Omnibearing intelligent advertisement management system

Abstract

The invention relates to the technical field of digital marketing, and discloses an omnibearing intelligent advertisement management system, which comprises an advertisement data acquisition module, an intelligent evaluation and analysis module, a strategy optimization engine module, a cross-channel execution module, a closed-loop feedback module and a safety audit module, a logic deviation between advertisement content characteristics and user behavior data is recognized in real time through a multi-source cooperation mechanism, and cooperation conflicts are eliminated through a dynamic compensation algorithm; the environment sensing unit continuously tracks equipment state and network condition change, and triggers self-adaptive decision path reconstruction to prevent release deviation; the spectral analysis engine deconstructs and evaluates the composite advertisement materials in a layered mode, and element-level precise optimization is achieved; the emergency response mechanism verifies the strategy feasibility through sandbox rehearsal, and quickly deals with environment mutation and public opinion risks. The core problems of multi-source data fusion failure, strategy environment adaptation misalignment, composite material evaluation deviation, crisis response lag and the like are solved, and the advertisement putting accuracy and the marketing efficiency are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital marketing, in particular to a comprehensive intelligent advertisement management system. BACKGROUND

[0002] Digital marketing technology is a technology system based on digital channels to achieve precise touch, user interaction and effect optimization, covering the whole process of strategy formulation, content distribution, data analysis and automatic operation. With the increasing complexity of digital media ecology, the comprehensive intelligent advertisement management system plays a key role in improving the precision of advertisement placement and marketing efficiency as a core platform integrating artificial intelligence and big data technology. Through multi-dimensional data collaborative analysis, the system realizes dynamic optimization of advertisement strategy and intelligent distribution across channels. Among them, the multi-source heterogeneous data acquisition module is one of the core components of the system, which captures real-time advertisement material features, user interaction behaviors and placement environment parameters to provide a data basis for intelligent decision-making. The dynamic placement decision engine generates optimization strategies based on deep learning models to ensure efficient allocation of advertisement resources. The cross-channel execution unit realizes precise scheduling of the budget according to the weight distribution model.

[0003] Currently, due to the high dynamicity of the advertisement placement environment, there are technical bottlenecks in implementing multi-source data-driven advertisement optimization: source data conflicts are difficult to reconcile: advertisement content feature data, user behavior feedback data and environment data have time asynchrony, which cannot detect the dynamic deviation between data sources in real time. When there is a logical contradiction between content features and user behavior data, it may cause errors in the generation of placement strategy optimization vectors, seriously affecting the effectiveness of the strategy, and the adaptability of the placement strategy is insufficient: traditional systems cannot detect abnormal shifts in the matching degree of advertisement placement parameters and the environment in real time, and the hierarchical evaluation mechanism is missing: for advertisement materials containing composite elements, the system cannot realize hierarchical quantitative evaluation of creative elements. When the advertisement material contains contrasting visual elements, the lack of multi-region hierarchical detection mechanism increases the error rate of creative optimization strategy generation, directly affecting the iteration effect of advertisement creativity, and the emergency response is lagging: in the context of public opinion crisis caused by sudden public events, the current system response delay is more than 20 minutes, which cannot detect the compatibility differences between environmental mutation parameter sets and regular placement strategies in real time, and exacerbates the risk of brand communication.

[0004] Therefore, the present application provides a comprehensive intelligent advertisement management system to solve the above problems. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a comprehensive intelligent advertisement management system to solve the problems raised in the background art.

