285 results about "Decision making support systems" patented technology

The invention discloses an auditing decision support system and method based on a dynamic knowledge graph, relates to the technical field of computers, and aims to solve the problems that auditing data are heterogeneous and complex, risk identification is not timely and causal interpretation is lacked. According to the system, multi-modal audit data is collected in real time through a streaming event processing framework, and a dynamic audit knowledge graph with timeliness weight is constructed. Based on a graph calculation engine and cross-domain rule mining, identifying a high-frequency risk mode, and generating a risk conduction path graph; further fusing a multi-modal graph attention network, identifying and positioning abnormal entities, and outputting abnormal nodes and risk links thereof; and finally, the abnormal node embedding representation is dynamically updated through the time sequence diagram attention network, an interpretable audit causal map is generated in combination with a structural causal model, and closed-loop support from data acquisition and risk identification to interpretive audit decision is realized. The intellectualization and transparency of audit decision making are improved, and an efficient and traceable decision making basis is provided for a complex audit scene.

The invention relates to an infection risk assessment method and system for nursing, and the method comprises the steps: collecting multi-source heterogeneous data of a patient, and fusing the multi-source heterogeneous data into a unified original data set; obtaining a standardized risk feature vector based on the original data set; inputting the risk feature vectors into a multi-modal risk assessment model, and outputting risk probabilities of individual infection and group infection; forming a personalized risk index based on the risk probability; according to the personalized risk indexes, the decision support system outputs hierarchical intervention measures matched with the risk levels, and records and feeds back execution conditions and intervention effect data of the hierarchical intervention measures for dynamically updating parameters of the risk assessment model; and integrating the intervention effect data and the early warning signal into feedback information to form a dynamic infection risk assessment system adapted to specific hospital characteristics. The accuracy of infection risk assessment of nursing can be improved.

The invention relates to the technical field of data processing, and discloses a coal mine area gas control and safety guarantee method and system under complex geological conditions. The method comprises the steps that geophysical prospecting, drilling and logging exploration are conducted on a coal mine area, and geological parameters are obtained; performing statistical analysis on the data, and constructing a three-dimensional gas model; identifying a high-gas area based on the model, and arranging a directional drilling network; differential extraction and anti-reflection measures are implemented according to the drilling network; fusing extraction and sensor data, and predicting a gas change trend by using a neural network; and performing intelligent diagnosis based on the early warning matrix to form a disposal scheme. Under the complex geological condition, the gas distribution rule is accurately mastered, scientific arrangement of directional drilling and optimization design of differential extraction parameters are achieved, meanwhile, a gas early warning decision support system based on artificial intelligence is established, the accuracy of gas treatment and the timeliness of early warning are improved, and therefore safe production of a coal mine is guaranteed.

The invention relates to the technical field of medical decision, and discloses a medical decision support system based on a knowledge graph, and the system comprises a knowledge graph construction module which constructs an initial knowledge graph based on a medical ontology library, and the knowledge graph comprises entities and association relationships of diseases, symptoms and drugs; the data acquisition module is used for acquiring data from an electronic medical record, wearable equipment, a medical literature library and a hospital information system and normalizing the data through a standardized protocol; the dynamic knowledge updating module is used for processing normalized data through an incremental graph neural network; a multi-source knowledge fusion module; a context awareness module; a dynamic deduction module; and a decision optimization closed loop module. And triggering a preset clinical rule in real time based on the pathological state of the patient, dynamically adjusting the intensity value of the related edge in the factor graph, and persistently storing the intensity value back to the knowledge graph, so that logic adaptation and individualized experience precipitation of general medical knowledge in a special pathological state are realized, and the individualized treatment accuracy is ensured.

The invention discloses a property operation decision support system based on digital twinning and generative AI, which relates to the technical field of property operation management and comprises a sensing layer, a data layer, a digital twinning layer, a generative AI layer and a decision execution layer which are connected in sequence. In the invention, the generative AI layer is based on a special Transform architecture model, fuses historical operation data and digital twinning real-time data, can automatically generate an executable optimization strategy in multiple scenes such as facility maintenance, resource allocation, personalized service, emergency disposal and the like, verifies feasibility and effects in a virtual environment, and improves the implementation efficiency. The decision execution layer efficiently implements a strategy subjected to simulation verification into a physical space by using an intelligent scheduling and equipment linkage mechanism, performs quantitative evaluation and strategy iterative optimization based on an execution effect, deeply fuses a high-fidelity digital twin with a generative AI, constructs an AI decision closed-loop system based on simulation verification, and performs decision making on the AI decision closed-loop system. And a high-feasibility and feasible optimization scheme can be generated for a complex and dynamic property operation environment.

