66 results about "Decision support tools" patented technology

Systems and methods for providing a space launch service platform (SLSP) that integrates artificial intelligence and data analytics to support launch operations. The SLSP may connect to multiple data sources, collect data, and standardize the collected data according to regulatory and operational standards. The SLSP may evaluate launch safety and risks using standardized data and generate a situational analysis for decision-making. The SLSP may provide graphical overlays and decision-support tools to highlight optimal launch windows and potential risks.

The invention discloses a shale oil reservoir geology-engineering double-dessert compressibility comprehensive evaluation method, and relates to the technical field of petroleum and natural gas yield increase transformation and geotechnical engineering, in particular to a shale oil reservoir geology-engineering double-dessert compressibility comprehensive evaluation method, which comprises the following steps of performing normalization processing on a logging basic parameter standard deviation; geological dessert parameters are established by calculating the weight of each factor; influence factors influencing the longitudinal expansion capacity of the hydraulic fracture of the target reservoir are obtained; constructing a geological-engineering double-dessert compressibility comprehensive evaluation model of three types of lithofacies'reservoir performance-transformation potential 'synergistic mechanisms; and establishing a geological-engineering double-dessert compressibility kernel density estimation map, and dividing the reservoir compressibility on the premise of adaptive bandwidth. According to the method, the operability and prediction precision of an evaluation result are remarkably improved, and an innovative decision support tool is provided for horizon optimization and scheme design of shale oil reservoir fracturing transformation.

The invention discloses a network space map surveying and mapping method and system based on multi-source data fusion, and the method comprises the steps: obtaining a multi-source data set in a unified format based on network flow data, equipment information data and geographic position data; obtaining a network asset entity and an incidence relation graph thereof through entity identification and correlation analysis based on the multi-source data set with the uniform format; obtaining a network space three-dimensional map model through three-dimensional space mapping and visual rendering based on the network asset entity and the association relationship map thereof; based on the network space three-dimensional map model, performing dynamic updating according to the accessed real-time data flow to obtain real-time network space situation data; and obtaining a network security risk assessment result through anomaly detection and threat identification based on the real-time network space situation data. According to the invention, visual display and security situation awareness of the network space are realized, and a brand new decision support tool is provided for network security management.

The invention relates to an ore control element extraction and weight determination method based on a knowledge graph, and the method comprises the following steps: constructing a geological mineral knowledge graph: collecting geological text data, extracting entities and semantic relationships in the geological text data, and constructing the geological mineral knowledge graph; network analysis and simplification: utilizing a community clustering algorithm to divide an ore deposit, and combing and simplifying a complex geological knowledge map in combination with a modularity algorithm process; subgraph generation: constructing a series of subgraphs according to community categories of nodes, and forming an information set which completely and structurally represents specific geologic features and metallogenic conditions; weight calculation and integration: quantifying the indicating significance of different geological entities on ore deposit prospecting through an entity-relation weight result, and defining the weight of ore control elements; and element screening: sorting according to the weight values, and rapidly screening out geological entities which have important influence on ore deposit mineralization as ore control elements. According to the invention, an intelligent and automatic decision support tool is provided for geological prospecting work.

The invention discloses a public facility reachability evaluation method and system based on geographic multiple agents, and belongs to the field of urban calculation and geographic information. According to the method, a multi-agent system comprising a user agent and a facility agent is firstly constructed, and then a dynamic utility function combined with an evolutionary game theory is introduced to simulate the dynamic decision and congestion avoidance behavior of a user; and user strategies are balanced through multiple rounds of iterative evolution, so that a macroscopic reachability pattern emerging from a microcosmic game is obtained. According to the method, starting from the microscopic individual level, by simulating the dynamic game process of a large number of agents, the defects of a static calculation model are overcome, and dynamic evaluation and predictive simulation of reachability of public facilities are achieved. Compared with the prior art, the evaluation result is more accurate, dynamic feedback of the real world can be reflected, and a scientific decision support tool is provided for urban planning.

