64 results about "Precision marketing" patented technology

The invention discloses a smart marketing system and device and a medium, and belongs to the field of big data analysis. The system comprises a data support layer used for collecting multi-source heterogeneous data including equipment data, user behavior data and third-party data, and forming data assets by preprocessing the multi-source heterogeneous data; the intelligent decision-making layer is used for identifying the life cycle stage of the current user based on an intelligent model matrix and the data assets, predicting the transition stage of the life cycle of the user and generating a precision marketing strategy and a channel selection scheme of each stage; and the business application layer is used for converting the precision marketing strategy and the channel selection scheme into a specific marketing task, monitoring marketing effect data and feeding back the marketing effect data to the data support layer and the intelligent decision-making layer. According to the system, the intelligent marketing process of full-link data fusion, dynamic life cycle management and automatic marketing closed loop can be realized.

The invention discloses a user behavior analysis and precision marketing system based on deep learning, and relates to the technical field of computers, and the system comprises the steps: S1, carrying out data access and management, collecting a user behavior log, a session sequence, position information, an end-side event and cross-domain Internet of Things data, combining a commodity portrait, a commercial tenant portrait and multi-modal content, and carrying out the recognition of the user behavior log, the session sequence, the position information, the end-side event and the cross-domain Internet of Things data; time alignment, anomaly repair, label completion and privacy desensitization are completed, and a unified data basis is formed. According to the method, seamless integration and high-quality processing of multi-source heterogeneous data are realized through unified data access and treatment capability, a solid data foundation is laid for subsequent deep learning and precision marketing, the problems of data dispersion and poor quality of an existing system are effectively solved, and the method has a good application prospect through multi-modal representation learning and feature construction. Text, image and video features are extracted by using a depth encoder, and cross-modal feature alignment and complex relation modeling are realized in combination with sequence modeling and a graph neural network.

The invention belongs to the technical field of user portrait analysis, and mainly relates to a cross-border e-commerce user portrait analysis method based on big data, and the method comprises the steps: extracting a user demand tag through multi-source data collection and standardization processing in combination with a semantic understanding model, and constructing a compliance knowledge graph of a target market; in the construction of user portraits, a framework based on cultural cognition is adopted, and preferences of users on types and components of health care products under different cultural backgrounds are deeply mined; a semantic network between a culture background and health care product attributes is constructed through a knowledge graph technology, and the recognition ability of the model to culture sensitive features is further improved; by dynamically updating the user portraits and combining marketing feedback data, commodity recommendation and marketing strategies are continuously optimized, so that the accurate marketing effect of cross-border e-commerce is improved on the premise of guaranteeing compliance; the user experience and the conversion rate of the cross-border e-commerce platform can be effectively improved, and the risk of violation is reduced.

The invention discloses a precision marketing label screening method and system based on high-dimensional interactive screening. The method comprises the following steps: S1, a user determines a core index variable y for measuring a service effect; s2, a user determines a to-be-screened label range and preliminarily selects a proper customer group to obtain a sample set x; s3, constructing a hierarchical screening model; and S4, the user selects a screening hierarchical mode: weak hierarchical structure screening and strong hierarchical structure screening. The system comprises an interactive display module, a data storage module, a screening analysis module and a feedback optimization module. According to the method, reference can be provided for bank marketing personnel when the marketing objects are determined, the situation that labels with information values are omitted when the labels are selected from numerous labels is avoided, and hidden interaction information among the labels can be explored.

The application discloses a cost-optimized out-of-season clothing renovation design method and an intelligent processing system, relates to the field of clothing intelligent design systems, and has the technical scheme as follows: a data acquisition module, which collects original data of out-of-season clothing and popular trend matching accurate marketing landing; a data analysis and prediction module, which evaluates potential risks and feasibility in the renovation process based on the collected data; a design scheme generation module, which automatically generates multiple feasible renovation design schemes according to the data analysis results and personalized requirements input by a user, and determines the best design scheme; and the application integrates a complete closed-loop process from data acquisition to evaluation, combines an artificial intelligence deep learning algorithm to complete demand prediction, design scheme generation and optimization, and significantly improves production efficiency and reduces artificial dependence.

