some embodiments, the method further comprises: when a subtask exists in which the execution result data is out of the fluctuation range, determining all upstream database tables of the database tables corresponding to the execution result data according to a production relation graph among all the database tables in the subtask; and determining an upstream warehouse data table which enables the execution result data of the subtasks to be out of the fluctuation range according to the execution result data of each upstream warehouse table.
On the other hand, an embodiment of the present invention provides an alarm device for data fluctuation, including: the splitting module is configured to respond to the operation of completing the input task by the user and split the input task into a plurality of subtasks according to a splitting rule; the detection module is configured to detect whether the execution result data of each subtask is out of a fluctuation range corresponding to the historical execution result data of the subtask; and the alarm module is configured to send alarm information according to a notification mode corresponding to the subtask when the subtask with the execution result data outside the fluctuation range exists.
In some embodiments, further comprising: and the acquisition module is configured to acquire the execution result data and the historical execution result data of one or more dimensions of the subtasks.
In some embodiments, further comprising: the aggregation module is configured to aggregate the execution result data of each dimensionality of the subtasks to obtain the execution result data of the subtasks; and aggregating the historical execution result data of each dimension of the subtasks to obtain the historical execution result data of the subtasks.
In some embodiments, further comprising: the determining module is configured to determine all upstream database tables of the database tables corresponding to the execution result data according to a production relation diagram among the database tables in the subtasks when the subtask with the execution result data outside the fluctuation range exists; and determining an upstream warehouse data table which enables the execution result data of the subtasks to be out of the fluctuation range according to the execution result data of each upstream warehouse table.
In another aspect, an embodiment of the present invention provides a storage medium storing a computer program, where the computer program is executed by a processor to implement the method provided in any embodiment of the present invention.
On the other hand, an embodiment of the present invention provides an electronic device, which at least includes a memory and a processor, where the memory stores a computer program, and the processor implements the method provided in any embodiment of the present invention when executing the computer program on the memory.
According to the method, the form output time of each subtask statistical form is determined according to the execution ending time and the data volume of each subtask, the statistical form of the whole task can be obtained within a short time after the last subtask is executed, the form output time of the statistical form is greatly improved, the execution result data of each subtask can be compared with the historical execution result data, the problem of which data exists can be known earlier, and the user experience is better.
Drawings
Fig. 1 is a flowchart of a data fluctuation alarm method according to a first embodiment of the present invention;
FIG. 2 is a flow chart of a method for alarming data fluctuation according to a second embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a data fluctuation warning apparatus according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention without any inventive step, are within the scope of protection of the invention.
Unless defined otherwise, technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs. The use of "first," "second," and similar terms in the present application do not denote any order, quantity, or importance, but rather the terms are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
To maintain the following description of the embodiments of the present invention clear and concise, a detailed description of known functions and known components of the invention have been omitted.
The first embodiment of the present invention provides a method for alarming data fluctuation, the flow of the method is shown in fig. 1, and the method includes steps S101 to S103:
s101, responding to the operation that a user completes an input task, and splitting the input task into a plurality of subtasks according to a splitting rule.
Generally, a task has a large data volume when being executed, and the embodiment of the present invention splits a certain task that a user wants to monitor into a plurality of subtasks according to a certain splitting rule, so that the data volume of each subtask is much smaller than that of the whole task when being executed.
During implementation, the splitting rule may be split according to a partitioning mode, for example, according to time partitioning, a task running and completed in one day is split into 24 subtasks, each hour can complete one subtask, after the subtask is completed, a statistical report of the subtask can be obtained quickly due to a small data volume, even if the current subtask is the last subtask, the statistical report can be obtained quickly, and the statistical report of the whole task does not need to be obtained until the whole task is completed. Of course, the partition method may include various methods as long as the amount of data processed by the task at one time can be reduced.
S102, detecting whether the execution result data of each subtask is out of the fluctuation range corresponding to the historical execution result data of the subtask.
After the data is split into the plurality of subtasks, because the data volume of each subtask is small, after each subtask is executed, the execution result data corresponding to the subtask can be determined in a short time, and the execution result data is compared with the historical execution result data of the subtask to determine whether the current execution result data is within a reasonable fluctuation range.
S103, when the subtask with the execution result data outside the fluctuation range exists, alarm information is sent out according to a notification mode corresponding to the subtask.