[0006] To achieve the above object, the present application provides the following technical solutions to achieve: a comprehensive intelligent advertising management system, the system comprises the following steps: S1, collecting multi-source advertising data, including advertising content feature data, user behavior feedback data and advertising environment data; S2, based on the advertising content feature data, user behavior feedback data and advertising environment data, multi-dimensional advertising effect evaluation analysis is carried out, and an advertising effect evaluation matrix is generated; S3, when the key indicators in the advertising effect evaluation matrix are lower than the preset threshold, the advertising strategy optimization process is triggered, otherwise S5 step is directly executed; S4, using a deep learning model to extract joint features of advertising content feature data and user behavior feedback data, and generating an advertising optimization strategy vector; S5, based on the advertising effect evaluation matrix and the advertising optimization strategy vector, a dynamic delivery decision tree is constructed, and a target advertising delivery parameter set is generated; S6, calling a cross-channel delivery engine according to the target advertising delivery parameter set, and adjusting the advertising delivery strategy in real time; S7, continuously monitoring the adjusted advertising data through a closed-loop feedback mechanism, updating the advertising effect evaluation matrix and iteratively executing S3 to S6 steps; S8, generating a visual analysis report and an automatic audit log based on the advertising delivery effect.

[0007] Preferably, the S1 comprises: S11, collecting advertising content feature data through API interface, including advertising material type, creative element label and sentiment tendency value; S12, collecting user behavior feedback data through user terminal burying point, including click rate, conversion rate, stay time and interaction heat map; S13, collecting advertising delivery environment data through environmental sensors, including geographic location information, network bandwidth state and terminal device type.

[0008] Preferably, the S2 comprises: S21, constructing a three-dimensional evaluation coordinate system, wherein the X axis is the advertising content influence dimension, the Y axis is the user conversion potential dimension, and the Z axis is the environment adaptation degree dimension; S22, calculating the weight coefficients of each dimension by using entropy weight method, and generating an advertising effect evaluation matrix: Wherein, , , is a dynamic weight, satisfying , is a content influence function, User conversion function, Environment adaptation function, function , , The output is a normalized dimensionless value, with a value range of [0, 1].

[0009] Preferably, the S4 comprises: S41, construct a double-channel convolutional neural network model, the first channel processes the advertisement content feature data, and the second channel processes the user behavior feedback data; S42, fuse the double-channel output features through the attention mechanism to generate an advertisement optimization strategy vector: Wherein, is the advertisement optimization strategy vector, is the content feature attention weight, is the user behavior attention weight, is the advertisement content feature extractor, is the advertisement content feature data, is the user behavior feature extractor, is the user behavior feedback data, and the attention weight and are calculated by the softmax function, ensuring that + =1.

[0010] Preferably, the S5 comprises: S51, establish a dynamic delivery decision tree, the root node is an advertisement effect evaluation matrix, and the branch node is an advertisement optimization strategy vector; S52, calculate the information gain using the ID3 algorithm to select the optimal delivery path: Wherein, is the information gain of attribute A, is the sample set of the current decision node, is the advertisement delivery parameter attribute to be evaluated, is the information entropy of the sample set S, is all possible values of attribute A, is a specific value of attribute A, is the sub-sample set of attribute A with value v, is the subset in the total sample.

[0011] Preferably, the S6 comprises: S61, constructing a cross-channel delivery engine, including a search engine channel module, a social media channel module, and a programmatic trading channel module; S62, generating channel allocation weights based on the target advertising delivery parameter set: wherein, is the allocation weight of channel k, is a natural exponential operation, is a smoothing coefficient normalized to [0, 1], is the performance indicator of channel , after normalization processing, is the performance indicator of channel , is the total number of candidate channels.

[0012] Preferably, the S7 comprises: S71, setting a feedback time window: wherein, is the reference time point, is the dynamic window length, and the advertising delivery incremental data is collected in real time; S72, updating the advertising effect evaluation matrix through a Kalman filter: wherein, is the updated advertising effect evaluation matrix at , is the effect evolution model, is the state estimate at , is an observation value weight adjuster, is the actual advertising effect data collected at , is a state-observation converter.

[0013] Preferably, the S8 comprises: S81, generating a multi-dimensional visual analysis report, including a spatiotemporal distribution heat map, a channel performance radar chart, and a user conversion funnel chart; S82, constructing an automated audit log blockchain, each log block containing a timestamp, an operator digital signature, and a data hash value.

