156 results about "Decision support system" patented technology

A portable agent package apparatus for coupling to one or more environment, energy or water infrastructure or water body sensors produce timestamped or temporal process data, includes a physics surrogate world model trained to predict at least one hydraulic, chemical, or biological state variable of the sensed water system, a connection memory that stores metadata describing data source identifiers, units, and sampling cadence, pointers to available analytical tools or peer agent packages, or streams of operational experience or a hierarchical options library, an emotion tensor continuously encodes normalized metrics comprising at least one of model accuracy, computational load, data quality, latency, and uncertainty, or further including an exploration bonus channel, a value estimate error, an anomaly score, or an alignment divergence flag, and a bidirectional, authenticated communication interface that receives the temporal or timestamped process data from the one or more sensors, transmits Memo updates, and accepts goal directives.

The invention discloses a big data risk control decision support system based on multi-modal data, and the system comprises a data access module which is used for accessing and analyzing multi-source entity information, building a grouping calibration rule base, and generating a compliance multi-modal data set; the preprocessing and characterization module is used for preprocessing the data, generating basic characterization of each modal and establishing a micro verifier library; the causal hypergraph module is used for constructing a causal hypergraph and outputting a stable causal structure and a gating mask; the fusion and anti-fact module is used for executing cross-modal fusion to generate a risk representation, generating and verifying a minimum change anti-fact sample, and outputting a reachable identifier and an adjustment representation; the evidence chain module is used for calculating a shortest evidence overpass, generating a structured evidence chain and outputting an evidence index; and the strategy module is used for generating and correcting scores, triggering strategy actions and recording execution information. Through multi-modal data fusion and causal reasoning, high-precision, low-delay and interpretable big data risk control decision support is realized.

A water pollution risk early warning and tracing method based on multi-source data fusion belongs to the technical field of water pollution monitoring and early warning, and comprises the following steps: step 1, constructing a multi-source heterogeneous data acquisition network and realizing real-time data transmission; 2, performing multi-source data fusion and feature enhancement processing based on space-time reference; 3, constructing a water pollution risk dynamic early warning system based on a WOA-LSSVM model; fourthly, reverse positioning of the pollution source is completed on the basis of a CNN-GRU-SE Attention model; and 5, carrying out development and emergency response on a multi-dimensional visual decision support system, and positioning a pollution source. Multi-source information such as water quality sensor data, unmanned aerial vehicle image data and geographic information data is fused, and an intelligent monitoring network is constructed; through multi-source data fusion and an intelligent algorithm, water pollution risk early warning accuracy and traceability efficiency are effectively improved, and the method has the advantages of high response speed, wide monitoring range, accurate positioning and the like, can be widely applied to the fields of urban water supply, drainage basin management and the like, and meets the requirements of water environment safety guarantee.

The invention provides an intelligent question number and index management engine and system based on dynamic reward optimization, relates to the technical field of data learning, and is used for enterprise index analysis, attribution diagnosis and decision support. The system is provided with an index governance layer, index caliber, computational logic, blood relationship, credibility score and version information are managed in a unified mode through a dynamic knowledge graph, unified semantic constraint is carried out on a multi-agent analysis process, and index consistency and traceability are guaranteed. The system also establishes a causal cognition module, based on time sequence data and in combination with expert priori, generates and corrects a business index causal directed acyclic graph, realizes root cause positioning and anti-fact simulation, and answers what change is and what intervention is. The multi-agent collaborative analysis core is responsible for natural language intention analysis, index compliance verification, automatic access, causal inference, narrative generation and chart presentation, calculates a multi-target composite reward value based on user feedback and interaction behaviors, and adaptively adjusts output; and the user corrects and writes back to form closed-loop learning.

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

The invention relates to an enterprise multi-source data intelligent association analysis method based on artificial intelligence and a large model, and the method comprises the steps: introducing time sequence dynamic analysis, a business rule base and statistical correlation test, carrying out the multi-dimensional and automatic cross verification and consistency test of an association pair outputted by a semantic association engine, and carrying out the analysis of the association pair. Screening out a high-confidence correlation set conforming to the business logic, the time sequence evolution rule and the statistical significance; and packaging to form a reusable business insight analysis model based on the enterprise data knowledge graph, receiving a business query request by the model, automatically generating a deep analysis report for business process optimization and potential risk early warning through graph reasoning, path discovery or an abnormal sub-graph detection algorithm, and pushing a result to a decision support system.

