CN113989004B - Resource allocation method and device, electronic equipment and readable storage medium - Google Patents
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Abstract
The disclosure provides a resource allocation method and electronic equipment, relates to an artificial intelligence technology, and particularly relates to the field of big data. The specific scheme is as follows: weighting the webpage browsing volumes of the articles to be distributed according to the weights of the articles to be distributed, which are acquired in advance, to obtain N weighted webpage browsing volumes of each article to be distributed, wherein the weight of each article to be distributed comprises N sub-weights; determining target weights of a plurality of articles to be distributed by utilizing first resource distribution proportions of partial articles of the plurality of articles to be distributed and the total quantity of N weighted webpage browsing volumes of the plurality of articles to be distributed, wherein the first resource distribution proportions of a target article are the resource distribution proportions of the target article in an article set to which the target article belongs, and the target article is any one of the partial articles; the target resource allocation proportion of the articles to be allocated is determined according to the target weight of the articles to be allocated and the web browsing volume of the articles to be allocated, so that the accuracy of the resource allocation proportion can be improved.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence technologies such as big data, and in particular, to a resource allocation method and apparatus, an electronic device, and a readable storage medium.
Background
With the development of internet technology, a large number of content sharing platforms are emerging, for example, authors can share articles on the platforms, and can obtain corresponding resources (for example, revenue, earnings, rewards, profits, and the like) in turn, and in order to complete a content production task with guaranteed quality and quantity, sufficient active authors on the platforms need to be ensured. The income of the author on the platform has a crucial influence on the active condition of the author. The reasonable resource allocation method directly influences the income level of the author so as to achieve the regulation effect of the content ecology.
The current common resource allocation method comprises the steps that firstly, operation and product personnel propose a new resource allocation scheme, research and development personnel carry out measurement according to the new scheme, a new strategy is applied to historical data of multiple-day authors, income difference and distribution generated by the new scheme and the old scheme are compared, then operation is carried out to observe whether the measurement and calculation result meets expectations or not, if not, the new scheme is modified, the previous steps are repeated, if the expectation is met, the new scheme is on line, the weight of articles shared by the authors is calculated through the new scheme, and the resources allocated to the authors are determined through the weight.
Disclosure of Invention
The disclosure provides a resource allocation method, a resource allocation device, an electronic device and a readable storage medium.
In a first aspect, an embodiment of the present disclosure provides a resource allocation method, where the method includes:
weighting the webpage browsing volumes of the articles to be distributed through the weights of the articles to be distributed, which are acquired in advance, to obtain N weighted webpage browsing volumes of each article to be distributed, wherein the weight of each article to be distributed comprises N sub-weights, and N is an integer greater than 1;
determining target weights of the articles to be distributed by using first resource distribution proportions of partial articles of the articles to be distributed and the total quantity of N weighted webpage browsing volumes of the articles to be distributed, wherein the first resource distribution proportions of a target article are the resource distribution proportions of the target article in an article set to which the target article belongs, and the target article is any article in the partial articles;
and determining the target resource allocation proportion of the articles to be allocated according to the target weights of the articles to be allocated and the webpage browsing amount of the articles to be allocated.
In this embodiment, first, according to weights of a plurality of articles to be distributed, which are obtained in advance, a weighting process is performed on web browsing volumes of the plurality of articles to be distributed, so as to obtain N weighted web browsing volumes of each article to be distributed, where a target resource allocation proportion of the plurality of articles to be distributed is determined by target weights of the plurality of articles to be distributed and web browsing volumes of the plurality of articles to be distributed, where the target weights of the plurality of articles to be distributed are determined by first resource allocation proportions of partial articles of the plurality of articles to be distributed and total amounts of the N weighted web browsing volumes of the plurality of articles to be distributed, and the first resource allocation proportion of the target article is a resource allocation proportion of the target article in an article set to which the target article belongs. In other words, in the process of determining the target resource allocation proportions of the articles to be allocated, it is considered that the accuracy of the determined target resource allocation proportions can be improved by the first resource allocation proportions of the partial articles of the articles to be allocated and the total amount of the N weighted web browsing volumes of the articles to be allocated, so that the resource allocation accuracy is improved.
In a second aspect, an embodiment of the present disclosure provides a resource allocation apparatus, including:
the first weighting module is used for weighting the webpage browsing volumes of the articles to be distributed through the weights of the articles to be distributed, which are acquired in advance, so as to obtain N weighted webpage browsing volumes of each article to be distributed, wherein the weight of each article to be distributed comprises N sub-weights, and N is an integer greater than 1;
a first determining module, configured to determine target weights of the multiple articles to be distributed by using first resource allocation proportions of partial articles of the multiple articles to be distributed and a total amount of N weighted web browsing amounts of the multiple articles to be distributed, where the first resource allocation proportion of a target article is a resource allocation proportion of the target article in an article set to which the target article belongs, and the target article is any one of the partial articles;
and the target proportion determining module is used for determining the target resource allocation proportion of the articles to be allocated according to the target weights of the articles to be allocated and the webpage browsing amount of the articles to be allocated.
