198 results about "Consumer demand" patented technology

The invention discloses an intelligent marketing system based on user portraits and behavior prediction, which belongs to the field of artificial intelligence, and comprises multi-source data acquisition and integration: a diversified data acquisition interface is configured, and user multi-type data can be acquired from multiple channels such as an e-commerce platform, social media, a search engine and an enterprise CRM system; comprising basic information, browsing and purchasing behavior records, social interaction information, search preferences and emotion-related text data. Data of different formats and sources are integrated into a unified data set by using preprocessing means such as data cleaning, duplicate removal and standardization; according to the invention, through accurate user portrait and behavior prediction, an enterprise can accurately grasp the demand preference of a target customer, accurate pushing of marketing information is realized, the hit rate and the conversion rate of marketing activities are greatly improved, and the marketing effect is effectively improved; according to user value and behavior prediction, marketing resources are reasonably allocated, resource waste is avoided, and enterprise marketing efficiency is improved.

The invention discloses a marketing strategy optimization management system based on six elements of order transaction, and belongs to the field of communication management systems, in terms of data acquisition, multi-source comprehensive information collection enables enterprises to perceive consumer demands in all directions and no longer blindly grope, a dynamic feature modeling unit fuses various kinds of data into six-dimensional feature vectors, and the six-dimensional feature vectors are integrated into a database; multiple factors of commodities, users and festivals are balanced and considered, a solid foundation is laid for a marketing strategy, a commodity-festival-user ternary association graph constructed by a festival graph generation engine enables commodities to be pushed in a targeted manner, a personalized strategy is generated by an intelligent decision module, the matching degree of the commodities and consumers is improved, and the marketing efficiency is improved. The overall continuous feedback optimization mechanism of the system enables the marketing strategy to be like a continuously evolved life entity, can better adapt to the market change, and improves the sales probability of commodities.

A system to personalize a shopping experience in a conversational commerce platform is disclosed. The system includes a processing subsystem having a user interface module for consumer input and an input conversion module that processes and translates this input using a large language model (LLM) engine. The engine module features a catalog facet creation module that structures product information, a facet enrichment module for detailed descriptions, images and buyers' profile, and a customer profiling module utilizing natural language processing to understand customer needs. An AI merchandising module presents optimal product facets to customers based on profiles and historical data. Additionally, a conversational commerce module facilitates product selection through guided conversations, while a personalization module tailors recommendations. The system also includes a data collection and analytics module for performance tracking and a training and optimization module for continuous improvement of the LLM.

The invention discloses a customer demand analysis method based on AI artificial intelligence CRM system data, and the method comprises the following steps: S1, collecting multi-source heterogeneous customer data, executing the preprocessing, and carrying out the fusion; s2, customer behavior features are extracted and fused to form feature vector representation, feature importance evaluation is carried out, and high-correlation feature subsets are screened; s3, inputting the high-correlation feature subset into a swarm intelligence optimization module, and outputting initial network configuration parameters; s4, constructing and initializing a customer demand prediction model according to the initial network configuration parameters; s5, training the customer demand prediction model, and dynamically adjusting the structure of the customer demand prediction model; s6, continuing to iteratively train the customer demand prediction model; and S7, generating a customer demand prediction result. According to the method, intelligent swarm optimization and deep learning methods are fused, accurate prediction of customer demands is realized, and the method has the advantages of high adaptability, accurate prediction and fast response.

The invention relates to a financial customer precision marketing system and method based on big data and artificial intelligence in the field of financial science and technology and intelligent marketing cross technology, and provides a financial customer precision marketing system and method based on big data and artificial intelligence by integrating multi-dimensional customer data, constructing a dynamic customer portrait, predicting customer demands by using a deep learning model and combining the real-time reaching capability of enterprise WeChat ecology. According to the method, automatic execution and closed-loop optimization of a personalized marketing strategy are realized, and the customer conversion rate and loyalty of a financial institution are improved; the method is innovatively embodied in deep combination of an emotion recognition technology and a financial scene, reinforcement learning optimization of a dynamic rule engine and a privacy compliance data desensitization mechanism, and solves the problems of lagging customer demand recognition and low strategy matching efficiency in traditional marketing.

