87 results about "Decision support system" patented technology

The invention relates to the technical field of enterprise management, in particular to an intelligent decision support system and method based on cognitive logic and scenarized semanteme, and the method comprises the steps: obtaining enterprise decision data through multi-source data monitoring, carrying out the preprocessing, and generating cross-modal enterprise scene cognitive information; analyzing cross-modal association among the data, fusing a cognitive logical reasoning rule and a semantic understanding model, and constructing scenarized semantic decision fusion features; training an enterprise decision-making quality evaluation model based on historical cases, performing quality perception on a current decision-making scene, and outputting decision-making quality information; and an execution effect is judged in combination with an expected target, an optimization mechanism is started if the target is deviated, candidate schemes are generated by using a historical knowledge base and a multi-target optimization algorithm, an optimal solution is screened, and intelligent adjustment of a decision scheme is realized. According to the method, multi-source heterogeneous data and cognitive logic are fused, semantic understanding, dynamic evaluation and adaptive optimization capabilities of a decision system are enhanced, and scientificity and real-time performance of enterprise decision are improved.

The invention relates to an irrigation decision-making method and system based on a crop model and deep reinforcement learning. The method comprises the following steps: creating a virtual environment for simulating crop growth through a decision support system crop growth model after parameter calibration; then constructing a time sequence state matrix, and encoding the time sequence state matrix into a context vector concentrated with historical dynamic information; outputting irrigation actions of each decision-making day through a strategy network of a deep reinforcement learning agent based on a soft strategy-value algorithm; therefore, a decision support system crop model and a deep reinforcement learning technology are deeply fused, an intelligent irrigation decision system with biological rationality is constructed, and the problems of insufficient training data and environment distortion of an existing deep reinforcement learning model in agricultural application are solved.

The invention discloses a power load prediction method, and the method comprises the steps: firstly solving an extreme event data sparsity problem through a generative adversarial network, and constructing an event time sequence library through a time sequence anomaly detection algorithm; then analyzing the causal relationship between the event and the load by applying a causal discovery algorithm, and converting prediction output into probability distribution by adopting a Bayesian neural network to quantify uncertainty; constructing a prediction model triggered by an event, and generating a multi-time scale probability prediction interval; and finally, generating a multi-scene prediction result through Monte Carlo simulation, quantifying the system recovery capability in combination with a toughness index, and integrating the system recovery capability to a decision support system to generate a risk response scheme. According to the method, the accuracy and robustness of load prediction under the extreme climate are remarkably improved, full-chain risk insight from early warning to recovery is realized, and prospective decision support is provided for safe operation of a power system.

The application provides a smart question and index management engine and system based on dynamic reward optimization, relates to the technical field of data learning, and is used for enterprise index analysis, attribution diagnosis and decision support. The system sets an index management layer to uniformly manage index caliber, calculation logic, blood relationship, credibility score and version information by means of a dynamic knowledge graph, uniformly implements semantic constraints on a multi-agent analysis process, and guarantees index consistency and traceability. The system also establishes a cause-effect cognition module, generates and corrects a business index cause-effect directed acyclic graph based on time series data and in combination with expert priori, realizes root cause positioning and counterfactual simulation, and answers "why change" and "what if intervention". A multi-agent collaborative analysis core is responsible for natural language intention analysis, index compliance verification, automatic data acquisition, cause-effect inference, narrative generation and chart presentation, and calculates a multi-target compound reward value based on user feedback and interaction behavior, and adaptively adjusts output; user correction write-back forms a closed-loop learning.

The invention discloses a mental stress assessment and intelligent dredging method and system, and belongs to the technical field of digital medical treatment and health informatics. Multi-modal physiological signals are continuously collected through the wearable device, medical diagnosis level pressure state evaluation is carried out based on the personalized physiological baseline, and personalized pressure indexes and levels are generated; secondly, when it is diagnosed that the pressure level exceeds the standard, the system serves as an intelligent decision support system, the environment and schedule information after privacy protection processing is fused, and an optimal grooming action is dynamically selected from a predefined intervention action library and executed; finally, the system serves as a continuous learning system, the intervention efficiency is evaluated in real time according to feedback data of the pressure index after execution, the decision model is updated, and collaborative self-optimization of the diagnosis strategy and the intervention strategy is achieved. According to the method, medical diagnosis, personalized treatment decision and adaptive learning are integrated, and the systematicness, accuracy and intelligent level of mental stress related health problem management are improved.