If the current execution result data is not within a reasonable fluctuation range, alarm information can be sent according to a notification mode corresponding to the subtask, wherein the notification mode can be various, such as sending an email, sending a short message or calling for voice broadcast, and the like, and is not limited herein.
According to the embodiment of the invention, one task is divided into a plurality of subtasks, and because the data volume of each subtask is small, the execution result data can be obtained within a short time after each subtask is executed, and then the execution result data is compared with the historical execution result data, and alarm information is sent out under the condition that the execution result data has large fluctuation.
According to the method, the form output time of each subtask statistical form is determined according to the execution ending time and the data volume of each subtask, the statistical form of the whole task can be obtained within a short time after the last subtask is executed, the form output time of the statistical form is greatly improved, the execution result data of each subtask can be compared with the historical execution result data, the problem of which data exists can be known earlier, and the user experience is better.
The second embodiment of the present invention provides a method for alarming data fluctuation, the flow of the method is shown in fig. 2, and the method includes steps S201 to S205:
s201, responding to the operation that the user completes the input task, and splitting the input task into a plurality of subtasks according to a splitting rule.
Generally, a task has a large data volume when being executed, and the embodiment of the present invention splits a certain task that a user wants to monitor into a plurality of sub-tasks according to a certain splitting rule, so that the data volume of each task is much smaller than that of the whole task when being executed.
During implementation, the splitting rule may be split according to a partitioning mode, for example, according to time partitioning, a task running and completed in one day is split into 24 subtasks, each hour can complete one subtask, after the subtask is completed, a statistical report of the subtask can be obtained quickly due to a small data volume, even if the current subtask is the last subtask, the statistical report can be obtained quickly, and the statistical report of the whole task does not need to be obtained until the whole task is completed. Of course, the partition method may include various methods as long as the amount of data processed by the task at one time can be reduced.
S202, acquiring execution result data of one or more dimensions of the subtasks and historical execution result data.
The execution result data is acquired according to the dimension, so that the processing result can be more refined, and specifically, the dimension can be region, age, gender, APP version and the like. For example, for the same subtask, the execution result data and the historical execution result data of the subtask may be acquired respectively according to different regions.
In the implementation process, if execution result data of multiple dimensions are acquired, for convenience of subsequent comparison, execution result data of each dimension and historical execution result data need to be aggregated according to a certain mode, and then execution result data and historical execution result data of the subtask are obtained.
S203, detecting whether the execution result data of each subtask is out of the fluctuation range corresponding to the historical execution result data of the subtask.
After the data is split into the plurality of subtasks, because the data volume of each subtask is small, after each subtask is executed, the execution result data corresponding to the subtask can be determined in a short time, and the execution result data is compared with the historical execution result data of the subtask to determine whether the current execution result data is within a reasonable fluctuation range.
And S204, when a subtask with execution result data outside a fluctuation range exists, sending alarm information according to a notification mode corresponding to the subtask, and determining all upstream database tables of the database tables corresponding to the execution result data according to a production relation diagram among all the database tables in the subtask.
If the current execution result data is not within a reasonable fluctuation range, alarm information can be sent according to a notification mode corresponding to the subtask, wherein the notification mode can be various, such as sending an email, sending a short message or calling for voice broadcast, and the like, and is not limited herein.
Because the production relational graph of each database table in the subtask can be obtained, all upstream database tables of the database table corresponding to the currently executed result data can be obtained based on the production relational graph.
And S205, determining an upstream warehouse data table which enables the execution result data of the subtask to be out of the fluctuation range according to the execution result data of each upstream warehouse table.
After all the upstream database tables are found, the cause of the problem can be found, namely, the execution result data of the upstream database tables are determined to be the change of which upstream database tables, so that the execution result data of the whole subtask has larger fluctuation.
For example, the execution result data and the historical execution result data of a certain subtask in the north china area, the central china area, the east china area, the yunobuchuan area and the northeast china area can be respectively obtained, when the execution result data is day activity, the day activity of the whole country is reduced by 200 thousands compared with the historical day activity, the day activity of the north china area is reduced by 10 thousands, the day activity of the central china area is reduced by 10 thousands, the day activity of the east china area is reduced by 10 thousands, the day activity of the yunhuchuan area is reduced by 30 thousands, the day activity of the northeast china area is reduced by 140 thousands, the day activity of the northeast china area can be directly determined to have a problem, and an upstream database table with a problem is searched in the execution result data of the upstream database table related to the day activity of the northeast china area, and the problem is also easily found.
