CN107665135B - Upgrading program pushing method and device - Google Patents
Upgrading program pushing method and device Download PDFInfo
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- CN107665135B CN107665135B CN201710985394.4A CN201710985394A CN107665135B CN 107665135 B CN107665135 B CN 107665135B CN 201710985394 A CN201710985394 A CN 201710985394A CN 107665135 B CN107665135 B CN 107665135B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/445—Program loading or initiating
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/55—Push-based network services
Abstract
The invention discloses a pushing method of an upgrading program, which comprises the following steps: acquiring a full amount of users and at least one user index of an application program; determining the number of trial users according to the at least one user index; determining trial users from the total number of users according to the number of the trial users; pushing the upgrading program of the application program to the trial user; wherein the full users are all users who install the application program. The invention also discloses a pushing device for the upgrading program.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a pushing method and a pushing device for an upgrading program.
Background
With the rapid development of internet technology, the number of Applications (APP) is increasing exponentially. In practical applications, since published applications may have problems such as bugs, incomplete functions, or insufficient optimization of page layouts, developers of the applications upgrade the applications to repair bugs, enrich functions, or adjust layouts.
After a new version of an application program is developed by a developer, if the new version of the application program is directly pushed to all users for upgrading, a potential vulnerability may cause a significant loss. To avoid this, the upgrade process of an application program can be divided into two stages: and after the feedback of the part of users is obtained, optimizing the new version of the application program until no problem exists, and then upgrading and using all the users. The software upgrading mode can be called gray scale upgrading, and through the gray scale upgrading mode, when a part of users use the new version of the application program, the stability of the new version can be checked, and the uncontrollable adverse effect of potential bugs on the new version of the application program is avoided.
At present, when an application needs to perform "grayscale upgrading", the number and the group of a part of users are mainly determined by the following two schemes: the first scheme is that a certain type of users are often designated, and the new version of the application program is tried out to collect the feedback of the users and expose possible defects of the new version of the application program; in the second scheme, the number of users and the group to which the users belong are selected in a random selection mode.
In the above scenario, if the number of users and the user group to which the users belong are selected according to the first scheme, the selected user group has a single attribute and a fixed use behavior, and the effect of "gray scale upgrading" cannot be achieved, so that the fed-back data is more comprehensive, and further optimization of the misleading application program is possible. When the number of users and the group of users are selected by the second scheme, the number of users is difficult to determine due to the random selection mode, and data feedback is affected.
Disclosure of Invention
The embodiment of the invention provides a pushing method of an upgrading program, which aims to solve the problem that a trial user who performs gray scale upgrading determined in the prior art is not comprehensive enough.
In order to solve the technical problem, the invention is realized as follows: in a first aspect, an embodiment of the present invention provides a method for pushing an upgrade program, including:
acquiring a full amount of users and at least one user index of an application program;
determining the number of trial users according to the at least one user index;
determining trial users from the total number of users according to the number of the trial users;
pushing the upgrading program of the application program to the trial user;
wherein the full users are all users who install the application program.
In a second aspect, an embodiment of the present invention further provides a pushing apparatus for upgrading a program, including:
the index acquisition unit is used for acquiring the full users and at least one user index of the application program;
the first determining unit is used for determining the number of trial users according to the at least one user index;
a second determining unit, configured to determine trial users from the total number of users according to the number of trial users;
the program pushing unit is used for pushing the upgrading program of the application program to the trial user;
wherein the full users are all users who install the application program.
In a third aspect, an embodiment of the present invention further provides a server, including: memory, a processor and a computer program stored on the memory and executable on the processor, which computer program, when executed by the processor, performs the steps of the push method as an upgrade program.
In a fourth aspect, the embodiment of the present invention further provides a readable storage medium, where a computer program is stored on the readable storage medium, and when the computer program is executed by a processor, the computer program implements the steps of the pushing method, such as the upgrading program.
In the embodiment of the invention, the problem that the trial users who need to perform gray scale upgrading determined in the prior art are not comprehensive enough can be solved by acquiring the total users and at least one user index of the application program, determining the number of trial users according to the user index, determining the trial users from the total users according to the determined number of the trial users, and finally pushing the upgrading program of the application program to the determined trial users.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
Fig. 1 is a schematic flowchart of a specific implementation process of a pushing method for an upgrade program according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a pushing device for upgrading a program according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a server according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. 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 invention.
