Multi-scene advertisement identification content management system based on cloud platform

Abstract

The invention discloses a multi-scene advertisement identification content management system based on a cloud platform, and particularly relates to the technical field of advertisement identification content management, which comprises a content adaptation module, a distribution stabilization module, a conversion balance module and a compatible processing module, the content adaptation module collects consistency information of the multi-scene advertisement identification content based on the cloud platform and obtains a content adaptation coefficient, and the distribution stabilization module collects stability information of the multi-scene advertisement identification content based on the cloud platform and obtains a distribution connection coefficient; the conversion balance module establishes a conversion balance model according to the content adaptation coefficient and the distribution connection coefficient, obtains a conversion balance index to perform efficacy evaluation and classifies evaluation results, and the compatibility processing module outputs a management optimization strategy according to classification of the evaluation results, so that the defect of effect evaluation after putting is emphasized in the prior art is overcome; effectiveness diagnosis in delivery of predictive judgment before delivery is optimized, and the efficiency of multi-scene advertisement identification content management is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of advertising logo content management, and more specifically, to a multi-scenario advertising logo content management system based on a cloud platform. Background Art

[0002] Multi-scenario advertising logo content management based on cloud platform uses cloud computing technology to centrally manage advertising logos and content in different scenarios, which is used to update and distribute advertising materials in real time, and optimize advertising effects according to user behavior, scenario requirements and data analysis, so as to improve the flexibility and accuracy of advertising delivery and obtain higher conversion rate. The existing multi-scenario advertising logo content management based on cloud platform focuses on the delivery effect analysis of advertising materials after delivery. However, for different scenarios and different target groups, the delivery effect of the same advertising material may have deviations before delivery, which can be predicted. The prediction and evaluation of advertising materials before delivery affects the delivery application efficiency of different advertising materials. There may be factors that interfere with the delivery consistency of advertising materials before and after delivery. Therefore, it is an urgent problem to evaluate the adaptation consistency and delivery stability of advertising materials.

[0003] In order to solve the above defects, a technical solution is now proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-scenario advertising logo content management system based on a cloud platform to address the deficiencies in the background technology.

[0005] In order to achieve the above-mentioned purpose, the present invention provides the following technical solutions: a multi-scenario advertising logo content management system based on a cloud platform, including a content adaptation module, a distribution stabilization module, a conversion balance module, and a compatible processing module;

[0006] The content adaptation module is used to collect consistency information of multi-scenario advertising identification content based on the cloud platform, obtain content adaptation coefficients based on the consistency information of multi-scenario advertising identification content based on the cloud platform, and transmit the content adaptation coefficients to the conversion balance module;

[0007] The distribution stability module is used to collect stability information of multi-scenario advertising identification content based on the cloud platform, obtain the distribution connection coefficient based on the stability information of the multi-scenario advertising identification content based on the cloud platform, and transmit the distribution connection coefficient to the conversion balance module;

[0008] The conversion balance module is used to establish a conversion balance model based on the content adaptation coefficient and the distribution connection coefficient, and obtain the conversion balance index. By comparing the preset conversion threshold with the conversion balance index, the effectiveness of multi-scenario advertising logo content management based on the cloud platform is evaluated, and the evaluation results are classified;

[0009] The compatible processing module is used to output management optimization strategies based on the classification of evaluation results.

[0010] Preferably, the method for obtaining consistency information is:

[0011] Obtain the adaptation coefficient of the multi-scene advertisement identification content within a periodic time T, and integrate the adaptation coefficients of the multi-scene advertisement identification content within several periodic time T into a data set, and mark the data set as Spe = {ad a}, where a={1,2,3,…,p}, and p is a positive integer, ad a It represents the adaptation coefficient of the multi-scene advertisement identification content within the qth cycle time T. The calculation expression of the adaptation coefficient is Spe = Siz Ais , where Siz represents the size of the advertising logo content, Ais represents the RGB channel value of the advertising logo content, and the RGB channel value is the symbol value of the RGB color channel in the advertising logo content. The calculation expression is F lv =R+G×256+B×256 2 , where F lv is the flag value, R is the red channel value in the RGB channel, G is the green channel value in the RGB channel, and B is the blue channel value in the RGB channel;

