66 results about "Strategic decision making" patented technology

The invention discloses a real-time computing system resource coordination and decision engine system based on a large language model. According to the system, a large language model is innovatively used as a central strategic decision engine, and a'decision-coordination-execution 'three-layer architecture is constructed. According to the system, macroscopic strategy generation and microscopic real-time control are decoupled by introducing a hierarchical decision-making mechanism (a strategic layer, a tactical layer and an execution layer), so that the core contradiction between LLM high reasoning delay and the microsecond / millisecond-level real-time requirement of the system is effectively solved, and the method is suitable for local computing equipment and a cloud data center. The system comprises a predictive strategy preloading system, and transient response can be achieved. Meanwhile, the system adopts an asynchronous event-driven decision-making mechanism for continuous intelligent optimization. According to the method, the top-down, semantic understanding-based and global collaborative intelligent management of the computing resources is realized, and the resource utilization efficiency, the system automation degree and the overall energy efficiency in a complex and dynamic computing environment are remarkably improved.

Computerized systems and methods are described for generating and optimizing vendor product roadmaps using predictive insights. Leveraging a Real-Time Data Mesh (RTDM) module, data from diverse sources including market trends, customer feedback, and technological advancements is aggregated and standardized. An Analytics and Machine-Learning (AAML) module analyzes this data to generate predictive insights, facilitating adjustments to existing product roadmaps. Dynamic adjustments are made using a roadmap optimization module, with communication facilitated through a Single Pane of Glass (SPoG) user interface (UI). Scenario analysis, decision-support systems, and continuous monitoring improve strategic decision-making.

The invention discloses a large language model auxiliary optimization strategic decision-making system and a large language model auxiliary optimization strategic decision-making method. The system comprises six core modules. The dynamic knowledge fusion module constructs a three-layer distributed knowledge network, constructs an entity association weight matrix through a bidirectional Transform model based on an attention mechanism, and realizes knowledge dynamic association in combination with a time attenuation factor and a hybrid coding technology. The large language model module performs field fine tuning by adopting incremental pre-training and low-rank adaptation technologies, and introduces an exclusive word segmentation list to improve professional analysis precision. The full-process intelligent writing module covers submodules for report generation, revision and the like, and supports full-life-cycle management of reports. The strategic decision intelligent deduction module integrates scene impact factors, and realizes multi-scene deduction through reinforcement learning and Monte Carlo tree search. The interaction display module provides a visual interface, and the multi-mode interaction module realizes full task chain management. According to the invention, real-time knowledge support and intelligent deduction capability are provided for strategic decision making, and decision making efficiency and accuracy are improved.

The invention provides an intelligence information integration method and device based on a multi-agent task chain strategy large model, and the method comprises the steps: collecting a plurality of pieces of open-source dynamic science and technology intelligence information of a target information source, and employing a literature analysis agent, an abstract induction agent, a content generation agent, a sequencing agent and a report generation agent which are constructed through a large language model; key information extraction, semantic analysis and induction, content evaluation and updating, information sequencing and version change recording and report generation are performed respectively, and automatic aggregation and sequencing of intelligence information are realized. According to the method, the defects of generalization, objectivity, cross-document information processing, flexibility, robustness, illusion rate, extrapolation ability, small sample learning ability and the like in intelligence information integration in the prior art are effectively overcome, the efficiency and effect of intelligence work are improved, and the intellectual property is improved. The method is of great significance in enhancing the service quality benefit of dynamic intelligence information and supporting strategic decision and policy making.

The invention discloses a graphene industry application discovery method based on a large model and knowledge graph analysis, which comprises the following steps: S1, collecting patent literatures, scientific research papers, market analysis reports and industry news related to graphene, and constructing a multi-source heterogeneous data set; s2, preprocessing the multi-source heterogeneous data set; s3, performing information extraction on the processed multi-source heterogeneous data set by utilizing a BERT model, wherein the extracted information comprises technical key points, application fields, market demands and technical trends; s4, constructing a graphene patent and technology trend knowledge graph, and integrating the extracted key information into a unified semantic network; and S5, performing application discovery analysis based on the constructed graphene patent and technology trend knowledge graph, identifying a potential application scene of the graphene material, and providing application optimization suggestions and new market opportunities. According to the method, the analysis efficiency of a research institution is remarkably improved, and a scientific basis is provided for strategic decision making of the research institution in an emerging market.

