90 results about "Digital marketing" patented technology

The invention discloses a multi-modal generation-based agricultural product brand digital marketing method and system, and the method and system collect multi-source data related to a target agricultural product, carry out the preprocessing, and construct an agricultural product culture knowledge graph; based on a graph convolutional network, a potential diffusion model and a multi-modal fusion Transform prediction network, constructing a visual generation model, receiving an agricultural product culture knowledge graph and a specific demand description statement input by a user by the visual generation model, and outputting a prediction value of a key marketing index; and performing parameter updating and iterative optimization on the visual generation model to form a closed loop, and applying the optimized visual generation model to complete the generation and effect prediction of the agricultural product marketing image-text. According to the method, a complete process from cultural element mining, visual content automatic generation, market effect prediction to strategy closed-loop optimization is constructed, and the problems of visual generation blindness and effect evaluation hysteresis in agricultural product brand digital marketing are solved.

The invention relates to the technical field of digital people, and provides a method and equipment for generating a digital people video, which can be applied to a digital marketing scene. According to the method, the generation of the digital human video is divided into an audio generation process and a video generation process, when the audio is generated, a target text, an emotion parameter and a reference audio are taken as input, and the target audio of the target text is broadcasted by the sound of a reference person in the reference video and a set emotion, so that sound cloning is realized, the degree of violation of the generated digital human video is reduced, and the user experience is improved. And the text features are extracted from two dimensions of characters and pronunciation, so that the pronunciation accuracy during sound cloning is ensured. When the video is generated, the cloned target audio and the cloned reference video are used as input, and the fidelity of the digital human video is improved through expression and mouth shape matching, so that a reference person can quickly and efficiently generate vivid digital human videos with different broadcast contents only by shooting one reference video without shooting one by one, thereby reducing the manufacturing cost of the digital human video, and improving the user experience. And the video production efficiency is improved.

The invention relates to the field of digital marketing, and discloses a video understanding-oriented hierarchical spatio-temporal semantic extraction method, which analyzes video content through a three-stage progressive analysis framework: firstly, detecting an object instance in a key frame sequence and constructing a spatio-temporal trajectory to ensure object identity continuity; modeling an object state evolution law by using a recurrent neural network, and quantifying an interaction relationship between objects; and finally, constructing an event causal graph based on the state mutation points, and generating a directed causal chain through a statistical causal test algorithm. Video content is abstracted into event causal logic from pixel features, a semantic path of'track-state-causal 'is formed, structured semantic description of decisions can be directly driven, video semantics and user behavior data are coupled, user preference portraits are dynamically constructed, and personalized material generation is guided.

The invention belongs to the technical field of advertisement putting, and discloses an accurate customer obtaining and advertisement putting system and method based on big data. The method comprises the following steps: S1, multi-source data acquisition: acquiring Internet public data, enterprise private domain data and third-party compliance data to form a comprehensive data pool; s2, data structured storage: the collected data are classified and stored, and an index is established. According to the method, through the collaborative application of multiple technologies such as integrated distributed crawlers, generative adversarial networks, Kalman filtering, edge calculation, multiple linear regression and Lasso regularization, full-link optimization from data acquisition to effect landing is realized, the core problems of inaccurate customer acquisition and poor advertisement effect in the prior art are solved, and the service life of the advertisement is prolonged. Manual intervention cost is reduced through an automatic mechanism, the comprehensive goals of improving customer obtaining precision, optimizing advertisement effect, reducing marketing cost and guaranteeing long-term effectiveness are finally achieved, and a more systematic and more efficient technical support is provided for enterprise digital marketing.

