CN112988542B - Application scoring method, device, equipment and readable storage medium - Google Patents
Application scoring method, device, equipment and readable storage medium Download PDFInfo
- Publication number
- CN112988542B CN112988542B CN202110378311.1A CN202110378311A CN112988542B CN 112988542 B CN112988542 B CN 112988542B CN 202110378311 A CN202110378311 A CN 202110378311A CN 112988542 B CN112988542 B CN 112988542B
- Authority
- CN
- China
- Prior art keywords
- target
- index
- value
- indexes
- application
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3452—Performance evaluation by statistical analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
- G06F11/302—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a software system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3466—Performance evaluation by tracing or monitoring
- G06F11/3476—Data logging
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
- G06F2201/865—Monitoring of software
Abstract
The application discloses an application scoring method, device, equipment and readable storage medium, and relates to the technical field of computers to improve accuracy of evaluation of applications. The method comprises the following steps: acquiring a current value of target indexes of a target application, wherein the number of the target indexes is more than or equal to 2; obtaining the same ratio of the target indexes according to the current values of the target indexes and the historical average values of the target indexes; obtaining a target score value of the target index according to the equal ratio; and obtaining the score value of the target application according to the target score value. The method and the device for scoring the application can improve the accuracy of scoring the application.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to an application scoring method, apparatus, device, and readable storage medium.
Background
Currently, the monitoring of the application mainly includes threshold monitoring of key indexes such as thread, memory, CPU utilization, disk utilization and the like of the application. However, in the conventional monitoring method, when a single index exceeds a corresponding threshold, the application cannot be evaluated directly according to the judgment result of the single index. At this time, manual access is often required to configure the corresponding evaluation rule. However, the manual access approach often results in inaccurate evaluation of the application.
Disclosure of Invention
The embodiment of the application provides an application scoring method, device and equipment and a readable storage medium, so that the accuracy of evaluation on the application is improved.
In a first aspect, an embodiment of the present application provides an application scoring method, including:
acquiring a current value of target indexes of a target application, wherein the number of the target indexes is more than or equal to 2;
obtaining the same ratio of the target indexes according to the current values of the target indexes and the historical average values of the target indexes;
obtaining a target score value of the target index according to the equal ratio;
and obtaining the score value of the target application according to the target score value.
In a second aspect, an embodiment of the present application further provides an application scoring apparatus, including:
the device comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring the current value of target indexes of a target application, and the number of the target indexes is greater than or equal to 2;
the first calculation module is used for obtaining the same ratio of the target indexes according to the current values of the target indexes and the historical average values of the target indexes;
the second acquisition module is used for acquiring a target score value of the target index according to the same ratio;
and the third acquisition module is used for acquiring the score value of the target application according to the target score value.
In a third aspect, an embodiment of the present application further provides an electronic device, including: a transceiver, a memory, a processor and a program stored on the memory and executable on the processor, the processor implementing the steps in the application scoring method as described above when executing the program.
In a fourth aspect, the present application further provides a readable storage medium, on which a program is stored, where the program, when executed by a processor, implements the steps in the application scoring method as described above.
In the embodiment of the application, the target scoring values of a plurality of target indexes are used for obtaining the scoring value of the target application, wherein the target scoring value of each target index is determined according to the proportion of the target indexes. Since the ratio of the same proportion is determined according to the current value and the historical average value of the target index, the fluctuation of the score value of the target index is considered; meanwhile, the number of the target indexes is more than or equal to 2, so that the influence of the indexes on the application can be fully considered when the score value of the target application is determined, manual access is not needed, and the accuracy of scoring the application is improved.
Drawings
Fig. 1 is a flowchart of an application scoring method provided in an embodiment of the present application;
fig. 2 is a second flowchart of an application scoring method according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a system for application scoring provided by embodiments of the present application;
fig. 4 is a block diagram of an application scoring apparatus according to an embodiment of the present application.
Detailed Description
In the embodiment of the present application, the term "and/or" describes an association relationship of associated objects, and means that there may be three relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
In the embodiments of the present application, the term "plurality" means two or more, and other terms are similar thereto.
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, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, fig. 1 is a flowchart of an application scoring method provided in an embodiment of the present application, and as shown in fig. 1, the method includes the following steps:
The target application may be any application.