The technical solutions provided by the embodiments of the present invention are described in detail below with reference to the accompanying drawings.
In order to solve the problem that the trial users who perform the gray scale upgrading determined in the prior art are not comprehensive enough, the invention provides a pushing method of an upgrading program, and an execution main body of the method can be but is not limited to at least one of a server, a personal computer and other computers which can be configured to execute the method provided by the embodiment of the invention.
For convenience of description, the following description will be made of an embodiment of the method, taking an execution subject of the method as a server capable of executing the method as an example. It is understood that the implementation of the method by the server is merely an exemplary illustration and should not be construed as a limitation of the method.
Specifically, the pushing method of the upgrade program provided by the invention comprises the following steps: firstly, acquiring a full amount of users and at least one user index of an application program; then, determining the number of trial users according to the obtained user indexes; then determining trial users from the total users according to the number of the trial users; finally, pushing the upgrading program of the application program to a trial user; wherein the total number of users are all users who install the application.
In the embodiment of the invention, the problem that the trial users who need to perform gray scale upgrading determined in the prior art are not comprehensive enough can be solved by acquiring the total users and at least one user index of the application program, determining the number of trial users according to the user index, determining the trial users from the total users according to the determined number of the trial users, and finally pushing the upgrading program of the application program to the determined trial users.
The invention is described in detail below with reference to the schematic flow chart of fig. 1, which includes the following steps:
wherein the total number of users are all users who install the application. As described in the background art, in the prior art, the way of determining the trial users of the application program of "grayscale upgrade" is not optimized enough, the number of the determined trial users and the group to which the trial users belong are often not comprehensive enough, and the application program cannot be fed back well.
Specifically, the user index includes a numerical class user index, and the numerical class user index includes at least one of: number of starts, duration of use, number of downloads, and number of clicks. The user metrics may also include a duty-ratio-type user metric including at least one of: an activity rate, a retention rate, and a vulnerability upload rate.
Preferably, the user indexes in the embodiment of the present invention may include numerical user indexes and proportion user indexes, where the numerical user indexes include numerical indexes such as start times, use duration, download times, click times, and the like, and the proportion user indexes may include proportion user indexes such as daily or monthly active rate, next daily or weekly or monthly retention rate, vulnerability uploading rate, and the like.
Specifically, the number of starts may include the number of starts per day (the number of times each user starts an application per day), and the number of starts per month (the number of times each user starts an application per month); the usage duration may include average daily usage duration (the duration each user uses the application each day), and monthly usage duration (the duration each user uses the application each month); the download times may include daily download times (each user downloads a certain page of the application program every day), and monthly download times (each user downloads a certain page of the application program every month); the number of clicks may include a number of clicks per day (each user clicks on a certain page of the application per day), a number of clicks per month (each user clicks on a certain page of the application per month).
The active rate can include daily active rate (ratio of daily active user number to full user number), monthly active rate (ratio of monthly active user number to full user number); the retention rate may include a next day retention rate (a ratio of the number of users starting the application on the first day and starting the application on the second day to the number of users starting the application on the first day), a week retention rate (a ratio of the number of users starting the application on the first week and starting the application on the second week to the number of users starting the application on the first week), a month retention rate (a ratio of the number of users starting the application on the first month and starting the application on the second month to the number of users starting the application on the first month); and (4) the uploading rate of the vulnerability (the ratio of the number of users uploading the vulnerability to the number of the full users).
The user index listed above is only an exemplary illustration, and other user indexes may be added according to the characteristics and actual needs of the application program in the embodiment of the present invention, which is not specifically limited by the present invention.
since the user indexes include numerical user indexes and proportion user indexes, the following will describe in detail how to determine the trial user number according to at least one user index, taking the example that the user indexes include numerical user indexes and the user indexes include proportion user indexes.
