1058 results about "Ad serving" patented technology

The invention discloses an artificial intelligence-based advertisement effect evaluation method and system, and the method comprises the steps: synchronously obtaining multi-source data containing a user behavior data flow and an advertisement putting index flow through a distributed collection engine, and generating a time-space synchronous multi-dimensional data cube; performing feature decoupling on the multi-dimensional data cube, and outputting a dynamic feature topology network with a weight; inputting the dynamic feature topology network into an adversarial training framework, and finally outputting an advertisement conversion probability space-time distribution diagram; based on the advertisement conversion probability space-time distribution map, deploying an attribution calculation unit for real-time feedback, and generating an incremental attribution map with a confidence interval; and inputting the incremental attribution atlas into a strategy generation adversarial network, and outputting an adversarial optimization advertisement putting strategy set meeting Pareto optimum. According to the embodiment of the invention, the accuracy and real-time performance of advertisement effect evaluation can be improved.

The invention discloses a video feature extraction and multi-dimensional matching-based movie advertisement real-time pushing system, and belongs to the technical field of digital advertisements. According to the system, multi-modal analysis is carried out on visual, audio, text and semantic features of a movie through a video feature extraction module, a dynamic interest tag constructed by a user portrait analysis module is combined, and the weighted matching degree of a video scene, user preference and an advertisement tag is calculated by utilizing a multi-dimensional matching engine; and millisecond-level advertisement putting is completed through the real-time pushing decision module. According to the method, context awareness and streaming computing technologies are fused, the relevance between advertisements and content scenes is remarkably improved, cross-platform deployment is supported, the method is suitable for short videos, live broadcast and other real-time scenes, the advertisement click rate is increased by 55%-72% through tests, the response delay is lower than 200 ms, and the method has the technical advantages of being efficient, accurate and low in delay.

The invention relates to the technical field of advertisement putting and intelligent decision making, in particular to a favorite advertisement putting system for predicting the advertisement click rate, which comprises a context awareness intelligent adaptation unit and a deep enhancement decision making unit. By means of deep semantic analysis and situational inference engine processing, interest keywords are extracted by constructing a specific model, user situational portraits are constructed in combination with multi-source data, a preliminary advertisement set is screened by matching with an advertisement material library, a multi-agent architecture is constructed by a deep reinforcement decision unit, a master agent performs overall planning, slave agents are responsible for different advertisement types, and a user can perform multi-agent interaction. A multi-dimensional reward function system is designed, each agent collects feedback data such as operation of a user on an advertisement page, a reward value is calculated according to the feedback data, a strategy network is updated, a main agent integrates information to optimize an overall advertisement pushing strategy, accurate pushing of advertisements is achieved, and the advertisement click rate and the putting effect are effectively improved.

The invention discloses an advertisement putting real-time optimization method and system based on user behavior prediction, and particularly relates to the technical field of advertisement putting. Real-time behavior data and historical behavior data of a target user group are collected, a dynamic behavior sequence is generated through time window division, the historical behavior data are classified to construct a user behavior feature library, potential behavior prediction features of users are extracted, and a unified prediction feature vector set is generated; comparing and analyzing with a preset advertisement content label library, and outputting a user interest association degree data set in combination with an advertisement inventory state; a dynamic optimization parameter set is generated in combination with putting strategy constraint conditions; finally, the advertisement putting content, the advertisement putting frequency and the display position are adjusted in real time according to the dynamic optimization parameter set, an optimization result is output, and an advertisement display queue is updated, so that accurate capture and intelligent matching of user interests are achieved, and the advertisement putting effect and the user experience are improved.

The invention relates to a data processing method and device based on big data and advertisement pushing, and the method comprises the following steps: obtaining the historical behavior data of a user on a multi-channel platform, constructing a dynamic interest label map according to the historical behavior data, and depicting a user interest evolution process. Combining with a social relation network to analyze an interest propagation path, forming a user social interest diffusion trajectory, and introducing a time decay weighting mechanism to generate a dynamic interest decay curve. According to the method, user interests and advertisement materials are subjected to semantic similarity matching, a personalized advertisement recommendation list is generated, an optimal advertisement putting strategy is determined through multi-target optimization configuration and comprehensive consideration of display positions, opportunities and forms, accurate and efficient advertisement pushing is achieved, and the problems that a traditional user portrait method often depends on a static label system, and the user experience is poor are solved. The dynamic characteristic that the user interest changes along with time is difficult to reflect, so that the advertisement recommendation content lags behind the real intention of the user.

