Intelligent advertisement material replacement method and system

Abstract

The invention discloses an intelligent replacement method and system for advertisement materials. The intelligent replacement method comprises the steps of S1, media layering and marking, wherein media resources are layered according to a layering standard; collecting media data; for the collected media data, carrying out tagging processing on the layered media resources; analyzing the labeled data to optimize an advertisement putting strategy; step S2, advertisement material quality discrimination and analysis: carrying out quality evaluation on the advertisement material, wherein evaluation standards comprise a visual effect, content correlation and a loading speed; evaluating a bidding strategy and a filling rate of the advertisement material; quality, bid and filling rate labels are printed on the advertisement materials; s3, user dimension analysis is carried out; detailed analysis is carried out on data of different dimensions, and the advertisement putting effect is optimized; the detailed analysis comprises display data analysis, CPM / CPC analysis and filling rate and conversion effect analysis; and S4, strategy execution: dynamically replacing the advertisement materials according to analysis results of the steps S1-S3.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an intelligent replacement method and system for advertising materials. Background Art

[0002] In the ecosystem of programmatic advertising platforms, demand-side advertisers have increasingly stringent requirements for ad click costs and conversion effects. They expect every penny invested to bring significant returns, so they pay special attention to controlling ad click costs and improving conversion effects. At the same time, media parties are paying more and more attention to user experience and have set higher standards for the quality of advertising materials. They hope to ensure that every advertisement can not only attract users' attention, but also generate positive interactions with users.

[0003] Therefore, media owners are eager to obtain higher-quality advertising materials to improve user experience and increase user stickiness. At the same time, advertisers are pursuing lower click costs and higher conversion rates to maximize the return on advertising investment.

[0004] In view of this, the present invention is proposed. Summary of the invention

[0005] The purpose of the present invention is to overcome the above technical deficiencies and provide a method and system for intelligently replacing advertising materials, which can accurately identify and optimize advertising materials through big data analysis and machine learning using intelligent algorithms, and achieve efficient resource allocation. The material replacement function can automatically replace advertising materials with poor effects based on real-time data and user behavior, ensuring that the best and most relevant advertising content is displayed each time.

[0006] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0007] According to one aspect of the present invention, a method for intelligently replacing advertising material is provided, comprising:

[0008] Step S1, media layering and labeling:

[0009] Layering media resources according to layering standards; the layering standards include region, advertising slot type, and device type;

[0010] Collect media data, including media traffic data, user behavior data, and advertising effect data;

[0011] For the collected media data, labeling is performed on the layered media resources;

[0012] Analyze the labeled data to optimize the advertising strategy;

[0013] Step S2: Advertising material quality screening and analysis:

[0014] Evaluate the quality of ad creatives based on visual quality, content relevance, and loading speed;

[0015] Evaluate bidding strategies and fill rates for creatives;

[0016] Label creatives with quality, bid, and fill rate;

[0017] Step S3, user dimension analysis:

[0018] Analyze the delivery effect by advertiser and label them as advertisers; analyze by media code position and label them as code position; analyze by user device type and ID and label them as devices;

[0019] Conduct detailed analysis on data of different dimensions to optimize the advertising effect; the detailed analysis includes display data analysis, CPM / CPC analysis, fill rate and conversion effect analysis;

[0020] Step S4, strategy execution:

[0021] According to the analysis results of steps S1-S3, the advertising material is replaced dynamically.

[0022] Furthermore, in step S1, media traffic data includes page visits, user dwell time, and bounce rate; user behavior data includes user click behavior, browsing history, and purchase history; and advertising effect data includes ad impressions, clicks, and conversion rates.

[0023] Furthermore, in step S1, the labeled data is analyzed to optimize the advertising delivery strategy, specifically including:

[0024] Data Analysis:

[0025] Click-through rate analysis: Analyze the click-through rates of ads under different tag combinations;

[0026] Conversion rate analysis: Analyze the advertising conversion rates under different tag combinations;

[0027] CPM / CPC analysis: Analyze the cost per thousand impressions and cost per click under different tag combinations;

[0028] Optimization strategy:

[0029] Efficient combination: Identify tag combinations with high click-through rate and conversion rate, and prioritize advertising;

[0030] Inefficient exclusion: Identify tag combinations with low click-through rate and conversion rate, and reduce or stop advertising;

[0031] Dynamic adjustment: Dynamically adjust label combinations and delivery strategies based on real-time data to optimize delivery effects.