The invention relates to the technical field of digital marketing management, and discloses a Malus hupehensis cloud digital marketing service management platform. The system comprises a marketing service management module, a marketing data stability analysis module, a marketing strategy configuration module and a marketing execution optimization module. Wherein the marketing service management module collects global marketing data and constructs a multi-dimensional historical marketing data set; a marketing data stability analysis module traverses the data set for stability analysis, and outputs product, channel and user behavior stability factors; the marketing strategy configuration module takes the stability factors as indexes, and searches and determines a target marketing strategy decision value in a marketing strategy configuration space in a centralized manner; the marketing execution optimization module generates an execution scheme based on the decision value and optimizes execution process data in real time through a marketing strategy analyzer. According to the platform, the whole-process systematic management of marketing data is realized, the marketing management efficiency and strategy accuracy are improved, and enterprises are assisted to adapt to the change of a digital marketing environment.

Embodiments of the present disclosure disclose a service request processing method and device, electronic equipment and a computer readable medium. A specific embodiment of the method comprises: in response to receiving a service participation request, querying object participation flow information corresponding to the service participation request; determining the number of historical participations of the object according to the object participation flow information; constructing an object participation flow identifier according to the number of historical participations of the object and service object information; and storing the object participation flow identifier in a database corresponding to the object participation flow information. The embodiment is related to digital marketing, realizes user concurrency inventory restriction in a lock-free manner, and meets the user dimension inventory restriction of a business scenario.

The application discloses a route data processing method and device, and relates to the technical field of digital marketing. The specific implementation of the method comprises the following steps: acquiring historical query data corresponding to a plurality of routes; performing continuous processing on the historical query data corresponding to the plurality of routes respectively, and determining continuous processing results; determining one or more target routes and a target coverage time period corresponding to each target route according to a preset coverage time period and the continuous processing results; and acquiring route data corresponding to each target route according to the target coverage time period corresponding to each target route, and storing the route data in a cache. According to the actual route resource use scene, the implementation can maximize the use of the cost of consideration, determine an accurate and continuous route resource coverage range, process special time periods, respond to users in time to improve user experience, prevent interference of abnormal data, and improve operation and management efficiency.

This invention relates to the field of commission settlement technology and discloses a real-time settlement system for digital marketing commissions based on a group-buying platform. The system includes a payment traceability unit, a commission analysis unit, a payment verification unit, and a real-time settlement unit. Through the collaboration of multiple units, it overcomes the shortcomings of traditional solutions where payment success and commission settlement status are inconsistent due to network fluctuations or lost callbacks. Specifically, the payment traceability unit generates an unforgeable settlement traceability identifier; the commission analysis unit generates a commission settlement benchmark form containing the total commission amount and the amounts for each promoter without relying on real-time callbacks; the payment verification unit performs anti-interference comparison using dynamic time-window verification factors and amount deviations, and automatically triggers a secondary push in case of anomalies; and finally, the real-time settlement unit generates a unique transfer authorization code to complete fund transfer and comparison storage. This solution ensures the real-time nature, consistency, and tamper-proof nature of marketing commission settlement.

The application is suitable for the technical field of data processing, and provides a visitor information processing method and device, a terminal and a storage medium, wherein the method comprises the following steps: obtaining visitor data based on a preset digital marketing material; a data collection item is set in the digital marketing material; a set number of target data are selected from the visitor data as information factors based on a target data collection item; the information factors are encoded based on data encoding parameters corresponding to the target data collection item, so as to obtain visitor identification information; and the visitor identification information and other data in the visitor data are stored in a visitor database in association, wherein the other data are data in the visitor data except the target data. The scheme can ensure that the generated visitor identification information can meet the information uniqueness requirement of digital marketing, and ensure the effective implementation of subsequent data analysis.