The invention relates to the technical field of land space planning, and discloses a territorial space planning multi-source data fusion decision support system, which comprises a data foundation and preprocessing module used for processing multi-source heterogeneous data related to territorial space planning; the macroscopic causal knowledge construction module is used for constructing a space-time causal map representing a macroscopic causal relationship among territorial space elements from a unified data basis; a coupling simulation and deduction execution module; the conflict perception and scene differentiation module is used for monitoring the consistency between the macroscopic trend and the microscopic behavior trend; the multi-scene toughness strategy evaluation module is used for receiving a planning strategy; and the model adaptive calibration module is used for comparing the deviation between the virtual data of the future space-time state and the real world observation data. The space-time causal atlas is fed back and updated by comparing simulation and reality deviations, closed-loop learning is formed, and the problems that macroscopic and microscopic disjunction occurs, and deep uncertainty is difficult to deal with are solved.

The invention relates to the technical field of first-aid medical engineering and intelligent medical equipment, and discloses a digestive tract hemorrhage patient liquid resuscitation decision support system which comprises a thermal wave signal generation and infusion execution module, a distributed thermal dispersion perception and vascular stiffness resolving module, a multi-physics field coupling modeling module and a double-target game decision control module. By superposing a micro-amplitude thermal wave signal in resuscitation liquid, the system calculates a vascular stiffness parameter of a patient in real time based on a thermal dispersion principle; and predicting the shear stress of the wall surface of the blood vessel at different flow velocities by utilizing a temperature change viscosity and blood vessel elastic mechanics coupling model. In decision control, the system determines the target flow velocity according to the microcirculation perfusion requirement, and reversely solves the required fluid dynamic viscosity according to the shear stress safety threshold value, so as to dynamically adjust the basic temperature of the infusion fluid. The fluid viscosity is reduced through physical field intervention, the high-flow-rate resuscitation requirement is met, meanwhile, the shear stress of the wall face of the blood vessel is effectively restrained, and the rebleeding risk is reduced.

The invention discloses a forest fire prevention decision support system based on a knowledge graph, and the system comprises the following modules: a multi-source heterogeneous data collection module which is used for synchronously collecting forest environment monitoring data; the forest situation knowledge graph construction module is used for performing cross-modal semantic fusion by using a graph attention network to generate a forest situation knowledge graph; the fire danger situation feature code extraction module is used for outputting forest fire danger situation feature codes based on a space-time diagram neural network; the fire evolution deduction map generation module introduces random noise by using a neural stochastic differential equation and outputs a fire evolution deduction map; the resource scheduling strategy generation module is used for generating an optimal resource scheduling strategy set based on the improved implicit quantile network model; and the visual decision support module is used for dynamically rendering and outputting a decision report on the three-dimensional digital twin map. According to the method, the fire evolution deduction precision and the decision robustness are improved.

The invention belongs to an intelligent traffic system and artificial intelligence technology, and discloses a tunnel emergency speed limit decision support system and method. The method comprises the steps that a natural language instruction of a traffic management terminal is received and analyzed, and an unstructured current emergency scene is converted into a standardized parameter; based on the current emergency scene after parameter standardization, similarity retrieval is carried out in the historical simulation case library, a historical case with the highest matching degree is recalled, and an optimal control scheme and a recommendation index thereof are extracted from the historical case and recommended to a traffic management terminal; and performing result integration and output on the optimal control scheme recommended to the traffic management terminal and the recommendation index thereof. According to the method, a complete historical simulation case library and an efficient AI Agent retrieval matching engine are constructed, so that full-process automation from scene recognition to scheme recommendation is realized.