[0032] Furthermore, the step S2 also includes: using image recognition technology to automatically evaluate the visual effect of the advertising material; and using natural language processing technology to automatically evaluate the relevance of the advertising content.

[0033] Furthermore, in step S3, the display data analysis includes: counting the number of advertisement displays and exposure rate, analyzing the display effect, and optimizing the display strategy;

[0034] The CPM / CPC analysis includes: analyzing the cost per thousand impressions and analyzing the cost per click;

[0035] The fill rate and conversion effect analysis includes: counting the fill rate of the ad slots and optimizing the fill strategy; and analyzing the conversion effect after the ad is clicked.

[0036] Furthermore, the step S4 specifically includes:

[0037] Manual strategy execution: Use high-quality materials to replace ads in specific media code locations, and manually adjust the delivery strategy based on actual conditions.

[0038] Furthermore, the step S4 specifically includes:

[0039] Intelligent replacement using algorithms: Based on the device number dimension, use materials with good historical delivery effects to perform dynamic ad replacement. Use machine learning algorithms to analyze user behavior and ad effects in real time and dynamically adjust ad delivery strategies.

[0040] According to one aspect of the present invention, there is also provided an intelligent replacement system for advertising materials, comprising:

[0041] Media layering and labeling unit, used for media layering and labeling:

[0042] Layering media resources according to layering standards; the layering standards include region, advertising slot type, and device type;

[0043] Collect media data, including media traffic data, user behavior data, and advertising effect data;

[0044] For the collected media data, labeling is performed on the layered media resources;

[0045] Analyze the labeled data to optimize the advertising strategy;

[0046] Advertisement material quality screening and analysis unit, used for advertisement material quality screening and analysis:

[0047] Evaluate the quality of ad creatives based on visual quality, content relevance, and loading speed;

[0048] Evaluate bidding strategies and fill rates for creatives;

[0049] Label creatives with quality, bid, and fill rate;

[0050] User dimension analysis unit, used for user dimension analysis:

[0051] Analyze the delivery effect by advertiser and label them as advertisers; analyze by media code position and label them as code position; analyze by user device type and ID and label them as devices;

[0052] Conduct detailed analysis on data of different dimensions to optimize the advertising effect; the detailed analysis includes display data analysis, CPM / CPC analysis, fill rate and conversion effect analysis;

[0053] Policy execution unit, used for policy execution:

[0054] Based on the analysis results, the advertising creatives are dynamically replaced.

[0055] According to another aspect of the present invention, there is also provided an electronic device, comprising: a processor and a memory;

[0056] The memory stores a computer-readable program executable by the processor;

[0057] When the processor executes the computer-readable program, the steps in the above method are implemented.

[0058] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores one or more programs. The one or more programs can be executed by one or more processors to implement the steps in the above method.

[0059] The invention provides a method and system for intelligently replacing advertising materials, which not only meets the needs of media owners for high-quality materials, but also helps advertisers reduce click costs and improve conversion effects. It is a key link in the application of intelligent algorithms in advertising delivery, truly realizing the intelligence and precision of advertising delivery, ensuring that each display is the best user experience and maximized advertising effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A flowchart of a method for intelligently replacing advertising material is provided in an embodiment of the present invention;

[0061] Figure 2 A schematic diagram of the structure of an intelligent replacement system for advertising materials is provided for an embodiment of the present invention;

[0062] Figure 3 The present invention is a block diagram of a terminal that can implement an AIGC-based brand graphic creative automatic generation method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0064] Embodiment 1

[0065] The embodiment of the present invention provides an intelligent replacement method for advertising materials. Figure 1 ,include:

[0066] Step S1, media layering and labeling:

[0067] Step S11, stratify the media resources according to stratification standards; the stratification standards include region, advertisement slot type, and device type.