[0014] Preferably, the following steps are further included: S91, when the advertising delivery environment data undergoes a mutation, starting an emergency response mechanism to collect a set of mutation environment parameters in real time; S92, reconstruct the dynamic delivery decision tree based on the set of mutated environment parameters, and generate a subset of emergency advertisement delivery parameters; S93, create an isolated sandbox environment in the cross-channel delivery engine, load the subset of emergency advertisement delivery parameters for simulation delivery test; S94, verify the effectiveness of the emergency strategy through A / B testing, and perform full switching when the conversion rate improvement value is greater than the preset threshold value; S95, record the whole process data of emergency response, and update the advertisement strategy optimization knowledge base.

[0015] Preferably, after S8, it further comprises: S101, establish an advertisement effect decay model to monitor the life cycle state of each channel advertisement: Among them, is the advertisement performance coefficient at time t, is the time variable, is the initial performance reference value, is the natural decay rate, is the periodic fluctuation amplitude, is the fluctuation frequency controller, , , is a dimensionless parameter, which is normalized to ensure dimensional consistency; S102, when λ(t) is lower than the continuous delivery threshold, automatically trigger the advertisement creative regeneration process; S103, cross and mutate the advertisement material gene sequence through genetic algorithm: Among them, is the new generation of advertisement gene code, is the cross operation input, is the genetic operation weight in the range of [0, 1], is the gene cross operation, is the gene mutation operation; S104, inject the regenerated advertisement material into the cross-channel delivery engine and reset the life cycle monitoring parameters.

[0016] Compared with the prior art, the present application provides a comprehensive intelligent advertisement management system, which has the following beneficial effects: 1. In the present application, by deploying a multi-source collaborative module, when performing dynamic optimization of advertising strategy, a multi-dimensional data bias detection mechanism is established to capture the logical conflicts between advertising content features, user behavior feedback and environmental data in real time, and a time-space alignment compensation algorithm is constructed to correct data deviation. The system can identify and eliminate dynamic bias problems in the multi-source data collaboration process in real time, compress strategy optimization errors, improve the reliability of advertising decisions, ensure the accuracy of cross-source data fusion, and reduce the risk of strategy failure from the bottom up.

[0017] 2. In the present application, by integrating an environmental perception engine, when performing real-time advertising, the device type, network status and geographic location changes are continuously monitored by environmental sensors, and the matching offset degree of the current environment is dynamically calculated. When network fluctuations are detected, the decision tree path reconstruction mechanism is automatically triggered. The system can reduce the delivery offset rate in the traditional scheme, generate correction parameter sets in milliseconds when the strategy deviates, and fundamentally solve the problem of misplacement of advertising, ensuring accurate reach of advertising.

