542 results about "Ad serving" patented technology

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 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.

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 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.

The invention discloses an advertisement putting platform based on AI dynamic creative optimization, and relates to the technical field of advertisement putting, and the platform comprises five modules: a dynamic creative basic network construction module deploys multi-modal nodes to construct a full-scene generation network; the creative reference index acquisition module acquires effect and adaptive data, and analyzes to obtain a quantitative index; the creative failure evaluation module evaluates whether the effect of the creative element is attenuated according to the index; the alternative creativity planning module analyzes available alternative elements and plans a scheme; and the creative emergency response scheme execution module executes the emergency scheme when the alternative elements are all attenuated. The effect data comprises a click conversion rate and the like, and the adaptive data comprises theme correlation degree and the like, and calculating indexes through normalization processing. Attenuation judgment refers to effect index decreasing amplitude and adaptive index fluctuation, alternative schemes are screened according to priorities, emergency schemes are rapidly generated, new creativity is tested, and the advertisement putting effect and efficiency are improved.

The invention provides a live broadcast advertisement effect real-time optimization method and system based on multi-modal AI, and relates to the technical field of advertisement optimization, and the method comprises the steps: collecting the multi-modal input information of a live broadcast scene, and extracting a feature vector after preprocessing; constructing a multi-modal-advertisement effect association graph, and integrating the multi-modal-advertisement effect association graph with the multi-modal knowledge graph to form a dynamically updated advertisement optimization knowledge network; based on real-time context awareness and a knowledge network, determining an optimal strategy through reinforcement learning evaluation sorting; the optimal strategy is converted into a specific instruction, and dynamic optimization is executed; collecting effect feedback data, and returning to a feature fusion and weight optimization link; the system comprises a multi-modal data acquisition and feature extraction module, a cross-modal feature alignment and knowledge network construction module, a reinforcement learning strategy generation and evaluation module and the like. According to the invention, the delay waiting time of the live broadcast advertisement optimization response is reduced, the real-time adaptability is improved, and the advertisement putting accuracy and conversion efficiency are improved.

The embodiment of the invention provides a published content recommendation method and device, equipment, a medium and a product, and the scheme can comprise the following steps: filling a search keyword used for searching a target advertisement and candidate published content matched with the search keyword into a cue word template containing thinking chain guide information; and generating prompt information for being input into the large language model. Wherein the thinking chain guiding information is guiding information of a thinking chain of a recommendation reason generated for the candidate published content; the recommendation reason is a specific reason basis for putting the target advertisement by utilizing the candidate release content. And inputting the cue word information into a large language model to obtain recommendation information at least comprising the recommendation reason and a thinking chain corresponding to the recommendation reason. According to the scheme, the advertisement publisher can fully understand the recommendation basis of the recommendation system, the recognition degree and the satisfaction degree of the recommendation content are improved, and the overall user experience and the advertisement putting effect of the recommendation system can be improved.

The invention relates to the technical field of advertisement putting, in particular to a real-time dynamic advertisement putting optimization method and system based on reinforcement learning. The method comprises the following steps: collecting multi-dimensional attribute data of an advertisement putting environment and user historical behavior data, and extracting adaptation degree characteristics of advertisement contents and the environment and user behavior preference characteristics; constructing an advertisement effect prediction model based on reinforcement learning, generating an optimal advertisement content form and putting strategy, and performing advertisement putting; based on the environment adaptation degree data and the real-time feedback data, linkage analysis of the advertisement content, the environment matching degree and the advertisement effect is carried out, and the comprehensive efficiency of an advertisement strategy is calculated; and dynamically optimizing and adjusting the advertisement strategy according to the analysis result. According to the method, through semantic correlation analysis of the advertisement and the environment and comprehensive evaluation of the semantic correlation analysis result and the user behavior, accurate prediction before advertisement putting is realized, and the advertisement putting accuracy, the user experience and the conversion effect are effectively improved.

The invention discloses an advertisement creative real-time optimization and putting control method and system based on artificial intelligence, electronic equipment and a computer readable medium, and the method achieves the precise matching, effect prediction and dynamic regulation and control of the advertisement creative through an intelligent technology, thereby improving the efficiency and effect of advertisement putting. The core process of the method comprises the following steps: firstly, carrying out fusion and joint modeling on multi-source features such as user portraits, advertisement creative contents and real-time context environments so as to realize high-matching-degree real-time optimization of creativity, users and scenes; and secondly, a deep cross network (DCN) model is adopted to capture a complex high-order interaction relationship among the user, the creativity and the environment, so that key indexes such as a click rate (CTR) and a conversion rate (CVR) are predicted with high precision. Besides, the system establishes a real-time monitoring mechanism based on a sliding time window and a change slope, creative fatigue is actively identified through the change trend of data such as exposure, clicking and conversion, and intervention and adjustment are carried out in time.

