CN116795618A - Data detection method and device - Google Patents
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Abstract
The embodiment of the disclosure provides a data detection method and device, wherein the method comprises the following steps: acquiring first index data of a first version of an application at a first running time, wherein the first running time is the running time of the application after updating the application of part of users from a second version to the first version; determining difference data between the first index data and second index data of a second version at a second running time, wherein the second running time is the running time of an application after partial users are updated from a third version to the second version; and determining whether the first index data is abnormal or not according to the difference data, the first index data, the confidence interval of the index data and the confidence interval of the difference data. In this way, whether the first index data is abnormal or not is determined according to the confidence interval of the index data and the confidence interval of the difference data, so that real-time detection of the index data of each version in the partial update period is realized.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of computers, in particular to a data detection method and device.
Background
The core service index in the mobile application is easily affected by various factors such as front end function abnormality, service link communication abnormality, back end release model effect fluctuation, etc., but the threshold value of the problem alarm of the core service index is often strongly related to the service background of the core service index, and cannot be effectively monitored.
In the related art, abnormal changes of the core service of the application are monitored irregularly by adopting a manual inspection mode, abnormal increases or decreases of the core index monitoring curve of each version of the mobile application at different time points during partial updating are observed, and therefore whether the core service index is abnormal or not is determined.
However, the manual inspection is facilitated, the human resources are occupied greatly, and the index evaluation standards are not uniform, so that real-time detection of index data of each version in the gray level operation period cannot be realized.
Disclosure of Invention
The embodiment of the disclosure provides a data detection method and device, which are used for solving the problem that real-time detection of index data during partial update of each version cannot be realized in the prior art.
In a first aspect, an embodiment of the present disclosure provides a data detection method, including:
acquiring first index data of a first version of an application at a first running time, wherein the first running time is the running time of the application after updating the application of part of users from a second version to the first version;
determining difference data between the first index data and second index data of a second version at a second running time, wherein the second running time is the running time of the application after partial users are updated from a third version to the second version;
And determining whether the first index data is abnormal or not according to the difference data, the first index data, the confidence interval of the index data and the confidence interval of the difference data.
In a second aspect, an embodiment of the present disclosure provides a data detection apparatus, including:
the system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is used for acquiring first index data of a first version of an application at a first running time, wherein the first running time is the running time of the application after updating the application of part of users from a second version to the first version;
the processing module is used for determining difference data between the first index data and second index data of a second version at a second running time, wherein the second index data is the running time of the application of the second version after the second running time is the running time of the application after a part of users are updated from a third version to the second version;
the detection module is used for determining whether the first index data is abnormal or not according to the difference data, the first index data, the confidence interval of the index data and the confidence interval of the difference data.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including: at least one processor and memory;
The memory stores computer-executable instructions;
the at least one processor executes computer-executable instructions stored in the memory, causing the at least one processor to perform the data detection method as described above in the first aspect and the various possible designs of the first aspect.
In a fourth aspect, embodiments of the present disclosure provide a computer-readable storage medium having stored therein computer-executable instructions which, when executed by a processor, implement the data detection method according to the first aspect and the various possible designs of the first aspect.
In a fifth aspect, embodiments of the present disclosure provide a computer program product comprising computer instructions which, when executed by a processor, implement the data detection method of the first aspect and the various possible designs of the first aspect.
According to the data detection method and device, first index data of a first version of an application at a first running time is obtained, and the first running time is the running time of the application after a part of users' applications are updated from a second version to the first version. Then, difference data between the first index data and second index data of a second version at a second runtime, which is a runtime of the application after updating a part of the users from the third version to the second version, is determined. And finally, determining whether the first index data is abnormal or not according to the difference data, the first index data, the confidence interval of the index data and the confidence interval of the difference data. In this way, whether the first index data is abnormal or not is determined according to the confidence interval of the index data and the confidence interval of the difference data, so that real-time detection of the index data of each version in the partial update period is realized.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or the solutions in the prior art, a brief description will be given below of the drawings that are needed in the embodiments or the description of the prior art, it being obvious that the drawings in the following description are some embodiments of the present disclosure, and that other drawings may be obtained from these drawings without inventive effort to a person of ordinary skill in the art.