[0012] Calculate the standard deviation of the adaptation coefficient of the multi-scene advertising logo content within a certain period of time T. The calculation expression is: In the formula, gbn is the average value of the adaptation coefficient of the multi-scene advertising logo content within several cycles T, and the calculation expression is:

[0013] Preferably, the method for obtaining the content adaptation coefficient according to the consistency information is:

[0014] The click rate is C lk , the residence time is S ti , then the calculation expression of content adaptation coefficient is In the formula, Spe is the adaptation coefficient, C of Serving conversion rate for advertising.

[0015] Preferably, the method for obtaining stability information is:

[0016] Obtain the smooth coefficient of the advertisement logo updated in real time within the cycle time T, and integrate the smooth coefficients of the advertisement logo updated in real time within several cycle times T into a data set, and mark the data set as Smo = {ne c}, where c = {1, 2, 3, ..., v}, and v is a positive integer, ne cIt represents the smooth coefficient of the real-time update of the advertising logo within the cth cycle time T. The calculation expression of the smooth coefficient is Among them, R up is the number of update requests, S up is the number of successful updates;

[0017] Calculate the standard deviation of the smooth coefficient of real-time update of advertising logos within several cycles T. The calculation expression is: In the formula, gpv is the average value of the smoothness coefficient of the real-time update of the advertising logo within several cycles T, and the calculation expression is:

[0018] Preferably, the method for obtaining the distribution connection coefficient according to the stability information is:

[0019] The network delay is calibrated as N ed , the packet loss rate is P lr , then the calculation expression of the distribution connection coefficient is Where D cc is the distribution connection coefficient, N th is the network delay threshold.

[0020] Preferably, a conversion balance model is established according to the content adaptation coefficient and the distribution connection coefficient, and a method for obtaining a conversion balance index is as follows:

[0021] Constructing a transformation equilibrium model Where, T rb is the transformation balance index, D et D is the duration of advertisement delivery. fr is the advertising frequency, α and β are and D cc *exp(D fr ), and both α and β are positive numbers.

[0022] Preferably, the effectiveness of multi-scenario advertising logo content management based on the cloud platform is evaluated by comparing the preset conversion threshold with the conversion balance index, and the logic of classifying the evaluation results is:

[0023] Preset conversion threshold T hd , the calculated transformation balance index T rb With conversion threshold T hd Compare, if the transformation balance index T rb Greater than or equal to the conversion threshold T hd , it indicates that the content management of multi-scenario advertising logos is in an unbalanced state;

[0024] If the transformation balance index T rb Less than the conversion threshold T hd, it marks that the multi-scene advertising logo content management is in a balanced state.

[0025] Preferably, the logic of the management optimization strategy according to the classification output of the evaluation results is:

[0026] When the management of multi-scenario advertising logo content is in an unbalanced state, notify the management personnel to conduct multi-scenario review, verify the scene validity of different advertising materials, and check the network quality;

[0027] When the multi-scenario advertising logo content management is in a balanced state, the advertising delivery data is recorded and stored and sent to the management personnel for analysis.

[0028] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0029] The present application collects and analyzes consistency information and stability information in the content management of multi-scenario advertising logos based on a cloud platform, extracts and obtains the content adaptation coefficient and the distribution connection coefficient, establishes a conversion balance model based on the content adaptation coefficient and the distribution connection coefficient, and obtains the conversion balance index. This makes up for the drawback of the existing multi-scenario advertising logo content management that focuses on post-delivery effect evaluation, optimizes the effectiveness diagnosis of the predictive judgment before delivery, improves the efficiency of multi-scenario advertising logo content management, reduces the possibility of delivery failure, and improves the return on advertising delivery. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0031] Figure 1 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0032] 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.

