CN110609780A - Data monitoring method and device, electronic equipment and storage medium - Google Patents
Data monitoring method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application discloses a data monitoring method and device, electronic equipment and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: for each monitoring item in all the created monitoring items, acquiring a monitoring object in the monitoring items as a target monitoring object, acquiring a monitoring algorithm used by the target monitoring object as a target monitoring algorithm, acquiring a monitoring index for monitoring the target monitoring object as a target monitoring index and acquiring a comparison standard under the target monitoring index; acquiring monitoring data, wherein the monitoring data is obtained by calculating the target monitoring index of the target monitoring object through the target monitoring algorithm; and when the monitoring data exceeds the preset range interval of the comparison standard, giving an alarm prompt, so that the alarm prompt is given in time when the data is abnormal.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a data monitoring method and apparatus, an electronic device, and a storage medium.
Background
In many internet data platforms, a large amount of operating data is generated, and the operating data represents whether the data platform is operating normally or not. However, if the operation data is only lack of monitoring, the operation abnormality of the data platform cannot be found in time according to the operation data.
Disclosure of Invention
In view of the above problems, the present application provides a data monitoring method, an apparatus, an electronic device, and a storage medium, which monitor data and alarm data exception, so as to improve the above problems.
In a first aspect, an embodiment of the present application provides a data monitoring method, where the method includes: for each monitoring item in all the created monitoring items, acquiring a monitoring object in the monitoring items as a target monitoring object, acquiring a monitoring algorithm used by the target monitoring object as a target monitoring algorithm, acquiring a monitoring index for monitoring the target monitoring object as a target monitoring index and acquiring a comparison standard under the target monitoring index; acquiring monitoring data, wherein the monitoring data is obtained by calculating the target monitoring index of the target monitoring object through the target monitoring algorithm; and when the monitoring data exceeds the preset range interval of the comparison standard, giving an alarm for prompting.
In a second aspect, an embodiment of the present application provides a data monitoring apparatus, where the apparatus includes: the first data acquisition module is used for acquiring a monitoring object in all the created monitoring items as a target monitoring object, acquiring a monitoring algorithm used by the target monitoring object as a target monitoring algorithm, acquiring a monitoring index for monitoring the target monitoring object as a target monitoring index and a comparison standard under the target monitoring index; the second data acquisition module is used for acquiring monitoring data, and the monitoring data is obtained by calculating the target monitoring index of the target monitoring object through the target monitoring algorithm; and the alarm module is used for giving an alarm prompt when the monitoring data exceeds the preset range interval of the comparison standard.
In a third aspect, an embodiment of the present application provides an electronic device, including a memory and a processor, where the memory is coupled to the processor, and the memory stores instructions, and when the instructions are executed by the processor, the processor executes the method described above.
In a fourth aspect, the present application provides a computer-readable storage medium, in which program codes are stored, and the program codes can be called by a processor to execute the method described above.
In the data monitoring method, the data monitoring device, the electronic device, and the storage medium provided in the embodiments of the present application, each monitoring item includes a monitoring object, a monitoring algorithm used for the monitoring object, a monitoring index for monitoring the monitoring object, and a comparison standard under the monitoring index. For each monitoring item, a monitoring object, a monitoring algorithm, a monitoring index and a comparison standard in the monitoring item can be obtained. And calculating the monitoring index of the monitored object according to the acquired monitoring algorithm to obtain monitoring data. Comparing the monitoring data obtained by calculation with the comparison standard of the monitoring object in the monitoring item, if the monitoring data exceeds the preset range interval of the comparison standard, indicating that the monitoring data is abnormal, and giving an alarm prompt to find the abnormality of the monitoring object as soon as possible.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 shows a flowchart of a data monitoring method provided in an embodiment of the present application.
Fig. 2 shows a schematic diagram of a monitoring entry provided in an embodiment of the present application.
Fig. 3 shows another flowchart of a data monitoring method provided in an embodiment of the present application.
Fig. 4 shows another flowchart of a data monitoring method provided in an embodiment of the present application.
Fig. 5 shows a further flowchart of a data monitoring method provided in an embodiment of the present application.
Fig. 6 is a functional block diagram of a data monitoring apparatus according to an embodiment of the present application.
Fig. 7 shows a block diagram of an electronic device according to an embodiment of the present application.
Fig. 8 is a storage medium for storing or carrying program codes for implementing a data monitoring method according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
Data monitoring is available on many data platforms that generate large amounts of operational data. For example, platforms related to information push, such as an application recommendation platform corresponding to an application store, a news push platform corresponding to a news website, and a short video recommendation platform corresponding to a short video application, are involved. Or other data platforms related to the user feedback data, such as a search engine, a service platform corresponding to music playing software, and the like. During monitoring, various operation data of the data platform can be obtained, and whether abnormal alarming is needed or not is monitored by analyzing the operation data.
The embodiment of the application provides a data monitoring method for monitoring whether various data of a data platform are abnormal, and the embodiment of the application mainly describes monitoring of running data of an information push platform, for example, mainly describes monitoring of recommendation information pushed to a mobile terminal. The data monitoring method according to the embodiment of the present application is described in detail below.
Fig. 1 shows a flowchart of a data monitoring method provided in an embodiment of the present application. In the data monitoring method, for each of all the monitoring entries created, the following steps are performed.
Step S110: acquiring a monitoring object in the monitoring items as a target monitoring object, acquiring a monitoring algorithm used by the target monitoring object as a target monitoring algorithm, acquiring a monitoring index for monitoring the target monitoring object as a target monitoring index and acquiring a comparison standard under the target monitoring index.
In the embodiment of the present application, one or more monitoring entries may be created in advance, where each monitoring entry includes a monitoring object, a monitoring algorithm, a monitoring index, and a comparison standard, as shown in fig. 2.
The monitoring object represents whose data is to be monitored, and represents a monitored subject or a monitoring target. And recommendation information which can be divided by the monitoring object from multiple dimensions. For example, the recommendation information pushed to the mobile terminal is monitored, and the monitoring object may be recommendation information displayed at a preset display position, such as a short video displayed at a display position of a home page of a short video application program, content at a position of an animation page when the application program is entered, and display content at some designated positions in the application program, such as display content in some cards. The monitoring object may be recommendation information of a preset category, such as advertisements pushed to the application program of the mobile terminal, games pushed to the mobile terminal, and the like. The monitoring object may be recommendation information pushed by each mobile terminal, that is, recommendation information pushed to the mobile terminal by the data platform in units of one mobile terminal. In addition, the monitoring object can also be a specific resource, and preset specific recommendation information. For example, if a certain video is a video that needs to be focused, the video may be used as a monitoring object. Of course, the monitoring object may be various, such as a detail recommendation page in an application program, a search association page, a search result page, a hot search installation, a user installation in a search, a detail page bottom recommendation, and the like, and all subjects needing to pay attention to user feedback may be used as the monitoring object.
The monitoring index indicates which aspect of monitoring needs to be performed on the monitored object, such as CTR (conversion Rate, Click-Through-Rate), download amount of downloadable data such as software, exposure amount, exposure ratio, advertisement CTR, advertisement download amount, advertisement exposure ratio, advertisement normalized ecpm (expected revenue of thousands of displays, expected Cost per mill), advertisement revenue, game CTR, game download amount, game exposure amount, game normalized exposure value, and the like.
The monitoring algorithm represents which algorithm is used for calculating the monitoring data under the monitoring index for the monitored object, and the monitoring data represents the value obtained by calculating the monitoring index of the monitored object by the monitoring algorithm in the monitoring item. For example, in a certain monitoring entry, the monitoring object is a top page, the monitoring index is CTR, and the monitoring algorithm is algorithm a, which indicates how much the conversion rate of the top page is calculated by algorithm a.
The comparison standard is basic data for determining whether to alarm or not, and is used for determining whether to alarm or not by comparing the monitoring data with the comparison standard after the monitoring data is obtained by calculation of a monitoring algorithm.
Each monitoring item is monitored. For each monitoring item, during monitoring, acquiring a monitoring object in the monitoring item to define the monitoring object as a target monitoring object, acquiring a monitoring algorithm in the monitoring item to define the monitoring algorithm as a target monitoring algorithm, acquiring a monitoring index in the monitoring item to define the monitoring index as a target monitoring index, and acquiring a comparison standard in the monitoring item.
Step S120: and acquiring monitoring data, wherein the monitoring data is obtained by calculating the target monitoring index of the target monitoring object through the target monitoring algorithm.
And calculating a target monitoring index of the target monitoring object through the obtained target monitoring algorithm, and taking the obtained result as the monitoring data under the item. For example, in a certain monitoring entry, the obtained target monitoring object is a top page, the target monitoring index is CTR, the target monitoring algorithm is algorithm a, and if the conversion rate of the top page calculated by algorithm a is 0.5, it means that the obtained monitoring data in the entry is 0.5.
Step S130: and when the monitoring data exceeds the preset range interval of the comparison standard, giving an alarm for prompting.
And comparing the monitoring data with the comparison standard, judging whether the monitoring data exceeds the preset range interval of the comparison identifier, and if so, giving an alarm.
If the preset range interval of the comparison standard is smaller than the comparison standard, the preset range interval exceeding the comparison standard is larger than or equal to the comparison standard; the preset range interval of the comparison standard can be smaller than or equal to the comparison standard, and the preset range interval exceeding the comparison standard is larger than the comparison standard; the preset range interval of the comparison standard can be greater than the comparison standard, and the preset range interval exceeding the comparison standard is less than or equal to the comparison standard; the preset range interval of the comparison standard can be greater than or equal to the comparison standard, and the preset range interval beyond the comparison standard is smaller than the comparison standard.
In the embodiment of the application, the preset range interval of the monitoring data exceeding the comparison standard is a triggering condition of alarm. The preset range section exceeding the comparison standard can be defined corresponding to each monitoring item, that is, when the monitoring item is set, not only the monitoring object, the monitoring index, the monitoring algorithm and the comparison standard in each monitoring item are set, but also the triggering condition of the alarm can be set, or the definition of the preset range section exceeding the comparison standard can be set. For example, in some entries, the predetermined range of the comparison criteria is greater than the comparison criteria, and in other entries, the predetermined range of the comparison criteria is less than the comparison criteria.
Optionally, when the definition of the preset range interval exceeding the comparison standard in the monitoring entry is set, a comparison mode option and a comparison standard option may be included. The comparison mode options comprise options which are greater than, less than, greater than or equal to and less than or equal to, and can be set according to actual monitoring requirements; the comparison criteria option includes determining a value of a comparison criteria. In addition, the time period of the monitoring entry may also be set, i.e. how often the monitoring data is calculated and compared with the comparison standard.
In some embodiments, the comparison criterion may be a fixed value, that is, in each monitoring entry, a fixed value is set as the comparison criterion, and the fixed value may measure a monitoring index in the monitoring entry, for example, in a certain monitoring entry, the monitored object is a first page, the monitoring index is CTR, the monitoring algorithm is algorithm a, and the comparison criterion may be a value that can measure CTR, for example, 0.4. The fixed value may be set according to monitoring needs, for example, if the CTR of the top page is lower than 0.4, it indicates that the setting of the top page may be unreasonable, and may include contents that the user is not interested in, or the layout of the top page may not arouse the user's interest, and the like, and if the top page needs to be improved, the comparison standard for monitoring the CTR of the top page may be set to 0.4, and the comparison mode may be smaller than, that is, smaller than 0.4, i.e., an alarm. For another example, for some recommendation information of preset categories, such as advertisements, the exposure amount can be monitored, and if the exposure amount is too high, an alarm is required to prevent too many advertisements displayed on a user page. Therefore, the exposure of the advertisement can be set as a comparison standard, the comparison mode is larger than the exposure of the advertisement, the overexposure of the advertisement is determined to be high when the exposure is larger than the comparison standard, and an alarm is given.
In other embodiments, if the operation data fluctuates too much, the fluctuation of one period may be relatively large compared to other periods, which indicates that the monitored object may not be adjusted in time according to the user requirement or adjusted according to the market environment, and needs to be adjusted, and therefore, the comparison criterion may be the monitored data of other periods. In this embodiment, for a monitoring item, the set comparison standard may be monitoring data obtained in the last time period in the monitoring item, or some other specified period; or the comparison standard can be a floating threshold value set upwards or a floating threshold value set downwards on the basis of monitoring data obtained in the last time period or some other specified period. Specific periods, time granularity of the periods, floating up or floating down and the like can be set corresponding to each monitoring item. Time granularity of cycle the time duration of the time cycle may be hours, days, weeks, months. If the selection is small, after the monitoring items are successfully configured, submitting a calculation task to calculate monitoring data of the Tth hour when the Tth hour is finished or the Tth +1 hour is started, for example, calculating the monitoring data of the period of 8-9 hours when the 9 hour is started, calculating successfully and judging that the monitoring data exceeds the preset range interval of the comparison standard to immediately alarm. If the day, the week and the month are selected, after the configuration is successful, the calculation task is submitted to calculate the monitoring data of the Tth time period when the Tth time period is ended or the Tth +1 th time period is started (can be in the morning), after the calculation is successful, the monitoring data obtained in the T-1 period is used as a comparison standard, and if the alarm condition is met, the alarm is given. For example, in a monitoring entry with a time period of day, a home page as a monitoring object, a CTR as a monitoring index, and an algorithm a as a monitoring algorithm, an alarm prompt in 12 and 11 months in 2018 and early morning may be: your indicator of interest CTR drops 5% compared to 2018/12/09 at 2018/12/10 in the front page module, Algorithm A. Specific data: 0.5vs 0.475(2018/12/10 first-page algorithm A vs 2018/12/09 first-page algorithm A).
In some embodiments, in addition, in an objective situation, the monitoring data of the same monitoring index of the same monitoring object in the same period should be consistent, so that the monitoring data obtained by calculating the same monitoring index of the same monitoring object by different monitoring algorithms should have a small difference, and if the difference is too large, it indicates that the monitoring algorithms may have a problem. Therefore, in this embodiment, the comparison criterion set in the monitoring entry may be monitoring data obtained by calculating the same monitoring object and monitoring index in the monitoring entry with different monitoring algorithms in the same time period, so as to alarm the abnormal monitoring algorithm. For example, in the monitoring entry of the monitoring data calculated by taking the home page as the monitoring object, the CTR as the monitoring index, the algorithm a as the monitoring algorithm, and the comparison standard as the algorithm B, the alarm prompt in 12 and 11 months in 2018 in early morning may be: the indicator CTR of interest is improved by 5% in 2018/12/10 in the home page module algorithm A compared with the comparison algorithm B. Specific data: 0.5vs 0.475(2018/12/10 first-page algorithm A vs 2018/12/1014 first-page algorithm B).
In the embodiment of the present application, the alarm is specifically triggered in what manner for each monitoring entry, that is, the definition of the preset range interval exceeding the comparison standard in each monitoring entry may be any one of the above definition forms, and may be set according to requirements. In addition, for the same monitoring index of the same monitoring object, a plurality of monitoring items can be set, and different alarm triggering conditions are set in different monitoring items, so that the monitoring object can be monitored from multiple dimensions.
In the embodiment of the application, after a time period of each monitoring item is completed, monitoring data of a monitoring index of a monitored object in the time period can be calculated through a monitoring algorithm in the monitoring item, and whether to alarm or not is determined according to comparison between the monitoring data and a comparison standard. If the monitored data exceeds the preset range interval of the comparison standard, alarm prompt is carried out, and therefore alarm can be given in time when the running data in the data platform is abnormal.
In the embodiment of the present application, each monitoring entry may be configured, and the configuration operation may include adding a monitoring entry, modifying a monitoring entry, deleting a monitoring entry, and the like.
In the embodiment of the application, if the monitoring data exceeds the preset range interval of the comparison standard, the monitoring object is abnormal or the monitoring algorithm is abnormal, so that when the alarm prompt is performed on the monitoring entry, the alarm prompt can be performed to prompt that the target monitoring object in the monitoring entry is abnormal, and the alarm prompt can also prompt that the monitoring algorithm is abnormal. For example, in the above embodiment, the comparison standard is a fixed value or other monitoring data of the monitoring index that periodically monitors the same monitored object by using the same monitoring algorithm, and if the monitored object is abnormal, an alarm may be given to prompt that the monitored object is abnormal. Of course, it is also possible that the monitoring algorithm is abnormal, so that an alarm can be given to prompt that the monitoring algorithm is abnormal.
In addition, in the monitoring items, if the comparison standard is the calculation result of the other monitoring algorithms on the same monitoring index of the same monitoring object in the same time period, because only the monitoring algorithms in the comparison standard are different, the monitoring algorithms in the monitoring items are possibly abnormal, and an alarm can be given to prompt that the monitoring algorithms are abnormal, so that related personnel improve the algorithms or troubleshoot problems.
Therefore, another embodiment of the present application provides a data monitoring method. Referring to fig. 3, the data monitoring method performs the following steps for each of all the monitoring entries created. Wherein, each execution time can be the time period end, and the following steps are carried out according to the operation data in the same period. When the following steps are executed according to each monitoring item, the running data of the used data platform is determined according to the monitoring object, the monitoring index, the monitoring algorithm and the comparison standard in the monitoring item, namely, which data are needed to be used in the monitoring item to calculate the monitoring data and obtain the comparison standard, and which running data in the data platform are obtained. For example, if the monitored object in the monitored item is a top page, the monitoring index is CTR, the parameters concerned by the monitoring algorithm are a and b, and the comparison standard is the monitoring data of the previous period in the monitored item, the running data to be used is the pushed amount of the recommendation information in the top page, the clicked amount of the recommendation information, the parameter a, the parameter b, and the monitoring data obtained by calculation of the previous period in the monitored item.
Step S210: acquiring a monitoring object in the monitoring items as a target monitoring object, acquiring a monitoring algorithm used by the target monitoring object as a target monitoring algorithm, acquiring a monitoring index for monitoring the target monitoring object as a target monitoring index and acquiring a comparison standard under the target monitoring index.
Step S220: and acquiring monitoring data, wherein the monitoring data is obtained by calculating the target monitoring index of the target monitoring object through the target monitoring algorithm.
Step S230: and acquiring a comparison standard, wherein the comparison standard is obtained by calculating the target monitoring index of the target monitoring object through a specified monitoring algorithm, and the specified monitoring algorithm is different from the target monitoring algorithm.
In the embodiment of the present application, the comparison criteria in the current monitoring entry are obtained. The comparison standard is monitoring data obtained by calculating the same monitoring index of the same monitoring object in the same time period by different monitoring algorithms, so that the comparison standard is obtained by calculating the target monitoring index of the target monitoring object by a specified monitoring algorithm which is different from the target monitoring algorithm in the same time period.
In one embodiment, the specified monitoring algorithm may be set directly when setting the comparison criteria for the monitoring entry. Optionally, when the comparison standard is obtained, the monitoring object as the target monitoring object, the monitoring index as the target monitoring index, and the monitoring algorithm as the monitoring item of the specified monitoring algorithm may be searched from other monitoring items, and the monitoring data obtained by calculation in the same time period in the found monitoring items is obtained as the comparison standard. Optionally, the target monitoring index of the target monitored object may be directly calculated by a specified monitoring algorithm to obtain the comparison standard.
In another embodiment, when the comparison criterion of the monitoring entry is set, another monitoring entry may be specified, which indicates that the monitoring data in the same cycle in the another monitoring entry is the comparison criterion. The monitoring object of the other monitoring item is a target monitoring object, the monitoring index is a target monitoring index, and the monitoring algorithm is a specified monitoring algorithm.
Step S240: and when the monitoring data exceeds the preset range interval of the comparison standard, alarming to prompt that the target monitoring algorithm is abnormal.
In the embodiment of the present application, the specified monitoring algorithm may be a verified and reliable monitoring algorithm, and the difference between the calculation result of the target monitoring index of the target monitoring object and the actual monitoring data by the specified monitoring algorithm is not large and is within the allowable difference range. Therefore, if the monitoring data exceeds the preset range interval of the comparison standard, the monitoring data is abnormal. And because the designated monitoring algorithm is a reliable monitoring algorithm, the target monitoring algorithm in the current monitoring item can be determined to be abnormal under the condition that the monitoring data of the current monitoring item is abnormal, the target monitoring algorithm is possibly not suitable for the current use environment, or the parameter of the target monitoring algorithm is poor or other possible problems, so that the target monitoring algorithm can be alarmed to prompt the abnormality of the target monitoring algorithm, and corresponding processing personnel can improve the abnormal algorithm in time.
In the embodiment of the application, monitoring data obtained by monitoring the same monitoring index of the same monitored object by using different monitoring algorithms is used as a comparison standard, so that whether the monitoring algorithm in the current monitoring item is abnormal or not is determined by comparing the comparison standard with the monitoring data, and an alarm is given to the abnormal monitoring algorithm in time.
In addition, the comparison standard is determined by using a reliable monitoring algorithm, and can also be determined by a plurality of monitoring algorithms together, so that the reliability of the comparison standard is improved. Specifically, the embodiment of the application provides a data monitoring method. As shown in fig. 4, the method includes:
step S310: for each monitoring item, acquiring a monitoring object in the monitoring item as a target monitoring object, acquiring a monitoring algorithm used by the target monitoring object as a target monitoring algorithm, acquiring a monitoring index for monitoring the target monitoring object as a target monitoring index and acquiring a comparison standard under the target monitoring index.
Step S320: and acquiring monitoring data for each monitoring item, wherein the monitoring data is obtained by calculating the target monitoring index of the target monitoring object through the target monitoring algorithm.
In the embodiment of the present application, for each of all the created monitoring entries, the monitoring data of the monitoring entry is obtained. As described above, the monitoring data of the monitoring entry is obtained by calculating the monitoring index of the monitored object in the monitoring entry through the monitoring algorithm in the monitoring entry.
Step S330: and acquiring a plurality of to-be-compared items from all the monitoring items, wherein the plurality of to-be-compared items are monitoring items with the same monitoring object, the same monitoring index and different monitoring algorithms.
After the monitoring data of all the monitoring items are obtained, a plurality of items to be compared can be selected as a group of items to be compared, and the selected items to be compared are monitoring items with the same monitoring object, the same monitoring index and different monitoring algorithms. For example, in all the monitoring entries, including 3 monitoring objects all being top pages, the monitoring indexes all being CTRs, the monitoring algorithms are first monitoring entries of algorithm a, algorithm B and algorithm C, respectively, including 4 monitoring objects all being top pages, the monitoring indexes all being exposures, the monitoring algorithms are second monitoring entries of algorithm a, algorithm B and algorithm C, respectively, and then the 3 first monitoring entries serve as a group of multiple entries to be compared; and 4 second monitoring entries are used as a group of multiple entries to be compared.
In addition, after the monitoring data of one monitoring item is obtained through calculation, the monitoring items which are the same as the monitoring object, the same as the monitoring index and different from the monitoring algorithm of the monitoring item can be obtained to jointly form a plurality of items to be compared.
Step S340: and obtaining the average value obtained by calculation after removing the maximum value and the minimum value from the monitoring data of the plurality of items to be compared.
Because the same index of the same monitoring object is calculated by different monitoring algorithms for a group of multiple items to be compared, the calculated monitoring data difference is not large for the normal monitoring algorithm and is within a smaller preset difference range. However, there may be an abnormality in the monitoring algorithm, resulting in the obtained monitoring data being too large or too small. Therefore, the maximum value and the minimum value can be taken out from the monitoring data of a plurality of items to be compared, and then the average value is obtained, wherein the average value can represent objective monitoring data and is close to real monitoring data.
Step S350: and determining that the items of which the monitoring data exceeds the preset threshold range of the average value in the plurality of items to be compared are abnormal items.
When the monitoring item is set, the set comparison standard may be an average value calculated from a group of multiple items to be compared where the monitoring item is located. When determining whether the monitoring item needs to give an alarm, if the monitoring item is one of a group of multiple items to be compared, comparing the monitoring data of the monitoring item with an average value calculated from the group of items to be compared, and if the monitoring data in the monitoring item exceeds the preset threshold range of the average value, determining that the monitoring item is abnormal, and defining the monitoring item as an abnormal item.
Therefore, in a group of multiple items to be compared, the monitoring data of each item to be compared can be compared with the calculated average value, wherein the items of which the monitoring data exceeds the preset threshold range of the average value can be defined as abnormal items.
Of course, in the embodiment of the present application, if the comparison standard of the monitoring entry in the plurality of entries to be compared is not the average value, it is not determined whether the monitoring data of the monitoring entry exceeds the preset threshold range of the average value, but it is determined according to the comparison standard actually set by the monitoring entry.
Step S360: and alarming to prompt that the monitoring algorithm corresponding to the abnormal entry is abnormal.
An alarm is raised for each exception entry. The average value obtained by calculating the plurality of items to be compared can represent objective monitoring data close to real monitoring data, the monitoring data of the abnormal items exceeds the range of the preset threshold value of the average value and represents that the monitoring data calculated by the monitoring algorithm in the abnormal items are abnormal, the calculation result of the monitoring algorithm is not accurate, and the monitoring algorithm is abnormal, so that the monitoring algorithm corresponding to the abnormal items can be alarmed and prompted to be abnormal.
In the embodiment of the application, the comparison standard is determined according to the monitoring items with the same monitoring object, the same monitoring index and different monitoring algorithms, so that the abnormal monitoring algorithm is effectively eliminated.
In the embodiment of the application, for the same monitoring item, an alarm may be given to prompt that the monitoring object is abnormal, and an alarm may also be given to prompt that the monitoring algorithm is abnormal. Specifically, if there are too many monitoring entries to remind the monitored object of abnormality, it may be that the monitored object is abnormal, but the monitoring algorithm is abnormal. Therefore, the present application further provides an embodiment, in the data monitoring method in this embodiment, as illustrated in fig. 5, the method includes:
step S410: for each monitoring item in all the created monitoring items, acquiring a monitoring object in the monitoring items as a target monitoring object, acquiring a monitoring algorithm used by the target monitoring object as a target monitoring algorithm, acquiring a monitoring index for monitoring the target monitoring object as a target monitoring index and acquiring a comparison standard under the target monitoring index.
Step S420: and acquiring monitoring data corresponding to each monitoring item. And the monitoring data is obtained by calculating the target monitoring index of the target monitoring object through the target monitoring algorithm.
Step S430: and when the monitoring data exceeds a preset range interval of the comparison standard, determining that the target monitoring object is abnormal.
In the embodiment of the present application, the comparison criterion in the monitoring entry is the monitoring data obtained by the monitoring entry in the previous time period. And after the monitoring item calculates the current time period to obtain the monitoring data, comparing the monitoring data with the monitoring data obtained in the previous time period, and if the monitoring data obtained in the current time period exceeds the preset range interval compared with the monitoring data obtained in the previous time period, determining that the monitored object in the monitoring item is abnormal.
Step S440: and acquiring a plurality of to-be-compared items from all the monitoring items, wherein the plurality of to-be-compared items are monitoring items with different monitoring objects but the same monitoring algorithm.
And acquiring a plurality of groups of to-be-compared items from all the monitoring items, wherein each group of to-be-compared items comprises a plurality of to-be-compared items. In this embodiment, a plurality of to-be-compared entries in each group of to-be-compared entries are monitoring entries with different monitoring objects but the same monitoring algorithm. In the embodiment of the present application, a plurality of to-be-compared entries in a group of to-be-compared entries are taken as an example for description.
Step S450: and determining the number of monitoring entries with abnormal monitoring objects in the plurality of entries to be compared.
And determining the abnormal to-be-compared items of the monitoring object which are prompted by an alarm from the plurality of to-be-compared items as the abnormal monitoring items of the monitoring object, and calculating the number of the abnormal monitoring items of the monitoring object in the plurality of to-be-compared items.
Step S460: if the number of the to-be-compared items with the abnormal monitoring objects is larger than a first preset number, for each abnormal to-be-compared item of the monitoring objects, acquiring the monitoring items which are the same as the monitoring objects of the to-be-compared items but different in monitoring algorithm from all the monitoring items as designated items.
The multiple items to be compared are calculated for different monitored objects by the same monitoring algorithm, and if the number of monitoring items in the multiple items to be compared, which are abnormal to the monitored object, is too many, it may be that the monitored object is actually not in a problem, but the monitored object is judged to be abnormal due to the problem of the monitoring algorithm. Therefore, in this embodiment, it may be determined whether the number of the monitoring entries with abnormal monitoring objects in the multiple entries to be compared is greater than a first preset number, where the first preset number may be set according to actual needs, and the first preset number set for each group of entries to be compared may be different.
If the number of the monitoring entries with abnormal monitoring objects in a group of multiple entries to be compared is larger than the first preset number, it indicates that there may be no problem in the monitoring objects, but the monitoring algorithms in the multiple entries to be compared may have abnormal. Therefore, for each abnormal item to be compared of the monitored object, whether the monitored object is abnormal or the monitoring algorithm is abnormal in the items to be compared can be judged according to the monitoring result of monitoring the same monitored object through other monitoring algorithms.
Specifically, for each abnormal item to be compared of the monitored object, the monitored item which is the same as the monitored object of the item to be compared but different in monitoring algorithm is obtained from all the monitored items, and is defined as the designated item, that is, each obtained designated item is the same as the monitored object of the item to be compared and different in monitoring algorithm. For example, in a group of items to be compared, if it is determined that the monitored objects of the item a to be compared and the item B to be compared are abnormal, acquiring the monitored items which are the same as the monitored objects of the item a to be compared but different in monitoring algorithm from all the monitored items, and using the monitored items as the designated items corresponding to the item a to be compared; and acquiring the monitoring items which are the same as the monitoring objects of the items B to be compared but have different monitoring algorithms from all the monitoring items as the appointed items corresponding to the items A to be compared.
Optionally, the designated entry may be a monitoring entry with the same monitoring object, the same monitoring index, and different monitoring algorithms, so as to improve the accuracy of the judgment.
Step S470: and judging whether the ratio of the monitoring items with abnormal monitoring objects in the corresponding appointed items is larger than a preset ratio or not for the items to be compared with each abnormal monitoring object, and if not, judging that the items to be compared are abnormal comparison items.
Step S480: and judging whether the number of the abnormal comparison items in the plurality of items to be compared is larger than a second preset number.
Step S490: and if the number of the entries to be compared is larger than the second preset number, alarming to prompt that the monitoring algorithm in the entries to be compared is abnormal.
Because each abnormal monitored object is in the appointed items corresponding to the items to be compared, the monitored objects are the same but the monitoring algorithms are different. If the proportion of the monitoring items with abnormal monitoring objects is too low in each designated item according to the monitoring data, the fact that the monitoring objects are possibly not abnormal is shown. However, in the to-be-compared item with the abnormal monitored object, the abnormal monitored object is judged according to the monitoring data, which indicates that the monitoring algorithm in the to-be-compared item may be abnormal to cause misjudgment in practice, but not the abnormal monitored object itself.
And for a to-be-compared item for judging the abnormity of a monitoring object, if the proportion of the monitoring item for judging the abnormity of the monitoring object in the corresponding appointed item is not more than the preset proportion, defining the to-be-compared item as an abnormal comparison item.
If the number of the abnormal comparison entries is too large, it can be determined that the monitoring algorithm in the abnormal comparison entries has a problem to cause a judgment error, and the monitoring object which is not abnormal is judged to be abnormal by mistake. Therefore, whether the number of the abnormal comparison items is larger than a second preset number or not can be judged, and if so, an alarm is given to prompt that the corresponding monitoring algorithm in the plurality of items to be compared is abnormal. Therefore, related workers are prompted to improve the monitoring algorithm, and the probability of false alarm caused by abnormal algorithm in the monitored items is reduced. If the number of the abnormal items is not larger than the second preset number, the monitoring algorithm may have no problem, and an alarm of the abnormality of the monitored object can be performed on the abnormal items to be compared of the monitored object.
