166 results about "Push technology" patented technology

The invention relates to the technical field of front-end resources, in particular to an intelligent caching method and device for loading front-end resources on demand, equipment and a medium, and the method comprises the following steps: obtaining user behavior data, predicting an access page, identifying required resources, and constructing a resource dependency graph; calculating resource use frequency and importance, and determining a target resource; selecting a resource format and a compression mode according to the user equipment type; according to the resource dependency relationship graph, loading and caching target resources required by a next page possibly accessed by the user in advance; meanwhile, the caching strategy is dynamically adjusted based on the resource use frequency and the importance index; the method comprises the following steps: monitoring a network condition, dynamically adjusting a resource loading strategy, pre-loading resources before a user requests by using a resource dependency graph and a server pushing technology, and determining a target loading mode when the user accesses. Therefore, the problems that in the prior art, resource loading is low in efficiency, a caching strategy is rigid, environment adaptability is poor, user behavior analysis is insufficient, and performance optimization automation is lack are solved.

The invention discloses a private domain data-based user portrait generation method and a private domain data-based content generation and touch method, and relates to the technical field of big data processing and pushing. Analyzing user behavior characteristics and interest preferences based on the collected data; marking static tags for the corresponding users based on the basic attributes; according to the behavior characteristics and interest preferences, constructing a primary dynamic tag; through user behavior data collected in real time, a time decay weighting algorithm is adopted to improve recent behavior weight and filter abnormal click data, a time sequence cross attention network is adopted to model a behavior feature sequence, interest preference and probability are output, and a dynamic threshold value is set to trigger dynamic label updating; and if the key behavior data of the user is collected, recalculating the dynamic tag based on the key behavior data and performing full-amount refreshing. According to the invention, a data-content-touch real-time linkage closed-loop system is constructed.

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 provides a multi-terminal collaborative AI model dynamic deployment method, which comprises the following steps that: a cloud terminal splits a complete AI model into lightweight sub-models according to a real-time state reported by edge equipment and AI model structure characteristics; calculating the sequence of the sub-models distributed to the edge devices through a priority queue algorithm; differential increment transmission is adopted to push difference parameters of the existing model of the edge device; the edge device triggers incremental learning based on an end side abnormal sample, and local model parameters are updated by adopting lightweight transfer learning; the cloud receives the encrypted local model parameters and the associated sample feature values, node parameters of the multiple edge devices are aggregated through federal learning, and an optimized global model is generated. According to the multi-terminal collaborative AI model dynamic deployment method, through a model fragmentation pushing technology and a side incremental learning mechanism, model dynamic adaptation and closed-loop optimization of resource awareness are realized, and the AI model deployment efficiency and scene adaptability in a complex industrial scene are remarkably improved.

The invention relates to the technical field of business propaganda and pushing, in particular to a telecommunication business propaganda and pushing method based on artificial intelligence. Comprising the following steps: S1, collecting behavior data, preference data and telecommunication service data of a user, and sending the collected data to a database for storage; s2, obtaining a push value of a telecommunication service product through multi-dimensional analysis of the user information and the telecommunication service product information; s3, performing comprehensive analysis on the user active time, the login frequency and the response behavior to realize accurate pushing of related telecommunication services; s4, obtaining real-time feedback of the user to the pushed content by providing a user feedback entrance; through analysis of the time period and the activity degree, pushing is carried out at the active time of the user, the opening rate and the click rate of pushing are improved, the pushing frequency and the pushing interval are adjusted, and excessive interference to the user is avoided; the invalid pushing frequency is reduced, pushing in a low-response-rate channel and an inactive time period is avoided, and resource use is optimized.

The invention relates to the technical field of advertisement pushing, in particular to an intelligent pushing strategy method and system for real-time user behavior monitoring. The method comprises the following steps: acquiring user behavior data; performing feature extraction according to the user behavior data to obtain user behavior feature data; performing user behavior change capture according to the user behavior feature data to obtain user behavior change data, and performing model updating according to the user behavior change data and a preset user interest model to obtain a user real-time interest model; and generating advertisement content according to the user behavior data and the user real-time interest model to obtain user real-time advertisement content data so as to carry out real-time user behavior advertisement pushing operation. According to the method, the pushed content is optimized, so that the pushed content is highly matched with user requirements, interests and environments, and the click rate and the conversion rate of pushing are remarkably improved.