According to the embodiment of the invention, one task is divided into a plurality of subtasks, and because the data volume of each subtask is small, the execution result data can be obtained within a short time after each subtask is executed, and then the execution result data is compared with the historical execution result data, and alarm information is sent out under the condition that the execution result data has large fluctuation.
According to the method, the form output time of each subtask statistical form is determined according to the execution ending time and the data volume of each subtask, the statistical form of the whole task can be obtained within a short time after the last subtask is executed, the form output time of the statistical form is greatly improved, the execution result data of each subtask can be compared with the historical execution result data, the problem of which data exists can be known earlier, and the user experience is better.
A third embodiment of the present invention provides a data fluctuation alarm apparatus, which is schematically shown in fig. 3 and includes:
the splitting module 10 is configured to split an input task into a plurality of subtasks according to a splitting rule in response to an operation of completing the input task by a user; a detection module 20, coupled to the splitting module 10, configured to detect whether the execution result data of each sub-task is outside a fluctuation range corresponding to the historical execution result data of the sub-task; and the alarm module 30 is coupled with the detection module 20 and configured to send alarm information according to a notification mode corresponding to the subtask when the subtask with the execution result data being out of the fluctuation range exists.
Generally, a task has a large data volume when being executed, and the embodiment of the present invention splits a certain task that a user wants to monitor into a plurality of sub-tasks according to a certain splitting rule, so that the data volume of each task is much smaller than that of the whole task when being executed.
During implementation, the splitting rule may be split according to a partitioning mode, for example, according to time partitioning, a task running and completed in one day is split into 24 subtasks, each hour can complete one subtask, after the subtask is completed, a statistical report of the subtask can be obtained quickly due to a small data volume, even if the current subtask is the last subtask, the statistical report can be obtained quickly, and the statistical report of the whole task does not need to be obtained until the whole task is completed. Of course, the partition method may include various methods as long as the amount of data processed by the task at one time can be reduced.
After the data is split into a plurality of subtasks, because the data volume of each subtask is small, after each subtask is executed, the execution result data corresponding to the subtask can be determined in a short time, and the execution result data is compared with the historical execution result data of the subtask to determine whether the current execution result data is in a reasonable fluctuation range.
If the current execution result data is not within a reasonable fluctuation range, alarm information can be sent according to a notification mode corresponding to the subtask, wherein the notification mode can be various, such as sending an email, sending a short message or calling for voice broadcast, and the like, and is not limited herein.
According to the embodiment of the invention, one task is divided into a plurality of subtasks, and because the data volume of each subtask is small, the execution result data can be obtained within a short time after each subtask is executed, and then the execution result data is compared with the historical execution result data, and alarm information is sent out under the condition that the execution result data has large fluctuation.
The above apparatus may further include: and the acquisition module is coupled with the splitting module and the detection module and is configured to acquire the execution result data and the historical execution result data of one or more dimensions of the subtasks. Obtaining the execution result data according to the dimension can make the processing result more refined, and specifically, the dimension may be a region, an age, a gender, an APP version, and the like. For example, for the same subtask, the execution result data and the historical execution result data of the subtask may be acquired respectively according to different regions.
The device can also comprise an aggregation module which is coupled with the acquisition module and is configured to aggregate the execution result data of each dimension of the subtask to obtain the execution result data of the subtask; and aggregating the historical execution result data of each dimensionality of the subtasks to obtain the historical execution result data of the subtasks. After the execution result data and the historical execution result data of each dimension are aggregated, the subsequent alarm process is more accurate.
The above apparatus may further include: the determining module is coupled with the alarm module and is configured to determine all upstream database tables of the database tables corresponding to the execution result data according to the production relation diagram among the database tables in the subtasks when the subtask with the execution result data outside the fluctuation range exists; and determining an upstream warehouse data table which enables the execution result data of the subtask to be out of the fluctuation range according to the execution result data of each upstream warehouse table.
Because the production relational graph of each database table in the subtask can be obtained, all upstream database tables of the database table corresponding to the currently executed result data can be obtained based on the production relational graph. After all the upstream database tables are found, the cause of the problem can be found, namely, the execution result data of the upstream database tables are determined to be the change of which upstream database tables, so that the execution result data of the whole subtask has larger fluctuation.
