530 results about "Structured analysis" patented technology

The invention discloses a structured decision-making method based on multi-agent collaborative decision-making and reinforcement learning, and relates to the technical field of natural language processing, knowledge engineering and agent collaboration, and the method comprises the steps: receiving an original rule document, analyzing the document type, complexity and constraint conditions, and defining a task target and a success standard; and according to the task target, matching and scheduling the intelligent agent from the registered intelligent agent library, and further analyzing the capacity configuration of the intelligent agent for standby. Through the multi-agent cooperation and reinforcement learning technology, full-process automation of rule documents from input to structured analysis is realized, document types, complexity evaluation and constraint condition analysis can be automatically identified, and a clear task target and a success standard are generated; and the large language model generates a structured workflow according to task requirements and agent capabilities, so that the performability is ensured through logic verification, manual intervention is greatly reduced, and the processing efficiency and the system intelligence degree are improved.

The invention discloses an AI-based animation split script automatic generation and visual preview method and system, and the method comprises the following steps: 1, receiving a natural language script text inputted by a user, the natural language script text comprising scene description, role action, dialogue and shot indication information; step 2, performing semantic analysis and structured analysis on the script text based on a natural language processing technology, and identifying and extracting key narrative elements; by introducing an artificial intelligence technology, end-to-end automatic generation and interactive optimization from a character script to a dynamic split rehearsal video are realized, the system can deeply understand scenes, actions, role emotions and shot languages in the script, corresponding visual elements are automatically matched and generated, and the dynamic split rehearsal effect is improved. And the timeline and the rhythm conforming to the film and television grammar are constructed, so that the efficiency and the consistency of the split creation are greatly improved, and the professional threshold and the manufacturing cost are reduced.

The invention relates to the field of constructional engineering, and discloses an intelligent construction management and control method for constructional engineering, which comprises the steps of collecting multi-dimensional data of a building construction site, including construction progress data, personnel positioning information, an equipment operation state and site environment parameters, and constructing a construction behavior feature data set in combination with a multi-source data fusion algorithm; performing automatic structured analysis on the construction behavior feature data set, constructing a construction process modeling framework based on a hierarchical feature clustering method, and introducing a dynamic feedback learning mechanism to perform adaptive optimization on a modeling result; judging whether the optimization process is stable or not according to the evolution trend of the process model, and if so, recording the stage state of the current construction process feature; and based on the corrected construction plan path, performing disturbance factor correction on the prediction progress by applying a multi-scale construction simulation algorithm, extracting corresponding control parameters, and performing intelligent adjustment on prediction nodes. The method has the advantage of improving intelligent construction management.

The invention discloses a remote sensing image semantic segmentation method and system based on a dynamic attention mechanism, and relates to the technical field of remote sensing image processing, and the method comprises the following steps: carrying out the labeling and enhancement processing of a multi-scene remote sensing image, and generating a standardized data set; processing the data set through an encoder, and extracting a multi-scale high-dimensional feature map; decoding the multi-scale high-dimensional feature map, and fusing a decoding result with scale features to generate an optimized feature map; based on the optimized feature map, adopting a joint loss function to synchronously optimize segmentation, detection and classification tasks; performing dynamic up-sampling and boundary refinement on the optimized feature map, and outputting a structured analysis result; according to the method, the boundary information of the ground objects in the remote sensing image is accurately extracted through a dynamic processing flow and introduction of a double-flow decoder network and a multi-task joint optimization strategy, accurate segmentation and classification of the ground objects in a complex scene are achieved, and the overall effect of analysis and processing of the remote sensing image is improved.

The invention discloses a memory enhancement and task planning method and system for large-model multi-agent. The method comprises the steps of performing structured analysis on historical task data, extracting planning experience and an abstract mode, and constructing and indexing an external memory library; retrieving related experience and modes from a memory library based on the new task description, and generating a memory enhanced task planning book; the planning scheme is distributed to a multi-agent network to be executed, an actual execution track is monitored and recorded, and the deviation between the actual execution track and the planning expectation is calculated; and evaluating the planning scheme based on the execution result and the deviation, extracting a planning correction rule, and updating the experience and the rule of the task to a memory bank. By introducing an external memory and closed-loop learning mechanism which can be dynamically evolved, the problems that existing system planning lacks experience guidance, knowledge cannot be precipitated and reused, and planning and execution are disjointed are solved, and the planning quality, the system reusability and the self-adaptive optimization capability are remarkably improved.

