736 results about "Hierarchical clustering" patented technology

The invention belongs to the technical field of knowledge retrieval, and discloses a multi-modal large model-based dynamic vector knowledge base construction and retrieval method, which comprises the following steps of: obtaining a multi-source heterogeneous modal data set, and carrying out preprocessing and modal standardization processing on the multi-source heterogeneous modal data set to obtain a standardized multi-modal data set; performing feature extraction and semantic vector representation generation by using the pre-trained multi-modal large model, and constructing a multi-modal knowledge vector set; semantic association analysis and hierarchical clustering are carried out on the multi-modal knowledge vector set, and a structured vector knowledge base is constructed; performing semantic similarity calculation and relation modeling on the vector knowledge base to form a vector relation network; intention analysis and vector representation are performed based on mixed modal query information input by a user, and efficient similarity retrieval is realized in combination with a vector relation network; dynamic optimization is carried out through user feedback, personalized retrieval result adjustment is achieved, and the problem of limitation of a traditional retrieval system during multi-modal data processing is effectively solved.

The present invention provides a method and system for extracting and analyzing long text information and association based on a large model, which relates to the technical field of natural language processing, including determining sliding window parameters based on semantic density values ​​to divide long text into blocks, extracting feature vectors of text blocks and calculating semantic relevance, and constructing a hierarchical clustering feature matrix to merge text blocks to obtain semantic fragments. Afterwards, the semantic fragments are syntactically analyzed to construct a semantic dependency tree, and the semantic network is extracted and the entity relationship strength value is calculated to construct a semantic association graph. Finally, the node features are extracted using a pre-trained large model, the semantic representation is fused through a hierarchical attention mechanism, the features are enhanced by propagating an adaptive graph network, and candidate paths are generated through a hierarchical decision network, the optimal path is selected, and the associated path and probability distribution are output. The present invention can effectively extract key information from long texts and analyze the association relationship between entities, thereby improving the accuracy and efficiency of information extraction.

A rolling bearing combined fault diagnosis method based on a multi-domain feature construction is provided, including: collecting vibration signals of rolling bearings, extracting time-domain and frequency-domain features of fault-sensitive vibration signals of the rolling bearings, and obtaining an in-domain feature set; performing an improved empirical mode decomposition on the vibration signals of the rolling bearings to obtain time-frequency features; constructing a multi-domain fault sample set based on the in-domain feature set and the time-frequency features; performing a fault mechanism analysis on the multi-domain fault sample set to obtain modulation features under each single fault; carrying out a feature engineering on the time-domain and frequency-domain features, and screening out relevant features, redundant features and irrelevant features; performing an unsupervised hierarchical clustering on test set fault samples, and then inputting the test set fault samples into a KNN model for a classification, and obtaining rolling bearing combined fault diagnosis results.

The invention discloses a safety control method based on a knowledge graph, and belongs to the field of safety control, and the method comprises the following steps: crawling various types of data according to keywords through a crawler program to construct a fireproof database; carrying out semantic analysis on data in the fireproof database by using a natural language processing technology, and carrying out data processing on the fireproof database after semantic analysis through word vector modeling and entity relationship extraction; constructing an internal large-scale language model by using the processed fireproof database, and performing hierarchical clustering on the internal large-scale language model by adopting a Leiden technology; establishing a multi-dimensional mapping model based on historic building spatial features, cultural relic features and disaster-inducing factors in a hierarchical clustering result, and constructing a multi-modal dynamic association fireproof knowledge graph by using a knowledge graph technology based on the multi-dimensional mapping model; and dynamically selecting a search mode based on a user query keyword, and generating a security control result according to the query keyword based on the search mode.