Fig. 1 is a schematic application scenario diagram of a data detection method according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of a data detection method according to an embodiment of the disclosure;
FIG. 3 is a schematic diagram of alignment of index data according to an embodiment of the disclosure;
fig. 4 is a flowchart of another data detection method according to an embodiment of the disclosure;
fig. 5 is a flowchart of another data detection method according to an embodiment of the disclosure;
fig. 6 is a block diagram of a data detection device according to an embodiment of the present disclosure;
fig. 7 is a schematic hardware structure of an electronic device according to an embodiment of the disclosure.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some embodiments of the present disclosure, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without inventive effort, based on the embodiments in this disclosure are intended to be within the scope of this disclosure.
The core service index in the mobile application is easily affected by various factors such as front end function abnormality, service link communication abnormality, back end release model effect fluctuation, etc., but the threshold value of the problem alarm of the core service index is often strongly related to the service background of the core service index, and cannot be effectively monitored.
In the related art, abnormal changes of the core service of the application are monitored irregularly by adopting a manual inspection mode, abnormal increases or decreases of the core index monitoring curve of each version of the mobile application at different time points during partial updating are observed, and therefore whether the core service index is abnormal or not is determined. However, in a manual inspection mode, human resources are occupied greatly, and index evaluation standards are not uniform, so that real-time detection of index data of each version in a partial update period cannot be realized.
In order to solve the above problems, embodiments of the present application provide a data detection method and apparatus, which determine whether index data is abnormal by comparing index data during partial update with difference data between different versions of index data, so as to implement real-time detection of index data during partial update for each version.
The application scenario of the data detection method according to the present application will be described below.
Fig. 1 is a schematic application scenario diagram of a data detection method according to an embodiment of the present disclosure. As shown in fig. 1, before the version of the application program is updated, the version to be updated may be gray-scale run (updating part of the user's application to the version to be updated). The server 102 may push an update package of the version to be updated to a part of the terminal device 101. When the terminal device 101 updates the version to be updated, the server 102 may collect, from the terminal device 101, index data of the version to be updated during the partial update. Server 102 may then compare the index data of the version to be updated during the partial update with the index data of the online version during the partial update to determine if the index data of the version to be updated during the partial update is anomalous.
The terminal device 101 may be a tablet computer (pad), a computer with a wireless transceiving function, a Virtual Reality (VR) terminal device, an augmented reality (augmented reality, AR) terminal device, a wireless terminal in industrial control (industrial control), a wireless terminal in self driving (self driving), a wireless terminal in teleoperation (remote medical surgery), a wireless terminal in smart grid (smart grid), a wireless terminal in smart home (smart home), or the like. In the embodiment of the present application, the device for implementing the function of the terminal may be the terminal, or may be a device capable of supporting the terminal to implement the function, for example, a chip system, and the device may be installed in the terminal. In the embodiment of the application, the chip system can be composed of chips, and can also comprise chips and other discrete devices.
Server 102 may be, but is not limited to, a single web server, a server group of multiple web servers, or a cloud based cloud computing consisting of a large number of computers or web servers. The cloud computing is a distributed computing type, and is a super virtual computer consisting of a group of loosely coupled computers.
It should be understood that the application scenario of the technical solution of the present application may be the scenario in fig. 1, but is not limited thereto, and may also be applied to other scenarios requiring data detection.
It may be understood that the above-mentioned data detection method may be implemented by using the data detection device provided by the embodiment of the present application, where the data detection device may be part or all of a certain device, for example, a server or a chip of the server.
The technical solutions of the embodiments of the present disclosure are described in detail below with specific embodiments. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.
Referring to fig. 2, fig. 2 is a flow chart of a data detection method according to an embodiment of the disclosure. The execution subject of the present embodiment is a server, and the present embodiment relates to a process of how to detect index data. The data detection method comprises the following steps:
S201: first index data of a first version of the application at a first running time is obtained, wherein the first running time is the running time of the application after the application of a part of users is updated from a second version to the first version.
In the embodiment of the application, when the first version of the application is partially updated, the server can acquire the first index data of the first version of the application at the first running time in real time, so as to detect whether the first index data is abnormal or not.
The partial update may be a smooth release manner, that is, in the version update process, a part of users continue to use the original version, and another part of users use the new version, so that the original version is gradually replaced by the release process of the new version. Accordingly, the first version related in the embodiment of the present application may be a version to be updated by an application, or an observed version, and the first runtime related in the embodiment of the present application may be a runtime of a partially updated application.