The invention relates to the technical field of information pushing, in particular to a platform user interest recommendation method and system based on artificial intelligence. The method comprises the following steps: acquiring user action data and environment perception data, analyzing the user action data and the environment perception data, and constructing a unified space-time semantic network; according to the unified space-time semantic network, analyzing scene features of the user, and determining a multi-dimensional dynamic scene feature set; and analyzing the multi-dimensional dynamic scene feature set, determining composite scene demand information of the current user, and retrieving and pushing real-time demand information of the user according to the composite scene demand information. According to the method and the device, the perception capability of the pushed information to the user demand scene is improved, the multi-dimensional dynamic adaptation of the pushed information to the platform user is realized, the information pushing accuracy for the platform user is improved, and the composite information demand of the user in a complex scene is met.

The invention relates to the technical field of message pushing, in particular to an SaaS (Software as a Service) intelligent concurrent pushing system which comprises a tenant flow monitoring module, a resource binding module, a lock-free object writing module, a handle layout construction module and a cache prefetching delivery module. According to the method, the high-frequency business object is identified by monitoring the flow characteristics of the tenants, and the complete Slab page is dynamically applied and bound to the thread exclusive local allocation buffer area, so that physical isolation of bottom layer resources is realized to eliminate global lock competition and context switching overhead in a multi-thread environment; lock-free memory writing is executed based on a privatized pointer, the object construction efficiency is greatly improved, a compact storage structure in which a bitmap is adjacent to a connection handle is constructed, a hardware cache prefetching mechanism is triggered by utilizing bitmap reading, connection data is loaded to a CPU cache line in advance, and the connection data is stored in the cache line. And the cache miss rate is reduced and the concurrent delivery performance of massive messages is remarkably improved by utilizing a memory locality principle.

The invention provides an advertisement cold start pushing method and system based on multi-source data fusion, and relates to the technical field of advertisement pushing. The advertisement cold start pushing method based on multi-source data fusion comprises the steps that user side information is collected, and the user side information comprises device information, behavior logs and third-party annotation data; extracting user portrait features according to the collected user equipment information, behavior logs and third-party annotation data, and extracting features of advertisement content needing to be pushed; the extracted user portrait features are matched with the content features of the advertisements needing to be pushed, and interest values of the users for all the advertisements are obtained respectively; and according to the obtained interest value of the user for each advertisement, judging the type of the advertisement needing to be pushed to the client, generating a pushing list of the advertisement, and completing pushing of the advertisement according to the pushing list. According to the method, the accuracy and the putting effect of the advertisement can be greatly improved, and better user experience and business return can be brought.

The invention discloses a product and service pushing method and system based on a payment value chain model, and belongs to the technical field of Internet product pushing, and the method specifically comprises the steps: collecting user payment value chain data in real time through closed-loop applications, such as digital catering, digital campus, and even digital business district, and carrying out the preprocessing; features are extracted, a mixed data model is constructed, user portraits are constructed based on the model, and user groups are divided through a clustering strategy; user derivative product demand information is identified, a value threshold value is set, a derivative product recommendation list meeting the demand is generated, and a collaborative filtering method is used for sorting; and according to the user portrait and the sorted recommendation list, a pushing strategy is formulated, the effect is tracked, and the strategy is optimized. According to the method, the recommendation accuracy and the user satisfaction degree are improved.

