79 results about "Decision aiding" patented technology

The invention provides a legal affair intelligent auxiliary processing method and a multi-agent system. The method comprises the following steps: constructing a dynamically updated legal affair knowledge system; according to the dynamically updated law knowledge system, constructing a case shunting agent Agent, a law application Agent, a class case retrieval Agent and a risk early warning Agent, and realizing a multi-Agent collaboration mechanism through a collaboration structure based on message transmission and state sharing; according to a multi-Agent cooperation mechanism, a preference mapping relation model is constructed through case complexity evaluation and quality evaluation, and multi-case relation perception preference mapping is realized; constructing a planning tree PLANTOR framework by combining a large language model and a knowledge management technology based on Prolog based on multi-case relationship perception preference mapping, generating a robot-oriented knowledge base and a planning program, and converting the robot-oriented knowledge base and the planning program into a behavior tree; according to the PLANTOR framework, multi-Agent inference chain alignment is achieved through joint optimization of transmission optimization and embedded optimization, and law decision assistance is completed. According to the scheme, the legal affair processing accuracy and efficiency are effectively improved.

The invention discloses an intelligent enterprise financial data analysis system and method based on artificial intelligence, and belongs to the technical field of enterprise informatization management and intelligent finance. The objective of the invention is to solve the technical problems of low processing efficiency, insufficient data insight, lagging risk identification, weak intelligent decision-making assistance capability and the like in the existing enterprise financial data analysis process. The system generally integrates a data automatic acquisition and integration module, a financial data deep analysis and modeling engine based on artificial intelligence, a key financial index and operation performance intelligent calculation module, a multi-dimensional financial risk intelligent identification and dynamic early warning module and an interactive visual analysis report and decision support module. The core of the method is that internal and external multi-source heterogeneous financial and operation data of an enterprise are automatically collected and fused, deep processing and intelligent analysis are performed on the data by applying artificial intelligence technologies such as machine learning and natural language processing, and accurate portrait of the financial condition of the enterprise, dynamic evaluation of operation performance and real-time monitoring and prediction of financial risks are realized. And analysis reports and optimization suggestions with insight are generated, so that enterprise managers can make efficient and scientific operation decisions. According to the method, the automation and intelligence level of enterprise financial data analysis can be remarkably improved, the financial information quality and decision support efficiency are improved, the risk prevention capability of enterprises is enhanced, and the enterprises are assisted to realize refined operation and value creation.

The invention discloses an AI data scheduling distribution system and method based on cloud computing, and relates to the technical field of intelligent cloud computing, and the system comprises an AI data collection module, a feature extraction module, a decision parameter generation module, a scheduling distribution module and a visual feedback module. The AI data acquisition module acquires equipment video data and temperature data, the feature extraction module extracts video temperature features, the decision parameter generation module outputs equipment vibration intensity and temperature rise rate, and the scheduling distribution module calculates equipment shutdown probability and node load score and triggers a shutdown instruction and a task migration decision. And the visual feedback module maps the equipment health state and node load distribution, and optimizes the feature weight. Through multi-modal data fusion and a closed-loop feedback mechanism, accurate early warning of equipment abnormity and dynamic optimal allocation of resources are realized, visual decision assistance and adaptive weight adjustment are combined, equipment safety and cluster load balancing efficiency in a cloud computing environment are improved, and manual intervention requirements and operation and maintenance cost are reduced.

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 data processing, and discloses a decision-making auxiliary method, device and equipment based on portrait data and a medium, which can be applied to financial and medical scenarios, and the method comprises the following steps: processing an original data set to obtain standard data; respectively constructing a basic attribute portrait, a service behavior portrait and a consumption behavior portrait based on the standard data to extract multi-dimensional feature information, and converting the multi-dimensional feature information into a customer portrait vector; identifying customer behaviors according to the customer portrait vector, and constructing a portrait dynamic updating mechanism to obtain dynamic portrait data; and constructing a decision-making auxiliary model by using the dynamic portrait data, and operating the decision-making auxiliary model to output a decision-making auxiliary scheme. In the invention, aiming at the problem of unreasonable resource allocation caused by incomplete client portrait construction, the decision-making auxiliary model can be constructed and operated by utilizing the calculated dynamic portrait data, so as to output the decision-making auxiliary scheme. Therefore, resources can be reasonably distributed, and the working efficiency of personnel is improved.