It should be understood that the first index data related to the embodiment of the present application may be index data of the first version at the first running time, and the embodiment of the present application does not limit the type of index data, and in some embodiments, the index data may specifically include a display Send Ratio (SSR), a click Rate (Click Through Rate, CTR), a Conversion Rate (CVR), or the like.
In some embodiments, the index data is data collected according to a preset time interval. The embodiment of the application does not limit the preset time interval, for example, the preset time interval can be a day, and the index data can be data taking the day as unit data.
Optionally, the server obtains first index data of the first version of the application during the first running period, and may align the first index data with historical version historical index data of the application. Correspondingly, the user magnitude of the first version and the user magnitude of the second version are aligned according to the collection time of the index data of the first version.
It should be noted that, in some embodiments of the present application, taking the first index data and the second index data as examples, the first index data and the second index data may be aligned according to the collection time of the first index data, and the second index data is the index data of the second version during the gray level operation.
Fig. 3 is a schematic diagram illustrating alignment of index data according to an embodiment of the disclosure. As shown in fig. 3, the index data of the same gradation days (days from the partial update start time) are aligned accordingly. For example, the index data of the third day of gray scale between different versions may be aligned, and the index data of the nth day of gray scale between different versions may be aligned.
In some embodiments, if the fluctuation data of the user level corresponding to the first runtime is smaller than the threshold value, first index data of the first version of the application at the first runtime is obtained.
Since the data magnitude of each version on the day before the start of partial update is too sparse and the index data is generally unstable, the index data on the day before can be subjected to the weight reduction processing. The numerical value of the index data can be severely dithered along with the number of users of the version, so that the monitoring of the core index is temporarily avoided before the number of the users is stable. When the updating time of each version part is close, the user magnitude is aligned by adopting a gray scale day alignment mode after the user magnitude is stable. Thus, in some embodiments, the weight of the index data corresponds to a collection time of the index data, the weight of the index data having a collection time less than the first time threshold being less than the weight of the index data having a collection time greater than or equal to the first time threshold.
It should be understood that, in the embodiment of the present application, the size of the first time threshold is not limited, and may be specifically set according to practical situations, and exemplary, the first time threshold may be the gray level of the second day.
S202: a number of differences between the first index data and the second index data of the second version at the second runtime is determined.
Wherein the second runtime is the runtime of the application after updating the portion of the users from the third version to the second version.
In this step, after the server acquires the first index data of the first version of the application at the first runtime, difference data between the first index data and the second index data may also be determined.
It should be understood that the embodiments of the present application are not limited as to how the type of difference data is, in some embodiments, the difference data may be a percentage of the difference between the first index data and the second index data. In the present disclosure, the difference between the first and second versions may be quantified by comparing the first index data of the first version with the second index data of the second version, and measuring the performance of the first version and the second version in the same development stage from two dimensions.
In other embodiments, the server may also determine index data for the historical version, and difference data between the index data for the historical version and a previous version of the historical version.
The difference data needs to be aligned according to the collection time of the index data during calculation.
By way of example, table 1 is a schematic table of difference data provided in an embodiment of the disclosure, and as shown in table 1, difference data between index data of each historical version may be determined.
TABLE 1
| Historical version number | Gray scale first day index data | Difference data |
| 160400 | 0.8 | (0.8-0.6)/0.6＝0.33 |
| 160300 | 0.6 | (0.6-0.7)/0.7＝-0.14 |
| 160200 | 0.7 | (0.8-0.4)/0.4＝0.75 |
| 160200 | 0.4 | (0.4-0.5)/0.4＝-0.25 |
| 160000 | 0.5 | …… |
Illustratively, table 2 is a schematic representation of difference data between first index data and second index data provided by embodiments of the present disclosure.
TABLE 2
| Current version number | Gray scale first day index data | Difference data |
| 160500 | 0.6 | (0.6-0.8)/0.6＝-0.33 |
S203: and determining whether the first index data is abnormal or not according to the difference data, the first index data, the confidence interval of the index data and the confidence interval of the difference data.
In this step, after the server determines the difference data between the first index data and the second index data, it may be determined whether the first index data is abnormal based on the difference data, the first index data, the confidence interval of the index data, and the confidence interval of the difference data.