An AI-driven system for personalized advertising in real time, where: ◯ an analytics unit to monitor and analyze user behavior in real time across multiple digital platforms such as websites, mobile applications, social media, and smart devices; the unit collects data on user engagement, browsing patterns, time spent, and content preferences to enable targeted, personalized advertising; ◯ a prediction module that predicts preferences and interests of a user, operatively connected to the user interaction data collection unit, wherein the prediction module uses machine learning models such as deep learning, recurrent neural networks (RNNs) and transformer-based architectures to predict interests of users based on historical interactions and inferred preferences; ◯ an emotion and sentiment analysis unit that assesses the user's mood in real time through computer vision, natural language processing (NLP) and voice analysis, whereby the analysis of facial expressions, voice pitch and linguistic mood is used to determine emotional states and receptivity to advertising content; ◯ an embodiment of a context awareness component in operational communication with the emotion analysis and mood unit, in some cases further augmented by various environmental and situational data such as the device type and its physical location, date and time, and the content processed in the device, processing methods, etc., in establishing adaptability and automatic ad placement to be as relevant as possible to the user and their status as prescribed; ◯ Use reinforcement learning algorithms and generative AI models to drive advertising with personalization engines. Creative elements, messaging, and presentations are dynamically adjusted in real time based on user responses to ensure advertising is personalized and always optimized for best performance; o a privacy-focused AI system with federated learning, differential privacy methods, and on-device AI processing to reduce targeted advertising while complying with international data protection laws such as the General Data Protection Regulation (GDPR) and California consumer privacy laws; o an operationally adapted ad delivery mechanism to engage with real-time bidding (RTB) networks, programmatic advertising exchanges and demand-side platforms (DSPs) and place advertisements through digital advertising networks, which guarantees the delivery of tailored advertising to the most relevant audience in real time; and o a contextual feedback loop in which the machine learning models used in the preference and interest prediction module and in advertising personalization The engine is continuously updated to reflect the latest user engagement data, improving personalization over time and optimizing advertising performance.

The invention discloses an intelligent recommendation method for optimizing advertisement keyword combination through cross validation, and relates to the technical field of advertisement technology and search engine marketing, which comprises the following steps: constructing a heterogeneous data set through multi-modal data fusion, and layering according to data sparseness: training a Transform-XL time sequence model by adopting time cross validation of a dynamic K value in a high resource layer; a graph neural network association graph is introduced into a low resource layer, semantic expression of a long tail word is enhanced, a stratified sampling-transfer learning two-channel mechanism is designed, and the generalization ability is improved in combination with exposure frequency weighting and a parameter freezing strategy; developing a Bayesian fusion engine, and dynamically weighting a high / low resource layer prediction result by using an improved Materon kernel function Gaussian process; and generating a confidence interval based on neural quantile regression, and outputting an optimal keyword combination sequence under ROI-risk-diversity constraint in combination with multi-target Pareto optimization. According to the method, the cold start efficiency and the long-tail resource utilization rate are improved, and high-robustness decision support is provided for advertisement putting.

The invention relates to a method for intelligently pushing commodity display according to user behavior habits, and belongs to the technical field of artificial intelligence and electronic commerce recommendation systems. Aiming at the problems of low recommendation accuracy and insufficient real-time performance caused by dynamic change of user behaviors in the existing commodity pushing technology, the method comprises the following steps of: acquiring multi-dimensional data such as user browsing tracks, click preferences, purchase records and page staying duration, and constructing a dynamic user portrait in combination with a time sequence analysis and clustering algorithm; fusing real-time behavior feedback by adopting an improved collaborative filtering algorithm, mining potential association between behavior characteristics and commodity attributes through a deep learning model, and establishing an adaptive weight adjustment mechanism; and finally, a personalized commodity sorting strategy is generated based on the current scene and behavior trend prediction of the user, and dynamic optimization of the pushed content is realized. The method can be applied to an e-commerce platform, an advertisement putting system and a mobile application, the recommendation accuracy, the user conversion efficiency and the platform sales volume are remarkably improved, and meanwhile computing resource consumption is reduced.