The invention discloses a non-point source pollution and waterlogging collaborative four-pre management and control system based on short temporary rainfall forecast, which relates to the technical field of urban water environment management and comprises a short temporary rainfall forecast module, a non-point source pollution control module, a waterlogging management module, a four-pre management and control platform, an intelligent decision support system, a real-time monitoring and feedback system and a water ecological management module. By integrating meteorological monitoring, hydrological monitoring and environment monitoring data, high-precision short and temporary rainfall forecast is realized by using a machine learning algorithm, the operation strategies of the intelligent catch basin and the initial rain storage pond are dynamically adjusted to control non-point source pollution, and meanwhile, the waterlogging risk is predicted and preventive measures are taken in combination with real-time hydrological data. According to the technology, integrated management of forecasting, early warning, rehearsal and plans is achieved through a four-pre-management and control platform, an intelligent decision support system, a real-time monitoring and feedback system and a water ecological management module under the sponge city design concept are used in an auxiliary mode, and the efficiency and effect of urban water environment management are comprehensively improved.

The invention relates to the field of environmental protection, and discloses an emergency pollution diffusion model construction method, which comprises the following steps: constructing a pollutant diffusion basic model, and describing the diffusion process of pollutants in a water body along with time and space; dynamically optimizing control parameters of the pollution diffusion model, adjusting control variables in real time, and optimizing a pollution diffusion path through an adaptive optimization control method; based on the real-time monitoring data and the prediction result of the pollution diffusion model, performing state estimation and correction on the pollution diffusion path by using a Kalman filtering algorithm; in combination with real-time data from different monitoring sources, optimizing a prediction result of the pollution diffusion model by using a spatio-temporal data fusion technology; and according to the optimized pollution diffusion result, an emergency response scheme is provided for an environment management department through a decision support system. According to the method, a spatio-temporal data fusion technology is adopted, and the prediction result of the pollution diffusion model is optimized through weighted averaging of multi-source monitoring data.

The invention provides an agricultural problem prediction and decision support system based on a large model, and the system comprises a data collection and preprocessing module which is used for collecting data resources of a plurality of channels, and carrying out the data cleaning, data conversion and data storage through a distributed calculation framework; the intelligent agent large model module comprises a large model for agricultural problem prediction, and the large model is trained by adopting historical data; the policy response module is used for extracting information in the policy file, converting the information into structured data and providing a calling interface; and the decision support module is used for generating specific farming activity suggestions based on the structured data in the policy response module and the prediction result of the agent large model module. The system provided by the invention can dynamically provide suggestions in response to farming activities according to crop growth conditions.

The invention discloses a ship equipment maintenance support system and method based on artificial intelligence, and the system collects the operation data of equipment in real time through a data collection and integration module, and digitalizes and integrates the historical maintenance data into a unified database; a fault diagnosis model is constructed through the model diagnosis module to carry out fault diagnosis and output a diagnosis result; an optimal maintenance scheme is provided through a maintenance decision support system according to a fault diagnosis result in combination with multi-dimensional data; real-time monitoring and trend analysis are carried out on operation data of the equipment through the fault prediction module, potential faults of the equipment are predicted, and a maintenance plan is made in advance. According to the invention, equipment operation data is collected in real time through a sensor, accurate fault diagnosis and prediction are realized in combination with a deep learning algorithm, an optimal maintenance scheme is formulated in combination with multi-dimensional data, and a maintenance plan is dynamically adjusted.