It should be understood that the embodiment of the present application does not limit the criterion for determining whether the first index data is abnormal, and in some embodiments, if the difference data between the first index data and the second index data is not within the confidence interval of the difference data and the first index data is not within the confidence interval of the index data, the first index data is determined to be abnormal. Accordingly, if the difference data between the first index data and the second index data is within the confidence interval of the difference data or the first index data is within the confidence interval of the index data, it may be determined that the first index data is normal.
It should be noted that, the method for determining the confidence interval of the difference data and the confidence interval of the index data in the embodiment of the application is not limited, and the method can be directly indicated by a user, and the confidence interval of the difference data can be determined through the difference data between the index data of the historical version, and the confidence interval of the index data can be determined through the index data of the historical version.
In some embodiments, the server may fit a normal distribution to the historical version of the index data during gray scale operation to determine a confidence interval for the index data. Correspondingly, the server may also determine difference data between the index data of the historical version and the index data of the previous version of the historical version according to the version update sequence of the application. And then, fitting normal distribution is carried out on the difference data between the index data of the historical version and the index data of the previous version of the historical version, and a confidence interval of the difference data is determined.
For example, normal distribution assumptions are made for the historical values and the difference data of the index data, normal distribution is fitted, and confidence intervals are set, respectively. And based on the confidence interval, a double-latitude decision rule is formulated. If the index data and the first difference data are both outside the confidence interval in one day, judging that the index data are in an abnormal change stage, and alarming.
By the data detection method, abnormal change of index data can be monitored and alarmed in real time, and the false alarm rate are reduced.
The data detection method provided in this embodiment first obtains a first version of an application during gray level running
According to the data detection method provided by the embodiment of the disclosure, first index data of a first version of an application at a first running time is obtained, wherein the first running time is the running time of the application after the application of a part of users is updated from a second version to the first version. Then, difference data between the first index data and second index data of a second version at a second runtime, which is a runtime of the application after updating a part of the users from the third version to the second version, is determined. And finally, determining whether the first index data is abnormal or not according to the difference data, the first index data, the confidence interval of the index data and the confidence interval of the difference data. In this way, whether the first index data is abnormal or not is determined according to the confidence interval of the index data and the confidence interval of the difference data, so that real-time detection of the index data of each version in the partial update period is realized.
On the basis of the above-described embodiment, a description will be given below of how to process the first index data after the first index data is acquired. Fig. 4 is a flowchart of another data detection method according to an embodiment of the disclosure. As shown in fig. 4, the data detection method includes:
s301: first index data of a first version of the application at a first running time is obtained, wherein the first running time is the running time of the application after the application of a part of users is updated from a second version to the first version.
S302: and aligning the first index data with the second index data according to the collection time of the first index data and the second index data.
The second index data is index data of a second version at a second running time, and the second running time is the running time of the application after part of users are updated from the third version to the second version.
S303: difference data between the first index data and the second index data is determined.
S304: it is determined whether the difference data is in a confidence interval of the difference data.
If yes, step S305 is executed, and if no, step S306 is executed.
S305: it is determined whether the first index data is within a confidence interval of the index data.
If yes, step S307 is executed, and if no, step S306 is executed.
S306: and determining that the first index data is normal.
S307: a first index data anomaly is determined.
In some embodiments, the first index data and the second index data are data collected according to a preset time interval.
In some embodiments, the weight of the index data corresponds to a collection time of the index data, the weight of the index data having a collection time less than the first time threshold being less than the weight of the index data having a collection time greater than or equal to the first time threshold.
In some embodiments, if the fluctuation data of the user level corresponding to the first runtime is smaller than the threshold value, first index data of the first version of the application at the first runtime is obtained.
In some embodiments, the index data includes display send rate, click rate, or conversion rate.
The technical terms, effects, features, and alternative embodiments of S301-S307 may be understood with reference to S201-S203 shown in fig. 2, and will not be described again here for repeated matters.
On the basis of the above-described embodiments, a description will be given below of how to determine the confidence interval of the index data and the confidence interval of the difference data. Fig. 5 is a flowchart of another data detection method according to an embodiment of the disclosure. As shown in fig. 5, the data detection method includes:
S401: index data of a historical version of the application is obtained.
S402: fitting normal distribution is carried out on the index data of the historical version, and a confidence interval of the index data is determined.