The invention relates to an intelligent delivery decision-making method and system based on multi-dimensional index association, and the method comprises the following steps: S1, collecting user, advertisement and context multi-dimensional data, and carrying out the preprocessing of the data to generate standardized features; s2, performing fusion calculation on the multi-dimensional standardized features, extracting key features, and constructing and generating a user-advertisement-context joint feature set; s3, constructing a multi-task prediction model, and training according to a user-advertisement-context joint feature set; s4, according to a prediction result and real-time features of the trained multi-task prediction model, rapidly matching an optimal advertisement for a given user in a real-time bidding process; and S5, performing causal analysis according to the exposure / click log of the optimal advertisement, verifying the real effect of the advertisement, correcting the index, and feeding back to the feature engineering in the S2 and the model training step in the S3 according to the corrected index. According to the invention, the advertisement putting efficiency and effect are effectively improved.

The invention discloses a real-time behavior monitoring and dynamic optimization system for an advertisement putting strategy, and relates to the technical field of advertisement putting and user behavior analysis. A real-time multi-source user behavior data acquisition module acquires data; the distributed storage and indexing module supports fragmented storage, capacity expansion and multi-dimensional indexing; the behavior feature extraction and analysis module extracts features and identifies a time sequence rule; the putting effect evaluation module compares data through an index system and positions an effect short board; the user portrait dynamic updating module adjusts the label weight according to the latest data; the strategy optimization decision module optimizes parameters in combination with the portrait and the evaluation result; the execution scheduling module issues an instruction and monitors a state; the historical data mining and model training module is used for mining rules and iteratively training the model; and the visual interaction configuration module provides an interface and supports parameter configuration and data, effect and optimization suggestion display. According to the invention, full-scene real-time monitoring and cross-platform identity association of user behaviors are realized, and support is provided for refined advertisement marketing.

The invention belongs to the field of advertisement putting, and provides an advertisement putting method and system based on multi-source data analysis, and the method comprises the steps: collecting multi-source original data related to a user; identifying a plurality of cognitive state nodes based on the click behavior data and the transaction path data; constructing a cognitive behavior causal atlas based on the plurality of cognitive state nodes, wherein nodes of the causal atlas represent user cognitive states; predicting an advertisement response probability and a conversion probability of a target user by adopting a Bayesian inference model in combination with a historical behavior sample and a path structure in the causal atlas, and estimating a state transition probability of the user from a current state node to a target state node based on different advertisement intervention contents; and based on the state transition probability and the causal atlas structure, determining an optimal advertisement intervention path of the user from the current cognitive state to the expected conversion state, and constructing a corresponding advertisement putting sequence based on the path.

The invention discloses an AI-based intelligent marketing advertisement putting method and system, and the method comprises the steps: collecting and fusing heterogeneous user behavior data streams from a plurality of independent platforms in real time, and generating a user behavior feature map with a unified space-time mark; on the basis of the user behavior characteristic spectrum, a deep auto-encoder and a space-time diagram convolutional network are used for joint modeling, and fine-grained scene-decoupled user interest preference vectors are generated; inputting the user interest preference vector into a pre-trained generative adversarial network, and dynamically generating a personalized advertisement material highly matched with the current user interest preference and scene; and taking the personalized advertisement material as a candidate arm, and dynamically generating an advertisement putting strategy including an advertisement display form, a display opportunity and a display channel in combination with a current user state and historical putting feedback data. By utilizing the embodiment of the invention, the accuracy and the real-time performance of advertisement putting can be improved, and the advertisement conversion rate and the user experience are improved.