[0033] Example 1: Please refer to Figure 1 As shown, the present invention is a multi-scenario advertising logo content management system based on a cloud platform, including a content adaptation module, a distribution stabilization module, a conversion balance module, and a compatible processing module;

[0034] The content adaptation module is used to collect consistency information of multi-scenario advertising identification content based on the cloud platform, obtain content adaptation coefficients based on the consistency information of multi-scenario advertising identification content based on the cloud platform, and transmit the content adaptation coefficients to the conversion balance module;

[0035] The distribution stability module is used to collect stability information of multi-scenario advertising identification content based on the cloud platform, obtain the distribution connection coefficient based on the stability information of the multi-scenario advertising identification content based on the cloud platform, and transmit the distribution connection coefficient to the conversion balance module;

[0036] The conversion balance module is used to establish a conversion balance model based on the content adaptation coefficient and the distribution connection coefficient, and obtain the conversion balance index. By comparing the preset conversion threshold with the conversion balance index, the effectiveness of multi-scenario advertising logo content management based on the cloud platform is evaluated, and the evaluation results are classified;

[0037] The compatible processing module is used to output management optimization strategies based on the classification of evaluation results.

[0038] In the multi-scenario advertising logo content management based on the cloud platform, it is difficult to ensure the content adaptation and consistency between different scenarios. Different scenarios such as online advertising, outdoor advertising, mobile applications, etc. may require different content formats, sizes and styles. For example, outdoor advertising usually requires a greater visual impact, while social media advertising may focus more on interactivity and sharing. In actual operations, it is difficult to flexibly adjust the advertising content and ensure the effectiveness of each version.

[0039] The core information of an advertisement includes brand logo, promotional information, etc., which must be consistent in different scenarios. If the information is not clearly conveyed or deviates from the brand image in a certain scenario, it may cause user confusion and affect brand recognition.

[0040] User behaviors and expectations may differ in different scenarios. For example, users may have lower patience for ads on mobile devices, but are willing to spend more time on desktop devices. Designing and distributing consistent ad content that meets user expectations in different scenarios is a major challenge.

[0041] The content adaptation module is used to collect consistency information of multi-scenario advertising identification content based on the cloud platform, obtain content adaptation coefficients based on the consistency information of multi-scenario advertising identification content based on the cloud platform, and transmit the content adaptation coefficients to the conversion balance module;

[0042] The method for obtaining consistency information is:

[0043] Obtain the adaptation coefficient of the multi-scene advertisement identification content within a periodic time T, and integrate the adaptation coefficients of the multi-scene advertisement identification content within several periodic time T into a data set, and mark the data set as Spe = {ad a}, where a={1,2,3,…,p}, and p is a positive integer, ad a It represents the adaptation coefficient of the multi-scene advertisement identification content within the qth cycle time T. The calculation expression of the adaptation coefficient is Spe = Siz Ais , where Siz represents the size of the advertising logo content, Ais represents the RGB channel value of the advertising logo content, and the RGB channel value is the symbol value of the RGB color channel in the advertising logo content. The calculation expression is F lv =R+G×256+B×256 2 , where F lv is the flag value, R is the red channel value in the RGB channel, G is the green channel value in the RGB channel, and B is the blue channel value in the RGB channel;

[0044] Calculate the standard deviation of the adaptation coefficient of the multi-scene advertising logo content within a certain period of time T. The calculation expression is: In the formula, gbn is the average value of the adaptation coefficient of the multi-scene advertising logo content within several cycles T, and the calculation expression is:

[0045] The method for obtaining the content adaptation coefficient based on consistency information is:

[0046] The click rate is C lk , the residence time is S ti , then the calculation expression of content adaptation coefficient is In the formula, Spe is the adaptation coefficient, C of Serving conversion rate for advertising.