The invention discloses an AI-based tobacco consumer behavior analysis and personalized marketing method and system, and relates to the technical field of data processing, and the method comprises the steps: collecting a multi-modal real-time behavior data stream; performing dynamic noise filtering on the multi-modal real-time behavior data stream to obtain an effective behavior sequence; extracting a plurality of spatio-temporal feature vectors of the effective behavior sequence, fusing the spatio-temporal feature vectors and compressing dimensions to obtain a target feature tensor; inputting the target feature tensor into an incremental learning engine for incremental learning; according to the public opinion event, performing matching in a preset marketing strategy library to obtain alternative actions, and then pushing differentiated marketing strategies; feedback behavior data is collected, and then a strategy utility index is calculated according to the feedback behavior data, and the strategy utility index is transmitted back to the incremental learning engine to serve as a regularization constraint term of next round of updating. According to the method, the incremental learning engine, the public opinion event and the marketing strategy mapping mechanism are combined, and dynamic optimization and precise marketing of the incremental learning engine are achieved.

The invention discloses a machine learning recommendation method for precision marketing and potential customer mining. The method comprises the following steps: S1, data acquisition: establishing a user tag table based on historical data and acquiring user behavior characteristics; s2, data preprocessing: preprocessing the data, including removing abnormal values, filling missing values, and converting category features into numeric features; s3, automatic box separation: performing variable analysis and automatic box separation on the preprocessed data; s4, feature processing: converting a data table obtained by binning into a numerical value form; s5, dividing a data set: dividing the processed data set into a training set and a test set; s6, stepwise regression is carried out to select features: stepwise logistic regression is used to select model key variables; s7, manual binning and modeling data set preparation are carried out; the method has the characteristics of high automation level and high decision-making efficiency.

A training method of a marketing prediction model for eliminating time deviation and a precision marketing method relate to the technical field of data processing, samples are grouped by setting time stamps and marketing scheme stamps, experimental samples and contrast samples in the same time group are ensured to be comparable in average value, and the accuracy of marketing prediction is improved. Therefore, the time deviation problem when historical samples are adopted is eliminated, meanwhile, purchase rate target pre-training is carried out on the model to solve the problem that high-latitude features cannot be effectively utilized due to the fact that the unbiased sample size is small, and finally the overall loss is calculated in a multi-task training mode, so that the accuracy of the model is improved. The marketing prediction model can achieve better prediction accuracy under the condition that biased samples and a small number of unbiased samples are adopted, so that the sampling cost caused by only using the unbiased samples is greatly reduced, and both the cost and the effect are achieved. By using the marketing prediction model, accurate putting of marketing schemes can be further realized, and the marketing prediction model has a good practical value.

The invention discloses a bank financial cloud customer potential tapping method based on machine learning. The mining method comprises the steps of S1, defining sample data; s2, performing customer portraying according to the sample data; s3, carrying out data processing on the customer portrait; s4, carrying out model construction and verification; s5, the model is applied; and step S6, evaluating the model. Through a LightGBM ensemble learning algorithm, a complex relationship between customer characteristics and purchase tendencies can be automatically learned, a response probability of a customer is output, and accurate marketing of the customer is realized. And customer omission caused by potential customer mining through artificial experience is avoided.

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.

This invention discloses an intelligent identification method for street-front shops based on an improved TF-IDF model, belonging to the field of intelligent shop identification technology. The method includes: extracting and normalizing internal customer information from the operator to obtain a local customer pool; performing a three-dimensional matching between a clean shop pool and the local customer pool to obtain a first pool of matched shops and a second pool of unmatched shops; forming text pairs using corresponding fields from the second shop pool and the local customer pool, calculating semantic similarity using an improved TF-IDF model, and identifying shops with similarity greater than a threshold as model-matched local shops; merging the first shop pool with the model-matched local shops to form the final local shop pool, and removing them from the clean shop pool to obtain a pool of shops from other networks; adding industry and product tags to the final local and other network shop pools to generate a shop profile library and outputting it to the marketing end. This invention can improve the accuracy of shop identification, reduce the cost of manual visits, and achieve one-time separation of local and other network shops for precise marketing.