The invention relates to the technical field of artificial intelligence and decision support, and discloses a large model agent-based strategic decision generation method, which comprises the following steps of: performing semantic analysis and multi-target game balance on a strategic instruction by a main control agent, and decomposing to generate subtasks; a corresponding domain agent calls an RAG enhancement module to retrieve related multi-modal data, and a preliminary strategy of embedded compliance verification is generated based on the data; and finally, dynamically arranging and coordinating the preliminary strategy by a hierarchical workflow engine, and generating a comprehensive decision report after receiving strategy correction information of a commander. According to the invention, through multi-agent cooperation and multi-target game balance, the decision-making globality and strategic depth are realized; by fusing dynamic multi-modal data, the adaptive capacity of the decision to the external environment is improved; through workflow arrangement and man-machine cooperation, logic self-consistency, execution feasibility and safety of a final scheme are ensured.

The invention discloses a cloud mobile phone market demand analysis method and related equipment, and relates to the technical field of cloud mobile phones, the method comprises the following steps: obtaining multi-source heterogeneous data, the multi-source heterogeneous data comprising structured sales data and unstructured user behavior data; based on a preset semantic alignment rule, performing cross-modal fusion on the multi-source heterogeneous data to generate space-time correlation features; based on a dynamic federated learning framework, training a market demand prediction model by using the space-time correlation features, and determining a prediction result of the cloud mobile phone market; and based on the association network atlas, performing association analysis on the prediction result, and determining market decision information. According to the application, the comprehensiveness, prediction precision and decision support efficiency of market analysis are improved, and an accurate and prospective strategic decision basis is provided for each link of the cloud mobile phone industry chain.

The invention discloses a business and group leader cockpit combined business scheduling method and related equipment. The method comprises the following steps: constructing a three-dimensional dynamic data matrix by acquiring equipment operation data and environment monitoring data of a plurality of power plants; analyzing the business team operation log and the group leader decision record based on a natural language processing technology, and generating dynamic user portraits of the business team and the group leader; real-time change characteristics are extracted according to the three-dimensional dynamic data matrix, and dynamic scheduling strategy information is generated through a multi-objective optimization algorithm according to the equipment operation frequency characteristic coefficient, the strategic index sensitivity coefficient and the real-time change characteristics; and pushing an equipment operation instruction of the dynamic scheduling strategy information to the business cockpit according to the business authority corresponding to the business cockpit, and pushing a strategic decision support graph of the visual scheduling instruction set to the group cockpit according to the business authority corresponding to the group cockpit. A comprehensive business scheduling solution is provided, and powerful support is provided for business and management layers.

The invention discloses an automatic construction system for financial data management, and relates to the related technical field of financial data management, and the system comprises a user interface module which is used for providing an intuitive graphical interface, and enabling a user to construct an application process in a mode of dragging a component; the business logic module is used for calculating the production cost of the product and automatically generating an asset balance sheet, a profit sheet and a cash flow sheet; and the financial trend prediction module needs historical financial data as input, the user inputs financial data and related characteristics, and the model outputs a prediction result. An automatic data processing and report generating system can be rapidly constructed through an automatic tool, the labor burden is relieved, a low-code platform system allows a user to customize and adjust the financial process in a visual mode, the individual requirement can be met without compiling a large number of codes, and the user experience is improved. The LLM technology can provide natural language understanding and intelligent analysis ability, and assists non-professionals in deep financial insight and strategic decision making.