The embodiment of the invention carries out fluctuation detection of data indexes in different dimensions and different zones, if the data indexes exceed a threshold value, the data indexes are determined to be abnormal fluctuation, and an alarm is sent to remind a user; the time of leaving the table of the statistical form is greatly improved, the execution result data of each subtask can be compared with the historical execution result data, and then the data which have problems can be known earlier, and the user experience is better.
A fourth embodiment of the present invention provides a storage medium storing a computer program, which when executed by a processor implements the method provided in any embodiment of the present invention, including the following steps S1 to S3:
s1, responding to the operation of completing the input task by the user, splitting the input task into a plurality of subtasks according to the splitting rule;
s2, detecting whether the execution result data of each subtask is out of the fluctuation range corresponding to the historical execution result data of the subtask;
and S3, when the subtask with the execution result data outside the fluctuation range exists, sending alarm information according to the notification mode corresponding to the subtask.
Before the computer program is executed by the processor to detect whether the execution result data of each subtask is out of the fluctuation range corresponding to the historical execution result data of the subtask, the computer program may further be executed by the processor to: and acquiring execution result data and historical execution result data of one or more dimensions of the subtasks.
After the computer program is executed by the processor to obtain the execution result data of the multiple dimensions of the subtasks and the historical execution result data, the following steps can be executed by the processor: aggregating the execution result data of each dimensionality of the subtasks to obtain the execution result data of the subtasks; and aggregating the historical execution result data of each dimension of the subtasks to obtain the historical execution result data of the subtasks.
The computer program may further be executable by the processor to perform the steps of: when a subtask with execution result data outside a fluctuation range exists, determining all upstream database tables of the database tables corresponding to the execution result data according to a production relation graph among all the database tables in the subtask; and determining an upstream warehouse data table which enables the execution result data of the subtask to be out of the fluctuation range according to the execution result data of each upstream warehouse table.
The embodiment of the invention carries out fluctuation detection of data indexes in different dimensions and different zones, if the data indexes exceed a threshold value, the data indexes are determined to be abnormal fluctuation, and an alarm is sent to remind a user; the time of the statistical form being out of the table is greatly improved, the execution result data of each subtask can be compared with the historical execution result data, and then which data have problems can be known earlier, and user experience is good.
Optionally, in this embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-only memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes. Optionally, in this embodiment, the processor executes the method steps described in the above embodiments according to the program code stored in the storage medium. Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation manners, and this embodiment is not described herein again. It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
A fifth embodiment of the present invention provides an electronic device, as shown in fig. 4, the electronic device at least includes a memory 901 and a processor 902, the memory 901 stores a computer program, and the processor 902 implements the method provided by any embodiment of the present invention when executing the computer program on the memory 901, for example, the computer program has the following steps S11 to S14:
s1, responding to the operation of completing the input task by the user, splitting the input task into a plurality of subtasks according to a splitting rule;
s2, detecting whether the execution result data of each subtask is out of the fluctuation range corresponding to the historical execution result data of the subtask;
and S3, when the subtask with the execution result data outside the fluctuation range exists, sending alarm information according to the notification mode corresponding to the subtask.
The processor 902 may further execute the following computer program before executing the computer program stored on the memory 901 that detects whether the execution result data of each subtask is out of the fluctuation range corresponding to the history execution result data of the subtask: and acquiring execution result data and historical execution result data of one or more dimensions of the subtasks.
The processor 902, after executing the computer program stored on the memory 901 to obtain the execution result data of one or more dimensions of the subtasks and the historical execution result data, may further execute the following computer program: aggregating the execution result data of each dimension of the subtasks to obtain the execution result data of the subtasks; and aggregating the historical execution result data of each dimension of the subtasks to obtain the historical execution result data of the subtasks.
The processor 902 may also execute the following computer programs stored on the execution memory 901: when a subtask exists in which the execution result data is outside the fluctuation range, determining all upstream database tables of the database tables corresponding to the execution result data according to a production relation diagram among the database tables in the subtask; and determining an upstream warehouse data table which enables the execution result data of the subtask to be out of the fluctuation range according to the execution result data of each upstream warehouse table.