The invention relates to the technical field of network advertisement promotion, in particular to a network advertisement promotion method and system based on the Internet, and the method comprises the following steps: obtaining user behavior data, analyzing a browsing track, a staying duration, a click frequency and an interactive behavior, calculating the activity of a user in real time, and recognizing the interest point of the user. And updating user tags in real time, classifying user groups, and generating a user tag data set. According to the method, the interest point of the user is identified, the user label is updated in real time, it is ensured that personalized recommendation of the advertisement conforms to the real-time behavior of the user, the active time period of the user is analyzed and predicted, resource waste caused by putting in a fixed time period is avoided, budget allocation is dynamically adjusted according to the adaptation degree between the behavior of the user and the platform, and the user experience is improved. The advertisement putting efficiency is improved, user behavior changes before and after advertisement display are compared, the advertisement putting effect is accurately evaluated, data support is provided for adjustment of advertisement strategies, and the advertisement putting effect and resource configuration are optimized.

The invention discloses an accurate advertisement putting method and system based on internet big data, and relates to the technical field of advertisement putting, the method comprises the steps that behavior data of a user is collected through multiple platforms, and the behavior data of the user at least comprises browsing records, search keywords and purchase history; cleaning, integrating and analyzing the collected data, and extracting valuable information and features; building a label system and a behavior model of the user, and forming a user portrait; according to the user portrait and the demand of the advertiser, an accurate advertisement putting strategy is formulated, and the advertisement putting strategy at least comprises putting time, a channel and advertisement content; the advertisement is put, the strategy is adjusted in real time according to the putting effect, and the advertisement effect is continuously optimized; the technical effects that the advertisement putting strategy can be dynamically adjusted according to the real-time data, and the flexibility and the real-time performance of advertisement putting are improved are achieved.

The invention relates to the technical field of Internet advertisement putting, in particular to an intelligent huge advertisement putting method, which comprises the following steps of: acquiring and preprocessing multi-dimensional operation data in real time, constructing a user response prediction model based on a deep interest network, and dynamically weighting a user behavior sequence by utilizing an attention mechanism. A dynamic budget allocation optimization model is established; an objective function is set according to the type of an advertiser; a budget weight is adjusted according to a real-time bidding success rate; a real-time decision engine is deployed; according to the method, each link is monitored through an exposure conversion funnel, an abnormal alarm is set, strategy backtracking analysis is started, and continuous optimization is performed in combination with online learning and model gray release, so that the problem of low efficiency of budget allocation in the traditional technology is solved, intelligent and accurate putting of a huge amount of advertisements is realized, the advertisement conversion rate is improved, and the customer obtaining cost is reduced.

The invention relates to the technical field of digital marketing, in particular to an intelligent marketing data analysis system and method. According to the invention, through deep analysis of the user interaction data, the user behavior offset is accurately identified, so that the marketing strategy can be dynamically adjusted, and the putting accuracy is improved. By means of the change trend of the residence time sequence, the relative change rate is calculated in combination with the access preference, user interest hotspot distribution information is effectively extracted, and the pertinence of content recommendation is improved. Based on access path stability analysis of interest hotspots, a stable mode of user behaviors is extracted, and an advertisement putting interval is optimized, so that user loss caused by frequent putting is reduced. And user groups with fluency decline or obvious jumping behaviors are screened by utilizing change calculation of interaction fluency, so that the advertisement pushing rhythm is reasonably adjusted, and the advertisement experience is improved. The user access data is synthesized to analyze the conversion change of the user behavior after the advertisement is displayed, and the influence of different advertisement intervals on the user behavior is accurately evaluated.

The invention discloses an advertisement putting strategy optimization method and system based on preference data collaboration, and relates to the technical field of advertisement putting, and the method comprises the steps: collecting the internal user data of a local advertisement putting platform, obtaining the external user data of a third-party platform through a safety data interface, carrying out the integration and preprocessing of the data, and obtaining an advertisement putting strategy; extracting explicit preference data and implicit preference data; a user-advertisement scoring matrix is constructed based on the preference data, and user preference vectors are generated after decomposition; acquiring copywriting text, image and category information of an advertisement to be put, constructing an advertisement feature expression model and generating an advertisement feature vector; calculating a matching degree score based on the user preference vector and the advertisement characteristic vector, and dynamically optimizing an advertisement putting strategy according to the score; according to the method, through comprehensive utilization of explicit and implicit preferences of the user, more accurate user interest modeling and advertisement matching are realized, and the advertisement putting effect and putting efficiency are effectively improved.