S403: difference data between the index data of the history version and the index data of the previous version of the history version is determined, respectively.
S404: and fitting normal distribution to the difference data, and determining a confidence interval of the difference data.
S405: first index data of a first version of the application at a first running time is obtained, wherein the first running time is the running time of the application after the application of a part of users is updated from a second version to the first version.
S406: difference data between the first index data and second index data of a second version at a second runtime, the second runtime being a runtime of the application after updating a portion of the users from the third version to the second version, is determined.
S407: and determining whether the first index data is abnormal or not according to the difference data, the first index data, the confidence interval of the index data and the confidence interval of the difference data.
The technical terms, effects, features, and alternative embodiments of S401-S307 may be understood with reference to S201-S203 shown in fig. 2, and will not be further described herein for repeated matters.
According to the data detection method provided by the embodiment of the disclosure, first index data of a first version of an application at a first running time is obtained, wherein the first running time is the running time of the application after the application of a part of users is updated from a second version to the first version. Then, difference data between the first index data and second index data of a second version at a second runtime, which is a runtime of the application after updating a part of the users from the third version to the second version, is determined. And finally, determining whether the first index data is abnormal or not according to the difference data, the first index data, the confidence interval of the index data and the confidence interval of the difference data. In this way, whether the first index data is abnormal or not is determined according to the confidence interval of the index data and the confidence interval of the difference data, so that real-time detection of the index data of each version in the partial update period is realized.
Fig. 6 is a block diagram of a data detection device according to an embodiment of the present disclosure, corresponding to a processing method of a stack according to the above embodiment. For ease of illustration, only portions relevant to embodiments of the present disclosure are shown. Referring to fig. 6, the data detection apparatus 500 includes: an acquisition module 501, a processing module 502, and a detection module 503.
An obtaining module 501, configured to obtain first index data of a first version of an application at a first runtime, where the first runtime is the runtime of the application after updating an application of a part of users from a second version to the first version;
a processing module 502, configured to determine difference data between the first index data and second index data of a second version at a second runtime, where the second index data is the second version and the second runtime is the runtime of the application after updating a part of users from the third version to the second version;
the detection module 503 is configured to determine whether the first index data is abnormal according to the difference data, the first index data, the confidence interval of the index data, and the confidence interval of the difference data.
In one embodiment of the present disclosure, the detection module 503 is specifically configured to determine that the first index data is abnormal if the difference data is not within the confidence interval of the difference data and the first index data is not within the confidence interval of the index data.
In one embodiment of the present disclosure, the first index data and the second index data are data collected according to a preset time interval.
In one embodiment of the present disclosure, the processing module 502 is further configured to align the first index data with the second index data according to a collection time of the first index data and the second index data.
In one embodiment of the present disclosure, the obtaining module 501 is specifically configured to obtain the first index data of the first version of the application at the first runtime if the fluctuation data of the user level corresponding to the first runtime is smaller than the threshold value.
In one embodiment of the present disclosure, the obtaining module 501 is further configured to obtain index data of a historical version of an application;
the processing module 502 is further configured to perform fitting normal distribution on the historical version of the index data, and determine a confidence interval of the index data.
In one embodiment of the present disclosure, the processing module 502 is further configured to determine difference data between the index data of the historical version and the index data of a previous version of the historical version according to the version update sequence of the application, respectively; and fitting normal distribution is carried out on the difference data between the index data of the historical version and the index data of the previous version of the historical version, and a confidence interval of the difference data is determined.
In one embodiment of the present disclosure, the index data includes a display transmission rate, click rate, or conversion rate.
The data detection device provided in this embodiment may be used to implement the technical solution of the foregoing method embodiment, and its implementation principle and technical effects are similar, and this embodiment will not be described herein again.
Referring to fig. 7, there is shown a schematic structural diagram of an electronic device 600 suitable for use in implementing embodiments of the present disclosure, the electronic device 600 may be a terminal device or a server. The terminal device may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, personal digital assistants (Personal Digital Assistant, PDA for short), tablet computers (Portable Android Device, PAD for short), portable multimedia players (Portable Media Player, PMP for short), car terminals (e.g., car navigation terminals), wearable electronic devices, etc., and fixed terminals such as digital TVs, desktop computers, smart home devices, etc. The electronic device shown in fig. 7 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.
