Internet advertising system management method, device, medium and equipment

Abstract

The present invention relates to an Internet advertising system management method, device, medium and equipment, including determining the basic advertising delivery revenue of each Internet advertisement at each broadcast and determining the delivery path of each Internet advertisement according to the audience group targeted by each Internet advertisement in the Internet advertising list of the Internet advertising system; predicting the initial delivery probability of each Internet advertisement at the next delivery time point on the delivery path according to the basic advertising delivery revenue of each Internet advertisement and the relative resource competition coefficient of other Internet advertisements on the same path in the Internet advertising list; correcting the initial delivery probability according to the available broadcasting time and delivery revenue at the next delivery time point to obtain the corrected delivery probability of each Internet advertisement at the next delivery time point; determining the target Internet advertisement to be delivered at the next delivery time point according to the corrected delivery probability, and delivering the target Internet advertisement when the next delivery time point is reached.

Description

Technical Field

[0001] The present disclosure relates to the field of advertising technology, and in particular to an Internet advertising system management method, device, medium and equipment. Background Art

[0002] With the rapid development of Internet technology, Internet advertising has become an important means for enterprises to promote their products and services. Traditional advertising methods often rely on manual judgment and experience-based decision-making, making it difficult to achieve accurate allocation and efficient use of advertising resources. Especially in the face of massive user data and complex and changing network environments, how to dynamically adjust advertising strategies based on the characteristics of advertising audiences and the competition for advertising resources has become an urgent problem to be solved.

[0003] Most existing Internet advertising systems use fixed rankings or simple bidding ranking mechanisms to determine the order and position of advertisements. However, this approach often ignores the differences in advertising audiences, the real-time changes in advertising resources, and the overall benefits of advertising. In particular, during the advertising delivery process, the value differences and available playback time at different delivery time points are not fully considered, resulting in low advertising delivery efficiency and serious waste of advertising resources. Summary of the invention

[0004] The purpose of the present invention is to provide an Internet advertising system management method, device, medium and equipment, aiming to achieve precision and efficiency in advertising delivery, thereby improving the overall performance and user experience of the Internet advertising system.

[0005] In order to achieve the above-mentioned purpose, a first aspect of the embodiments of the present disclosure provides an Internet advertising system management method, which is applied to an Internet advertising system for delivering Internet advertisements, and the method includes:

[0006] According to the audience groups targeted by each Internet advertisement in the Internet advertisement list of the Internet advertisement system, determine the basic advertisement delivery revenue of each Internet advertisement at each broadcast and determine the delivery path of each Internet advertisement;

[0007] Predicting the initial delivery probability of each of the Internet advertisements at the next delivery time point on the delivery path by the Internet advertising system based on the basic advertisement delivery revenue of each of the Internet advertisements and the resource competition coefficient of other Internet advertisements on the same path in the Internet advertisement list relative to the Internet advertisement;

[0008] According to the available broadcasting time and the delivery income at the next delivery time point, the initial delivery probability of delivering each of the Internet advertisements at the next delivery time point on the delivery path is corrected to obtain the corrected delivery probability of delivering each of the Internet advertisements at the next delivery time point on the delivery path, wherein the delivery income is used to represent the ratio of the golden delivery income at the delivery time point to each golden delivery time point;

[0009] According to the modified delivery probability, the target Internet advertisement to be delivered at the next delivery time point on the delivery path is determined, and when the next delivery time point is reached, the target Internet advertisement is delivered.

[0010] According to a second aspect of the present disclosure, there is provided an Internet advertising system management device, the device comprising:

[0011] A determination module configured to determine the basic advertising revenue of each Internet advertisement at each broadcast and the delivery path of each Internet advertisement according to the audience group targeted by each Internet advertisement in the Internet advertisement list of the Internet advertisement system;

[0012] A prediction module is configured to predict the initial delivery probability of each of the Internet advertisements to be delivered by the Internet advertising system at the next delivery time point on the delivery path according to the basic advertisement delivery revenue of each of the Internet advertisements and the resource competition coefficient of other Internet advertisements on the same path in the Internet advertisement list relative to the Internet advertisement;

[0013] A correction module is configured to correct the initial delivery probability of each Internet advertisement at the next delivery time point on the delivery path according to the available broadcasting time and delivery income at the next delivery time point, and obtain a corrected delivery probability of each Internet advertisement at the next delivery time point on the delivery path, wherein the delivery income is used to represent the ratio of the golden delivery income of the delivery time point to each golden delivery time point;

[0014] The delivery module is configured to determine the target Internet advertisement to be delivered at the next delivery time point on the delivery path according to the modified delivery probability, and deliver the target Internet advertisement when the next delivery time point is reached.

[0015] According to a third aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the steps of any one of the methods described in the first aspect are implemented.

[0016] According to a fourth aspect of the embodiments of the present disclosure, an electronic device is provided, including:

[0017] a memory having a computer program stored thereon;

[0018] A processor is used to execute the computer program in the memory to implement the steps of any one of the methods in the first aspect.

[0019] The present invention provides an Internet advertising system management method, device, medium and equipment. Compared with the prior art, it has the following beneficial effects:

[0020] By analyzing the characteristics of the audience and the resource competition coefficient, the system can more accurately predict the effect of advertising and achieve precise delivery. This helps to improve the exposure and click-through rate of advertising, and thus improve the conversion rate of advertising. The introduction of the evaluation mechanism of the delivery time point value and the available playback time makes the advertising delivery strategy more flexible and efficient. The delivery strategy can be dynamically adjusted according to real-time data to ensure that advertising resources are fully utilized at the most valuable time point. By dynamically predicting and correcting the delivery probability, the system can ensure that advertising resources are delivered to the user group that is most likely to generate benefits. It helps to reduce the waste of advertising resources and improve the utilization rate of overall advertising resources. The precisely delivered advertising content is more in line with the interests and needs of users, which helps to improve the user experience. When users browse the web or use applications, they can receive more personalized advertising information, which increases user stickiness. In summary, by introducing mechanisms such as audience analysis, resource competition coefficient evaluation, delivery time point value evaluation, and dynamic delivery probability correction, precise delivery and efficient utilization of Internet advertising are achieved.

[0021] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0023] Figure 1 It is a flow chart of an Internet advertising system management method shown according to an embodiment of the specification.

[0024] Figure 2 It is a block diagram of an Internet advertising system management device shown according to an embodiment of the specification.

[0025] Figure 3 It is a block diagram of another Internet advertising system management device shown according to an embodiment of the specification. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0028] In order to achieve the above objectives, the present disclosure provides an Internet advertising system management method. Figure 1 This is a flow chart of a method for managing an Internet advertising system according to an embodiment. The method comprises:

[0029] In step S11, according to the audience groups targeted by each Internet advertisement in the Internet advertisement list of the Internet advertisement system, the basic advertisement delivery revenue of each Internet advertisement at each broadcast and the delivery path of each Internet advertisement are determined;

[0030] Among them, basic advertising revenue is the basic revenue value estimated based on the characteristics of the target audience when an Internet advertisement is played once. The effect of advertising depends largely on the matching degree between the advertising content and the target audience. The characteristics of the target audience may include age, gender, geographic location, interests, purchasing behavior, browsing history and other dimensions. By analyzing these characteristics, it is possible to more accurately predict the attractiveness of an advertisement to a specific group of people, thereby estimating the basic advertising revenue.

[0031] The estimated basic revenue value is obtained by predicting the revenue that an ad may bring in a single broadcast based on historical data and algorithm models. This estimate takes into account a variety of factors, such as the ad's click-through rate, conversion rate, viewing time, user interaction, etc. The estimated basic revenue value helps the advertising system compare the delivery effects of different ads.

[0032] For example, suppose there is an advertisement for a fashion brand targeting young women. The target audience of the advertisement is women aged 18-30 who are interested in fashion and beauty. By analyzing historical data and user portraits, it is estimated that the basic advertising revenue value of the advertisement is 20 when it is played once. This means that when the advertisement is played to users who meet the characteristics of the target audience, it is expected to obtain a basic advertising revenue value of 20. This estimate will serve as an important reference for the advertising system to decide whether to run the advertisement and how to compete with other advertisements for the advertisement space.

[0033] Among them, the delivery channel is the specific channel through which Internet advertisements are displayed through Internet platforms, such as search engines, social media, video platforms, bus stop advertisements, TV advertisements, etc. Choose the most appropriate delivery channel according to the characteristics of the target audience of the advertisement. For example, young users may prefer social media and video platforms. At the same time, different types of advertising content are suitable for different delivery channels. For example, video advertisements are more suitable for display on video platforms. The advertising costs of different delivery channels vary greatly. Advertisers need to reasonably allocate delivery resources according to their budgets. Analyze the situation of competitors on the same delivery channel and choose relatively advantageous locations and time periods for delivery.