The invention discloses an intelligent touch system and method based on user characteristics, and belongs to the technical field of message pushing. According to the system, user portraits are generated through user behavior data, the user portraits comprise tags such as reach channels, reach time and reach content, and preference characteristics such as channel preference, time preference, algorithm preference and copywriting preference are generated based on click behaviors of users. And the system calculates the weight of each reaching channel, time and content packet through an element preferential module, and selects an optimal combination to generate a message sending scheme. And if the message is not successfully sent, the system automatically selects to generate a new sending scheme again and sends the new sending scheme again. According to the method, accurate and personalized message pushing can be realized according to historical behaviors and preferences of the user, the reaching rate of the message and the response rate of the user are remarkably improved, and the method is suitable for scenes needing large-scale personalized pushing such as e-commerce platforms and social media.

The invention is suitable for the technical field of advertisement pushing, and particularly relates to a digital media advertisement pushing method and system, and the method comprises the steps: dividing an internal region of a shopping mall into a plurality of sub-regions, each sub-region corresponding to a category, marking the deployment position of a Bluetooth gateway in each sub-region, and carrying out the pushing of a digital media advertisement through the Bluetooth gateways, reading a Bluetooth beacon signal of the visitor, extracting a unique identifier, acquiring consumption data of the visitor through pre-bound identity information, and determining an interest category of the visitor; acquiring video monitoring data of an entrance and an exit of the shopping mall, and identifying the number of visitors; and if the number is 1, passing the identity information. According to the method, the same item is pushed to all the visitors of the same bank, so that not only can personalized commodity recommendation and promotion information be pushed, but also attention and interaction of the visitors to the information can be increased, the attraction of the advertisement is improved, and thus the advertisement pushing accuracy and the participation degree of the visitors are improved.

The invention belongs to the technical field of advertisement pushing, and relates to an intelligent real-time advertisement pushing method based on a multilevel graph neural network, which comprises the following steps of: firstly collecting dynamic data and static data of a parking lot and preprocessing, then designing two graph structures of a global graph and a local graph, and introducing a self-attention mechanism; a multi-level graph neural network is constructed and trained to obtain a trained model, finally, the trained model is utilized to calculate embedding of each license plate and a frequently-going place so as to generate a prediction score, for each user, advertisements are sorted according to the prediction score, and the advertisement with the highest score is selected as a push result. Through a multi-level graph structure, the model can capture a user behavior mode more comprehensively and meticulously, and the recommendation accuracy and correlation are improved; and moreover, the calculation complexity is reduced, the calculation efficiency of the model is improved, the method can be efficiently operated in an actual application scene with limited resources, and the requirement of real-time recommendation is met.

The invention discloses an advertisement pushing method and device based on artificial intelligence and a medium, and relates to the technical field of advertisement pushing, and the method comprises the steps: collecting data information of a user, carrying out the preprocessing operation, and dividing the data into a scene data set and a user interest data set; constructing a scene recognition model, and training the scene recognition model through the scene data set; constructing a user interest model, and training the user interest model through the user interest data set; analyzing the advertisement content, extracting an advertisement keyword, defining a scene keyword for the scene according to the current scene of the user, and matching and pushing the scene keyword; and carrying out effect evaluation and optimization on the pushed advertisement. According to the method, advertisements related to the current scene can be pushed to the user through scene perception advertisement pushing, so that the click rate and the conversion rate of the advertisements are improved, the satisfaction degree of the user is improved, and the loyalty of the user to the brand is enhanced.

The invention discloses an advertisement pushing method and system based on age and gender recognition, and relates to the technical field of intelligent advertisement pushing, and the method comprises the steps: carrying out the polymorphic fusion of extracted user features through a multi-modal fusion network, and comprehensively analyzing the age and gender information of a user; according to the age and gender information of the user, identifying an advertisement material which is most matched with the age and gender of the user from an advertisement library; based on the selected advertisement materials, generating personalized advertisement copywriting by using NLP, and generating personalized advertisement pictures and videos by using GANs; and combining the personalized advertisement copywriting with the personalized advertisement picture and video to generate personalized advertisement content. Through multi-modal data acquisition and fusion, the age and gender identification precision is improved, the advertisement content is accurately matched, personalized copywriting and vision are generated, and the advertisement attraction and the user experience are improved.