The invention provides a visual communication advertisement design system and method, and the method comprises the steps: carrying out the cross-modal feature alignment of a background voiceprint feature extracted from environment voiceprint data of an advertisement putting scene and a frequency domain feature of illumination intensity in illumination intensity data, and obtaining an environment perception state tensor; generating a dynamic illumination matrix by combining the spectral irradiance distribution in the environmental perception state tensor with the anisotropic highlight coefficient of each sub-region in the target advertisement picture; determining an audience space domain according to the space depth data of the advertisement putting scene and the audience motion trail, and performing space audio redirection on the target advertisement based on the audience space domain and the sound field energy distribution in the environment perception state tensor to obtain an acoustic beam width angle; and performing acousto-optic synchronous dynamic rendering on the advertisement content of the target advertisement according to the dynamic illumination matrix and the acoustic beam width angle. By adopting the scheme of the invention, acousto-optic collaborative rendering and acousto-optic parameter dynamic adaptation can be performed on the target advertisement so as to realize directional propagation to audiences.

The invention discloses a personalized content recommendation and ROI (Region of Interest) improvement method and system based on a multi-dimensional user portrait, and the method comprises the following steps: collecting user data, and generating the multi-dimensional portrait of a user through feature extraction and weighted fusion; taking the advertisement platform content feature vectors and the multi-dimensional portraits as nodes of a graph structure, taking the interactive behavior data of the users as edges of the graph structure, and constructing an association graph; analyzing the association map and the multi-dimensional portrait by using a causal reasoning model, and identifying invalid correlation variables and key variables which influence a user decision; carrying out recommendation decision making based on a causal reasoning result under a multi-objective optimization framework, and generating a personalized recommendation list of each user; and calculating potential conversion values of different user groups based on a time sequence prediction model, and optimizing an advertisement budget allocation strategy. According to the invention, the personalized degree of advertisement recommendation and the ROI of advertisement putting can be improved.

The invention discloses an advertisement putting method and system based on big data analysis, a medium and a program product, and the method comprises the steps: calculating the acceptance degree data of a target user for various advertisement contents; determining consumption conversion links of the target user in different time periods based on the purchase behavior data; determining a key advertisement content combination and an optimal display time sequence according to the consumption conversion link; screening out the advertisement content from a preset advertisement library, wherein the combination similarity of the advertisement content and the key advertisement content is greater than a preset first similarity threshold value, and the click frequency of the advertisement content is smaller than a preset frequency, and obtaining an advertisement content exploration pool; the advertisement content in the advertisement content exploration pool is inserted into the optimal display time sequence in a progressive mode, and a dynamic advertisement putting sequence is formed; and putting the final advertisement content corresponding to the dynamic advertisement putting sequence to the target user according to the dynamic advertisement putting sequence. According to the method and the device, advertisement contents conforming to potential interests are brought to users, and continuous enhancement of the delivery effect is realized on the basis of keeping the existing delivery effect.

The invention discloses a multi-channel advertisement effect prediction and optimization method and system based on artificial intelligence. The method comprises the following steps: obtaining and preprocessing advertisement putting data, user behavior data and external environment data; performing feature extraction on the preprocessed data, mapping different types of content feature vectors to a unified semantic space through a cross-channel feature projection matrix, and generating an advertisement feature set; fusing a prediction model of Transform and a graph neural network, and synchronously predicting a conversion rate, a delivery return rate and a user interaction index of the target advertisement in each channel based on the advertisement feature set; constructing a strategy optimization network based on reinforcement learning, taking a prediction result output by the prediction model as state input, and dynamically generating a budget allocation matrix and a channel selection weight through a strategy gradient algorithm; and pushing the adjusted advertisement putting strategy to an advertisement management platform according to the optimization strategy, and executing advertisement putting. The accuracy and stability of advertisement effect prediction can be improved.