In practical application, the current values of a plurality of indexes can be collected by a netdata (index monitoring data collection agent). Wherein the plurality of metrics may include: the number of threads, the memory usage rate, the CPU usage rate, the number of calls Per minute, the number of abnormal calls Per minute, the average response time, QPS (Query Per Second), full gc (garbage collection), the rate of the CPU of the system occupied by the application, tp99, tp90, tp50, and the like. The current value refers to a current value of a certain index, such as a specific thread number, a value of memory usage rate, and the like.
In order to improve the accuracy of the obtained data, the netdata can also send the obtained index values of the indexes, the information of the indexes and the like to the flink, and the flink screens and classifies the indexes according to different applications, different indexes and specific use requirements.
For example, the flink may select a part (may be set as needed, for example, 4) of the obtained plurality of indexes as an index for the evaluation program, that is, a target index. Since the information provided by netdata may come from different applications, flink may also classify the screened-out metrics according to the application.
In the embodiment of the application, the number of the target indexes is greater than or equal to 2, so that the application can be scored by combining a plurality of indexes, and the accuracy of the obtained scoring result is improved.
And 102, obtaining the same ratio of the target indexes according to the current values of the target indexes and the historical average values of the target indexes.
In this step, an index parameter of the target index may be obtained, where the index parameter includes an index variation trend parameter, and then a ratio of the target index is obtained according to the index variation trend parameter, a current value of the target index, and a historical average value of the target index.
Wherein the historical average of the target index refers to an average of index values of the target index obtained within a certain period of time before the current time. The length of the time period may be set arbitrarily, for example, to one week, 1 month, and the like. The geometric proportion refers to the change amplitude of the current value of a certain index compared with the historical average value of the index. The geometric ratio of the target metric may then represent a fluctuation of the current value of the target metric compared to the historical average of the target metric.
In an embodiment of the present application, the index parameter may further include: the weight dynamic change parameter is used for indicating whether the weight is a dynamic change weight. When the index variation trend parameter is the increase of the same ratio, the same ratio is the difference between the quotient of the current value and the historical average value of the target index and 1, namely: m is p/q-1; when the index variation trend parameter is the same-ratio reduction, the same-ratio is 1 minus the quotient of the current value and the historical average value of the target index, namely: and m is 1-p/q. Where m represents the geometric ratio, p represents the current value, and q represents the historical average.
Of course, the same ratio can also be expressed in terms of a percentage.
In the embodiment of the application, in order to improve the accuracy of evaluating a target application, when obtaining a historical average value of a target index, a time stamp of a current value of the target index may be obtained, and then, according to the time stamp of the current value of the target index, the historical average value of the target index is obtained, where the time stamp of the historical average value of the target index matches the time stamp of the current value of the target index. Due to the fact that the time labels of the current value and the historical average value are matched, the current value and the historical average value can be better evaluated to the fluctuation situation so far, and therefore the accuracy of the obtained score for the target application is improved.
The Time tag may represent the Time of obtaining the current value, and may be represented in a UTC (Universal Time Coordinated) format.
In this step, when the current value of the target index is obtained, its time tag, e.g., the time at which the current value was obtained, may be obtained. Then, based on this tag, a historical average is obtained that matches the time tag of the current finger. The historical average value may be understood as the average of index values of a certain index having the same time stamp over a period of time. For example, 8 per day for a week: 00: 00, 8: 00: 00 is the time stamp of the obtained historical average.
Matching with the time stamp of the current value may be understood as having the same time of day (including hour, minute, second) information as the time stamp of the current value, and may also be understood as having the same day of the week, time of day information, etc. as the time stamp of the current value. For example, the time stamp for the current value is 14: 30: 00, then the time stamp for the historical average is also 14: 30: 00.
in practical application, according to a preset statistical requirement, obtaining a historical score value of the target application, and then storing the historical score value, wherein the historical score value has a corresponding time label. Wherein, the statistical requirement may include a statistical period, a statistical accuracy, and the like.
The historical averages may be stored in a database. During storage, the classified storage can be performed according to different applications, indexes and time labels. For example, the obtained historical average may be stored in an elastic search. The elastic search is an open source search and data analysis engine, and is also a distributed real-time document storage and full text search engine.