The invention relates to the technical field of data processing, and discloses an advertisement putting state real-time monitoring method and system. The method comprises the following steps: collecting an advertisement putting event stream, and extracting an instantaneous index and a fluctuation rate; counting historical abnormal conduction time delay and intensity, and constructing a conduction matrix; when single-point abnormity is detected, downstream abnormity is predicted according to the conduction matrix, the deviation degree is calculated, and an abnormal conduction chain is searched; conducting chain features are extracted to form abnormal fingerprints, fingerprint database similarity is matched, high similarity is directly confirmed, and low similarity is injected into probe flow verification. By constructing the real-time monitoring method based on the abnormal conduction matrix, the problems that in the prior art, the conduction relation between indexes cannot be quantified, the downstream abnormal moment cannot be predicted, the false report and missing report rate is high, and the abnormal confirmation response is slow are solved, and the accuracy, the real-time performance and the intelligent level of advertisement putting abnormal detection are improved.

The invention discloses an advertisement putting ROI dynamic optimization method and system based on real-time data driving. The method comprises the steps of collecting advertising data such as advertisement display quantity, click quantity, conversion quantity and consumption cost in a minute-level period through a multi-platform data interface; based on historical and real-time data, utilizing a machine learning model to predict the rate of return on investment (ROI) of each advertisement platform in a future time period; constructing an optimization problem with the overall ROI maximization as a target under the constraint of the total budget, and solving an optimal budget allocation strategy of each platform in each time period through an optimization algorithm; and synchronizing the budget allocation strategy to an advertisement platform for execution through an automatic control module, and recording ROI changes before and after optimization by a monitoring module to realize iterative updating of the model. Compared with the prior art, data acquisition, prediction and optimization can be completed in a minute-level period, dynamic allocation and rolling optimization of advertisement budget are realized, the overall ROI is effectively improved, putting waste is reduced, and the method is suitable for a multi-platform advertisement scene.

The invention belongs to the field of electronic commerce, and provides a multi-channel advertisement putting effect analysis method and system based on real-time calculation, and the method comprises the steps: generating a unique click identifier CLICKID for each advertisement putting channel, and writing the CLICKID and corresponding click information into a real-time data storage when a user clicks an advertisement; obtaining order data through an order return mechanism or a timed task, and inputting the order data into an attribution program; retrieving a corresponding click record in a preset multi-dimensional index data structure on the basis of the CLICKID; and associating the order data with the corresponding advertisement putting plan information to form an attribution result, and writing the attribution result and the order information into an attribution result database. According to the invention, the attribution accuracy and efficiency can be improved.

The invention provides a product advertisement putting method and system. Relates to the technical field of advertisement putting, and comprises the following steps: obtaining product advertisement putting data with a timestamp from an e-commerce platform, a mobile application and an advertisement media, and carrying out cross-terminal association on the product advertisement putting data and a user identifier to form a multi-source data set so as to optimize product promotion, advertisement putting and marketing strategies and improve commercial benefits. According to the product advertisement putting method and system, the interest, the purchase intention and the cross-channel activeness of the user can be reflected more accurately through cross-terminal association of the multi-source data and construction of the multi-dimensional audience portrait, so that the target audience can be matched more accurately during advertisement putting, the advertisement effect is improved, and meanwhile, the user experience is improved. The combination of the incremental transformation prediction model and the multi-target reinforcement learning decision engine enables the advertisement putting strategy to be adjusted in real time, and improves the putting real-time performance and the rate of return on investment.

The invention discloses a cross-border intelligent advertisement putting optimization method and system based on reinforcement learning, and aims to solve the problems of delayed sparse reward reconstruction and credit distribution under the conditions of privacy attribution and cross-border settlement. The method comprises the following steps: reconstructing instant conversion intensity and estimating uncertainty by constructing a reversible deconvolution integral solution masked by a privacy window; the net profit is calibrated according to a unified reference currency by combining a hierarchical Bayesian model, and bidding, orientation, material selection and budget allocation are optimized under the budget constraint based on risk sensitive reinforcement learning of condition risk value; the technical effects of accurately reconstructing rewards under privacy limitation and cross-border cost fluctuation, stabilizing credit distribution, reducing tail risks and improving putting income and compliance are achieved.