The invention discloses a mechanical part cost decision support system and method driven by an industrial knowledge graph, and relates to the technical field of mechanical manufacturing cost decision, and the system comprises a multi-modal data collection module which collects multi-type data of the whole process of mechanical manufacturing and processes the multi-type data to form a unified set; the multi-modal knowledge graph construction module constructs a graph and establishes an updating mechanism; the intelligent reasoning decision module inputs new part parameters to screen an optimal scheme; the dynamic optimization adjustment module collects data in real time to update a result reversely; the decision result output module generates a visual report and supports multi-format export; according to the method, manufacturing data dispersion pain points are broken, multi-source data fusion precipitation and knowledge systematization are realized, and accurate support is provided for cost decision making; meanwhile, an optimal scheme is screened through multi-algorithm cooperation and multi-target evaluation, field data are collected in real time to dynamically optimize decisions, the crossing from static estimation to dynamic optimization is achieved, the core target is effectively balanced, and the economical efficiency and the stability are improved.

The invention discloses an electric power engineering project dynamic cost and risk prediction system based on digital twinning, and relates to the technical field of electric power engineering project management and digital twinning. The problem that in the prior art, it is difficult to dynamically and accurately predict the construction cost and the construction period and quantitatively evaluate the chain influence of risk events is solved. According to the method, the digital twinborn body with the autonomous evolution capability is constructed, the physical constraint and the time delay causal relationship are fused, and the structural causal model and the anti-factual reasoning are combined, so that the probabilistic dynamic prediction of the project cost and the construction period and the visual map generation of the risk situation are realized; and multi-scheme comparison and robust decision support are provided. The system also has a self-learning optimization capability, and can continuously improve the accuracy of prediction and decision, thereby remarkably improving the fine management and anti-risk level of the whole life cycle of the electric power engineering project.

The application discloses a content generation and interactive dialogue method and system based on AI artificial intelligence, which models semantic label sequences in multiple rounds of conversation, identifies semantic derailment points in behavior evolution trajectory templates and triggers an intent clarification mechanism; constructs a semantic tension map based on user feedback, forms a tension closed cluster and generates multi-path response candidate segments; uses a misleading chain cross-analysis unit to identify high-risk response chains associated with historical negative feedback, and eliminates and shields content; and selects the optimal response segment through a context break span model and dynamically updates the behavior evolution model. The application can significantly improve the semantic stability, consistency and safety of the dialogue system, reduce semantic drift, misleading answers and illusion risks, and has high reliability and technical advancement. The system scheme can realize the modularization of the method functions and is suitable for application in intelligent customer service, general large models, decision support systems and other scenes.

The invention discloses an intelligent decision support system for urban water conservancy project multi-source data fusion, and relates to the field of engineering planning. Forming a feature region containing a green belt; analyzing to obtain at least one terrain low point and at least one terrain high point; obtaining an operation labor-consuming index of the distribution block; forming at least one first planning scheme; forming a second planning scheme corresponding to the first planning scheme; and combining the first planning scheme and the corresponding second planning scheme into a third planning scheme, and calculating an operation difficulty value of the third planning scheme. The urban water conservancy project to be repaired is classified to form a feature region containing a green belt, the operation labor-consuming index of a distribution block is obtained, and the operation difficulty value of a third planning scheme is calculated, so that the soil excavation difficulty, the excavation length and the relative position relationship between the water conservancy project and the green belt can be synthesized; and the reasonability of the designed planning scheme is ensured.

A Real-Time Enterprise Conversation Analyzer and Decision Enabler (RECA&DE) that enhances the capabilities of enterprise applications, transforming them from transactional or operational support systems into decision support systems. In one aspect, the RECA&DE is capable of performing the following: generating a first graphical user interface for implementing a decision support module within an enterprise application, obtaining data for a discussion event from the enterprise application based on input received via the first graphical user interface, transferring event data to a discussion service, receiving feedback data from an artificial-intelligence platform, the feedback data comprises sentiment data derived by the artificial-intelligence platform based on transcript data generated from conversations of the participants using the discussion service, analyzing the feedback data, and rendering one or more dashboards in a second graphical user interface based on analyzing of the feedback data.