[0034] In the disclosed embodiment, the target audience characteristics (such as age, gender, interest preferences, etc.) of each Internet advertisement are first analyzed, and the basic revenue expectation of the advertisement when it is played once is calculated by combining the historical advertisement performance data (such as click-through rate, conversion rate, etc.). At the same time, the most appropriate delivery channel can also be determined based on the delivery demand, the characteristics of the advertisement content, and the platform resources. For example, a fashion brand advertisement targeting a young user group may choose a social platform as a delivery channel.

[0035] For example, a cosmetics brand wants to promote its new facial mask. By analyzing that the target audience is mainly young women, it can usually determine that the basic advertising revenue may be 10 based on this identity. The basic advertising revenue can be further combined with the preference to learn about product information through short videos, so it is decided to place advertisements on the video software platform.

[0036] In step S12, based on the basic advertisement delivery revenue of each Internet advertisement and the resource competition coefficient of other Internet advertisements on the same path in the Internet advertisement list relative to the Internet advertisement, the initial delivery probability of each Internet advertisement delivered by the Internet advertisement system at the next delivery time point on the delivery path is predicted;

[0037] Among them, the resource competition coefficient is used to reflect the intensity of competition among advertisements in the Internet advertising list when competing for limited advertising space resources.

[0038] The initial delivery probability is the probability of an advertisement being selected at the next delivery time point on a specific delivery channel, which is predicted based on the basic advertising delivery revenue and resource competition coefficient.

[0039] In the disclosed embodiment, the basic advertising revenue of each advertisement and its resource competition coefficient in the advertisement list (which may involve factors such as the bid price, historical performance, and advertiser reputation of the advertisement) are comprehensively considered. The initial delivery probability of each advertisement on a specific delivery channel at the next delivery time point can be predicted through calculation by an algorithm model.

[0040] For example, continuing with the cosmetics brand example above, assume that at the same time point, there are multiple other brands competing for the platform's advertising space. Based on the basic advertising revenue, bidding situation and historical performance of each brand, the initial delivery probability of the cosmetics brand advertisement is calculated to be 30%.

[0041] In step S13, based on the available broadcasting time and the delivery revenue at the next delivery time point, the initial delivery probability of each Internet advertisement at the next delivery time point on the delivery path is corrected to obtain the corrected delivery probability of each Internet advertisement at the next delivery time point on the delivery path.

[0042] The delivery income is used to indicate the ratio of the delivery time point to the golden delivery income of each golden delivery time point;

[0043] Among them, the revised delivery probability is the probability value of delivery at the next delivery time point on the delivery path after adjusting the initial delivery probability based on the available playback time at the next delivery time point, the delivery revenue and the revenue ratio of the golden delivery time point.

[0044] Among them, the golden delivery time points are divided into multiple delivery time points based on the delivery time periods with the best advertising performance and the highest returns in history, such as the prime time in the evening. Among them, multiple golden delivery points can have different preset delivery returns. It can be understood that the closer to the golden delivery time point, the higher the delivery return.

[0045] In the disclosed embodiment, the initial delivery probability is dynamically adjusted by considering the specific situation of the next delivery time point, such as the available playback time, the profit ratio of this time point relative to the golden delivery time point, etc. This helps to optimize the advertising delivery strategy and ensure the maximum profit with limited resources.

[0046] For example, if the next delivery time is predicted to be a non-prime time (such as weekday mornings), and the advertising revenue during this time is only 50% of that during the prime time. Considering the low user activity during this time, the revised delivery probability of cosmetics brands may be lowered from the initial 30% to 20% to reduce advertising investment in non-efficient time periods, while retaining resources for subsequent more efficient delivery opportunities, and the revised delivery probability of elderly products (such as reading glasses) may be raised from the initial 30% to 70%. This will enable a more scientific and reasonable allocation of advertising resources, improve the accuracy and efficiency of advertising delivery, and achieve a win-win situation for both advertisers and platforms.

[0047] In step S14, the target Internet advertisement to be delivered at the next delivery time point on the delivery path is determined according to the modified delivery probability, and the target Internet advertisement is delivered when the next delivery time point is reached.

[0048] In the disclosed embodiment, based on the modified delivery probability, the modified delivery probabilities of different advertisements are compared. The advertisement with the highest modified delivery probability can be selected as the target Internet advertisement to be delivered at the next delivery time point. When the next delivery time point is reached, the delivery process of the advertisement is triggered, and the target Internet advertisement will be displayed on the corresponding advertisement position on the delivery path for users to browse and interact.

[0049] For example, based on the revised delivery probability, the advertising system of the social media platform will compare the delivery probabilities of different advertisements. Assume that the advertisement of the e-commerce platform has the highest revised delivery probability due to its high bid, high target audience match, and the positive influence of real-time data. Therefore, this advertisement will be selected as the target Internet advertisement to be delivered at the next delivery time point. When the next delivery time point is reached, the advertising system of the social media platform will trigger the delivery process of the advertisement. The advertisement of the e-commerce platform will be displayed on the corresponding advertising space on the social media for users to browse and interact. Users can see the content of the advertisement and click to enter the promotion page of the e-commerce platform to shop.

[0050] The method of the above technical solution can more accurately predict the effect of advertising delivery and achieve precise delivery by analyzing the characteristics of the audience and the resource competition coefficient. This helps to improve the exposure and click-through rate of advertisements, thereby improving the conversion rate of advertisements. The introduction of the evaluation mechanism of the delivery time point value and the available playback time makes the advertising delivery strategy more flexible and efficient. The delivery strategy can be dynamically adjusted according to real-time data to ensure that advertising resources are fully utilized at the most valuable time point. By dynamically predicting and correcting the delivery probability, the system can ensure that advertising resources are delivered to the user group that is most likely to generate benefits. It helps to reduce the waste of advertising resources and improve the utilization rate of overall advertising resources. The precisely delivered advertising content is more in line with the interests and needs of users, which helps to improve the user experience. When users browse web pages or use applications, they can receive more personalized advertising information and increase user stickiness. In summary, by introducing mechanisms such as audience analysis, resource competition coefficient evaluation, delivery time point value evaluation and dynamic delivery probability correction, precise delivery and efficient utilization of Internet advertising are achieved.

[0051] In a possible implementation, in step S12, predicting the initial delivery probability of each Internet advertisement delivered by the Internet advertising system at the next delivery time point on the delivery path based on the basic advertisement delivery revenue of each Internet advertisement and the resource competition coefficient of other Internet advertisements on the same path in the Internet advertisement list relative to the Internet advertisement includes:

[0052] In step S121, a preset number of votes are issued to each of the Internet advertisements corresponding to the delivery channels;

[0053] The preset number of votes is used to simulate the voting mechanism in the advertising decision-making process to fairly allocate an initial and equal chance of selection to each competing ad. This value can be dynamically determined by the system based on historical data, current system load, or advertiser bids.

[0054] In the disclosed embodiment, a voting process is simulated. For the sake of fairness, the same number of preset votes are issued to each Internet advertisement competing on the same delivery channel. These votes represent the possibility of the advertisement being selected at the next delivery time point, but initially, the number of votes for all advertisements is the same, that is, they all have the same chance to vote or be voted for according to their competitive relationship with other Internet advertisements.

[0055] For example, suppose there are three Internet ads A, B, and C that are all planned to be delivered on social media platforms, and their delivery time points overlap. Each ad (A, B, C) is issued 100 votes with a preset number. This means that before the voting begins, the three ads A, B, and C are all standing on the same starting line, each with 100 votes, waiting for the subsequent voting process to determine their delivery probability at the next delivery time point.

[0056] In step S122, based on the resource competition coefficient of each Internet advertisement relative to other Internet advertisements on the same path in the Internet advertisement list and the preset number of votes corresponding to each Internet advertisement, the number of votes obtained by each Internet advertisement on the delivery path at each delivery time point of the delivery path is determined by voting;

[0057] Among them, the resource competition coefficient is an indicator used to reflect the intensity of competition between advertisements on the same delivery channel. It may be calculated based on multiple factors such as the bid, quality score, historical performance, audience matching, etc. The higher the resource competition coefficient, the more advantageous the advertisement is in the competition.

[0058] In the disclosed embodiment, the resource competition coefficient is used to influence the number of votes for an advertisement. Specifically, the preset number of votes initially obtained by each advertisement is weighted according to the resource competition coefficient of each advertisement. Advertisements with high resource competition coefficients will receive more additional votes, thereby gaining an advantage in the voting process. Ultimately, the number of votes each advertisement receives at the time of delivery will reflect its relative competitiveness.

