50 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.

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 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 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 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.

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.

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.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an enterprise technology demand insight method and system based on a large model. According to the method, an intelligent insight framework is constructed and comprises four core modules, namely a multi-dimensional data intelligent acquisition module, an enterprise technical capability portrait module, a technical gap intelligent identification module and a large-model-driven technical demand intelligent reasoning and generation module. The data acquisition module acquires multi-source data of patents, papers, products and the like. And the technical capability portrait module identifies the core technical field and capability level of the enterprise by using an achievement transformation large model. And the technical gap identification module encodes technical data into a high-dimensional vector through semantic space mapping, and identifies the technical gap by adopting cosine similarity and a dynamic threshold mechanism. The demand reasoning generation module adopts a causal reasoning and technical decomposition tree method to decompose technical gaps into hierarchical demands. According to the method, a fusion result transformation large model semantic understanding and an intelligent gap recognition algorithm are adopted, and scientific support is provided for enterprise technology strategy decision making.

A system and method for performing simulations of uncertain future events may use statistical analysis via computerized simulations associated with processes including project scheduling; project budgeting, project risk assessments (or confidence percentages), strategic decision-making of alternatives, investment alternatives, etc. In some embodiments, the system and method may utilize probability and convolution theory to arrive at ideal theoretical probabilistic solutions (PDF [probability density function] and “S” Curve [cumulative distribution function] outcomes).

The application belongs to the technical field of mineral resources management, and provides a strategic mineral resource demand prediction method and system, which comprises the following steps: performing a first prediction path based on bottom-up logic, obtaining a first prediction result by summarizing demand predictions of various application departments; performing a second prediction path based on social and economic evolution rules, obtaining a second prediction result by predicting in-use inventory evolution and inversely calculating demand based on material flow balance; and fusing the first and second prediction results to generate a comprehensive future trend prediction result. Through double-path prediction and fusion, the application overcomes the defects of a single model, improves the accuracy and robustness of prediction, solves the problems of single prediction dimension and poor robustness, and provides support for resource strategic decision-making.

The invention discloses a new energy automobile charging mode analysis method based on an evolutionary game theory, and the method comprises the steps: firstly, determining a government, an automobile manufacturing enterprise and a consumer as game participants, and defining a strategy set and influence factor variables of the game participants; based on this, a payment matrix coupling benefits of three parties is constructed, and then a replication dynamic equation is established by taking the strategy selection probability of each party as a state variable; a system equilibrium point is obtained by solving an equation, stability analysis is carried out by using a Jacobian matrix eigenvalue method, and a stability condition for enabling the equilibrium point to become an evolution stability strategy point is deduced; and finally, based on the condition analysis, factor variables needing to be adjusted for realizing the expected stable strategy are analyzed, and a decision support result is generated. According to the method, the defect that the prior art lacks a dynamic view angle, a system framework and a quantitative model is overcome, the three-party strategy interaction evolution process can be quantitatively depicted, the stability condition is determined, and a scientific and quantitative basis is provided for industrial policy making and enterprise strategic decision making of a battery charging and replacing mode.

The invention discloses an intelligent retrieval method based on industrial intelligence data and electronic equipment, and aims to solve the three problems of high information noise interference, low entity and relationship extraction precision and performance bottleneck caused by tight system coupling in the prior art. According to the system, a modular loose coupling design is adopted, functions such as noise recognition, entity extraction and relation mining are decoupled into independent service units, and cooperation is carried out through a standardized interface. The core is to construct an integrated intelligent processing mechanism, so that noise filtering and semantic analysis share features in a unified framework, collaborative optimization is realized, and error accumulation is avoided while the data quality is improved. The system has high expandability and maintainability, supports independent upgrading of modules and on-demand expansion and contraction of resources, remarkably improves intelligence processing precision and efficiency, and provides high-quality and high-credibility intelligence support for enterprise strategic decision making.

The application provides a kind of cloud computing-based intellectual property data analysis method and system, related to intellectual property data analysis field, by extracting the local context vector of technical concept in patent literature, question-solution label and time stamp, structured identification body is constructed, massive real-time data is managed by time dimension partition index using distributed storage system, the similarity between newly generated target identification body and historical data is matched, equivalence relationship determination, evolution path tracking and convergence relationship identification are executed, fragmented patent layout is penetrated, and unified technical thought and evolution context are restored. Finally, a technology strategy map is generated, a risk assessment layer is superimposed with semantic conflict degree verification, and is pushed to the user terminal in real time through the WebSocket protocol. The application can effectively support enterprises in accurately analyzing the technology roadmap of competitors, optimizing R&D investment, and dynamically evaluating infringement risks, significantly improving the timeliness and accuracy of intellectual property strategy decision-making.