The application relates to the technical field of digital marketing data processing, and in particular to a marketing data synthesis and enhancement method and system based on a generative adversarial network. The method comprises the following steps: acquiring original marketing data, extracting multi-modal features, and inputting the multi-modal features into a generative adversarial network comprising a generator, a reality discriminator and an effect discriminator; using the effect discriminator to evaluate the probability of the synthesized data producing a positive marketing effect and generating a signal; calculating a joint loss function of the generator and dynamically adjusting a weight coefficient, and updating the generator parameters according to the joint loss function and the weight coefficient to optimize the quality of the synthesized data; and finally mixing the optimized synthesized data and the original data to form an enhanced data set. The application realizes the automation and intelligentization of marketing data synthesis, significantly improves the marketing effect while guaranteeing the data authenticity, and effectively protects the data privacy and security.

The present application relates to the technical field of digital marketing and search marketing, and particularly relates to a large model global search marketing layered delivery attribution joint optimization method and system, the method and system acquire global marketing data and generate query semantic representation and intent recognition, construct layered delivery units and randomly shunt four groups; in the trigger time window, the trigger value is obtained by correcting the search volume based on the benchmark prediction, and the inter-layer migration strength is counted; in the capture time window, the capture value is obtained by predicting and correcting the conversion; the layered delivery strategy is output under the joint optimization of the total budget constraint, the migration strength and the cost combined with the double reward signals, the unified trigger and capture incremental criteria are reduced, the individual tendency and fluctuation are reduced; the layered fine regulation is realized by the semantic intent and the inter-layer migration strength; the two types of costs and the double rewards are jointly optimized under the total budget constraint, the collaborative delivery strategy is output, and the overall income is improved.

This invention belongs to the field of digital marketing technology. It discloses an intelligent analysis and optimization system based on short video platform traffic data, comprising: acquiring short video platform traffic trajectory data and interaction fingerprint features to construct a content performance profile library; classifying traffic flows and drawing a domain popularity star map; detecting algorithm pulses and user preference migration traces to form a fluctuation feature spectrum; interpreting historical traffic data, mapping seasonal traffic rhythm rules, and constructing a trend prediction model; applying it to target content to generate performance prediction results and transforming them into content frequency adjustment schemes; implementing real-time effect monitoring and establishing a feedback loop. This invention achieves intelligent analysis and automatic optimization of short video content delivery, accurately predicting content performance trends and dynamically adjusting delivery strategies, effectively improving platform traffic conversion efficiency.

An information processing device stores medical content information viewable or browsable on a Web site and tag information classifying the medical content information to be associated with each other; stores user behavior information that associates user identification information, the tag information corresponding to the medical content information viewed or browsed by a user, and a number of times of viewing or browsing per the tag information with one another; stores user attribute information that associates the user identification information and attribute information including viewing / browsing situation information of the medical content information viewed or browsed by the user with each other; normalizes the number of times of viewing or browsing per the tag information to represent a frequency of viewing or browsing by the user; and applies factor analysis processing to the user behavior information after the normalizing to calculate a predetermined number of common factors.

The invention provides a data processing method and device, a rule configuration method and device, equipment, a medium and a product, and relates to the field of user interface design, the technical field of streaming data processing and the technical field of digital marketing. The data processing method comprises the following steps: calling a real-time stream calculation engine to calculate a plurality of order transaction data based on a pre-configured conversion rule in response to receiving a plurality of order transaction data for a plurality of to-be-processed orders issued by an order system, and generating conversion data, the conversion data represents a conversion value for converting the transaction resources of the plurality of orders to be processed into special resource consumption, and the conversion rule represents a conversion mode for converting the transaction resources of the orders into special resource consumption.

The invention provides a method and system for dynamically optimizing an advertisement script, electronic equipment and a storage medium, and relates to the technical field of digital marketing, and the method comprises the steps: obtaining behavior data of a target user, carrying out the emotion analysis of the behavior data, and obtaining the emotion state of the target user; based on the emotional state, a script module is selected from a preset script material library, and a personalized advertisement script matched with the emotional state is generated; displaying the personalized advertisement script to the target user, and monitoring an attention behavior index of the target user in real time; based on the attention behavior index and a preset intervention node, triggering attention intervention compensation on the personalized advertisement script based on an emotional state, and generating a push advertisement script; the conversion result of the advertisement pushing script is counted, and data return is performed on the personalized advertisement script, the advertisement pushing script, the compensation record corresponding to the advertisement pushing script and the conversion result, so that the script module is automatically updated, and the advertisement conversion rate can be improved.

