367 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 the technical field of skin disease diagnosis, and discloses an AI auxiliary decision support system for skin disease diagnosis, which integrates an image acquisition module, an image analysis module and a comprehensive analysis and diagnosis decision module. A high-definition image of the skin of a patient is obtained through high-precision skin photographing equipment, and a training data set is formed by combining basic information of the patient. A deep learning algorithm is utilized to train a skin disease recognition model and an illness state evaluation model, and accurate recognition of skin disease types and objective evaluation of severity are realized. And the comprehensive analysis module fuses output of the two models, constructs a fusion decision model and provides comprehensive diagnosis suggestions. And the diagnosis decision module is used for generating a disease type judgment and illness state evaluation report based on the trained model in combination with real-time image data so as to effectively assist doctors in making decisions. The accuracy, the efficiency and the popularization degree of skin disease diagnosis are improved, and complex and changeable diagnosis requirements in clinical practice are met.

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.

Described are systems and methods for artificial intelligence (AI)-based clinical decision support. Systems can include a platform configured with a user input processing module, a context matching module, a retrieval-augmented generation (RAG) module, and an output generation module. Outputs of the platform can include a differential diagnosis, an assessment and treatment plan, or a clinical reference. The platform can further include an AI-copilot module and an AI-notebook module.

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.

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 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 discloses a campus smart canteen management platform based on AI and IOT. The campus intelligent canteen management platform comprises a global sensing system, an intelligent decision support system, a credible tracing system, a safety and supervision system and a user experience optimization system. The global sensing system is used for collecting multi-modal data and carrying out data fusion and preprocessing on the multi-modal data; the intelligent decision support system is used for performing multifunctional analysis on the fused and preprocessed multi-modal data to obtain a management decision; the credible traceability system is used for recording digital traceability information, storing reserved food samples and detecting pesticide residues in real time; the safety and supervision system is used for providing food reserved sample management, food quality event monitoring and analysis, supervision record management and parent applet functions; and the user experience optimization system is used for providing health inspection, health monitoring and personalized suggestions, identity verification and payment modes. In this way, the overall level of canteen management is improved.

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.

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

The invention discloses a 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 an intelligent electrical equipment maintenance task decision support system and method, particularly relates to the field of motor maintenance, is used for solving the problem of running-in mismatching of new and old parts, and comprises the following steps: collecting multi-dimensional performance parameters of the new and old parts, constructing a performance feature database, and detailedly describing key parameter differences; a personalized running-in curve is generated by using a support vector machine regression algorithm, the load change rate, the rotating speed adjustment step length and the running-in time are accurately set, and vibration and current fluctuation caused by initial mismatching of new and old parts are avoided; by monitoring the temperature, the vibration spectrum and the current fluctuation of the equipment in real time, the load and the rotating speed are automatically adjusted, and the running-in process is ensured to be stable; the frequency spectrum sharpness index and the time sequence entropy difference index are combined, running-in state classification analysis is carried out, and potential anomalies are recognized in time; in an ambiguous state, running-in is optimized by adjusting PID parameters, it is ensured that new and old parts are gradually matched, the operation stability of equipment is improved, and faults and damage caused by poor running-in for a long time are avoided.

The invention provides an unknown environment adaptive navigation control system based on deep learning, and aims to improve the autonomous navigation capability of a machine in a complex and unknown environment. According to the system, an advanced sensing module is adopted, multi-sensor data are combined, and a dynamic environment is quickly and accurately sensed through a deep learning algorithm. The online environment modeling mechanism can update the environment information in real time without depending on a pre-constructed map. And by utilizing a reinforcement learning algorithm, the system can autonomously learn and optimize path planning, and meanwhile, energy consumption, time efficiency and safety are considered. An innovative multi-mode obstacle avoidance strategy is adopted, and obstacle avoidance behaviors are dynamically adjusted according to environment complexity and machine states. The intelligent decision support system combines a decision tree and fuzzy logic, and provides fast and accurate decision ability. The adaptive motion control algorithm optimizes path tracking performance and adapts to dynamic characteristics and environmental conditions of a machine. In addition, the collaborative job mechanism supports efficient communication and task collaboration in a multi-machine system.

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 discloses a purchase management decision support system driven by an AI large model, and particularly relates to the technical field of intelligent purchase management. A data acquisition module obtains historical and current purchase, market price, inventory, sales and customer feedback data of commodities; the clustering analysis module divides purchasing groups based on inventory states and market prices; the self-portrait construction module generates a comprehensive self-portrait through client feedback data; the market analysis module analyzes sales data and predicts future demand changes; the optimization adjustment module optimizes the purchase quantity in combination with demand prediction, customer preferences and purchase group data, so that the accuracy and efficiency of purchase decision making are improved; through intelligent data processing and model prediction, the limitation of dependence on experience and static rules in traditional purchase management is avoided, fluctuation of market demands can be responded in real time, the purchase quantity is ensured to be matched with the market demands, inventory overstock and shortage phenomena are avoided, and customer satisfaction and market competitiveness are improved.

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 invention discloses a complex pavement damage condition detection method integrated with an unmanned aerial vehicle. The method specifically comprises the following steps: S1, an unmanned aerial vehicle platform and a sensor system; s2, image preprocessing and feature extraction; the invention relates to the technical field of intelligent traffic. According to the complex road surface damage condition detection method and device fused with the unmanned aerial vehicle, the unmanned aerial vehicle is high in inspection speed and wide in coverage range, the detection period is greatly shortened, the detection cost is reduced, and artificial intelligence technologies such as deep learning and machine learning are combined, so that accurate recognition and classification of road surface damage conditions are realized, and the detection precision is improved; a comprehensive detection report and three-dimensional visual analysis are provided, a scientific basis is provided for road maintenance decision, traffic safety management and urban planning, road maintenance is promoted to develop towards the intelligent and precise direction, the road maintenance efficiency and quality are improved, traffic safety is guaranteed, an interactive decision support system is developed, and the development of the road maintenance system is facilitated. And the user operation convenience and the decision-making efficiency are improved.

The invention relates to an automatic order dispatching method based on civil aviation airport aircraft ground support, which adopts a dynamic causal relationship identification module based on a Bayesian network, and combines a context awareness module of a convolutional neural network (CNN) and a long short-term memory (LSTM) network to improve prediction accuracy and task allocation efficiency. An improved multi-objective optimization algorithm is combined with a genetic algorithm and a particle swarm optimization algorithm, and a dynamic weight adjustment mechanism is introduced, so that self-adaptive optimization configuration of resources is realized. A path planning algorithm combining multi-level A * search and deep reinforcement learning can adjust a path in real time and dynamically avoid obstacles, and the system enhances real-time response capability and decision accuracy through a self-learning algorithm based on a depth deterministic policy gradient (DDPG) and a comprehensive decision support system based on a graph neural network (GNN). According to the method, the operation efficiency, the safety and the emergency response capability of the airport ground guarantee service are 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.