The invention discloses an agricultural product pesticide residue detection and analysis system and method based on big data, and relates to the field of hyperspectral analysis, and the method comprises the steps: obtaining hyperspectral data, environmental data and pesticide application parameters of the surface of a target agricultural product, space pixel coordinates in the hyperspectral data serve as sampling points, space-time alignment is conducted on the sampling points, the environment data and the pesticide application parameters, and a space-time correlated spectral feature matrix is generated; calculating the spectral gradient and noise disturbance of the quantum optimal feature set through a spectral gradient back propagation method, and generating an antagonism enhanced sample and target domain feature distribution; and associating the pesticide residue detection value and the standard exceeding early warning signal with the geographic coordinates to generate a pesticide residue distribution map and a traceability analysis result. According to the method, high-sensitivity detection of the pesticide residue concentration is realized through quantum feature optimization, adversarial domain adaptation training and space-time dynamic fusion technologies, the traceability matching accuracy is improved, and an accurate and efficient decision support tool is provided for agricultural safety supervision.

The invention discloses a corn crossbreeding intelligent matching system and method based on artificial intelligence, and the method comprises the steps: collecting molecular marker genotype data and phenotype data of candidate parents, calculating the genetic distance between the parents, and constructing a genetic distance matrix; analyzing the phenotypic complementarity degree of the parent pair, and calculating a phenotypic complementarity index; constructing a heterosis prediction model based on genetic distance and phenotype complementarity, and predicting the yield heterosis value of the candidate hybrid combination; evaluating the environmental adaptability and resistance comprehensive score of the candidate combination; and adopting a Pareto multi-objective optimization strategy to screen an optimal matching scheme. According to the method, molecular marker data and phenotype data are integrated, a heterosis prediction model considering a genetic distance nonlinear effect is constructed, collaborative optimization of multiple breeding targets such as yield, resistance and environmental adaptability is achieved, the prediction accuracy is improved by about 20% compared with that of a traditional method, and the method is suitable for large-scale popularization and application. And an intelligent and precise decision support tool is provided for corn crossbreeding.

Decision support tool for real estate selection having at least one computer system and user interface. A content database of real estate properties includes spatial, temporal, material, risk, and financial variables for a set of at least one real estate property from the real estate properties, wherein the at least one real estate property can include a set of at least one real estate property with at least one second real estate property. At least one software program is disposed on at least one computer system designed to calculate user vectors wherein at least one machine learning program is designed to build the set of at least one real estate property from the real estate properties. The user interface is designed to receive inputs and present outputs wherein users may create user sets of at least one selected real estate property from the real estate properties.

The invention discloses a railway construction management multi-objective equilibrium optimization method based on an improved MOPSO algorithm, and the method comprises the steps: 1) constructing four objective function optimization models on the premise of meeting the basic constraint conditions of a railway construction project, and enabling each objective function optimization model to correspond to an optimization objective; 2) according to the four objective function optimization models constructed in the step 1), constructing a multi-objective equilibrium optimization model; and 3) performing iterative solution on the multi-objective equilibrium optimization model constructed in the step 2) by using an improved multi-objective particle swarm optimization MOPSO algorithm to obtain an optimal solution set of the project under equilibrium in four aspects of construction period, resource, cost and safety. According to the method, the convergence speed, the diversity maintenance capability and the constraint processing flexibility of the MOPSO algorithm can be remarkably improved while the four optimization objectives of the construction period, the resources, the cost and the safety of the railway construction project are effectively considered, and an efficient and reliable multi-objective comprehensive decision support tool is provided for railway construction management.

The embodiment of the invention provides a self-adaptive pollution diffusion forecasting method and system based on FLEXPART-WRF, and is applied to the technical field of pollution detection. The method comprises the following steps: generating a self-adaptive WRF simulation grid of a pollution accident; determining a research area, and performing three-dimensional meteorological field simulation on the research area by using the downscaling meteorological diagnosis model to obtain refined three-dimensional meteorological field data; utilizing an FLEXPART-WRF model to determine a simulated migration track of the pollutants, simulated particle concentration of a self-adaptive WRF simulation grid and simulated dry-wet sedimentation; and determining simulated weather forecast and simulated pollutant diffusion forecast. In this way, through adaptive grid generation, accurate meteorological field simulation and real-time prediction of pollutant diffusion, accurate pollutant diffusion information, efficient automatic simulation and fine pollution source analysis can be provided, and a quantitative and practical decision support tool is provided for environment management.