[0068] The purpose of stratification is to subdivide media resources into smaller and more precise delivery units so as to optimize advertising delivery strategies according to different characteristics. The main stratification criteria include region, ad slot type and device type. Specifically, they include:

[0069] Regional stratification:

[0070] Country level: Divide media resources by country.

[0071] Provincial / state level: Based on the national level stratification, it is further divided by province or state.

[0072] City level: Based on the province / state level, it is broken down into cities.

[0073] Ad slot type hierarchy:

[0074] Display ads: including banner ads, rectangular ads, pop-up ads, etc.

[0075] Video ads: including pre-roll, mid-roll, and post-roll ads, etc.

[0076] Native advertising: an advertising format that blends naturally with page content.

[0077] Search ads: Ads on search engine results pages.

[0078] Social media ads: Advertising placements on social media platforms.

[0079] Device type stratification:

[0080] PC side: including desktop computers and laptops.

[0081] Mobile devices: This includes smartphones and tablets.

[0082] Smart TV: Includes smart TVs and streaming devices.

[0083] Step S12, collecting media data, wherein the media data includes media flow data, user behavior data, and advertising effect data;

[0084] In order to perform accurate stratification and labeling, a large amount of data must be collected and processed. These data mainly include media traffic data, user behavior data, and advertising effect data.

[0085] Data sources include: Media traffic data: including page visits, user dwell time, bounce rate, etc. User behavior data: including user click behavior, browsing history, purchase history, etc. Advertising effect data: including ad impressions, clicks, conversion rate, etc.

[0086] Data processing includes: Data cleaning: remove duplicate data, abnormal data and noise data to ensure data accuracy. Data preprocessing: standardize and normalize the data to facilitate subsequent analysis.

[0087] Step S13, labeling the collected media data and the layered media resources; labeling is labeling the layered media resources so as to accurately match them when advertising is delivered. Specifically, it includes:

[0088] Regional labeling: Regional labeling: Label each media resource with the corresponding country, province / state, and city. Ad slot type labeling: Ad slot labeling: Label each ad slot with display ads, video ads, native ads, search ads, social media ads, etc. Device type labeling: Device labeling: Label each media resource with PC, mobile device, smart TV, etc.

[0089] Step S14, analyzing the labeled data to optimize the advertising delivery strategy;

[0090] By analyzing the labeled data, you can further optimize the advertising strategy and improve the advertising effect. Data analysis includes: Click-through rate analysis: analyze the click-through rate of ads under different label combinations. Conversion rate analysis: analyze the conversion rate of ads under different label combinations. CPM / CPC analysis: analyze the cost per thousand impressions (CPM) and cost per click (CPC) under different label combinations.

[0091] Optimization strategies include: High-efficiency combination: Identify tag combinations with high click-through rate and conversion rate, and give priority to advertising. Low-efficiency exclusion: Identify tag combinations with low click-through rate and conversion rate, and reduce or stop advertising. Dynamic adjustment: Dynamically adjust tag combinations and delivery strategies based on real-time data to optimize delivery results.

[0092] Step S2: Advertising material quality screening and analysis:

[0093] Ensure high quality of advertising materials to enhance user experience and advertising effectiveness.

[0094] Step S21, evaluate the quality of the advertising material, the evaluation criteria include visual effect, content relevance and loading speed; visual effect includes: clear image, beautiful design, strong visual appeal. Content relevance includes: the advertising content is relevant to the interests and needs of the target audience. Loading speed includes: the loading speed of the advertising material is fast and does not affect the page performance.

[0095] In this step, the following automatic recognition modes can be used: Image recognition: Use image recognition technology to automatically evaluate the visual effects of advertising materials. Natural language processing: Use natural language processing technology to automatically evaluate the relevance of advertising content. This can improve the intelligence and automation of recognition.

[0096] Step S22, evaluating the bidding strategy and fill rate of the advertising material;

[0097] Bidding strategies include: Bidding analysis: Analyze the advertiser's bidding strategy and adjust the bid based on the quality of the creative.

[0098] Fill rate analysis includes: Fill rate statistics: count the fill rates of advertising creatives and identify creatives with poor filling effects.