The present invention belongs to the technical field of data mining, and particularly relates to a commodity recommendation method and system based on mining of high-utility repeated negative sequence patterns. The present invention no longer restricts that the negative transformation operation must occur within the itemset range, but allows the transformation operation to be performed on each commodity element at a finer granularity, thereby generating more repeated negative sequence candidate patterns; moreover, when the existing HUNSPM algorithm calculates the utility value of the repeated negative sequence candidate pattern, the utility value of the negative commodity element is set to 0, and only the utility value based on the positive commodity element is considered to obtain the utility value of the negative sequence candidate pattern; while when the present application calculates the utility value of the repeated negative sequence candidate pattern based on the commodity element influence rate, the present application not only utilizes the utility value of the non-overlapping positive sequence candidate pattern, but also utilizes IDSE(P i ,S i ) and considers the commodity element influence rate corresponding to the negative commodity element obtained by the negative transformation process. The commodity recommended by the present application can better meet the customer requirements.

The invention relates to the technical field of bottom protection plates, and provides an engine bottom protection plate and a preparation method thereof. The engine bottom protection plate is prepared from the following raw materials in parts by weight: 40 to 50 parts of polypropylene, 15 to 20 parts of glass fiber, 5 to 10 parts of silicon carbide modified carbon fiber, 5 to 9 parts of nano silicon dioxide, 2 to 3 parts of compatilizer, 0.5 to 1 part of antioxidant, 0.3 to 0.5 part of calcium stearate and 0.4 to 0.6 part of polyethylene wax. The engine bottom guard plate provided by the invention is high in hardness, also has certain acid and alkali resistance, keeps good mechanical properties, and meets the development of the modern automobile industry and the change of consumer demands.

The invention discloses an intelligent customer obtaining method and system based on multi-modal data, and relates to the technical field of multi-modal data. Corresponding customer demand information is determined according to detection of a demand space; according to the content of the customer demand information, the information weight and the multi-modal data, multiple pieces of target data in the multi-modal data are determined, and the final customer obtaining path is determined based on the multiple pieces of target data, the enterprise database and the customer demand information, so that the accuracy of the customer obtaining path is improved. Therefore, the corresponding customer information is determined according to the detection of the customer obtaining node of the customer obtaining path, and the corresponding customer obtaining mode is matched based on the customer information and the customer demand information; and determining a corresponding customer obtaining number according to the execution of the customer obtaining mode, and triggering autonomous optimization of the customer obtaining mode according to the customer obtaining number and the execution condition of the customer obtaining mode until the customer obtaining number is greater than a preset customer obtaining number threshold value, so as to improve the intelligent customer obtaining effect.

The invention provides an automobile after-service pricing model optimization method under dynamic supply and demand balance, and relates to the technical field of pricing model optimization. The pricing model optimization method comprises the following steps of accessing a government policy issuing platform and an industry association database, capturing related policies and macroeconomic indicators of automobile post-service in real time, performing semantic analysis on financial news and policy interpretation articles, extracting potential factors, and performing classification and analysis on the potential factors. A competitor official website, an e-commerce platform and a social media account are monitored for 7 * 24 hours, a real-time feedback module is embedded in a service platform, customers are encouraged to evaluate services, prices and new product demands in time, a market signal threshold model is established, and the market quality is improved. And early warning thresholds are set for the policy change frequency, the competitor price fluctuation amplitude and the customer demand growth rate index. Through real-time market dynamic monitoring, scientific quantitative analysis, intelligent and accurate decision making and rapid iteration strategies, the enterprise pricing ability and market competitiveness are effectively improved.

The invention provides a logistics resource sharing and dynamic optimization configuration method based on the industrial internet in order to solve the technical problems that an existing distribution task distribution mode is mostly based on static data and is not flexible and efficient enough, and most distribution systems do not fully utilize real-time state information of vehicles, so that dynamic adjustment and optimization cannot be achieved in the distribution process. On the basis of perception of logistics resource real-time state information and customer demand dynamic information, a vehicle path model suitable for mixed taking and delivering is constructed, and the model is a multi-target model considering vehicle cost, time window deviation punishment cost and the like. In combination with the state information of the current logistics resources and the dynamic change of the customer demand, the hybrid delivery vehicle path model is solved through an improved adaptive large-scale neighborhood search algorithm to obtain an optimized delivery path scheme, so that the logistics vehicles can dynamically respond to the customer demand in the delivery process, and the delivery efficiency is improved. The method is more flexibly suitable for dynamically changing distribution requirements and complex urban traffic environments, and efficient logistics services are realized.