The invention provides an emergency decision-making auxiliary system based on a knowledge graph. Comprising a data acquisition and processing module, a knowledge graph construction and reasoning module, an information retrieval and generation module, a multi-level reasoning and thinking chain prompting module, a multi-agent cooperation and game optimization module, an incremental learning and dynamic knowledge updating module and a block chain trust guarantee module. The emergency decision-making efficiency, accuracy, cooperation capability and safety are broken through, dynamic and complex emergency scenes can be effectively dealt with, and efficient, reliable and intelligent technical support is provided for decision makers.

The invention discloses a hydropower line economic dispatching and load collaborative optimization system and method, and relates to the technical field of hydropower dispatching, and the system comprises a data collection module which is used for collecting hydropower station unit water consumption characteristic curve data, power grid real-time load demand data, reservoir water level dynamic data and unit vibration area constraint data; the model construction module is used for preprocessing the data acquired by the data acquisition module and constructing a multi-target optimization model with the minimum total water consumption and the minimum line transmission loss as targets in combination with constraint conditions; the improved optimization module is used for receiving the target function and the constraint condition output by the multi-target optimization model module and dynamically calculating the optimal load distribution scheme of each unit; and the result output module is used for receiving the optimal load distribution scheme output by the improvement and optimization module and realizing visual output and decision assistance of the scheme. The problem that a traditional scheduling scheme is low in traditional scheduling decision-making efficiency is solved, and the scheduling decision-making efficiency and engineering practicability are improved.

The invention belongs to the technical field of decision-making assistance, and provides a medical decision-making assistance method and system based on a large reasoning model. The method comprises the following steps: recording voice interaction content between a doctor and a patient, and extracting first key medical information from the voice interaction content; extracting second key medical information from an electronic medical record written by a doctor, and carrying out consistency analysis and conflict analysis on the first key medical information and the second key medical information by using the large reasoning model; and outputting corresponding abnormity prompt information to a doctor according to an analysis result. The method can achieve the automatic verification of the medical information, guarantees that the electronic medical record is consistent with the oral medical advice, reduces the drug use and re-visit risks of a patient, and improves the accuracy and safety of a medical decision.

The invention discloses an electric power system data anomaly prediction method based on deep learning, and relates to the field of data anomaly prediction.The method comprises the steps that firstly, a graph convolutional network and LSTM are used for deeply mining time series data of an electric power system and spatial-temporal characteristics of a topological structure, and high-precision anomaly detection and positioning are achieved; and when an exception is detected, an exception attribution mechanism is introduced, and exception signal features (such as exception fragments and positioning information) at a mathematical level are converted into a structured query object at a semantic level. And then, a barrier between numerical data and the unstructured operation and maintenance knowledge base is broken through a vector retrieval technology, and related maintenance regulations and historical cases are accurately recalled. And finally, an executable recommended disposal scheme is automatically generated based on recall knowledge in combination with the generation capability of a large language model, so that an intelligent closed loop from anomaly perception and knowledge matching to decision assistance is constructed, and the accuracy and efficiency of power system fault handling are remarkably improved.

The invention provides a disaster site-oriented multi-unmanned aerial vehicle rapid three-dimensional reconstruction and decision-making auxiliary method and related equipment, and belongs to the technical field of emergency surveying and mapping and three-dimensional reconstruction. The method comprises the steps that multiple unmanned aerial vehicles are used for cooperatively collecting image data of a disaster site, and uncertainty indexes used for representing the data missing degree are constructed; taking the uncertainty index as input, selecting an optimal viewpoint to generate a supplementary collection task and executing the supplementary collection task based on the contribution degree of candidate viewpoints to reduction of uncertainty and the comprehensive cost of executing the viewpoints, and obtaining an image data set with coverage integrity; screening a key frame set for three-dimensional reconstruction from the image data set; performing motion recovery structure and space partitioning based on the key frame set, and fusing depth priori and semantic information to obtain an initialized point cloud; and based on a three-dimensional Gaussian sputtering model, extracting structured elements, constructing a risk cost map, and obtaining a rescue route and a disaster situation decision report. According to the invention, emergency command and rescue actions can be supported.