The invention discloses a multi-dimensional attribution analysis method and system for a cross-media advertisement putting effect, relates to the technical field of data analysis and advertisement, and is used for solving the problems that the advertisement conversion path recognition accuracy is reduced and the putting strategy optimization strategy of an attribution result is low. The method comprises the following steps: collecting contacts, time sequences and advertisement creative information of a user in a multimedia channel through a point burying technology, classifying according to a channel mapping rule, calculating the conversion contribution degree and propagation efficiency of each channel in combination with historical conversion data, obtaining an influence weight, constructing a user cross-channel access path diagram, and counting node switching frequency. And a multi-layer perceptron model is adopted to calculate a key node coupling coefficient, extract a nonlinear interaction relationship, fuse the coupling coefficient, a time sequence and advertisement originality, extract an attribution variable based on principal component analysis, generate a structured attribution result, and realize refined optimization of an advertisement strategy.

The invention relates to the technical field of machine learning, in particular to an AI large model-driven short video advertisement putting optimization system, which comprises a node feature extraction module, a turning point detection module, a resource scheduling module and an interest trend analysis module. According to the method, through deep semantic vectorization of multi-dimensional data, in combination with dynamic capture of user behaviors and content interests and key turning of sensing node influence, fine-grained classification is performed according to a user interest change trend, advertisement pushing parameters are dynamically adjusted, and real-time weight optimization of a resource allocation strategy is realized; advertisement pushing content is highly matched with user interests, the advertisement putting effect is promoted to be continuously improved, the advertisement resource utilization rate is improved, the user experience is also positively improved, cooperation from multi-dimensional data collection to putting strategy optimization is achieved in the whole process, user interest offset is quickly responded, advertisement matching and interaction performance is enhanced, and the advertisement putting effect is improved. And income improvement and data value release are brought to the short video advertising service.

The invention discloses a global intelligent marketing method based on multi-modal information fusion. The method comprises the following steps: firstly, collecting multi-source heterogeneous data, extracting text, vision and behavior characteristics by using deep learning, and mapping the text, vision and behavior characteristics to a public semantic space for fusion through an attention mechanism; and then, constructing a dynamic user portrait based on the fusion features and identifying a user intention stage. On the basis, the click rate is estimated by using a double-tower network, and a dynamic bidding strategy is generated in combination with a reinforcement learning DQN model and PID control. And finally, aiming at customer inquiry, generating a quoted price in combination with a cost model or performing automatic negotiation by using a time-dependent game strategy, and updating model parameters in real time through online incremental learning. Through full-link intelligent closed-loop control, accurate user intention recognition, efficient advertisement putting and automatic business negotiation are realized, the marketing conversion rate is effectively improved, and the operation cost is reduced.

The invention relates to the technical field of advertisement putting, and discloses an online and offline advertisement attribution method and system based on multi-dimensional anchor point matching, and the method comprises the following steps: obtaining online advertisement interaction and offline purchase events, and extracting feature vectors; generating a dynamic intention potential field for the online event according to the situational features; in response to offline purchase, calculating a potential energy value of each online event by taking the offline purchase as a multi-dimensional anchor point; and finally, normalizing the potential energy value to obtain an attribution contribution degree, and aggregating to generate an efficiency analysis result. The system comprises a data and feature module, a potential field modeling module, an attribution calculation module and a result aggregation module. According to the method, the intention potential field dynamically determined by the situation is constructed, and offline purchase is used as the multi-dimensional anchor points of the space-time and semantics for matching calculation, so that the real influence of the advertisement is refined and dynamically quantified.

The invention relates to the technical field of internet advertisement marketing, in particular to an internet advertisement marketing method and system based on artificial intelligence, and the method comprises the steps: integrating the browsing history, demographic information and behavior preference data of a user, generating a dynamic user portrait, and predicting a user demand; generating personalized advertisement creative content based on the user portrait and the predicted user demand; performing multi-dimensional automatic violation review on the advertisement creative content, and performing originality verification on the advertisement creative content passing the violation review; based on the dynamic user portraits and the advertisement putting targets, advertisement creative contents passing violation review and originality verification are accurately pushed; according to the method, the advertisement can be automatically examined before the advertisement is put, so that the examination efficiency is improved, the compliance risk caused by omission due to manual detection and the brand reputation loss caused by the compliance risk are reduced, and the advertisement marketing effect can be improved.