The invention discloses an assembling device and method of a high-efficiency filter in the technical field of machining and assembling, and aims to solve the problems that in the prior art, the process of detaching and replacing filter paper of the high-efficiency filter by a customer is complicated, and the quality is easy to cause problems. The assembly line comprises an assembly line used for conveying folded filter paper, the assembly line is sequentially provided with a gluing assembly, a screen plate placing assembly, a side plate assembly assembly and a connecting strip assembly assembly from upstream to downstream, the upstream and the downstream of the assembly line are divided into a first section and a second section respectively, and the feeding width of the second section of the assembly line is smaller than the length of the filter paper; a jacking conveying line is further arranged on the side of the upstream position of the second section of the assembly line, and a second conveying line is further arranged on the side of the downstream position of the second section of the assembly line. The invention is used for providing a new filter paper assembly mode, so that the efficient filter equipment of a customer can be efficiently replaced according to the customer demand, the manual operation error can be reduced while the assembly efficiency is guaranteed, and the assembly efficiency and quality are better.

The invention discloses a product recommendation method and device, computer equipment and a storage medium, belongs to the technical field of artificial intelligence, and is applied to a recommendation scene of financial products. According to the method, end-to-end data standardization, semantic analysis and privacy protection are carried out; performing disturbance and noise injection based on differential privacy; the method comprises the following steps: accurately capturing short-term fluctuation and long-term trend of customer demands by virtue of a multi-level time sequence algorithm, and extracting a typical demand change mode through segmented clustering and modeling; when significant demand fluctuation is detected, the online learning and deep reinforcement learning model can update a recommendation strategy in real time; the interpretability analysis module transparently presents a recommendation decision basis; user feedback is continuously absorbed through incremental learning, model parameters are continuously optimized, and finally it is guaranteed that an accurate personalized recommendation result is output under millisecond-level response. According to the method, the recommendation accuracy, the user satisfaction and the service conversion rate are remarkably improved, and the compliance and the expansibility are considered at the same time.

The invention discloses a public transportation auxiliary unmanned aerial vehicle cooperative distribution method considering client point dynamic demands, introduces a public transportation auxiliary unmanned aerial vehicle with large service range coverage to carry out express distribution, and aims to reduce the terminal logistics distribution cost and improve the distribution timeliness. The method comprises the following steps: constructing a generation model which simulates a dynamic customer demand and considers a time window in combination with an actual scene, and establishing a bus auxiliary unmanned aerial vehicle cooperative distribution optimization model which considers the customer point dynamic demand based on a customer point generation mechanism and a bus unmanned aerial vehicle cooperative operation mechanism in the generation model; and aiming at the established model, designing a dynamic information processing strategy based on bus departure time, and fusing the dynamic information processing strategy into an adaptive large neighborhood search framework for solving, thereby obtaining a heuristic algorithm suitable for solving a bus-assisted unmanned aerial vehicle cooperative distribution problem considering the dynamic demand of a client point. The distribution path can be dynamically planned according to the demand points generated at any time, the logistics delivery efficiency is improved, and the distribution cost is reduced.

According to the embodiment of the invention, the instrument type selection method comprises the following steps: demand collection: obtaining a spoken type selection demand input by a user; the demand analysis is used for converting the spoken type selection demand input by the user into a demand of applying a standard instrument term; type selection searching: searching in an instrument type selection knowledge graph based on the analyzed demand to obtain an instrument entity meeting the demand; and performing model selection evaluation, calculating weighted scores of the instrument entities meeting the requirements based on weights of different model selection elements, and obtaining the instrument entities of final model selection, based on the method, customer requirements can be more fully and accurately understood, meanwhile, the entity association ability and retrieval ability of the knowledge graph are further enhanced, and the user experience is improved. Therefore, the instrument type which better meets the requirements of the user is recommended for the user. The invention further provides an instrument type selection system and a storage medium.