Disclosed in the present invention is a point-cloud-based operation and maintenance decision aid method for a distribution network corridor. The method comprises: a data collection module acquiring point cloud data of a distribution network corridor by means of a laser scanning device scanning the distribution network corridor, and preprocessing the collected point cloud data; a defect detection module using a constructed point cloud model to perform defect detection and classification on the distribution network corridor; a three-dimensional visualization module performing integration and visual display on the point cloud data and defect information, in order to realize the three-dimensional visualization of the distribution network corridor; and an operation and maintenance module formulating a dynamic operation, maintenance and overhaul strategy on the basis of device defect information. In the present invention, point cloud data and a three-dimensional model are used to perform device state monitoring and defect detection, so as to realize the comprehensive and rapid detection and evaluation on distribution network corridors, thereby improving the operation and maintenance efficiency.

The invention belongs to the technical field of man-machine interaction equipment, and particularly relates to an intelligent man-machine interaction method and system based on a multi-modal large model, and the system comprises a multi-modal input processing unit, an intelligent analysis decision unit, an interface protocol management unit and a visual man-machine interaction interface unit. Through modular design and tight data flow direction control, efficient and accurate man-machine interaction is achieved, all the modules cooperate with one another to jointly complete multi-modal data acquisition, processing, fusion, decision support, instruction execution and feedback tasks, and the system is high in practicability and easy to popularize. Decision-making auxiliary supports such as autonomous task planning, dynamic environment perception and real-time intelligent control are provided for underwater operation equipment, the safety and execution efficiency of the underwater operation equipment are improved, and the problems that a traditional man-machine interaction system is insufficient in perception, rigid in planning and delayed in control in complex scenes such as underwater operation are solved.

The invention provides a power distribution network voltage quality online evaluation method and system supporting new energy group access, and belongs to the field of power system automation. Comprising the following steps: generating a node dynamic feature vector through multi-source data fusion and feature extraction; constructing a dynamic causal association diagram, and synchronously outputting a voltage quality multi-dimensional evaluation index and disturbance source contribution degree analysis by using a gating space-time diffusion mechanism; and generating graded early warning based on evaluation and traceability results, and generating auxiliary decision-making information of recommended control measures through strategy matching, effect simulation and multi-target optimization. The system comprises a data acquisition and fusion module, a dynamic causal association diagram construction module, a causal time sequence diagram diffusion evaluation module, an endogenous traceability analysis module, a grading early warning and decision auxiliary module and the like. According to the invention, real-time evaluation, accurate traceability and intelligent decision support of the voltage quality problem of the new energy power distribution network are realized.

The invention provides a mental disorder aided decision-making method and system based on multi-modal data, and belongs to the technical field of disease aided decision-making, the method is applied to a system comprising a data acquisition module, a preliminary screening module and an aided decision-making module, and the method specifically comprises the following steps: preprocessing the multi-modal data of a patient; in combination with the mental disorder risk level of the patient of the preliminary screening model, decision assistance is triggered according to the mental disorder risk level, or corresponding decision suggestions are matched and output; when decision assistance is triggered, the disease classification probability and severity are obtained through a diagnosis model based on a cross-modal attention mechanism, meanwhile, according to a time-dependent risk prediction model, a survival probability curve of a recurrence risk is generated in combination with historical diagnosis data of a patient, and decision assistance suggestions are determined by integrating outputs of the two models. According to the method, on the basis of multi-modal data, multiple types of intelligent models are fused, objective data support is provided in the assessment and intervention process, and the missed diagnosis and misdiagnosis risks of mental disorders are reduced.

The invention relates to a trade bill multi-dimensional processing method, device and system, and the method comprises the steps: obtaining a plurality of pieces of trade bill data, and carrying out the intelligent recognition and structural processing, and obtaining bill field information; performing service association integration on the bill field information based on a preset service identifier to generate bill service data; performing multiple verification and analysis on the bill business data to obtain verification information; and performing multi-dimensional trade process analysis according to the verification information and the bill business data, and outputting decision-making auxiliary information. According to the invention, the automatic conversion from the original bill to the decision auxiliary information can be realized, and the transparency, controllability and operation efficiency of the whole trade process are improved.