An example provides a method, including: obtaining, using one or more sensor systems, sensor data for one or more persons in an environment comprising machinery; analyzing, using a processor, the sensor data using a trained model to identify the one or more persons and an associated time series of actions; determining, using the associated time series of actions, if the one or more persons are engaging in behavior indicative of an unsafe practice; and thereafter presenting safety information to a visual display system, connected software system or other decision support tool.

The invention provides an intelligent cargo warehouse management system and management method. The intelligent cargo warehouse management system comprises a data acquisition module, a cargo pick-and-place module, a data transmission and storage module and an intelligent control module. The data acquisition module is used for reading cargo information; the goods taking and placing module is used for taking and placing goods on the goods shelf; and the intelligent control module is used for constructing a warehouse three-dimensional virtual scene, and carrying out optimal path goods taking on the goods in combination with the goods taking and placing module. High automation of the goods warehousing and ex-warehouse process is achieved, and the requirement for manual intervention is reduced. The intelligent control module is deployed in the local server, and the goods storage and taking operation can be automatically executed only by inputting a goods taking order number or issuing a goods storage command, so that the operation process is simplified, and the working efficiency is improved. The system constructs a three-dimensional virtual scene by using a panoramic jigsaw algorithm, and displays the inventory distribution condition in combination with a manifold visualization technology, thereby providing a visual decision support tool for managers, and enhancing the visual management level of the storage state.

The invention provides an urban green infrastructure comprehensive benefit applicability evaluation method based on subjective and objective weights. The urban green infrastructure comprehensive benefit applicability evaluation method comprises the steps of establishing a basic evaluation system, screening indexes, calculating index weights, calculating quantitative indexes, calculating qualitative indexes, performing grading evaluation, performing standardized score assignment and performing comprehensive evaluation. The basic evaluation system is of a three-layer structure and covers four criteria of runoff regulation and storage benefits, environmental benefits, economic benefits and social benefits and 19 indexes; a subjective weight is determined through an analytic hierarchy process, an objective weight is determined through an entropy weight method, and a comprehensive weight is obtained in combination with a D-S evidence theory fusion method; quantitative indexes are calculated through methods such as hydrological simulation, and qualitative indexes are rated by public; the indexes are unified into dimensionless scores through standardized score assignment, and finally an evaluation result is obtained through weighted summation. According to the method, the applicability quantitative evaluation of matching of the typical green infrastructure and the site condition construction target is realized, and a scientific and quantitative decision support tool is provided for sponge city planning.

The invention discloses a multi-energy complementary system site selection method based on dynamic weight and geographic information system (GIS) cooperation, and belongs to the technical field of renewable energy planning. According to the method, a technical framework of'multi-dimensional data acquisition-dynamic weight optimization-collaborative decision verification 'is constructed, so that the core problems of weight staticizing, space-time splitting, insufficient robustness and the like in a traditional site selection method are solved. According to the technical scheme, an analytic hierarchy process and a space-time dynamic correction mechanism are innovatively combined, and self-adaptive weight optimization of multi-dimensional criteria such as climate resources, power grid access and environmental constraints is achieved; through fusion of spatial clustering analysis and Monte Carlo simulation, a site selection verification system considering geographical proximity and system collaboration is established. According to the method, the dynamic response capability of site selection decision to policy change and technical evolution is remarkably improved, the scientificity and implementation reliability of a multi-energy complementary system planning scheme are ensured, and an intelligent decision support tool is provided for efficient deployment of renewable energy sources in a novel power system.

The present invention discloses a method and system for predicting the risk of high wind meteorological disasters based on a quantitative model. The method comprises obtaining historical data, including geographic location, environmental data, meteorological data, and disaster data. The geographic location includes latitude, longitude, and altitude. The environmental data includes vegetation index and terrain type. The meteorological data includes wind speed, air pressure, and temperature. The method classifies the geographic location and disaster data to obtain risk areas. First data is obtained based on the risk areas. Second data is obtained based on the correlation between the environmental data and the disaster data. The method fuses the first and second data to obtain a dynamic risk score. A risk prediction model is established based on the risk score, meteorological data, and disaster data. The method inputs the data to be predicted into the risk prediction model to obtain a prediction result. The present invention provides a more scientific and efficient decision support tool for risk prediction and management of high wind meteorological disasters through a quantitative model.

