14 results about "Operational strategy" patented technology

The invention relates to the technical field of big data analysis, in particular to an operation management system and method based on financial marketing big data, and the system comprises a data standardization module, a customer portrait disambiguation module, a strategy matching module, a real-time transmission module and an operation strategy generation module. According to the method, a data table with a unified structure is constructed through combined judgment of field semantics and numerical rules, the data consistency is improved, customer information integration is executed based on field conflict time, credibility level and logic verification, the recognition accuracy is enhanced, and the matching relation is judged by combining the transaction amount, the product association degree and the interaction frequency with strategy attributes; according to the method, strategy dynamic screening is achieved, pushing content is screened according to the strategy emergency degree and the channel state, the transmission efficiency is improved, customer clicking, conversion and feedback participate in behavior trend judgment, strategy updating linkage is supported, an overall processing chain is of a closed-loop structure, and the fusion accuracy and the strategy response timeliness are improved under the conditions of data fragmentation and multi-source heterogeneous.

This invention discloses a digital twin cold chain management method and system based on a multi-dimensional operation algorithm, belonging to the field of smart logistics and digital twin technology. By constructing a digital twin model of the cold chain, a physical foundation is provided for the digital twin simulation of cold chain operation, facilitating the simulation and generation of cold chain management and operation strategies. Furthermore, a cold chain operation target management model is constructed using a multi-dimensional operation algorithm, comprising multiple sub-models for cold chain operation target management. This allows for responses to different operational scenarios based on different sub-models, achieving adaptation and compatibility across multiple operational scenarios, resulting in more accurate and targeted operational strategies. In addition, the operational scenario judgment layer of the cold chain operation target management model can achieve autonomous scenario recognition and sub-model matching, greatly improving the adaptability of cold chain management. Through multiple rounds of simulation and target calculation, digital-level strategy execution is achieved, significantly improving the generation efficiency of cold chain management and operation strategies.

This invention discloses a strategy generation method, apparatus, medium, and device based on intelligent agent coordination processing, relating to the field of data analysis and processing technology. It primarily addresses the problems of inaccurate group customer operations and low automation levels. The method includes: collecting full-dimensional data from various group customers to construct a multi-dimensional dataset; performing multi-modal feature fusion processing on each multi-dimensional dataset to obtain a comprehensive feature vector corresponding to each group customer; employing an adaptive clustering algorithm to dynamically group each comprehensive feature vector to obtain structured grouping results corresponding to different types of group customer groups; combining the structured grouping results, historical interaction data, and business data to construct a group-level customer profile; and based on the group-level customer profile, generating operational strategies corresponding to the target group customer group from three dimensions—communication content, service plan, and reach path—through an AI Agent and a multi-agent collaborative mechanism.

The invention provides an e-commerce operation method based on an intelligent agent, and the method comprises the steps: collecting current e-commerce operation data, and transmitting the e-commerce operation data to the intelligent agent according to the type of the intelligent agent; first operation information generated by the intelligent agents based on the e-commerce operation data and the current operation strategy is obtained, the current operation strategy is optimized based on the first operation information, the e-commerce operation data, a preset optimization algorithm, a constraint condition and a reward function, a target operation strategy is obtained, and the current operation strategies corresponding to different intelligent agents are different; and obtaining target operation information generated by the intelligent agent based on the target operation strategy and the e-commerce operation data, and performing e-commerce operation according to the target operation information. The e-commerce operation strategy can be dynamically adjusted according to the real-time e-commerce operation data, the problem that the operation strategy needs to be made manually is effectively solved, the timeliness and accuracy of strategy making are improved, the strategy making efficiency is improved, the current market environment can be accurately fitted, and the e-commerce operation effect is improved.

The invention discloses a post-fault reliability improvement method for embedded multi-master multi-slave end-to-end transaction and power distribution network operator-producer interaction and cooperation. The method comprises the following steps: step 1, establishing a producer energy consumption benefit measurement model considering differentiated reliability preference; 2, in combination with the production and consumer energy consumption benefit measurement model, establishing a post-fault power distribution network operator-production and consumer double-layer interaction reliability improvement model of the embedded multi-master multi-slave end-to-end transaction; and step 3, based on the balance proof of the multi-master multi-slave game, and based on a sample boundary compression algorithm, solving the power distribution network operator-production and consumer double-layer interaction reliability improvement model after the fault, and finally outputting a network reconstruction strategy of the power distribution network operator and an operation strategy of the production and consumer after the power distribution network fault. According to the technical scheme provided by the embodiment of the invention, mutual benefit and win-win of a power distribution network operator and a producer after a power distribution network fault event occurs are realized, and the social welfare level is effectively improved.

The invention discloses a marketing copywriting automatic generation method based on deep learning, and belongs to the technical field of marketing copywriting generation. The method comprises the following steps: extracting visual features, functional attributes and text description of a product through a multi-modal fusion model to obtain a comprehensive product representation vector; calculating weight distribution among the features by adopting an attention mechanism to obtain a weighted feature set; mapping the features to a brand style inheritance space through an embedded layer to obtain an embedded representation with a consistent style; carrying out sequence decoding on the basis to generate a preliminary copywriting draft; filtering the efficacy selling point phrases through an operation strategy module to form refined document fragments; and carrying out matching verification by adopting an iterative optimization loop, carrying out backtracking adjustment through consistency verification, and finally generating a marketing copywriting conforming to a brand style and an operation target.

