15results about How to "Improve marketing efficiency" patented technology

The invention belongs to the technical field of electric power system operation control, and relates to a unit operation strategy optimization method under cooperation of an electric energy market and a capacity market, which comprises the following steps: 1, constructing a decision optimization model of an upper-layer power generation enterprise participating in an electric energy spot market and an electric power capacity market, 2, constructing a lower-layer electric energy spot market clearing model, the method comprises the steps of 1, establishing a lower-layer power capacity market clearing model, 4, converting a lower-layer market clearing problem into a KKT optimality condition, 5, carrying out model linearization processing, and 6, calling a Gurobi solver to solve a linearized mixed integer linear programming problem. According to the method, the collaborative operation relationship between the electric energy spot market and the electric power capacity market is fully considered, short-term optimization of the electric energy market and long-term planning of the electric power capacity market supplement each other, the linkage between the markets is enhanced, the overall market efficiency is further improved, and the reliability adequacy of the system is effectively guaranteed.

The invention relates to the technical field of power generation management, in particular to an evaluation method and device based on a new energy power station. And five dimensions of resource environment, system design, equipment quality, construction quality and production operation are evaluated, so that the overall condition of the new energy power station is comprehensively and deeply known, and an important basis and an effective systematic support are provided for normal promotion of asset transaction of the new energy power station. The dynamic evaluation method is adopted and is different from static evaluation, and the dynamic evaluation is concentrated in the aspects of data collection and processing, prediction and modeling, subjective factor processing and the like, so that the accuracy and effectiveness of the overall evaluation are ensured.

The invention provides a marketing clue scoring method and system based on data source grading. The marketing clue scoring method based on data source grading comprises the following steps: extracting data stream feature data information and a data source classification scheme of a data source corresponding to a marketing clue; classifying data sources corresponding to the obtained marketing clues by using the data stream feature data information and a data source classification scheme to obtain different categories of data sources; setting a data acquisition frequency by using the target classification coefficients corresponding to the different types of data sources in combination with a preset initial data acquisition frequency, and obtaining marketing clue data; performing data quality safety judgment on the marketing clue data to obtain marketing clue data meeting a preset safety standard; and calling a deep learning model for scoring, and obtaining a scoring score corresponding to the marketing clue data meeting the preset safety standard by using the deep learning model for scoring. The system comprises modules corresponding to the steps of the method.

The invention provides an information triggering channel optimization method and system, electronic equipment and a storage medium. The method comprises the following steps: determining first data of a user; extracting basic features related to a touch effect from the portrait data, and constructing a feature matrix in combination with a touch channel, historical marketing information and user response data; training the feature matrix by using a machine learning algorithm to obtain a response prediction model capable of predicting user response data; the response prediction model is used for predicting user response data of each arrival channel according to the portrait data of the user, the arrival channel data and the marketing information; predicting the user according to the trained response prediction model, and determining an optimal access channel of different marketing information of the user; namely, by collecting and analyzing the response data of the user on different channels, the model capable of predicting the response data of the customer is trained, then the bank is guided to select the optimal touch channel, the touch efficiency is high, and the marketing efficiency and the customer satisfaction degree are improved.

The invention discloses a business data processing method and system based on multiple channels, and the method comprises the steps: obtaining the basic information of a to-be-sold commodity and a preset advertisement putting amount, calculating the average re-purchase period of the commodity through analyzing the historical business data of each channel, comparing the remaining re-purchase time of a user with the average re-purchase period, and determining a first intention user. If the number of the first intention users meets the advertisement putting demand, allocating the advertisement putting amount and screening the advertisement putting users based on the remaining re-purchase time and a preset advertisement putting preference prediction algorithm; otherwise, the second intention users who have purchased the commodities recently are found out by analyzing the commodity set with high relevance, and the final advertisement putting object is determined by combining the conditions of the two types of intention users. According to the invention, by analyzing the multi-channel business data and the user re-purchase cycle, the intentional user is accurately positioned, the advertisement putting is optimized, and the advertisement conversion rate and the resource utilization rate are effectively improved.

