331 results about "Consumer demand" patented technology

The invention relates to the technical field of business adaptation, in particular to a business adaptation degree intelligent analysis method and system combined with a customer portrait. The method comprises the following steps: firstly, collecting customer data from a plurality of data sources, then processing the collected data, extracting interest labels of customers, constructing a customer-interest label matrix, comprehensively evaluating the purchase ability and loyalty of the customers, updating the portraits of the customers according to the interest preferences of similar customers, and then collecting the business demand data of the customers. The method comprises the following steps: constructing a business demand model, quantifying the demand intensity of a customer, generating a demand score vector, matching a customer portrait with the demand score vector, calculating an adaptation degree score, generating personalized recommendation for the customer, preferentially recommending a product or service with the highest matching degree with the customer demand, and pushing related recommendation through multiple channels. And the satisfaction degree and the conversion rate of customers are improved. According to the invention, the resource utilization efficiency and the customer satisfaction are improved, so that more efficient enterprise operation and customer service are realized.

The invention relates to the technical field of automatic arrangement, and discloses a method and device for automatically arranging a business relation chain and a storage medium, and the method comprises the steps: constructing a business rule base based on supplier information, customer information and business information, and carrying out the standardization processing of customer demand data, and obtaining the standardized business demand information; performing multi-dimensional matching operation on the standardized business demand information based on the business rule base to obtain an initial supplier set, and performing dynamic weight adjustment on the initial supplier set to obtain an optimal supplier sequence; constructing a chain node graph according to the optimal supplier sequence, calculating and verifying a business constraint relationship between nodes, and generating a business relationship chain; the business relation chain is converted into a standard order format, order data synchronization is completed and an order synchronization result is output through encryption signature and a concurrent distribution mechanism, and the accuracy and efficiency of business relation chain arrangement are improved.

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.

The invention discloses an intelligent commodity management method, and relates to the technical field of commodity management. Through the design of the whole method, the efficiency can be improved, the intelligent system can automatically manage a plurality of goods management tasks such as inventory management, purchase decision making and demand prediction, manual intervention is reduced, the management efficiency is improved, inventory optimization is facilitated, the intelligent system can accurately predict demands through data analysis and prediction algorithms, and the management efficiency is improved. The inventory level is optimized according to the prediction result, the inventory overstock or stockout phenomenon is avoided, the inventory turnover rate is improved, the cost is reduced conveniently, the intelligent system can help enterprises to optimize purchasing strategies and reduce the inventory cost, the purchasing cost is reduced through price optimization and other modes, and therefore the overall cost of the enterprises is reduced, and the enterprise experience is improved. The customer satisfaction can be improved conveniently under the overall application, and the customer satisfaction can be improved and the customer loyalty can be enhanced by meeting customer requirements in time, accurately predicting the market trend and providing personalized services and intelligent commodity management.

The invention relates to the technical field of data analysis, and discloses a consumer demand analysis method and device based on a multi-objective optimization algorithm, and the method comprises the steps: obtaining and preprocessing a plurality of pieces of consumer comment data, and obtaining a plurality of pieces of effective comment data; analyzing the effective comment data by using a preset recognition model to obtain theme probability distribution and emotion probability distribution of the effective comment data; combining the topic probability distribution and the emotion probability distribution to obtain an overall joint probability matrix; calculating by using a multi-objective optimization algorithm to obtain an optimal weight matrix; and multiplying the overall joint probability matrix by the optimal weight matrix to obtain a demand analysis result. According to the method, by obtaining the comment data of the consumer and analyzing the theme and emotional tendency of the consumer, the coverage and real-time performance of data collection are improved, the demand and preference information of the consumer is deeply mined, the demand preference of the consumer for the target product is comprehensively and dynamically mastered, and a more accurate and timely basis is provided for the market decision of an enterprise.