The present disclosure provides a system (100) and a method (700) for implementing chaos engineering in a cloud environment (106). The system (100) monitors the cloud environment (106), introducing disruptions such as network latency, resource depletion, and service failures. The system models operational strategies for each cloud component in response to disruptions using game theory techniques, evaluates the payoffs of these strategies based on predefined performance metrics, and identifies optimal strategies via Nash Equilibrium. These strategies are then implemented across the cloud environment. Additionally, the system monitors the response to the implemented strategies and uses this feedback to refine future strategy modeling and payoff calculations, enhancing decision-making capabilities over time. The system is communicatively coupled to computing devices (110) for displaying performance metrics, operational strategies, payoffs, and health of the cloud environment.

We provide the system. [Solution] A means of constructing a virtual model of a physical system using a generative model, A means of performing calculations based on a virtual model and obtaining data about the physical system, A means of formulating an operational strategy for the physical system by analyzing the acquired data, A means of operating a physical system based on a formulated operational strategy and acquiring the operational results again as data, A means of updating the virtual model using the acquired operational results, A system that includes this.

The embodiment of the invention discloses a customer management method, device and equipment. The method comprises the following steps: receiving a customer management request for a target customer; in response to the client management request, determining a candidate operation strategy corresponding to the target client based on the client value contribution, the client value change and the business stickiness of the target client; determining a model by using a pre-trained strategy, and selecting a target operation strategy corresponding to the target customer from the candidate operation strategies; wherein the strategy determination model is constructed based on a preset cloud drift algorithm, and the cloud drift algorithm is used for searching a global optimal solution and a local optimal solution in a preset search space by using cloud particles related to the candidate operation strategy based on a preset fitness function, and determining the candidate operation strategy based on the global optimal solution and the local optimal solution. Determining the target operation strategy; and performing customer management on the target customer based on the target operation strategy.

This invention relates to the field of big data analytics, specifically to an operation and management system and method based on financial marketing big data. The system includes a data standardization module, a customer profiling disambiguation module, a strategy matching module, a real-time transmission module, and an operation strategy generation module. In this invention, a unified data table is constructed through joint judgment of field semantics and numerical rules to improve data consistency. Customer information integration is performed based on the time, credibility level, and logical verification of field conflicts to enhance identification accuracy. Transaction amount, product relevance, and interaction frequency are combined with strategy attributes to determine matching relationships, enabling dynamic strategy filtering. Push content is filtered and arranged according to strategy urgency and channel status to improve transmission efficiency. Customer clicks, conversions, and feedback behavior trends are determined, supporting linked strategy updates. The overall processing chain has a closed-loop structure, improving fusion accuracy and strategy response timeliness under conditions of data fragmentation and multi-source heterogeneity.

The invention belongs to the technical field of marketing, and discloses a marketing recommendation method and system for cultural products in electronic commerce. The method comprises the following steps: constructing a user semantic vector and a user culture membership matrix based on original data of a target user; generating a product culture vector based on the original information of the to-be-recommended culture product; jointly inputting the user semantic vector and the user culture membership matrix into a configured fuzzy embedded deep learning model, and outputting a culture sensitive vector matrix capable of reflecting deep culture preferences of the user; screening and sorting the users based on the comprehensive similarity between the culture sensitive vector matrix and the product culture vector, and generating an initial recommendation candidate set; and fusing the initial recommendation candidate set with an operation strategy rule of the platform, and determining and pushing the to-be-recommended cultural product to the user in the final recommendation object set. According to the method, the potential user groups with high culture matching degree with the to-be-recommended culture products can be accurately screened out from massive users.

The invention relates to the technical field of business marketing, and particularly provides an operation strategy causal quantification method, a storage medium, electronic equipment and a product, and the method can comprise the steps: obtaining call behavior state attributes of an outbound user under different preset scales, the preset scale is related to the call number and / or the operation cycle of the outbound user; according to the call behavior state attribute and a multi-intervention gain model corresponding to the preset scale, a gain estimated value is obtained, and the gain estimated value represents the sensitive degree of the outbound user to a plurality of marketing strategies; and adjusting a marketing scheme for the outbound user through the gain estimated value. According to the embodiment of the invention, the marketing scheme for the user can be accurately optimized, and the marketing efficiency and the user experience are improved.

The invention relates to the technical field of e-commerce marketing, in particular to an e-commerce operation strategy optimization method based on multi-AI agent collaboration, which comprises the following steps: acquiring historical user behavior data and commodity sales peak data of a target e-commerce platform, performing operation scene classification on the target e-commerce platform based on the historical user behavior data and the commodity sales peak data to obtain at least one e-commerce operation sub-scene; and obtaining real-time operation monitoring data of at least one e-commerce operation sub-scene, and performing feature extraction on the real-time operation monitoring data to obtain a state vector corresponding to the e-commerce operation sub-scene. According to the method, the user behavior data, the commodity sales data and the like of the e-commerce platform are acquired in real time, a customized operation strategy can be accurately generated for each e-commerce operation sub-scene based on historical data and real-time monitoring data, the strategies can accurately cope with requirements and fluctuations in different scenes, and the operation efficiency is maximized.

We provide the system. [Solution] Means for collecting large amounts of information, A generation means for extracting user interests and trends from the aforementioned collected information, A decision-making tool for formulating operational strategies based on identified interests and trends, A means for generating and distributing advertisements based on the aforementioned operational strategy, A feedback means for measuring the effectiveness of the delivered advertisement and for improving the generation means and determination means, A system that includes this.