The invention discloses an automatic mining method for a marketing document popular formula based on big data analysis, and belongs to the field of digital marketing intelligent generation. Emotional expression vectors and structural design modes are obtained by analyzing historical document data, emotion generality categories are determined after clustering, and user response intensity is evaluated; and the association rule of the high-response emotion type and the structure mode is mined. A platform exclusive creation rule is determined according to a platform difference refining rule subset, copywriting templates suitable for various crowds are generated by fusing crowd data, and finally the copywriting is output and optimized by matching promotion information and adjusting parameters, so that the problems that traditional copywriting creation depends on artificial experience and is difficult to quantify and optimize are solved, and the copywriting efficiency is improved. And data-driven accurate copywriting generation is realized.

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.

The invention relates to the technical field of marketing service, in particular to a big data-based digital marketing service system, which comprises the following components: a data collection and analysis module which is used for acquiring browsing records, purchasing behaviors and social media interaction data of users in real time through butt joint with various digital platforms, processing and analyzing the browsing records, the purchasing behaviors and the social media interaction data, and sending the data to a cloud server; and the quality and the integrity of the data are ensured. And the target market positioning module is used for dividing the users into different subdivided markets by utilizing a machine learning and data mining algorithm according to the user portraits and the behavior patterns, and determining a target user group with the most potential. And the personalized recommendation module is used for utilizing a personalized recommendation algorithm machine learning and data mining algorithm based on the target user group and the marketing target. By collecting, processing and analyzing massive user data and combining an artificial intelligence algorithm and a personalized recommendation technology, accurate target market positioning, a personalized marketing strategy and a whole-course tracking analysis function are provided for enterprises.

This application relates to the interdisciplinary fields of digital marketing and electronic information technology, and specifically to a method for intelligent demand mining and precision marketing for small and medium-sized enterprises (SMEs) in the electronic information industry. The method includes: generating anonymized simulated customer data for SMEs in the electronic information industry, with the customer data generated based on customer characteristics in the electronic information industry, including customer attribute information and demand expression information; constructing a mapping model between customer attributes and product characteristics based on the customer data; analyzing the customer data based on the mapping model to mine potential customer needs and quantify the intensity of those needs; and generating marketing solutions adapted to the electronic information industry scenario based on these potential needs. This solves the problems of difficulty in demand mining, extensive marketing methods, and lack of digital marketing capabilities among SMEs in the electronic information industry. While ensuring data compliance, it lowers the technical application threshold and achieves accurate demand identification, automatic generation of marketing solutions, and continuous model evolution.

The invention discloses a customer intelligent marketing method and device, electronic equipment and a storage medium, and relates to the technical field of digital marketing, and the method comprises the steps: collecting and standardizing the behavior event flow of an enterprise customer on an electronic channel in real time, fusing the static attribute, historical transaction and real-time behavior sequence data to construct a multi-dimensional customer feature representation, and performing joint prediction through a pre-trained machine learning model to obtain a demand probability value for the target marketing product, generating a marketing decision instruction according to the probability value and the business rule, and issuing the marketing decision instruction to a current operation interface of a client in real time for scene display. According to the method, through multi-source data fusion and real-time calculation, the problems of incomplete customer portrait, inaccurate demand prediction and missed marketing opportunity caused by single data dimension, model staticization and marketing response lag are solved; the technical effects of accurately predicting customer demands, immediately mastering marketing opportunities, and naturally fusing into a business scene to improve user experience and conversion efficiency are achieved.