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 invention discloses a low-oil-absorption fried food hanging paste and a preparation method thereof, and belongs to the technical field of food processing and making. The hanging paste comprises the following components in parts by weight: 10-20 parts of corn starch, 15-20 parts of highland barley flour, 10-15 parts of rice flour, 1-2 parts of edible salt, 0.5-1 part of baking powder and 50-60 parts of drinking water. The highland barley flour and the rice flour are compounded to replace wheat flour used in a traditional pasting formula, compact gel formed after high amylose in the rice flour is gelatinized can seal pores after being cooled, beta-glucan in the highland barley flour absorbs water to increase the viscosity, the pores of the rice starch gel are filled, a continuous oil-resistant layer is formed, and the pasty effect is improved. In the frying process, a denser and smoother surface can be formed, grease permeation is hindered, the water binding capacity and stability of hanging paste are improved, the hanging paste rate is increased, a starch-beta-glucan colloid barrier is formed on the surface of food, the content of permeated grease and the total oil content in the hanging paste after frying are reduced, grease intake is reduced, and the food quality is improved. The requirements of consumers on low-oil healthy foods are met.

The invention discloses a consumer protection method and device based on artificial intelligence and a storage medium. The method comprises the following steps: acquiring multi-modal customer communication corpus data; performing feature extraction on the client communication corpus data to obtain target features, and determining an emotion type corresponding to the client and emotion intensity corresponding to the emotion type according to the target features; according to the individual user portrait, the emotion type and the emotion intensity of the customer, the complaint probability corresponding to the customer is determined, and the individual user portrait is at least used for representing the historical complaint preference of the customer; and when the complaint probability exceeds a probability threshold value corresponding to the target business handled by the client, sending complaint early warning information to a responsible department corresponding to the target business. The technical problem that the customer complaint early warning effect is poor and the consumer demand cannot be met due to the fact that complaint management in the related technology still stays in a manual judgment and processing mode is solved.

The invention belongs to the technical field of knowledge maps, and discloses an industry knowledge structured modeling method of a knowledge map based on semantic association. Collecting industry production data; constructing a production knowledge graph according to the industry production data; obtaining personalized demand content; performing feature extraction on the personalized demand content to obtain actual demand feature data; inputting the actual demand feature data into the demand evaluation model to obtain a demand evaluation score, and analyzing and processing the demand evaluation score to obtain a demand rationality level; inputting the actual demand feature data and the production knowledge graph into a parameter construction model to obtain a corresponding production parameter set; inputting the demand rationality level, the production parameter set and the production knowledge graph into a demand construction model to obtain predicted demand feature data; the matching degree of the customer demand and the system prediction is dynamically evaluated, a production parameter set meeting the personalized demand of the customer is generated, and the production accuracy is improved.

The invention discloses a management method based on order-driven production, and the method comprises the following steps: S1, maintaining and sorting material information, determining customer demands, and forming a sales order; s2, generating an exclusive processing order of the sales order, and adjusting the BOM of the exclusive processing order and the process information of the exclusive processing order according to customer demands; s3, material preparation is conducted according to the exclusive processing list, and production is conducted; and S4, after production of the exclusive processing list is completed, cost accounting is carried out on the exclusive processing list. The processing order is created according to the sales order, the sales order is bound with the production process, the product BOM and the production process are customized through the sales order, different detail requirements of customers are met, and meanwhile production of more types of products is supported.

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 relates to the technical field of customer management, in particular to a cross-channel customer interaction management system and method based on data fusion. The method comprises the following steps: acquiring a customer communication demand, analyzing the customer communication demand, and determining customer basic information and customer communication content; based on multi-channel data, according to the basic information of the customer and the communication content of the customer, determining a consultation intention of the customer; and determining a reply thought according to the customer consultation intention, and responding to the customer according to a preset reply logic and the reply thought. User requirements are quickly responded, and the waiting time of customers is shortened. The personalized service is based on multi-channel data and communication history of the customer, and can provide services better meeting customer requirements, thereby improving customer satisfaction. And by utilizing machine learning and natural language processing technologies, the system can more accurately identify the consultation intention of the customer, and misunderstanding and repeated consultation are reduced. The accuracy of consultation response is improved, and the customer is ensured to obtain correct information and suggestions.