In a third aspect, an embodiment of the present disclosure further provides an electronic device, including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the resource allocation method as provided by the first aspect of the disclosure.
In a fourth aspect, an embodiment of the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the resource allocation method provided by the first aspect of the present disclosure.
In a fifth aspect, an embodiment of the present disclosure provides a computer program product comprising a computer program, which when executed by a processor, implements the resource allocation method of the present disclosure as provided in the first aspect.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
fig. 1 is one of the flow diagrams of a resource allocation method according to an embodiment provided in the present disclosure;
fig. 2 is a second schematic flowchart of a resource allocation method according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of target weight determination in a resource allocation method according to an embodiment of the present disclosure;
FIG. 4 is one of the block diagrams of a resource allocation apparatus of an embodiment provided by the present disclosure;
fig. 5 is a second block diagram of a resource allocation apparatus according to an embodiment of the present disclosure;
fig. 6 is a block diagram of an electronic device for implementing a resource allocation method of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of embodiments of the present disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
As shown in fig. 1, according to an embodiment of the present disclosure, the present disclosure provides a resource allocation method, including:
step S101: and weighting the webpage browsing volumes of the articles to be distributed through the weights of the articles to be distributed, which are acquired in advance, so as to obtain N weighted webpage browsing volumes of each article to be distributed.
The weight of each article to be distributed comprises N sub-weights, and N is an integer greater than 1.
An article to be allocated may be understood as an article to which resources are to be allocated, which may include, but are not limited to, revenue, earnings, rewards, profits, and the like. The weights of the articles to be distributed can be preset, and after the webpage browsing volumes of the articles to be distributed are obtained, the webpage browsing volumes of the articles to be distributed can be weighted through the weights of the articles to be distributed, which are obtained in advance, so that N weighted webpage browsing volumes of each article to be distributed are obtained. It should be noted that the web browsing amount in the embodiment of the present disclosure is a web browsing amount in a preset time period, for example, if resource allocation is performed once a day with a period of one day, the web browsing amount is a web browsing amount in one day. The resource allocation method of the embodiment of the disclosure can be applied to a content sharing platform, for example, can be applied to a resource allocation scene in the content sharing platform.
Step S102: and determining the target weights of the articles to be distributed by utilizing the first resource distribution proportion of the partial articles of the articles to be distributed and the total quantity of the N weighted webpage browsing volumes of the articles to be distributed.
The first resource allocation proportion of the target article is the resource allocation proportion of the target article in the article set to which the target article belongs, and the target article is any one article in the partial articles.
It can be understood that the articles to be distributed include N article sets, each article in the articles to be distributed has a corresponding article set, and the first resource allocation proportion of the target article is a resource allocation proportion of the target article in the article set to which the target article belongs, for example, as an example, the first resource allocation proportion of the target article may be a ratio of a weighted web browsing amount of the target article to a sum of weighted web browsing amounts of the article sets to which the target article belongs (i.e., a ratio of the weighted web browsing amount of the target article to the sum of weighted web browsing amounts of the article sets to which the target article belongs), so as to improve accuracy of the first resource allocation proportion. The target weights of the articles to be distributed are determined according to the first resource distribution proportion of the partial articles and the total quantity of N weighted webpage browsing volumes of the articles to be distributed, namely, each article to be distributed has a corresponding target weight. For example, the number of the articles to be distributed is M, and the total amount of the N weighted web browsing volumes of the articles to be distributed is the sum of the M multiplied by the N weighted web browsing volumes. The weights of the plurality of articles to be distributed, which are acquired in advance, can be understood as initial weights, and the target weights of the plurality of articles to be distributed can be determined through the process.
Step S103: and determining the target resource allocation proportion of the articles to be allocated according to the target weights of the articles to be allocated and the web browsing amount of the articles to be allocated.
After the target weights of the articles to be distributed are determined, the target resource distribution proportions of the articles to be distributed can be determined by using the target weights of the articles to be distributed and the webpage browsing volumes of the articles to be distributed subsequently, the target distribution resources of the articles to be distributed can be determined subsequently according to the total resources and the target resource distribution proportions of the articles to be distributed, and the target distribution resources of an article in the embodiment of the disclosure can be understood as resources distributed to the article under the corresponding webpage browsing volume.
As an example, in the process of determining the target resource allocation proportion of the articles to be allocated according to the target weights of the articles to be allocated and the web browsing volumes of the articles to be allocated, the target weighted web browsing volumes of the articles to be allocated may be calculated according to the target weights of the articles to be allocated and the web browsing volumes of the articles to be allocated, where the target weighted web browsing volume of any one of the articles to be allocated is obtained by weighting the web browsing volumes of the articles by the target weights of the articles; and then, calculating a target resource distribution proportion of the articles to be distributed according to the target weighted webpage browsing amount of the articles to be distributed and the total amount of the N weighted webpage browsing amounts of the articles to be distributed, wherein the target resource distribution proportion of any article in the articles to be distributed is the ratio of the target weighted webpage browsing amount of the article to the total amount.