[0059] For example, continuing with the above example, assume that the resource competition coefficient of advertisement A is 1.2 (indicating that its competitiveness is slightly above average), the resource competition coefficient of advertisement B is 1.0 (indicating that its competitiveness is average), and the resource competition coefficient of advertisement C is 0.8 (indicating that its competitiveness is slightly below average). In step S122, the initial 100 votes for advertisements A, B, and C are weighted according to these resource competition coefficients. Ultimately, advertisement A may receive more than 100 votes (e.g., 120), advertisement B remains unchanged at 100, and advertisement C may receive less than 100 votes (e.g., 80). In this way, the probability of advertisement A being delivered at the next delivery time point is higher than that of advertisements B and C, and it is more likely to be selected for delivery.

[0060] In step S123, the competition index of each of the Internet advertisements is determined according to the basic advertisement delivery revenue of each of the Internet advertisements and the number of votes corresponding to each of the delivery time points in the delivery path;

[0061] Among them, the competition index is used to measure the competitiveness of each Internet advertisement relative to other advertisements in a specific delivery channel and delivery time. It is usually calculated based on the basic advertising revenue, number of votes, and other possible factors (such as advertising quality, audience matching, etc.).

[0062] In the disclosed embodiment, the basic advertising revenue of each advertisement is first considered, which is a direct reflection of the value of the advertisement itself. Then, the number of votes corresponding to each delivery time point in the delivery channel is combined, and these votes reflect the popularity or relative advantage of the advertisement in the competition. By comprehensively considering these two factors, the competition index of each advertisement can be calculated. The higher the competition index, the more competitive the advertisement is under specific delivery conditions.

[0063] Using the example for illustration, assume that there are three Internet ads A, B, and C competing for the same delivery time on a social media platform. Ad A has a higher basic advertising delivery revenue and the most votes at the delivery time; Ad B has a medium basic revenue and a medium number of votes; Ad C has a lower basic revenue and a smaller number of votes. The competition index of each ad can be calculated based on these data. Since Ad A performs well in both basic revenue and votes, its competition index will be the highest; Ad B is second; and Ad C is the lowest.

[0064] In step S124, based on the competition index and the ratio of the number of votes to the sum of the preset number, the initial delivery probability of the Internet advertising system to deliver each of the Internet advertisements at the next delivery time point on the delivery path is predicted.

[0065] Among them, the initial delivery probability is the possibility of each Internet advertisement being selected in a specific delivery channel and delivery time point, predicted based on current data and algorithms, without interference from other real-time factors.

[0066] In the disclosed embodiment, the initial delivery probability of each advertisement at the next delivery time point can be predicted based on the calculated competition index and the ratio of the number of votes received by each advertisement at the delivery time point to the total number of votes of a preset number (this ratio reflects the relative proportion of the number of votes received by the advertisement). Specifically, the higher the competition index and the greater the relative proportion of the number of votes received by the advertisement, the higher the initial delivery probability will be. Such predictions help the system to more accurately allocate limited delivery resources and improve the overall advertising effect.

[0067] Continuing with the above example, assume that the calculated competition indexes of ads A, B, and C are high, medium, and low, respectively. At the same time, the relative proportion of votes for ad A (i.e., the number of votes for ad A divided by the total number of votes for all ads) is also the highest. Based on these data and algorithms, the initial delivery probability of each ad is predicted. Since ad A has an advantage in both the competition index and the relative proportion of votes, its initial delivery probability will be the highest; ad B is second; and ad C is the lowest. In this way, when the next delivery time point is reached, ad A is more likely to be selected for delivery.

[0068] The above technical solution issues a preset number of votes to the Internet advertisements corresponding to each delivery channel. The solution ensures that all advertisements have the same competitive starting point in the initial stage, which reflects fairness. At the same time, the voting mechanism based on the resource competition coefficient allows advertisements to dynamically adjust their competitiveness according to their own strength and market feedback, increasing the flexibility of the system. When evaluating the competitiveness of advertisements, the solution not only considers the basic advertising revenue, but also combines market feedback (i.e., the number of votes), thereby realizing a multi-dimensional and comprehensive evaluation of advertisements. This evaluation method is closer to the actual market environment and helps to more accurately reflect the true value of advertisements. Based on the ratio of the competition index and the number of votes to the sum of the preset number, the initial delivery probability of each advertisement delivered by the Internet advertising system at the next delivery time point can be predicted. This prediction not only helps to optimize the advertising delivery strategy, improve advertising exposure and conversion rate, but also can be dynamically adjusted according to real-time data to ensure the accuracy and effectiveness of advertising delivery. At the same time, by comprehensively evaluating the competitiveness and market feedback of advertisements, limited delivery resources can be allocated more reasonably. Advertisements with high competitiveness and high market feedback will obtain more delivery opportunities, thereby realizing efficient allocation and maximum utilization of resources. The entire delivery optimization process is highly automated and intelligent, reducing the impact of manual intervention and subjective judgment. This not only improves work efficiency, but also reduces operating costs, making advertising more scientific and efficient.

[0069] In summary, by building a fair, flexible, and multi-dimensional evaluation system, combined with accurate prediction and intelligent resource allocation, we have achieved comprehensive optimization of Internet advertising. This not only helps to improve advertising effectiveness, but also provides advertisers with more accurate and efficient advertising solutions.

[0070] In a possible implementation, in step S122, voting to determine the number of votes obtained by each of the Internet advertisements on the delivery path at each delivery time point of the delivery path according to the resource competition coefficient of each of the Internet advertisements relative to other Internet advertisements on the same path in the Internet advertisement list and a preset number of votes corresponding to each of the Internet advertisements includes:

[0071] In step S1221, the plurality of Internet advertisements corresponding to the delivery path are sequentially selected as voted advertisements, and according to the resource competition coefficient of each Internet advertisement relative to other Internet advertisements on the same path in the Internet advertisement list, voting is performed on all delivery time points of each Internet advertisement on the delivery path by having other Internet advertisements vote for the voted advertisement, until all Internet advertisements on the delivery path are voted as voted advertisements at all delivery time points of the delivery path, and a preset number of votes for each Internet advertisement are cast, thereby obtaining the number of votes obtained by each Internet advertisement on the delivery path at each delivery time point of the delivery path;

[0072] Among them, after the voted advertisement is voted by the other Internet advertisements at any of the delivery time points, the resource competition coefficient of other Internet advertisements on the same path in the Internet advertisement list relative to the Internet advertisement is adjusted according to the number of votes at the delivery time point.

[0073] The voted advertisements are Internet advertisements that are evaluated in the current voting round and may receive votes from other advertisements.

[0074] In the disclosed embodiment, a simulated voting mechanism is used to evaluate the competitiveness of each Internet advertisement in a specific delivery channel and at each delivery time point. This process is similar to voting in an election, but the voters are not human voters, but other Internet advertisements on the same delivery channel.

[0075] Initialization: First, all Internet ads currently on the delivery channel are identified and a preset number of votes are assigned to each of them. These votes represent the initial "voice" of the ads in the competition.

[0076] Voting cycle: Set each ad as a "voted ad" in turn. For each voted ad, other ads on the same delivery channel will cast a certain number of votes for the voted ad at all delivery time points based on their resource competition coefficients. The number of votes cast may be allocated based on the relative proportion of the resource competition coefficients to ensure that highly competitive ads can get more votes.

[0077] This process is repeated until all ads on the delivery path have been voted for by other ads at all delivery time points, and the preset number of votes for each ad has been fully cast.

[0078] Voting statistics: After the voting is over, the system counts the number of votes each ad received at each time point of delivery. These votes reflect the competitiveness of the ad at that time point.

[0079] For example, suppose there is an advertising channel (such as a sidebar ad space on a social platform), and there are three Internet ads A, B, and C competing on this channel. Each ad initially receives 10 votes for voting.

[0080] Ad A: The resource competition coefficient is 0.5 (relatively low), but it may attract a specific audience due to its novel content.

[0081] Ad B: The resource competition coefficient is 0.8 (medium), historical performance is stable, and the target audience is wide.

[0082] Ad C: The resource competition coefficient is 1.0 (the highest), it is placed by a large brand and has a sufficient budget.

[0083] During the voting process, Ad C, due to its high resource competition coefficient, may cast more votes to Ad A and B at multiple delivery time points. Ad B will also vote for A and C based on its resource competition coefficient, but the number may be less than C. Ad A, due to its weaker competitiveness, may receive relatively fewer votes.

[0084] Finally, the system counts the number of votes for each ad at each time point. For example, if ad C has the highest number of votes at multiple time points, it means that ad C is likely to achieve the best results at these time points. This result will serve as an important basis for subsequent ad placement decisions.