The invention belongs to the field of digital delivery, and particularly relates to a digital delivery topology mapping method and system for multi-source real-time data fusion, and the method comprises the steps: obtaining factory building distribution, equipment distribution and operation control logic and preset function block operation logic, and constructing a hierarchical clustering function mapping space through combining an association analysis and clustering algorithm; in response to a target function demand, obtaining a layered response mapping path in combination with a deep search algorithm; layered synchronous response and distributed node anomaly monitoring are realized based on the path, the three-dimensional simulation model and the display system equipment performance and the network state. Tracing abnormities based on a monitoring result in combination with a hidden Markov algorithm and a forward reasoning model, performing iterative verification after conflict resolution until the function is free of abnormities, and updating a mapping space; and adjusting the demand repeating steps to obtain a complete and updated mapping space, and realizing accurate function and picture collaboration under multi-source data fusion.

The invention discloses a personalized care method, device and equipment based on an AI technology and a storage medium, and solves the technical problem that an artificial intelligence dialogue system lacks dynamic memory, multi-dimensional user portrait updating and personalized care strategies in the prior art. The method comprises the steps of collecting data and preprocessing the data; the method comprises the following steps: generating a dynamic user feature vector by constructing an LSTM-GRU hybrid neural network structure; dynamic evolution of a user portrait is realized through incremental learning and memory attenuation, and a user multi-dimensional portrait is constructed; constructing a memory storage hierarchical architecture; extracting and optimizing keywords based on a medium-term memory storage system, and optimizing and constructing an interest graph by utilizing a hierarchical clustering method according to a keyword weight enhancement formula; based on a memory storage hierarchical architecture, establishing a causal relationship and a sequential relationship between entity nodes and emotion nodes to construct a memory association knowledge graph; and establishing a trigger mechanism, performing emotion memory composite analysis based on the memory association knowledge graph, and generating a care strategy. The method can be widely applied to the artificial intelligence technology field.

The invention relates to the technical field of knowledge extraction, in particular to a knowledge graph construction method and system based on a large model, and the method comprises the following steps: obtaining a current input statement of a user through a dialogue state tracker, inputting the statement into a BERT intention classification model for domain label analysis, behavior type recognition and emotional tendency detection, and outputting a three-dimensional classification vector; and extracting entity lexical items and relation predicates based on an LSTM sequence tagging device, and generating an original semantic structural body. According to the method, intention classification, behavior recognition and emotion detection are fused through three-dimensional semantic analysis, semantic comprehension granularity is improved, dynamic entity disambiguation is combined with a Manhattan distance threshold value and dialogue history tracking, semantic boundaries are defined to reduce anaphora ambiguity, and cross-modal alignment is enhanced through relation predicate hierarchical clustering and knowledge base dynamic matching; generative reply and semantic coherence reordering collaboratively keep topic continuation, and structured analysis and unstructured generation closed loop optimize semantic output and interaction fluency.

The embodiment of the invention provides a data processing method and system for an enterprise digital transformation platform, and belongs to the field of data processing. The method comprises the steps that structured field information from all heterogeneous data sources is acquired, and preprocessing operation is executed on the structured field information; constructing the processed structured field information into an embedded input sequence, splicing the embedded input sequence into a natural language fragment according to a preset template, and inputting the natural language fragment into a fine-tuned semantic coding model to obtain a corresponding semantic embedded vector; identifying similar field groups by adopting a clustering algorithm based on density or a hierarchical structure, and classifying each group of structured field information into a semantic cluster; and generating a corresponding standard field identifier for each semantic clustering cluster, and storing the generated standard field identifier in a standard field index database of the platform after digital transformation. According to the scheme, the field unified management and cross-system data alignment capability of the enterprise digital platform is remarkably enhanced.

The invention discloses an altered mineral analysis-based prospecting target prediction method and system, and relates to the technical field of prospecting target prediction. An altered mineral analysis-based prospecting target prediction system comprises a data acquisition module, a clustering analysis module, an alteration combination discrimination module, a spatial modeling module, a space-time coupling module and a metallogenic evaluation module. According to the method, altered minerals and symbiotic combinations of the altered minerals are subjected to layered clustering treatment by introducing mineral thermodynamic phase diagram constraints, multi-stage superposed alteration information in a complex structure area is effectively analyzed, and a mineral symbiotic combination structure model with cause difference expression ability is established; by constructing cause period labels and forming a time sequence decoupling model, systematic distinguishing of alteration bodies formed under the mineralization effect of different times is achieved, and a time sequence basis is provided for identification of the multi-stage mineralization process.