The invention provides a water conservancy knowledge structured extraction and verification method and device, and belongs to the technical field of artificial intelligence, and the method comprises the steps: carrying out the differential text processing of different formats of files, and generating an intermediate file; classifying the intermediate file into a regulation class or a non-regulation class based on a preset rule base; performing hierarchical title identification on the regulatory files to form entry knowledge blocks, and converting table contents into HTML (Hypertext Markup Language) knowledge blocks; performing semantic segmentation on the non-regulation file to generate knowledge blocks; performing knowledge block checking and filing, and marking an abnormal alarm block; converting the table knowledge blocks into natural language description by utilizing a large model; and positioning the context of the original text of the alarm knowledge block, and performing intelligent correction through a large model. According to the method, a traditional semantic analysis model and a large language model are creatively fused, a closed-loop process of preprocessing, extraction, verification and correction is formed, the problems of structured analysis and error correction of complex texts in the water conservancy field are solved, and the knowledge processing efficiency and accuracy are remarkably improved.

The invention relates to the technical field of computer vision, image recognition and intelligent medicine management, in particular to a medicine bottle label content recognition method based on multiple cameras and YOLOv8. The method at least comprises the following steps: S1, deploying a medicine bottle label generation system, and generating a label; s2, multi-camera image acquisition and preprocessing; s3, carrying out chessboard calibration and space positioning; s4, medicine bottle label detection and label character recognition and structured analysis; and S5, system integration and application. According to the invention, through combination of multi-camera and multi-angle acquisition and checkerboard calibration positioning and combination with YOLOv8 label detection and OCR identification, high precision, high efficiency, end-to-end automation and system integration of medicine bottle label identification are realized, the defects of precision, efficiency, environmental adaptability and management integration in the prior art are overcome, and the system is suitable for popularization and application. The method has obvious technical advantages and practical value.

The invention provides an automatic consumption tag analysis system and method based on multi-agent collaboration, and the system comprises a data processing agent which is used for collecting data from a social media platform and carrying out the data preprocessing; the label identification intelligent agent is used for extracting consumption labels of different dimensions from the preprocessed data; the sentiment analysis agent is used for carrying out context modeling and sentiment tendency recognition and binding the recognized sentiment tendency to the corresponding consumption label; the label normalization agent is used for performing clustering and normalization processing on all consumption labels bound with emotional tendencies to generate a structured multi-layer label atlas; the central scheduling agent is used for scheduling other agents, generating a label analysis result by using the multi-layer label atlas and sending the label analysis result to the user; according to the invention, based on a multi-agent architecture, structured analysis is carried out on user tags, behavior attributes and consumption intentions in social media contents, so that high-precision and high-efficiency intelligent consumption insight is realized.

The invention provides a mathematical question and answer process teaching system, which comprises a user interaction module, a question analysis and standardization module, a cognitive diagnosis engine, a generative teaching module, a mathematical knowledge graph database and a user portrait database, teaching content and guiding questions are presented to the user in a multi-mode form; the question analysis and standardization module is used for performing semantic understanding and structured analysis on mathematical questions and generating standardized question description and at least one standard question solving path; according to the method, the structured reasoning of the knowledge graph is combined with the natural language generation ability of the generative AI, so that a closed loop from'problem solving 'to'diagnosis' to'heuristic teaching 'is realized, thinking blockage points of students can be accurately positioned and personalized tutoring can be provided like human teachers, and the learning efficiency and the autonomous thinking ability are remarkably improved.

The invention discloses a property operation report generation method and device based on multiple agents and a related medium, and the method comprises the steps: calling an intention recognition agent to carry out intention analysis, and then calling a task planning agent to carry out path construction and scheme solving; determining an execution sequence of tasks in combination with the task planning data to obtain task processing data; calling a data collection agent to carry out data collection on the task processing data to obtain primary processing data; and calling a code calculation agent to carry out code generation and model operation on the primary processing data, calling an outline making agent to carry out outline arrangement and report generation processing, calling a report writing agent to complete report packaging, and outputting a property operation report. According to the invention, the structured analysis result obtained through calculation is subjected to outline arrangement and report generation processing, and the property operation report is packaged and output, so that manual report arrangement is not needed, the process is simplified, and the report output efficiency is improved.