(1) For the features of some application programs (such as video application programs, developers of these application programs often pay more attention to numerical user indexes such as usage duration and download times of such application programs), in order to determine the trial user number, in an embodiment of the present invention, the trial user number may be determined based on user indexes of some users in the total number of users as sample users, when the user indexes include the numerical user indexes, the trial user number is determined according to at least one user index, specifically, the mean U and the variance σ of each user index may be calculated first2(ii) a Then according to the mean U and variance sigma2And a first preset error value delta, determining the trial user number n.
Optionally, according to the mean U, the variance σ2And a first preset error value delta, determining the number n of trial users, and then calculating an absolute value | U-U | of a difference value between the average value U of the user indexes of the trial users and the average value U of the user indexes of the full users; if the absolute value | U-U | of the difference is not greater than a first preset error value delta when the confidence coefficient is 1-alpha, the absolute value | U-U | of the difference passes through a formula
Determining the number n of trial users; wherein
In a standard normal distribution, probability
Corresponding critical value.
Taking the total number of users of a certain video application program as N, the mean values corresponding to at least one user index including index 1 (using time per month), index 2 (downloading time per month) and index 3 (starting time per month) are respectively U1, U2 and U3, and the corresponding variances are respectively U1, U2 and U3
For example, it is clear that these three indicators can reflect the monthly activity of the user using the application. When n trial users are randomly selected from the total number of users as sample users, the average value u corresponding to the indexes 1, 2 and 3 of the n trial users can be known according to the central limit theorem1、u2、u3Are respectively obeys
Is determined. If the mean values u1, u2 and u3 corresponding to the indexes 1, 2 and 3 of the n trial users and the mean value difference values corresponding to the indexes 1, 2 and 3 of the full number of users are ensured to be 1-alpha (0) in confidence degree<α<1) When respectively do not exceed delta1、δ2、δ3(i.e. | U1-U1| ≦ δ1、|u2-U2|≤δ2、|u3-U3|≤δ3) If the confidence is 1-alpha, then there is
Thereby having
Thereby determining
Wherein
In a standard normal distribution, probability
Corresponding critical value.
Now suppose the number of users1 hundred million, the average value of the monthly service time of the total users is 5, the variance is 83, the average value of the monthly download times is 13, the variance is 114, the average value of the monthly startup times is 30, and the variance is 181, so that 99% confidence is ensured that the error between the average value of the monthly service time of n trial users and the average value of the monthly service time of the total users is less than or equal to 0.1, the error between the average value of the monthly download times of the n trial users and the average value of the monthly startup times of the total users is less than or equal to 0.1, the error between the average value of the monthly startup times of the n trial users and the average value of the monthly startup times of the total users is less than or equal to 0.2, and the number n of sample users is greater than or equal to 0
Wherein 2.575 is the probability in a standard normal distribution
The corresponding threshold, n ≧ 75589, is determined 75589 users as trial users from at least the full number of users.
Alternatively, in some cases, such as the format requirements for some data in the calculation process, the mean U and variance σ are determined2And a first preset error value delta, determining the number n of trial users, and specifically, calculating the ratio of the absolute value | U-U | of the difference value between the average value U of the user indexes of the trial users and the average value U of the user indexes of the full users to the average value U of the user indexes of the full users
If the ratio is
When the confidence coefficient is 1-alpha, the first preset error value delta is not larger than the first preset error value delta, the first preset error value delta can be obtained through a formula
Determining a trialUsing the number n of users; wherein
In a standard normal distribution, probability
Corresponding critical value.
The total number of users is used as N, the mean values corresponding to at least one user index including index 1 (using time per month), index 2 (downloading times per month) and index 3 (starting times per month) are respectively U1, U2 and U3, and the corresponding variances are respectively U1, U2 and U3
For example. The first preset error value of the N trial users and the full users can be expressed by relative values, i.e. the first preset error value is expressed by the relative value
Substituting it into the above formula
Then there is
Specifically, if the relative error between the index 1 of the n trial users and the index 2 of the full users is 0.1, the relative error between the index 2 of the n trial users and the index 2 of the full users is 0.01, and the relative error between the index 3 of the n trial users and the index 3 of the full users is 0.01, the number n of trial users should be greater than or equal to
I.e., n ≧ 220137, i.e., 220137 users are determined as trial users from among at least the full number of users.