The invention relates to the technical field of building informatization and intelligent construction, and discloses a building engineering digital twinning progress management and control method and system.The building engineering digital twinning progress management and control method comprises the steps that multi-source heterogeneous data in the building engineering construction process is collected; carrying out fusion processing on the original data set by adopting a space-time alignment network; updating the digital twin body by adopting an incremental state estimation method; predicting a future progress by adopting a model fusing a graph neural network and a time sequence attention mechanism; a multi-level detection mechanism is adopted to identify progress deviation and analyze a root cause; generating an optimized resource scheduling scheme by adopting a reinforcement learning method; an intelligent decision support system is constructed, and visual display and interaction functions are provided; according to the method, the graph neural network and the time sequence attention mechanism are fused, the progress prediction model capable of capturing the task topology dependency relationship and the time sequence evolution characteristics at the same time is constructed, and the accuracy of progress prediction is improved.

The invention discloses a medical risk intelligent prediction system based on time series data mining. The system comprises a multi-dimensional time sequence data acquisition and preprocessing module, a time sequence mode deep mining engine, a multi-dimensional risk assessment engine, an intelligent intervention decision support system and a real-time monitoring feedback module. A time sequence mode mining engine adopts a layered architecture, and short, medium and long-term time sequence modes are respectively analyzed through a bidirectional LSTM-attention network, a wavelet transform-convolutional network and a seasonal decomposition-gating circulation network. The risk assessment engine integrates an isolated forest, an auto-encoder, a Transform multi-task network and knowledge graph reasoning, and realizes all-around risk quantification. The decision support system generates a personalized intervention strategy based on deep Q network reinforcement learning and case reasoning. According to the system, early prediction and accurate intervention of medical risks are realized, and the prediction accuracy and the medical safety level are remarkably improved.

The invention relates to the field of finance, and discloses an AI-based investment project feasibility intelligent analysis and decision support system, which comprises a multi-modal data fusion engine used for extracting a feature vector of an unstructured text through a BERT variant model, carrying out joint coding on the feature vector and a structured data feature, and outputting fused multi-modal data; the dynamic knowledge graph construction module is connected with the multi-modal data fusion engine; the deep reinforcement learning decision module comprises a feasibility prediction network and a risk assessment network which are cooperatively trained; an interpretable AI module; and a closed loop feedback module. By means of a multi-modal data fusion engine and a deep reinforcement learning decision module, automatic processing and intelligent analysis of data are achieved, and the analysis time of a single project is greatly shortened to 8 minutes. The high-efficiency processing speed enables the investment institution to quickly respond to the market change, timely grasp the investment opportunity, and occupy the decision-making precedence in the financial market which changes instantaneously.

The invention discloses an intelligent fusion analysis and decision support system and method for multi-source heterogeneous data of a spacecraft. The intelligent fusion analysis and decision support system for the multi-source heterogeneous data of the spacecraft and the intelligent fusion analysis and decision support method for the multi-source heterogeneous data of the spacecraft are included. The system comprises a dynamic adaptation access layer, a unified semantic modeling layer, a space-time semantic fusion engine, a digital twin collaborative decision-making center, a knowledge enhancement decision-making auxiliary subsystem, a cross-department collaborative interaction platform and a side cloud collaborative intelligent processing framework. The method comprises the steps of multi-source heterogeneous information dynamic access and semantic modeling, space-time and semantic deep fusion, digital twin driven decision deduction, knowledge enhanced decision assistance, cross-department collaborative decision and interaction, edge cloud collaborative intelligent processing and the like. According to the method, the safety, reliability and decision-making efficiency of spacecraft tasks can be remarkably improved, and the method is suitable for development, testing and on-orbit task stages of various spacecrafts.

The invention relates to the technical field of Chinese trumpet creeper breeding, and discloses a Chinese trumpet creeper-based efficient breeding decision support system and method. The method comprises the following steps: acquiring multi-dimensional phenotypic data in a Chinese trumpet creeper breeding test, wherein the multi-dimensional phenotypic data comprises plant height, leaf area, flowering period and yield indexes; performing normalization processing on the multi-dimensional phenotypic data, eliminating influences of different dimensions, and calculating a variable coefficient of each phenotypic index; then screening out phenotypic indexes of which the stability is higher than a preset threshold value based on the variable coefficient, and constructing a Chinese trumpet creeper phenotypic feature matrix; carrying out dimensionality reduction on the Chinese trumpet creeper phenotypic feature matrix by adopting an adaptive weighting algorithm, and extracting key phenotypic features; inputting the key phenotypic features into a genetic algorithm optimization module, and calculating the contribution degree of each genetic locus in combination with the Chinese trumpet creeper genotype data; and constructing a Chinese trumpet creeper breeding decision model according to the contribution degree of the gene locus, and outputting an optimal breeding combination scheme. The system can optimize the Chinese trumpet creeper breeding process and provide effective support for Chinese trumpet creeper breeding work.