The invention provides an automatic waybill ordering method and system based on logistics docking, and the method comprises the steps: constructing a logistics resource intelligent agent set and a logistics resource market based on logistics resource data, obtaining customer demand data, constructing a customer demand intelligent agent based on the customer demand data, and carrying out the customer demand intelligent agent set and the logistics resource market; and generating a corresponding logistics risk portrait in combination with a preset logistics risk map and a multi-factor risk convolution mechanism, putting the customer demand agent into a logistics resource market for game prediction, obtaining a corresponding game prediction result, generating a corresponding logistics risk assessment report based on the game prediction result, and performing logistics risk assessment. The method comprises the steps of performing logistics resource screening based on a logistics risk assessment report, generating corresponding logistics waybill data, generating a corresponding electronic logistics waybill in combination with a waybill adaptive conversion mechanism, obtaining an actual performance data set of the electronic logistics waybill, and performing feedback optimization on a module corresponding to an automatic waybill ordering system in combination with the actual performance data set. Therefore, the quality of service provided for merchants is improved.

The invention provides a non-standard conveying equipment production enterprise management method and system, and relates to the technical field of non-standard conveying equipment production enterprise management. The method comprises the steps of obtaining customer demand information, and matching a historical model to generate a workpiece demand list; comparing a price library to trigger a grading quotation strategy; the contract is stored after the order is confirmed; design information is obtained, and workpiece parameters are analyzed; comparing the outsourcing article library to generate outsourcing and production lists; classifying components based on an identification rule base; generating a task instruction set in combination with equipment and employee information; collecting production data to form completion information; the system comprises a quotation module, an order splitting module, a task distribution module and the like. Through a rapid quotation and order splitting mechanism, a dynamic classification decision and a full-link quality tracing coding system, the production efficiency of non-standard parts is improved, responsibility is accurately positioned, resource configuration is optimized, the management cost is reduced, and the market competitiveness of enterprises is enhanced.

The invention provides a heating control method of an air fryer, the air fryer comprises a cooking cavity, a hot air assembly and a temperature sensor, the hot air assembly comprises a fan and a heating piece, the heating piece comprises a first heating pipe and a second heating pipe, and the power of the first heating pipe is larger than that of the second heating pipe; the heating control method comprises a temperature rise stage and a constant temperature stage, the temperature control logic of the constant temperature stage is selected according to the target control temperature, the target control temperature is not smaller than 180 DEG C, the first temperature control logic is selected, the second heating pipe is continuously started, and the first heating pipe is intermittently started for compensation; and if the target control temperature is not greater than 120 DEG C, selecting a second temperature control logic, intermittently opening the second heating pipe, and intermittently opening the first heating pipe for compensation. Different temperature control logics are set for different target control temperatures, so that the coloring requirements of different food materials during high-temperature and low-temperature cooking can be met, cooking is more professional, and the cooking requirements of consumers on'good color, aroma and taste 'at present are met.

The application discloses a product design intelligent evaluation system based on big data, and particularly relates to the technical field of product design evaluation, and the system comprises an anchor point account establishing module, a product evaluation module, a scheme rating module and a result output module; the anchor point account establishing module forms a design anchor point table according to a demand preliminary evaluation sheet and initial scheme information; the product evaluation module acquires a scheme change sheet and calls scheme verification data, and performs product undertaking verification; the scheme rating module combines the undertaking verification result and an auxiliary verification result to output a scheme evaluation state; and the result output module outputs product design intelligent evaluation results in a summarized manner. The application can continuously verify whether customer demand is undertaken by design parameters, versions, materials, processes, samples, costs and market feedback after scheme change, and can locate abnormalities to specific change fields, change components, anchoring relationships and data sources, thereby improving the traceability and review efficiency of evaluation results.

The invention discloses a scene self-adaptive scoring method and system based on double-agent collaboration, and belongs to the technical field of artificial intelligence and data processing. The method comprises the following steps: identifying an intelligent agent through a user intention, and receiving and identifying a customer demand containing a target marketing scene; the user intention recognition agent transmits recognized target marketing scene information to a scoring modeling agent; the score modeling agent adaptively screens and obtains personalized user data related to the target marketing scene from a database containing operator data according to the target marketing scene; and the score modeling agent fuses the personalized user data and the universal user data, and performs score modeling on the users based on the fused data to obtain scores of the users in the target marketing scene. According to the invention, automation, precision and high efficiency of the scoring system are realized, and the efficiency and accuracy of marketing scoring are significantly improved.