For a certain application, every minute there is detailed data of each application, including the score, current value, etc. of each index. In the embodiment of the present application, the historical average may refer to an average of all historical data of a certain index on the current day of the week, the current clock, and the current minute. Assume the current Sunday number w, the current clock number is h, and the current minute number is m. When the number of days of the stored historical scoring data is less than 7 days (namely, the data less than 7 days is stored), the historical average value is calculated by the index value of h hour m minutes of each day of the stored data. The specific calculation method is as follows, assuming that the current clock number is 22 and the current minute number is 22, inquiring the index value at the time point of 22:22 every day, and calculating the average value of each index of each application as the historical average value according to the application and index grouping. When the days of historical scoring data stored by the system are more than 7 days (namely, data of more than or equal to 7 days are stored), index values of w minutes, h minutes and m minutes every week in all data are inquired to calculate an average value. Then, the historical data of how many Weeks are total can be calculated first, and the week number Weeks of the historical data is obtained. Assuming Weeks equals 3, the current sunday 2020-09-2022: 22:00 queries the application scoring data at the time points of the last sunday 2020-09-1322: 22:00 and the last sunday 2020-09-0622: 22:00, respectively, and then calculates the average value of each metric for each application, grouped by application, metric.
And 103, obtaining a target score value of the target index according to the equal ratio.
In this step, the weight of the target index is obtained, and then the product of the weight and the ratio of equal to each other is taken as the target score value.
As previously mentioned, the metric parameters may further include: the weight dynamic change parameter is used for indicating whether the weight is a dynamic change weight or not. In practical application, specific index parameters of each index can be set according to specific use scenes. The weight represents the score weight occupied by a certain index; the dynamic weight change parameter indicates whether the weight of a certain index is adjusted according to the fluctuation condition of the index ring ratio; the maximum weight indicates that a certain index can occupy the maximum weight value of the score when the weight is adjusted. In setting these parameters, the impact on the final application score may be set according to the metrics. For example, if a certain metric is relatively important, then its weight may be set to a dynamically changing weight.
When the weight of the target index is a dynamically changing weight, the weight of the target index is a minimum value between a maximum weight value of the target index and a first value, wherein the first value is a product of the proportional ratio and a basic weight of the target index; when the weight of the target index is not a dynamically changing weight, then the weight is a product of a base weight of the target index and a second value, wherein the second value is a minimum between 1 and the same-ratio. That is, when the ratio is greater than or equal to 1, the second value takes the value of 1; and when the same ratio is less than 1, the second value is the value of the same ratio.
When the weight of the index is determined, the dynamic change weight is taken as a consideration factor, and the actual change condition of the change of the index value relative to the historical average value can be fully considered, so that the score of the index can be reasonably calculated when the score of the target application is calculated, and the accuracy of the obtained score of the target application is improved.
And 104, obtaining the score value of the target application according to the target score value.
In this step, the score reference value is used to subtract the target score value of each target index to obtain a third value, and then the third value is used as the score value of the target application, that is: and F-F1- … -Fx, wherein the Score is a Score reference value, F represents a Score reference value, and F1 … … Fx represents target Score values of the target indexes respectively.
Wherein the score reference value may be set to 100.
In the embodiment of the application, the target scoring values of a plurality of target indexes are used for obtaining the scoring value of the target application, wherein the target scoring value of each target index is determined according to the proportion of the target indexes. Since the ratio of the same proportion is determined according to the current value and the historical average value of the target index, the fluctuation of the score value of the target index is considered; meanwhile, the number of the target indexes is more than or equal to 2, so that the influence of the indexes on the application can be fully considered when the score value of the target application is determined, manual access is not needed, and the accuracy of scoring the application is improved.
On the basis of the above embodiment, to improve the scoring efficiency, before step 101, the method may further include: and acquiring candidate indexes, and selecting the target indexes from the candidate indexes according to a preset rule.
Wherein the candidate index may include: the number of threads, the memory usage rate, the CPU usage rate, the number of calls per minute, the number of abnormal calls per minute, the average response time, the QPS, the full gc number, the ratio of the applications occupying the system CPU, tp99, tp90, tp50 and the like. The preset rule may be, for example, selecting an index having a large influence on the score of the target application, randomly selecting the index, setting the index by the user, or the like.