The invention provides an energy storage power station optimization operation mode decision-making method and system, and relates to the technical field of energy storage power station optimizing.A hybrid prediction model is constructed to realize high-precision decomposition prediction of power load, and meanwhile, the internal resistance of a battery is estimated in real time by adopting a recursive least square method; and the battery capacity and internal resistance parameters are dynamically corrected in combination with a temperature compensation mechanism. Through health state multi-index fusion evaluation, self-adaptive distribution of charging and discharging power is achieved, and compared with the prior art, the problem that a traditional static model cannot adapt to complex environment changes is solved. The battery capacity fading risk can be predicted in advance by introducing a double-compensation mechanism of an environmental influence index and an electric power influence index. According to the scheme, the response speed and economical efficiency of energy storage in a high fluctuation load scene are remarkably improved, and a reliable dynamic optimization decision support system is provided.

The invention discloses a motion intervention multi-dimensional decision support system and method for CIPN remission, and relates to the technical field of decision support, and the system comprises an intervention effect prediction unit which is used for collecting the multi-dimensional data of a patient, constructing a patient simulation model, and carrying out the simulation of different motion intervention schemes through the Markov decision process theory, establishing an intervention effect prediction model based on the simulation result; an exercise intervention recommendation unit; an encouraging and exciting unit; the optimal motion recommendation unit is used for updating a patient simulation model according to the constructed encouraging and incentive mechanism, analyzing a causal relationship between a motion effect and neural restoration through a hypergraph gradient causal discovery algorithm, and screening out an optimal motion recommendation scheme; and a visualization unit. According to the invention, a personalized exercise intervention scheme can be customized according to the individual characteristics and physiological status of the patient, and the effects of different intervention strategies are predicted, so that the pertinence and effectiveness of exercise treatment are improved.

The invention relates to the technical field of disaster relief path planning, in particular to a disaster relief path planning and mode optimization method and system based on a large model, and the method comprises the following steps: collecting and preprocessing data; constructing a prediction model; planning and optimizing a path; a decision support system; real-time adjustment and feedback are carried out; the method has the beneficial effects that the disaster condition and the influence thereof can be quickly analyzed by using big data and a machine learning technology, so that the time required by traditional rescue path planning is greatly shortened, and the emergency response speed is improved. Within golden 72 hours of post-disaster rescue, each second is crucial. The system can provide an optimal rescue path and a resource allocation scheme in an extremely short time, help rescue workers to quickly arrive at a disaster area, and save more lives.

A decision support system and a computer-implemented method of enterprise decision support include a data consolidation module, domain-specific machine learning models, an enterprise decision intelligence model, and a user interface layer. The data consolidation module collects data from both internal and external data sources. The domain-specific machine learning models generate decisions based on the collected data. The enterprise decision intelligence model integrates real-time trends and the decisions to provide context-aware recommendations. The enterprise decision intelligence model maintains a decision graph that connects decision variables of the domain-specific machine learning models in a causal relationship, with the domain-specific machine learning models interacting as an interconnected network. The decisions are influenced by affects of decision variables from other domain-specific machine learning models. The user interface layer facilitates interactive decision-making by way of the enterprise decision intelligence model and visualizing the recommendations.

The invention discloses an intelligent decision support system and method for grassroots social governance based on multi-modal fusion. The method comprises the following steps: S1, acquiring and preprocessing multi-modal data in the field of grassroots social governance; s2, constructing a heterogeneous graph structure according to the data type and the treatment subject category; s3, extracting single-modal features respectively and generating cross-modal dynamic association features; s4, performing collaborative optimization on the heterogeneous graph structure and the cross-modal fusion parameters by adopting a flying fox optimization algorithm; s5, analyzing data in real time according to the optimized heterogeneous graph, and generating potential risk early warning, event trend prediction and event association deduction information; s6, pushing an auxiliary decision-making scheme and a disposal strategy in real time; and S7, continuously optimizing the heterogeneous graph structure by using a feedback result. The decision-making efficiency and accuracy of grassroots social governance are effectively improved.

The invention belongs to the field of medical rehabilitation, and particularly discloses a diabetic foot ulcer patient non-load exercise rehabilitation exercise decision support system which comprises an evaluation grading module used for obtaining multi-mode ulcer data of a patient and scientifically grading diabetic foot ulcers of the patient in combination with a layering algorithm to obtain a grading result; the scheme generation module is used for matching a non-load exercise scheme suitable for the patient in a database according to the grading result and generating an initial exercise scheme; the exercise feedback module is used for sending the initial exercise scheme to a patient terminal, acquiring the exercise completion condition of the patient and performing personalized adjustment on the exercise intensity and frequency through a dynamic regulation and control mechanism; according to the method, static ulcer features and dynamic physiological features are combined through a multi-modal data fusion technology, automatic grading is realized based on an AI algorithm, and scientificity and consistency of grading evaluation are remarkably improved.