This invention relates to an intelligent decision support system for rectal cancer prognosis integrating digital pathological images, belonging to the field of medical image processing technology. The system includes a data acquisition module for collecting multiple sets of sample data; a feature selection module for identifying multiple key medical imaging features of rectal cancer based on the multiple sets of sample data; a model building module for acquiring a pre-trained deep learning model and constructing a rectal cancer prognosis prediction model based on transfer learning, the pre-trained deep learning model, the multiple sets of sample data, and the multiple key medical imaging features of rectal cancer; an image acquisition module for acquiring pre- and post-operative medical images of the rectal cancer patient to be evaluated; and a recurrence prediction module for predicting the recurrence probability of the rectal cancer patient to be evaluated based on the pre- and post-operative medical images of the patient using the rectal cancer prognosis prediction model. This system has the advantage of improving the accuracy of non-invasive prediction of postoperative recurrence of rectal cancer.

The invention relates to the technical field of smart city management and planning decision, in particular to a smart city planning decision support system and method based on multi-source data fusion. The smart city planning decision support system based on multi-source data fusion comprises a data acquisition module for acquiring functional area data of a target lag and stagnation area and original city planning data; the data processing module is used for processing the functional area data and the original city planning data to obtain functional area core strength data and planning influence data with time sequence marks; and the model matching module is used for constructing a planning data set according to related planning case data of each region, and acquiring recommended planning data based on the planning influence data and the planning data set. According to the method, a decision support system fusing multi-source data and integrating intelligent matching and dynamic evaluation is constructed, and the key defects that in traditional city planning, the data source is single, analysis is static and one-sided, decision depends on subjective experience, and scheme rigidity lacks elasticity are effectively overcome.

We provide a highly practical snow removal decision support system, program, and road management method that can accurately and flexibly determine the need for snow removal by integrating and analyzing a variety of information such as patrol status, traffic conditions, weather information, and resident reports, and enables prioritization and the formulation and updating of dynamic implementation plans. [Solution] The snow removal decision support system comprises an information acquisition unit that acquires at least two types of information such as road surface, traffic, weather, driving, resident reports, and snow removal and disposal; an analysis unit that generates analysis results based on the acquired information; and a road snow removal and disposal unit that at least formulates dispatch orders to snow removal and disposal companies based on the analysis results and advance plans registered for each route or construction section (including the responsible snow removal and disposal company and snow removal and disposal standards).

The application discloses an industry analysis and decision support system based on big data, which comprises a data collection module, a data processing module and a data application module, wherein the data collection module is connected with the data processing module and the data application module respectively. The data collection module is used for collecting industry-related data, the data processing module is used for processing, arranging and calculating and statistically processing the collected data, and the data application module is used for presenting the data processed by the data processing module to users in different ways and providing recommendations for the users through industry status, development and hot sensitive topics.

The invention provides a military plotting-based auxiliary tactical calculation and decision-making system, and belongs to the technical field of computers. Comprising a data processing and acquisition module, a plotting system development module, a tactical calculation model module, a decision support system module, a communication and network technology module, a man-machine interaction module, a security and encryption technology module and a continuous improvement and optimization module. The system provided by the invention improves the decision-making efficiency, greatly improves the decision-making efficiency of a commander through automatic and intelligent data processing, tactical analysis and decision-making suggestion, shortens the decision-making period, carries out data analysis and tactical prediction by using an advanced artificial intelligence technology, can provide a more accurate and reliable decision-making basis, and improves the decision-making efficiency of the commander. Dynamic analysis and adjustment can be carried out according to real-time battlefield data, it is ensured that a commander can make a decision according to the latest situation, and the real-time performance and accuracy of the decision are improved.