[0018] 3. In the present application, by configuring a spectrum analysis engine, when performing composite advertising material optimization, the creative element deconstruction unit decomposes the text and video into independent visual components, calculates the color difference evaluation value of each element based on the color space model, and generates a targeted optimization strategy vector according to the multi-region deviation. The system can avoid creative misjudgment, implement pixel-level material hierarchical diagnosis, eliminate the one-size-fits-all evaluation defects of traditional schemes for complex advertising, and ensure the scientificity and accuracy of creative optimization from the source, so as to improve the conversion efficiency of advertising. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 The figure is a schematic diagram of the framework of the present application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0021] Please refer to Figure 1 The all-around intelligent advertising management system S1, collects multi-source advertising data, including advertising content feature data, user behavior feedback data and advertising delivery environment data; S2, multi-dimensional advertising effect evaluation analysis is performed based on advertising content feature data, user behavior feedback data and advertising delivery environment data, and an advertising effect evaluation matrix is generated; S3, when the key indicators in the advertisement effect evaluation matrix are lower than the preset threshold, triggering the advertisement strategy optimization process, otherwise directly executing S5 step; S4, using a deep learning model to jointly extract features from advertisement content feature data and user behavior feedback data, generating an advertisement optimization strategy vector; S5, based on the advertisement effect evaluation matrix and the advertisement optimization strategy vector, constructing a dynamic delivery decision tree to generate a target advertisement delivery parameter set; S6, calling a cross-channel delivery engine according to the target advertisement delivery parameter set to adjust the advertisement delivery strategy in real time; S7, continuously monitor the adjusted advertisement data through a closed-loop feedback mechanism, update the advertisement effect evaluation matrix and iterate S3 to S6 steps; S8, generating a visual analysis report and an automatic audit log based on the advertisement delivery effect; S11, collecting advertisement content feature data through API interface, including advertisement material type, creative element label and sentiment tendency value; S12, collecting user behavior feedback data through user terminal burying points, including click rate, conversion rate, dwell time and interaction heat map; S13, collecting advertisement delivery environment data through environmental sensors, including geographic location information, network bandwidth status and terminal device type; S21, construct a three-dimensional evaluation coordinate system, wherein the X-axis is the advertisement content influence dimension, the Y-axis is the user conversion potential dimension, and the Z-axis is the environment adaptation degree dimension; S22, calculate the weight coefficients of each dimension using the entropy weight method to generate the advertisement effect evaluation matrix: wherein, , , is the dynamic weight, satisfying , is the content influence function, user conversion function, is the environment adaptation function, function , , The output is a dimensionless value after normalization, with a value range of [0, 1]; S41, construct a double-channel convolutional neural network model, the first channel processes advertisement content feature data, and the second channel processes user behavior feedback data; S42, fuse the double-channel output features through the attention mechanism to generate an advertisement optimization strategy vector: wherein, Optimize the strategy vector for advertising. Attention weights for content features User behavior attention weight, For advertising content feature extractor, For advertising content feature data, For user behavior feature extractor, User behavior feedback data, attention weight and Calculated using the softmax function, ensuring + =1; S51. Establish a dynamic delivery decision tree, with the root node being the advertising effectiveness evaluation matrix and the branch nodes being advertising optimization strategy vectors. S52. Calculate the information gain using the ID3 algorithm and select the optimal delivery path: in, For the information gain of attribute A, The sample set for the current decision node. For the advertising delivery parameter attributes to be evaluated, The information entropy of the sample set S, For all possible values ​​of attribute A, For a specific value of attribute A, For a subset of samples where attribute A has a value of v, For subset The proportion in the total sample; S61. Build a cross-channel delivery engine, including a search engine channel module, a social media channel module, and a programmatic trading channel module; S62. Generate channel allocation weights based on the target ad delivery parameter set: in, Assign weights to channel k. This is for natural index calculations. The smoothing coefficients are normalized to [0,1]. For channels Performance indicators After normalization For channels Performance indicators Total number of candidate channels; S71. Set feedback time window: in, As the reference time point, The dynamic window length is real-time collected advertising increment data; S72, updating the advertising effect evaluation matrix through the Kalman filter: wherein, is the advertising effect evaluation matrix updated at the moment, is the effect evolution model, is the state estimation at the moment, is the observation value weight adjuster, is the advertising effect data actually collected at the moment, is the state-observation converter; S81, generating a multi-dimensional visual analysis report, including a space-time distribution heat map, a channel performance radar chart and a user conversion funnel chart; S82, constructing an automatic audit log blockchain, each log block containing a timestamp, an operator digital signature and a data hash value; S91, when the advertising delivery environment data mutates, starting an emergency response mechanism, and real-time collecting the mutation environment parameter set; S92, reconstructing the dynamic delivery decision tree based on the mutation environment parameter set, and generating an emergency advertising delivery parameter subset; S93, creating an isolated sandbox environment in the cross-channel delivery engine, and loading the emergency advertising delivery parameter subset for simulation delivery test; S94, verifying the effectiveness of the emergency strategy through A / B test, and performing full switching when the conversion rate improvement value is greater than the preset threshold; S95, recording the whole-process data of the emergency response, and updating the advertising strategy optimization knowledge base; S101, establishing an advertising effect decay model to monitor the life cycle state of each channel advertising: wherein, is the advertising performance coefficient at t moment, is the time variable, is the initial performance reference value, is the natural decay rate, is the periodic fluctuation amplitude, is the fluctuation frequency controller, , , is a dimensionless parameter, which is normalized to ensure dimensional consistency; S102, when λ(t) is lower than the continuous delivery threshold, automatically triggering the advertising creative regeneration process; S103. Perform crossover and mutation operations on the gene sequence of advertising materials using a genetic algorithm: in, Encoding the next generation of advertising genes, For cross operation input, The genetic operation weights are in the range [0,1]. For gene crossover operations, This is a gene mutation operation; S104. Inject recycled ad creatives into the cross-channel delivery engine and reset lifecycle monitoring parameters.