The invention belongs to the field of data processing, and discloses a precision marketing analysis system based on big data, which comprises an analysis terminal and an identity recognition terminal, the analysis terminal is used for acquiring a face image of an analyst and transmitting the face image to the identity recognition terminal; the identity recognition terminal comprises a noise reduction module and a recognition module; the noise reduction module is used for carrying out noise reduction on the face image to obtain a to-be-recognized image; the recognition module is used for recognizing the to-be-recognized image and judging whether the face recognition of the analyst is successful or not. According to the method, the noise reduction efficiency is higher, and compared with the first noise reduction mode adopted for all the pixel points, the edge information can be more effectively reserved. Therefore, the overall noise reduction time can be effectively shortened and the noise reduction efficiency is improved while the noise reduction effect is ensured, so that the customer type determination efficiency is improved and the marketing efficiency is improved.

The invention provides a customer screening apparatus, a customer screening method, and a computer program product. The customer screening device for performing customer screening based on the game adversarial network comprises a recessive code generation module which generates a recessive code vector based on pre-prepared data of a target customer, and the recessive code vector maps features which are meaningful to judgment of the target customer; the virtual customer information generation module is used for generating virtual customer information based on the implicit coding vector and the random noise vector by utilizing a generative network in the game adversarial network; the game confrontation module is used for discriminating the virtual customer data based on the real customer data by using a confrontation network in the game confrontation network; and the auxiliary network module is used for providing training feedback for the generative network by using an auxiliary neural network according to the implicit coding vector and the virtual customer data so as to improve the correspondence between the generated virtual customer information and the implicit coding vector, and alternately training the generative network and the adversarial network based on a loss function so as to improve the correspondence between the generated virtual customer information and the implicit coding vector. The loss function comprises mutual information operation between a nonlinear difference function of the generative network and the adversarial network and an output function of the generative network and an output function of the auxiliary neural network.

The present invention discloses a micro-enterprise marketing auxiliary method and system based on a large language model, which uses a large language model to improve the efficiency and effectiveness of marketing. The method integrates a vector database, an embedding model and contextual learning technology to achieve real-time understanding of user intentions, personalized responses and the ability to quickly adapt to business changes. The core advantages of this method lie in its accurate user demand analysis, personalized service provision and efficient knowledge retrieval system. By analyzing user behavior and conversation records, the present invention can provide product recommendations and marketing strategies that meet user preferences. In addition, the present invention also has the ability to update real-time speech to ensure the timeliness and accuracy of information; the automated order information extraction technology improves information processing efficiency and reduces the time and cost of manual operations. The present invention not only improves marketing efficiency, but also enhances the interactive experience between users and customer service, providing companies with a competitive advantage in a highly competitive market.

The application provides a multi-channel cooperative target customer cultivation system and method, and relates to the technical field of computers. User data from multiple channels is received to identify the identity of a user and generate a unique marketing user identifier for the user. When a cultivation task is performed on the user, all channels associated with the marketing user identifier of the user are obtained, the highest priority single channel is selected according to a preset channel priority strategy, and the marketing content of this time is sent to the user only through the single channel. A cultivation workflow containing conditional branch nodes is constructed, the cultivation workflow is driven by user behavior events, and subsequent predefined branch actions are triggered based on user behavior events and conditional branch nodes. The application solves the problem of marketing resource waste and user experience fragmentation caused by data silos, realizes the transformation of marketing processes from extensive broadcasting to precise personalized reach, and improves marketing efficiency, customer experience and business conversion rate.

The invention relates to a photovoltaic enterprise green certificate transaction and electricity price subsidy mechanism integration effect evaluation method, and aims to optimize the cooperation process between a renewable energy source matching and forekeeping system (RPS) and a fixed electricity price system (FIT) and measure the RPS and FIT integration effect under various subsidy time limit and intensity scenes so as to support the stable operation of a photovoltaic enterprise. The method comprises the following steps: firstly, establishing an evolutionary game model of a government and a photovoltaic enterprise, and analyzing policy parameters when the game reaches a stable equilibrium condition; carrying out system dynamics modeling, and verifying the correctness of the game model; and then analyzing the influence of policy parameters on an evolution result, a green certificate transaction volume and social welfare. Research results show that the provided method is helpful for measuring integration policy effects, is helpful for the country to formulate reasonable subsidy combination or substitution policies, and realizes stable development of photovoltaic enterprises.