[0085] Furthermore, during the voting process, when the voted advertisement receives votes from other Internet advertisements at any point in time, its resource competition coefficient is not fixed, but will be dynamically adjusted according to the number of votes received. This adjustment mechanism is intended to more accurately reflect the competitiveness of advertisements in a real-time market environment.

[0086] In one embodiment of the present disclosure, voting statistics: at each delivery time point, the number of votes obtained by each voted advertisement is recorded. These votes represent the degree of recognition of the advertisement by other advertisements at the current time point.

[0087] Resource competition coefficient adjustment: The resource competition coefficient is adjusted accordingly based on the number of votes the voted ad receives. The specific adjustment algorithm may vary from system to system, but the general principle is: the more votes the ad receives, the more the resource competition coefficient increases; the fewer votes the ad receives, the less the resource competition coefficient increases, or it remains unchanged or even decreases (depending on the specific implementation).

[0088] Real-time update: The resource competition coefficient is adjusted in real time to ensure that the system can respond quickly to market changes. The adjusted resource competition coefficient will be used in subsequent voting rounds and advertising decisions.

[0089] For example, suppose there are two Internet ads A and B competing on the same delivery channel. In the initial stage, the resource competition coefficients of ads A and B are 0.6 and 0.4 respectively (assuming that these coefficients are pre-calculated based on factors such as ad quality and budget).

[0090] First round of voting: At the first delivery time point, Ad B received more votes (assuming 8 votes) than other ads due to its novel content and high target audience match, while Ad A received fewer votes (assuming 4 votes).

[0091] Resource competition coefficient adjustment: Based on the voting results, the system adjusts the resource competition coefficients of ads A and B. Since ad B received more votes than ad A, the resource competition coefficient of ad B may increase from 0.4 to 0.5 or higher (the specific increase depends on the adjustment algorithm). The resource competition coefficient of ad A may remain unchanged or increase slightly (but the increase is smaller than that of ad B).

[0092] Subsequent impact: In subsequent voting rounds and ad placement decisions, the adjusted resource competition coefficient will serve as an important reference factor. For example, when predicting the initial placement probability at the next placement time point, the system may give higher priority to ad B with a higher resource competition coefficient.

[0093] In this way, the competitiveness of advertisements in a real-time market environment can be dynamically evaluated and adjusted, thereby optimizing advertising delivery strategies and improving advertising effectiveness.

[0094] In a possible implementation, after the voted advertisement is voted by the other Internet advertisements at any delivery time point, the resource competition coefficient of other Internet advertisements on the same path in the Internet advertisement list relative to the Internet advertisement is adjusted according to the number of votes at the delivery time point, including:

[0095] In step S21, the number of votes cast by other Internet advertisements on the same channel in the Internet advertisement list for the voted advertisement at any of the delivery time points is determined;

[0096] In the disclosed embodiment, at each delivery time point, the number of votes cast by other Internet advertisements for the voted advertisement is recorded and counted. These votes directly reflect the degree of recognition of the voted advertisement by other advertisements at the current time point. For example: Assuming that at the first delivery time point, Ad C received 5 votes from Ad A and 3 votes from Ad B, then the number of votes for Ad C at that time point is 8 votes.

[0097] In step S22, the acceptance degree of the other Internet advertisements to the voted advertisement is determined according to a first ratio of the number of votes to the preset number and a second ratio of the number of votes to the number of remaining votes of other Internet advertisements on the same channel in the Internet advertisement list before voting at the delivery time point;

[0098] The remaining number of votes refers to the number of votes that have not been cast for an ad before the delivery time. The yield is an indicator used to measure the degree of recognition or concession of an ad to other ads (especially the voted ads) during the voting process. The higher the yield, the more willing the ad is to give up its own competitive opportunities to the voted ads.

[0099] The calculation of the yield is based on two ratios: the first ratio of the number of votes to the preset number, which reflects the relative strength of the voting behavior; the second ratio of the number of votes to the remaining number of votes, which takes into account the relative scarcity of voting behavior. By combining these two ratios, the system can more accurately evaluate the yield relationship between advertisements.

[0100] The first ratio: number of votes / preset number. The higher this ratio is, the more actively the ad supports the ad being voted for during the voting process.

[0101] The second ratio: number of votes / number of remaining votes. The higher this ratio is, the more willing the advertisement is to cast more votes for the voted advertisement when its own voting resources are limited, which reflects a higher degree of acceptance.

[0102] For example, suppose Ad A has a preset number of 10 votes and a remaining number of 6 votes, and it casts 5 votes for Ad C. Then, the first ratio is 5 / 10=0.5, and the second ratio is 5 / 6≈0.83. Based on these two ratios and a specific algorithm (such as weighted average), the system calculates Ad A's acceptance of Ad C. If Ad B also votes for Ad C, but has a larger number of remaining votes or a smaller preset number, its acceptance may be lower than Ad A.

[0103] In this way, the competitive relationship and market recognition between Internet advertisements can be evaluated more finely. The introduction of the acceptance rate not only takes into account the absolute number of voting behaviors (number of votes), but also the relative strength and scarcity of voting behaviors (measured by two ratios).

[0104] In step S23, when the yield exceeds the first preset threshold, the resource competition coefficient of other Internet advertisements on the same path in the Internet advertisement list relative to the Internet advertisement is reduced according to a preset step size;

[0105] The preset threshold value may be a standard value set in advance for judging or triggering a specific operation. In this context, there is a first preset threshold value and a second preset threshold value, which correspond to different yield levels. The preset step size is used to adjust the fixed amount or proportion of each adjustment when adjusting the resource contention coefficient.

[0106] In the disclosed embodiment, when the degree of concession of a certain advertisement to the voted advertisement exceeds the first preset threshold, it indicates that the advertisement has shown an extremely high degree of recognition or concession during the voting process. In order to reflect this high degree of recognition, the system reduces the resource competition coefficient of the advertisement relative to the voted advertisement according to a preset step size. The purpose of this is to give the voted advertisement more priority when allocating resources, because other advertisements have expressed their high recognition of it through voting. For example, assuming that the degree of concession of advertisement A to advertisement C is 0.9 (much higher than the first preset threshold of 0.7), the system may reduce the resource competition coefficient of advertisement A relative to advertisement C according to a preset step size (such as 0.05). If the original resource competition coefficient of advertisement A is 0.6, it may become 0.55 after adjustment.

[0107] In step S24, when the yield exceeds a second preset threshold and does not exceed the first preset threshold, the resource competition coefficient of other Internet advertisements on the same path in the Internet advertisement list relative to the Internet advertisement is maintained, wherein the second preset threshold is less than the first preset threshold;

[0108] In the disclosed embodiment, when the yield is between the second preset threshold and the first preset threshold, it indicates that the degree of recognition of the advertisement for the voted advertisement is moderate, neither too high nor too low. In this case, the system keeps the resource competition coefficient of the advertisement unchanged to maintain the existing competition situation. For example, assuming that the yield of advertisement B to advertisement C is 0.6 (between the second preset threshold of 0.5 and the first preset threshold of 0.7), the system will keep the resource competition coefficient of advertisement B relative to advertisement C unchanged.

[0109] In step S25, when the yield does not exceed the second preset threshold, the resource competition coefficient of other Internet advertisements on the same path in the Internet advertisement list relative to the Internet advertisement is increased according to a preset step size.

[0110] In the disclosed embodiment, when the yield is lower than the second preset threshold, it indicates that the advertisement has a low degree of recognition of the voted advertisement, or that it maintains a strong position in the competition. In order to encourage competition and reflect this position, the system increases the resource competition coefficient of the advertisement according to a preset step size. The purpose of this is to give the advertisement more opportunities when allocating resources to balance the competition situation in the market.

[0111] For example, assuming that the yield of Ad D to Ad C is 0.3 (much lower than the second preset threshold of 0.5), the system may increase the resource competition coefficient of Ad D relative to Ad C according to a preset step size (such as 0.05). If the original resource competition coefficient of Ad D is 0.4, it may become 0.45 after adjustment.

[0112] The above technical solution is based on the resource competition coefficient adjustment mechanism of the concession degree. By setting different preset thresholds and preset step sizes, the resource competition coefficient can be dynamically adjusted according to the voting behavior and recognition degree between advertisements, thereby optimizing the efficiency and effect of advertising delivery. This mechanism not only takes into account the direct competition relationship between advertisements, but also indirectly reflects the complex situation of market recognition and concession through the introduction of the concession degree.

[0113] In a possible implementation, in step S13, the initial delivery probability of delivering each of the Internet advertisements at the next delivery time point on the delivery path is corrected according to the available playback time and delivery revenue at the next delivery time point to obtain the corrected delivery probability of delivering each of the Internet advertisements at the next delivery time point on the delivery path, including:

[0114] In step S131, a candidate Internet advertisement is determined from the Internet advertisements according to the available playback duration at the next delivery time point and the playback duration of each Internet advertisement;

[0115] Among them, available playback time: the total time available for playing ads at a certain delivery time point. Playback time: the playback time required for a single ad from start to end.