The embodiment of the application provides a kind of wisdom shop data processing method and device based on multidimensional data analysis, and the alignment and mapping of space-time feature sequence are innovatively realized by collecting sensor, intelligent terminal and social media data.Multiple dimensional data fusion is carried out based on feature weight coefficient, and a commodity recommendation model is constructed using convolutional neural network and recurrent neural network.The system continuously optimizes the model performance through incremental learning, realizes personalized commodity recommendation and shop operation trend analysis.Introduce marketing decision optimization model, intelligently evaluate and dynamically optimize marketing strategy, and continuously update the model through feedback data.The method breaks through the limitation of traditional single data analysis, and provides a comprehensive data processing solution for precision marketing of wisdom shop.

This application discloses a modeling method and apparatus for customer conversion prediction, a customer marketing method and apparatus, equipment, and computer program product. The method includes: constructing dynamic panel data of sample customers; performing feature engineering processing on the dynamic panel data of sample customers using a preset feature engineering strategy to obtain multi-dimensional modeling feature data of sample customers; and performing adaptive modeling using a preset classification model based on the multi-dimensional modeling feature data of sample customers to obtain a customer conversion prediction model. This application effectively captures the impact of changes in customer behavior over time on marketing conversion by constructing a dynamic panel data structure and utilizing its "intra-group correlation and inter-group independence" characteristics. Feature engineering analysis and mining based on panel data ensure the effectiveness and robustness of the mined features. Through refined adaptation of different models and data structures, the model's accuracy in identifying high-potential conversion customers is improved, providing reliable support for precision marketing.

The embodiment of the invention relates to the field of data processing, and provides a marketing method based on a customer portrait and a related device, and the method comprises the steps: obtaining customer behavior information of a target customer during consumption, carrying out the construction of the customer portrait according to the customer behavior information, and obtaining a target customer portrait, therefore, marketing activity preference information can be generated according to the target customer portrait, target marketing activity information corresponding to the target customer is further determined according to the marketing activity preference information, and the target marketing activity information can be sent to a terminal corresponding to the target customer. Precise marketing based on customer consumption behaviors can be realized, and the matching degree of marketing activities and customer demands and the customer response rate can be improved.

The invention relates to a two-dimensional code interaction system based on an outer package or a bottle cap of a beverage bottle. The method comprises the steps that a first-time interaction module obtains a two-dimensional code image, analyzes the two-dimensional code image to obtain a unique bottle body identifier and a server address, and generates a first-time interaction request in combination with an equipment encryption hardware identifier and user identity data; the mapping construction module verifies request validity and extracts core data to generate a mapping relation table; the user authentication module retrieves the mapping table through the re-scanned subsequent request, verifies the consistency of the device identifier and then generates a user identity authentication token; the recommendation generation module generates a user portrait based on the token associated social data, and generates an exclusive recommendation list in combination with historical interaction data. All the modules cooperate to provide more accurate services for users, and effective technical support is provided for enterprises to achieve accurate marketing and user management based on consumption scenes.

The application provides an intelligent marketing method and system driven by an ai large model, which comprises the following steps: extracting a behavior feature vector based on the micro interaction behavior of a user by using a multi-scale behavior mode coding network; extracting the core marketing interest of the user by using an adaptive multi-layer interest clustering, and generating a behavior label; analyzing the real influence of different marketing strategies on the purchase decision of the user by using double-layer causal reasoning, and outputting an individualized marketing effect label; scoring the applicability of the marketing strategy based on the individualized marketing effect label, and optimizing the recommended strategy by using a reinforcement learning framework; pushing the individualized marketing content to the user according to the individualized marketing strategy distribution result, and optimizing the recommended strategy by using a dynamic closed-loop feedback optimization model based on the real-time feedback of the user. The application overcomes the limitations of the existing precision marketing technology by introducing innovative technologies such as fine-grained behavior analysis, causal reasoning optimization and dynamic marketing strategy adjustment, and improves the precision of the recommendation and the marketing ROI.