(2) For the characteristics of some application programs (for example, social application programs, developers of these application programs often pay more attention to percentage-based user indexes such as the active rate and the retention rate of such application programs), in order to make the determined trial user number more comprehensive, the embodiment of the present invention may also determine the trial user number based on the total number of users, that is, by taking the percentage-based user indexes as examples. When the user indexes comprise the percentage user indexes, determining the number of trial users according to at least one user index, and specifically calculating the variance P (1-P) corresponding to each user index P; and then determining the number n of trial users according to the user index P, the variance P (1-P) corresponding to the user index and a second preset error value e.
Optionally, determining the number n of trial users according to the user index P, the variance P (1-P) corresponding to the user index, and the second preset error value e, and specifically, calculating an absolute value | P-P | of a difference between the user index P of the trial users and the user index P of the full users; if the absolute value | P-P | of the difference is determined to be not greater than a second preset error value e when the confidence coefficient is 1-alpha, the formula is passed
Determining the number n of trial users; wherein
In a standard normal distribution, probability
Corresponding critical value.
Taking at least one user index of the social application program including index 1 (daily activity rate), index 2 (retention rate of the next day), and index 3 (vulnerability uploading rate) as an example, it is obvious that these three indexes can reflect the activity of the user using the application program every day and the vulnerability problem of the application program itself. Suppose that the index 1, index 2, index 3 values of the full users are P1, P2 and P3 respectivelyIf the variances corresponding to the total user index 1, index 2 and index 3 are P1(1-P1), P2(1-P2) and P3(1-P3), the formula is based on the above formula
It can be determined that the number n of trial users is greater than or equal to
Taking index 1 as 80%, index 2 as 15% and index 3 as 10% as an example, the number n of trial users can be determined to be greater than or equal to the above formula
I.e., n ≧ 10609, i.e., 10609 users are determined as trial users from among at least the full number of users.
Optionally, in some cases, for example, for format requirements of some data in the calculation process, the number n of trial users is determined according to the user index P, the variance P (1-P) corresponding to the user index and the second preset error value e, and specifically, the ratio of the absolute value | P-P | of the difference between the user index P of the trial users and the user index P of the full users may be calculated first
If the ratio is determined
When the confidence coefficient is 1-alpha, the second preset error value e is not larger than the first preset error value e, the second preset error value e is obtained through the formula
Determining the number n of trial users; wherein
In a standard normal distribution, probability
Corresponding critical value.
The example of the at least one user index of the social application program, including an index 1 (daily activity rate) of P1, an index 2 (next-day retention rate) of P2, and an index 3 (vulnerability uploading rate) of P3, is carried out, and here, in addition to the user index capable of reflecting the user activity, an index reflecting the vulnerability of the application program itself is also included. Assuming that the index 1 of the trial user is the ratio of the difference value between p1 and the index 1 of the full user to the index 1 of the full user, the index 2 of the trial user is the ratio of the difference value between p2 and the index 2 of the full user to the index 2 of the full user, the index 3 of the trial user is the ratio of the difference value between p3 and the index 3 of the full user to the index 3 of the full user, and when the confidence coefficient is 1-alpha, the confidence coefficient is not greater than a second preset error value f1、f2And f3I.e. by
According to the above formula
The number n of trial users can be determined to be more than or equal to
Due to the fact that the active rate, the retention rate and the vulnerability uploading rate of the application program used by the user are combined, the number of trial users determined according to the three indexes can generate a better feedback effect on the upgrade version of the application program.
Now, assume that index 1 of the trial user is the ratio of the difference between p1 and index 1 of the full user to index 1 of the full user, index 2 of the trial user is the ratio of the difference between p2 and index 2 of the full user to index 2 of the full user, and index 3 of the trial user is the ratio of index 2 of the full userThe ratio of the difference between p3 and index 3 of the full-volume user to index 3 of the full-volume user is not greater than second preset error values 0.01, 0.01 and 0.01 when the confidence degree is 1-alpha, and then the sample user volume n should be greater than or equal to
I.e., n ≧ 596756, i.e., 596756 users are determined as trial users from among at least the full number of users.