The application discloses a 7T magnetic resonance head and neck tumor radiotherapy response evaluation system, belongs to the field of medical imaging and tumor radiotherapy, and comprises an ultrahigh-field multi-modal image acquisition module, a multi-phase accurate registration and radiomics feature extraction module, an early response biomarker identification module and an adaptive treatment decision support module. The high resolution, high signal-to-noise ratio and multi-nuclide imaging capability of the 7T ultrahigh-field magnetic resonance are utilized to acquire multi-modal images before and during treatment, more than 200 features are extracted through accurate registration and radiomics analysis, the treatment response is predicted based on the Delta feature and the deep learning model 2 to 3 weeks after the start of radiotherapy, the prediction accuracy reaches 87.5%, the prediction is 3 to 4 weeks earlier than the conventional evaluation, decision support is provided for timely adjustment of the treatment strategy, a closed-loop feedback mechanism is established, the clinical verification results are used to optimize the parameters of each module reversely, and continuous improvement is realized.

This invention provides a method for injecting industrial knowledge based on retrieval enhancement, belonging to the field of industrial knowledge technology. This invention establishes a time-series data flow matrix by collecting multi-source heterogeneous data, constructs a time-series causal knowledge graph using Granger causality tests, establishes an industrial knowledge document library with a hybrid index structure, performs time-series-aware query expansion on the query input, performs a hybrid retrieval of dense vectors and sparse inverted indexes and cross-encodes and reorders the data, associates the refined documents with the time-series causal knowledge graph to extract event evolution paths and fuses them to generate a time-series enhanced knowledge representation, inputs it into a time-series knowledge enhancement model to generate answer text containing fault analysis and prediction suggestions, injects it into an industrial decision support system after quality assessment, and stores feedback data for continuous model optimization. This solves the technical problem of industrial knowledge retrieval being disconnected from real-time temporal status, resulting in a lack of time-series causal reasoning ability in the generated answers.

The invention discloses an electric power digital intelligent base adaptation detection method, which relates to the technical field of intelligent monitoring, and comprises the following steps: acquiring operation data and environment data of an electric power system through an intelligent electric meter and an environment sensor, and carrying out data fusion to obtain enhanced multi-source electric power data; carrying out load feature extraction based on the enhanced multi-source power data to construct a self-adaptive load model, preliminarily adjusting a data sampling strategy according to real-time load fluctuation, and carrying out power suitability analysis; inputting a power suitability analysis result into a decision support system to generate a visual report, and carrying out collaborative optimization on the data sampling strategy by adopting multi-agent reinforcement learning; and updating the adaptive load model based on the data sampling strategy after collaborative optimization, and evaluating the detection precision through online cross validation to complete intelligent adaptive detection of the electric power digital intelligent base. According to the method, long-term adaptability and real-time precision guarantee can be kept at low cost, and prediction reliability and operation economy are improved.

The invention discloses a safety manufacturing control method and device, relates to the technical field of intelligent manufacturing digitization, and constructs a set of intelligent monitoring and decision support system oriented to safety management of a manufacturing process through deep fusion of machine learning and a knowledge graph technology. And performing dynamic feature extraction and pattern recognition on the real-time data stream by using a machine learning model, and obtaining security disposal measures through a knowledge graph. The machine learning module is responsible for mining potential abnormal modes from data to predict safety risks in advance, the knowledge graph provides semantic association and causal logic support, multi-level and high-precision prediction of the safety risks is achieved through cooperation of the machine learning module and the knowledge graph, and the production safety of the manufacturing process is effectively improved.

The present application relates to the field of low-altitude flight control and digital twin technology, and in particular to a flight path intelligent optimization decision support system based on a low-altitude digital twin model, which comprises a multi-source data perception fusion module, a low-altitude digital twin modeling module, an intelligent path optimization decision module, and a virtual-real linkage control feedback module.A multi-scale dynamic digital twin model is constructed to realize real-time high-precision mapping of static terrain and dynamic environment.A three-stage path optimization algorithm combining imitation learning and reinforcement learning is proposed to complete closed-loop training in combination with a digital twin simulation environment.A distributed federated reinforcement learning collaboration mechanism is designed to ensure the safety and privacy of multi-aircraft path collaborative optimization.A space-time evolution type risk assessment model is established to realize early warning of dynamic risks and adaptive adjustment of paths.The present application can significantly improve the safety, economy and real-time performance of low-altitude flight paths.