In this embodiment, first, according to weights of a plurality of articles to be distributed, which are obtained in advance, a weighting process is performed on web browsing volumes of the plurality of articles to be distributed, so as to obtain N weighted web browsing volumes of each article to be distributed, where a target resource allocation proportion of the plurality of articles to be distributed is determined by target weights of the plurality of articles to be distributed and web browsing volumes of the plurality of articles to be distributed, where the target weights of the plurality of articles to be distributed are determined by first resource allocation proportions of partial articles of the plurality of articles to be distributed and total amounts of the N weighted web browsing volumes of the plurality of articles to be distributed, and the first resource allocation proportion of the target article is a resource allocation proportion of the target article in an article set to which the target article belongs. In other words, in the process of determining the target resource allocation proportions of the articles to be allocated, it is considered that the accuracy of the determined target resource allocation proportions can be improved by the first resource allocation proportions of the partial articles of the articles to be allocated and the total amount of the N weighted web browsing volumes of the articles to be allocated, so that the resource allocation accuracy is improved.
In one embodiment, the articles to be distributed comprise N article sets, and each article set corresponds to a weight strategy;
the N sub-weights of the first article are calculated through N weight strategies, and the first article is any one of the articles to be distributed.
It should be noted that the weight policy is used for calculating the weight of an article, for example, in the process of calculating a sub-weight of an article in a plurality of articles to be assigned by using one weight policy, the calculation may be performed according to attribute information of the article, different weight policies may be different in at least one of the attribute information of the article and a calculation manner according to the attribute information, and the attribute information of the article may include, but is not limited to, at least one of the following: the web browsing amount, the publishing time, the article publisher level and the number of target users associated with the article publisher. The target user may be understood as an active user, for example, the active user may be understood as a user who has a preset number of related operation behaviors (for example, a browsing behavior, a clicking behavior, a sharing behavior, a collecting behavior, a praise behavior, and the like) on an article published by an article publisher in a preset historical time period, or the active user may also be a user who has a related operation behavior on the article, and the like, that is, the active user is not limited in this embodiment of the disclosure. It should be noted that any one of the N sub-weights of the first article is determined by a weight policy, and the N sub-weights of the first article correspond to the N weight policies one to one.
In this embodiment, the articles to be distributed are divided into N article sets, each article set corresponds to one weight policy, that is, there are N weight policies, and N sub-weights of the first article are calculated by the N weight policies, that is, the N weight policies are configured in advance, and the weight of each article to be distributed in the articles to be distributed can be calculated in advance by the N weight policies, so as to obtain N sub-weights of each article to be distributed. That is, in this embodiment, a certain weight policy corresponds to a certain article set, but the weight policy may be applied to each article to be distributed to calculate N corresponding sub-weights, so that each weight policy is applied to each article to be distributed to obtain N sub-weights of a plurality of articles to be distributed, which are used as a basis for subsequently determining the target weight, and the target resource allocation proportions of the plurality of articles to be distributed are determined by using the target weight and the web browsing volumes of the plurality of articles to be distributed, so that the accuracy of the determined target resource allocation proportions may be improved.
In one embodiment, the N article sets comprise N-1 first article sets and a second article set, the total quantity of the articles of the N-1 first article sets is smaller than that of the second article set, and the partial articles are the N-1 first article sets.
It can be understood that the first article set belongs to a small flow of articles, that is, the number of articles in the article set is small, and the second article set can be understood as belonging to a large disc of articles, that is, the number of articles in the article set is large.
In this embodiment, the target weights of the articles to be distributed are determined by using the first resource allocation proportions of N-1 first-class article sets and the total amount of N weighted web browsing volumes of the articles to be distributed, that is, the first resource allocation proportions of the N-1 first-class article sets with smaller data are applied to the determination process of the target weights, and the determination of the target weights of the articles of the second-class article sets in the articles to be distributed takes the influence of the first resource allocation proportions of the N-1 first-class article sets into account, so that the accuracy of the target weights can be improved, and the target resource allocation proportions of the articles to be distributed are determined by using the target weights and the web browsing volumes of the articles to be distributed, so that the accuracy of the resource allocation proportions can be improved, and the accuracy of the resource allocation can be improved.