The present application provides a method for intelligent legal assistance and a multi-agent system, the method including: constructing a dynamically updated legal knowledge system; constructing a case diversion agent, a legal application agent, a similar case retrieval agent and a risk warning agent based on the dynamically updated legal knowledge system, and realizing a multi-agent collaborative mechanism through a collaborative structure based on message passing and state sharing; constructing a preference mapping relationship model through case complexity assessment and quality assessment based on the multi-agent collaborative mechanism, and realizing multi-case relationship perception preference mapping; based on multi-case relationship perception preference mapping, combining a large language model with Prolog-based knowledge management technology to construct a planning tree PLANTOR framework, generate a robot-oriented knowledge base and planning program, and convert them into a behavior tree; according to the PLANTOR framework, realize multi-agent reasoning chain alignment through the joint optimization of transmission optimization and embedding optimization, and complete legal decision-making assistance. The above scheme effectively improves the accuracy and efficiency of legal processing.

The invention discloses an end-stage kidney disease patient dialysis mode decision-making auxiliary system, and relates to the technical field of data processing, and the system comprises a multi-dimensional data collection module which is used for carrying out the data collection of a patient, and building a patient data set; the integrated geographic information module is used for generating a dialysis center reachability index, and the reachability index comprises real-time traffic time cost and public traffic coverage density; the feature coupling module is used for mapping the patient data set and the reachability index to a unified vector space and then executing adaptive weight distribution; the fitness analysis unit is used for configuring a balance fitness function based on the self-adaptive weight distribution result, performing balance optimization by using the balance fitness function and the vector data mapped to the unified vector space, and establishing a balance optimization result; and the visual terminal is used for interactively displaying the balance optimization result. Therefore, the technical effect of providing visual data support and accurate decision-making assistance is achieved.

The application provides a liver cancer risk stratification and treatment recommendation information generation method and device, the method comprises the following steps: obtaining liver cancer related structured clinical data of a target object; based on a pre-set electronic medical record narrative template, converting the structured clinical data into an electronic medical record style narrative text containing clinical semantics, embedding key clinical variable markers corresponding to the structured clinical data into the electronic medical record style narrative text, and constructing a model input sequence; inputting the model input sequence into a pre-trained large language model for inference operation, outputting a structured text stream, and the large language model is obtained through decision tree constraint based on a liver cancer diagnosis and treatment guideline and multi-objective reinforcement learning strategy training; and extracting comprehensive decision assistance information from the structured text stream by using a pre-set analysis rule, realizing high-credibility clinical assistance decision, and significantly improving the accuracy and logical consistency of the treatment recommendation information.

The invention discloses a non-invasive hemodynamic detection and decision-making assistance method and device. The method comprises the steps of obtaining a first data set; preprocessing the first data set to obtain a second data set; an intracardiac prediction model based on a Transform architecture is constructed and trained; inputting the second data set to the trained intracardiac prediction model, and outputting the second data set as a third data set; collecting expert experience knowledge; constructing a relational expert knowledge base by adopting a knowledge representation method of a semantic network; performing trend analysis and correlation analysis on the third data set, the real-time electrocardiogram monitor physiological parameters and the electronic medical record to obtain an analysis result; performing matching reasoning on the analysis result and a treatment scheme of a corresponding case in an expert knowledge base to obtain a reasoning result; and generating personalized decision suggestions based on the reasoning result. According to the method, the treatment scheme decision is generated only based on the electrocardiograph monitor, and an efficient, intelligent and non-invasive diagnosis and treatment assistant is provided for clinical medical staff.

The invention discloses a power-economic mapping matching method and system based on an improved hierarchical neural network model, and the method comprises the steps: inputting power data and economic data into a feature coding layer for feature extraction, and obtaining context semantic features of the power-economic data; inputting the context semantic features into an event relation modeling layer, constructing a multi-type graph structure by taking time nodes as event nodes in the graph structure, and modeling the multi-type graph structure to obtain a power-economic state sequence; performing feature matching on the electric power-economic state sequence through a preset electric power-economic feature matching mechanism of a feature matching layer to obtain a semantic similarity between the current electric power economic state and a target economic structure; and the output prediction layer constructs a task output structure according to different task requirements on the basis of semantic similarity, and obtains a final conclusion of power-economy mapping in combination with structural features and a matching result. According to the invention, the expression ability and decision-making assistance ability of the model to a complex coupling system are significantly improved.