[0116] In the disclosed embodiment, all Internet advertisements are traversed and their playing durations are compared with the available playing durations at the delivery time point. If the playing duration of an advertisement is less than or equal to the available playing duration, and the sum of the playing durations of the advertisement and other selected candidate advertisements does not exceed the available playing duration, the advertisement is added to the candidate advertisement set.

[0117] For example, suppose that at a certain delivery time point, the available playback time is 1 minute, and there are three ads A, B, and C with playback time of 30 seconds, 20 seconds, and 15 seconds respectively. Filter out alternative ads based on these playback time and available playback time. In this example, all three ads can be used as alternative ads because their total playback time does not exceed 1 minute.

[0118] In step S132, when the Internet advertisement is the candidate Internet advertisement, the single delivery revenue of the Internet advertisement at each delivery point in time on the delivery path is determined according to the delivery revenue and the exposure cost on each delivery path;

[0119] Among them, the revenue from advertising is the expected revenue after advertising, which may be calculated based on multiple factors such as click-through rate and conversion rate. Since it is impossible to accurately judge the revenue directly, it is determined based on the relative relationship of the golden time point for advertising. Exposure cost is the fee that needs to be paid to display an advertisement on a certain delivery channel, which is usually related to factors such as the number of exposures and the quality of the advertisement.

[0120] In the disclosed embodiment, for each candidate advertisement, the single delivery revenue at a specific delivery channel and delivery time point is calculated based on the delivery revenue and exposure cost. This revenue may be a predicted value based on multiple factors (such as click-through rate, conversion rate, advertisement quality, etc.). By comparing the single delivery revenue of different advertisements, the system can decide which advertisements to deliver first under limited resources.

[0121] For example, suppose the exposure cost of Ad A on a specific delivery channel is 0.5 yuan / time, the expected click-through rate is 2%, and the average revenue per click is 1 yuan. Then, the single delivery revenue of Ad A on this delivery channel and delivery time point (calculated based on exposure) can be roughly estimated as: Revenue per exposure = Click-through rate * Revenue per click - Exposure cost = 2% * 1 yuan - 0.5 yuan / time = -0.3 yuan / time (this is just an example, the actual calculation may be more complicated and may take more factors into account). However, it should be noted that the calculation method here is relatively simplified, and the actual delivery revenue may be predicted based on a more complex model. In real scenarios, the single delivery revenue of each advertisement is calculated based on more comprehensive data and more sophisticated models, and their revised delivery probabilities are adjusted accordingly.

[0122] In step S133, according to the ratio of the single delivery revenue of the Internet advertisement at each delivery point in time on the delivery path to the basic advertisement delivery revenue of the Internet advertisement at each broadcast, the correction parameter corresponding to the next delivery point in time on the delivery path of the Internet advertisement is determined;

[0123] In the disclosed embodiment, the ratio between the single delivery revenue and the basic advertisement delivery revenue of each Internet advertisement in a specific delivery channel and delivery time point is calculated. This ratio reflects the relative revenue capacity of the advertisement under the delivery conditions, that is, the actual revenue potential of the advertisement relative to its basic revenue level. By comparing this ratio, the system can evaluate which advertisements are more likely to bring excess revenue under specific conditions, and thus give them a higher delivery priority.

[0124] For example, suppose there are three ads A, B, and C. In a specific delivery channel and delivery time, their individual delivery revenues are 0.8 yuan, 1.2 yuan, and 0.5 yuan respectively, and their basic advertising delivery revenues are all 0.5 yuan. Then, the ratio of ad A is 0.8 / 0.5=1.6, the ratio of ad B is 1.2 / 0.5=2.4, and the ratio of ad C is 0.5 / 0.5=1. Based on this ratio, the system can assume that ad B is more likely to bring excess revenue than other ads under this delivery condition, and therefore should be given a higher delivery priority.

[0125] In order to convert this ratio into a correction parameter, a conversion function or mapping relationship can be set to map the ratio to a reasonable correction parameter range (for example, between 0 and 1, or a larger range, depending on the design and requirements of the system). In this example, the system may map the ratio of advertisement B, 2.4, to a higher correction parameter (such as 0.8), and map the ratios of advertisements A and C to lower correction parameters (such as 0.6 and 0.4).

[0126] In step S134, based on the correction parameters corresponding to each of the Internet advertisements, the initial delivery probability of each of the Internet advertisements at the next delivery time point on the delivery path is corrected to obtain a corrected delivery probability of each of the Internet advertisements at the next delivery time point on the delivery path.

[0127] In the disclosed embodiment, the correction parameter calculated in the previous step is used to adjust the initial delivery probability of each advertisement at a specific delivery channel and delivery time point. The corrected delivery probability more accurately reflects the actual delivery priority of each advertisement after considering the ratio of delivery revenue to basic revenue. Usually, the correction parameter is used as a multiplier or weighting factor, multiplied or combined with the initial delivery probability to obtain the corrected delivery probability.

[0128] Continuing with the above example, assume that the initial delivery probabilities of ads A, B, and C in a specific delivery channel and delivery time point are 0.3, 0.4, and 0.3 respectively. After considering the correction parameter, the system may multiply the initial delivery probability of ad B of 0.4 by the correction parameter of 0.8 (assuming it is the mapped value), and obtain a corrected delivery probability of 0.32; and multiply the initial delivery probabilities of ads A and C by lower correction parameters (such as 0.6 and 0.4), respectively, to obtain corrected delivery probabilities of 0.18 and 0.12. In this way, ad B occupies a higher proportion in the corrected delivery probability, reflecting its relative advantage under this delivery condition.

[0129] The above technical solution

[0130] In a possible implementation, in step S131, determining a candidate Internet advertisement from each Internet advertisement according to the available playback duration at the next delivery time point and the playback duration of each Internet advertisement includes:

[0131] In step S1311, when the playing time of the Internet advertisement is longer than the available playing time at the next delivery time point, the Internet advertisement is used as a non-alternative Internet advertisement, wherein the non-alternative Internet advertisement will not be delivered at the next delivery time point;

[0132] In the embodiment of the present disclosure, according to the available playback time at the next delivery time point, the candidate advertisements that can be delivered at the next delivery time point are screened from all available Internet advertisements, which involves comparing the playback time of each advertisement with the available playback time.

[0133] When the playback time of an Internet ad is longer than the available playback time at the next delivery time point, the ad cannot be fully played at the current delivery time point and is therefore not suitable as an alternative ad. The system will mark such ads as non-alternative ads and exclude them from subsequent delivery decisions. For example, suppose the available playback time at the next delivery time point is 30 seconds, and the playback time of Ad A is 45 seconds. Since the playback time of Ad A exceeds the available playback time, the system will treat Ad A as a non-alternative ad and will not consider delivering Ad A at the current delivery time point.

[0134] In step S1312, when the playing time of the Internet advertisement is equal to the available playing time at the next delivery time point, the Internet advertisement is used as a candidate Internet advertisement;

[0135] In the disclosed embodiment, when the playing time of a certain Internet advertisement is equal to the available playing time at the next delivery time point, the advertisement can be played completely at the current delivery time point, and is therefore a suitable candidate advertisement. Such advertisements are added to the candidate advertisement set. For example, continuing with the above example, assume that the playing time of advertisement B is exactly 30 seconds, which is equal to the available playing time at the next delivery time point. Advertisement B is regarded as a candidate advertisement and is added to the candidate advertisement set.

[0136] In step S1313, when the playback duration of the Internet advertisement is less than the available playback duration at the next delivery time point, the Internet advertisement is used as an alternative Internet advertisement, and the following steps are repeatedly performed: the duration difference between the available playback duration at the next delivery time point and the playback duration of the Internet advertisement is calculated, and the alternative Internet advertisement is continued to be determined from the Internet advertisement list based on the duration difference, until the remaining duration difference is less than the playback duration of any Internet advertisement in the Internet advertisement list.

[0137] In the disclosed embodiment, when the playing time of an Internet advertisement is less than the available playing time at the next delivery time point, the advertisement is initially determined as a candidate Internet advertisement. However, since the remaining available playing time is still sufficient to play other advertisements, the system needs to continue to evaluate and determine whether more advertisements can be filled in the current delivery time point.

[0138] Initial screening: First, all ads with a playback duration less than the available playback duration are considered as potential candidates.

[0139] Duration difference calculation: For each initially filtered ad, calculate the difference between it and the currently remaining available playback duration.

[0140] Recursive filtering: Use the calculated duration difference as the new available playback duration, and filter out ads with a playback duration less than or equal to the new duration from the ad list again.