The present invention relates to a cognitive computing-assisted clinical decision support system designed to enable personalized neurological rehabilitation. The system acquires structured user data across clinical, anatomical, radiological, etiological, pathological, and rehabilitation domains to create individualized profiles. These profiles are mapped against a repository of historical cases using analog matching and similarity scoring to generate stratified, evidence-based rehabilitation recommendations. Real-time monitoring of rehabilitation progress is performed using global recovery and function outcome indicators, allowing for dynamic adjustment of treatment plans. Clinician intervention modules ensure safety, interpretability, and context-aware customization. The system incorporates a continuous feedback mechanism to refine future predictions and recommendations, making it increasingly adaptive over time. The invention improves rehabilitation outcome prediction accuracy, reduces recovery variability, and optimizes functional outcomes by transforming static rehabilitation models into intelligent, responsive, and personalized care pathways.

The invention provides a medical information retrieval enhancement method and device based on a large model and related equipment, and relates to the technical field of artificial intelligence. The method comprises the following steps: in response to a medical information query request, executing double-path joint retrieval of keywords and semantics in a pre-constructed medical knowledge base to obtain a candidate data set containing at least one medical information query result, the medical knowledge base is a database which analyzes the multi-modal medical data based on a multi-modal medical data deep analysis large model and is constructed according to the analyzed medical text data; and adopting a plurality of sorting tools to resort the medical information query results in the candidate data set, and performing fusion processing on a plurality of resorting results obtained by resorting to obtain a medical information query result after retrieval enhancement. According to the method and the device, the precision improvement and response efficiency optimization of medical knowledge retrieval can be realized, a high-credibility knowledge enhancement service is provided for a clinical decision support system, and the method and the device have important application value.

Described herein is a platform and supported graphical user interface (GUI) decision-making tools for use by medical practitioners and / or their patients, e.g., to aide in the process of making decisions about a course of cancer treatment and / or to track treatment and / or the progress of a disease.

The invention relates to the technical field of project site selection decision support, in particular to an intelligent project site selection decision support system and method based on ArcEngine. The method specifically comprises the following steps: integrating site selection data, and realizing unified management of geographic information and coordinate system conversion by utilizing ArcEngine; missing values and abnormal values are processed by adopting multilayer perception interpolation, Bayesian interpolation, adaptive density clustering and random forest correction technologies, and data reliability is ensured through a standard verification code mechanism; building a comprehensive evaluation model of resource accessibility, environmental suitability and cost control, generating an optimal solution and simulating a long-term industrial evolution trend; through industrial chain modeling, multi-agent game analysis and a network optimization algorithm, node benefits and a synergistic effect are quantified, and supply chain dynamic scheduling is optimized in combination with reinforcement learning. According to the method, site selection scientificity and industrial cooperation benefits are remarkably improved, and the method is suitable for complex site selection requirements of the modern coal chemical industry heavy asset industry.

The invention discloses a river health evaluation method suitable for northern water-deficient cities, and relates to the technical field of environmental science and engineering.The system comprises the following steps that the pH value, the dissolved oxygen concentration and the total electrolyte amount are monitored through a pH sensor, a dissolved oxygen sensor and a conductivity sensor, the river water surface, the river channel and the surrounding environment are monitored through a GIS, and the river health evaluation result is obtained. Comprising a land utilization type, a vegetation coverage rate and a river bank erosion condition, collecting environmental problems uploaded by the public through a mobile phone APP, and investigating the satisfaction degree of the public to the environment; a river health evaluation system is constructed, the trend of river health is predicted, and potential factors affecting river health are identified; and according to a prediction result, configuring a decision support system to assist in making a management strategy, and regularly reviewing and evaluating system effectiveness. The river health evaluation method and management system are continuously optimized by dynamically adjusting river management and protection work according to a dynamic adjustment result, and the river ecological system of northern water-deficient cities is effectively protected for a long time.