The invention provides a multimodal transport logistics scheme recommendation algorithm based on customer demands, and the algorithm employs a feature extraction module which is used for converting original data into a model processable form, extracting features related to a logistics transport scheme from the original data, and transmitting the features to a database; a composite neural network model module fusing an embedded layer, an attention mechanism, a cross network and a deep network is constructed, meanwhile, the module comprehensively considers factors such as comprehensive score, diversity and deduplication of model prediction so as to ensure high quality and practicability of recommendation results, a weighted Huber loss function is introduced, and the recommendation accuracy is improved. The prediction performance of transportation cost and time is optimized at the same time, sorting loss (ListMLE loss) is introduced to optimize the sorting performance of the model, the model accurately recognizes customer demands, a personalized scheme is generated, an efficient decision support tool is provided for coastal port logistics enterprises, the service quality is improved, the operation cost is reduced, and the competitiveness is enhanced. And intelligent logistics technology application and port logistics intelligent upgrading are promoted.

The invention discloses a policy information recommendation method, which solves the problem of low matching degree between policy recommendation and enterprise requirements in the prior art by combining the information extraction capability of a closed source large language model and the semantic understanding capability of an open source large language model, and achieves the technical effects of improving the policy recommendation accuracy and the user experience. Complex relations between enterprises and policies can be deeply understood and matched, the correlation and practicability of policy recommendation are improved, and an efficient and intelligent decision support tool is provided for science and technology enterprises and policy formulators.

The invention discloses an energy-saving city design-oriented building performance proxy model construction and application method, which comprises the following steps of: establishing a parameterized city block model, and defining city form design variables and building performance evaluation indexes; based on the parameterized city block model, generating a training data set comprising a plurality of groups of city form design variables and building performance evaluation indexes corresponding to the city form design variables through building performance simulation software; constructing and training a deep neural network model by using the training data set, so that the deep neural network model learns a nonlinear mapping relationship between urban form design variables and building performance evaluation indexes to obtain a building performance proxy model; and applying the trained building performance agent model to predict a corresponding building performance evaluation index according to the input urban form design variable. According to the method, real-time prediction of the building performance is realized through the constructed agent model, and an efficient and reliable decision support tool is provided for early scheme evaluation and optimization of energy-saving city design.

The invention provides a configuration optimization system and method for a water and soil conservation type protection forest, and relates to the technical field of protection forest configuration, and the system comprises an information collection module which is used for obtaining multi-source data of a target area; the information matching module is used for screening a candidate tree species set; the grid generation module is used for dividing a region into a plurality of evaluation unit grids with geographic attributes; the configuration strategy generation module is used for generating an initial configuration scheme; the strategy optimization module is used for constructing a water and soil conservation benefit evaluation model and generating a recommendation configuration scheme with optimal comprehensive benefits; and the man-machine interaction module is used for finely adjusting and confirming the recommended configuration scheme. According to the invention, through multi-module cooperation and intelligent analysis, the improvement of protection forest configuration from extensive experience decision making to scientific and accurate planning is realized, the scientificity, high efficiency and sustainability of protection forest construction are significantly improved, and a powerful decision support tool is provided for the synergistic interaction of water and soil loss treatment and ecological system service functions.

The invention discloses a forearm radial side branch penetrating flap operation scheme generation and prognosis prediction method based on a generative model and a Bayesian model, and belongs to the field of computer-aided surgery. According to the method, three types of medical data of the structure, the function and the form of the forearm of a patient are acquired and processed, a plurality of personalized flap operation alternative schemes and rationality scores thereof are generated by using a conditional generative adversarial network, and then risk quantitative prediction with a confidence interval is performed on each scheme through a Bayesian neural network. And finally synthesizing an intelligent decision report. According to the invention, intelligent generation and quantitative evaluation of the skin flap operation scheme are realized, and a practical decision support tool is provided for core surgical preoperative planning.