The invention discloses a cotton textile industry decision-making auxiliary management system in the technical field of textile, which comprises a management system, a basic model, a vertical enhancement module and a mechanism model fusion module are integrated on the management system, and a technical training module, a core function module and a data set construction and computing power system are further loaded on the management system. The technical training module is loaded with a basic training concept, data stream fusion and an operation engine. According to the method, a domestic general large model and deep industry vertical enhancement are combined, and digital and intelligent application of knowledge is realized. The international advanced OODA design concept is introduced, an enterprise model with decision as the center is constructed, and instant perception and response are achieved. A physical transformation rule of textile material science and a yarn forming mechanism model are integrated into large model training, and the illusion problem of a general large model in the professional industrial field is solved. Three intelligent agents of AI cotton assorting, AI production scheduling and order whole process are constructed, and deep mapping and automatic closed loop of a business scene are realized.

The invention discloses a policy network optimization method and a related device, and relates to the technical field of artificial intelligence, and the method employs a reward function at least comprising a basic reward and a cognitive reward to carry out deep reinforcement learning on a policy network, is multi-target collaborative reinforcement learning, can synchronously improve the scoring accuracy and interpretability in an inference chain, and improves the learning efficiency. The generation of the reasoning chain is optimized, a new solution is provided for realizing more intelligent and more transparent model evaluation, and the method has wide application prospects and important practical values in the fields of intelligent question answering, decision assistance, content auditing and the like.

The present application relates to the technical field of data processing, in particular to a conference decision assistance method and system based on a multi-modal real-time perception AI exo-brain, comprising: converting historical conversations in a current conference into a relationship graph by using a large model; marking a conference speaker who needs decision assistance as a target speaker and establishing a reference graph; performing a conference decision warning when the similarity between a decision result recently made by the target speaker and a decision result in the reference graph is less than a decision threshold; calculating an adverse index of the decision result of the target speaker; calculating an adverse decision utilization index of the adverse decision result; and adjusting the decision threshold by using the adverse index and the adverse decision utilization index. The present application advances risk identification from after the meeting to during the meeting, and realizes a dynamic balance between guaranteeing user decision autonomy and avoiding organizational systematic decision risks by dynamically adjusting the decision threshold.

The invention relates to a Chinese ancient city space-time knowledge graph construction method and device and a storage medium. The method comprises the steps of obtaining ancient document data related to a target city based on a city place name set of the target city; performing sentence segmentation processing and knowledge extraction based on the ancient book literature data to obtain a plurality of ancient book text entries and triples corresponding to the plurality of ancient book text entries; semantic disambiguation is carried out on each triad corresponding to each ancient book text entry, and attribute values of entities of time and space types uniquely corresponding to each triad are determined, so that the time-space knowledge graph of the ancient Chinese cities is constructed. According to the embodiment of the invention, the time-space knowledge graph can be constructed by taking the ancient cities as the ontology, and the constructed time-space knowledge graph of the ancient Chinese cities can support deep semantic retrieval and analysis related to the ancient Chinese cities. And efficient knowledge support and decision-making assistance can be provided for the fields of urban historical research, cultural heritage protection, urban planning and construction and the like.

The invention provides an intelligent grading decision-making and automatic response method and device in an infectious disease hospital, and a medium, relates to the field of public health information, and aims to carry out comprehensive calculation through a dynamic risk decision-making engine by fusing a real-time prevention and control situation and individual case characteristics, change a traditional static judgment mode based on a fixed rule or a single keyword, and improve the accuracy of judgment. Decision-making auxiliary information including disease types, quantitative risk levels and clinical guidance is actively pushed to clinicians, complex code selection and report judgment are simplified into clear risk prompting and click operation, confirmation operation of the clinicians is automatically converted into executable response operation instructions, a hospital system is driven to execute differentiated prevention and control processes, and the risk prevention and control efficiency of the clinicians is improved. A data-driven continuous optimization system is constructed, so that high-risk unresponded cases can be automatically backtracked, a traceability report is generated, and the management efficiency and pertinence are improved; the closed-loop linkage capability of medical and defense cooperation is enhanced, and the hospital is no longer an information island by simultaneously receiving a macroscopic epidemic situation monitoring platform signal and hospital diagnosis and treatment data.