The present invention relates to the field of computer technology, and to a method, system, device and storage medium for recommending financial products for banks. The method comprises the following steps: collecting customer data and product data; dividing customer data into existing customer data and new customer data; making user portraits based on existing customer data and new customer data; making product portraits based on product data; establishing a first association relationship between the feature tags of existing customers and the product feature tags of products, and establishing a second association relationship between the feature tags of new customers and the product feature tags of products; outputting product recommendation results for existing customers based on the first association relationship, and outputting product recommendation results for new customers based on the second association relationship. The present invention can solve the problems that the existing bank product recommendation method cannot deeply mine customer data, relies on business scenarios and lacks interconnection and interaction between business scenarios, and the customer manager can only search and identify individually through the list, which is inefficient and low in accuracy.

The application discloses an intelligent marketing document generation device and method based on multi-source data fusion, and the method comprises four processes: user instruction analysis, regular cleaning, model extraction parameter, Neo4j knowledge graph verification, and output of structured parameters; external data acquisition, parameter calling of a Baidu large model to collect data, de-duplication, semantic enhancement, and weighted screening of high-quality data; internal case processing, regular slicing of case storage, filtering, propagation calculation, multi-dimensional scoring, and generation of a CoT reasoning chain report; multi-modal output, format adaptation, data integration by a BART model, and output of a metadata document. The device and the method are coordinated, multi-source fusion, knowledge graph association and multi-modal conversion are realized, high-quality marketing documents suitable for multiple industries are efficiently generated, decision-making is supported, and the market response and output efficiency of enterprises are improved.

The invention relates to a big data private domain accurate user pushing method, and relates to the technical field of data processing, and the method comprises the steps: determining a plurality of commodity classes based on the commodity feature information of a plurality of on-sale commodities; determining a plurality of test users corresponding to the commodity classes based on the shopping behavior data of the plurality of test users on the e-commerce platform, and calculating commodity class related parameters of the two commodity classes; determining a plurality of user clusters according to the user portraits of the plurality of test users and the shopping behavior data on the e-commerce platform; based on the shopping behavior data of the to-be-analyzed user on the e-commerce platform and the plurality of user clusters, establishing a user portrait of the to-be-analyzed user, and combining commodity features of at least one promotion commodity corresponding to each piece of to-be-pushed preferential information, a plurality of commodity classes and commodity class related parameters of any two commodity classes, and determining the to-be-pushed commodity and / or the to-be-pushed preferential information matched with the to-be-analyzed user. The method has the advantage of accurately pushing the adaptive commodity or preferential information related to the user.

The invention relates to the technical field of data information processing, solves the problem that a non-performing asset marketing scheme has significant defects in information symmetry and transaction efficiency in the prior art, and provides a multi-platform-based non-performing asset information pushing method and equipment. The method comprises the steps of obtaining recommendation information of each non-performing asset, pushing the recommendation information to each specified platform through a preset standardized interface, and calculating an asset popularity value of each non-performing asset according to behavior data of a user in each specified platform; pushing recommendation information of the interested unhealthy assets on the own channel platform according to the asset popularity value and / or the behavior data; and withdrawing the recommendation information of the corresponding non-performing assets in all the specified platforms through the preset standardized interface in response to the condition that the non-performing assets meet the preset withdrawing condition. According to the invention, the marketing efficiency of the unhealthy assets is improved, and the accuracy and correlation of the information are ensured.

The invention belongs to the technical field of power system operation control, and relates to a power generation enterprise strategy behavior-considered unit effective flexibility capacity evaluation method, which comprises the steps of 1, constructing a decision model of an upper-layer power generation enterprise participating in a spot electric energy market and a power capacity market, 2, constructing a clearing model of a lower-layer spot electric energy market, the method comprises the following steps: 1, constructing a lower-layer power capacity market clearing model, 4, converting a lower-layer market clearing problem into a KKT optimality condition, 5, converting the model into a linear form, 6, solving an MILP model by using a Gurobi solver, and 7, constructing a lower-layer power capacity market clearing model. The method not only considers the technical characteristics of the unit, but also fully considers the strategic behaviors of the power generation enterprises in the market, and more truly reflects the decision preferences of the power generation enterprises under different market conditions. Therefore, the decision-making results of each power generation enterprise in the spot electric energy market and the electric power capacity market can be ensured to accord with the equilibrium state of the market, and efficient operation of the whole market and optimal allocation of resources are realized.