[0022] Example 1: Multi-source data collaborative optimization scenario In an actual advertising campaign, a beauty brand needed to promote its new serum across multiple platforms. The system first collects ad creative feature data in real time through API interfaces, including the color distribution matrix of the product's main visual image, the emotional polarity value of the copy, and the dynamic element tags of the video. Simultaneously, it captures click heatmaps and conversion funnel data through user terminal tracking. Combined with the mobile device ratio and 4G network status obtained by the environmental perception unit, when the system detects that the click-through rate is abnormally high but the conversion rate is below the threshold in the user behavior data, the intelligent evaluation and analysis module starts the three-dimensional evaluation coordinate system calculation. It finds that the weight of the Y-axis user conversion dimension has dropped sharply. At this time, the deep learning model activates the dual-channel convolutional neural network. The content channel extracts that the ad creative is suspected of being over-beautified, and the behavior channel detects that the user's dwell time is less than 2 seconds. The attention mechanism dynamically allocates weights of α=0.7 and β=0.3 to generate an optimization strategy vector pointing to "weakening visual effects and strengthening ingredient descriptions". The decision tree construction unit reconstructs the branch path accordingly. The cross-channel execution module increases the weight of social media channels and adds ingredient comparison ads on the programmatic trading platform. The closed-loop feedback module detects that the conversion rate has rebounded in the next time window. The entire process takes less time and is more efficient than traditional manual optimization.

[0023] Example 2: Adaptive Scenario for Sudden Environments When a car brand launched a new car advertisement during a live sports event, the system environment perception unit detected that the user terminal switched to a 5G network, and the geographic location information showed that the audience was concentrated in second- and third-tier cities. The original high-definition video advertisement had a sharp drop in loading completion rate due to bandwidth demand, and the Z-axis environmental adaptation index in the advertisement effect evaluation matrix fell below the threshold. The strategy optimization engine immediately started the emergency response mechanism, loading three emergency strategies through the isolation sandbox: compressing the video into a lightweight version, replacing it with a combination of text and graphics materials, and adding a preloading buffer prompt. The A / B testing module completed parallel verification of the three strategies within 150 seconds, and the data showed that the conversion rate of the combination of text and graphics scheme reached the preset threshold. The cross-channel delivery engine immediately switched the strategy, and the programmatic bidding unit simultaneously reduced the video advertisement bidding weight, ultimately increasing the advertisement loading completion rate and avoiding damage to the brand image caused by lag. The entire process automatically completes strategy switching and effect tracking, and automatically stores environmental mutation parameters and coping strategies in the knowledge base for decision-making reference for similar scenarios in the future.