The invention provides a large model retrieval enhancement generation method fused with a multi-source knowledge base, and relates to the technical field of large model retrieval enhancement generation. The method comprises the steps that firstly, user query is analyzed, multiple implied independent retrieval intentions are recognized and separated, and a knowledge source identifier is distributed to each retrieval intention subtask based on a mapping rule; according to the method, related evidence fragments are acquired in parallel from a heterogeneous knowledge source library in a targeted manner, multi-level relation analysis is further performed on the original evidence fragments by introducing a cross validation engine, so that a structured evidence conflict graph is constructed, and then the evidence conflict graph is intelligently processed by applying a game theory excitation algorithm. The method comprises the following steps: obtaining an evidence set with internal consistency, forming a guide signal through structured analysis, and finally, inputting original user query, the synthesized evidence set and the guide signal into a large language model in combination with a pre-constructed cue word template to generate a final answer under constraint, so as to realize retrieval enhancement generation fused with multi-source knowledge base retrieval.

The invention discloses a power grid fault report decision verification method based on digital twin replay and an interpretable neural network, and relates to the technical field of intelligent operation and maintenance and artificial intelligence decision verification of a power system. According to the method, structured analysis of a fault report is generated through AI, power grid digital twinning simulation replay and neural network reasoning with characteristic interpretability are combined, report conclusions and simulation results are automatically compared, interpretability analysis such as SHAP and LIME is integrated, and transparent tracing and characteristic contribution degree quantitative verification of the fault report reasoning process are achieved. Compared with a traditional power grid fault report processing method depending on manual checking or black box AI model output, the method can achieve automatic consistency criterion verification and whole-process data traceability of a fault report reasoning conclusion, and the credibility, transparency and batch engineering application efficiency of a power grid operation and maintenance decision are effectively improved.

The invention discloses a mining equipment state prediction method based on digital twinning and multi-modal fusion, and relates to the technical field of intelligent operation and maintenance, and the method comprises the steps: inputting a multi-modal alignment working condition window sequence into a digital twinning mechanism link, carrying out the same-window ideal response deduction, outputting an ideal multi-modal response, and constructing a twinning dynamic residual error; meanwhile, segmenting a residual structure event to generate a residual event token set; positioning a multi-modal data fragment based on the residual event token set, executing cross-modal attention alignment, and performing topology propagation aggregation in combination with a mechanical topology observation mapping table to generate a topology constraint fusion table set; and inputting the topological constraint fusion table set into the graph neural network, carrying out message passing in combination with a mechanical topological graph, outputting a component state and a complete machine state, and packaging the component state and the complete machine state into a mining equipment state prediction set. According to the method, structured analysis of the twinborn dynamic residual error is realized, and the method is used for accurately positioning an abnormal event and improving the sensitivity and timeliness of state prediction.

The invention provides an intelligent conference management system, method and device based on a large model and a medium, and belongs to the technical field of computers. The system comprises a pre-meeting module used for automatically generating and sending a meeting invitation through an integrated enterprise schedule system, using telephone notification and IVR interaction to confirm a meeting participation form, and counting a meeting participation condition and a meeting arrival state; the in-conference module is used for collecting multi-source audios based on a microphone array in the conference process, transferring conference content in real time and generating subtitles in combination with a voice recognition technology, synchronously recording videos, recording process information of the conference, and performing structured analysis; and the post-conference module is used for extracting key indication information and backlogs in the process information of the conference, generating a standardized conference summary, automatically distributing tasks, tracking the progress and supporting semantic retrieval of historical conference records. According to the invention, a large model technology is utilized, and technologies of cloud computing, big data, artificial intelligence and the like are combined, so that automatic management of the whole conference process is realized.

The invention discloses a video compliance monitoring system based on multistage event aggregation, which adopts a distributed two-stage architecture of an edge end and a center end, the edge end realizes video stream acquisition, dynamic sampling, multi-modal AI structured analysis and video segmentation buffer storage, and pushes an analysis result to the center end in real time, and the center end receives data and then sends the data to the edge end. Through a three-stage analysis system including atomic event extraction, session scene recognition and cross-time-period violation detection, violation detection is completed, a backtracking video with a superimposed mark can be generated, and a violation record and a video URL are pushed to a service system, so that efficient analysis and accurate backtracking of video compliance monitoring are realized; the invention further relates to a video compliance monitoring method based on multi-level event aggregation.