The invention provides a big data advertisement putting method and system based on a cloud platform. The method comprises the following steps of performing multi-source data collection and preprocessing; dynamically constructing a user portrait; performing multi-dimensional analysis on the behavior pattern; target audience subdivision is based on a Gaussian mixture model; dynamically generating personalized advertisement creativity; a step of optimizing a multi-target delivery strategy; a step of real-time bidding and bidding optimization; a step of putting execution and real-time monitoring; a step of effect evaluation and multi-dimensional feedback; and carrying out adaptive system adjustment and iterative optimization. The big data advertisement putting method based on the cloud platform has the advantages that putting precision and efficiency are improved, audiences are subdivided through complex statistics, dynamic creativity is generated, advertisement correlation is enhanced, the predicted click rate is increased by 15-20%, and budget waste is reduced. And the cloud platform supports stream processing and automatic adjustment, so that the system can quickly respond to market changes, and the delay is reduced to a millisecond level.

The invention relates to the technical field of advertisement putting, and discloses an intelligent automatic putting method and system, a terminal and a storage medium, and the method comprises the steps: receiving a putting instruction of a management end, dynamically accessing original data according to a business target, and carrying out the dimensionality fusion, and generating a unified data environment; fusing user historical transaction value features and behavior sequence willingness features, identifying high-value users and extracting common portraits; decomposing a business target into stage sub-targets in combination with the delivery constraint, and generating a multi-step delivery execution strategy; compiling into a standardized instruction, deploying a task on a target advertisement platform, and monitoring feedback to generate an execution state report; based on index abnormity and a unified data environment in the execution state report, performing multi-dimensional root cause analysis on the delivery task to obtain an intelligent diagnosis conclusion; according to the intelligent diagnosis conclusion, state transition of the release task is driven, and an effect verification report is generated according to feedback data after state transition; the intelligent automatic putting efficiency can be improved.

The embodiment of the invention provides a computing advertisement creative agent construction method and related equipment, and belongs to the cross technical field of artificial intelligence and computing advertisement. The method comprises the following steps: sensing behaviors, environments and intentions of customers in real time through multi-modal data, and constructing dynamically updated scene semantic digital twins; based on the difference between the digital twinborn feedback and the expectation of the advertiser, disassembling a creative generation task into sub-tasks by using an RPA technology; and retrieving materials from the creative knowledge base by adopting a learning-decision-modeling-prediction (LDMP) model in combination with a retrieval enhancement generation (RAG) technology, and performing effect estimation and iterative optimization on the generated new creativity in a digital twin simulation environment until the personalized advertisement creativity meeting the expectation is output. Through deep fusion of digital twinning, RPA and generative AI, high intelligence, automation and individuation of advertisement creative generation are realized, and the advertisement putting effect and the user experience are remarkably improved.

The invention relates to a water dispenser advertisement playing method, system and device based on cloud linkage and a medium, and relates to the field of advertisement putting and device linkage control, and the method comprises the steps that the playing behavior of a customized advertisement is adjusted based on a preset triggering rule or a cloud instruction, and the playing behavior comprises an autonomous playing event and a user triggering event; recording the playing behavior and the equipment state change in real time, and reporting the playing behavior and the equipment state change to a cloud according to a preset reporting strategy; and receiving an analysis report generated by the cloud based on the reported advertisement playing behavior and the equipment state change, and optimizing the advertisement putting rule according to the analysis report. According to the invention, remote centralized management, real-time pushing, accurate putting and playing state monitoring of the advertisement content can be realized, so that the efficiency and flexibility of advertisement release are improved.

The invention provides an AI deep analysis method and device for multi-platform advertisement putting effect data, and the method comprises the steps: carrying out the incremental updating of the advertisement putting effect data of each putting platform based on a preset time-space attenuation factor, and generating a conversion behavior graph; performing association analysis on the dynamic conversion behavior map to obtain a conversion behavior pattern; the advertisement effect data is divided into associated data blocks according to the conversion behavior modes, each associated data block corresponds to a unique conversion behavior mode, and the conversion behavior modes corresponding to different associated data blocks meet orthogonality constraints; and inputting the associated data blocks with the same conversion behavior pattern into a preset analysis model to obtain a putting contribution degree score of each putting platform.