[0141] Repeat: Repeat the above process until the remaining duration difference is less than the playing duration of any ad in the ad list, that is, no more ads can be filled in the current delivery time point.

[0142] For example: Assume that the initial available playback time at the next delivery time point is 60 seconds, and the ad list contains the following three ads:

[0143] Ad A: Playing time 20 seconds;

[0144] Ad B: Playing time 15 seconds;

[0145] Ad C: Playing time 30 seconds;

[0146] Preliminary screening: All ads are considered preliminary candidates because they are shorter than 60 seconds in duration.

[0147] Duration difference calculation and screening (first round):

[0148] Select the longest advertisement C (30 seconds) for playback.

[0149] After playback, the remaining available playback time is 60 seconds - 30 seconds = 30 seconds.

[0150] At this point, the playing time of Ad A (20 seconds) and Ad B (15 seconds) are both less than the remaining 30 seconds, so they continue to be selected.

[0151] Duration difference calculation and screening (second round):

[0152] Select Ad A (20 seconds) with a longer playing time for playback.

[0153] After playback, the remaining available playback time is 30 seconds - 20 seconds = 10 seconds.

[0154] At this point, only Ad B (15 seconds) has a playback duration greater than the remaining 10 seconds, so Ad B is no longer an alternative ad for the current delivery time point.

[0155] End filtering: The remaining available playback time is 10 seconds, which is less than the playback time of any ad in the ad list (neither ad A nor ad B meets this requirement), so the filtering stops.

[0156] Finally, at the current delivery time point, Ad C and Ad A are selected for playback, while Ad B is not played because it cannot be fully played within the remaining duration of the current delivery time point. This ensures the continuity and efficiency of ad delivery, while avoiding the degradation of user experience caused by playing incomplete ads.

[0157] In a possible implementation, in step S11, determining the basic advertising revenue of each Internet advertisement at each broadcast and determining the delivery path of each Internet advertisement according to the audience group targeted by each Internet advertisement in the Internet advertisement list of the Internet advertisement system includes:

[0158] In step S111, according to the target audience of each Internet advertisement in the Internet advertisement list of the Internet advertisement system, a target historical Internet advertisement with the same target audience is determined from the historically delivered Internet advertisements;

[0159] In the disclosed embodiment, according to the target audiences targeted by each advertisement in the current Internet advertisement list, the historical delivery records can be searched for target historical Internet advertisements with the same or similar audience characteristics. The purpose of this step is to use the existing historical data to assist in predicting the performance of new advertisements. For example, suppose that the current advertisement list contains an Internet advertisement A targeted at "young professionals." All advertisements that are also targeted at "young professionals" or have similar audience characteristics (such as age, occupation, interests, etc.) are searched in the historical delivery records, such as historical advertisements B, C, etc., as target historical Internet advertisements.

[0160] In step S112, the user conversion rate of each Internet advertisement at each playback is determined according to the historical conversion rate estimates and historical revenue estimates of the target historical Internet advertisement in different delivery channels;

[0161] In the disclosed embodiment, after determining the target historical Internet advertisements, the system further analyzes the historical conversion rate valuations and historical revenue estimates of these advertisements in different delivery channels (such as social media, video platforms, search engines, etc.). The historical conversion rate valuation refers to the proportion of clicks or conversions of specific audience groups on a specific delivery channel into actual purchases, registrations, etc. during the past delivery process; the historical revenue estimate is the advertising revenue estimated based on the conversion rate and other factors (such as advertising unit price, click-through rate, etc.). By analyzing these data, the user conversion rate on different delivery channels can be predicted for each advertisement in the current advertising list.

[0162] Continuing with the above example, the system finds that the historical conversion rate of historical ad B on social media is estimated to be 3%, and the historical revenue is estimated to be 1,000 yuan; while the historical conversion rate on the video platform is estimated to be 2%, and the historical revenue is estimated to be 800 yuan. Based on this data, the system can predict that the user conversion rate of ad A on social media may also be high, so the basic advertising revenue on this channel may be higher. Accordingly, the system will also consider social media as a priority delivery channel for ad A.

[0163] In step S113, the basic advertising revenue of the Internet advertisement on each delivery channel is determined according to the user conversion rate of each Internet advertisement at each broadcast and the exposure cost on each delivery channel;

[0164] Among them, user conversion rate is the proportion of users who perform specific actions (such as clicks, purchases, registrations, etc.) expected by advertisers after being exposed to ads. A high conversion rate usually means that the ad content is highly matched with the target audience and the ad creative is attractive. Exposure cost is the fee paid by advertisers for a certain number of ad exposures on a specific delivery channel. This can be based on the number of clicks (CPC), cost per thousand impressions (CPM) or other forms of billing models.

[0165] For example, suppose an e-commerce ad is placed on three delivery channels (social media A, search engine B, content platform C) and the following data is collected:

[0166] Social media A: User conversion rate is 5%, unit exposure cost is 0.1 yuan, and the basic advertising revenue (relative value) is 5% ÷ 0.1 = 0.5.

[0167] Search engine B: user conversion rate is 3%, unit exposure cost is 0.08 yuan, and the basic advertising revenue (relative value) is 3% ÷ 0.08 = 0.375.

[0168] Content platform C: user conversion rate is 4%, unit exposure cost is 0.12 yuan, and the basic advertising revenue (relative value) is 4% ÷ 0.12 ≈ 0.333.

[0169] It can be seen that although the unit exposure cost of social media A is higher, its user conversion rate is also the highest, so the basic advertising revenue (relative value) is the highest. This shows that advertising on social media A may be more efficient, especially when pursuing direct conversion effects.

[0170] In step S114, the delivery channel corresponding to each of the Internet advertisements is determined according to the basic advertisement delivery revenue of the Internet advertisement on each of the delivery channels.

[0171] In the disclosed embodiment, based on the calculated basic advertising revenue (or relative value), it can be decided how to allocate its advertising budget to different delivery channels. Other factors may also be involved, such as brand awareness enhancement, user portrait matching, competitive environment, etc., but basic advertising revenue is usually one of the core considerations. In this way, more resources can be invested in delivery channels that can provide higher cost-effectiveness (i.e., lower cost to obtain higher conversion rate). At the same time, the diversity of delivery channels will also be considered to cover a wider range of potential user groups and reduce dependence on a single channel.

[0172] Continuing with the above scenario, according to the calculation result of step S113, most of the advertising budget may be allocated to social media A, because this channel provides the highest basic advertising revenue (relative value). At the same time, in order to maintain the diversity of delivery and cover more potential users, advertisers may allocate part of the budget to search engine B and content platform C, although their relative benefits are lower.

[0173] Such an allocation strategy aims to maximize the overall effectiveness of advertising while reducing risk and ensuring the wide dissemination of brand information. In actual operations, the delivery strategy may also be adjusted based on market feedback and real-time data to achieve more refined advertising management.

[0174] The present disclosure also provides an Internet advertising system management device. Figure 2 As shown, the device comprises:

[0175] The determination module 210 is configured to determine the basic advertising revenue of each Internet advertisement at each broadcast and determine the delivery path of each Internet advertisement according to the audience group targeted by each Internet advertisement in the Internet advertisement list of the Internet advertisement system;

[0176] The prediction module 220 is configured to predict the initial delivery probability of each of the Internet advertisements at the next delivery time point on the delivery path by the Internet advertising system based on the basic advertisement delivery revenue of each of the Internet advertisements and the resource competition coefficient of other Internet advertisements on the same path in the Internet advertisement list relative to the Internet advertisement;

[0177] The correction module 230 is configured to correct the initial delivery probability of each Internet advertisement at the next delivery time point on the delivery path according to the available broadcasting time and delivery income at the next delivery time point, and obtain the corrected delivery probability of each Internet advertisement at the next delivery time point on the delivery path, wherein the delivery income is used to represent the ratio of the golden delivery income of the delivery time point to each golden delivery time point;

[0178] The delivery module 240 is configured to determine the target Internet advertisement to be delivered at the next delivery time point on the delivery path according to the modified delivery probability, and deliver the target Internet advertisement when the next delivery time point is reached.

[0179] In a possible implementation, the prediction module 220 is configured to:

[0180] Issuing a preset number of votes to each of the Internet advertisements corresponding to the delivery channels;

[0181] Determine the number of votes obtained by each of the Internet advertisements on the delivery path at each delivery time point of the delivery path according to the resource competition coefficient of each of the Internet advertisements relative to other Internet advertisements on the same path in the Internet advertisement list and a preset number of votes corresponding to each of the Internet advertisements;

[0182] Determining the competition index of each of the Internet advertisements according to the basic advertisement delivery revenue of each of the Internet advertisements and the number of votes corresponding to each of the delivery time points in the delivery channel;

[0183] The initial delivery probability of each of the Internet advertisements to be delivered by the Internet advertising system at the next delivery time point on the delivery path is predicted based on the competition index and the ratio of the number of votes to the sum of the preset number.