The utility model discloses a traction pillow which comprises a pillow body, a neck traction part is arranged on the rear side of the pillow body, a prefabricated supporting strip is wrapped on the inner side of the neck traction part, inner exhaust holes are formed in the inner side of the prefabricated supporting strip, and outer exhaust holes corresponding to the inner exhaust holes are formed in the outer side of the neck traction part. The prefabricated supporting strip is a polyurethane prefabricated supporting strip, and the inner side of the prefabricated supporting strip and the inner side of the neck traction part are each connected with a steel wire. According to the utility model, the manufacturing is simple, the neck traction part is embedded into the prefabricated supporting strip, the hardness of the prefabricated supporting strip can be adjusted according to customer requirements, the use requirements of different consumers are met, a steel wire can penetrate through the middle of the prefabricated supporting strip to fix the prefabricated supporting strip at the required mold cavity position, and the steel wire is pulled out after foaming is completed, so that the production efficiency is improved. The inner exhaust holes and the outer exhaust holes are used for improving the odor dispersing and thorough drying rate or the exhaust effect of polyurethane, residual odor is avoided, and the use effect of consumers is good.

This invention discloses a method for producing ginger gummy candy, belonging to the field of gummy candy processing technology. Potato resistant starch, locust bean gum, monoglycerides, and water are added to a mixing tank, stirred, and heated to boiling. A gelatin solution is then added dropwise to the mixing tank while stirring continues to obtain a starch mixture. Ginger juice and a sweetener are added to the mixing tank, followed by the starch mixture and lemon juice. The mixture is poured into a mold and cooled to set, yielding the finished ginger gummy candy. The method utilizes a combination of potato resistant starch, locust bean gum, and gelatin, leveraging the high thermal stability and low coagulation properties of the starch-polysaccharide gel system to improve the sensory characteristics of the ginger gummy candy and meet consumer demands for taste.

This invention provides a method for cultivating cigar wrapper tobacco leaves, belonging to the field of tobacco cultivation technology. The cultivation method includes the following steps: ridging and drip irrigation cultivation; before transplanting tobacco seedlings, the ridges are moistened to increase soil moisture, controlling the drip irrigation flow rate at 1.0-1.5 L / h and the drip irrigation duration at 3-5 hours; shading begins after the tobacco plants enter the bush stage, with the shade net having a light transmittance of 60%-80%; during the vigorous growth period of the tobacco plants until the end of harvest, spraying is carried out when the temperature exceeds 33℃, with a spraying volume of 800-1500 kg / mu. Through the cultivation method of this invention, the appearance quality of cigar wrapper tobacco leaves can be significantly improved, such as color uniformity and leaf tissue integrity, as well as physical properties such as leaf thickness and elasticity. High-quality wrappers can enhance the overall quality of cigars, strengthen their competitiveness in the market, meet consumer demand for high-quality cigars, and thus increase the added value of cigar products.

The invention discloses an intelligent de-inventory method for idle real estate resources, and relates to the technical field of real estate management, and the method comprises the following steps: S1, obtaining the idle real estate resources, carrying out the recognition, classification and labeling of the idle real estate resources, and constructing a dynamic resource feature library; s2, analyzing the customer demand text, extracting a multi-level keyword, and generating a customer demand vector; s3, establishing a resource matching model, and sequentially generating a first recommendation scheme, a second recommendation scheme to an Nth recommendation scheme in combination with a resource feature library and a government preferential policy; and S4, outputting all the generated recommendation schemes as a stepped recommendation scheme set, and sorting the recommendation schemes according to the comprehensive score for the user to select, thereby effectively improving the inventory removal efficiency of the idle real estate resources.

Lime-incorporated chitosan (LCh) beads are a non-toxic, environmentally friendly, and efficient preservative for food products, showcasing their superiority in microbial control, alcohol inhibition, color retention, and maintenance of physicochemical parameters. The all-natural LCh beads described here exhibit an on-par preservation performance with synthetic sodium benzoate at its maximum permissible limit in fruit juices. Additionally, the LCh beads offer the unique advantage of reusability, maintaining their preservation efficacy over multiple fruit juice preservation cycles, contributing to economic and environmental sustainability. The all-natural and biodegradable LCh beads exhibit comparable or superior preservation performance to synthetic preservatives like sodium benzoate at its maximum permissible limit in fruit juices. In light of the growing concerns about the harmful effects of synthetic preservatives, LCh beads present an eco-friendly and biocompatible solution, aligning with the increasing consumer demand for fresh and safe foods without chemically synthesized additives.