The invention discloses a network advertisement putting path optimization method based on causal inference, and the method comprises the steps: S1, collecting historical advertisement putting data, and constructing an advertisement putting data set; s2, constructing a causal inference model based on the improved structure equation, and obtaining a causal relationship between the advertisement form and the advertisement effect; s3, designing an advertisement path selection strategy in combination with a causal relationship and a dobby machine algorithm, and obtaining an optimal advertisement form; s4, exploring a new advertisement putting path by using a dobby machine algorithm, and performing advertisement putting; s5, monitoring an advertisement putting process in real time, and dynamically adjusting an advertisement putting path according to a monitoring result; and S6, regularly updating the causal inference model according to the advertisement form, the user behavior and the change of the market environment. According to the method, an efficient and scientific optimization scheme can be provided in network advertisement putting path optimization, and remarkable technical values and economic benefits are brought to practical application.

The invention relates to the field of artificial intelligence, discloses a crowd advertisement accurate putting determination method based on a knowledge graph, and aims to solve the problem of mismatching caused by interest model lag in traditional advertisement putting. The method comprises the following steps: constructing a dynamic evolution type user interest knowledge graph, fusing multi-source behavior flow and static portrait data, and introducing a time decay factor and an event triggering mechanism to realize node weight self-adaptive updating; a short-term interest pulse is accurately captured through a time sequence attention propagation and cross-domain association edge dynamic generation algorithm; and constructing a dual-channel graph neural network inference engine, respectively processing long-term stable and short-term sudden interest paths, and generating a delivery decision through confidence weighted fusion. According to the invention, the timeliness and accuracy of advertisement matching are improved, the mismatching rate is reduced, and the user experience and the self-calibration capability are enhanced.

An adaptive dependency replay control system for use in a latency-sensitive ad serving backend, comprising: a microcontroller-based replay control engine configured to receive ad serving requests and interface with a variety of downstream microservices; a latency monitoring unit operatively coupled to the microcontroller, the latency monitoring unit continuously sampling and maintaining real-time latency histograms for each downstream microservice over sliding time windows; a health status aggregator communicatively coupled to the retry control engine, the aggregator configured to receive service-level health indicators, including, but not limited to, HTTP status codes, circuit breaker states, request timeout counters, and error rate thresholds; a retry decision processing unit stored in a memory accessible to the microcontroller, wherein the matrix can generate a retry action vector based on one or more of the following factors: dependency health, request priority, estimated ad impression value, and system resource metrics; a policy execution engine configured to evaluate retry policies expressed in a domain-specific retry policy language, wherein the execution engine resolves the policies into bytecode rules that are executed by the retry control engine in real time on a per-request basis; and at least one fallback path generator capable of returning an approximate or synthetic response instead of retrying a degraded dependency, where the fallback path is selected based on runtime evaluation of the policy conditions and a calculated retry confidence value.

The embodiment of the invention relates to the technical field of artificial intelligence, and discloses an advertisement putting strategy optimization method and system based on AI learning, and the method comprises the steps: firstly, capturing real-time state parameters of an external environment through a multi-source environment sensor, and analyzing and associating hotspot online shopping data in combination with big data; quantifying the matching degree between each candidate advertisement and the environment state through a pre-training model, and generating a first putting strategy; and then, in an advertisement display process, performing secondary optimization on the first putting strategy to obtain a second putting strategy by taking recessive behavior data such as user watching duration and the like as feedback indexes. Therefore, by combining the environment parameters and the online shopping hotspot data and utilizing the model score sorting to optimize the initial strategy, the advertisement putting better fits the environment state and the online shopping consumption trend, the adaptability of the advertisement putting with the scene environment and the online shopping hotspot is greatly improved, the advertisement putting effect and the user attention are improved, and the advertisement putting experience is improved. And intelligent and precise optimization of an advertisement putting strategy is realized.

The invention discloses an intelligent advertisement putting system based on big data analysis, and the system comprises the following modules: a multi-source data collection module which is used for collecting user side behavior data, advertiser demand data and media environment data in real time; the user portrait construction module is used for generating dynamically updated user feature tags based on the multi-source data; through multi-source real-time data acquisition and dynamic user portrait construction, the comprehensiveness and timeliness of user feature recognition are remarkably improved; based on an intelligent matching mechanism of deep learning and a double-tower neural network, high-precision association between advertisements and users is realized; the advertisement putting efficiency and the user experience are optimized by combining a dynamic bidding strategy, frequency control and cross-platform coordination; and real-time iteration and multi-target balance of the strategy are realized by relying on a closed-loop evaluation optimization framework and deep reinforcement learning, and finally, collaboration of ROI (Region of Interest) improvement of advertisers, platform income increase and user advertisement fatigue reduction is achieved.