As shown in fig. 2, in an embodiment, determining a target weight of an article to be distributed by using a first resource distribution proportion of a part of articles of a plurality of articles to be distributed and a total amount of N weighted web browsing volumes of the plurality of articles to be distributed S102 includes:
s1021: calculating the target weight of the partial articles according to the webpage browsing amount of the partial articles, the first resource allocation proportion of the partial articles and the total amount of N weighted webpage browsing amounts of the articles to be allocated;
s1022: calculating a first weighted webpage browsing amount of a part of articles according to the target weight of the part of articles;
s1023: and calculating the target weight of the rest articles according to the total amount of the first weighted webpage browsing amount of the partial articles and the total amount of the N weighted webpage browsing amounts of the articles to be distributed.
The rest articles are the articles except part of the articles in the plurality of articles to be distributed.
The target weight of each article in the partial articles can be calculated firstly to obtain the target weight of the partial articles, and the target weight of any article in the partial articles can be calculated according to the web browsing volume of the article, the first resource allocation proportion of the article and the total amount of the N weighted web browsing volumes of the articles to be allocated. The method includes the steps of weighting the web browsing volumes of a plurality of articles to be distributed through the weights of the plurality of articles to be distributed acquired in advance, wherein the weighted web browsing volumes can be understood as initial weighted web browsing volumes in N weighted web browsing volumes of each article to be distributed, calculating a first weighted web browsing volume of a part of articles after calculating the target weight of the part of articles, and calculating the target weights of the rest of articles by using the total amount of the first weighted web browsing volumes of the part of articles and the total amount of the N weighted web browsing volumes of the articles to be distributed, so that the calculation of the target weights of the plurality of articles to be distributed is achieved.
In this embodiment, the target weights of a part of articles may be calculated first, then the first weighted webpage browsing amount of the part of articles is calculated according to the target weights of the part of articles, and then the target weights of the remaining articles are calculated according to the total amount of the first weighted webpage browsing amount of the part of articles and the total amount of the N weighted webpage browsing amounts of the articles to be distributed, so that the calculation of the target weights of the plurality of articles to be distributed is implemented, and thus, the accuracy of the calculation of the target weights can be improved, and the accuracy of the target resource distribution proportion is further improved, so as to improve the accuracy of resource distribution for the plurality of resources to be distributed.
In one embodiment, calculating the target weight of the remaining articles according to the total amount of the first weighted web browsing volumes of the partial articles and the total amount of the N weighted web browsing volumes of the articles to be distributed comprises:
calculating the difference between the total quantity of the N weighted webpage browsing quantities of the articles to be distributed and the total quantity of the first weighted webpage browsing quantities of the partial articles;
calculating a weight adjustment value according to the difference value and the weighted webpage browsing amount of the rest articles;
and adjusting the first sub-weights of the remaining articles by using the weight adjustment value to obtain the target weights of the remaining articles, wherein the first sub-weight of the second article is a sub-weight calculated by a weight strategy corresponding to an article set to which the second article belongs in the N sub-weights of the second article, and the second article is any one of the remaining articles.
In this embodiment, the target weight of the second article is obtained by adjusting the sub-weight calculated by the weight policy corresponding to the article set to which the second article belongs in the N sub-weights of the second article, and the target weight of each article in the remaining articles is obtained after calculating the target weight of the remaining article, so that the accuracy of the target weight of the remaining articles can be improved.
As one example, the target weight of the second article may be a product of the weight adjustment value and a first sub-weight value of the second article.
In one embodiment, calculating the weight adjustment value according to the difference value and the weighted web browsing amount of the remaining articles comprises:
and determining the ratio of the difference value to the sum of the weighted webpage browsing amounts of the rest articles as a weight adjustment value.
It can be understood that the weight adjustment values of the remaining articles are the same one, and are all calculated by the ratio between the difference and the sum of the weighted web browsing amounts of the remaining articles, after the weight adjustment value is determined, the first sub-weights of the remaining articles are adjusted by using the weight adjustment value to obtain the target weights of the remaining articles, and the weight adjustment value takes into account the difference between the total amount of the N weighted web browsing amounts of the articles to be distributed and the total amount of the first weighted web browsing amounts of the partial articles, and is the ratio between the difference and the sum of the weighted web browsing amounts of the remaining articles, so that the accuracy of the weight adjustment value can be improved, and thus the accuracy of the target weights of the remaining articles can be improved, and thus the accuracy of the target resource distribution ratio of the remaining articles can be improved, and the accuracy of subsequent resource distribution can be improved.
In one embodiment, the target weight of the target article is positively correlated with the total amount of the N weighted web browsing volumes of the articles to be distributed and the first resource distribution proportion of the target article, and the target weight of the target article is inversely correlated with the web browsing volume of the target article.
That is, the target weight of any article in a part of articles is positively correlated with the total amount of the N weighted web browsing volumes of the articles to be distributed and the first resource distribution proportion of the article, and inversely correlated with the web browsing volume of the article.