The invention discloses a personalized customization pushing system and method of a user portrait technology, and relates to the technical field of personalized pushing, a terminal obtains a user portrait through authorization of a target user and uploads non-privacy information in the target user portrait to a server, and the server generates a universal recommendation result of the target user; the server sends the universality recommendation result to the terminal, the terminal obtains a particularity recommendation result based on the universality recommendation result and the target user portrait, and the terminal obtains a personalized customization pushing method according to the universality recommendation result and the particularity recommendation result; transmission of the user portrait containing privacy information is not involved, and the risk of user portrait information leakage in the transmission process is reduced; transmission of personalized customization push results is not involved, and the risk that personalized customization push content exposes privacy information of a user is reduced; and the privacy information of the user is reserved in the terminal equipment, so that the security of the user information is improved.

The invention discloses an intelligent notification pushing method and system based on applet ecology, relates to the technical field of information pushing, and solves the technical problem that an existing applet pushing system is difficult to perform information pushing according to the real-time state of a user, so that the satisfaction degree of the user to applet pushing is relatively low. The data acquisition module is used for acquiring user data in real time through a user terminal; the data processing module is used for acquiring the state of the applet when the applet is in a running state; extracting input information and browsing information in the user data, generating a target keyword group according to the input information and the browsing information, and generating push content according to the target keyword group; obtaining a user state, and generating a push instruction according to the user state and the applet state; the message pushing module is used for packaging the pushing content into pushing information; pushing the push information according to a push instruction; the content pushed by the applet is more suitable for the corresponding user; and the satisfaction of the user is enhanced.

The invention discloses an advertisement directional pushing method based on video content, relates to the technical field of advertisement pushing, and solves the technical problem that target audiences really having demands cannot be effectively reached by fully considering personalized characteristics and demand differences of each user. According to the method, information such as browsing history records, search records and use time of the push users is obtained in detail, and scientific analysis methods such as time period division, classification of advertisements of the same type and frequency calculation are applied, so that a user portrait which is more accurate than that of a traditional mode can be constructed; therefore, advertisements meeting user requirements are pushed in a targeted mode, the matching degree of the advertisements and the users is improved, invalid advertisement putting is reduced, and by analyzing software use behaviors and advertisement browsing conditions of the users in different time segments, for example, the browsing ratio of different advertisement types and the software use frequency in the target time period are calculated, so that the user experience is improved. And the advertisement pushing times and types are reasonably adjusted.

The invention discloses a commodity pushing method and system based on a consumer address, and particularly relates to the technical field of positioning and message pushing, and the system comprises a data collection module which is used for monitoring and collecting consumer personal information and mobile phone historical record information data; according to the invention, based on the consumer outdoor information, the environmental noise information and the network connection information generated in the daily activities of the consumer, the demand degree of the consumer for commodity pushing is evaluated, the pushing frequency is preliminarily adjusted according to the evaluation result, and then the machine learning algorithm model is constructed to optimize the pushing process, so that the recommendation accuracy is improved, and the user experience is improved. After a system sends commodity push to consumers according to an algorithm, a post-push emotion evaluation model is constructed through emotion abnormal information, software retention information and commodity evaluation information of consumers who do not purchase commodities after information acquisition, and the emotion tendency of the consumers to user software is judged; and the commodity pushing frequency of the consumer is adjusted for the second time according to the emotion tendency of the user.

The embodiment of the invention discloses a finance and taxation service request processing method and device, and the method comprises the following steps: receiving a finance and taxation service request, carrying out the semantic analysis through a BERT model trained based on a finance and taxation field corpus, and extracting a user intention and a key service entity; based on a finite-state machine model, managing a dialogue flow of a business scene, triggering state transition according to a current dialogue state, a user intention and a key business entity, and orderly promoting an invoicing, checking or diagnosing business process; scheduling a corresponding AI capability module to execute a task according to the user intention and a service scene defined by the key service entity; an execution result of the AI capability module is fed back to a front end in real time through a server pushing technology and is displayed in linkage with information input by a user; based on key business entities and operation combinations recorded in a user operation log, the key business entities and the operation combinations are automatically abstracted and packaged into independent reusable functional components. According to the embodiment of the invention, finance and tax business requirements can be adapted through multi-step and scenarized dialogue response.