The invention discloses an e-commerce intelligent operation management system and an ecological construction method. According to the invention, through mining and analyzing a large amount of user behavior data and market trends, accurate market positioning and strategic decision support can be provided for an e-commerce platform. By identifying the user behavior mode, predicting the sales trend and evaluating the marketing activity effect, the e-commerce platform is helped to optimize resource configuration, and the operation efficiency is improved. And the user portrait analysis sub-module constructs a detailed user portrait, so that the e-commerce platform can implement personalized marketing and service, and the satisfaction and loyalty of the user are improved. And the competitive product analysis sub-module monitors and analyzes products, prices, marketing strategies and the like of competitors to help the e-commerce platform to formulate effective competition strategies and maintain market advantages, so that the e-commerce platform can better understand the market and users, thereby making more intelligent decisions and improving the overall operation performance.

The invention discloses an industrial chain association degree quantitative analysis method and system based on multi-source enterprise big data. The method comprises the following steps: collecting multi-dimensional data such as enterprise transaction, capital, innovation, talents and the like, establishing industry association through a knowledge graph and a semantic embedding technology, and constructing a basic traffic matrix; constructing and carrying out weighted fusion on the contribution matrix and the dependency matrix of each dimension to form a composite industry contribution matrix and a dependency matrix; the weight is dynamically determined by adopting an adjusting coefficient entropy weight method, and objectivity is enhanced in combination with network topology and priori knowledge; and further calculating a full-order indirect correlation effect through a matrix power series, and finally synthesizing an industrial total correlation matrix simultaneously covering forward supply driving and backward demand pulling. The method can efficiently and accurately quantify the direct and multiplication conduction effect of the performance change between the industries, and provides a reliable basis for industrial policy making, industrial financial risk management and enterprise strategic decision making.

The invention belongs to the technical field of computers, particularly relates to an enterprise consultation service system based on big data, and aims to solve the problems of data islands, decision lagging and insufficient predictive ability in traditional enterprise consultation service. The system comprises a global data access module, a knowledge graph construction module, a dynamic simulation engine, an intelligent consultation generation module and an interactive feedback optimization module, a time sequence evolution knowledge network is constructed by fusing internal and external multi-source heterogeneous data, multi-scene parallel simulation and intelligent suggestion generation are realized, and closed-loop feedback learning is supported. According to the scheme, the accuracy, foresight and response efficiency of enterprise strategic decision can be improved, and large-scale concurrent service and real-time dynamic optimization are supported.

The invention relates to a risk management and decision support method, system and platform for the pharmaceutical industry. The method comprises strategic investment portfolio management, supervision affair, clinical drug development, pharmaceutical economics, investment strategy optimization, risk management, merger and purchase full-duty investigation and stock markets. The system uses artificial intelligence and diverse data from public and private sources, including clinical trials, regulatory decisions, economic data, pharmacological data, and company data. The system integrates a plurality of decision modules for clinical development, such as clinical risk, supervision risk, pharmacological risk and economic risk. This risk factor network generates predictive and normative information for strategic decisions.

The invention discloses a reinforcement learning-based mine card scheduling optimization method and device and a medium, and relates to the technical field of intelligent scheduling, and the method comprises the steps: dividing a high-level strategic decision layer and a low-level tactical execution layer of mine card scheduling, respectively defining a state space and an action space, and constructing a layered reinforcement learning framework; based on a hierarchical reinforcement learning framework, receiving a current global state by using a high-level strategic decision-making layer, and obtaining a macroscopic strategic instruction through a strategic neural network; on the basis of the atomic task and the low-layer tactical execution layer, the actual execution action and the instant tactical reward are obtained in combination with the local state of itself; and according to the actually executed action and the instant tactical reward, calculating the total reward obtained in the period of the high-level strategic decision-making layer, updating the high-level strategic decision-making layer and the low-level tactical execution layer through the tetrad, and generating a mine card scheduling optimization report. According to the invention, multi-target balanced optimization of mine card scheduling in a dynamic mine environment is realized, and the overall transportation efficiency is improved.