In this embodiment, the target weight of the target article is calculated by using the web browsing volume of the target article, the first resource allocation proportion of the target article, and the total amount of the N weighted web browsing volumes of the articles to be allocated, and the target weight of the target article is positively correlated with the total amount of the N weighted web browsing volumes of the articles to be allocated and the first resource allocation proportion of the target article, and inversely correlated with the web browsing volume of the target article, and the target article is any one of a part of articles.
In one embodiment, the target weight of the target article is the result of dividing the sum of the N weighted web browsing volumes of the articles to be distributed by the web browsing volume of the target article after multiplying the first resource distribution proportion of the target article.
In this embodiment, the result of dividing the value obtained by multiplying the total amount of the N weighted web browsing volumes of the articles to be distributed by the first resource distribution proportion of the target article by the web browsing volume of the target article is used as the target weight of the target article, so as to improve the accuracy of the target weight of part of the articles.
The following describes the procedure of the resource allocation method in an embodiment. The first article sets with N of 3,2 are respectively an article set J1 (corresponding to the weight policy being policy 1) and an article set J2 (corresponding to the weight policy being policy 2), the second article set is an article set J3 (corresponding to the weight policy being policy 3), the article set J1 includes an article a, the article set J2 includes an article B, and the article set J3 includes an article C, an article D, and an article F. Fig. 3 is a schematic diagram of determining target weights in the resource allocation method of this embodiment.
First, each of the 3 policies is applied to the 5 articles to be distributed, and 3 sub-weights of each article to be distributed are calculated, so that 15 sub-weights (i.e., 5 times 3) can be obtained, and 3 sub-weights of each article to be distributed are multiplied by the web browsing volume of the article to be distributed to obtain 3 weighted web browsing volumes of the article to be distributed, and each article to be distributed of the 5 articles to be distributed is subjected to the process, so that N weighted web browsing volumes of a plurality of articles to be distributed, i.e., 15 weighted web browsing volumes, including 3 weighted web browsing volumes of the article a (i.e., A1, A2, and A3), 3 weighted web browsing volumes of the article B (i.e., B1, B2, and B3), 3 weighted web browsing volumes of the article C (i.e., C1, C2, and C3), and 3 weighted web browsing volumes of the article D (i.e., D1, D2, and D3) and 3 weighted web browsing volumes of the article F (i.e., F1, F2, and F3), as shown in a table-3.
TABLE 1
TABLE 2
TABLE 3
Then, a first resource allocation proportion of each article in the partial articles (article set J1 and article set J2, i.e., article a and article B) is calculated and a target weight of each article in the partial resources is calculated.
For article a, belonging to article set J1, the first resource allocation ratio _ a of article a is A1/(A1 + B1+ C1+ D1+ F1), the first resource allocation ratio _ B of article B is B2/(A2 + B2+ C2+ D2+ F2), the first resource allocation ratio _ C of article C is C3/(A3 + B3+ C3+ D3+ F3), the first resource allocation ratio _ D of article D is D3/(A3 + B3+ C3+ D3+ F3), and the first resource allocation ratio _ F of article F is F3/(A3 + B3+ F3+ D3+ F3). The total amount of 3 weighted web browsing volumes of 5 articles to be distributed is PV _ Z, that is, the sum of the 15 weighted web browsing volumes (A1 + B1+ C1+ D1+ F1+ A2+ B2+ C2+ D2+ F2+ A3+ B3+ C3+ D3+ F3).
For article a, the corresponding target weight W _ a is PV _ Z ratio _ a/PV _ a, where a represents a multiplier, and PV _ a is the web browsing volume of article a. For article B, the corresponding target weight W _ B is PV _ Z ratio _ B/PV _ B, which represents a multiplier, and PV _ B is the web browsing volume of article B. The first weighted web browsing volume PA of the article a is pv _ a × W _ a, and the first weighted web browsing volume PB of the article B is pv _ B × W _ B.
Second, the target weights of the remaining articles (article C, article D, and article F) are calculated.
The total amount P0 of the first weighted webpage browsing volume of the partial article is PA + PB, a difference H between PV _ Z and P0 is calculated, that is, PV _ Z-P0, and a value of a weight adjustment parameter r, that is, a weight adjustment value is H/(C3 + D3+ F3) is calculated by using a formula (C3 + D3+ F3) × r = H.
For the article C, belonging to the article set J3, corresponding to the strategy 3, a sub-weight of the article C calculated by the strategy 3 is W 3C I.e. the first sub-weight of the article C, is multiplied by the first sub-weight W of the article C using the weight adjustment value obtained above 3C And obtaining the target weight W _ C of the article C. For the article D, which belongs to the article set J3 and corresponds to the strategy 3, a sub-weight of the article D calculated by the strategy 3 is W 3D I.e. the first sub-weight of the article D, is multiplied by the first sub-weight W of the article D using the weight adjustment value obtained above 3D The target weight W _ D of article D is obtained. For the article F, the article F belongs to the article set J3, corresponding to the strategy 3, and a sub-weight of the article F calculated by the strategy 3 is W 3F I.e. the first sub-weight of the article F, the obtained weight adjustment value is multiplied by the first sub-weight W of the article F 3F The target weight W _ F of the article F is obtained.