The embodiment of the invention carries out fluctuation detection of data indexes in different dimensions and different zones, if the data indexes exceed a threshold value, the data indexes are determined to be abnormal fluctuation, and an alarm is sent to remind a user; the time of the statistical form being out of the table is greatly improved, the execution result data of each subtask can be compared with the historical execution result data, and then which data have problems can be known earlier, and user experience is good.
Moreover, although exemplary embodiments have been described herein, the scope thereof includes any and all embodiments based on the present invention with equivalent elements, modifications, omissions, combinations (e.g., of various embodiments across), adaptations or alterations. The elements of the claims are to be interpreted broadly based on the language employed in the claims and not limited to examples described in the present specification or during the prosecution of the application, which examples are to be construed as non-exclusive. It is intended, therefore, that the specification and examples be considered as exemplary only, with a true scope and spirit being indicated by the following claims and their full scope of equivalents.
The above description is intended to be illustrative and not restrictive. For example, the above-described examples (or one or more versions thereof) may be used in combination with each other. For example, other embodiments may be used by those of ordinary skill in the art upon reading the above description. In addition, in the foregoing detailed description, various features may be grouped together to streamline the disclosure. This should not be interpreted as an intention that a disclosed feature not claimed is essential to any claim. Rather, inventive subject matter may lie in less than all features of a particular disclosed embodiment. Thus, the following claims are hereby incorporated into the detailed description as examples or embodiments, with each claim standing on its own as a separate embodiment, and it is contemplated that these embodiments may be combined with each other in various combinations or permutations. The scope of the invention should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.
While the embodiments of the present invention have been described in detail, the present invention is not limited to these specific embodiments, and those skilled in the art can make various modifications and modifications of the embodiments based on the concept of the present invention, which fall within the scope of the present invention as claimed.
Claims (8)
1. A method for alarming data fluctuation is characterized by comprising the following steps:
responding to the operation of completing the input task by a user, and splitting the input task into a plurality of subtasks according to a splitting rule;
detecting whether the execution result data of each subtask is out of a fluctuation range corresponding to the historical execution result data of the subtask;
when a subtask exists in which the execution result data is outside the fluctuation range, sending alarm information according to a notification mode corresponding to the subtask, and determining all upstream database tables of the database tables corresponding to the execution result data according to a production relation graph among all the database tables in the subtask;
and searching the upstream database tables which enable the execution result data of the subtasks to be out of the fluctuation range in each upstream database table according to the execution result data of each upstream database table.
2. The method of claim 1, wherein detecting whether the execution result data of each subtask is outside a fluctuation range corresponding to the historical execution result data of the subtask further comprises:
and acquiring execution result data and historical execution result data of one or more dimensions of the subtasks.
3. The method of claim 2, wherein after obtaining the execution result data and the historical execution result data for the multiple dimensions of the subtasks, further comprising:
aggregating the execution result data of each dimension of the subtasks to obtain the execution result data of the subtasks;
and aggregating the historical execution result data of each dimension of the subtasks to obtain the historical execution result data of the subtasks.
4. An alarm device for data fluctuation, comprising:
the splitting module is configured to respond to the operation of completing the input task by the user and split the input task into a plurality of subtasks according to a splitting rule;
the detection module is configured to detect whether the execution result data of each subtask is out of a fluctuation range corresponding to the historical execution result data of the subtask;
the alarm module is configured to send alarm information according to a notification mode corresponding to the subtask when the subtask with the execution result data outside the fluctuation range exists;
and the determining module is configured to determine all upstream database tables of the database tables corresponding to the execution result data according to a production relation graph among the database tables in the subtasks when the subtask with the execution result data outside the fluctuation range exists, and search upstream database tables with the execution result data of the subtask outside the fluctuation range in each upstream database table according to the execution result data of each upstream database table.
5. The apparatus of claim 4, further comprising:
and the acquisition module is configured to acquire the execution result data and the historical execution result data of one or more dimensions of the subtasks.
6. The apparatus of claim 5, further comprising:
the aggregation module is configured to aggregate the execution result data of each dimension of the subtasks to obtain the execution result data of the subtasks; and aggregating the historical execution result data of each dimension of the subtasks to obtain the historical execution result data of the subtasks.
7. A storage medium storing a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 3 when executed by a processor.
8. An electronic device comprising at least a memory, a processor, the memory having a computer program stored thereon, characterized in that the processor realizes the steps of the method of any of claims 1 to 3 when executing the computer program on the memory.
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