As shown in fig. 7, the electronic device 600 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 601, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage means 608 into a random access Memory (Random Access Memory, RAM) 603, to implement the above-described data detection method defined in the method of the embodiment of the present disclosure. In the RAM 603, various programs and data required for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM602, and the RAM 603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
In general, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like; an output device 607 including, for example, a liquid crystal display (Liquid Crystal Display, LCD for short), a speaker, a vibrator, and the like; storage 608 including, for example, magnetic tape, hard disk, etc.; and a communication device 607. The communication means 607 may allow the electronic device 600 to communicate wirelessly or by wire with other devices to exchange data. While fig. 7 shows an electronic device 600 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communications device 607, or installed from the storage device 608, or installed from the ROM 602. When executed by the processing device 601, the computer program performs the above-described data detection method defined in the method of the embodiments of the present disclosure.
It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
The computer-readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to perform the method shown in the above-described embodiments, for example, to perform the above-described data detection method defined in the method of the embodiments of the present disclosure.
Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a local area network (Local Area Network, LAN for short) or a wide area network (Wide Area Network, WAN for short), or it may be connected to an external computer (e.g., connected via the internet using an internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The name of the element does not in some case constitute a limitation of the element itself, for example the acquisition module may also be described as "an element that acquires first index data of the first version of the application during the greyscale operation".
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
In a first aspect, a data detection method, the method comprising:
acquiring first index data of a first version of an application at a first running time, wherein the first running time is the running time of the application after updating the application of part of users from a second version to the first version;
determining difference data between the first index data and second index data of a second version at a second running time, wherein the second running time is the running time of the application after partial users are updated from a third version to the second version;
and determining whether the first index data is abnormal or not according to the difference data, the first index data, the confidence interval of the index data and the confidence interval of the difference data.
In one embodiment of the disclosure, the determining whether the first index data is abnormal includes:
and if the difference data is not in the confidence interval of the difference data and the first index data is not in the confidence interval of the index data, determining that the first index data is abnormal.
In one embodiment of the present disclosure, the first index data and the second index data are data collected according to a preset time interval.
In one embodiment of the present disclosure, before said determining the difference data between the first index data and the second index data of the second version at the second runtime, further comprising:
and aligning the first index data with the second index data according to the collection time of the first index data and the second index data.
In one embodiment of the disclosure, the obtaining the first index data of the first version of the application at the first runtime includes:
and if the fluctuation data of the user magnitude corresponding to the first running time is smaller than a threshold value, acquiring first index data of the first version of the application in the first running time.
In one embodiment of the present disclosure, before the determining whether the first version of the index data is abnormal, the method further includes:
acquiring index data of a historical version of the application;
fitting normal distribution is carried out on the index data of the historical version, and a confidence interval of the index data is determined.
In one embodiment of the present disclosure, after the obtaining the index data of the historical version of the application, the method further includes:
according to the version updating sequence of the application, difference data between the index data of the historical version and the index data of the previous version of the historical version are respectively determined;
And fitting normal distribution is carried out on the difference data between the index data of the historical version and the index data of the previous version of the historical version, and a confidence interval of the difference data is determined.
In one embodiment of the present disclosure, the index data includes a display transmission rate, a click rate, or a conversion rate.
In a second aspect, an embodiment of the present disclosure provides a data detection apparatus, including:
the system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is used for acquiring first index data of a first version of an application at a first running time, wherein the first running time is the running time of the application after updating the application of part of users from a second version to the first version;
the processing module is used for determining difference data between the first index data and second index data of a second version at a second running time, wherein the second index data is the running time of the application of the second version after the second running time is the running time of the application after a part of users are updated from a third version to the second version;
the detection module is used for determining whether the first index data is abnormal or not according to the difference data, the first index data, the confidence interval of the index data and the confidence interval of the difference data.
In one embodiment of the disclosure, the detection module is specifically configured to determine that the first index data is abnormal if the difference data is not within a confidence interval of the difference data and the first index data is not within the confidence interval of the index data.
In one embodiment of the present disclosure, the first index data and the second index data are data collected according to a preset time interval.
In one embodiment of the disclosure, the processing module is further configured to align the first index data with the second index data according to a collection time of the first index data and the second index data.
In an embodiment of the disclosure, the obtaining module is specifically configured to obtain the first index data of the first version of the application at the first running time if the fluctuation data of the user level corresponding to the first running time is smaller than a threshold value.