A clinical decision support system and method for patients with pulmonary arterial hypertension is disclosed herein. The system may comprise a processor to process instructions to execute one or more pulmonary arterial hypertension risk algorithms configured to generate a risk score value associated with a patient surviving within a given time period. The system may comprise a means for input and output, wherein input variable data may be received and a set of risk score values may be displayed. A method for operating the clinical decision support system is also disclosed.

The invention discloses an industry and trade enterprise public health service resource optimization decision support system, and relates to the technical field of public health management, and the system comprises a data perception and access layer, a semantic fusion and modeling layer, a knowledge graph and risk calculation layer, an intelligent early warning and decision layer, and a feedback optimization closed loop. Multi-source heterogeneous data are collected through a protocol adapter, a unified data view is constructed through industry and trade public health field ontology semantic fusion, a space-time knowledge graph is dynamically generated, risks are deduced in combination with an improved SEIR space-time propagation model and a Monte Carlo method, visual early warning and interactive decision making are achieved based on digital twinborn bodies, and the method is applied to the field of industry and trade public health. According to the method, data islands can be broken, accurate advanced early warning of public health risks is realized, resource allocation is optimized, and the method is suitable for industry and trade enterprises with dense personnel such as machine manufacturing and electronic processing.

The invention relates to the technical field of intelligent medical multi-modal data analysis and nursing aid decision making, in particular to a neurology nursing risk dynamic early warning and intelligent decision support system. Comprising the steps that a data acquisition module acquires a physiological time sequence and nursing logs to obtain a multi-modal original data set; the feature extraction module performs nonlinear dynamic analysis on the physiological data to generate a state manifold vector, and performs semantic embedding on a log to generate an intervention state tensor containing spatio-temporal features; the causal reasoning module constructs a dynamic causal map, maps vectors and tensors as nodes, and adjusts transmission weights by using the tensors to suppress non-pathological fluctuations; the decision-making module calculates dynamic risks and difference values based on atlas anti-fact deduction, and generates graded early warning and an optimal nursing path; the feedback module updates the atlas parameters according to the processing result. According to the system, the problem of high-frequency false alarm caused by daily nursing actions of traditional monitoring equipment is effectively solved, the alarm fatigue of medical staff is relieved, and focusing on real critical events is ensured.

The invention discloses an infectious disease intelligent prediction and decision support system and method based on multi-source data, and relates to the technical field of infectious disease intelligent prediction, and the system comprises a prediction module and a decision module. Predicting the outbreak time period, the propagation scale and the propagation path of the target infectious disease; and the decision module is used for making a decision according to the predicted outbreak time period, the propagation scale and the propagation path of the target infectious disease. According to the method, the limitation of a traditional single data source is broken through by multi-source data integration, the multi-dimensional characteristics of infectious disease transmission can be comprehensively captured, and the accuracy and reliability of prediction are remarkably improved. The prevention and control strategy can be dynamically adjusted according to the real-time change of the epidemic situation, resource waste and excessive prevention and control are avoided, and the utilization efficiency of public health resources is improved.

The invention relates to a large-model-driven data governance decision support system, which belongs to the technical field of intelligent manufacturing and industrial automation control, and comprises a physical field evolution modeling module for establishing a real-time global stress field based on a collected multi-modal state data set and outputting a time sequence stress field data set; the dynamic drift evaluation module is used for receiving the time-sequenced stress field data set, comparing the time-sequenced stress field data set with a preset reference stress field data set and calculating a drift risk level; the defect evolution prediction module is used for inputting the time sequence stress field data set and the collected defect correlation characteristic data set into a fusion prediction model to generate a latent defect risk index; and the closed-loop self-adaptive control module is used for generating self-adaptive control gain by integrating the drift risk level and the latent defect risk index, calculating the deviation between the time sequence and the reference stress field data set, and generating a control instruction for adjusting production parameters based on the gain and the deviation. According to the method, the crossing from discrete sensing data to a continuous dynamic physical field model is realized, and a high-fidelity data basis is provided for decision making.