[0099] Step S23, labeling the advertising material with quality, bid, and fill rate;

[0100] Step S3, user dimension analysis:

[0101] Improve the accuracy and effectiveness of advertising through multi-dimensional analysis.

[0102] The analysis dimensions include:

[0103] Demand-side advertiser dimension: Advertiser label: Analyze the delivery effect by advertiser and label the advertiser.

[0104] Media code position dimension: Code position label: Analyze by media code position and add code position label.

[0105] User device dimension: Device label: Analyze by user device type and ID, and label the device. ;

[0106] Conduct detailed analysis on data of different dimensions to optimize advertising effectiveness; the detailed analysis includes display data analysis, CPM / CPC analysis, fill rate and conversion effect analysis.

[0107] Display data includes: Number of impressions: statistics of ad impressions and exposure rate. Display effect: analysis of display effect and optimization of display strategy.

[0108] CPM / CPC analysis includes: CPM analysis: analysis of cost per thousand impressions (CPM). CPC analysis: analysis of cost per click (CPC).

[0109] Fill rate and conversion effect include: Fill rate analysis: statistics on the fill rate of ad slots and optimization of filling strategies. Conversion effect analysis: analysis of conversion effects after ad clicks, such as registration, purchase, etc.

[0110] Step S4, strategy execution:

[0111] According to the analysis results of steps S1-S3, the advertising material is replaced dynamically.

[0112] Specifically, they may include:

[0113] Step S41, manual strategy

[0114] Support operations personnel to manually set strategies and optimize advertising delivery.

[0115] Strategy settings:

[0116] High-quality material delivery: Use high-quality materials to replace advertisements in specific media code locations.

[0117] Manual adjustment: Manually adjust the delivery strategy based on actual conditions.

[0118] Step S42, algorithm strategy

[0119] Use intelligent algorithms to dynamically optimize advertising and improve delivery results.

[0120] Smart Replacement:

[0121] Dynamic replacement: Based on the device number dimension, use materials with good historical delivery effects to perform dynamic ad replacement.

[0122] Algorithm optimization: Use machine learning algorithms to analyze user behavior and advertising effectiveness in real time and dynamically adjust advertising strategies.

[0123] Machine Learning:

[0124] Model training: Develop and train algorithms for creative quality assessment and user behavior analysis.

[0125] Real-time adjustment: adjust the delivery strategy in real time to optimize the delivery effect.

[0126] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0127] When applying, you can proceed through the following method flow:

[0128] 1. Data collection and processing: Collect media traffic data, user behavior data and advertising effect data, and perform pre-processing and cleaning.

[0129] 2. Algorithm development and model training: Develop advertising material quality assessment and user behavior analysis algorithms, and train models.

[0130] 3. System design and development: Design and develop media layering labeling system, advertising material screening system and strategy execution system.

[0131] 4. Testing and optimization: Conduct system testing, verify functions and effects, and make optimization adjustments.

[0132] 5. Launch and operation: The system is officially launched to support operators in setting and optimizing strategies, monitoring delivery effects in real time and making adjustments.

[0133] Through the above solution, the accuracy and effectiveness of advertising can be effectively improved, the needs of media owners and advertisers can be met, the ROI of overall advertising can be improved, and a higher user experience and better conversion effects can be achieved.

[0134] The core advantages of intelligent material replacement are:

[0135] 1. Real-time optimization: Through stratification and labeling of multiple dimensions such as region, ad slot type, and device, intelligent algorithms can analyze the performance of advertising materials in real time and automatically replace poorly performing materials.

[0136] 2. Improve user experience: High-quality material replacement can improve user experience, reduce advertising interference to users, and increase advertising acceptance and click-through rate.

[0137] 3. Improve conversion rate: By continuously optimizing and replacing advertising materials, intelligent algorithms can ensure that the materials displayed each time are the ones that are most likely to attract users and promote conversions, thereby significantly improving conversion rates.

[0138] 4. Reduce click costs: By optimizing the delivery of materials, the click-through rate and conversion rate of advertisements can be increased, ultimately achieving the goal of reducing click costs.

[0139] Embodiment 2

[0140] According to an embodiment of the present invention, a smart replacement system for advertising material is also provided. Figure 2 ,include

[0141] Media layering and labeling unit, used for media layering and labeling:

[0142] Layering media resources according to layering standards; the layering standards include region, advertising slot type, and device type;

[0143] Collect media data, including media traffic data, user behavior data, and advertising effect data;

[0144] For the collected media data, labeling is performed on the layered media resources;

[0145] Analyze the labeled data to optimize the advertising strategy;

[0146] Advertisement material quality screening and analysis unit, used for advertisement material quality screening and analysis:

[0147] Evaluate the quality of ad creatives based on visual quality, content relevance, and loading speed;

[0148] Evaluate bidding strategies and fill rates for creatives;

[0149] Label creatives with quality, bid, and fill rate;

[0150] User dimension analysis unit, used for user dimension analysis:

[0151] Analyze the delivery effect by advertiser and label them as advertisers; analyze by media code position and label them as code position; analyze by user device type and ID and label them as devices;

[0152] Conduct detailed analysis on data of different dimensions to optimize the advertising effect; the detailed analysis includes display data analysis, CPM / CPC analysis, fill rate and conversion effect analysis;

[0153] Policy execution unit, used for policy execution:

[0154] Based on the analysis results, the advertising creatives are dynamically replaced.

[0155] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment will not be described in detail here.

[0156] It should be noted that the examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the contents disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can be run in a corresponding hardware environment, can be implemented by software, and can also be implemented by hardware, wherein the hardware environment includes a network environment.

[0157] Figure 3 is a structural block diagram of a terminal according to an embodiment of the present application, such as Figure 3 As shown, the terminal may include: one or more (only one is shown) processors 101, a memory 103, and a transmission device 105, such as Figure 3 As shown, the terminal may also include an input and output device 107 .

[0158] Among them, the memory 103 can be used to store software programs and modules, such as program instructions / modules corresponding to the methods and devices in the embodiments of the present application. The processor 101 executes various functional applications and data processing by running the software programs and modules stored in the memory 103, that is, implementing the above-mentioned method. The memory 103 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 103 may further include a memory remotely arranged relative to the processor 101, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0159] The transmission device 105 is used to receive or send data via a network, and can also be used for data transmission between a processor and a memory. Specific examples of the above-mentioned network may include a wired network and a wireless network. In one example, the transmission device 105 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices and routers via a network cable so as to communicate with the Internet or a local area network. In one example, the transmission device 105 is a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0160] Specifically, the memory 103 is used to store application programs.

[0161] The processor 101 may call the application program stored in the memory 103 through the transmission device 105 to execute the steps in the above method.

[0162] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment will not be described in detail here.

[0163] Those skilled in the art will appreciate that the above terminal structures are for illustration only, and the terminals may be smart phones (such as Android phones, iOS phones, etc.), tablet computers, PDAs, mobile Internet devices (MID), PADs, and other terminal devices. Figure 3 It does not limit the structure of the above electronic device. For example, the terminal may also include Figure 3 More or fewer components (such as network interfaces, display devices, etc.) shown in, or having Figure 3 Different configurations are shown.

[0164] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0165] The embodiment of the present application further provides a storage medium. Optionally, in this embodiment, the storage medium can be used to execute the program code of the above method.

[0166] Optionally, in this embodiment, the storage medium may be located on at least one network device among a plurality of network devices in the network shown in the above embodiment.

[0167] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment will not be described in detail here.

[0168] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for intelligently replacing advertising materials, characterized in that: include: Step S1, media layering and labeling: Layering media resources according to layering standards; The stratification criteria include region, ad slot type, and device type; Collect media data, including media traffic data, user behavior data, and advertising effect data; For the collected media data, labeling is performed on the layered media resources; Analyze the labeled data to optimize the advertising strategy; Step S2: Advertising material quality screening and analysis: Evaluate the quality of ad creatives based on visual quality, content relevance, and loading speed; Evaluate bidding strategies and fill rates for creatives; Label creatives with quality, bid, and fill rate; Step S3, user dimension analysis: Analyze the delivery effect by advertiser and label them as advertisers; analyze by media code position and label them as code position; analyze by user device type and ID and label them as devices; Conduct detailed analysis on data of different dimensions to optimize the advertising effect; the detailed analysis includes display data analysis, CPM / CPC analysis, fill rate and conversion effect analysis; Step S4, strategy execution: According to the analysis results of steps S1-S3, the advertising material is replaced dynamically.

2. The method for intelligently replacing advertising material according to claim 1, characterized in that: In step S1, media traffic data includes page visits, user dwell time, and bounce rate; user behavior data includes user click behavior, browsing history, and purchase history; and advertising effect data includes ad impressions, clicks, and conversion rates.

3. The method for intelligently replacing advertising material according to claim 1, characterized in that: In step S1, the labeled data is analyzed to optimize the advertising delivery strategy, specifically including: Data Analysis: Click-through rate analysis: Analyze the click-through rates of ads under different tag combinations; Conversion rate analysis: Analyze the advertising conversion rates under different tag combinations; CPM / CPC analysis: Analyze the cost per thousand impressions and cost per click under different tag combinations; Optimization strategy: Efficient combination: Identify tag combinations with high click-through rate and conversion rate, and prioritize advertising; Inefficient exclusion: Identify tag combinations with low click-through rate and conversion rate, and reduce or stop advertising; Dynamic adjustment: Dynamically adjust label combinations and delivery strategies based on real-time data to optimize delivery effects.

4. The method for intelligently replacing advertising material according to claim 3, characterized in that: The step S2 also includes: using image recognition technology to automatically evaluate the visual effect of the advertising material; and using natural language processing technology to automatically evaluate the relevance of the advertising content.

5. The method for intelligently replacing advertisement material according to claim 1, characterized in that: In step S3, the display data analysis includes: counting the number of advertisement displays and exposure rate, analyzing the display effect, and optimizing the display strategy; The CPM / CPC analysis includes: analyzing the cost per thousand impressions and analyzing the cost per click; The fill rate and conversion effect analysis includes: counting the fill rate of the ad slots and optimizing the fill strategy; and analyzing the conversion effect after the ad is clicked.

6. The method for intelligently replacing advertisement material according to claim 1, characterized in that: The step S4 specifically includes: Manual strategy execution: Use high-quality materials to replace ads in specific media code locations, and manually adjust the delivery strategy based on actual conditions.

7. The method for intelligently replacing advertisement material according to claim 1, characterized in that: The step S4 specifically includes: Intelligent replacement using algorithms: Based on the device number dimension, use materials with good historical delivery effects to perform dynamic ad replacement. Use machine learning algorithms to analyze user behavior and ad effects in real time and dynamically adjust ad delivery strategies.

8. An intelligent replacement system for advertising materials, characterized in that: include Media layering and labeling unit, used for media layering and labeling: Layering media resources according to layering standards; The stratification criteria include region, ad slot type, and device type; Collect media data, including media traffic data, user behavior data, and advertising effect data; For the collected media data, labeling is performed on the layered media resources; Analyze the labeled data to optimize the advertising strategy; Advertisement material quality screening and analysis unit, used for advertisement material quality screening and analysis: Evaluate the quality of ad creatives based on visual quality, content relevance, and loading speed; Evaluate bidding strategies and fill rates for creatives; Label creatives with quality, bid, and fill rate; User dimension analysis unit, used for user dimension analysis: Analyze the delivery effect by advertiser and label them as advertisers; analyze by media code position and label them as code position; analyze by user device type and ID and label them as devices; Conduct detailed analysis on data of different dimensions to optimize the advertising effect; the detailed analysis includes display data analysis, CPM / CPC analysis, fill rate and conversion effect analysis; Policy execution unit, used for policy execution: Based on the analysis results, the advertising creatives are dynamically replaced.

9. An electronic device, characterized in that: include: Processor and memory; The memory stores a computer-readable program executable by the processor; When the processor executes the computer-readable program, the steps in the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps in the method according to any one of claims 1 to 7.

Images