The invention is suitable for the technical field of intelligent advertisement pushing, and provides an advertisement pushing method and system based on a user portrait, and the method comprises the steps: judging whether a first exhibition advertisement belongs to a high-information-load advertisement or not after detecting that a current user enters a designated application interface, and if yes, executing the step 1; and if yes, obtaining an advertisement recommendation weight of the first exhibition advertisement, wearable device monitoring data of the current user and specified application interaction behavior data. According to the invention, by introducing wearable device monitoring data and combining the current cognitive load state and the historical behavior trend of the user, a recommendation weight dynamic correction mechanism taking the first adjustment factor and the second adjustment factor as the core is constructed.

The invention relates to the technical field of hot search pushing, and discloses a hot search information pushing method based on big data, which screens user effective behavior data through a multi-dimensional data cleaning strategy, constructs a user interest weight model in combination with a time attenuation factor, and dynamically balances the diversity and correlation of pushed contents by using information entropy. The method specifically comprises the following steps: cleaning historical browsing data of a user, removing invalid behaviors, and generating a second heat collection set; calculating interest weights of the user for different types of hot search based on the time decay factor, and optimizing weight distribution through normalization processing; pushing strategies are designed for the new users and the old users respectively, the new users adopt global information entropy to screen high-diversity content, the old users dynamically adjust a pushing list in combination with weighted information entropy, it is ensured that the content conforms to interests, and information cocoon rooms are avoided.

The invention discloses an intelligent mobile terminal tourism information personalized pushing system based on a user portrait, and relates to the technical field of mobile terminal tourism pushing, and the system comprises a user portrait construction module which collects basic information, mines behavior data and generates an interest label; the tourism information processing module is used for collecting multi-source tourism data, carrying out structured processing and evaluating the quality; the personalized push decision module is used for matching user interests and situations and determining a push strategy; the interaction feedback module is used for tracking user operation and optimizing portraits; and the privacy protection module is used for desensitizing the data, managing the authority, encrypting and storing. According to the method, the pushing accuracy and timeliness are improved, the user experience is optimized, the information quality and privacy security are guaranteed, and the method is adaptive to the whole travel process demand.

The invention discloses an employment post information push query method, computer equipment and a storage medium, and relates to the technical field of post push, and the method comprises the steps: collecting the post description text of an enterprise post, the enterprise employee demission rate and the enterprise growth index, constructing a post evaluation matrix, and obtaining a post evaluation result; performing evaluation calculation by using the job seeker multi-dimensional label vector and the post evaluation matrix, generating post scores, inputting enterprise geographic coordinates and job seeker residence coordinates into a path planning API (Application Program Interface) to calculate commuting time, executing post recommendation screening by using the post scores and the actual commuting time, and obtaining a post recommendation list. And pushing the post recommendation list to the job seeker in real time through the intelligent terminal. According to the post pushing method, the post matching accuracy is improved, and the problem of inadaptive work caused by inconvenient commuting and inaccurate matching is reduced.

The invention discloses an online advertisement intelligent pushing system based on big data, and relates to the technical field of advertisement pushing, the online advertisement intelligent pushing system comprises a data acquisition module, a data processing module, a data storage module, an intelligent pushing module and a user feedback module, the intelligent pushing module comprises a user interest analysis unit, a pushing opportunity unit and an effect evaluation unit, the user interest analysis unit analyzes the interest degrees of different advertisement categories, the pushing opportunity unit divides a day into a plurality of time periods, and determines the advertisement pushing strength and the advertisement pushing time periods, so that the preference of the user for the advertisement categories is analyzed through an interest evaluation algorithm, the most popular advertisement category is selected for pushing, and the advertisement pushing efficiency is improved. The optimal advertisement pushing time period and pushing strength are determined through the segmentation evaluation algorithm, the pushing effect is evaluated through the advertisement effect evaluation algorithm, the threshold value is set, the competitive advertisement influence is adjusted in real time, the advertisement strategy is dynamically adjusted in the high competitive time period, and the advertisement pushing accuracy is improved.