[0184] In a possible implementation, the prediction module 220 is configured to:

[0185] The plurality of Internet advertisements corresponding to the delivery path are sequentially used as voted advertisements, and according to the resource competition coefficient of each Internet advertisement relative to other Internet advertisements on the same path in the Internet advertisement list, voting is performed on all delivery time points of each Internet advertisement on the delivery path by having other Internet advertisements vote for the voted advertisements, until all Internet advertisements on the delivery path are voted as voted advertisements at all delivery time points of the delivery path, and a preset number of votes for each Internet advertisement are cast, thereby obtaining the number of votes obtained by each Internet advertisement on the delivery path at each delivery time point of the delivery path;

[0186] Among them, after the voted advertisement is voted by the other Internet advertisements at any of the delivery time points, the resource competition coefficient of other Internet advertisements on the same path in the Internet advertisement list relative to the Internet advertisement is adjusted according to the number of votes at the delivery time point.

[0187] In a possible implementation, the prediction module 220 is configured to:

[0188] Determine the number of votes cast by other Internet advertisements on the same channel in the Internet advertisement list for the voted advertisement at any of the delivery time points;

[0189] Determine the yield of the other Internet advertisements to the voted advertisement according to a first ratio of the number of votes to the preset number and a second ratio of the number of votes to the number of remaining votes of other Internet advertisements on the same channel in the Internet advertisement list before voting at the delivery time point;

[0190] When the yield exceeds a first preset threshold, reducing the resource competition coefficient of other Internet advertisements on the same path in the Internet advertisement list relative to the Internet advertisement according to a preset step length;

[0191] When the yield exceeds a second preset threshold and does not exceed the first preset threshold, maintaining the resource competition coefficient of other Internet advertisements on the same path in the Internet advertisement list relative to the Internet advertisement, wherein the second preset threshold is less than the first preset threshold;

[0192] When the degree of acceptance does not exceed the second preset threshold, the resource competition coefficient of other Internet advertisements on the same path in the Internet advertisement list relative to the Internet advertisement is increased according to a preset step size.

[0193] In a possible implementation, the correction module 230 is configured to:

[0194] Determining a candidate Internet advertisement from among the Internet advertisements according to the available broadcasting duration at the next delivery time point and the broadcasting duration of each Internet advertisement;

[0195] In the case where the Internet advertisement is the candidate Internet advertisement, determining a single delivery revenue of the Internet advertisement at each delivery point in time on the delivery path according to the delivery revenue and the exposure cost on each delivery path;

[0196] Determine the correction parameter corresponding to the next delivery time point of the Internet advertisement on the delivery path according to the ratio of the single delivery revenue of each delivery time point of the Internet advertisement on the delivery path to the basic advertisement delivery revenue of the Internet advertisement at each broadcast;

[0197] According to the correction parameters corresponding to each of the Internet advertisements, the initial delivery probability of each of the Internet advertisements at the next delivery time point on the delivery path is corrected to obtain a corrected delivery probability of each of the Internet advertisements at the next delivery time point on the delivery path.

[0198] In a possible implementation, the correction module 230 is configured to:

[0199] In the case where the playing time of the Internet advertisement is longer than the available playing time at the next delivery time point, the Internet advertisement is used as a non-alternative Internet advertisement, wherein the non-alternative Internet advertisement will not be delivered at the next delivery time point;

[0200] In the case where the playing time of the Internet advertisement is equal to the available playing time at the next delivery time point, the Internet advertisement is used as a candidate Internet advertisement;

[0201] In the case that the playback duration of the Internet advertisement is less than the available playback duration at the next delivery time point, the Internet advertisement is used as an alternative Internet advertisement, and the following steps are repeatedly performed: calculating the duration difference between the available playback duration at the next delivery time point and the playback duration of the Internet advertisement, and continuing to determine alternative Internet advertisements from the Internet advertisement list based on the duration difference, until the remaining duration difference is less than the playback duration of any Internet advertisement in the Internet advertisement list.

[0202] In a possible implementation, the determining module 210 is configured to:

[0203] According to the target audience of each Internet advertisement in the Internet advertisement list of the Internet advertisement system, determine the target historical Internet advertisement with the same target audience from the historically delivered Internet advertisements;

[0204] Determine the user conversion rate of each of the Internet advertisements at each playback according to the historical conversion rate estimates and historical revenue estimates of the target historical Internet advertisements in different delivery channels;

[0205] Determine the basic advertising revenue of the Internet advertisement on each delivery channel according to the user conversion rate of each Internet advertisement at each broadcast and the exposure cost on each delivery channel;

[0206] The delivery channel corresponding to each of the Internet advertisements is determined according to the basic advertisement delivery revenue of the Internet advertisement on each of the delivery channels.

[0207] The present disclosure also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the method described in any one of the aforementioned embodiments are implemented.

[0208] According to a fourth aspect of the embodiments of the present disclosure, an electronic device is provided, including:

[0209] a memory having a computer program stored thereon;

[0210] A processor is used to execute the computer program in the memory to implement the steps of the method in any one of the aforementioned embodiments.

[0211] Figure 3 The Internet advertising system management device 100 shown includes: a processor 1001 and a memory 1003. The processor 1001 and the memory 1003 are connected, such as through a bus 1002. Optionally, the Internet advertising system management device 100 may also include a communication component, which can be used for data interaction between the device 100 and other devices, such as data transmission and / or data reception. It should be noted that the communication component in actual scheduling is not limited to one, and the structure of the Internet advertising system management device 100 does not constitute a limitation on the embodiments of the present application.

[0212] Processor 1001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 1001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0213] The bus 1002 may include a path to transmit information between the above components. The bus 1002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 1002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0214] The memory 1003 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), magnetic disk storage medium, other magnetic storage devices, or any other medium that can be used to carry or store program code and can be read by a computer, without limitation herein.

[0215] The memory 1003 is used to store program codes for executing the embodiments of the present disclosure, and the execution is controlled by the processor 1001. The processor 1001 is used to execute the program codes stored in the memory 1003 to implement the steps shown in the above-mentioned Internet advertising system management method embodiment.

[0216] The embodiment of the present disclosure also provides a computer-readable storage medium having program code stored thereon. When the program code is executed by a processor, the steps and corresponding contents of the aforementioned Internet advertising system management method embodiment can be implemented.

[0217] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments; within the technical concept of the present disclosure, various changes, modifications, substitutions and variations may be made to these embodiments, and these changes, modifications, substitutions and variations all fall within the protection scope of the present disclosure.

[0218] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction, and they should also be regarded as the contents disclosed in this disclosure. In order to avoid unnecessary repetition, this disclosure will not further describe various possible combinations. The technical scope of this application is not limited to the contents in the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A method for managing an Internet advertising system, characterized in that: An Internet advertising system for placing Internet advertisements, the method comprising: According to the audience groups targeted by each Internet advertisement in the Internet advertisement list of the Internet advertisement system, determine the basic advertisement delivery revenue of each Internet advertisement at each broadcast and determine the delivery path of each Internet advertisement; Predicting the initial delivery probability of each of the Internet advertisements at the next delivery time point on the delivery path by the Internet advertising system based on the basic advertisement delivery revenue of each of the Internet advertisements and the resource competition coefficient of other Internet advertisements on the same path in the Internet advertisement list relative to the Internet advertisement; According to the available broadcasting time and the delivery income at the next delivery time point, the initial delivery probability of delivering each of the Internet advertisements at the next delivery time point on the delivery path is corrected to obtain the corrected delivery probability of delivering each of the Internet advertisements at the next delivery time point on the delivery path, wherein the delivery income is used to represent the ratio of the golden delivery income at the delivery time point to each golden delivery time point; Determine, according to the modified delivery probability, a target Internet advertisement to be delivered at the next delivery time point on the delivery path, and deliver the target Internet advertisement when the next delivery time point is reached; The method of predicting the initial delivery probability of each Internet advertisement at the next delivery time point on the delivery path by the Internet advertising system based on the basic advertisement delivery revenue of each Internet advertisement and the resource competition coefficient of other Internet advertisements on the same path in the Internet advertisement list relative to the Internet advertisement includes: Issuing a preset number of votes to each of the Internet advertisements corresponding to the delivery channels; Determine the number of votes obtained by each of the Internet advertisements on the delivery path at each delivery time point of the delivery path according to the resource competition coefficient of each of the Internet advertisements relative to other Internet advertisements on the same path in the Internet advertisement list and a preset number of votes corresponding to each of the Internet advertisements; Determining the competition index of each of the Internet advertisements according to the basic advertisement delivery revenue of each of the Internet advertisements and the number of votes corresponding to each of the delivery time points in the delivery channel; Predicting the initial delivery probability of each of the Internet advertisements delivered by the Internet advertising system at the next delivery time point on the delivery path according to the competition index and the ratio of the number of votes to the sum of the preset number; The method of correcting the initial delivery probability of each Internet advertisement at the next delivery time point on the delivery path according to the available broadcasting time and the delivery revenue at the next delivery time point to obtain the corrected delivery probability of each Internet advertisement at the next delivery time point on the delivery path includes: Determining a candidate Internet advertisement from among the Internet advertisements according to the available broadcasting duration at the next delivery time point and the broadcasting duration of each Internet advertisement; In the case where the Internet advertisement is the candidate Internet advertisement, determining a single delivery revenue of the Internet advertisement at each delivery time point on the delivery path according to the delivery revenue and the exposure cost on each delivery path; Determine, according to the ratio of the single delivery revenue of the Internet advertisement at each delivery time point on the delivery path to the basic advertisement delivery revenue of the Internet advertisement at each broadcast, the correction parameter corresponding to the next delivery time point of the Internet advertisement on the delivery path; According to the correction parameters corresponding to each of the Internet advertisements, the initial delivery probability of each of the Internet advertisements at the next delivery time point on the delivery path is corrected to obtain a corrected delivery probability of each of the Internet advertisements at the next delivery time point on the delivery path.