The invention relates to the technical field of database construction, in particular to a construction method and system for a reproductive medicine database. The method comprises the following steps: acquiring a biological tissue sample of a patient; acquiring reproductive endocrine hormone data, embryo morphological parameter data, immune cell function data and genome variation data from the biological tissue sample of the patient; performing time sequence analysis processing on the hormone fluctuation mode according to the reproductive endocrine hormone data to obtain endocrine dynamic characteristic data; performing image recognition processing based on coherent superposition characteristics of light on the embryo morphological parameter data, and performing embryo development potential evaluation to obtain embryo development potential data; and performing hierarchical clustering processing on the individual heterogeneity of the patient according to the immune cell function data and the genome variation data to obtain immune-genetic typing data of the patient. By means of the quantum dot sensing technology and the multi-channel micro-fluidic chip, standardization of data acquisition is achieved, and the heterogeneity problem of a data source is solved.

The invention provides a large model text generation method and system based on self-adaptive cue words, and belongs to the technical field of large model text generation. The technical problem to be solved is that the current large language model text generation has the defects of selection deviation and uncertainty. According to the technical scheme, hierarchical clustering processing is carried out on problems contained in a large-scale multi-task language understanding MMLU data set, a text embedding and similarity calculation matrix is adopted, and a plurality of problem clusters containing similar features are constructed and obtained; randomly extracting representative problem examples from each problem cluster, and constructing excellent cue words with remarkable guide effects for each problem based on domain knowledge and potential semantic association; a Qwen model is adopted to generate candidate cue words with diversity and pertinence for various question clusters divided in advance, a cue word bank is constructed, and each question and cue word pair in the cue word bank is manually scored; the method is applied to text generation.

The invention discloses a fault model construction method for adaptive testing of abnormal values of a system-on-chip. The method relates to the technical field of fault construction of chip testing, and comprises the following steps: testing chips on a large scale under the conditions of different temperatures, voltages, frequencies, loads and the like, acquiring performance parameters such as signal delay and power consumption, screening abnormal values by applying a Z-score or box plot method, and processing by using a sliding window; extracting features through CNN or RNN, and normalizing the features; fault features are classified through K-Means or hierarchical clustering, and the accuracy rate is larger than or equal to 80%; a model is built through an SVM or a decision tree, faults can be predicted, self-updating can be achieved, self-adaptive adjustment can be achieved according to chip parameter changes, independent data verification optimization, one-out-of-leave evaluation and gradient descent optimization are achieved. According to the method, multi-condition testing can be carried out on the system-level chip, abnormal values are accurately screened, features are extracted, faults are classified, a fault model capable of being self-updated is constructed, self-adaptive adjustment and verification optimization are achieved, the accuracy is improved, and the chip testing and fault diagnosis efficiency and accuracy are improved.

The invention relates to the field of 3D rendering, in particular to an intelligent scenic area three-dimensional image rendering method, which introduces a differentiable discrete decision into 3D fusion, supports end-to-end learning of a k value, performs discrete-continuous optimization based on an activation function, predicts an optimal k value of each voxel, introduces feature adaptive fusion based on dynamic neighborhood bidirectional retrieval, and realizes the 3D image rendering of the scenic area. The alignment of the color image and the point cloud is enhanced, the false detection rate of a small target is reduced, the detail reconstruction capability of a large target is improved, and the comprehensive rendering capability of a scenic spot is improved; according to the method, a lightweight grid is adopted to express a scenic spot subject, residual Gaussian is introduced to supplement high-frequency detail features, the number of Gaussian is reduced, rendering efficiency and capability are improved, textures are generated based on initial rendering back projection, fuzzy view angle dependence is avoided, hierarchical clustering and contour extraction from bottom to top are adopted on the basis, and the method is more efficient and efficient. The point cloud vertical structure change is dynamically detected, the point cloud is complemented, the accurate contour is extracted, the number of grid vertexes is reduced, and the rendering integrity is improved.