Referring to fig. 2, fig. 2 is a block flow diagram of an application scoring method provided in an embodiment of the present application. Wherein, the whole process can include: key index data acquisition, index data historical average calculation, score calculation, calculation result storage and the like. In the scoring calculation process, the collected key index data and the historical average value are sent into a message queue, a 5-minute sliding window is created according to application shunting, the window step length is 1 minute, and the scoring of each index of each application is calculated in each window.
Referring to fig. 3, fig. 3 is a schematic diagram of a scoring system provided in the embodiment of the present application. As shown in fig. 3, mainly includes netdata, a first flash, kafka, fastx, a second flash, a first elastic search, and a second elastic search.
The netdata is mainly used for collecting current values of indexes such as thread number, memory utilization rate, cpu utilization rate, calling times per minute, abnormal calling times per minute, average response time, QPS (quick Path per second), full gc times, container occupation system cpu ratio, tp99, tp90 and tp50 of each deployed application. The netdata then sends it to the first flink.
The netdata is an index monitoring data acquisition agent, can provide key index data of a physical machine, a container, a database, middleware and java application in real time, provides various data transmission plug-ins, and transmits the monitoring data to information middleware such as kafka.
The first flink filters the received current values of the indexes of the applications according to the indexes; and then classifying according to the application. The first blink then sends the data to kafka.
In the embodiment of the application, the flink executes any stream data program in a data parallel and pipeline mode, and the pipeline runtime system of the flink can execute batch processing and stream processing programs.
The fastx queries the first elastic search to obtain the application log number (for example, within the first 1 minute of the current time) and the abnormal log number of each application in a preset time period, and sends the obtained log numbers to the kafka.
fastx obtains the history data of each application from the second elastic search, calculates the history average value of each index according to the application, and sends the history average value to kafka.
The elastic search is a search and data analysis engine, and is also a distributed real-time document storage and full-text search engine. Collecting monitoring data of a physical machine, a virtual machine, a container, a service buried point and the like on a fastx support; and supporting alarm, alarm convergence and data visualization based on the monitoring data.
The second flink calculates the current value of credit for an application based on the data obtained from kafka.
When the second flink carries out scoring calculation, the obtained data has current index data from the netdata; there is historical scoring data from fastx. In order to ensure the accuracy of the calculation, the multiple data sources ensure that the index used for calculating the score of the same application can be applied to the same data set for calculation.
For this reason, in the embodiment of the present application, the format of each data is unified using the message middleware kafka. The key of the timestamp mark of all indexes is timestamp, and the value is UTC format of the current minute 0 second time of Beijing time, such as timestamp:2020-09-23T12:24:00Z, so that the requirement of the elastic search on the time format can be met. Meanwhile, for the same index of the same application, the index name of the current index is used as a mark key, the index name and the character spliced by the _ history are used as mark keys of the history score of the historical index, the timestamp of the historical average value also adopts the current time, and the UTC format is replaced in a unified mode. Thus, when the second flink consumes the data in the kafka, the indexes which need to be calculated in the same time period can be grouped for subsequent calculation by grouping according to time.
In the above system, the second flink is a flink streaming computing engine, and the specific process may include:
(1) a FlinkKafkaConsumer010 object is created to consume the metric data from kafka.
(2) And (3) adding the object in the step (1) as a data source to an execution environment of a second flight to obtain DataStreamSource.
(3) And (3) calling a fltMap method of the object in the step (2) to convert the message data of the kafka into score index data which can be calculated, wherein the data type is Tuple2< String, Map > data conversion.
(4) And (4) calling the assignTimestampsAndWatermarks method of the object in the step (3), uniformly taking a timestamp field timestamp as a time watermark, and calculating all time correlations according to the field of the index.
(5) And (4) calling a keyBy (0) method of the object in the step (4), and shunting by using the application name field of each index to ensure that the data calculated in each data stream is the data of the same application.
(6) And (5) calling a timeWindow (time. minutes (5), time. minutes (1)) method of the object in the step (5), and creating a sliding time window on the basis of the application stream data, wherein the window length is 5 minutes, and the window sliding length is 1 minute. That is, data within 5 minutes of application will be aggregated into a window, and after the calculation is completed, the window time moves forward by one minute.