2. The method according to claim 1, characterized in that The method of voting to determine the number of votes obtained by each of the Internet advertisements on the delivery path at each delivery time point of the delivery path according to the resource competition coefficient of each of the Internet advertisements relative to other Internet advertisements on the same path in the Internet advertisement list and the preset number of votes corresponding to each of the Internet advertisements includes: The plurality of Internet advertisements corresponding to the delivery path are sequentially used as voted advertisements, and according to the resource competition coefficient of each Internet advertisement relative to other Internet advertisements on the same path in the Internet advertisement list, voting is performed on all delivery time points of each Internet advertisement on the delivery path by having other Internet advertisements vote for the voted advertisements, until all Internet advertisements on the delivery path are voted as voted advertisements at all delivery time points of the delivery path, and a preset number of votes for each Internet advertisement are cast, thereby obtaining the number of votes obtained by each Internet advertisement on the delivery path at each delivery time point of the delivery path; Among them, after the voted advertisement is voted by the other Internet advertisements at any of the delivery time points, the resource competition coefficient of other Internet advertisements on the same path in the Internet advertisement list relative to the Internet advertisement is adjusted according to the number of votes at the delivery time point.

3. The method according to claim 2, characterized in that After the voted advertisement is voted by the other Internet advertisements at any delivery time point, the resource competition coefficient of other Internet advertisements on the same path in the Internet advertisement list relative to the Internet advertisement is adjusted according to the number of votes at the delivery time point, including: Determine the number of votes cast by other Internet advertisements on the same channel in the Internet advertisement list for the voted advertisement at any of the delivery time points; Determine the yield of the other Internet advertisements to the voted advertisement according to a first ratio of the number of votes to the preset number and a second ratio of the number of votes to the number of remaining votes of other Internet advertisements on the same channel in the Internet advertisement list before voting at the delivery time point; When the yield exceeds a first preset threshold, reducing the resource competition coefficient of other Internet advertisements on the same path in the Internet advertisement list relative to the Internet advertisement according to a preset step length; When the yield exceeds a second preset threshold and does not exceed the first preset threshold, maintaining the resource competition coefficient of other Internet advertisements on the same path in the Internet advertisement list relative to the Internet advertisement, wherein the second preset threshold is less than the first preset threshold; When the degree of acceptance does not exceed the second preset threshold, the resource competition coefficient of other Internet advertisements on the same path in the Internet advertisement list relative to the Internet advertisement is increased according to a preset step size.

4. The method according to claim 1, characterized in that: The step of determining the candidate Internet advertisements from the Internet advertisements according to the available playback duration at the next delivery time point and the playback duration of the Internet advertisements includes: In the case where the playing time of the Internet advertisement is longer than the available playing time at the next delivery time point, the Internet advertisement is used as a non-alternative Internet advertisement, wherein the non-alternative Internet advertisement will not be delivered at the next delivery time point; In the case where the playing time of the Internet advertisement is equal to the available playing time at the next delivery time point, the Internet advertisement is used as a candidate Internet advertisement; In the case that the playback duration of the Internet advertisement is less than the available playback duration at the next delivery time point, the Internet advertisement is used as an alternative Internet advertisement, and the following steps are repeatedly performed: calculating the duration difference between the available playback duration at the next delivery time point and the playback duration of the Internet advertisement, and continuing to determine alternative Internet advertisements from the Internet advertisement list based on the duration difference, until the remaining duration difference is less than the playback duration of any Internet advertisement in the Internet advertisement list.

5. The method according to any one of claims 1 to 4, characterized in that The method of determining the basic advertising revenue of each Internet advertisement at each broadcast and determining the delivery path of each Internet advertisement according to the audience group targeted by each Internet advertisement in the Internet advertisement list of the Internet advertisement system includes: According to the target audience of each Internet advertisement in the Internet advertisement list of the Internet advertisement system, determine the target historical Internet advertisement with the same target audience from the historically delivered Internet advertisements; Determine the user conversion rate of each of the Internet advertisements at each playback according to the historical conversion rate estimates and historical revenue estimates of the target historical Internet advertisements in different delivery channels; Determine the basic advertising revenue of the Internet advertisement on each delivery channel according to the user conversion rate of each Internet advertisement at each broadcast and the exposure cost on each delivery channel; The delivery channel corresponding to each of the Internet advertisements is determined according to the basic advertisement delivery revenue of the Internet advertisement on each of the delivery channels.

6. An Internet advertising system management device, characterized in that: The device comprises: A determination module configured to determine the basic advertising revenue of each Internet advertisement at each broadcast and the delivery path of each Internet advertisement according to the audience group targeted by each Internet advertisement in the Internet advertisement list of the Internet advertisement system; A prediction module is configured to predict the initial delivery probability of each of the Internet advertisements at the next delivery time point on the delivery path by the Internet advertising system based on the basic advertisement delivery revenue of each of the Internet advertisements and the resource competition coefficient of other Internet advertisements on the same path in the Internet advertisement list relative to the Internet advertisement; A correction module is configured to correct the initial delivery probability of each Internet advertisement at the next delivery time point on the delivery path according to the available broadcasting time and delivery income at the next delivery time point, and obtain a corrected delivery probability of each Internet advertisement at the next delivery time point on the delivery path, wherein the delivery income is used to represent the ratio of the golden delivery income of the delivery time point to each golden delivery time point; A delivery module is configured to determine the target Internet advertisement to be delivered at the next delivery time point on the delivery path according to the modified delivery probability, and deliver the target Internet advertisement when the next delivery time point is reached; Wherein, the prediction module is configured as follows: Issuing a preset number of votes to each of the Internet advertisements corresponding to the delivery channels; Determine the number of votes obtained by each of the Internet advertisements on the delivery path at each delivery time point of the delivery path according to the resource competition coefficient of each of the Internet advertisements relative to other Internet advertisements on the same path in the Internet advertisement list and a preset number of votes corresponding to each of the Internet advertisements; Determining the competition index of each of the Internet advertisements according to the basic advertisement delivery revenue of each of the Internet advertisements and the number of votes corresponding to each of the delivery time points in the delivery channel; Predicting the initial delivery probability of each of the Internet advertisements delivered by the Internet advertising system at the next delivery time point on the delivery path according to the competition index and the ratio of the number of votes to the sum of the preset number; Wherein, the correction module is configured as follows: Determining a candidate Internet advertisement from among the Internet advertisements according to the available broadcasting duration at the next delivery time point and the broadcasting duration of each Internet advertisement; In the case where the Internet advertisement is the candidate Internet advertisement, determining a single delivery revenue of the Internet advertisement at each delivery time point on the delivery path according to the delivery revenue and the exposure cost on each delivery path; Determine, according to the ratio of the single delivery revenue of the Internet advertisement at each delivery time point on the delivery path to the basic advertisement delivery revenue of the Internet advertisement at each broadcast, the correction parameter corresponding to the next delivery time point of the Internet advertisement on the delivery path; According to the correction parameters corresponding to each of the Internet advertisements, the initial delivery probability of each of the Internet advertisements at the next delivery time point on the delivery path is corrected to obtain a corrected delivery probability of each of the Internet advertisements at the next delivery time point on the delivery path.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method described in any one of claims 1 to 5 are implemented.

8. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 5.

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