The invention relates to the field of big data analysis, and discloses a business collaborative management method and system based on a power grid comprehensive plan, and the method comprises the steps: fusing multi-source heterogeneous data through ontology modeling, and constructing a global business entity relation graph; intelligently identifying a relationship between service elements by using a graph neural network and generating plan elements; performing multi-target collaborative optimization by adopting multi-agent reinforcement learning to generate a global optimal plan scheme; performing real-time deduction and dynamic early warning on plan execution by means of a digital twinborn technology; and finally, key data is stored through the block chain, and closed-loop self-optimization of the AI model is realized by using an evaluation result. According to the method, the problems of data islands, difficulty in collaboration, management and control lagging and the like in the prior art are effectively solved, the whole-process intelligent, collaborative and closed-loop management of power grid business from planning, execution monitoring to evaluation optimization is realized, and the resource configuration efficiency, the operation management level and the strategic decision-making capability of power grid enterprises are remarkably improved.

The invention discloses an interpretable patent recommendation method and device based on a knowledge graph and a large language model, and the method comprises the following steps: obtaining patent text information and patent transfer information of a target enterprise in a preset period, and constructing a corresponding patent knowledge graph; a CPC co-occurrence matrix is constructed, and the patent research direction of the target enterprise is obtained; optimizing the patent knowledge graph based on a diffusion model, and performing knowledge reasoning on the optimized patent knowledge graph based on a self-supervised learning model to obtain a patent recommendation path of the target enterprise; and fusing the patent recommendation path and the patent research direction by using a large language model to generate a recommendation text corresponding to the target enterprise. The patent recommendation system effectively solves the problems that an existing patent recommendation system is insufficient in transparency and poor in technology trend prediction capacity, through knowledge reasoning and semantic generation, the accuracy of recommendation results is improved, and the credibility of enterprise technology strategy decisions is remarkably enhanced.

The application discloses a business scheduling method combined with business and group leader cockpit and related equipment. The method constructs a three-dimensional dynamic data matrix by obtaining equipment operation data and environmental monitoring data of multiple power plants; analyzes business team operation logs and group leader decision records based on natural language processing technology to generate dynamic user portraits of business teams and group leaders; extracts real-time change characteristics according to the three-dimensional dynamic data matrix, generates dynamic scheduling strategy information according to equipment operation frequency characteristic coefficients, strategic indicator sensitivity coefficients and real-time change characteristics through a multi-objective optimization algorithm; and pushes equipment operation instructions of the dynamic scheduling strategy information to the business cockpit according to the corresponding business authority of the business cockpit, and pushes strategic decision support graphs of the visual scheduling instruction set to the group cockpit according to the corresponding business authority of the group cockpit. A comprehensive business scheduling solution is provided, which provides strong support for business and management.

This invention is a method, system, and platform for risk management and decision support in pharmaceuticals, including strategic portfolio management, regulatory affairs, clinical drug development, pharmacoeconomic, investment strategy optimization, risk management, due diligence for mergers and acquisitions, and the stock market. The system uses artificial intelligence and diverse data from open and private sources, including clinical trials, regulatory decisions, economic data, pharmacological data, and corporate data. It integrates multiple decision-making modules for clinical development, such as clinical risk, regulatory risk, pharmacological risk, and economic risk. This network of risk factors generates predictive and prescriptive information for strategic decision-making.

The invention discloses a welding production process cost management system and method. The method comprises the following steps: acquiring basic production process data of each process in the welding production process through a data acquisition module; business data analysis is conducted on the basic production process data through a business processing module based on a preset variable dictionary, business detailed data of all production process businesses are obtained, and variable parameters in data calculation are stored in the variable dictionary; and generating a cost summary report corresponding to the to-be-calculated cost item type through a cost summary module according to the to-be-calculated cost item type and the business detailed data. Unified integration of multi-source data, automatic and accurate cost calculation and full-life-cycle management are achieved, and accurate data support is provided for enterprise cost control and strategic decision making.

The invention discloses an enterprise strategic decision-making method and system based on affair graph enhancement, and belongs to the technical field of enterprise intelligent decision-making. The method comprises the following steps: constructing a affair graph according to multi-source business intelligence, wherein the affair graph comprises event nodes, entity nodes and relation edges; extracting a decision chain of the target subject based on the confidence coefficients of the event nodes and the relation edges in the event atlas; and mapping each decision chain to a predefined decision mode to obtain an enterprise strategic decision corresponding to the multi-source commercial information. According to the invention, an accurate and explainable enterprise decision chain and mode analysis result can be generated based on multi-source business intelligence.