(7) And (6) calling an application () method of the object in the step (6), calculating a comparison ratio according to the current value and the historical average value of the index, and finally calculating a score according to the index rule. The obtained data is then assembled into a data format that needs to be saved to the elasticsearch.
(8) Calling a map () method of the object in (7), fine-tuning the data, and then sending the data to the elastic search.
In the embodiment of the present application, as shown in table 1, 14 indexes for performing application health scoring are determined from four dimensions of log, application performance, resource usage and error rate, and detailed data of each index is as follows:
TABLE 1
In table 1, the weight represents the score weight occupied by a certain index; the dynamic weight change parameter indicates whether the weight of a certain index is adjusted according to the fluctuation condition of the index ring ratio; the maximum weight represents the maximum weight value of the score which can be occupied by a certain index when the weight is adjusted; description is a detailed explanation of a certain index; the calculation rule is a calculation method of certain index fluctuation, the geometric increase represents that points are deducted if the current value of a certain index is increased relative to the historical average value, and the geometric decrease represents that points are deducted if the certain index is decreased relative to the historical index.
Wherein, the calculation formula of the score value of a certain index is as follows:
Fx＝Wx×Rx
wherein, FxRepresents the value of the index x, WxRepresenting the final weight, R, of the index xxIndicating the proportional ratio corresponding to the index x.
If the index variation trend parameter of a certain index is in the increase of the same ratio, the same ratio is the difference between the quotient of the current value and the historical average value of the target index and 1, namely: m is p/q-1; if the index variation trend parameter is the same-proportion reduction, the same-proportion ratio is 1 minus the quotient of the current value and the historical average value of the target index, namely: and m is 1-p/q. Where m represents the geometric ratio, p represents the current value, and q represents the historical average.
When the rule of the index is a dynamically changing weight, then Wx＝min(Wmax，RxXw), i.e., the maximum value of the weight and the ratio of the same to the minimum value of the weight W. W is the weight in table 1. When R isxxW is less than the maximum weight WmaxWhen W isxIs equal to Rx×W(ii) a When R isxX W is greater than the maximum weight value WmaxWhen W isxIs equal to Wmax。
When the rule of the index is not a dynamically changing weight, then Wx＝W×min(1，Rx) I.e. when the ratio R is equal toxWhen less than 1, Wx＝W×Rx(ii) a Otherwise, Wx＝W。
Then, Score is F-F1- … -Fx, where Score is a Score reference value, F denotes a Score reference value, and F1 … … Fx denotes a target Score value of each target index, respectively. Wherein the score reference value may be set to 100.
It can be seen from the above description that, by using the scheme of the embodiment of the present application, the health condition of the current application can be automatically calculated according to the fluctuation degree of the multiple indexes of the application relative to the historical average value, and manual intervention is not required, so that the accuracy of scoring the application is improved. In addition, real-time streaming calculation based on the flash can ensure the real-time performance of data, the health feedback of application can be accurately acquired in a short time when the key indexes of the application fluctuate, and meanwhile, the deduction data of a plurality of key indexes are stored in the elastic search, so that accurate feedback application in alarm notification is the deduction caused by which key indexes fluctuate relatively to history at the same time, and the problem of user troubleshooting is facilitated.
The embodiment of the application also provides an application scoring device. Referring to fig. 4, fig. 4 is a structural diagram of an application scoring device according to an embodiment of the present application. As shown in fig. 4, the application scoring apparatus includes:
a first obtaining module 401, configured to obtain a current value of target indicators of a target application, where the number of the target indicators is greater than or equal to 2; a first calculating module 402, configured to obtain a ratio of the target indicators according to a current value of the target indicators and a historical average value of the target indicators; a second obtaining module 403, configured to obtain a target score value of the target indicator according to the equal ratio; a third obtaining module 404, configured to obtain a score value of the target application according to the target score value.
Optionally, the first calculating module 402 may include:
the first obtaining submodule is used for obtaining index parameters of the target index, wherein the index parameters comprise index change trend parameters; and the first calculation submodule is used for obtaining the same ratio of the target indexes according to the index change trend parameter, the current values of the target indexes and the historical average values of the target indexes.