Furthermore, a weight file is generated, which includes the target weights of 5 articles, namely article a, article B, article C, article D and article F. As an example, it may be determined whether the distribution of the first weighted web browsing amounts of the partial articles meets expectations, that is, whether the distributions of the first weighted web browsing amounts PA and PB of the articles a and B meet expectations, and if the distribution of the first weighted web browsing amounts of the partial articles meets expectations, a weight file may be generated.
In the resource allocation process, the target weighted web browsing volume of the articles to be allocated is calculated according to the target weights of the articles to be allocated and the web browsing volumes of the articles to be allocated, for example, the target weighted web browsing volume LA of the article a is pv _ a × W _ a, and the target weighted web browsing volumes of other articles are similar, but the difference is that the articles are different, the article changes, and the corresponding target weights and the web browsing volumes change with the article changes. Then, according to the target weighted web browsing amount of the articles to be distributed and the total amount of the N weighted web browsing amounts of the articles to be distributed, calculating a target resource distribution ratio of the articles to be distributed, for example, the target resource distribution ratio RA of the article a is LA/PV _ Z, and for other articles, the target resource distribution ratio is calculated similarly, except that the articles are different and the article distribution ratio is changed, and the corresponding target resource distribution ratio is changed along with the article change. The target resource allocation proportion RA of the article A multiplied by the total resource ZZ is the target allocation resource of the article A, the target resource allocation proportion RB of the article B multiplied by the total resource ZZ is the target allocation resource of the article B, the target resource allocation proportion RC of the article C multiplied by the total resource ZZ is the target allocation resource of the article C, the target resource allocation proportion RD of the article D multiplied by the total resource ZZ is the target allocation resource of the article D, and the target resource allocation proportion RF of the article F multiplied by the total resource ZZ is the target allocation resource of the article F.
In the resource allocation method of the embodiment of the disclosure, the actual webpage browsing volume of the article is considered to determine the first resource allocation proportion of the partial thermal navigation, the target weights of the articles to be allocated are determined by using the first resource allocation proportion of the partial article and the total amount of the N weighted webpage browsing volumes of the articles to be allocated, then the target weights of the articles to be allocated and the webpage browsing volumes of the articles to be allocated are used to determine the target resource allocation proportion of the articles to be allocated, and the resource allocation is performed by using the target resource allocation proportion, so that the resource allocation effect is improved.
As shown in fig. 4, according to an embodiment of the present disclosure, the present disclosure also provides a resource allocation apparatus 400, including:
the first weighting module 401 is configured to perform weighting processing on the web browsing volumes of the articles to be distributed through weights of a plurality of articles to be distributed, which are obtained in advance, to obtain N weighted web browsing volumes of each article to be distributed, where the weight of each article to be distributed includes N sub-weights, and N is an integer greater than 1;
a first determining module 402, configured to determine target weights of multiple articles to be distributed by using first resource allocation proportions of partial articles of the multiple articles to be distributed and a total amount of N weighted web browsing volumes of the multiple articles to be distributed, where the first resource allocation proportion of a target article is a resource allocation proportion of the target article in an article set to which the target article belongs, and the target article is any one of the partial articles;
the target proportion determining module 403 is configured to determine a target resource allocation proportion of the multiple articles to be allocated according to target weights of the multiple articles to be allocated and web browsing volumes of the multiple articles to be allocated.
In one embodiment, the articles to be distributed comprise N article sets, and each article set corresponds to a weight strategy;
the N sub-weights of the first article are calculated through N weight strategies, and the first article is any one of the articles to be distributed.
In one embodiment, the N article sets include N-1 first-type article sets and a second-type article set, the total quantity of articles in the N-1 first-type article sets is less than the total quantity of articles in the second-type article set, wherein a part of the articles are the N-1 first-type article sets.
As shown in fig. 5, in one embodiment, the first determining module 402 includes:
a first weight determining module 4021, configured to calculate a target weight of a partial article according to a web browsing amount of the partial article, a first resource allocation proportion of the partial article, and a total amount of N weighted web browsing amounts of a plurality of articles to be allocated;
the second weighting module 4022 is configured to calculate a first weighted amount of browsing web pages of a part of articles according to the target weights of the part of articles;
the second weight determining module 4023 is configured to calculate a target weight of a remaining article according to a total amount of the first weighted web browsing amount of the partial article and a total amount of the N weighted web browsing amounts of the plurality of articles to be distributed, where the remaining article is an article other than the partial article in the plurality of articles to be distributed.