Computerized systems and methods for generating and optimizing vendor product roadmaps using predictive insights are described. A real-time data grid (RTDM) module is utilized to summarize and standardize data (including market trend, customer feedback and technical progress) from different sources. An analysis and machine learning (AAML) module analyzes the data to generate predictive insights, facilitating adjustments to existing product roadmaps. Dynamic adjustments are made using a roadmap optimization module, where communications are facilitated through a single glass pane (SPoG) user interface (UI). The scenario analysis, decision support system and continuous monitoring improve strategic decision making.

The invention discloses a tourism internship and employment system based on big data sharing, and aims to solve the technical problems that talent training and industrial requirements in the current tourism industry are disjointed, internship and employment information is asymmetric, and vocational development lacks data support. According to the system, multi-source data of colleges, enterprises, students and government / industry associations are integrated by constructing a'government-enterprise-school-bank 'multi-dimensional data sharing and fusion architecture; high-precision bidirectional recommendation of students and posts is realized by utilizing an intelligent matching engine driven by dynamic ability portraits and deep learning; a full life cycle tracking and feedback closed loop throughout'practice-employment-occupational development 'is established, and industry talent demand prediction and personalized occupational planning are provided based on big data analysis. The method effectively breaks the information island, improves the man-post matching efficiency, meets the production and study gap, enables the personalized development of students, provides data support for talent strategy decision-making in the tourism industry, and is suitable for talent cultivation and human resource allocation scenes in the tourism industry.

The present invention provides a metadata query method and system, belonging to the technical field of cloud computing. The method includes: when the cluster name in the metadata query request matches the local autonomous metadata cluster, obtaining a query result from the local autonomous metadata cluster according to the metadata query information in the metadata query request; otherwise, determining a target metadata proxy in the pre-constructed metadata query network according to the cluster name; sending the metadata query request to the target metadata proxy, so that the target metadata proxy obtains a query result from the corresponding non-local autonomous metadata cluster according to the metadata query information; and receiving the query result returned by the target metadata proxy. The present invention circumvents the problem of multiple copies of metadata on the basis of retaining the efficiency of metadata autonomy and fault isolation within the information system, and simplifies the technical difficulty and management cost of strategic decision-making support across information systems in large enterprises.

A group purchase and supply chain operation management and control platform construction method comprehensively expounds the overall design of the platform from the two aspects of group organization management and informatization construction, covers three levels of group headquarters, platform companies and member units by constructing a '1 + 1 + N' overall architecture, and combines a public network with an internal private network organically to form a group purchase and supply chain operation management and control platform. Real-time penetration and longitudinal penetration of key business data such as purchasing, performance, warehousing and logistics are realized, efficient operation and scientific decision making are enabled, and a foundation is laid for full-chain visual management; the group headquarters serve as a strategy making layer, a unified supply chain control tower is built, first-level management and control are achieved, and macroscopic monitoring, strategy decision making and performance management are focused. According to the method, the defects of the group purchase and supply chain in management and technology can be made up, a new construction idea is provided, a new management concept and an informatization technology are fully applied, and toughness enhancement, decision-making capability improvement and strategic collaborative value are brought to the group purchase and supply chain.

The invention provides an intelligent moral decision-making and target auditing system, and aims to perform auditing evaluation on strategic decisions or targets submitted by users in terms of moral and value under the assistance of artificial intelligence. The system comprises a target analysis module, a value comparison module, an influence prediction module, an audit evaluation module, an optimization suggestion module, an interaction feedback module and the like: firstly, target extraction key information is analyzed, then consistency between a target and human common value and user value is examined by comparing a built-in ethical principle library, and consequences and risks of target implementation are predicted in combination with a domain model; and the system integrates the results to carry out moral conformity scoring and risk rating on the target, and outputs a detailed value analysis auditing report. For the found problem, the system gives a suggested scheme for modifying or optimizing the target, such as adjusting a target index and increasing a moral constraint condition, and allows a user to interact feedback preferences to iteratively perfect the target. Through the above process, automatic auditing and optimization of the human target by the AI as a moral advisor are realized, and it is ensured that the decision-making target meets the requirements of moral specifications, long-term benefits and sustainable development in the making stage. The technology that the wisdom layer directly acts on the intention layer fills the blank of existing AI governance, organizations and individuals are helped to avoid decision directivity errors, and decision moral and sustainability are improved.