[0047] A mathematical model is established by combining content adaptability, information consistency and user experience to conduct a comprehensive evaluation of adaptability consistency. In the process of comprehensive evaluation combining multiple dimensions, a comprehensive understanding of the performance of advertising content in different scenarios is obtained to avoid one-sided analysis. Scientific decisions are made based on quantitative data and model evaluation to optimize advertising strategies and thus improve the effectiveness of advertising. The various indicators in the model help identify specific directions for improvement, such as content incompatibility or insufficient information communication, so that targeted adjustments can be made. The model is dynamically adjusted according to market changes and user feedback to make advertising content more flexible and adapt to different audience needs and scenario changes. By optimizing advertising content and delivery strategies, the return on advertising investment can be improved to ensure that resources are effectively utilized. Through comprehensive evaluation, advertising content can be more effectively managed in a changing market environment to improve the overall effectiveness of advertising.

[0048] The distribution stability module is used to collect stability information of multi-scenario advertising identification content based on the cloud platform, obtain the distribution connection coefficient based on the stability information of the multi-scenario advertising identification content based on the cloud platform, and transmit the distribution connection coefficient to the conversion balance module;

[0049] The method for obtaining stability information is:

[0050] Obtain the smooth coefficient of the advertisement logo updated in real time within the cycle time T, and integrate the smooth coefficients of the advertisement logo updated in real time within several cycle times T into a data set, and mark the data set as Smo = {ne c}, where c = {1, 2, 3, ..., v}, and v is a positive integer, ne c It represents the smooth coefficient of the real-time update of the advertising logo within the cth cycle time T. The calculation expression of the smooth coefficient is Among them, R up is the number of update requests, S up is the number of successful updates;

[0051] Calculate the standard deviation of the smooth coefficient of real-time update of advertising logos within several cycles T. The calculation expression is: In the formula, gpv is the average value of the smoothness coefficient of the real-time update of the advertising logo within several cycles T, and the calculation expression is:

[0052] The method for obtaining the distribution connection coefficient based on stability information is:

[0053] The network delay is calibrated as N ed , the packet loss rate is P lr , then the calculation expression of the distribution connection coefficient is Where D cc is the distribution connection coefficient, N th is the network delay threshold;

[0054] The method for obtaining network delay is to measure and record the average delay through network monitoring tools. The packet loss rate is to analyze the ratio of the number of data packets lost in a certain period of time to the total number of packets sent through network traffic monitoring software. Commonly used network monitoring tools include Ping command.

[0055] Evaluate the impact of unstable network connection on real-time update effect, understand the impact of network delay and packet loss rate on content update, optimize user experience, and ensure that users can obtain the latest information in a timely manner. The evaluation results will help to reasonably configure server and bandwidth resources under different network conditions, improve overall system efficiency, identify the impact of network problems on update effect, and take measures in advance, such as adding redundancy or improving data transmission protocols to improve system stability. Data-driven evaluation provides a basis for the technical team to make decisions on network optimization, content push strategy and user feedback. By identifying and resolving potential network problems, business losses caused by update failures can be reduced and brand image can be maintained. The evaluation provides a basis for dynamically adjusting content update strategies and optimizing content loading methods when network conditions are poor to reduce the impact on users.

[0056] The conversion balance module is used to establish a conversion balance model based on the content adaptation coefficient and the distribution connection coefficient, and obtain the conversion balance index. By comparing the preset conversion threshold with the conversion balance index, the effectiveness of multi-scenario advertising logo content management based on the cloud platform is evaluated, and the evaluation results are classified;

[0057] The method of establishing a conversion balance model based on the content adaptation coefficient and the distribution connection coefficient and obtaining the conversion balance index is as follows:

[0058] Constructing a transformation equilibrium model Where, T rb is the transformation balance index, D et D is the duration of advertisement delivery. fr is the advertising frequency, α and β are and D cc *exp(D fr ), and both α and β are positive numbers;

[0059] The effectiveness of multi-scenario advertising logo content management based on the cloud platform is evaluated by comparing the preset conversion threshold with the conversion balance index, and the logic for classifying the evaluation results is as follows:

[0060] Preset conversion threshold T hd , the calculated transformation balance index T rb With conversion threshold T hd Compare, if the transformation balance index T rb Greater than or equal to the conversion threshold T hd , it indicates that the content management of multi-scenario advertising logos is in an unbalanced state;

[0061] If the transformation balance index T rb Less than the conversion threshold T hd , it marks that the multi-scene advertising logo content management is in a balanced state.