The invention discloses a flood prevention decision-oriented inference text generation method and system, and the method comprises the steps: obtaining historical flood prevention brief report data, carrying out the text segmentation preprocessing, and constructing a flood prevention brief report corpus; performing key information knowledge extraction on the preprocessed flood prevention brief report by using a large language model to realize semantic association of multi-dimensional decision elements; a reverse prompt method is adopted, a Beam Width beam width parameter is dynamically adjusted, a selection strategy of a multi-path generation result is optimized, logical reasoning is carried out in combination with a thinking chain guide model, an IP-CoT algorithm module is designed, and iterative reasoning type text generation is achieved; a reasoning type text generation system suitable for flood prevention decision making is constructed based on Django, and flood prevention brief reports which are complete in structure and clear in logic are automatically generated. According to the method, intelligent generation from the unstructured text to the decision support information is realized by fusing the generation capability of the large language model and the knowledge in the flood prevention field, the compiling efficiency and quality of the flood prevention brief report can be effectively improved, and the flexibility of decision assistance is improved.

The invention relates to the technical field of spaceflight measurement and control communication, in particular to a receiving performance evaluation method for a spaceflight ground measurement and control system. The method comprises the steps of task data acquisition and preprocessing, G / T value time sequence calculation, multi-dimensional receiving performance evaluation, evaluation conclusion generation and the like, and realizes multi-dimensional evaluation of index dimension, capability dimension and trend dimension. A quantitative and qualitative aerospace ground measurement and control system receiving performance evaluation conclusion is formed and comprises a qualitative evaluation conclusion (normal, decline and deterioration), a quantitative score F (score 0-100) and a trend evaluation conclusion (decline and deterioration estimation date: T2), an image-text evaluation report is formed, and decision-making assistance is provided for equipment scheduling use and repair maintenance.

The wisdom heating full-dimensional perception and decision assistance system and method based on a large model of the present application comprise the following steps: constructing a multi-modal heterogeneous data perception and preprocessing network; performing semantic alignment and unified representation of multi-modal features, extracting feature vectors of heterogeneous data through respective modal dedicated encoders; performing retrieval enhancement generation based on space-time constraints, retrieving matching disposal plans and mechanism knowledge in the pre-constructed heating field knowledge base and historical case base according to the fusion context vector corresponding to the current working condition; performing causal reasoning and decision generation based on thought chains, comprehensively analyzing the fusion context vector and the enhanced prompt context by using a large-scale pre-trained language model; and performing decision closed-loop optimization based on human feedback. The present application realizes the continuous accumulation of knowledge assets; and the special cold start strategy and online evolution mechanism ensure that the system can be quickly put into operation and produce actual benefits in old pipe networks or newly built areas with missing data.

An artificial-intelligence-based decision-assisting system and method are disclosed for generating ranked recommendations through adaptive multi-source analysis. The system includes a processor and a non-transitory computer-readable storage medium storing executable instructions that implement a scoring engine and a ranking engine. Decision parameters and user-defined weighting factors are received from user devices and combined with system-defined weighting factors retrieved from behavioral, historical, external-context, and scoring-criteria databases. Composite decision scores are computed and used to rank candidate options. The system iteratively updates weighting factors or rankings based on feedback data and dynamically restricts data retrieval to relevant parameters to reduce latency and improve throughput. Results are displayed via a graphical user interface, and anonymization procedures protect user identity. The method supports concurrent processing and adaptive learning to refine decision predictions.

The application discloses a kind of personal thinking simulation method and system, comprising: first, target user is carried out multi-source data acquisition and privacy preprocessing in public, private and dynamic questionnaire, and obtains desensitization behavior data.Secondly, features are extracted by three-level segmentation strategy, and multidimensional joint semantic vector is obtained by cross-modal association of graph attention network.Accordingly, graph database and vector storage database containing five-dimensional knowledge graph of cognition are constructed.Combined with five-dimensional knowledge graph, preset template and real-time environmental data, dynamic prompt words are constructed, and after multi-level retrieval enhancement, the final decision suggestion for target user is generated by combining large model, to realize accurate personal thinking simulation and efficient decision assistance, and privacy protection is considered.