The application discloses a railway construction management multi-objective balanced optimization method based on an improved MOPSO algorithm, which comprises the following steps: 1) under the premise of meeting the basic constraint conditions of a railway construction project, four objective function optimization models are constructed, and each objective function optimization model corresponds to an optimization target; 2) according to the four objective function optimization models constructed in the step 1), a multi-objective balanced optimization model is constructed; 3) the multi-objective balanced optimization model constructed in the step 2) is iteratively solved by using an improved multi-objective particle swarm optimization MOPSO algorithm, and an optimal solution set of the project under the balance of four aspects of a construction period, resources, cost and safety is obtained. The method can effectively consider the four optimization targets of the construction period, resources, cost and safety of the railway construction project, can significantly improve the convergence speed, diversity maintenance capability and constraint processing flexibility of the MOPSO algorithm, and provides an efficient, reliable multi-objective comprehensive decision support tool for railway construction management.

This application relates to the field of artificial intelligence knowledge graph technology, and discloses a customs emergency decision-making method and system based on a multi-agent large language model. The method includes: constructing a knowledge graph from unstructured customs emergency procedure documents through a multi-agent collaborative framework; the multi-agent collaborative framework includes at least a procedure parsing agent, a key point filtering agent, an emergency element extraction agent, and a logic construction agent; based on the constructed knowledge graph, a graph navigation agent driven by a large language model provides support for users' customs emergency decision-making problems and provides customs emergency decision-making answers. This application can achieve high-fidelity automated construction of customs emergency knowledge graphs, ensuring the accuracy and completeness of knowledge; it can transform static knowledge graphs into interactive dynamic decision support tools, truly connecting the entire chain from unstructured procedure documents to intelligent decision support.

The application relates to the technical field of data prediction, and discloses a marine foundation bearing capacity prediction method and system based on scour pit fractal reconstruction and deep learning, a terminal and a storage medium.The method comprises the following steps: acquiring measured three-dimensional point cloud data, performing interpolation and reconstruction, and generating a three-dimensional scour pit digital geometric model; based on the three-dimensional scour pit digital geometric model, the foundation limit bearing capacity under different scour pit morphologies is calculated, and a scour pit morphology quantitative descriptor is extracted to form a data pair sample library; the deep learning model is trained by taking the data pair sample library as a training set, and a trained deep learning prediction model is obtained; the newly detected scour pit morphology data is input into the deep learning prediction model after being reconstructed and quantitatively described, and the foundation prediction bearing capacity under the current morphology is obtained. The application realizes end-to-end rapid prediction from detection data to bearing capacity evaluation, and provides an efficient and accurate decision support tool for the safe operation and maintenance of marine structures.

The invention relates to the technical field of industrial automation and intelligent manufacturing, in particular to an equipment scheduling management method for coal preparation plant production, which comprises an information sharing platform, a dynamic scheduling algorithm, an adaptive scheduling system and a decision support tool, and monitoring equipment operation data in real time, and transmitting the data to the cloud server for centralized processing and analysis. The dynamic scheduling algorithm utilizes a machine learning model to carry out real-time analysis on equipment efficiency and production requirements, and an optimized scheduling scheme is automatically generated. The self-adaptive scheduling system can quickly evaluate other available equipment and perform resource reconfiguration when the equipment fails or production conditions change, so that the production continuity is ensured. The decision support tool generates maintenance demand prediction based on historical data and real-time information, and helps dispatchers to arrange a maintenance plan in advance.

The invention discloses a mountain city land utilization change prediction method based on a gray model and deep learning, and the method comprises the following steps: S1, collecting data, and constructing a data set; s2, data preprocessing; s3, constructing a state transition matrix; s4, establishing a gray system model; s5, simulating and predicting the change trend of the land utilization type in combination with a Markov model; s6, training and evaluating the suitability probability of the land utilization type through a deep learning ANN module; s7, simulating the mutual relation among the land use types; s8, constructing a land utilization change prediction model according to the land utilization transfer change and the land utilization change driving factors; s9, performing multi-scene prediction on future land utilization change through the trained model; according to the method, efficient land utilization prediction is realized by integrating multi-source data, introducing a self-adaptive mechanism, combining a decision support tool and providing ecological environment influence assessment.