Optionally, the first computing submodule includes:
the first calculation unit is used for calculating the index variation trend parameter of the target index according to the current value and the historical average value of the target index;
and the second calculation unit is used for subtracting the quotient of the current value and the historical average value of the target index from 1 by the same ratio if the index change trend parameter is the same ratio reduction.
Optionally, the third obtaining module includes:
the first obtaining submodule is used for obtaining the weight of the target index;
and the second obtaining submodule is used for taking the product of the weight and the equal ratio as the target scoring value.
Optionally, the first obtaining sub-module includes:
a first obtaining unit, configured to, when a weight of the target indicator is a dynamically changing weight, determine the weight as a minimum value between a maximum weight value of the target indicator and a first value, where the first value is a product of the proportional-to-proportional ratio and a base weight of the target indicator;
a second obtaining unit, configured to, when the weight of the target index is not a dynamically changing weight, obtain the weight as a product of a base weight of the target index and a second value, where the second value is a minimum value between 1 and the same-ratio.
Optionally, the fourth obtaining module includes:
the first obtaining submodule is used for subtracting the target scoring value of each target index from the scoring reference value to obtain a third value;
and the second obtaining submodule is used for taking the third value as the credit value of the target application.
Optionally, the apparatus may further include:
the fourth acquisition module is used for acquiring candidate indexes;
and the selection module is used for selecting the target index from the candidate indexes according to a preset rule.
Optionally, the apparatus may further include: the fifth acquisition module is used for acquiring the historical average value of the target index; the method comprises the following steps:
the first obtaining submodule is used for obtaining the time tag of the current value;
and the second obtaining submodule is used for obtaining the historical average value of the target index according to the time tag of the current value, wherein the time tag of the historical average value of the target index is matched with the time tag of the current value.
Optionally, the apparatus further comprises:
the sixth acquisition module is used for acquiring the historical credit value of the target application according to a preset statistical requirement;
and the storage module is used for storing the historical scoring value, wherein the historical scoring value has a corresponding time tag.
The apparatus provided in the embodiment of the present application may implement the method embodiment, and the implementation principle and the technical effect are similar, which are not described herein again.
It should be noted that the division of the unit in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation. In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented as a software functional unit and sold or used as a stand-alone product, may be stored in a processor readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
An embodiment of the present application further provides an electronic device, including: a memory, a processor, and a program stored on the memory and executable on the processor; the processor is used for reading the program in the memory to realize the steps in the application scoring method.
The embodiment of the present application further provides a readable storage medium, where a program is stored on the readable storage medium, and when the program is executed by a processor, the program implements each process of the above-mentioned embodiment of the application scoring method, and can achieve the same technical effect, and in order to avoid repetition, the detailed description is omitted here. The readable storage medium may be any available medium or data storage device that can be accessed by a processor, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO), etc.), optical memory (e.g., CD, DVD, BD, HVD, etc.), and semiconductor memory (e.g., ROM, EPROM, EEPROM, nonvolatile memory (NAND FLASH), Solid State Disk (SSD)), etc.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. With such an understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the methods according to the embodiments of the present application.
While the present embodiments have been described with reference to the accompanying drawings, it is to be understood that the invention is not limited to the precise embodiments described above, which are meant to be illustrative and not restrictive, and that various changes may be made therein by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. An application scoring method, comprising:
acquiring a current value of target indexes of a target application, wherein the number of the target indexes is more than or equal to 2;
obtaining the same ratio of the target indexes according to the current values of the target indexes and the historical average values of the target indexes;
obtaining a target score value of the target index according to the equal ratio;
obtaining the score value of the target application according to the target score value;
wherein, the obtaining the same ratio of the target indexes according to the current value of the target indexes and the historical average value of the target indexes comprises:
acquiring index parameters of the target index, wherein the index parameters comprise index variation trend parameters;
and obtaining the same ratio of the target indexes according to the index change trend parameter, the current values of the target indexes and the historical average values of the target indexes.