A method and apparatus for integrating multiple disjunct processes into a cohesive workflow can involve receiving proposal development inputs through a system linked to a back-end processor and user interface, and collecting with the proposal development, data for staffing and cost estimation, customizable per case. The data can be processed by a baseline development component to confirm realistic paths, establish target dates, and maintain cost and schedule metrics within tolerances. The system can update tasks, displays critical paths, and monitor performance metrics while integrating with a portfolio workforce planning component, staffing data across proposal, baseline development, and program execution components. This integration of staffing data can allow for strategic decision making and / or program prioritization.

This invention discloses a holographic ecological chain dynamic steady-state quantum computing power fission strategic decision-making system, belonging to the field of quantum computing and intelligent decision-making technology. The system uses the Zu Chongzhi quantum computer as its core, achieving precise allocation and fission-like growth of computing power through a ternary system and a universe-wide hedging logic. The system includes a dedicated computing power interface, quantum computing firmware, and a computing power gain calibration module to ensure efficient utilization of computing power. The ternary system achieves directional gain of computing power through ternary stability adaptation and system integration optimization. The universe-wide hedging logic achieves fission-like growth of computing power gain through positive and negative cosmic hedging. The holographic ecological chain dynamic steady-state strategic decision-making system integrates the above technologies to form an intelligent decision-making system with autonomous iteration capabilities, applicable to multiple fields such as people's livelihood, enterprises, education, military industry, ecology, and technology. The system ensures the originality and security of the technology through legal protection and a unique support system. This system aims to improve computing power utilization efficiency, achieve seamless integration across all scenarios, and promote technological progress and social development.

This invention discloses a method and system for generating and strategically analyzing technology maps in the construction industry based on knowledge-constrained AI, belonging to the field of building information modeling (BIM). This invention constructs a multi-agent system comprising an agent for market importance assessment and an agent for technology advancement assessment. It automatically converts structured text into a visualized technology map with a seven-level hierarchical structure and invokes the aforementioned agents to evaluate technology nodes. During market importance assessment, a structured knowledge base of the construction industry is introduced as a contextual constraint to guide the large language model, suppressing illusions and ensuring the factual reliability of the assessment. Technology advancement assessment employs a quantitative model that integrates patent data and Delphi expert maturity scores, and the optimal integration weights are determined through backtesting. Finally, a four-quadrant matrix is ​​constructed using the market importance results and technology advancement scores as dimensions, automatically outputting resource allocation strategy recommendations. This invention provides construction enterprises with a dynamic, objective, reliable, and efficient intelligent strategic decision support tool.

This invention provides a method for evaluating enterprise value symbiosis reports based on an artificial intelligence big data model. The evaluation target is general enterprises with a unified social credit code starting with 91 and possessing complete operational data. The method combines internal historical transaction data, stakeholder supplementary data, and publicly available external big data. Through the deep integration of large language models, knowledge graphs, multi-agent collaboration, and machine learning algorithms, it effectively evaluates the quality of the symbiotic relationship and the total ecological value between the enterprise and its core stakeholders. This provides comprehensive information support for enterprises to optimize customer cooperation, supply chain management, employee motivation, environmental governance, and social responsibility fulfillment. Simultaneously, the system can automatically generate multiple versions of value symbiosis reports, output risk items and collaborative suggestions, effectively supporting enterprise strategic decision-making and sustainable development management. It also supports scenario simulation and natural language interaction, significantly improving the intelligence and automation level of enterprise value assessment.

The invention discloses an enterprise employee dynamic point management method. The method comprises the following steps: acquiring strategic decision information of an enterprise and capability qualification information of employees; inputting the strategic decision information into a pre-trained linear regression model, and identifying a skill demand signal of the enterprise; calculating a skill attenuation factor and a project value attenuation factor according to the skill demand signal; calculating a fixed integral value based on the education background authentication information, calculating a first dynamic integral value based on the historical project completion record and the project value attenuation factor, and calculating a second dynamic integral value based on the skill certificate information and the skill attenuation factor; and integrating the fixed integral value, the first dynamic integral value and the second dynamic integral value to obtain a total integral value of the employee. According to the invention, fixed and dynamic multi-dimensional scientific calculation of employee credits can be realized, real-time synchronization of employee skill evaluation and enterprise demands is realized, and reasonable allocation of resources by enterprises is facilitated.