The invention relates to a building engineering green low-carbon construction evaluation and decision support system, in particular to the field of building engineering, and aims to construct a space-time atlas to comprehensively characterize a construction process by integrating an Internet of Things sensor, a building information model and meteorological data, and accurately estimate a future carbon emission trend by using a dynamic prediction mechanism. And key influence factors and contribution degrees thereof are identified through factor analysis, and finally real-time optimization suggestions are provided by virtue of a visual interface, so that scientific decision support for green low-carbon construction is realized, carbon emission is effectively reduced, the resource utilization efficiency is improved, and the environmental sustainability of constructional engineering is promoted.

The invention provides a GIS-based intelligent livestock breeding full-chain visualization method and system, and the method comprises the steps: collecting the basic information of a farm, livestock information, production process data and other multi-source data through a standardized data interface, and carrying out the correlation mapping of business data and GIS spatial data based on a geographic coordinate system; constructing a three-dimensional data model containing spatial information, attribute information and relation information; three-dimensional scene rendering is realized by utilizing a WebGL technology, a macroscopic visual engine, a mesoscopic visual engine and a microcosmic visual engine are constructed, and multi-scale visual display is realized; constructing a full-life-cycle data model of the livestock products, and realizing visual display of full-chain traceability of the livestock products; based on a visual data analysis method, a decision support system for industrial layout analysis, breeding scale prediction, epidemic disease risk assessment and market supply and demand analysis is constructed. Visual management of the whole industry chain of livestock breeding is achieved, the industry management efficiency is improved, the epidemic disease risk prevention and control capacity is enhanced, and data support is provided for decision making of the livestock industry.

The invention relates to the technical field of hepatic fibrosis treatment strategy optimization, in particular to a hepatic fibrosis treatment strategy optimization system and method based on deep reinforcement learning. The strategy generation module is in communication connection with the database module; the simulation module is in communication connection with the strategy generation module and is used for simulating the application effect of the treatment strategy through a simulation system; the evaluation module is in communication connection with the simulation module and is used for determining an effect value of treatment strategy application through the simulation module and transmitting the effect value to the strategy generation module; the scheme recommendation module is in communication connection with the strategy generation module and is used for recommending the treatment strategy to a doctor; the invention constructs a comprehensive, dynamic and intelligent hepatic fibrosis treatment decision support system. The system not only can process high-dimensional and non-linear medical data, but also can capture dynamic characteristics of disease progression, so that a major span from static decision to dynamic optimization is realized, and the overall efficiency and expandability of the system are greatly improved.

The invention relates to the technical field of new energy power generation and control, and discloses a new energy station operation decision support system based on multi-objective optimization, which comprises a multi-source state space reconstruction module, a Riemannian manifold geometry engine module, a self-adaptive inertia Hamiltonian evolution module and a symplectic geometric integral and instruction mapping module. The system collects station data to construct a dimensionless state space, constructs a Riemannian metric tensor according to physical constraints to reconstruct a Riemannian manifold space, and calculates a geometric connection strength factor; the factor is used to adaptively modulate a virtual inertia matrix, and a dissipative Hamiltonian kinetic model is constructed; and finally, solving the steady-state generalized coordinates through a pungent-preserving numerical integration algorithm, and decoding the steady-state generalized coordinates into an equipment control instruction. According to the method, physical constraints are converted into geometric measurements, and a self-adaptive inertia mechanism is introduced, so that the problem of optimization convergence under multivariable strong constraints is solved, and the safety and accuracy of a control instruction are ensured.

The invention relates to the technical field of medical intelligent decision, in particular to an operating room nursing key node decision support system, which comprises the following steps: establishing a nursing operation reference containing a standard execution sequence and a parameter interval based on historical cases; by integrating discrete operation and continuous monitoring data in a real-time operation, a nursing process event chain with unified semantics is formed. The system compares the chain of events with an execution criterion to identify potentially risky segments and automatically extracts, for each segment, a complete operating room multi-dimensional state snapshot within a specific time window before and after its occurrence. And dynamically constructing a cause deduction network according to the state snapshots, and speculating cause chains possibly causing risks by traversing the network. And integrating all deviation information and cause analysis results, and generating a structured decision intervention prompt. According to the invention, deepening from automatic risk identification to root intelligent diagnosis can be realized, and accurate and efficient decision support is provided for operating room nursing.