The invention relates to the technical field of artificial intelligence, in particular to a self-organizing knowledge base construction method, system and device based on a multi-agent system and a storage medium, and the method comprises the steps: S1, monitoring a dialogue information flow, calculating the self-reply confidence coefficient of an agent, and if the self-reply confidence coefficient exceeds a preset dynamic threshold value, judging that a potential knowledge value exists and triggering an extraction request; s2, in response to the request, performing semantic coding on a dialogue fragment, extracting knowledge elements, and generating a structured knowledge unit by utilizing graph neural network modeling and relation calibration; s3, through a pre-training semantic coding model, mapping the multi-dimensional semantic coding model into a multi-dimensional semantic vector; s4, carrying out hierarchical clustering comparison and combination, and if a threshold value is exceeded, establishing a new classification and adjusting a knowledge base index; and S5, knowledge fingerprints are generated and compared, and fusion updating is carried out based on the reputation scoring model when semantics conflict or redundancy occurs. According to the method, unstructured dialogue high-precision knowledge extraction is realized, so that the knowledge base structure is dynamically self-organized according to semantics, multi-source knowledge is coordinated, and the knowledge management automation level and quality are improved.

The invention discloses a high-dimensional time series data cluster structure prediction method based on dynamic hierarchical clustering and LSTM fusion. The method comprises the following steps: receiving a high-dimensional heterogeneous time series data set; performing clustering processing on the high-dimensional heterogeneous time series data set to obtain a cluster structure and a centroid matrix at the current moment; based on the cluster structure and the centroid matrix, time sequence features are extracted from three dimensions of a centroid track, a topological structure and scale dynamic, and a multi-channel time sequence feature tensor is generated through coding; and inputting the multi-channel feature tensor into the LSTM, and outputting a prediction result of the centroid offset and the topology change probability at the next moment by applying an attention gating mechanism. According to the method, the system and the equipment provided by the invention, efficient clustering and accurate prediction of high-dimensional heterogeneous time series data are realized through deep fusion of dynamic hierarchical clustering and LSTM, and the timeliness, the accuracy and the calculation efficiency of time series data processing are remarkably improved.

The invention provides an intelligent data analysis and knowledge question and answer method and system based on a multi-modal large model. The method comprises the following steps: receiving a multi-modal query request, performing semantic analysis and intention recognition technologies, determining a question core and a required knowledge field, and generating a personalized question understanding framework; performing manifold embedding processing by applying a manifold learning enhancement algorithm, revealing an internal geometric structure, constructing a shared representation space by adopting a multi-task learning technology, training a plurality of sub-tasks at the same time, and generating cross-modal unified representation; performing recurrent processing by using a recurrent neural network algorithm, recursively updating an internal state, constructing an organized information structure by using a hierarchical clustering method, and generating an ordered information structure chart; a context awareness feedback mechanism is introduced, instant response and environment change are monitored in real time, an answer strategy is dynamically adjusted, instant feedback is recorded, and an intelligent data analysis and knowledge question and answer scheme is generated. According to the technical scheme provided by the invention, the accuracy of intelligent data analysis and knowledge questions and answers and the user experience are improved.

The invention discloses a high-precision distributed photovoltaic output prediction method and system based on micrometeorology and feature hierarchical clustering, and relates to the technical field of distributed photovoltaic output prediction.The method comprises the steps that numerical weather forecast data are collected, an initial micrometeorological field is generated through space-time alignment and self-adaptive KNN interpolation, and the initial micrometeorological field is subjected to feature clustering; a WRF-LES system and a bidirectional LSTM are combined to establish cross-scale mapping, a dynamic residual correction field is fused to generate hectometer-level high-resolution micrometeorological data, and the problem of insufficient resolution of traditional numerical forecasting is solved. MIC and PA-DTW are used for jointly analyzing the characteristics of the power station, and dynamic clustering is achieved through a sliding time window and incremental spectral clustering. According to the method, a physical information graph network and causal expansion convolution are coupled to extract features, federal learning cross-power-station cooperative training is combined, the distributed photovoltaic output prediction precision and robustness are improved, and privacy security is considered.