The invention relates to a crop phenotype information generation system for unmanned aerial vehicle multispectral remote sensing images. The method comprises the following steps: acquiring remote sensing images by using an unmanned aerial vehicle with a multispectral sensor and a visible light sensor; constructing and using a user-defined vegetation index calculation module, and extracting characteristic variables with biological significance from the data; performing preliminary screening on all vegetation indexes; carrying out model training, and carrying out system evaluation on a training result by adopting multiple performance evaluation indexes; loading the trained model to carry out field prediction; predicting and generating a two-dimensional prediction image consistent with the original image in size; the topdressing amount is calculated, and a fertilization prescription map suitable for guiding agricultural machinery operation is generated in combination with the unmanned aerial vehicle remote sensing image and the grid width input by the user. According to the method, full-chain visualization from remote sensing data preprocessing to prediction result output, crop parameter inversion estimation and farmland management prescription map automatic generation functions are realized, and an intuitive decision support tool is provided for precision agricultural practice.

The invention discloses a big data processing analysis system, which comprises a multi-source sensing module, a dynamic analysis module, a cloud decision module and a visual output module, and is characterized in that the multi-source sensing module comprises a water temperature probe, a pH probe, a dissolved oxygen sensor, a conductivity sensor, a turbidity meter, a flow meter and a water level gauge; the multi-source sensing module is used for being deployed in a target water area and a coastal area; by integrating the multi-source sensor, the dynamic analysis model, the cloud intelligent decision and the GIS visual platform, accurate monitoring and efficient treatment of water quality pollution are realized, the accuracy of pollution source positioning can be greatly improved by utilizing the LSTM prediction model, the limitation of a traditional time difference method is overcome, and meanwhile, the method has the advantages of being simple in structure and convenient to operate. Different pollution levels are generated by combining rainfall, soil and other multi-dimensional data, treatment measures are automatically matched, a reliable decision support tool is finally provided for water environment treatment, and the intelligence and refinement level of water environment management is effectively improved.

The invention particularly relates to a lung cancer death education knowledge map system and method based on a triple semantic network, and the system comprises a patient terminal, a family member terminal, a medical care terminal and a server, and the patient terminal inputs a patient demand for a patient; the family member end is used for inputting basic information, own roles and family member demands of patients by family members; medical staff can inquire education records and feedback suggestions of patients through a patient data inquiry function of the medical care terminal, inquire related knowledge graph contents through a knowledge graph management function, and formulate an education plan through a decision support tool; an interaction module in the server is used for interaction and calling a reasoning module for reasoning, the reasoning module screens out a feedback result matched with the resistance based on the user demand and / or the recognized resistance from the knowledge graph with the defined triple semantics, and the feedback result is fed back to the patient end, the family member end or the medical care end through the interaction module. According to the scheme, comprehensive, accurate and dynamic knowledge support and psychological guidance are provided for patients and family members.

The invention belongs to the field of heat exchanger performance evaluation, and particularly relates to a heat exchanger comprehensive performance evaluation method, a use method and a system. The heat exchanger comprehensive performance evaluation method comprises the following steps: S1, establishing a comprehensive performance evaluation diagram: establishing a logarithmic coordinate system to form the comprehensive performance evaluation diagram; s2, constructing constraint performance lines: in the comprehensive performance evaluation diagram, constructing at least one constraint performance line based on a preset evaluation constraint condition, the constraint performance line being defined by a performance relation model; and S3, performance evaluation: quantitatively evaluating the comprehensive performance of the enhanced heat exchange structure under the selected evaluation constraint condition. According to the heat exchanger comprehensive performance evaluation method, the use method and the system, the limitation of a traditional evaluation system is overcome, and a reliable and efficient decision support tool is provided for design and model selection of an efficient, compact and lightweight heat exchanger.

The present application belongs to the field of heat exchanger performance evaluation, and particularly relates to a heat exchanger comprehensive performance evaluation method, a use method and a system. The heat exchanger comprehensive performance evaluation method comprises the following steps: S1, establishing a comprehensive performance evaluation graph: establishing a logarithmic coordinate system to form a comprehensive performance evaluation graph; S2, constructing a constraint performance line: in the comprehensive performance evaluation graph, at least one constraint performance line is constructed based on a preset evaluation constraint condition, and the constraint performance line is defined by a performance relationship model; and S3, performance evaluation: quantitatively evaluating the comprehensive performance of the enhanced heat exchange structure under the selected evaluation constraint condition. The present application provides a heat exchanger comprehensive performance evaluation method, a use method and a system, overcomes the limitations of the traditional evaluation system, and provides a reliable and efficient decision support tool for the design and selection of efficient, compact and lightweight heat exchangers.