In one embodiment, the second weight determination module includes:
the difference determining module is used for calculating the difference between the total quantity of the N weighted webpage browsing volumes of the articles to be distributed and the total quantity of the first weighted webpage browsing volumes of part of the articles;
the adjustment value determining module is used for calculating a weight adjustment value according to the difference value and the weighted webpage browsing amount of the rest articles;
and the adjusting module is used for adjusting the first sub-weights of the remaining articles by using the weight adjusting value to obtain the target weights of the remaining articles, the first sub-weight of the second article is a sub-weight obtained by calculating a weight strategy corresponding to an article set to which the second article belongs in the N sub-weights of the second article, and the second article is any one of the remaining articles.
In one embodiment, calculating the weight adjustment value of the remaining articles according to the difference value and the weighted web browsing amount of the remaining articles comprises:
and determining the ratio of the difference value to the sum of the weighted webpage browsing volumes of the remaining articles as the weight adjustment value of the remaining articles.
In one embodiment, the target weight of the target article is positively correlated with the total amount of the N weighted web browsing volumes of the articles to be distributed and the first resource distribution proportion of the target article, and the target weight of the target article is inversely correlated with the web browsing volume of the target article.
In one embodiment, the target weight of the target article is a result of multiplying the total amount of the N weighted web browsing volumes of the articles to be distributed by the first resource distribution proportion of the target article and then dividing the result by the web browsing volume of the target article.
In one embodiment, the first resource allocation proportion of the target article is a ratio of the weighted web browsing amount of the target article to the sum of the weighted web browsing amounts of the article sets to which the target article belongs.
The resource allocation apparatus in each of the embodiments is an apparatus for implementing the resource allocation method in each of the embodiments, and the technical features and the technical effects correspond to each other, and are not described herein again.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
A non-transitory computer-readable storage medium of an embodiment of the present disclosure stores computer instructions for causing a computer to perform a resource allocation method provided by the present disclosure.
The computer program product of the embodiments of the present disclosure includes a computer program for causing a computer to execute the resource allocation method provided by the embodiments of the present disclosure.
FIG. 6 illustrates a schematic block diagram of an example electronic device 900 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 6, the electronic device 600 includes a computing unit 601 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 602 or a computer program loaded from a storage unit 606 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data necessary for the operation of the device 600 can also be stored. The calculation unit 601, the ROM 602, and the RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Various components in the electronic device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, or the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the electronic device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated artificial intelligence (I) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 601 performs the respective methods and processes described above, such as the resource allocation method. For example, in some embodiments, the resource allocation method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into RAM603 and executed by the computing unit 601, one or more steps of the resource allocation method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the resource allocation method in any other suitable manner (e.g., by means of firmware). Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
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. A 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 compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), the internet, and blockchain networks.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server can be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (16)
1. A method of resource allocation, the method comprising:
weighting the webpage browsing volumes of a plurality of articles to be distributed through the weight of a plurality of articles to be distributed, which is acquired in advance, to obtain N weighted webpage browsing volumes of each article to be distributed, wherein the weight of each article to be distributed comprises N sub-weights, N is an integer larger than 1, the N sub-weights of a first article are obtained through calculation of N weight strategies, and the first article is any one of the articles to be distributed;
determining target weights of the articles to be distributed by using first resource distribution proportions of partial articles of the articles to be distributed and the total quantity of N weighted webpage browsing volumes of the articles to be distributed, wherein the first resource distribution proportions of a target article are the resource distribution proportions of the target article in an article set to which the target article belongs, and the first resource distribution proportions of the target article are as follows: the ratio of the weighted webpage browsing volume of the target article to the weighted webpage browsing volume of the article set to which the target article belongs, wherein the target article is any article in the partial articles;
and determining the target resource allocation proportion of the articles to be allocated according to the target weights of the articles to be allocated and the webpage browsing amount of the articles to be allocated.
2. The method of claim 1, wherein the plurality of articles to be assigned comprises N article sets, each article set corresponding to a weight policy.
3. The method of claim 2, wherein the N sets of articles comprise N-1 first sets of articles and a second set of articles, a total volume of articles in the N-1 first sets of articles being less than a total volume of articles in the second set of articles, wherein the portion of articles is the N-1 first sets of articles.
4. The method of claim 1, wherein the determining the target weights of the articles to be distributed by using the first resource distribution proportion of the partial articles of the articles to be distributed and the total amount of the N weighted web browsing volumes of the articles to be distributed comprises:
calculating the target weight of the partial articles according to the webpage browsing amount of the partial articles, the first resource allocation proportion of the partial articles and the total amount of the N weighted webpage browsing amounts of the articles to be allocated;
calculating a first weighted webpage browsing amount of the partial articles according to the target weights of the partial articles;
and calculating the target weight of the rest articles according to the total amount of the first weighted webpage browsing amount of the partial articles and the total amount of the N weighted webpage browsing amounts of the articles to be distributed, wherein the rest articles are articles except the partial articles in the articles to be distributed.