[0024] Example Three, Compound Material Layered Optimization Scene During a promotion activity of an e-commerce platform, the system detected that the compound advertisement material containing dynamic price tags, multiple person scenes, and gradient backgrounds had evaluation conflicts. The spectrum analysis engine started the creative deconstruction process, decomposed the material into price tag layer, character subject layer, and background layer, calculated the color difference evaluation value of each layer, and detected that the sentiment tendency value of the gradient purple in the background layer was negative, which deviated from the promotion atmosphere. The flicker frequency of the price tag layer caused the visual fatigue index to exceed the standard. The strategy optimization engine generated a layered correction vector accordingly: replace the background color with warm yellow, reduce the flicker frequency of the price tag, and keep the original parameters of the character layer. The material regeneration unit executed the adjustment, and the advertisement effect evaluation matrix showed that the X-axis content influence dimension improved, and the user conversion potential dimension also increased. This layered optimization scheme was continuously iterated in the subsequent 3 rounds of delivery, and ultimately extended the material life cycle, with the single-day conversion record improved to the top three in the platform history.

[0025] Example Four, Public Opinion Crisis Emergency Scene When a certain food brand encounters a sudden food safety public opinion, the system captures the signal of negative information surge through the public opinion monitoring interface, the security audit module is immediately started, the data desensitization unit homomorphically encrypts the user comments involved, the operation traceability unit records all data access behaviors, the environment perception unit synchronously detects that the advertisement exposure conversion rate drops within 45 minutes, and the user stay time is shortened, the emergency response mechanism is automatically activated, the strategy optimization engine retrieves the crisis response template from the knowledge base, generates an emergency strategy vector of "suspend brand hard advertising, and strengthen the image of public welfare", the cross-channel execution module implants food safety knowledge popularization advertisements in the search engine channel, reduces the weight of the social media channel, and freezes all bidding behaviors on the programmatic trading platform, the closed-loop feedback module updates the public opinion heat coefficient every 5 minutes, and when the negative voice volume is monitored to decrease, the system gradually restores the brand product advertisement delivery and injects third-party detection report materials, the whole process shortens the brand public opinion warming cycle and avoids potential economic losses.

[0026] It should be noted that the relational terms herein such as first and second and the like are used solely to distinguish one entity from another entity without necessarily requiring or implying any actual relationship or order between such entities. Also, the terms "comprises", "comprising", or any other variation thereof are intended to cover a non-exclusive inclusion, such that a process, system, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, system, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the presence of additional identical elements in the process, system, article, or apparatus that comprises the element.

[0027] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A comprehensive intelligent advertisement management system, characterized in that: include: Advertising data collection module, intelligent evaluation and analysis module, strategy optimization engine module, cross-channel execution module, closed-loop feedback module; The advertising data collection module includes: The content feature extraction unit is used to analyze the visual elements and semantic features of advertising materials; User behavior capture unit, used to track user interaction paths in real time; An environmental sensing unit is used to detect the status of devices and networks. The intelligent evaluation and analysis module includes: Multi-dimensional evaluation matrix generation unit; Dynamic weight calculation unit; The strategy optimization engine module includes: Deep learning model training unit; Decision tree building unit; The cross-channel execution module includes: Programmatic bidding unit; Channel weight allocation unit; The closed-loop feedback module includes: Real-time data monitoring unit; Matrix iterative update unit.

2. The omnichannel intelligent advertisement management system of claim 1, wherein: The system also includes a security audit module, which contains: The data anonymization unit is used to perform homomorphic encryption on user privacy data; An operation traceability unit is used to record operation logs based on blockchain technology. The access control unit is used to implement dynamic access control policies based on the RBAC model.

3. A comprehensive intelligent advertisement management system, characterized in that, The system includes the following steps: S1. Collect multi-source advertising data, including advertising content feature data, user behavior feedback data, and advertising placement environment data; S2. Based on the advertising content feature data, user behavior feedback data, and advertising placement environment data, perform multi-dimensional advertising effectiveness evaluation and analysis to generate an advertising effectiveness evaluation matrix; S3. When the key indicators in the advertising effectiveness evaluation matrix are lower than the preset threshold, the advertising strategy optimization process is triggered; otherwise, step S5 is executed directly. S4. Use a deep learning model to perform joint feature extraction on advertising content feature data and user behavior feedback data to generate an advertising optimization strategy vector. S5. Construct a dynamic delivery decision tree based on the advertising effectiveness evaluation matrix and advertising optimization strategy vector to generate a target advertising delivery parameter set; S6. Call the cross-channel delivery engine based on the target ad delivery parameter set to adjust the ad delivery strategy in real time; S7. Continuously monitor the adjusted advertising data through a closed-loop feedback mechanism, update the advertising effectiveness evaluation matrix, and iteratively execute steps S3 to S6. S8. Generate visual analysis reports and automated audit logs based on advertising performance.