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

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

The invention discloses a multi-dimensional analysis house building health monitoring and early warning system and method, and relates to the technical field of monitoring and early warning. During operation of the system, various sensors are deployed to monitor the physical states of different materials in a building, collect surrounding environment data and transmit the data to a central processing unit in real time, and through a data fusion algorithm, real-time monitoring and early warning are achieved; the method comprises the following steps: integrating sensor data from different materials into a unified data set, preprocessing and normalizing the data, constructing a multi-material interaction mathematical model, calculating an effective stress sigma eff of a building structure, constructing a building overall health assessment model, and calculating a building health comprehensive index HI; according to the building health comprehensive index HI and the comparison result of the health index of each material and the safety threshold value, the health change of the building is detected in real time, an intelligent decision support system is provided, and building management personnel are helped to carry out long-term maintenance planning and resource optimization distribution.

The invention relates to a photovoltaic low-carbon park smart energy management system and method based on digital twinning. Accurate mapping and simulation analysis of a park energy system are realized by constructing a digital twinborn model, dynamic optimization scheduling of energy equipment is realized by adopting a multi-agent cooperative control algorithm, an energy optimization strategy is formulated by taking low carbon as a target, the health state of the equipment is predicted by utilizing machine learning, and an intelligent operation and maintenance plan is generated. And decision support is provided through energy big data analysis. The system integrates multi-source data, and efficient utilization and low-carbon operation of park energy are realized through the steps of digital twin modeling, intelligent agent collaboration, low-carbon scheduling, equipment prediction, big data analysis and the like. Practical application shows that the system can improve the photovoltaic efficiency by 8%, reduce the energy cost by 18%, reduce the carbon emission intensity by 30%, and significantly improve the park energy management level and the low-carbon degree.

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

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

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

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

The invention discloses a water pollution traceability analysis and ecological restoration decision support system. The system comprises a data acquisition layer, a data management and fusion layer, a pollution traceability analysis layer, an ecological restoration decision layer and a user interaction and visualization layer. The system collects multi-source water quality, hydrology and discharge data by deploying a distributed water quality sensing network, remote sensing and unmanned aerial vehicle monitoring equipment and a pollution discharge source online access module; through data cleaning, fusion and knowledge modeling, a pollution propagation model and a traceability inversion algorithm are constructed, and a pollution source and a contribution rate are identified; and simulating a multi-class repair measure response process in the digital twin water environment, and automatically recommending an optimal repair scheme in combination with a multi-objective optimization algorithm. The method has the advantages of accurate traceability, scientific decision, eco-friendliness, dynamic optimization and the like, and is suitable for pollution treatment and ecological restoration scenes of various water bodies such as drainage basins, lakes, urban rivers and the like.

The invention belongs to the field of medical care informatics, and provides an extracorporeal circulation real-time risk early warning and decision support system based on multi-modal physiological data, which comprises the following steps: collecting multi-modal data in an extracorporeal circulation process; determining the current activation state of each mode, and numbering and identifying the modes in the activation state; respectively executing independent state updating operation on the modes in the activated state; selecting a plurality of reference state units from a historical state set to identify the difference degree between the input feature vector and the plurality of reference state units on the modal structure, and adjusting the participation degree and the influence weight of the reference state units based on the difference degree; and based on the risk evolution trajectory of the reference state unit and the corresponding participation degree and influence weight, determining a risk score value at the current moment, and finally generating a control suggestion.

The invention relates to the technical field of city construction, in particular to a city built-up environment intelligent evaluation system based on multi-source data fusion, which comprises multi-source data acquisition and processing, a city update potential recognition model, cross-scale quantitative evaluation system construction and built-up environment and city vitality nonlinear interaction mechanism analysis. The construction of the cross-scale quantitative evaluation system specifically comprises the step of establishing a comprehensive evaluation model of facility density, population matching degree and traffic network structure in combination with an AHP-entropy weight method and spatial syntactic analysis. According to the invention, through multi-source data fusion, cross-scale quantitative evaluation, machine learning modeling and interpretability analysis, an accurate, efficient and generalizable city update decision support system is constructed.

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

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

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

The invention discloses a DIKWP natural language analysis enhancement method combined with a large language model, and belongs to the technical field of artificial intelligence and natural language processing. The method is realized through the following steps of: firstly, performing semantic pre-analysis on natural language input of a user by utilizing a large language model (LLM), and extracting five semantic fragments of data, information, knowledge, wisdom and intention; then the content is mapped to a DIKWP semantic map node in a structured mode through a DIKWP semantic mapping template system; then semantic consistency verification and logical reasoning are carried out through an LLM and DIKWP dual-channel cooperation mechanism; and finally, iteratively optimizing ambiguous, missing or conflicting contents by means of a language-semantic interaction closed loop until a complete and consistent DIKWP semantic map is generated. According to the method, the semantic comprehension ability of a large language model and the structural reasoning advantage of the DIKWP map are effectively fused, the accuracy, consistency and interpretability of complex natural language analysis are remarkably improved, and the method can be widely applied to intelligent question answering, knowledge modeling and decision support systems.

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

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

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

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