5. The method of claim 4, wherein the calculating a target weight of a remaining article according to a total amount of the first weighted web browsing amount of the partial article and a total amount of the N weighted web browsing amounts of the plurality of articles to be distributed, the remaining article being an article other than the partial article in the plurality of articles to be distributed, comprises:
calculating the difference between the total quantity of the N weighted webpage browsing volumes of the articles to be distributed and the total quantity of the first weighted webpage browsing volumes of the partial articles;
calculating a weight adjustment value of the remaining articles according to the difference value and the weighted webpage browsing amount of the remaining articles;
and adjusting the first sub-weight of the remaining article by using the weight adjustment value to obtain the target weight of the remaining article, wherein the first sub-weight of the second article is a sub-weight calculated by a weight strategy corresponding to an article set to which the second article belongs in the N sub-weights of the second article, and the second article is any one of the remaining articles.
6. The method of claim 5, wherein the calculating a weight adjustment value for the remaining articles based on the difference and the weighted amount of web browsing of the remaining articles comprises:
and determining the ratio of the difference value to the sum of the weighted webpage browsing volumes of the remaining articles as the weight adjustment value of the remaining articles.
7. The method of claim 4, wherein the target weight of the target article is positively correlated with the total amount of the N weighted web browsing volumes of the articles to be distributed and the first resource distribution proportion of the target article, respectively, and the target weight of the target article is inversely correlated with the web browsing volume of the target article.
8. The method of claim 7, wherein the target weight of the target article is a result of multiplying a total amount of the N weighted web browsing volumes of the articles to be distributed by the first resource distribution proportion of the target article and dividing the result by the web browsing volume of the target article.
9. An apparatus for resource allocation, the apparatus comprising:
the first weighting module is used for weighting the webpage browsing volumes of the articles to be distributed through the weights of the articles to be distributed, which are acquired in advance, so as to obtain N weighted webpage browsing volumes of each article to be distributed, wherein the weight of each article to be distributed comprises N sub-weights, N is an integer greater than 1, the N sub-weights of a first article are obtained through calculation of N weight strategies, and the first article is any one of the articles to be distributed;
a first determining module, configured to determine target weights of the multiple articles to be distributed by using first resource allocation proportions of partial articles of the multiple articles to be distributed and a total amount of N weighted web browsing amounts of the multiple articles to be distributed, where the first resource allocation proportion of a target article is a resource allocation proportion of the target article in an article set to which the target article belongs, and the first resource allocation proportion of the target article is: the ratio of the weighted webpage browsing volume of the target article to the weighted webpage browsing volume of the article set to which the target article belongs, wherein the target article is any article in the partial articles;
and the target proportion determining module is used for determining the target resource allocation proportion of the articles to be allocated according to the target weights of the articles to be allocated and the webpage browsing amount of the articles to be allocated.
10. The apparatus of claim 9, wherein the plurality of articles to be assigned comprises N article sets, one weight policy for each article set.
11. The apparatus of claim 10, wherein the N sets of articles comprise N-1 first sets of articles and a second set of articles, a total volume of articles in the N-1 first sets of articles being less than a total volume of articles in the second set of articles, wherein the portion of articles is the N-1 first sets of articles.
12. The apparatus of claim 9, wherein the first determining means comprises:
a first weight determining module, configured to calculate a target weight of the partial article according to the web browsing volume of the partial article, the first resource allocation proportion of the partial article, and a total amount of N weighted web browsing volumes of the articles to be allocated;
the second weighting module is used for calculating the first weighted webpage browsing amount of the partial articles according to the target weights of the partial articles;
and the second weight determining module is used for calculating the target weight of the rest articles according to the total amount of the first weighted webpage browsing amount of the partial articles and the total amount of the N weighted webpage browsing amounts of the articles to be distributed, wherein the rest articles are articles except the partial articles in the articles to be distributed.
13. The apparatus of claim 12, wherein the second weight determining module comprises:
a difference determining module, configured to calculate a difference between a total amount of the N weighted web browsing volumes of the articles to be distributed and a total amount of the first weighted web browsing volumes of the partial articles;
an adjustment value determining module, configured to calculate a weight adjustment value of the remaining article according to the difference and the weighted web browsing amount of the remaining article;
and the adjusting module is used for adjusting the first sub-weights of the remaining articles by using the weight adjusting value to obtain target weights of the remaining articles, the first sub-weights of the second articles are calculated by weight strategies corresponding to the article set to which the second articles belong in the N sub-weights of the second articles, and the second articles are any one of the remaining articles.
14. The apparatus of claim 13, wherein the calculating a weight adjustment value for the remaining articles based on the difference and a weighted amount of web browsing of the remaining articles comprises:
and determining the ratio of the difference value to the sum of the weighted webpage browsing amounts of the remaining articles as the weight adjustment value of the remaining articles.
15. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of resource allocation according to any one of claims 1 to 8.
16. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the resource allocation method of any one of claims 1-8.
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