It should be noted that the numerical user index and the percentage user index may be combined with each other, or may be used alone to determine the number of trial users, and how to select which user indexes in an actual application scenario may be determined according to the actual requirements of the test application and the characteristics of the application itself.
103, determining trial users from the total number of users according to the number of the trial users;
specifically, the trial users are determined from the total users according to the number of the trial users, and the total users can be divided into at least one user group; then determining the proportion of the number of users in each user group in the number of users of the total number of users; and finally, determining trial users according to the proportion of the number of the users in each user group in the number of the users of the total number of users and the number of the trial users.
Firstly, dividing the total users of the application program into at least one user group;
because the liveness and the payment amount of a user of a certain application program can be often used as two core dimensions reflecting the characteristics of the application program, on one hand, the user can be divided into a high-activity user, a medium-activity user and a low-activity user according to the starting times and the using duration of the user using the application program; on the other hand, the users can be classified into high-value users, medium-value users and low-value users according to the payment amount when the users use the application program. And the user can be divided into 9 user groups of high activity high value, high activity medium value, high activity low value, medium activity high value, medium activity medium value, medium activity low value, low activity high value, low activity medium value and low activity low value by crossing the activity and the payment amount.
Then, determining the proportion of the number of users in each user group in the number of users of the total number of users;
in a practical scenario, for a user of an application, each user corresponds to a unique identifier for the backend server of the application, the identifier can be uniquely determined according to the characteristics of the user, such as the activity of the user and the payment amount, and by analyzing the identifiers of the full amount of users, the backend server can determine to calculate the ratio of the number of users of the 9 user groups to the number of users of the full amount of users, which is denoted as a1, a2, … and a 9.
And finally, determining trial users according to the proportion of the number of the users in each user group in the number of the users of the total number of users and the number of the trial users.
Continuing with the above example of dividing the total users into 9 user groups according to the activity and the business value, after the ratios a1, a2, …, a9 of the 9 user groups in the total users are determined respectively, the trial user numbers of the 9 user groups, namely n × a1, n × a2, …, n × a9, can be determined respectively according to the ratios a1, a2, …, a9 of the 9 user groups in the total users and the determined trial user number n. Finally, respectively aiming at the 9 user groups, sequentially extracting n × a1, n × a2, … and n × a9 users from the 9 user groups through a random algorithm to carry out gray scale upgrading.
The total users of the application program can be grouped according to the activity of the users and the payment amount, the proportion of the number of the users of each group of users in the number of the users of the total users can be determined, the trial users of each group of users can be respectively determined by combining the determined number of the trial users, the determined number of the trial users is more purposeful, the obtained feedback data of the upgrading program of the application program is more representative, and the method is more beneficial to subsequently improving the upgrading program of the application program.
And 104, pushing the upgrading program of the application program to the trial user.
After the trial users are determined, the server can push the upgrading program of the application program to the trial users, so that the trial users can feed back the upgrading program of the application program after receiving the upgrading program of the application program.
In the embodiment of the invention, the problem that the trial users who need to perform gray scale upgrading determined in the prior art are not comprehensive enough can be solved by acquiring the total users and at least one user index of the application program, determining the number of trial users according to the user index, determining the trial users from the total users according to the determined number of the trial users, and finally pushing the upgrading program of the application program to the determined trial users.
An embodiment of the present invention further provides a pushing device for upgrading a program, as shown in fig. 2, including:
an index acquisition unit 201 for acquiring a full user and at least one user index of an application;
a first determining unit 202, configured to determine, according to the at least one user index, a number of trial users;
a second determining unit 203, configured to determine trial users from the total number of users according to the number of trial users;
a program pushing unit 204, configured to push an upgrade program of the application program to the trial user;
wherein the full users are all users who install the application program.
In one embodiment, the user metrics include numeric class user metrics including at least one of: number of starts, duration of use, number of downloads, and number of clicks.
In one embodiment, the user metrics include a duty-based user metric including at least one of: an activity rate, a retention rate, and a vulnerability upload rate.
In an embodiment, the first determining unit 202 is configured to:
calculating the mean U and variance σ of each user index2；
According to the mean U and the variance sigma2And a first preset error value delta, determining the trial user number n.