The invention discloses an e-commerce platform pushing system based on user portrait analysis, and relates to the technical field of platform pushing, the e-commerce platform pushing system comprises a control center, and the control center is in communication connection with a database, a portrait generation module and a pushing module; the database is used for storing user information, commodity information, order information and behavior information, and generating a relative correlation coefficient between commodities according to the commodity information and the order information; the portrait generation module is used for generating user keywords according to the user information and the behavior information; acquiring user payment behavior data, and generating user attributes according to the payment behavior data; generating a user portrait according to the user keyword and the user attribute; and the pushing module is used for generating a corresponding pushing strategy according to the relative correlation coefficient between the user portrait and the commodity.

This application belongs to the field of digital push technology and relates to a method and system for collaborative online and offline marketing content delivery, aiming to solve the problems of rigid delivery, low accuracy, and disconnect between online and offline in existing technologies. The method includes: acquiring user behavior characteristics and scenario characteristics collected by offline display terminals when displaying content, and determining interests; when preset conditions are met, sending an instruction to cause the offline display terminal to dynamically generate a unique interaction identifier and output an interaction entry point; receiving online access requests and extracting the unique interaction identifier from them, associating display events and online interaction events based on the unique interaction identifier to obtain event-level attribution results; and adjusting the generation control parameters and / or delivery strategy parameters of subsequent target marketing content based on the attribution results. This application can achieve event-level association and attribution between offline display and online interaction, forming a closed loop of content generation, display, interaction, and optimization, improving the efficiency of marketing content generation, delivery accuracy, and conversion effect.

The present invention belongs to the technical field of advertising push and relates to an intelligent real-time advertising push method based on a multi-level graph neural network. The method first collects dynamic data and static data of a parking lot and performs preprocessing. Then, two graph structures, a global graph and a local graph, are designed. The self-attention mechanism is introduced to construct and train a multi-level graph neural network to obtain a trained model. Finally, the trained model is used to calculate the embedding of each license plate and frequently visited places, thereby generating a predicted score. For each user, advertisements are sorted according to the predicted score, and the advertisement with the highest score is selected as the push result. The multi-level graph structure enables the model to capture user behavior patterns more comprehensively and meticulously, thereby improving the accuracy and relevance of recommendations. In addition, the computational complexity is reduced, the computational efficiency of the model is improved, and it can run efficiently in actual application scenarios with limited resources, meeting the needs of real-time recommendations.

The invention discloses an advertisement playing method and device for a shelf propeller and a storage medium, and belongs to the technical field of advertisement pushing. The method comprises the steps that an advertisement playing action is triggered based on a pulse signal generated by a pulse signal sensor, the pulse signal is generated by the pulse signal sensor based on commodity number change information, an advertisement to be played is obtained based on the advertisement playing action, the advertisement to be played is displayed in a display screen, and the advertisement to be played is stored in a micro-control unit in advance, or remotely issuing through network connection. According to the method and the device, matching between advertisement pushing and user requirements is realized by sensing the taking and placing behaviors of the user on the specific commodity in real time, and the advertisement pushing accuracy is improved.

The invention discloses a context-based Internet of Things data pushing method, which relates to the technical field of data pushing, and comprises the following steps: collecting original data, inputting preprocessed data into a context fusion engine, fusing the preprocessed data into context data, identifying agricultural context events based on a context data application rule engine and a machine learning reasoning model, and outputting pushing event data. According to the method, the soil humidity data, the air temperature and humidity data, the illumination intensity data and the meteorological platform data are fused through the unified agricultural situation model, unified processing and situation reasoning of the multi-source data are achieved, and the accuracy and practicability of the pushed information are improved. The agricultural situation event is recognized by combining the rule engine and the machine learning reasoning model, and the pushing frequency and content are adaptively adjusted according to the pushing event data, so that the communication and calculation burden is reduced, the user experience is optimized, and a good application effect is achieved.