In one embodiment of the disclosure, the obtaining module is further configured to obtain the index data of the historical version of the application;
the processing module is further used for carrying out fitting normal distribution on the index data of the historical version and determining a confidence interval of the index data.
In one embodiment of the disclosure, the processing module is further configured to determine difference data between the index data of the historical version and index data of a previous version of the historical version according to a version update order of the application, respectively; and fitting normal distribution is carried out on the difference data between the index data of the historical version and the index data of the previous version of the historical version, and a confidence interval of the difference data is determined.
In one embodiment of the present disclosure, the index data includes a display transmission rate, a click rate, or a conversion rate.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executes computer-executable instructions stored in the memory such that the at least one processor performs data detection as described above in the first aspect and the various possible designs of the first aspect.
In a fourth aspect, embodiments of the present disclosure provide a computer-readable storage medium having stored therein computer-executable instructions which, when executed by a processor, implement the data detection described above for the first aspect and the various possible designs of the first aspect.
In a fifth aspect, embodiments of the present disclosure provide a computer program product comprising computer instructions which, when executed by a processor, implement the data detection of the first aspect and the various possible designs of the first aspect.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in this disclosure is not limited to the specific combinations of features described above, but also covers other embodiments which may be formed by any combination of features described above or equivalents thereof without departing from the spirit of the disclosure. Such as those described above, are mutually substituted with the technical features having similar functions disclosed in the present disclosure (but not limited thereto).
Moreover, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims.
Claims (12)
1. A method of data detection, the method comprising:
acquiring first index data of a first version of an application at a first running time, wherein the first running time is the running time of the application after updating the application of part of users from a second version to the first version;
determining difference data between the first index data and second index data of a second version at a second running time, wherein the second running time is the running time of the application after partial users are updated from a third version to the second version;
and determining whether the first index data is abnormal or not according to the difference data, the first index data, the confidence interval of the index data and the confidence interval of the difference data.
2. The method of claim 1, wherein the determining whether the first metric data is anomalous comprises:
And if the difference data is not in the confidence interval of the difference data and the first index data is not in the confidence interval of the index data, determining that the first index data is abnormal.
3. The method of claim 1, wherein the first index data and the second index data are data collected according to a preset time interval.
4. A method according to claim 3, further comprising, prior to said determining difference data between said first indicator data and said second version of second indicator data at a second runtime:
and aligning the first index data with the second index data according to the collection time of the first index data and the second index data.
5. The method of claim 1, wherein the obtaining first index data of the first version of the application at the first runtime comprises:
and if the fluctuation data of the user magnitude corresponding to the first running time is smaller than a threshold value, acquiring first index data of the first version of the application in the first running time.
6. The method of claim 1, wherein prior to the determining whether the first version of the index data is anomalous, the method further comprises:
Acquiring index data of a historical version of the application;
fitting normal distribution is carried out on the index data of the historical version, and a confidence interval of the index data is determined.
7. The method of claim 6, wherein after the obtaining the historical version of the metrics data for the application, the method further comprises:
according to the version updating sequence of the application, difference data between the index data of the historical version and the index data of the previous version of the historical version are respectively determined;
and fitting normal distribution is carried out on the difference data between the index data of the historical version and the index data of the previous version of the historical version, and a confidence interval of the difference data is determined.
8. The method of any of claims 1-7, wherein the indicator data comprises a display send rate, click-through rate, or conversion rate.
9. A data detection apparatus, comprising:
the system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is used for acquiring first index data of a first version of an application at a first running time, wherein the first running time is the running time of the application after updating the application of part of users from a second version to the first version;
The processing module is used for determining difference data between the first index data and second index data of a second version at a second running time, wherein the second index data is the running time of the application of the second version after the second running time is the running time of the application after a part of users are updated from a third version to the second version;
the detection module is used for determining whether the first index data is abnormal or not according to the difference data, the first index data, the confidence interval of the index data and the confidence interval of the difference data.
10. An electronic device, comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing computer-executable instructions stored in the memory causes the at least one processor to perform the method of any one of claims 1 to 8.
11. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor implement the method of any one of claims 1 to 8.
12. A computer program product comprising computer instructions which, when executed by a processor, implement the method of any one of claims 1 to 8.
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