[0062] Combined with comprehensive evaluations from multiple dimensions, we can fully understand the performance of advertisements in different scenarios, so as to better formulate delivery strategies, identify specific problems, and optimize advertising content in a targeted manner, improve the consistency of information transmission and user experience, and enhance advertising effectiveness. By evaluating the impact of network connections on real-time updates, we can take measures to optimize the network environment and ensure that advertising content is updated in a timely manner, thereby improving user satisfaction. By optimizing advertising content and update strategies, we can increase users' willingness to interact, thereby improving conversion rates and increasing returns on investment. Comprehensive evaluations can be used to identify potential system and network risks, assist in advertising identification content management, improve the resilience of the overall system, and reduce uncertainty in management operations.

[0063] The compatible processing module is used to output the management optimization strategy according to the classification of the evaluation results;

[0064] The logic of the management optimization strategy based on the classification output of the evaluation results is:

[0065] When the management of multi-scenario advertising logo content is in an unbalanced state, notify the management personnel to conduct multi-scenario review, verify the scene validity of different advertising materials, and check the network quality;

[0066] When the multi-scenario advertising logo content management is in a balanced state, the advertising delivery data is recorded and stored and sent to the management personnel for analysis.

[0067] The present application collects and analyzes consistency information and stability information in the content management of multi-scenario advertising logos based on a cloud platform, extracts and obtains the content adaptation coefficient and the distribution connection coefficient, establishes a conversion balance model based on the content adaptation coefficient and the distribution connection coefficient, and obtains the conversion balance index. This makes up for the drawback of the existing multi-scenario advertising logo content management that focuses on post-delivery effect evaluation, optimizes the effectiveness diagnosis of the predictive judgment before delivery, improves the efficiency of multi-scenario advertising logo content management, reduces the possibility of delivery failure, and improves the return on advertising delivery.

[0068] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0069] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of computer program goods. The computer program goods include one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0070] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0071] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0072] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0073] If the functions are implemented in the form of software functional units and sold or used as independent goods, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can essentially or in other words, the part that contributes to the prior art or the part of the technical solution can be embodied in the form of software goods, and the computer software goods are stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0074] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A multi-scenario advertising logo content management system based on a cloud platform, characterized in that: Including content adaptation module, distribution stabilization module, conversion balance module, and compatible processing module; The content adaptation module is used to collect consistency information of multi-scenario advertising identification content based on the cloud platform, obtain content adaptation coefficients based on the consistency information of multi-scenario advertising identification content based on the cloud platform, and transmit the content adaptation coefficients to the conversion balance module; The distribution stability module is used to collect stability information of multi-scenario advertising identification content based on the cloud platform, obtain the distribution connection coefficient based on the stability information of the multi-scenario advertising identification content based on the cloud platform, and transmit the distribution connection coefficient to the conversion balance module; The conversion balance module is used to establish a conversion balance model based on the content adaptation coefficient and the distribution connection coefficient, and obtain the conversion balance index. By comparing the preset conversion threshold with the conversion balance index, the effectiveness of multi-scenario advertising logo content management based on the cloud platform is evaluated, and the evaluation results are classified; The compatible processing module is used to output management optimization strategies based on the classification of evaluation results.