The application is specifically an e-commerce analysis and diagnosis method based on artificial intelligence, comprising: behavior data collection; behavior analysis; comprehensive judgment; intention diagnosis and evaluation.In the application, a multi-dimensional analysis system is constructed, the behavior data of a user from the whole link of contacting a commodity to browsing details, comments and the like is comprehensively collected, a foundation is laid for accurate analysis, two core indexes of a matching coefficient and an interest coefficient are introduced, the former quantifies the matching degree of the user and the commodity information from the angles of browsing fluency, information acquisition efficiency, key information attention and the like, the latter combines three dimensions of comment interaction, historical portrait matching and picture browsing to mine the potential interest of the user, finally, the intention evaluation coefficient is calculated and graded by comprehensively adding the purchase behavior and the collection behavior, the purchase intention of the user can be more accurately judged, the waste of marketing resources is avoided, the conversion rate of marketing activities and the input-output ratio are improved, and a scientific basis is provided for accurate marketing of e-commerce.

The invention discloses a label data query method, system and device and a storage medium. The method comprises the steps that a query statement which is input by a user side and contains a label combination is received; analyzing the query statement based on a preset global tag key database to generate an abstract syntax tree; mapping nodes of the abstract syntax tree into bitmap operation and generating a bitmap execution plan, and executing the bitmap execution plan based on the two-dimensional bitmap matrix to obtain a bitmap result; and converting the user index in the bitmap result into a target user identifier list corresponding to the query statement through a user space bidirectional mapping model, and outputting the target user identifier list. Millisecond-level response of complex label combination query is realized, query efficiency and real-time performance are remarkably improved, system resource occupation is optimized, high-frequency query requirements of business scenes such as user portraits, precision marketing and real-time recommendation are effectively supported, and rapid and reliable technical support is provided for fine operation decision making of enterprises.

The application relates to the technical field of Internet, and provides a precise marketing method, device and equipment based on user consumption data, which comprises the following steps: dividing a data set into a majority class data set and a minority class data set after normalizing the consumption data; sorting the Mahalanobis distance of each sample in the minority class data set and then dividing the sorted samples into several subsets to calculate the minimum Mahalanobis distance; taking the samples in different subsets as parent samples by using the MAHAKIL algorithm with a random parameter, iteratively generating offspring samples, and training a user group classifier by taking the offspring samples with a Mahalanobis distance greater than or equal to the minimum Mahalanobis distance from the parent samples as a new minority class data set to perform precise marketing on different user groups; and balancing the sample quantity relationship between the minority class data set and the majority class data set can make the classifier accurately identify the minority class users, avoid the bias towards a certain class of users caused by the quantity difference of the training data set, and improve the precision of marketing.

The invention provides a commodity information pushing system and method based on precision marketing, and relates to the technical field of electronic commerce and information, and the method comprises the steps: collecting multi-source flight operation data and GIS data through a branch and bound method; performing field importance analysis and feature engineering analysis on the data, and selecting feature fields pushed to the commodity information to obtain feature data; according to the feature data, performing deep mining evaluation analysis on the flight operation data to construct a flight operation guarantee capability evaluation model and a sliding optimal path algorithm model; and according to the operation guarantee capability evaluation model, the sliding optimal path algorithm model and the historical behavior data of the user, constructing an accurate portrait of the target user. Through personalized user portrait construction and matching and real-time accurate pushing and marketing effect evaluation, the commodity information pushing accuracy and the user satisfaction degree are improved.

This invention provides a method and apparatus for generating digital SMS content, relating to the field of data processing technology. The method involves obtaining first historical marketing SMS information from a first merchant; obtaining first SMS content elements and second SMS content elements based on the first historical marketing SMS information; obtaining a first degree of influence of the first SMS content elements on the marketing effect of the first merchant and a second degree of influence of the second SMS content elements on the marketing effect of the first merchant; determining whether both the first degree of influence and the second degree of influence exceed a first preset threshold; when the first degree of influence and / or the second degree of influence exceed the first preset threshold, obtaining a first SMS content template based on the first SMS content elements and / or the second SMS content elements; and obtaining a first optimal SMS content based on the first merchant's first marketing task and the first SMS content template. This achieves the technical effects of improving readability and conversion rates, realizing precise marketing strategies, and increasing output value.