In an embodiment, the first determining unit 202 is configured to:
calculating an absolute value | U-U | of a difference value between the average value U of the user indexes of the trial users and the average value U of the user indexes of the full users;
if the absolute value | U-U | of the difference is not greater than a first preset error value delta when the confidence coefficient is 1-alpha, passing through a formula
Determining the trial user number n;
wherein the content of the first and second substances,
in a standard normal distribution, probability
Corresponding critical value.
In an embodiment, the first determining unit 202 is configured to:
calculating the ratio of the absolute value | U-U | of the difference between the average value U of the user indexes of the trial users and the average value U of the user indexes of the full users to the average value U of the user indexes of the full users
If the ratio is
When the confidence coefficient is 1-alpha, the first preset error value delta is not larger than the first preset error value delta, the first preset error value delta is obtained through the formula
Determining the trial user number n;
wherein the content of the first and second substances,
in a standard normal distribution, probability
Corresponding critical value.
In an embodiment, the first determining unit 202 is specifically configured to:
calculating the variance P (1-P) corresponding to each user index P;
and determining the trial user number n according to the user index P, the variance P (1-P) corresponding to the user index and a second preset error value e.
In an embodiment, the first determining unit 202 is configured to:
calculating an absolute value | P-P | of a difference value between the user index P of the trial user and the user index P of the full-volume user;
if the absolute value | P-P | of the difference is determined to be not greater than a second preset error value e when the confidence coefficient is 1-alpha, the formula is passed
Determining the trial user number n;
wherein the content of the first and second substances,
in a standard normal distribution, probability
Corresponding critical value.
In an embodiment, the first determining unit 202 is configured to:
calculating the ratio of the absolute value | P-P | of the difference between the user index P of the trial user and the user index P of the full user to the user index P of the full user
If the ratio is determined
When the confidence coefficient is 1-alpha, the second preset error value e is not larger than the first preset error value e, the second preset error value e is obtained through the formula
Determining the trial user number n;
wherein the content of the first and second substances,
in a standard normal distribution, probability
Corresponding critical value.
In an embodiment, the second determining unit 203 is configured to:
dividing the total number of users into at least one user group;
determining the proportion of the number of users in each user group in the number of users of the full number of users;
and determining trial users according to the percentage of the number of the users in each user group in the number of the users of the total users and the number of the trial users.
The pushing device of the upgrade program provided in the embodiment of the present invention can implement each process implemented by the pushing method of the upgrade program in the method embodiment of fig. 1, and is not described herein again to avoid repetition.
Figure 3 is a hardware architecture diagram of a server implementing various embodiments of the present invention,
the server 300 includes but is not limited to: a receiving unit 301, a network module 302, a transmitting unit 303, an interface unit 305, a processor 306, a memory 307, and a power supply 304. Those skilled in the art will appreciate that the server architecture shown in FIG. 3 is not intended to be limiting, and that a server may include more or fewer components than shown, or some components may be combined, or a different arrangement of components.
The receiving unit 301 is configured to obtain a total number of users and at least one user index of an application;
a processor 306 configured to determine a number of trial users according to the at least one user indicator; determining trial users from the total number of users according to the number of the trial users;
a sending unit 303, configured to push an upgrade program of the application program to the trial user; wherein the full users are all users who install the application program.
In the embodiment of the invention, the problem that the trial users who need to perform gray scale upgrading determined in the prior art are not comprehensive enough can be solved by acquiring the total users and at least one user index of the application program, determining the number of trial users according to the user index, determining the trial users from the total users according to the determined number of the trial users, and finally pushing the upgrading program of the application program to the determined trial users.
It should be understood that, in the embodiment of the present invention, the receiving unit 301 may be configured to receive and transmit signals during a message transmission or a call, and specifically, receive downlink data from a base station and then process the received downlink data to the processor 306; in addition, the uplink data is transmitted to the base station. Generally, the receiving unit 301 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like. In addition, the receiving unit 301 may also communicate with a network and other devices through a wireless communication system.
The server performs data transmission with other devices through the network module 302.