2. The method of claim 1, wherein obtaining the ratiometric ratio of the target indicator from the indicator variation trend parameter, the current value of the target indicator, and the historical average of the target indicator comprises:
when the index variation trend parameter is the increase of the same ratio, the same ratio is the difference between the quotient of the current value of the target index and the historical average value of the target index and 1;
when the index variation trend parameter is the same-proportion reduction, the same-proportion ratio is 1 minus the quotient of the current value of the target index and the historical average value of the target index.
3. The method of claim 1, wherein obtaining a target score value of the target indicator according to the ratiometric ratio comprises:
acquiring the weight of the target index;
taking the product of the weight and the ratio of equal to each other as the target score value.
4. The method of claim 3, wherein the obtaining the weight of the target index comprises:
when the weight of the target index is a dynamically changing weight, the weight is the minimum value between the maximum weight value of the target index and a first value, wherein the first value is the product of the same ratio and the basic weight of the target index;
when the weight of the target index is not a dynamically changing weight, then the weight is a product of a base weight of the target index and a second value, wherein the second value is a minimum between 1 and the same-ratio.
5. The method of claim 1, wherein obtaining the target application's score value based on the target score value comprises:
subtracting the target score value of each target index from the score reference value to obtain a third value;
and taking the third value as the credit value of the target application.
6. The method according to claim 1, wherein the method for obtaining the historical average value of the target index comprises:
acquiring a time tag of a current value of the target index;
and acquiring the historical average value of the target index according to the time label of the current value of the target index, wherein the time label of the historical average value of the target index is matched with the time label of the current value of the target index.
7. An application scoring apparatus, comprising:
the device comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring the current value of target indexes of a target application, and the number of the target indexes is greater than or equal to 2;
the first calculation module is used for obtaining the same ratio of the target indexes according to the current values of the target indexes and the historical average values of the target indexes;
the second acquisition module is used for acquiring a target score value of the target index according to the same ratio;
the third acquisition module is used for acquiring the score value of the target application according to the target score value;
the first computing module includes:
the first obtaining submodule is used for obtaining index parameters of the target index, wherein the index parameters comprise index change trend parameters; and the first calculation submodule is used for obtaining the same ratio of the target indexes according to the index change trend parameter, the current values of the target indexes and the historical average values of the target indexes.
8. An electronic device, comprising: a memory, a processor, and a program stored on the memory and executable on the processor; processor for reading a program in a memory implementing the steps in the application scoring method according to any one of claims 1 to 6.
9. A readable storage medium storing a program, wherein the program, when executed by a processor, implements the steps in the application scoring method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110378311.1A CN112988542B (en) | 2021-04-08 | 2021-04-08 | Application scoring method, device, equipment and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110378311.1A CN112988542B (en) | 2021-04-08 | 2021-04-08 | Application scoring method, device, equipment and readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112988542A CN112988542A (en) | 2021-06-18 |
| CN112988542B true CN112988542B (en) | 2021-11-30 |
Family
ID=76339494
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110378311.1A Active CN112988542B (en) | 2021-04-08 | 2021-04-08 | Application scoring method, device, equipment and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112988542B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102473132A (en) * | 2009-07-24 | 2012-05-23 | 伦敦大学玛丽女王和威斯特菲尔特学院 | Method of monitoring the performance of a software application |
| CN104572409A (en) * | 2015-01-14 | 2015-04-29 | 东莞宇龙通信科技有限公司 | Application parameter management method and system and electronic device |
| CN107315671A (en) * | 2017-06-16 | 2017-11-03 | 东软集团股份有限公司 | Application state monitoring method, device and its equipment |