In the embodiment of the application, the alarm mode can be a responsible person set by short message notification, and can also be an alarm mailbox set by mail notification. The specific manner and specific alarm object can be set corresponding to each monitoring item.
In the embodiment of the application, the abnormal monitoring algorithm is determined through double judgment. The first judgment is to determine the abnormal monitoring items of each judged monitoring object. And determining the number of the monitoring items with abnormal monitoring objects from the monitoring items with different monitoring objects but the same monitoring algorithm, if the number is larger than the preset number, indicating that the monitoring algorithm is possibly wrong, and further determining whether the monitoring objects are abnormal. If the monitored object has no abnormity, the monitoring algorithm is indicated to have abnormity, and therefore the abnormal monitoring algorithm is monitored more accurately.
The embodiment of the present application further provides a data monitoring apparatus 500. Referring to fig. 6, the data monitoring apparatus 500 includes a first data obtaining module 510, a second data obtaining module 520 and an alarm module 530. The first data obtaining module 510 is configured to, for each monitoring entry in all the created monitoring entries, obtain a target monitoring object as a monitoring object in the monitoring entries, obtain a target monitoring algorithm as a monitoring algorithm used for the target monitoring object, obtain a target monitoring index as a monitoring index for monitoring the target monitoring object, and obtain a comparison standard under the target monitoring index. A second data obtaining module 520, configured to obtain monitoring data, where the monitoring data is obtained by calculating the target monitoring index of the target monitored object through the target monitoring algorithm. And the alarm module 530 is configured to perform alarm prompting when the monitoring data exceeds a preset range interval of the comparison standard.
Optionally, the alarm module 530 may be configured to alarm to prompt that the target monitoring object is abnormal and/or the target monitoring algorithm is abnormal when the monitoring data exceeds the preset range of the comparison standard.
Optionally, the apparatus 500 may further include a third data obtaining module, configured to obtain a comparison standard, where the comparison standard is obtained by calculating the target monitoring index of the target monitored object through a specified monitoring algorithm, and the specified monitoring algorithm is different from the target monitoring algorithm. The alarm module 530 may be configured to alarm to prompt that the target monitoring algorithm is abnormal when the monitoring data exceeds the preset range of the comparison standard.
Optionally, the comparison standard may be monitoring data obtained by the monitoring entry in a previous time period. The alarm module 530 may be configured to alarm to prompt that the target monitored object is abnormal when the monitored data exceeds the preset range of the comparison standard.
Optionally, the apparatus may further include another alarm module, configured to obtain multiple to-be-compared entries from all monitoring entries, where the multiple to-be-compared entries are monitoring entries with different monitoring objects but the same monitoring algorithm; determining the number of monitoring items with abnormal monitoring objects in the plurality of items to be compared; if the number of the abnormal items to be compared of the monitored object is larger than a first preset number, acquiring the monitored items which are the same as the monitored objects of the items to be compared but different in monitoring algorithm from all the monitored items as designated items for the abnormal items to be compared of each monitored object; judging whether the proportion of the monitoring items with abnormal monitoring objects in the appointed items is larger than a preset proportion or not, and if not, judging that the items to be compared are abnormal comparison items; judging whether the number of abnormal comparison items in the plurality of items to be compared is larger than a second preset number or not; and if the number of the entries to be compared is larger than the second preset number, alarming to prompt that the monitoring algorithm in the entries to be compared is abnormal.
Optionally, the apparatus may further include a fourth data obtaining module, configured to obtain multiple to-be-compared entries from all monitoring entries, where the multiple to-be-compared entries are monitoring entries with the same monitoring object, the same monitoring index, and different monitoring algorithms; and obtaining the average value obtained by calculation after removing the maximum value and the minimum value from the monitoring data of the plurality of items to be compared. The alarm module 530 may be configured to determine that, of the multiple to-be-compared entries, an entry whose monitoring data exceeds the preset threshold range of the average value is an abnormal entry; and alarming to prompt that the monitoring algorithm corresponding to the abnormal entry is abnormal.
Optionally, the device may be configured to monitor recommendation information pushed to the mobile terminal, where the monitored object includes the recommendation information displayed at a preset display position, recommendation information of a preset category, recommendation information pushed by each mobile terminal, and a preset specific recommendation information.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and modules may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, the coupling between the modules may be electrical, mechanical or other type of coupling.
In addition, functional modules in the embodiments of the present application may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. Various implementation manners in this application embodiment may be implemented by using corresponding modules, and corresponding descriptions are not repeated in this application embodiment.
Referring to fig. 7, a block diagram of an electronic device 600 according to an embodiment of the present disclosure is shown. The electronic device 600 may be a mobile phone, a tablet computer, a laptop computer, a server, etc. The electronic device 600 may include a memory 610 and a processor 620. The memory 610 is coupled to the processor, the memory 610 stores instructions that, when executed by the processor 620, perform the method described in one or more embodiments above.
Processor 620 may include one or more processing cores. The processor 620 interfaces with various components throughout the electronic device 600 using various interfaces and circuitry to perform various functions of the electronic device 600 and process data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 610 and invoking data stored in the memory 610. Alternatively, the processor 620 may be implemented in hardware using at least one of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor 620 may integrate one or a combination of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a modem, and the like. Wherein, the CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing display content; the modem is used to handle wireless communications. It is understood that the modem may not be integrated into the processor 620, but may be implemented by a communication chip.