The interface unit 305 is an interface for connecting an external device to the server 300. The interface unit 304 may be used to receive input (e.g., data information, power, etc.) from an external device and transmit the received input to one or more elements within the server 300 or may be used to transmit data between the server 300 and an external device.
The memory 307 may be used to store software programs as well as various data. The memory 307 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. Further, the memory 307 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The processor 306 is a control center of the server, connects various parts of the entire server using various interfaces and lines, and performs various functions of the server and processes data by running or executing software programs and/or modules stored in the memory 307 and calling data stored in the memory 307, thereby performing overall monitoring of the server. Processor 306 may include one or more processing units; preferably, the processor 306 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 306.
The server 300 may further include a power supply 304 for supplying power to the various components, and preferably, the power supply 304 may be logically connected to the processor 306 through a power management system, so as to manage charging, discharging, and power consumption management functions through the power management system.
In addition, the server 300 includes some functional modules that are not shown, and are not described in detail herein.
Preferably, an embodiment of the present invention further provides a server, which includes a processor 306, a memory 307, and a computer program stored in the memory and capable of running on the processor 306, where the computer program, when executed by the processor 306, implements each process of the above pushing method embodiment of the upgrade program, and can achieve the same technical effect, and in order to avoid repetition, details are not described here again.
The embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements each process of the pushing method embodiment of the upgrade program, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here. The computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk.
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. Based on such understanding, the technical solutions of the present invention 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 method according to the embodiments of the present invention.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (16)
1. A pushing method of an upgrading program is characterized by comprising the following steps:
acquiring a full amount of users and at least one user index of an application program;
determining the number of trial users according to the at least one user index;
determining trial users from the total number of users according to the number of the trial users;
pushing the upgrading program of the application program to the trial user;
wherein the full users are all users who install the application program;
the user indexes comprise numerical user indexes and proportion user indexes, and the numerical user indexes comprise at least one of the following items: starting times, using duration, downloading times and clicking times; the percentage-based user index comprises at least one of the following: an active rate, a retention rate and a vulnerability uploading rate;
wherein, the determining trial users from the total number of users according to the number of trial users comprises:
dividing the total number of users into at least one user group;
determining the proportion of the number of users in each user group in the number of users of the full number of users;
and determining trial users according to the percentage of the number of the users in each user group in the number of the users of the total users and the number of the trial users.
2. The method of claim 1, wherein determining a number of trial users based on the at least one user indicator comprises:
calculating the mean U and variance σ of each user index2；
According to the mean U and the variance sigma2And a first preset error value delta, determining the trial user number n.
3. The method of claim 2, wherein the variance σ is determined according to the mean U and the variance U2And a first preset error value δ, determining the trial user number n, comprising:
calculating an absolute value | U-U | of a difference value between the average value U of the user indexes of the trial users and the average value U of the user indexes of the full users;
if the absolute value | U-U | of the difference is not greater than a first preset error value delta at the confidence level 1-alpha, passing through a formula
Determining the trial user number n;
wherein the content of the first and second substances,
in a standard normal distribution, probability
Corresponding critical value.
4. The method of claim 2According to the mean U and the variance σ2And a first preset error value δ, determining the trial user number n, comprising:
calculating the ratio of the absolute value | U-U | of the difference between the average value U of the user indexes of the trial users and the average value U of the user indexes of the full users to the average value U of the user indexes of the full users
If the ratio is
When the confidence coefficient is 1-alpha and is not greater than a first preset error value delta, the first preset error value delta is obtained through a formula
Determining the trial user number n;
wherein the content of the first and second substances,
in a standard normal distribution, probability
Corresponding critical value.
5. The method of claim 1, wherein determining a number of trial users based on the at least one user indicator comprises:
calculating the variance P (1-P) corresponding to each user index P;
and determining the trial user number n according to the user index P, the variance P (1-P) corresponding to the user index and a second preset error value e.
6. The method according to claim 5, wherein the determining the trial user number n according to the user index P, the variance P (1-P) corresponding to the user index P, and a second preset error value e comprises:
calculating an absolute value | P-P | of a difference value between the user index P of the trial user and the user index P of the full-volume user;
if the absolute value | P-P | of the difference is determined to be not greater than a second preset error value e when the confidence coefficient is 1-alpha, the formula is passed
Determining the trial user number n;
wherein the content of the first and second substances,
in a standard normal distribution, probability
Corresponding critical value.