The invention relates to a multi-level data collection method, system and device and a storage medium. The method comprises the steps that an engineering project cost file is received; performing structured analysis on the construction cost file of the engineering project, and extracting an original data field; performing term mapping processing on the original data field to generate a standardized data entity; and according to a preset project cost index system, carrying out hierarchical collection on the standardized data entities to generate a structured knowledge base, and storing the project cost file and the corresponding structured knowledge base into a data collection case library. The method has the effect of improving the data collection efficiency and accuracy of the engineering project cost file.

The invention relates to the technical field of emergency rescue intelligent decision, and discloses an emergency rescue management system and method based on rescue timeliness. The method comprises the following steps: constructing a space-time three-dimensional grid by fusing multi-source information, and performing dynamic scene reconstruction by using a multi-level mapping rule to obtain preliminary and deep rescue scenes. And after combined anomaly diagnosis is carried out on the double scenes, a task topological structure is driven to dynamically evolve according to a diagnosis report, and a rescue task sequence is generated. And then cross-dimension resource adaptation is executed, a resource list is formed, key bottleneck nodes are predicted and identified in combination with real-time feedback, and finally a rescue instruction chain is generated. According to the method, deep fusion and structured analysis of field information in a space-time dimension are realized, a closed-loop decision-making mechanism with task and resource dynamic cooperation and adaptive optimization is established, and timeliness and overall toughness of rescue actions in a complex disaster environment are enhanced.

The invention discloses a document content generation method based on a title structuring demand and related equipment, and relates to the technical field of artificial intelligence, the method comprises the following steps: performing structured analysis on a target electronic document to extract at least one title element in the target electronic document; receiving personalized requirements input by a user for the title elements, and performing data association on the personalized requirements and the corresponding title elements; the personalized requirements are used for guiding content generation; the title elements and the personalized requirements corresponding to the data association are combined into a generation request, and the generation request is sent to an artificial intelligence model; using an artificial intelligence model to obtain generation content according to the generation request; and inserting the generated content into a position related to the corresponding title element in the target electronic document. According to the method, the document structure can be deeply understood, a user is allowed to set independent generation requirements for different title elements, a document processing scheme for batch generation and selective optimization can be realized, and the flexibility of document processing is greatly improved.

The invention discloses an electromechanical industry digital process evaluation method based on data analysis, and particularly relates to the technical field of digital evaluation, a behavior path map, a time sequence causal dependence model and an operation frequency density model are constructed based on information provided by the electromechanical industry, whether structural deviation or behavior abnormity exists is analyzed, and the evaluation result is obtained. If the initial deviation label is generated, calculating a behavior consistency score and a process integrity score, inputting the scores into a fuzzy logic device for risk reasoning, and outputting a credible level; according to the credibility level, judging whether the data is suitable for digital process evaluation, and if the data does not meet the preset requirement, feeding back a corresponding optimization strategy; according to the method, digital behavior structured analysis is realized by constructing a behavior path map, a time sequence causal dependence model and an operation frequency density model; a deviation triggering mechanism is set, and abnormal operation behaviors are accurately recognized; the credibility level is output based on the scoring result, the information applicability is judged according to the credibility level, an optimization strategy is fed back, and the evaluation intelligence level is improved.

The invention discloses a multi-model inference chain data analysis method and device based on natural language input, a medium and a program product. The method comprises the steps that an analysis problem text in a natural language form is analyzed into a semantic task structure, and the semantic task structure is mapped into a structured analysis model conforming to a preset specification; registering the structured analysis model as an instance to an analysis model library, allocating a unique identifier ID to the structured analysis model during registration, and writing attribution relation information of the structured analysis model; an upstream model ID and a downstream model ID in the attribution relation information of the instance are read, the attribution relation information of the upstream model and the downstream model is analyzed, layer-by-layer expansion is carried out in the mode, and a multi-model reasoning chain of the instance is constructed according to the dependency relation between the models; and according to a sequence determined by the multi-model reasoning chain, executing each model instance in sequence or in parallel, and integrating the obtained final output into a comprehensive analysis report. According to the invention, automatic execution from a natural language to full-process data analysis can be realized.