The application discloses a kind of information system security vulnerability risk early warning method based on big data, comprising the following steps: step S1, the index library is established to enterprise internal various levels various types of information systems, network equipment, application software, and safety related data is collected;Step S2, integrate the security intelligence on Internet and third party vulnerability database, obtain the latest security vulnerability information, and carry out classified storage;Step S3, the original data collected is cleaned and preprocessed;Step S4, big data analysis and mining;Step S5, security vulnerability assessment and classification;Step S6, early warning trigger and notification;Step S7, establish security decision support system, provide reasonable security decision suggestion, the third party vulnerability database is collected, stored and analyzed in the application, realize the accurate detection and evaluation of potential vulnerability of business system, network and application program etc. each aspect, and timely security risk early warning and repair suggestion.

The invention relates to the technical field of artificial intelligence big data, and discloses a decision support method and system based on artificial intelligence big data, and the method comprises the steps: receiving instruction information inputted by a human expert for a specific situation, analyzing the instruction information, converting the instruction information into structured instruction data, and further converting the structured instruction data into decision constraint representation in the system; and injecting the decision constraint as a high-priority condition into resource scheduling decision logic. On the basis, the system adjusts the decision process of the resource optimization program according to the decision constraint so as to generate a resource scheduling scheme meeting the decision constraint, and evaluation of the system on the decision ability of the system is not reduced in the adjustment process. And finally, the system integrates the resource scheduling scheme of the decision constraint, and provides feedback and tracing functions for the resource scheduling scheme. According to the technical scheme, the intelligent medical decision support system effectively solves many problems of an existing intelligent medical decision support system when facing unconventional scenes such as public health emergencies and the like.

An artificial intelligence decision support system for manufacturing management is disclosed, which is implemented as a computer system that comprises a data warehouse and a host device, wherein the host device includes a processor and a storage device. In particular, the storage device stores an application program, and the processor executes the application program to perform the following functions: displaying a user operation interface on a display of a client device; converting natural language content and / or unstructured data provided through the user operation interface into a semantic embedding vector; processing the semantic embedding vector to obtain a task objective; activating a corresponding intelligent agent module to perform a retrieval-augmented generation (RAG) operation in the data warehouse based on the task objective to produce RAG data; and activating a large language model to generate a natural language specification and / or a visualization specification according to the RAG data.

The application relates to the field of data processing and discloses a customer satisfaction visual analysis and decision support system, which comprises a data access and verification module, a main data correlation module, an index calculation module, a threshold grading module, a decision output module, a visual interaction module, a permission security module and a data storage module. The data access and verification module receives customer satisfaction survey data and performs verification according to a unique dealer code rule and a contact or questionnaire code hierarchy consistency rule, writes the verified data into an original data area and generates import records. The index calculation module forms an index result set and generates an unsatisfied mark, the threshold grading module generates an interval grading result set, and the decision output module outputs an organization hierarchy ranking result and a to-be-handled list including a sample execution progress list, a follow-up handling list and an un-repaired unsatisfied object list.

The invention discloses an AI-based network switch maintenance decision support system, and relates to the technical field of intelligent operation and maintenance of network switches, the system obtains multiple layers of operation parameters such as port flow, packet loss rate, CPU and memory occupation, temperature, fan speed and energy consumption in real time through a data acquisition module; the data processing module fuses the time sequence and the topological features, and constructs a multi-dimensional operation parameter matrix and a weighted topological feature map; the intelligent state evaluation module calculates a load balancing index and automatically adjusts the load; establishing an AI dynamic diagnosis model, evaluating a health state based on a convolutional neural network, and generating a speed reduction and heat dissipation strategy; the abnormal trend monitoring module predicts future temperature and load by using LSTM, and triggers an adjustment strategy; and the structure health assessment module calculates a topology health coefficient, executes port self-inspection and maintenance log records, and realizes real-time monitoring, intelligent diagnosis and maintenance decision support of the switch state.