| CN107871190A (en) * | 2016-09-23 | 2018-04-03 | 阿里巴巴集团控股有限公司 | A kind of operational indicator monitoring method and device |
| CN108733532A (en) * | 2017-04-18 | 2018-11-02 | 北京京东尚科信息技术有限公司 | Health degree management-control method, device, medium and the electronic equipment of big data platform |
| CN109960650A (en) * | 2018-09-04 | 2019-07-02 | 中国平安人寿保险股份有限公司 | Application assessment method, apparatus, medium and electronic equipment based on big data |
| CN111241151A (en) * | 2019-12-27 | 2020-06-05 | 北京健康之家科技有限公司 | Service data analysis early warning method, system, storage medium and computing device |
| CN111552607A (en) * | 2020-03-27 | 2020-08-18 | 深圳壹账通智能科技有限公司 | Health evaluation method, device and equipment of application program and storage medium |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080133141A1 (en) * | 2005-12-22 | 2008-06-05 | Frost Stephen J | Weighted Scoring Methods and Use Thereof in Screening |
| CN106096799A (en) * | 2016-07-11 | 2016-11-09 | 国网浙江省电力公司经济技术研究院 | Minimum construction investment forecast system based on electric network performance index evaluation |
| US20200117576A1 (en) * | 2018-10-12 | 2020-04-16 | Ca, Inc. | Assessing the container-readiness of software applications |
-
2021
- 2021-04-08 CN CN202110378311.1A patent/CN112988542B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102473132A (en) * | 2009-07-24 | 2012-05-23 | 伦敦大学玛丽女王和威斯特菲尔特学院 | Method of monitoring the performance of a software application |
| EP2457164A1 (en) * | 2009-07-24 | 2012-05-30 | Queen Mary & Westfield College, University of London | Method of monitoring the performance of a software application |
| CN104572409A (en) * | 2015-01-14 | 2015-04-29 | 东莞宇龙通信科技有限公司 | Application parameter management method and system and electronic device |
| CN107871190A (en) * | 2016-09-23 | 2018-04-03 | 阿里巴巴集团控股有限公司 | A kind of operational indicator monitoring method and device |
| CN108733532A (en) * | 2017-04-18 | 2018-11-02 | 北京京东尚科信息技术有限公司 | Health degree management-control method, device, medium and the electronic equipment of big data platform |
| CN107315671A (en) * | 2017-06-16 | 2017-11-03 | 东软集团股份有限公司 | Application state monitoring method, device and its equipment |
| CN109960650A (en) * | 2018-09-04 | 2019-07-02 | 中国平安人寿保险股份有限公司 | Application assessment method, apparatus, medium and electronic equipment based on big data |
| CN111241151A (en) * | 2019-12-27 | 2020-06-05 | 北京健康之家科技有限公司 | Service data analysis early warning method, system, storage medium and computing device |
| CN111552607A (en) * | 2020-03-27 | 2020-08-18 | 深圳壹账通智能科技有限公司 | Health evaluation method, device and equipment of application program and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112988542A (en) | 2021-06-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111158977B (en) | Abnormal event root cause positioning method and device | |
| CN111064614B (en) | Fault root cause positioning method, device, equipment and storage medium | |
| US9852041B2 (en) | Systems and methods for categorizing exceptions and logs | |
| US9392022B2 (en) | Methods and apparatus to measure compliance of a virtual computing environment | |
| CN110661659A (en) | Alarm method, device and system and electronic equipment | |
| CN108363657B (en) | Method, equipment and medium for monitoring integrity of embedded data acquisition of APP client | |
| CN109684162B (en) | Equipment state prediction method, system, terminal and computer readable storage medium | |
| US11635985B2 (en) | Using degree of completeness of real-time data to maximize product revenue | |
| CN111552607A (en) | Health evaluation method, device and equipment of application program and storage medium | |
| CN111400288A (en) | Data quality inspection method and system | |
| CN109800269A (en) | Data managing method, device, computer equipment and storage medium | |
| CN105159925B (en) | A kind of data-base cluster data distributing method and system | |
| US8543552B2 (en) | Detecting statistical variation from unclassified process log | |
| JP2017174334A (en) | Production plan improvement assist system | |
| CN113986595A (en) | Abnormity positioning method and device | |
| CN107330031B (en) | Data storage method and device and electronic equipment | |
| CN111338888B (en) | Data statistics method and device, electronic equipment and storage medium | |
| CN112988542B (en) | Application scoring method, device, equipment and readable storage medium | |
| CN113780906A (en) | Machine management method and device and computer readable storage medium | |
| CN106161058B (en) | Alarm classification method and device | |
| CN108334524A (en) | A kind of storm daily records error analysis methodology and device | |
| CN110020348B (en) | Early warning method and device for circled events | |
| CN109086309B (en) | Index dimension relation definition method, server and storage medium | |
| CN112559023A (en) | Method, device and equipment for predicting change risk and readable storage medium | |
| CN111737555A (en) | Method and device for selecting hot keywords and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |