A content delivery method and system

Abstract

The application discloses a content delivery method and system, relates to the technical fields of internet digital content distribution, artificial intelligence application and digital marketing, and comprises the following components: S1, content double-dimension labeling and import period test analysis, S2, burst period resource focus delivery and dynamic strategy optimization, S3, AI-driven content disassembly and reorganization and scene adaptation detection in the recession period, and S4, new scene secondary delivery monitoring and full-cycle review iteration; the application adopts the complete process from content import period test analysis, burst period resource focus delivery, AI-driven content disassembly and reorganization in the recession period to new scene secondary delivery monitoring and full-cycle review iteration, automatically disassembles and reorganizes content elements by adopting AI technology, generates adaptive content in combination with new scene requirements, continuously monitors and evaluates the whole life cycle value of content, updates the element library, the user portrait library and the delivery strategy library based on feedback data, and forms a closed-loop iteration mechanism.

Description

Technical Field

[0001] This invention relates to the fields of internet digital content distribution, artificial intelligence applications, and digital marketing technologies, specifically a content delivery method and system. Background Technology

[0002] In today's digital marketing era, content delivery has become a key means for companies to promote their products and services and enhance their brand influence. With the rapid development of the Internet and the diversification of user behavior, how to efficiently and accurately deliver content to the target audience has become an important issue that needs to be addressed in the field of digital marketing.

[0003] Traditional content delivery technologies suffer from several drawbacks: First, they often lack precise user profiles and content tagging systems, leading to a mismatch between content recommendations and user needs, resulting in low delivery efficiency. Second, delivery strategies rely heavily on human experience and historical data, lacking real-time and dynamic adjustment capabilities, making it difficult to cope with rapidly changing market environments. Third, traditional methods are inadequate in content lifecycle management, failing to effectively extend content lifespan and improve its market adaptability. Finally, due to the lack of a closed-loop iteration mechanism, traditional content delivery methods struggle to continuously optimize delivery strategies and improve ROI based on feedback data.

[0004] In view of the shortcomings of traditional content delivery technologies, such as low accuracy, poor real-time performance, insufficient lifecycle management, and lack of closed-loop iteration mechanism, it is therefore of great importance to develop a content delivery method and system. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a content delivery method and system. It can accurately identify the optimal delivery channel, presentation format and core user characteristics by using dual-dimensional tagging of core elements and scene attributes, combined with a multi-dimensional comprehensive scoring algorithm for delivery effect. At the same time, through AI-driven content decomposition and reorganization and scene adaptation detection, as well as a full-cycle review and iteration mechanism, it effectively extends the lifespan of content and improves the market adaptability and return on investment of content.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: On one hand, a content delivery method, the specific steps of which are as follows: S1. Content Dual-Dimensional Tagging and Introductory Period Test Analysis: Perform dual-dimensional tagging of the content to be delivered, combining core elements and scenario attributes. Select 3-5 differentiated delivery channels, allocate small-scale test resources according to 10%-15% of the total budget, design 2-3 differentiated content presentation formats, collect delivery data for 7-10 days, and analyze the data through algorithms to output the optimal delivery channel, optimal presentation format, core user characteristics, and content element priority ranking results. S2. Focus on resource allocation and dynamic strategy optimization during the peak period: Concentrate 60%-70% of the total budget on the top two best channels, carry out large-scale campaigns using the optimal presentation format determined in S1, and reserve 5% of the total budget as contingency resources; build a real-time performance monitoring dashboard and set dual early warning thresholds. When the channel performance triggers an early warning, analyze the influencing factors and adjust the campaign strategy, and simultaneously record the high-value user behavior paths. S3. AI-driven content decomposition, reorganization, and scenario adaptation testing during the decline phase: When the campaign performance is lower than 50% of the peak performance during the peak period for 3 consecutive days and there is no significant recovery after optimization, visual elements in the content are extracted through multi-dimensional feature matching and screening technology, and core text information is extracted through natural language processing technology. The content is then decomposed into reusable element units. New content is generated by reorganizing the content in combination with new scenario requirements, and scenario adaptation testing is performed on the new content. S4. New Scenario Secondary Deployment Monitoring and Full-Cycle Review and Iteration: Allocate 10%-15% of the total budget to new scenario channels, and target the new content generated in S3 with a deployment cycle of 1.5 times that of the S1 introductory period; monitor the new scenario adaptability indicators, generate a full lifecycle report of the content after the deployment ends, and update the element library, user profile library and deployment strategy library based on the report to form an iterative closed loop.

[0007] Furthermore, the dual-dimensional tagging process of core elements and scene attributes in S1 specifically includes: core elements are divided into five primary tags: visual, text, audio, product, and creative. Each primary tag corresponds to a unique secondary sub-tag: visual secondary sub-tags include key video clips, image materials, and color schemes; text secondary sub-tags include core copywriting and layout structure; audio secondary sub-tags include background music and voice narration; product secondary sub-tags include core selling points and parameter information; and creative secondary sub-tags include theme style and interactive format. Scene attribute tags are divided into three levels: channel type, user time period, and behavioral scenario: channel type is labeled as short video. Platforms, news apps, audio platforms, and social media channels; user time periods are labeled as morning peak 7:00-9:00, lunch break 12:00-14:00, and evening peak 18:00-22:00; behavioral scenarios are labeled as commuting, relaxing at home, working breaks, and outdoor activities. The tagging process uses a combination of manual tagging and AI-assisted recognition: AI-assisted recognition uses an improved MobileNetV3 image classification model to identify visual element types and an improved TF-IDF keyword extraction model to identify core text information. The tagging results are stored in a structured content element library, which supports retrieval and access by tag type, priority, and other dimensions.

[0008] Furthermore, the algorithm used to analyze the delivery data in S1 is a multi-dimensional comprehensive scoring algorithm for delivery effectiveness. The comprehensive score for each delivery plan is calculated using this formula: ,in This is the overall score for the campaign strategy, ranging from 0 to 100. A higher score indicates a better strategy. The number of core indicators used in the scoring. The weights for each indicator are determined using the entropy weighting method based on historical data. This represents the maximum value of the metric in the test data. It is the minimum value.

[0009] Furthermore, the dual early warning thresholds in S2 include a click-through rate (CTR) warning threshold and a conversion cost (CBC) warning threshold. Both are determined through a dynamic model combined with industry benchmarks and actual project data. The CTR warning threshold is based on the average CTR of similar content in the same industry on the current channel and the average CTR of the project during the first three days of its peak period. Weights are allocated according to the stability of channel traffic. For short video platforms, where traffic fluctuates greatly, the industry data weight is 0.3, and the project's peak data weight is 0.7. For audio platforms, where traffic is stable, the industry data weight is 0.5. An early warning is triggered when the channel's CTR is below this threshold for two consecutive days. The CBC warning threshold is based on the current channel's industry average conversion cost and the conversion cost on the first day of the project's peak period. The weights are adjusted according to the company's cost tolerance. For cost-sensitive companies, the industry cost weight is 0.6, and for cost-tolerant companies, it is 0.4. An early warning is triggered when the conversion cost is above the threshold for two consecutive days. After the warning, the system automatically compares user profiles, channel traffic structure, and competitor behavior to determine the reasons, and adjusts targeting tags, content selling points, or resource allocation accordingly.

[0010] Furthermore, the adjustment ratio of mobile resources in S2 is determined through channel effect stratified response technology. The specific implementation steps are as follows: First, preset channel priority weights, setting the weight of the TOP1 optimal channel determined in S1 to 0.6, the TOP2 optimal channel to 0.4, and non-optimal channels to 0.2. The weight allocation is calibrated based on the effect contribution of different priority channels in historical deployments. Second, calculate the channel effect deviation in real time, compare the difference between the actual channel effect index and the target effect index, and divide the deviation degree into three levels: mild, moderate, and severe. Then, based on the priority-deviation level matching adjustment rule, adjust by 10%-20% of the total mobile resources for mild deviation, 20%-40% for moderate deviation, and 40%-60% for severe deviation, with the adjustment ratio of high-priority channels increased by 30%. Finally, after the resource adjustment is completed, a 72-hour effect monitoring cycle is started. If the channel effect recovers to more than 80% of the target value, the adjustment is maintained; otherwise, the above steps are repeated for secondary optimization to ensure that mobile resources are accurately directed to high-performance channels and avoid resource waste.

[0011] Furthermore, in step S3, when extracting visual elements using computer vision technology, high-value visual elements are determined through multi-dimensional feature matching and screening technology. The specific implementation steps are as follows: First, the visual materials in the initial content are preprocessed. An improved Gaussian filtering algorithm is used to eliminate noise interference in the video / image. Simultaneously, dynamic key segments in the video are extracted using the inter-frame difference method, and the core subject area in the image is located using an edge detection algorithm. Second, feature parameters of the visual elements are extracted based on CV technology, including the motion trajectory complexity of dynamic elements, the color contrast of static elements, the area ratio of the subject element, and the outline clarity. These feature parameters are then standardized to 0-1. The first step is to quantify the value of each visual element. The second step is to associate the user behavior data from the S1-S2 stages, and to count the frequency of user reviews, the percentage of dwell time, and the click-through rate of each visual element. The feature parameters and user behavior data are weighted using the analytic hierarchy process, with a weight of 0.4 for review frequency, 0.35 for dwell time, and 0.25 for feature quantification value. The third step is to set a high-value judgment threshold. After weighting the data of each dimension, visual elements with a score ≥6 are marked as high-value. Their feature tags and source information are recorded simultaneously, and they are stored in the reusable element library in descending order of score for priority use in subsequent content reorganization. This technology improves the accuracy of element selection through dual verification of features and behavior, and avoids misjudgment based on a single dimension.

[0012] Furthermore, when extracting core text information using natural language processing technology in S3, core text fragments are determined through semantic association and behavior anchoring filtering techniques. The specific implementation steps are as follows: First, the text content is preprocessed. Chinese word segmentation is performed using the jieba word segmentation tool, and redundant information is removed using an industry-specific stop word list. Simultaneously, sentence components are annotated through syntactic analysis to locate the core subject-verb-object structure. Second, the semantic vectors of each text fragment are extracted based on the BERT semantic understanding model and compared with the core tag vectors such as product selling points and discount rules annotated in S1 using cosine similarity. The first step involves calculating the semantic matching degree and filtering candidate segments with a semantic matching degree ≥ 0.7. The second step involves anchoring user behavior data, tracing the text browsing trajectory of users who converted in the S2 stage, and statistically analyzing the post-viewing conversion rate and complete reading rate of each candidate segment, eliminating segments with a post-viewing conversion rate < 10%. The third step involves determining the weight ratio of semantic matching degree and behavioral indicators using the Delphi method, marking segments with a weighted score ≥ 0.6 as core copy, and storing their semantic tags and behavioral association data in the text unit library. This technology solves the problem of misjudging core information caused by single semantic analysis through dual verification of semantics and behavior.

[0013] Furthermore, the scene adaptability detection in S3 is achieved through a scene adaptability scoring formula, with differentiated indicators designed for different new scenes. The scene adaptability formula for short video platforms is as follows: ,in The short video scene adaptability score is used to determine the appropriateness of the video. The first 3 seconds of a short video are scored to determine its attractiveness. For short video length matching, For better subtitle adaptation in short videos, , , The weighting coefficients are determined by combining the algorithm rules of the short video platform with user behavior data. , , .

[0014] Furthermore, the core of the content lifecycle report in S4 includes a total value assessment of the content lifecycle, calculated using the following formula: ,in Total value over the content's lifecycle. These correspond to the S1 introductory phase, the S2 explosive growth phase, and the S4 secondary deployment phase, respectively. The weighting coefficients for each stage are determined based on historical data to statistically analyze the value contribution of each stage. For the rate of return on investment at each stage, To illustrate the duration of each phase, the report also includes an element reuse efficiency table, which tracks the number of times each core element is reused, reuse scenarios, and the improvement rate of effectiveness after reuse. The specific iteration mechanism is as follows: elements reused ≥2 times and with an effectiveness improvement rate ≥10% are marked as core asset elements and included in the enterprise's shared element library; new scenario user characteristics during the second round of deployment are added to the user profile library; and the life cycle patterns of each channel are updated to the strategy library to provide a basis for the next round of deployment.

[0015] On the other hand, a content delivery system includes: a content tagging and introductory period analysis module, a peak period delivery optimization module, an AI content processing and adaptation module, a secondary delivery and review iteration module, and a closed-loop iteration module. The content tagging and import period analysis module performs dual-dimensional tagging processing of the content to be delivered, combining core elements and scenario attributes. It selects differentiated delivery channels and allocates small-volume test resources. After collecting delivery data, it analyzes the data using algorithms to output the optimal delivery plan and element priority results. The peak period optimization module: concentrates the budget on the best channels for large-scale deployment, reserves flexible resources and builds a real-time monitoring dashboard, triggers early warnings through dynamic thresholds and adjusts the deployment strategy, and records the high-value user behavior paths in a synchronous manner; The AI ​​content processing and adaptation module has a built-in computer vision unit and a natural language processing unit, which are used to decompose core elements and reorganize them to generate new content during the content decay period, while performing scene adaptability detection of the new content. The secondary deployment and review iteration module is used to allocate the budget to new scenario channels for content reorganization, monitor adaptation indicators and generate full lifecycle reports, and update the element library, user profile library and deployment strategy library based on the reports. The process forms a closed-loop iteration; the data storage module is used to store tag data, delivery data and user behavior data throughout the entire process, providing data support for the interaction of each module.

[0016] Compared with existing technologies, this content delivery method and system have the following advantages: I. This invention, through a complete process from content introduction testing and analysis, resource-focused deployment during the growth phase, AI-driven content decomposition and reorganization during the decline phase, to secondary deployment monitoring in new scenarios and full-cycle review and iteration, uses AI technology to automatically decompose and reorganize content elements, generate adapted content based on the needs of new scenarios, and continuously monitor and evaluate the value of the content throughout its entire lifecycle. Based on feedback data, it updates the element library, user profile library, and deployment strategy library, forming a closed-loop iteration mechanism. This process not only extends the lifespan of the content but also continuously optimizes and improves the market adaptability and return on investment of the content.

[0017] Second, this invention uses a dual-dimensional tagging process of core elements and scene attributes, combined with a multi-dimensional comprehensive scoring algorithm for delivery performance, to accurately identify the optimal delivery channels, presentation formats, and core user characteristics. This method not only improves the targeting of content delivery but also allows for rapid verification of delivery performance through small-scale testing resources, ensuring that strategies are optimized before large-scale delivery, thereby significantly improving the efficiency and accuracy of content delivery and reducing the cost of ineffective delivery.

[0018] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0020] Figure 1 This is a flowchart of a content delivery method. Figure 2 A flowchart illustrating the operational logic and branch decision-making process of a content delivery system; Figure 3This is a schematic diagram illustrating the association and closed-loop iteration of core modules in a content delivery system. Detailed Implementation

[0021] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0022] Example 1: As Figure 1 As shown, the content for the new low-sugar sparkling water was tagged using a two-dimensional approach: core elements and scenario attributes. For core elements, the first-level tags for visual elements included key video clips, image materials, and color schemes; for text elements, the first-level tags included core copy and layout structure; for audio elements, the first-level tags included background music and narration; for product elements, the first-level tags included core selling points and specifications; and for creative elements, the first-level tags included theme style and interactive format. Regarding scenario attributes, the channel type was tagged with three differentiated channels: short video platforms, news apps, and social media channels; the user time slots were tagged with morning peak hours (7:00-9:00), lunch break (12:00-14:00), and evening peak hours (18:00-22:00); and the behavioral scenarios were tagged with commuting, relaxing at home, and working breaks. The tagging process combined manual annotation with AI-assisted recognition. AI used an improved MobileNetV3 image classification model to identify visual element types and an improved TF-IDF keyword extraction model to identify core text information. The results were stored in a structured content element library.

[0023] Allocate 12% of the total budget for small-scale testing resources, designing two differentiated content presentation formats: Format 1: bottle opening experience + short video of drinking scenarios; Format 2: ingredient science + promotional text and images. Collect 8 days of campaign data, and analyze the comprehensive performance evaluation algorithm based on multiple dimensions. The formula is: ,in To determine the overall score of the campaign, The number of core indicators used in the scoring. The weights for each indicator are determined using the entropy weighting method based on historical data. This represents the maximum value of the metric in the test data. To minimize the impact of the minimum value, the optimal distribution channels are short video platforms and social media channels, the optimal presentation format is short videos featuring bottle opening experiences and drinking scenarios, the core user characteristics are young people aged 18-35, and the content elements are prioritized accordingly.

[0024] 68% of the total budget will be concentrated on the two top-performing channels: short video platforms and social media channels. A large-scale campaign will be launched using the "opening experience + scenario-based drinking" short video format determined in S1. 5% of the total budget will be reserved as contingency resources. A real-time performance monitoring dashboard will be built, with click-through rate (CTR) and conversion cost (CBC) warning thresholds set: Short video platform traffic fluctuates greatly, so industry data will be weighted at 0.3, and the average CTR weight for the first 3 days of the project's peak period will be 0.7; As the company is cost-sensitive, the industry average CBC weight will be 0.6, and the CBC weight for the first day of the project's peak period will be 0.4.

[0025] like Figure 2 As shown, when the channel click-through rate is below the threshold for two consecutive days or the conversion cost is above the threshold for two consecutive days, the system automatically compares user profiles, channel traffic structure, and competitor behavior positioning reasons, and adjusts targeting tags, content selling points, or resource allocation accordingly. At the same time, it records the user's high-value behavior path. The adjustment of flexible resources adopts channel effect layered response technology, with a preset weight of 0.6 for short video platforms, 0.4 for social media channels, and 0.2 for non-optimal channels. Based on the channel effect deviation level calculated in real time, adjustments are made according to 15%, 30%, and 50% of the total flexible resources, respectively, with the adjustment ratio for high-priority channels increasing by 30%. After the adjustment, a 72-hour effect monitoring cycle is initiated to ensure that the channel effect recovers to more than 80% of the target value.

[0026] like Figure 2 As shown, when the campaign performance is below 50% of the peak performance for three consecutive days and there is no significant recovery after optimization, it enters the decline phase. High-value visual elements in the content are extracted through multi-dimensional feature matching and screening technology: First, the short video and image materials are preprocessed, and an improved Gaussian filtering algorithm is used to eliminate noise. The dynamic key segments of the bottle opening bubble are extracted by the inter-frame difference method, and the core main body area of ​​the product is located by the edge detection algorithm. Then, the feature parameters of the visual elements are extracted and standardized. The user behavior data of the S1-S2 stage are correlated to count the review frequency, dwell time ratio and click-through rate of each visual element, and weights are assigned by the hierarchical analysis method. A high-value judgment threshold is set, and visual elements with a score ≥6 are marked as high-value and stored in the reusable element library.

[0027] The text was segmented using the jieba word segmentation tool, and redundant information was removed using a stop word list specific to the FMCG industry. The core subject-verb-object structure was located through syntactic analysis. Semantic vectors of text fragments were extracted based on the BERT semantic understanding model, and cosine similarity was calculated with core tag vectors such as product selling points and discount rules to screen candidate fragments with a semantic matching degree ≥0.7. The text browsing trajectory of converting users was anchored, and fragments with a conversion rate <10% after browsing were removed. The weight ratio was determined by the Delphi method, and fragments with a score ≥0.6 were marked as core text and stored in the text unit library.

[0028] Based on the needs of new scenarios, new content formats are generated and then tested for scenario suitability using a scenario suitability scoring formula. The formula is as follows: ,in The short video scene adaptability score is used to determine the appropriateness of the video. The first 3 seconds of a short video are scored to determine its attractiveness. For short video length matching, For better subtitle adaptation in short videos, , , The weighting coefficients are used to evaluate the attractiveness of the first 3 seconds of the short video, the length matching degree, and the subtitle adaptation degree.

[0029] Allocate 15% of the total budget to new channels such as outdoor lifestyle communities and camping-themed short video accounts. Targeted advertising will be placed on short videos featuring essential camping drinks generated by S3 and shared by multiple users. The campaign period will be 1.5 times the introductory period of S1, i.e., 12 days. Monitor the adaptability metrics of the new scenarios. After the campaign ends, generate a full content lifecycle report. The core of the report includes an assessment of the total value of the content throughout its lifecycle, using the formula: ,in Total value over the content's lifecycle. These correspond to the S1 introductory phase, the S2 explosive growth phase, and the S4 secondary deployment phase, respectively. These are the weighting coefficients for each stage. For the rate of return on investment at each stage, The duration of each stage, such as Figure 3 As shown, the report updates the reusable element library, user profile library, and delivery strategy library to form an iterative closed loop.

[0030] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A content delivery method, characterized in that, The specific steps of this method are as follows: S1. Content Dual-Dimensional Tagging and Introductory Period Test Analysis: Perform dual-dimensional tagging of the content to be delivered, combining core elements and scenario attributes. Select 3-5 differentiated delivery channels, allocate small-scale test resources according to 10%-15% of the total budget, design 2-3 differentiated content presentation formats, collect delivery data for 7-10 days, and analyze the data through algorithms to output the optimal delivery channel, optimal presentation format, core user characteristics, and content element priority ranking results. S2. Focus on resource allocation and dynamic strategy optimization during the peak period: Concentrate 60%-70% of the total budget on the top two best channels, carry out large-scale campaigns using the optimal presentation format determined in S1, and reserve 5% of the total budget as contingency resources; build a real-time performance monitoring dashboard and set dual early warning thresholds. When the channel performance triggers an early warning, analyze the influencing factors and adjust the campaign strategy, and simultaneously record the high-value user behavior paths. S3. AI-driven content decomposition, reorganization, and scenario adaptation testing during the decline phase: When the campaign performance is lower than 50% of the peak performance during the peak period for 3 consecutive days and there is no significant recovery after optimization, visual elements in the content are extracted through multi-dimensional feature matching and screening technology, and core text information is extracted through natural language processing technology. The content is then decomposed into reusable element units. New content is generated by reorganizing the content in combination with new scenario requirements, and scenario adaptation testing is performed on the new content. S4. New Scenario Secondary Deployment Monitoring and Full-Cycle Review and Iteration: Allocate 10%-15% of the total budget to new scenario channels, and target the new content generated in S3 with a deployment cycle of 1.5 times that of the S1 introductory period; monitor the new scenario adaptability indicators, generate a full lifecycle report of the content after the deployment ends, and update the element library, user profile library and deployment strategy library based on the report to form an iterative closed loop.

2. The content delivery method according to claim 1, characterized in that, The core element + scene attribute dual-dimensional tagging process in S1 specifically includes: core elements are divided into 5 primary tags: visual, text, audio, product, and creative. Each primary tag corresponds to a unique secondary sub-tag: visual secondary sub-tags include key video clips, image materials, and color schemes; text secondary sub-tags include core copywriting and layout structure; audio secondary sub-tags include background music and voice narration; product secondary sub-tags include core selling points and parameter information; creative secondary sub-tags include theme style and interaction format. Scene attribute tags are divided into three levels: channel type, user time period, and behavioral scenario: channel type is marked as short video platform, etc. Information apps, audio platforms, and social media channels are used for tagging. User time periods are labeled as morning peak (7:00-9:00), lunch break (12:00-14:00), and evening peak (18:00-22:00). Behavioral scenarios are labeled as commuting, relaxing at home, working breaks, and outdoor activities. The tagging process combines manual tagging with AI-assisted recognition: AI-assisted recognition uses an improved MobileNetV3 image classification model to identify visual element types and an improved TF-IDF keyword extraction model to identify core text information. The tagging results are stored in a structured content element library, which supports retrieval and access by tag type, priority, and other dimensions.

3. The content delivery method according to claim 1, characterized in that, The algorithm used in S1 to analyze the delivery data is a multi-dimensional comprehensive scoring algorithm for delivery effectiveness. The comprehensive score for each delivery plan is calculated using this formula: ,in To determine the overall score of the campaign, The number of core indicators used in the scoring. The weights for each indicator are determined using the entropy weighting method based on historical data. This represents the maximum value of the metric in the test data. It is the minimum value.

4. The content delivery method according to claim 1, characterized in that, The dual early warning thresholds in S2 include a click-through rate (CTR) early warning threshold and a conversion cost (CBC) early warning threshold. The CTR early warning threshold is based on the average CTR of similar content in the same industry on the current channel and the average CTR of the project in the first 3 days of its peak period. The weights are allocated according to the stability of channel traffic. Since the traffic of short video platforms fluctuates greatly, the industry data weight is 0.3 and the project peak data weight is 0.

7. The audio platform has stable traffic, and the industry data weight is set at 0.

5. When the channel click-through rate is lower than this threshold for two consecutive days, an alert is triggered. The conversion cost alert threshold is based on the current channel's industry average conversion cost and the conversion cost on the first day of the project's peak period. The weight is adjusted according to the company's cost tolerance. The industry cost weight is set at 0.6 for cost-sensitive companies and 0.4 for cost-tolerant companies. If the conversion cost is higher than the threshold for two consecutive days, an alert is triggered. After the alert, the system automatically compares the user profile, channel traffic structure, and competitor behavior and positioning reasons, and makes targeted adjustments to targeting tags, content selling points, or resource allocation.

5. The content delivery method according to claim 1, characterized in that, The adjustment ratio of mobile resources in S2 is determined through channel effect stratified response technology. The specific implementation steps are as follows: First, preset channel priority weights, setting the weight of the TOP1 optimal channel determined in S1 to 0.6, the TOP2 optimal channel to 0.4, and non-optimal channels to 0.

2. The weight allocation is calibrated based on the effect contribution of different priority channels in historical deployments. Second, calculate the channel effect deviation in real time, compare the difference between the actual channel effect index and the target effect index, and divide the deviation degree into three levels: mild, moderate, and severe. Then, based on the priority-deviation level matching adjustment rule, adjust by 10%-20% of the total mobile resources for mild deviation, 20%-40% for moderate deviation, and 40%-60% for severe deviation, with the adjustment ratio of high-priority channels increased by 30%. Finally, after the resource adjustment is completed, start a 72-hour effect monitoring cycle. If the channel effect recovers to more than 80% of the target value, maintain the adjustment; otherwise, repeat the above steps for secondary optimization.

6. The content delivery method according to claim 1, characterized in that, When extracting visual elements using computer vision technology in S3, high-value visual elements are determined through multi-dimensional feature matching and screening technology. The specific implementation steps are as follows: First, the visual materials in the initial content are preprocessed. An improved Gaussian filtering algorithm is used to eliminate noise interference in the video / image. Simultaneously, dynamic key segments in the video are extracted using inter-frame difference, and the core subject area in the image is located using an edge detection algorithm. Second, feature parameters of visual elements are extracted based on CV technology, including the motion trajectory complexity of dynamic elements, the color contrast of static elements, the area proportion of the subject element, and the outline clarity. These feature parameters are standardized to quantized values ​​of 0-1. Third, user behavior data from S1-S2 stages are correlated. The frequency of user reviews, dwell time proportion, and click-through rate of each visual element are statistically analyzed. The feature parameters and user behavior data are weighted using the analytic hierarchy process. Fourth, a high-value judgment threshold is set. After weighted calculation of the data from each dimension, visual elements with a score ≥ 6 are marked as high-value, and their feature labels are recorded simultaneously. Along with the source information, they are stored in a reusable element library in descending order of score for priority access in subsequent content reorganization.

7. The content delivery method according to claim 1, characterized in that, When extracting core text information using natural language processing technology in S3, core text fragments are determined through semantic association and behavioral anchoring filtering techniques. The specific implementation steps are as follows: First, the text content is preprocessed by using the jieba word segmentation tool to complete Chinese word segmentation, removing redundant information by combining an industry-specific stop word list, and simultaneously annotating sentence components through syntactic analysis to locate the core subject-verb-object structure. Second, the semantic vectors of each text fragment are extracted based on the BERT semantic understanding model, and cosine similarity is calculated with the core tag vectors such as product selling points and discount rules annotated in S1 to filter candidate fragments with a semantic matching degree ≥0.

7. Third, user behavior data is anchored, tracing the text browsing trajectory of users who converted in S2, and the conversion rate and complete reading rate after browsing of each candidate fragment are statistically analyzed, eliminating fragments with a conversion rate <10%. Fourth, the weight ratio of semantic matching degree and behavioral indicators is determined by the Delphi method, and fragments with a weighted score ≥0.6 are marked as core text, and their semantic tags and behavioral association data are stored in the text unit library.

8. The content delivery method according to claim 1, characterized in that, The scene adaptability detection in S3 is achieved through a scene adaptability scoring formula. Differentiated indicators are designed for different new scenes. The scene adaptability formula for short video platforms is as follows: ,in The short video scene adaptability score is used to determine the appropriateness of the video. The first 3 seconds of a short video are scored to determine its attractiveness. For short video length matching, For better subtitle adaptation in short videos, , , These are the weighting coefficients.

9. The content delivery method according to claim 1, characterized in that, The core of the content lifecycle report in S4 includes an assessment of the total value of the content lifecycle, calculated using the following formula: ,in Total value over the content's lifecycle. These correspond to the S1 introductory phase, the S2 explosive growth phase, and the S4 secondary deployment phase, respectively. These are the weighting coefficients for each stage. For the rate of return on investment at each stage, The duration of each stage.

10. A content delivery system, applicable to the content delivery method according to any one of claims 1-9, characterized in that, The system includes: a content tagging and introductory phase analysis module, a peak period deployment optimization module, an AI content processing and adaptation module, a secondary deployment and post-mortem iteration module, and a closed-loop iteration mechanism. The content tagging and import period analysis module performs dual-dimensional tagging processing of the content to be delivered, combining core elements and scenario attributes. It selects differentiated delivery channels and allocates small-volume test resources. After collecting delivery data, it analyzes the data using algorithms to output the optimal delivery plan and element priority results. The peak period optimization module: concentrates the budget on the best channels for large-scale deployment, reserves flexible resources and builds a real-time monitoring dashboard, triggers early warnings through dynamic thresholds and adjusts the deployment strategy, and records the high-value user behavior paths in a synchronous manner; The AI ​​content processing and adaptation module has a built-in computer vision unit and a natural language processing unit, which are used to decompose core elements and reorganize them to generate new content during the content decay period, while performing scene adaptability detection of the new content. The secondary deployment and review iteration module is used to allocate the budget to new scenario channels for content reorganization, monitor adaptation indicators and generate full lifecycle reports, and update the element library, user profile library and deployment strategy library based on the reports. The process forms a closed-loop iteration; the data storage module is used to store tag data, delivery data and user behavior data throughout the entire process, providing data support for the interaction of each module.

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