The Memory 610 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). The memory 610 may be used to store instructions, programs, code, sets of codes, or sets of instructions, such as instructions or sets of codes for implementing the data monitoring methods provided by embodiments of the present application. The memory 610 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function, instructions for implementing the various method embodiments described above, and the like. The data storage area can also store data (such as a phone book, audio and video data, chatting record data) and the like created by the electronic equipment in use.
Referring to fig. 8, a block diagram of a computer-readable storage medium according to an embodiment of the present application is shown. The computer-readable storage medium 700 has stored therein program code that can be called by a processor to execute the methods described in the above-described method embodiments.
The computer-readable storage medium 700 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM. Optionally, the computer-readable storage medium 700 includes a non-volatile computer-readable storage medium. The computer readable storage medium 800 has storage space for program code 710 to perform any of the method steps of the method described above. The program code can be read from or written to one or more computer program products. The program code 710 may be compressed, for example, in a suitable form.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not necessarily depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. A method for monitoring data, the method comprising:
for each monitoring entry of all monitoring entries created,
acquiring a monitoring object in the monitoring items as a target monitoring object, acquiring a monitoring algorithm used by the target monitoring object as a target monitoring algorithm, acquiring a monitoring index for monitoring the target monitoring object as a target monitoring index and acquiring a comparison standard under the target monitoring index;
acquiring monitoring data, wherein the monitoring data is obtained by calculating the target monitoring index of the target monitoring object through the target monitoring algorithm;
and when the monitoring data exceeds the preset range interval of the comparison standard, giving an alarm for prompting.
2. The method of claim 1, wherein when the monitoring data exceeds a preset range interval of the comparison standard, performing an alarm prompt comprises:
and when the monitoring data exceeds the preset range interval of the comparison standard, alarming to prompt that the target monitoring object is abnormal and/or the target monitoring algorithm is abnormal.
3. The method according to claim 1 or 2, characterized in that the method further comprises:
acquiring a comparison standard, wherein the comparison standard is obtained by calculating the target monitoring index of the target monitoring object through a specified monitoring algorithm, and the specified monitoring algorithm is different from the target monitoring algorithm;
when the monitoring data exceeds the preset range interval of the comparison standard, alarming and prompting are carried out, and the method comprises the following steps:
and when the monitoring data exceeds the preset range interval of the comparison standard, alarming to prompt that the target monitoring algorithm is abnormal.
4. The method according to claim 2, wherein the comparison criterion is monitoring data obtained by the monitoring item in a previous time period, and when the monitoring data exceeds a preset range interval of the comparison criterion, performing an alarm prompt includes:
and when the monitoring data exceeds the preset range interval of the comparison standard, alarming to prompt that the target monitoring object is abnormal.
5. The method of claim 4, further comprising:
acquiring a plurality of to-be-compared items from all monitoring items, wherein the plurality of to-be-compared items are monitoring items with different monitoring objects but the same monitoring algorithm;
determining the number of monitoring items with abnormal monitoring objects in the plurality of items to be compared;
if the number of the abnormal items to be compared of the monitored object is larger than the first preset number, for each abnormal item to be compared of the monitored object,
acquiring monitoring items which are the same as the monitoring objects of the items to be compared but have different monitoring algorithms from all the monitoring items as designated items;
judging whether the proportion of the monitoring items with abnormal monitoring objects in the appointed items is larger than a preset proportion or not, and if not, judging that the items to be compared are abnormal comparison items;
judging whether the number of abnormal comparison items in the plurality of items to be compared is larger than a second preset number or not;
and if the number of the entries to be compared is larger than the second preset number, alarming to prompt that the monitoring algorithm in the entries to be compared is abnormal.
6. The method of claim 1, further comprising:
acquiring a plurality of to-be-compared items from all monitoring items, wherein the plurality of to-be-compared items are monitoring items with the same monitoring object, the same monitoring index and different monitoring algorithms;
obtaining the average value obtained by calculation after the maximum value and the minimum value are removed from the monitoring data of the plurality of items to be compared;
when the monitoring data exceeds the preset range interval of the comparison standard, giving an alarm, including:
determining that the items of which the monitoring data exceeds the preset threshold range of the average value in the plurality of items to be compared are abnormal items;
and alarming to prompt that the monitoring algorithm corresponding to the abnormal entry is abnormal.
7. The method according to claim 1, wherein the method is used for monitoring the recommendation information pushed to the mobile terminal, and the monitoring object includes the recommendation information displayed at a preset display position, the recommendation information of a preset category, the recommendation information pushed by each mobile terminal, and a preset specific recommendation information.
8. A data monitoring apparatus, the apparatus comprising:
the first data acquisition module is used for acquiring a monitoring object in all the created monitoring items as a target monitoring object, acquiring a monitoring algorithm used by the target monitoring object as a target monitoring algorithm, acquiring a monitoring index for monitoring the target monitoring object as a target monitoring index and a comparison standard under the target monitoring index;
the second data acquisition module is used for acquiring monitoring data, and the monitoring data is obtained by calculating the target monitoring index of the target monitoring object through the target monitoring algorithm;
and the alarm module is used for giving an alarm prompt when the monitoring data exceeds the preset range interval of the comparison standard.
9. An electronic device comprising a memory and a processor, the memory coupled to the processor, the memory storing instructions that, when executed by the processor, the processor performs the method of any of claims 1-7.
10. A computer-readable storage medium, having stored thereon program code that can be invoked by a processor to perform the method according to any one of claims 1 to 7.
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