The invention discloses a medical informatization data intelligent analysis system based on a large model, and belongs to the technical field of medical informatization and artificial intelligence, and the system comprises a data collection and standardization module, a medical knowledge graph construction module, a knowledge enhancement inference analysis module, a data quality evaluation module and a structured output module. The system collects multi-source heterogeneous medical data from an electronic medical record system, a laboratory information system, a medical image information system and a hospital information system, performs standardization processing, automatically constructs a medical knowledge graph, and performs intelligent analysis and reasoning on the medical data by using a knowledge retrieval enhanced medical field large language model. Meanwhile, the data quality is evaluated in four dimensions of integrity, accuracy, timeliness and relevance, and finally a structured analysis report is generated. The multi-source medical data can be effectively integrated, the accuracy and interpretability of medical data analysis are improved, and intelligent support is provided for clinical decision making.

The invention relates to the crossing field of an artificial intelligence technology and an automotive electronic technology, and discloses a voice interaction question answering method and system based on a vehicle-mounted large model knowledge base, and the method comprises the following steps: receiving a user query text, and executing intention recognition and structured analysis; based on an analysis result, constructing and executing a mixed retrieval request including structured filtering, full-text keyword retrieval and semantic vector retrieval; fusing and reordering the plurality of retrieval result sets obtained respectively to obtain a high-quality final retrieval result set; and finally, constructing a context based on the final retrieval result set and a user query text, and calling a large language model to generate and output a final answer. By introducing a hybrid retrieval and fusion reordering mechanism, comprehensive context information is provided for a large language model, the accuracy and reliability of questions and answers are improved, and the ability of a system to understand and process complex fuzzy queries is enhanced.

The invention relates to an intelligent identification and import method for a building industry communication project contract scanning copy. The method comprises the steps of obtaining a target template and a to-be-processed contract scanning copy, preprocessing the contract scanning copy, performing structured analysis on the target template, extracting a field standard value, marking a field type, and generating a field type mapping table; inputting the preprocessed contract scanning copy into a multi-modal feature analysis model to obtain a contract information extraction set; performing semantic matching on the contract information extraction set and a field type mapping table to generate a field matching result, and inputting successfully matched contract information into a target template; and if the necessary fields are missing, retrieving associated historical information from the dynamic knowledge base through an entity identification and link technology, obtaining contract information of missing field mapping, and inputting the contract information of missing field mapping into the target template. According to the invention, the recognition accuracy is improved, and the labor cost is reduced.

The invention discloses a geological data description data structured analysis method based on a multi-modal large model. The method comprises the following steps: acquiring multi-modal elements including texts, tables, curves and sheet symbols; dynamically generating a limit mapping matrix of a Sheaf neural network according to the geological description ontology and the multi-modal elements; splicing and consistency evolution from a local cross section to a global cross section are executed, and description field candidates are obtained; performing consistency verification on the description field candidates, and identifying fields with contradictions; calculating a corresponding minimum evidence cut set to form minimum conflict interpretation; inputting the minimum conflict interpretation as an additional loss function into a training process, and updating parameters; and outputting the optimized geological data description field result. According to the method, the consistency, interpretability and traceability of geological data multi-modal description analysis are improved through the dynamic constraint field and the minimum conflict interpretation mechanism.

The invention discloses a formatted document intelligent generation method and system, and the method comprises the steps: obtaining a multi-source formatted document sample, carrying out the structural analysis through a deep learning technology, and constructing a hierarchical document structure knowledge base; performing template matching through neural manifold representation learning and comparison self-supervised learning on the basis of the knowledge base in combination with document requirements input by a user, and outputting an optimal document template; performing structure analysis on the template, generating document content through a diffusion model in combination with user content, and performing quality diagnosis and optimization by adopting optimal loss value estimation; carrying out format self-adaptive adjustment and style consistency check through a value-free strategy optimization algorithm of the reward partition; and performing multi-format conversion export and quality evaluation on the finished document. The technical problems that an existing system is insufficient in flexibility, format adjustment is lack of intelligence, and document semantic coherence and style consistency are difficult to maintain are solved.