The application discloses an AIGC material generation method and device of industry characteristics and multi-dimensional verification, and belongs to the technical field of artificial intelligence and digital marketing. Based on a merchant input data set and an industry characteristic weighted algorithm, a standardized prompt word is dynamically constructed. The prompt word is input into an AIGC text generation model, a batch of marketing material drafts are generated, and quality scores are executed. The candidate materials are subjected to graphic-text compliance verification and platform self-adaptive adaptation processing, and are one-key published through a multi-platform publishing interface. The application solves the problems of insufficient industry characteristic adaptation, low compliance verification and multi-platform publishing efficiency in the traditional method, and realizes efficient, compliant and self-adaptive material automatic generation and distribution.

The invention provides a training method, a resource recommendation method and device, equipment, a medium and a program product, and relates to the field of artificial intelligence, the field of digital marketing and the field of intelligent recommendation. The training method comprises the steps that sample resource features related to a sample object are obtained, the sample resource features comprise incremental sample features and first basic sample features, the incremental sample features and the first basic sample features are different in feature type, and a basic feature fusion network is obtained through training according to second basic sample features, the sampling time span of the second basic sample features is greater than the sampling time span of the sample resource features; and performing joint training on the basic feature fusion network and the reference feature fusion network by using the sample resource features to obtain a trained resource recommendation model, the trained resource recommendation model being used for predicting a reference recommendation index related to the target object so as to facilitate resource recommendation according to the predicted reference recommendation index.

This invention discloses a method for evaluating advertising creative quality based on the prediction of creative competitiveness decline, specifically relating to the field of digital marketing technology. The method includes the following steps: acquiring multimodal feature data and historical delivery data of advertising creatives; constructing a multi-agent game model that models advertising creatives and platform algorithms as agents; using the model to predict the squeeze-out effect and substitution risk of target creatives from competitor creatives; and dynamically adjusting the delivery strategy of target creatives to one of defensive, evolutionary, guerrilla, or silent control based on the prediction results. This invention overcomes the limitations of isolated evaluation by constructing a multi-agent game model that includes both creative agents and platform algorithm agents, improving the accuracy of creative quality evaluation in a dynamic competitive environment. By predicting the squeeze-out effect and substitution risk, it achieves a refined characterization of traffic competition and mindshare invasion. The delivery strategy is dynamically adjusted based on the combination of these two indicators, effectively extending the creative lifecycle.

The invention discloses a digital marketing recommendation method and system based on big data, and relates to the technical field of commodity recommendation. The method comprises the steps of obtaining original data of an e-commerce platform, obtaining a structured data set through preprocessing, performing fuzzy clustering on a scoring matrix, generating and iteratively updating a membership matrix, reducing the influence of initial clustering deviation, calculating clustering attribution in combination with user geographic information, and correcting scores according to the clustering attribution to obtain a final scoring matrix. Inputting the matrix and the user and commodity feature vectors into a recommendation model, and outputting a commodity recommendation result; clustering multiple membership degrees of the users by adopting fuzzy clustering, and adapting to complex overlapping characteristics of user preferences under sparse data of the distributors; the initial deviation is reduced by iteratively optimizing the membership degree, attribution is determined in combination with geographic information, and the clustering fitness and distinction degree are improved; the commodity recommendation model effectively solves the problem of low accuracy of the commodity recommendation result under the condition of reducing the calculation complexity.

A digital marketing method, system, program product and storage medium. In the method, after receiving a new task instruction of a user, relevant information is extracted, and a material script is put into a preset marketing channel to start marketing; during the marketing, every time the local time of the system reaches a synchronization node, the task quantity value is obtained, the local and external time are synchronized by means of a distributed algorithm, an encrypted time stamp is generated, and it is verified whether the quantity reaches the standard; if it reaches the standard, it continues to be put into; if it does not reach the standard, it is marked as a potential fault node and continues. The technical scheme provided by the application can execute marketing according to the preset time and quantity, effectively reduce the marketing failure caused by time deviation or data anomaly, improve the accuracy and stability of the execution of the digital marketing task, and thus improve the overall marketing effect.