7. The method according to claim 5, wherein the determining the trial user number n according to the user index P, the variance P (1-P) corresponding to the user index P, and a second preset error value e comprises:
calculating the ratio of the absolute value | P-P | of the difference between the user index P of the trial user and the user index P of the full user to the user index P of the full user
If the ratio is determined
When the confidence coefficient is not more than the second preset error value e at 1-alpha, the formula is passed
Determining the trial user number n;
wherein the content of the first and second substances,
in a standard normal distribution, probability
Corresponding critical value.
8. A pushing apparatus for upgrading a program, comprising:
the index acquisition unit is used for acquiring the full users and at least one user index of the application program;
the first determining unit is used for determining the number of trial users according to the at least one user index;
a second determining unit, configured to determine trial users from the total number of users according to the number of trial users;
the program pushing unit is used for pushing the upgrading program of the application program to the trial user;
wherein the full users are all users who install the application program;
the user indexes comprise numerical user indexes and proportion user indexes, and the numerical user indexes comprise at least one of the following items: starting times, using duration, downloading times and clicking times; the percentage-based user index comprises at least one of the following: an active rate, a retention rate and a vulnerability uploading rate;
wherein the second determination unit is configured to:
dividing the total number of users into at least one user group;
determining the proportion of the number of users in each user group in the number of users of the full number of users;
and determining trial users according to the percentage of the number of the users in each user group in the number of the users of the total users and the number of the trial users.
9. The apparatus of claim 8, wherein the first determining unit is configured to:
calculating the mean U and variance σ of each user index2；
According to the mean U and the variance sigma2And a first preset error value delta, determining the trial user number n.
10. The apparatus of claim 9, wherein the first determining unit is configured to:
calculating an absolute value | U-U | of a difference value between the average value U of the user indexes of the trial users and the average value U of the user indexes of the full users;
if the absolute value | U-U | of the difference is not greater than a first preset error value delta at the confidence level 1-alpha, passing through a formula
Determining the trial user number n;
wherein the content of the first and second substances,
in a standard normal distribution, probability
Corresponding critical value.
11. The apparatus of claim 9, wherein the first determining unit is configured to:
calculating the ratio of the absolute value | U-U | of the difference between the average value U of the user indexes of the trial users and the average value U of the user indexes of the full users to the average value U of the user indexes of the full users
If the ratio is
When the confidence coefficient is 1-alpha and is not greater than a first preset error value delta, the first preset error value delta is obtained through a formula
Determining the trial user number n;
wherein the content of the first and second substances,
in a standard normal distribution, probability
Corresponding critical value.
12. The apparatus according to claim 8, wherein the first determining unit is specifically configured to:
calculating the variance P (1-P) corresponding to each user index P;
and determining the trial user number n according to the user index P, the variance P (1-P) corresponding to the user index and a second preset error value e.
13. The apparatus of claim 12, wherein the first determining unit is configured to:
calculating an absolute value | P-P | of a difference value between the user index P of the trial user and the user index P of the full-volume user;
if the absolute value | P-P | of the difference is determined to be not greater than a second preset error value e when the confidence coefficient is 1-alpha, the formula is passed
Determining the trial user number n;
wherein the content of the first and second substances,
in a standard normal distribution, probability
Corresponding critical value.
14. The apparatus of claim 12, wherein the first determining unit is configured to:
calculating the ratio of the absolute value | P-P | of the difference between the user index P of the trial user and the user index P of the full user to the user index P of the full user
If the ratio is determined
When the confidence coefficient is not more than the second preset error value e at 1-alpha, the formula is passed
Determining the trial user number n;
wherein the content of the first and second substances,
in a standard normal distribution, probability
Corresponding critical value.
15. A server, comprising: memory, processor and computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the push method of an upgrade program according to any one of claims 1 to 7.
16. A computer-readable storage medium, characterized in that a computer program is stored thereon, which, when being executed by a processor, carries out the steps of the push method of an upgrade program according to any one of claims 1 to 7.
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