4. The omnichannel intelligent advertisement management system of claim 3, wherein: S1 includes: S11, collecting advertising content feature data through API interface, including advertising material type, creative element tags and sentiment value; S12. Collect user behavior feedback data through user terminal tracking, including click-through rate, conversion rate, dwell time and interaction heatmap; S13. Collect advertising environment data through environmental sensors, including geographic location information, network bandwidth status, and terminal device type.

5. The omnichannel intelligent advertising management system of claim 3, wherein: The S2 includes: S21, constructing a three-dimensional evaluation coordinate system, where the X-axis is the dimension of advertising content influence, the Y-axis is the dimension of user conversion potential, and the Z-axis is the dimension of environmental adaptability; S22. Calculate the weight coefficients of each dimension using the entropy weight method to generate an advertising effectiveness evaluation matrix: wherein, , , is a dynamic weight satisfying , is a content influence function, a user conversion function, is an environment adaptation function, the function , , The output is a normalized dimensionless value, with a value range of [0, 1].

6. The omnichannel intelligent advertising management system of claim 3, wherein: S4 includes: S41, constructing a dual-channel convolutional neural network model, where the first channel processes advertising content feature data and the second channel processes user behavior feedback data; S42, fuse the double-channel output features through the attention mechanism to generate an advertisement optimization strategy vector; wherein, is an advertisement optimization policy vector, is a content feature attention weight, is a user behavior attention weight, is an advertisement content feature extractor, is advertisement content feature data, is a user behavior feature extractor, is user behavior feedback data, attention weight and is computed by a softmax function, ensuring + = 1.

7. The omnichannel intelligent advertising management system of claim 3, wherein: The S5 includes: S51, establishing a dynamic delivery decision tree, the root node is an advertisement effect evaluation matrix, and the branch node is an advertisement optimization strategy vector; S52, calculating information gain by using an ID3 algorithm to select an optimal delivery path: wherein, information gain of attribute A, sample set of the current decision node, advertising delivery parameter attribute to be evaluated, information entropy of sample set S, all possible values of attribute A, a particular value of attribute A, sub-sample set of attribute A with value v, subset proportion in the total sample.

8. The omnichannel intelligent advertising management system of claim 3, wherein: The S6 includes: S61, constructing a cross-channel delivery engine, including a search engine channel module, a social media channel module, and a programmatic transaction channel module; S62, generating a channel distribution weight based on a target advertisement delivery parameter set: wherein, is the assigned weight for channel k, is a natural exponential operation, is a smoothing factor normalized to [0,1], is the performance indicator for channel after normalization, is the performance indicator for channel after normalization, is the performance indicator for channel is the total number of candidate channels.

9. The omnichannel intelligent advertising management system of claim 3, wherein: The S7 includes: S71, setting a feedback time window: Wherein, As a reference time point, As a dynamic window length, real-time collection of advertising increment data; S72, updating the advertisement effect evaluation matrix through a Kalman filter; wherein, is the updated advertisement effectiveness evaluation matrix, is the effectiveness evolution model, is the state estimate at time, is the observation weight adjuster, is the actually collected advertisement effectiveness data at time, is the state-observation converter.

10. The omnichannel intelligent advertising management system of claim 3, wherein: The S8 includes: S81, generating a multi-dimensional visual analysis report, including a space-time distribution heat map, a channel efficiency radar chart, and a user conversion funnel chart; S82, constructing an automatic audit log blockchain, and each log block includes a timestamp, an operator digital signature, and a data hash value.

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