The application discloses a kind of based on commercial circle big data to the method, device and electronic equipment of customer classification, the method includes: obtaining sales area, and the commercial data corresponding to sales area and the customer information of customer;Sales area is divided into commercial circle grid, and according to customer information, customer is mapped into corresponding commercial circle grid;According to commercial data, data filling is carried out to commercial circle grid;After data filling, commercial circle grid is classified, and the commercial circle type of each commercial circle grid is obtained;Customer is classified based on customer information and commercial circle type.The application divides region to sales market, and according to the commercial data of each region, the customer in region is classified more accurately, establishes the analysis level with commercial circle grid as core, realizes the fine intelligent management of customer, and through more accurate customer classification, effectively improve the level of accurate marketing to customer, promote the accurate delivery of goods source.

The invention relates to the technical field of marketing data processing, in particular to a user unified behavior representation and cross-scene marketing application method and system based on multi-task learning, and the method comprises the steps: collecting and preprocessing the original behavior data of a multi-scene user, and obtaining a standard behavior sequence with a scene identifier; inputting the sequence into a hierarchical multi-task learning model, and generating a decoupled user unified behavior representation vector through scene perception information extraction, multi-task collaborative learning and unified representation fusion; obtaining a target scene identifier and a marketing target, and obtaining a scene adaptation representation by using the lightweight adaptation network transformation vector; and calling a marketing prediction model based on the representation to generate a personalized decision and applying the personalized decision. Through multi-task collaborative learning, scene perception representation fusion and a dynamic adaptation mechanism, unified modeling and precise marketing of user cross-scene behaviors are realized, the personalization and the real-time performance of marketing decision making are improved, and meanwhile, the manual intervention cost and the error risk are reduced.

The invention relates to the technical field of e-commerce services, and discloses a sales service platform and method based on a lottery drawing incentive mechanism, and the platform comprises an account authorization management module, a user access management module, an intelligent lottery drawing engine, a real-time screen recording notarization module, a real-time quality supervision module, and a business event bus module. The method corresponds to the platform. According to the application, the sales service platform integrating account authorization, user access, intelligent lottery drawing, real-time screen recording notarization and real-time quality supervision is constructed, and cooperative operation among the modules is realized through the business event bus, so that a non-intermediate sales mode that a factory is directly connected with consumers is established, and the commodity circulation cost is reduced; fairness and transparency of the lottery drawing process are ensured through equipment fingerprint identification, two-way screen recording and block chain evidence storage technologies, and the user credibility is improved; precise marketing incentive is realized by means of intelligent lottery drawing and user grading; and meanwhile, a whole-process quality control closed loop is formed through a systematic quality real-time supervision and reputation management mechanism.

This invention discloses a method and system for precise marketing resource allocation based on the user's marketing value lifecycle, specifically relating to the field of marketing resource allocation technology. The method includes acquiring multi-source heterogeneous data of the target user group and constructing a user state representation that integrates static user attributes, dynamic behavioral sequences, and market dynamic characteristics. Based on the user state representation, a dynamic CLV prediction model is used to simultaneously predict the user's future lifecycle value and user state transition probability. This invention solves the technical problems of static prediction, disconnect between optimization and execution, and rigid budgeting in traditional marketing resource allocation by constructing a closed-loop collaborative architecture of dynamic CLV prediction, heterogeneous MDP planning, and contextual multi-armed learning. This enables the marketing resource allocation process to combine the foresight of long-term planning with the agility of short-term execution, improving the accuracy of CLV and state transition probability predictions. Through asynchronous updates and knowledge transfer mechanisms, it ensures the system's stable adaptation in dynamic environments.

The invention belongs to the technical field of furniture marketing management, and discloses an AI customized furniture precision marketing system and method based on a user portrait. Comprising the steps of collecting user portrait feature vectors; constructing a user life scene model based on the home space image, the house type plane graph and the space size input information uploaded by the user; calculating a space adaptation degree and a style coordination degree of the furniture commodity and the target room, and generating a furniture candidate combination set on the premise of meeting a space constraint condition; inputting the user portrait feature vector, the user life scene model and the furniture candidate combination set into a multi-task recommendation model, predicting a purchase transformation probability and a display click intention of each furniture candidate combination in the current marketing scene, and outputting a sorted target recommendation combination; and on the basis of the pre-trained copywriting generation model, generating a personalized furniture marketing copywriting strongly related to the actual life scene of the user, and realizing AI customized furniture precision marketing based on the user portrait.