2. The multi-scenario advertising logo content management system based on a cloud platform according to claim 1, characterized in that: The method for obtaining consistency information is: Obtain the adaptation coefficient of the multi-scene advertisement identification content within a periodic time T, and integrate the adaptation coefficients of the multi-scene advertisement identification content within several periodic time T into a data set, and mark the data set as Spe = {ad a }, where a={1,2,3,…,p}, and p is a positive integer, ad a It represents the adaptation coefficient of the multi-scene advertisement identification content within the qth cycle time T. The calculation expression of the adaptation coefficient is Spe = Siz Ais , where Siz represents the size of the advertising logo content, Ais represents the RGB channel value of the advertising logo content, and the RGB channel value is the symbol value of the RGB color channel in the advertising logo content. The calculation expression is F lv =R+G×256+B×256 2 , where F lv is the flag value, R is the red channel value in the RGB channel, G is the green channel value in the RGB channel, and B is the blue channel value in the RGB channel; Calculate the standard deviation of the adaptation coefficient of the multi-scene advertising logo content within a certain period of time T. The calculation expression is: In the formula, gbn is the average value of the adaptation coefficient of the multi-scene advertising logo content within several cycles T, and the calculation expression is:

3. The multi-scenario advertising logo content management system based on a cloud platform according to claim 2, characterized in that: The method for obtaining the content adaptation coefficient based on consistency information is: The click rate is C lk , the residence time is S ti , then the calculation expression of content adaptation coefficient is In the formula, Spe is the adaptation coefficient, C of Serving conversion rate for advertising.

4. The multi-scenario advertising logo content management system based on a cloud platform according to claim 1, characterized in that: The method for obtaining stability information is: Obtain the smooth coefficient of the advertisement logo updated in real time within the cycle time T, and integrate the smooth coefficients of the advertisement logo updated in real time within several cycle times T into a data set, and mark the data set as Smo = {ne c }, where c = {1, 2, 3, ..., v}, and v is a positive integer, ne c It represents the smooth coefficient of the real-time update of the advertising logo within the cth cycle time T. The calculation expression of the smooth coefficient is Among them, R up is the number of update requests, S up is the number of successful updates; Calculate the standard deviation of the smooth coefficient of real-time update of advertising logos within several cycles T. The calculation expression is: In the formula, gpv is the average value of the smoothness coefficient of the real-time update of the advertising logo within several cycles T, and the calculation expression is:

5. The multi-scenario advertising logo content management system based on a cloud platform according to claim 4, characterized in that: The method for obtaining the distribution connection coefficient based on stability information is: The network delay is calibrated as N ed , the packet loss rate is P lr , then the calculation expression of the distribution connection coefficient is Where D cc is the distribution connection coefficient, N th is the network delay threshold.

6. The multi-scenario advertising logo content management system based on a cloud platform according to claim 5, characterized in that: The method of establishing a conversion balance model based on the content adaptation coefficient and the distribution connection coefficient and obtaining the conversion balance index is as follows: Constructing a transformation equilibrium model Where, T rb is the transformation balance index, D et D is the duration of advertisement delivery. fr is the advertising frequency, α and β are and D cc *exp(D fr ), and both α and β are positive numbers.

7. The multi-scenario advertising logo content management system based on a cloud platform according to claim 6, characterized in that: The effectiveness of multi-scenario advertising logo content management based on the cloud platform is evaluated by comparing the preset conversion threshold with the conversion balance index, and the logic for classifying the evaluation results is as follows: Preset conversion threshold T hd , the calculated transformation balance index T rb With conversion threshold T hd Compare, if the transformation balance index T rb Greater than or equal to the conversion threshold T hd , it indicates that the content management of multi-scenario advertising logos is in an unbalanced state; If the transformation balance index T rb Less than the conversion threshold T hd , it marks that the multi-scene advertising logo content management is in a balanced state.

8. The multi-scenario advertising logo content management system based on a cloud platform according to claim 7, characterized in that: The logic of the management optimization strategy based on the classification output of the evaluation results is: When the management of multi-scenario advertising logo content is in an unbalanced state, notify the management personnel to conduct multi-scenario review, verify the scene validity of different advertising materials, and check the network quality; When the multi-scenario advertising logo content management is in a balanced state, the advertising delivery data is recorded and stored and sent to the management personnel for analysis.

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