The invention belongs to the technical field of big data customer information matching, and discloses an intelligent customer information matching method based on big data, which comprises the following steps of: firstly, establishing a scene customer behavior semantic dictionary according to three groups of new customers, high-value customers and loss early-warning customers, and determining behavior label mapping rules of different customer types; for example, the browsing duration of a new customer exceeds 3 minutes, the consultation frequency exceeds 2, the behavior is defined as a high-intention behavior, the semantic dictionary is dynamically updated every 24 hours based on cross-platform semantic conflict feedback, and the cross-platform data semantic standard is unified; secondly, screening weak association features by adopting a mutual information and customer value double-weight algorithm, setting weights according to customer value differences, and preferentially retaining features with higher association degree with customer demands to generate structured feature vectors; and finally, deploying a preprocessing effect verification module at an edge computing node, performing field secondary labeling, null interpolation complementation and time format unification operation on the data, and improving the data standardization level.

The invention provides an insurance customer demand matching and efficiency management and control method based on multi-source data fusion. The method comprises the following steps: S1, collecting and inputting customer basic information and user-defined tags; s2, receiving customer basic information and a self-defined label, and performing customer demand analysis; s3, performing matching degree evaluation on a customer evaluation result and a company platform product; s4, the salesperson determines whether to invitation according to the evaluation result, and the management personnel performs platform quality control according to the evaluation result; s5, acquiring a customer participation platform record and a transaction product record; and S6, performing model optimization according to the customer participation platform record and the transaction record to further improve the accuracy of the prediction information. According to the invention, accurate screening of platform customers is realized, a scientific sales basis is provided, and the problems of large flow and low transaction efficiency of an insurance sales platform are solved.

The invention discloses a customer financial product recommendation method and device. The method comprises the following steps: step 10, collecting multi-dimensional data of a new customer; step 20, carrying out preprocessing on the multi-dimensional data; step 30, integrating features from the preprocessed multi-dimensional data, and constructing a feature set; and step 40, inputting the feature set into a recommendation model, and selecting a financial product to be recommended to a new customer according to a result output by the recommendation model. According to the customer financial product recommendation method and device provided by the invention, customer demands are accurately identified, and personalized financial product recommendation is provided for customers.

The invention provides a financial lease method and equipment based on dynamic portrait matching, and belongs to the technical field of financial lease. The financing leasing method comprises the following steps: acquiring user information and equipment leasing information of an equipment leasing party, and constructing a user portrait of the equipment leasing party according to a leasing keyword of the information; using a multi-dimensional feature matching algorithm to match the user portraits of the device leasee and the plurality of device providers in the database to obtain a multi-dimensional feature matching degree; pushing the equipment provider information and the corresponding equipment information to an equipment lessee according to a sequence of the multi-dimensional feature matching degrees from high to low; and according to the operation instruction of the equipment leasee and the communication information with the equipment providers, adjusting the user portrait of the equipment leasee in real time, and according to the adjusted user portrait, dynamically matching the multi-dimensional feature matching degree and reordering the equipment providers to promote financing lease between the equipment leasee and the equipment providers. According to the method, the customer demand can be accurately met.

The utility model discloses a combined concave swing seat frame mechanism based on a slide rail, which comprises a base, a chair seat and a first electric push rod, the chair seat comprises a sitting and leaning frame and a backrest frame, a sliding frame is arranged on the base, at least two first hinge shafts are arranged on the left side and the right side of the sliding frame, and the first electric push rod is arranged on the first hinge shafts. A swing arm capable of swinging relative to the sliding frame is arranged on each first hinge shaft, the bottom of each swing arm is hinged to the sitting and leaning frame, the sitting and leaning frame is hung on the sliding frame through the swing arms, a swing arm driving assembly is further arranged on the sliding frame, the first electric push rod is hinged to the base, and the output end of the first electric push rod acts on the sliding frame. On the basis of the existing back-and-forth sliding and back rising and falling functions, the electric swing function technology is upgraded and added, the product configuration function is improved, more consumption requirements are met, the foot bath tub is suitable for more types of foot bath tubs and use places, swing in the mode is more stable, the action gives people a hypnotic feeling, people are promoted to have a rest and relax, and the user experience is improved. And anxiety and nervousness can be relieved.