To save times for evaluation and guidance and allow employees to focus on strategic decision-making by providing a system for precisely analyzing employees' skills and performance, identifying specific improvements and growth opportunities, and providing personalized feedback and guidance.SOLUTION: A system includes means for: analyzing employees' skills and performance; identifying improvements and growth opportunities from behavioral patterns and performance data; and providing personalized feedback and guidance.SELECTED DRAWING: Figure 1

To provide a theme park system without any visitors for improving attractiveness of a theme park by providing an attraction where next-generation high-speed communication can be experienced, creating personalized AI for each user, and providing objects which can be taken home.SOLUTION: A theme park system without any visitors includes: means for providing an attraction where next-generation high-speed communication can be experienced; means for creating personalized AI for each user and providing objects which can be taken home; and means for carrying out operational management and strategic determination with generative AI.SELECTED DRAWING: Figure 1

The invention relates to the technical field of digital economy, and provides a digital economy integration and optimization method and system based on an information chain, and the method comprises the following steps: data collection and integration: building a data integration platform, collecting market data, customer data and supply chain data, and integrating the data into a centralized data warehouse through ETL (extraction, conversion and loading); data analysis and mining: analyzing the integrated data based on a Transform model, and mining the market trend and demand; and real-time monitoring and prediction: predicting future market dynamics through an autoregression model by using an information chain and historical data, and supporting strategic decisions. Through ETL and standardization processing, the problem of multi-source data isomerism is solved, through combination of a Transform model and a self-attention mechanism, the market trend analysis accuracy is improved by 30%, the method is significantly superior to a traditional statistical method, through combination of an autoregression model and an information chain, real-time market monitoring and prediction are achieved, and the prediction error rate is reduced to 5% or below.

This invention discloses a multi-dimensional, hierarchical performance evaluation management method and system, relating to the field of enterprise management. The method includes: obtaining an organizational structure tree diagram; converting the hierarchical relationships between the strategic decision-making level, departmental management level, and grassroots execution level into a hierarchical database; extracting annual strategic goals based on the hierarchical database, breaking them down into quarterly OKRs for departmental management, and further subdividing them into monthly KPIs for the grassroots execution level, forming a three-level indicator mapping table; and using the Analytic Hierarchy Process (AHP) to assign weights to the three-level indicator mapping table, generating a strategic decision-making level weight vector, a departmental management level weight matrix, and a grassroots execution level weight tensor. This invention achieves cross-level collaborative adjustment by converting the organizational structure tree diagram into a hierarchical database and constructing a three-level indicator mapping table, using the AHP method for weight allocation, generating a strategic decision-making level weight vector, a departmental management level weight matrix, and a grassroots execution level weight tensor, and further constructing a three-body entangled state through qubit encoding.

The present application relates to the technical field of industrial competitiveness evaluation, and discloses an industrial competitiveness quantitative evaluation method based on a graph neural network and data fusion, comprising: constructing a heterogeneous graph; collecting real-time data and updating it to the heterogeneous graph to obtain a real-time heterogeneous graph, training and feature learning on the real-time heterogeneous graph based on a graph neural network model to obtain a competitiveness feature vector of a target node; constructing an evaluation mapping system, mapping the competitiveness feature vector of the target node to a quantitative evaluation result based on the evaluation mapping system, effectively integrating multi-source heterogeneous data in real time, capturing complex network structures and dynamic relationships within an industry through a graph neural network, and realizing accurate and dynamic quantitative evaluation of industrial competitiveness, thereby providing strong quantitative support for enterprise strategic decision-making and industrial policy formulation.