To realize selection of products that satisfies needs of customers and optimization in sales strategies by a system that performs emotion analysis on customer reviews using generative AI, and provides data such as satisfaction level and regret level for a product to a tenant to achieve adjustment in sales planning and sales improvement.SOLUTION: A system comprises: means for utilizing generative AI to perform emotion analysis on customer reviews to provide data such as satisfaction level and regret level for a product to a tenant; and means for allowing the tenant to utilize the data to adjust sales planning and improve sales.SELECTED DRAWING: Figure 1

The invention relates to the technical field of product management, in particular to an intelligent product after-sales management method and system. The method comprises the following steps: obtaining an intelligent product after-sales problem record set, wherein the intelligent product after-sales problem record set comprises at least one or more intelligent product after-sales problem records; performing customer demand degree quantification on the after-sales problem record of the intelligent product, and generating after-sales demand emergency degree data of the intelligent product; comparing the after-sales demand emergency degree data of the intelligent product with a preset standard demand emergency degree threshold value, and generating priority response demand data and delay response demand data; and performing fault processing association analysis on the after-sales problem record set of the intelligent product through the priority response demand data and the delay response demand data to generate a product fault processing automatic solution. According to the invention, through demand quantification, fault correlation analysis, feedback diagnosis and trend evaluation, the after-sales management efficiency and time efficiency are improved.

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 belongs to the technical field, and provides an intelligent response processing method and system. The method comprises the steps of collecting information input by a current user, extracting key demands and preferences of the current user, and determining user demands; inputting the determined user demand into a demand classification model to obtain a demand classification result, and when the result is a suggested demand, responding to the customer by matching a preset reply; when the result is a recommendation demand, determining a pre-pushed commodity meeting the recommendation demand according to a keyword in the recommendation demand, further performing comprehensive evaluation on the pre-pushed commodity, and determining a pushed commodity; and determining the customer category of the current user according to the consumption information data of the current user, performing sorting calculation on the determined pushed commodities, determining the optimal commodity as response information, and pushing the response information to the current user. According to the method, the retrieval problem of the customer can be effectively identified, and customized and dynamically calculated and adjusted commodity recommendation services can be provided on the basis of fully understanding customer demands.

The invention relates to a production scheduling optimization method and system in a packaging and printing factory. The method comprises the steps of obtaining time constraints of tasks in a plurality of orders to be scheduled and resource consumption of the tasks; based on a resource inventory and the resource consumption, adjusting a default schedule to obtain a first schedule; and determining a target scheduling algorithm by using an order scheduling model based on the resource consumption, the first scheduling and the time constraint, and sorting the plurality of orders based on the target scheduling algorithm to obtain a scheduling result. According to the invention, in the scheduling process, the resource inventory, the customer demand (order) and the production capacity are considered, the variable relationship among production, supply and demand can be coped, and the time constraint of each task in the order is also considered, so that the probability of obtaining the optimal order scheduling which reasonably utilizes resources, saves time and meets the customer demand is improved.

The invention discloses a bank customer manager point processing method and system based on a performance model. The processing method comprises the following steps: collecting performance data of a customer manager; performing cleaning and normalization processing on the performance data, and extracting key performance features; constructing and training a performance model for estimating performance points of the customer manager; capturing and processing performance data in real time, and dynamically calculating points of a customer manager; summarizing credits obtained by the customer manager from the plurality of business lines to obtain a total credits; and providing a personalized reward scheme for a customer manager according to the total points and personal preferences. Based on the flexibility of a large language model, the assessment indexes are dynamically adjusted to adapt to market changes and customer demands.

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 discloses a weaving method of screen cloth for a solar photovoltaic screen printing plate, which adopts various warp and weft combinations to weave the screen cloth, and meets the requirements of various performances and service life. According to the method, warp and weft different materials, different yarn diameters and different meshes are adopted for mixed weaving, according to customer requirements and market requirements, different weft yarn diameters and different warp and weft mesh numbers are set for weaving, and the specific steps are as follows: S1, mesh wires used for weaving are wound on a raw material shaft according to reed parameter indexes, and the length of each group needs to be consistent; s2, net wires used for weaving a net penetrate through a palm sheet and a reed, the palm sheet is divided into a front sheet and a rear sheet, and it is ensured that one of every two adjacent wires penetrates through the front sheet, and the other one penetrates through the rear sheet; s3, the warp yarn penetrating through the palm sheet and the reed is evenly adhered to an adhesive tape, certain tension is given, and net weaving equipment is started; s4, the rapier belt reciprocates to drive the weft to be woven, the front and rear palm sheets change angles once every time the rapier belt reciprocates, and it is ensured that the warp and the weft are woven alternately up and down.