The invention relates to the technical field of industrial automatic control, and discloses an industrial configuration software cross-platform cooperative control method and system based on a swan gap, which realize flexible initialization and dynamic task distribution of a control terminal by fully utilizing distributed communication and microkernel characteristics of a swan gap system. Through structured analysis and task graph construction of a configuration control model, granularity recombination and task parallel execution of a control process are supported; a task terminal recommendation mapping table is further generated in combination with the execution requirement of the control task and the terminal capability label information, and the adaptation matching degree of the control task and the execution terminal is effectively improved; and meanwhile, a distributed cooperative channel is constructed, a communication binding channel between the master control terminal and the slave control terminal is established, and a guarantee is provided for rapid scheduling and execution of tasks. Compared with the prior art, the heterogeneous compatibility, the task scheduling efficiency and the protocol adaptation flexibility can be remarkably improved, and the method is suitable for high-complexity and multi-terminal collaborative industrial automation control scenes.

The invention relates to the technical field of computer vision and edge computing, and discloses a multi-computing-power collaborative video structured analysis method, which comprises the following steps of: acquiring a plurality of paths of original video streams, and synchronously decoding the plurality of paths of original video streams; gradient analysis is adopted to evaluate the decoded multiple paths of original video streams, and a preprocessing algorithm combination is selected based on a quality feature vector; establishing a data transmission channel, and adopting an asynchronous communication protocol and a multi-level cache system; performing content complexity analysis, and performing model matching according to the content complexity analysis to obtain a scheduling decision; carrying out distributed deployment, and carrying out pipeline reasoning on the deployed model; pipeline reasoning results are fused, and consistency constraint verification is carried out on fusion results; optimizing system configuration parameters according to a video structured analysis result to realize video structured analysis; the real-time performance, the accuracy and the system stability of video analysis are improved.

The invention relates to a record text mining method for telecommunication fraud prevention, which belongs to the field of clue mining, and comprises the following steps: inputting telecommunication fraud records formed in a certain region within a period of time into a KnowLM-13b-i e model, extracting 9 types of entities and relationships, and generating structured data; importing the generated structured data into a Neo4j database, defining three layers of entities of "victim-attribute-structuring" and a corresponding relationship, performing structured analysis and centrality calculation, and further constructing a knowledge graph; initializing the constructed knowledge graph by using a reasoning technology, generating a data set and optimizing a loss function, performing model training on a reasoning model by using the data set, performing multi-task fine tuning in combination with the reasoning model, and enhancing the fraud recognition capability; and inputting a case description, and outputting a fraud type and a subsequent fraud means by the reasoning model. According to the method, the electric fraud record text can be combined with the pre-trained large model to realize knowledge reasoning of the electric fraud record.

The invention provides a quality inspection rule generation and deployment method which can be applied to the technical field of artificial intelligence, and relates to application of a large model in the fields of information security and financial science and technology. The quality inspection rule generation and deployment method comprises the steps of obtaining a to-be-analyzed file; inputting a preset prompt word, the target quality inspection rule and the constraint set into the first large model for semantic structured analysis to obtain structured data corresponding to the target quality inspection rule; comparing the structured data with historical rule data in a quality inspection rule production library, screening out target structured data, and constructing an object relation attribute graph based on the target structured data; performing rule logic analysis and code generation on the object relation attribute graph by utilizing a second large model to obtain intermediate rule language data; and generating a quality inspection rule code based on the intermediate rule language data, and deploying the quality inspection rule code to a quality inspection rule production library. The invention further provides a quality inspection rule generation and deployment device, equipment, a storage medium and a program product.

The invention discloses a Transform-based digital textbook automatic hierarchical design method. The method comprises the steps of obtaining digital textbook content, performing structured analysis, dividing the digital textbook content into a plurality of content units, and allocating a unique identifier; an improved Transform model is input, semantic modeling is executed, the cognitive hierarchy is controlled, and folding representation is generated; based on the folded semantic representation, initial layering is executed, a layered structure is constructed, and a sequence and hierarchical relationship is generated; constructing a self-evolution hierarchical decision maker, detecting conflicts in a hierarchical structure, and identifying a hierarchical inconsistent relationship; re-executing hierarchical processing based on the conflict relation updating rule to generate an updated hierarchical structure; and performing disturbance processing on the textbook structure, repeating modeling and layering processes, and verifying a hierarchical stability result. According to the method, the stable and automatic hierarchical design of the digital textbook is realized by improving Transform semantic modeling and self-evolution hierarchical decision.