The invention provides an advertisement putting method and device, equipment, a medium and a program product, relates to the technical field of advertisement systems, and aims to solve the problem of low flexibility of task scheduling in advertisement putting. The method comprises the following steps: receiving a first advertisement request sent by a client; determining a first declarative configuration file corresponding to the first advertisement request; the first declarative configuration file is analyzed, execution process information and a plurality of data access strategies corresponding to a plurality of task nodes in the execution process information in a one-to-one mode are obtained, and the data access strategies comprise to-be-accessed advertisement data sources and data scheduling strategies of the advertisement data sources; executing tasks corresponding to the plurality of task nodes according to the execution process information and the plurality of data access strategies to obtain an execution result; and sending the execution result to the client. According to the invention, the flexibility of task scheduling in advertisement putting can be improved.

The invention discloses an advertisement putting position management method and system based on big data analysis, and relates to the technical field of advertisement putting management, and the method comprises the steps: constructing an advertisement position value evaluation network, combining real-time market competition intelligence, calculating comprehensive dynamic potential energy through a digital ecological niche theory, and recognizing a high-value advertisement position; according to the high-value advertisement sites, carrying out advertisement request and delivery test, obtaining corresponding real-time data in the test process, and constructing a multi-modal feature data set; constructing an advertisement space value abnormity early warning model fused with dynamic budget constraint by utilizing an improved competitive symbiotic model and combining a pheromone deposition volatilization mechanism; and inputting the real-time bidding flow, the market fluctuation parameter and the external environment variable into the model to obtain a real-time value score and a risk level, and adjusting a bidding strategy and budget allocation of each advertisement space. According to the method, the accuracy and timeliness of advertisement space value evaluation are improved, and the dynamic adaptability and budget use efficiency of the putting strategy are enhanced.

The invention provides an advertisement putting strategy optimization method, system and platform, and belongs to the technical field of advertisement putting, and the method comprises the steps: obtaining multi-dimensional attribute information of each advertisement position on a target display screen from a record database, and carrying out the quantitative processing of the information, and constructing an attribute representation matrix; extracting a representation vector of each attribute representation matrix, and obtaining a multi-dimensional feature of each advertisement position depending on the original distribution of the advertisement positions on the target display screen; matching a historical change base line matched with each multi-dimensional feature from a historical delivery database, and predicting delivery advantages of the corresponding advertisement space in a preset time period by depending on the historical change base line and combining with the added virtual advertisement; time alignment processing is carried out according to the putting advantages of all the advertisement positions, the advertisement putting highlighting coefficient of each alignment time point is determined, the time point with the highlighting coefficient larger than a preset coefficient is locked to serve as a cutting point, and the advertisement putting strategy is optimized. The pertinence and effect of advertisement putting are effectively improved, and resource waste is reduced.

The invention relates to the technical field of recruitment advertisement putting, in particular to an intelligent recruitment advertisement putting decision-making system based on a big data model, which comprises an advertisement putting management center, a data acquisition module, a recruitment matching module, a putting precision module, a post division module, a putting effect module and a response management module. According to the method, based on multi-dimensional feature matching of cosine similarity, the recommendation list is dynamically adjusted in combination with job seeker behaviors, it is ensured that high-matching-degree job seekers are preferentially reached, through secondary verification and interaction score weighting, high-intention crowds are further screened out, invalid advertisement putting is reduced, the enterprise recruitment cost is reduced, and the enterprise recruitment efficiency is improved. And the post priority is converted from qualitative description to quantitative score, so that an objective basis is provided for advertisement budget distribution and putting strategy adjustment, and meanwhile, the pushing time period, channel and content are dynamically optimized through comparison between prediction and actual effects, so that a traditional experience-driven recruitment mode is promoted to be transformed into a data-driven intelligent decision-making mode.

The invention discloses a new media advertisement putting and monitoring integrated system, and the system comprises a user portrait construction module which obtains the browsing behavior, geographic position and social interaction data of a user at a mobile terminal, a PC terminal and an intelligent terminal through a multi-source data collection unit, and generates a dynamically updated user interest tag; the intelligent advertisement putting engine is used for matching advertisement contents with user portraits in real time based on a deep reinforcement learning algorithm and supporting programmed cross-platform bidding and private market direct putting; according to the invention, through multi-dimensional effect evaluation, emotion analysis and user life cycle value prediction are introduced, and a comprehensive evaluation system is constructed; data security is enhanced, and through homomorphic encryption and block chain technologies, user privacy is guaranteed and a non-tampering effect auditing voucher is provided. The customer obtaining cost of advertisers can be obviously reduced, and the rate of return on investment is improved.