The invention discloses a customer label clustering analysis method based on semantic embedding, comprising the following steps: S1, collecting original text data of customer labels, and preprocessing to generate a standardized label text data set; s2, executing semantic embedding operation, and generating a label semantic embedding vector data set; s3, the improved sparse coding model is used for dictionary learning, and sparse feature representation is obtained; s4, performing mean shift clustering analysis to obtain a tag clustering grouping result; s5, generating customer label groups and customer portraits according to a label clustering and grouping result; and S6, outputting customer label grouping information for customer management, precision marketing or label recommendation. According to the method, through fusion of semantic embedding, sparse coding and improved mean shift clustering algorithm, high-precision automatic grouping and personalized portrait generation of customer tags are realized.

1. Name of the product in this design: Graphical User Interface for Visualizing Electronic Devices. 2. Purpose of this design: An electronic device. 3. The key design features of this product are the graphical user interface content on the screen. 4. The picture or photo that best illustrates the key design points: Design 1 front view. 5. The product hardware is a standard design, and the rear, left, right, top, and bottom views of Design 1-2 are omitted. 6. Design 1 is designated as the basic design. 7. Purpose of the graphical user interface: This product design is used to add target audiences and comparison audiences in the profile insight function module, and select profile information to view the group characteristics of the profile audience and their characteristics compared with the comparison audience in real time, providing data support for the formulation of precision marketing strategies. 8. Human-computer interaction method of graphical user interface: Click "+Add Target Audience" on the main view interface of Design 1 to enter the design 1 change state diagram 1, click "Create Customer Group" to enter the design 1 change state diagram 2, click the "Save" button to enter the design 1 change state diagram 3, click "Add Profile Information" to enter the design 1 change state diagram 4, click "Transaction Indicator Distribution" to enter the design 1 change state diagram 5, click the "OK" button to enter the design 1 change state diagram 6, click "+Add Target Audience" on the main view interface of Design 2 to enter the design 2 change state diagram 1, click "Create Customer Group" to enter the design 2 change state diagram 2, click the "Save" button to enter the design 2 change state diagram 3, click "Add Comparison Audience" in the upper right corner of the interface to enter the design 2 change state diagram 4, select any existing customer group and click "Save" to enter the design 2 change state diagram 5.

The invention discloses a user consumption behavior prediction method under cross-tenant privacy protection and a related product. After a consumption prediction request of a tenant for a target user is received, interaction data and current geographic position data of the target user are firstly obtained. Converting the interaction data into a first semantic note for describing the consumption motivation of the user by using a large language model, and abstracting the geographic position data into a regional commercial attribute tag which does not contain specific street information to obtain a first tag; vectorizing and splicing the first semantic note and the first tag to form a target feature vector; and then a reference vector with high similarity with the target feature vector is retrieved in a public vector database, group features associated with the reference vector are combined, reasoning analysis is performed through a large language model, and finally a consumption intention prediction result of the target user is obtained. According to the invention, on the premise of ensuring the privacy security of the user, the cross-brand data can be effectively utilized to carry out precision marketing.

The invention discloses an information retrieval service and method for all entities such as e-commerce precise repurchase, equipment and implement precise retrieval and the like based on WeChat official account code scanning, keyword automatic reply and self-developed WeChat official account intelligent reply. The system serves as a multi-tenant intelligent reply system deployed in a cloud server and is in butt joint with a plurality of WeChat official accounts; a unique identification bar code is arranged on an entity, and an official account code scanning menu is configured; after a user scans a bar code, the self-developed intelligent reply system receives bar code data, retrieves uniquely corresponding multi-modal resource information from a multi-tenant system database, and assembles the information into a structured message to be returned to the user through an official account. According to the invention, through linkage of "object-code-intelligent system", the technical problems of limited entity information bearing and unsmooth sales and service channels are solved, and the beneficial effects of precise marketing, efficient after-sales and private domain traffic precipitation are realized.