The invention relates to the technical field of advertisement effect evaluation, in particular to a cross-screen interactive advertisement effect evaluation system based on multi-modal agent driving, which comprises the steps of collecting advertisement playing data of an advertisement on target equipment, obtaining a login account and a geographic position of the target equipment, performing account verification and distance verification within advertisement putting time, and obtaining the advertisement playing data of the target equipment. Identifying to obtain a cross-screen associated device of the target device; monitoring the running state of the cross-screen associated equipment to obtain user behavior data; constructing a user cross-screen behavior link according to the time sequence of the user behavior data; performing analysis based on the advertisement playing data of the target equipment and the user cross-screen behavior link of the cross-screen associated equipment to obtain cross-screen associated behavior data; and performing user conversion analysis of different advertisement space types based on the cross-screen association behavior data, and calculating an advertisement cross-screen interaction index. According to the method and the device, the advertisement effect is accurately evaluated by identifying the cross-screen behavior of the user.

The invention provides an elevator multi-mode crowd feature perception and intelligent advertisement putting method and system, and relates to the technical field of intelligent advertisement putting, and the method comprises the steps: collecting multi-mode perception data in an elevator through an edge computing terminal; performing feature decoupling on the data, performing cross-modal semantic alignment, establishing a directed association relationship between modals, and constructing a scene feature map; calculating the topology importance degree of map nodes, screening feature nodes, and extracting context information for semantic coding; mapping the scene semantic code and the advertisement audience semantic code to a two-dimensional coordinate system to construct a semantic matching graph, and extracting an optimal matching path to form a candidate set; predicting a scene evolution trend based on the scene characteristic spectrum evolution trajectory, and calculating an advertisement adaptive score to generate a playing sequence; putting and collecting user interaction data feedback according to the sequence to update the graph structure. According to the invention, accurate crowd feature recognition and advertisement dynamic matching are realized, and the advertisement putting efficiency and the user experience are improved.

The invention belongs to the field of advertisement analysis, and provides an advertisement putting effect evaluation and analysis system and method based on data analysis, and the method comprises the steps: constructing a cognitive state vector of a user based on multi-source behavior data before and after the contact between the user and an advertisement; modeling is conducted on user cognitive state vector changes caused by advertisement putting, cognitive intervention vectors of the advertisements are generated, and the cognitive intervention vectors represent the variable quantity of the cognitive states of the users after the users make contact with the advertisements; based on the language feedback, the visual behavior and the emotion track of the user, evaluating the consistency between the user behavior response and the cognitive intervention vector direction, and outputting a multi-modal behavior consistency score; tracking a behavior path of the user from advertisement click to final conversion, forming a conversion chain feature, and identifying a path length, a path node type and a path response time delay; and fusing the cognitive intervention vector, the multi-modal behavior consistency score and the conversion chain feature, and calculating a cognitive effect comprehensive score of advertisement putting for putting effect evaluation.

The invention relates to the technical field of cross-border advertisement putting, and provides a cross-border e-commerce advertisement accurate putting method and system based on a user portrait, and the method comprises the steps: building a unified data flow through multi-source data collection and standardization processing; calculating and fusing the real-time statistical features, the historical interest vectors and the context features in real time by using a stream processing engine to generate multi-dimensional feature vectors; inputting the feature vector into a multi-modal time sequence neural network model which captures a user behavior rule through time sequence coding and an attention mechanism and outputs a user behavior weight and an interest attenuation coefficient; dynamically updating the real-time interest score of the user by adopting an exponential decay model; and when the interest score reaches a threshold value, triggering a reinforcement learning agent to make a decision according to the comprehensive state information, and dynamically outputting an action strategy including release triggering, a bidding coefficient and a creative type. The problems that in the prior art, user interest modeling lags behind, and the self-adaptive capacity of the putting strategy is poor are effectively solved.