This invention relates to the fields of digital marketing technology and artificial intelligence optimization technology, and particularly to a method and system for joint budget allocation and ROI optimization of cross-platform advertising expenditures. It addresses the channel contribution assessment bias caused by traditional "last click" attribution through the Shapley value attribution model; it achieves quantitative prediction of inter-channel synergy and crowding-out effects by constructing a joint prediction model using nonlinear saturated response functions and cross-influence coefficients; it overcomes budget inefficiencies caused by independent optimization on a single platform through global optimization solutions using nonlinear programming and sequential quadratic programming algorithms; and it ensures the safe implementation of automated decision-making through safety executors and anomaly circuit breakers. This method elevates advertising budget allocation from a "local, static, and biased" level to a "global, dynamic, and scientific" level, significantly improving the overall advertising ROI by an estimated 15%-30%.

The invention discloses a cross-system data integration and value layering method based on customer numbers, and belongs to the field of digital marketing and financial science and technology. The method comprises the following steps: cleaning and standardizing source data of an electronic bank, credit management and customer channel system by taking a customer number as a unique association key to form a unified customer view; based on the view, non-linear fitting is carried out on the non-sensitive features by utilizing a gradient boosting tree model, and a customer value sorting result is generated; screening target clients according to the value sequence, and outputting a desensitization list only containing client numbers; and a full-process automatic closed-loop system covering data integration, portrait generation, multi-channel touch and effect tracking is constructed, and data interaction and task distribution among the modules are realized through an API (Application Program Interface). According to the method, the cross-system data integration efficiency and the accuracy and automation level of customer value layering are improved.

The invention provides a trusted agent generation method and system for enterprise full-flow digital marketing, and the method comprises the steps: obtaining the marketing execution flow data and user behavior data of an enterprise, and constructing a marketing flow chart and a user state machine; establishing a marketing agent structure based on the marketing flow chart and a user state mechanism, and generating a legal action set; scheduling and executing the legal action set of the marketing agent structure, and generating an action set which is actually successfully executed and a corresponding scheduling score; and performing structured recording and auditing tracking on the action set which is actually and successfully executed. The core problems that a traditional system intelligent agent is uncontrollable, behaviors cannot be checked, and the state cannot be known are solved.

To provide a system for enabling anyone to easily execute digital marketing without expert knowledge.SOLUTION: Means for receiving a merchandise concept from a user, means for automatically generating a delivery scenario in a AI based on the received merchandise concept, means for automatically generating advertisement content in the AI based on the automatically generated delivery scenario, means for adjusting the generated content and delivery scenario by a designer, means for detecting legal risks and conformity to brand guidelines of the advertisement content using the AI and presenting the detected legal risks and conformity to the brand guidelines to a legal department, means for delivering the generated advertisement content to influential target users and performing an AB test, means for evaluating delivery results with a BI tool and measuring CTR, CVR, and ROI, and means for analyzing evaluation results with the AI and improving the delivery scenario and the content.SELECTED DRAWING: Figure 1

The application discloses a kind of digital marketing user portrait generation method and system based on deep learning, it is related to digital marketing technical field, including, to original fine-grained interaction event flow is standardized, and the output standardized user microcosmic behavior time sequence;Standardized user microcosmic behavior time sequence is input to deep time sequence neural network, and the output user cognitive load quantification score and visual attention focus distribution vector;Based on user cognitive load quantification score and visual attention focus distribution vector, to static long-term interest portrait is weighted and focused fusion, generates situational dynamic user portrait vector;Situational dynamic user portrait vector is input counterfactual explanation engine.The application solves the problem that the marketing communication opportunity and form adaptability caused by lack of real-time psychological state assessment of user in prior art, improves the precision of digital marketing, user experience and communication efficiency.