The invention provides a data processing method for seabed-based monitoring equipment, which belongs to the technical field of electric digital data processing, and comprises the following steps: normalizing acquired data, removing abnormal values, extracting key features by using a singular value decomposition method, and segmenting by using a self-adaptive sliding time domain window to form time sequence data blocks. According to the method, a CUDA parallel processing flow is creatively used to construct a disorder matrix and a drift matrix, and a disorder mode feature vector, a drift principal component and an abnormal feature matrix are fused through a tensor decomposition technology to construct a seabed environment state tensor model. Meanwhile, a drift rule in a long-time sequence is learned by utilizing a deep sea environment perception attention network model, real-time monitoring data is corrected and compensated, and finally a seabed state evaluation standard is established by adopting a hierarchical clustering algorithm, so that the key technical problem that environment change and equipment drift in seabed monitoring data are difficult to distinguish is effectively solved.

The invention provides an inclined real spot cloud building top surface segmentation method, and the method comprises the steps: carrying out the segmentation of an inclined building top surface point cloud through employing a clustering-recognition-detail supplement progressive fine classification method, and obtaining a building top plane point cloud. Firstly, the normal of each point is determined by adopting a point cloud gridding method, the plane and the vertical face of the top of a building are distinguished according to the angle of the normal, and point cloud category marking is achieved. Carrying out rough clustering segmentation on the top surface of the building by adopting a hierarchical clustering method based on height statistics, and reducing the interference between clusters in a plane segmentation process; and finally, edge point cloud supplementation is carried out on the building top plane point cloud to realize supplementation of holes in the plane, and the effect of perfecting the details of the point cloud plane is achieved.

The invention relates to the technical field of health management of old people, and discloses a method and a system for predicting multiple diseases of old people. The method comprises the following steps: acquiring multi-modal health monitoring data of a target old person in a preset time period, wherein the multi-modal health monitoring data comprises physiological index time sequence data, a medication record sequence and a daily activity ability evaluation result; and performing cross-modal correlation analysis on the multi-modal health monitoring data to generate a disease interaction characteristic matrix containing metabolic disease correlation degree, circulatory system disease coordination index and neurodegenerative disease progress rate. And performing hierarchical clustering processing on the feature matrix by adopting a dynamic weight distribution algorithm, and outputting potential common disease combinations of the target old people and a priority score of each common disease combination. And generating a personalized intervention strategy set including a drug interaction avoidance scheme, a rehabilitation training intensity adjustment scheme and a nutrition intake ratio scheme according to the priority score.

The invention relates to the technical field of underground coal mine safety, and discloses an underground coal mine early warning system and method. The method comprises the following steps: collecting multidimensional monitoring data of an underground coal mine environment; performing dynamic window division on the multi-dimensional monitoring data to generate a data fragment set; performing abnormal mode recognition based on the data fragment set, and extracting abnormal feature points exceeding a dynamic threshold in each data fragment; inputting the abnormal feature points into a pre-constructed space-time correlation model, and analyzing diffusion paths of the abnormal feature points in a time dimension and a space dimension; according to the diffusion path, generating an abnormal propagation map containing the propagation direction and the intensity attenuation gradient of the abnormal feature points; hierarchical clustering is carried out on the abnormal propagation atlas, and abnormal clusters with similar propagation characteristics are divided; generating an early warning signal containing a priority label by using the spatial and temporal distribution characteristics of the abnormal cluster; performing synchronous verification on the early warning signal and underground equipment state data, and screening out a target early warning signal needing to be responded; and triggering a corresponding emergency control instruction according to the target early warning signal.

The invention discloses a short-term wind power cluster power prediction method supporting conventional meteorological typing and extreme weather small sample expansion, and the method comprises the steps: comprehensively utilizing NWP data, wind power plant operation data and other related information; the technologies such as a Granger causal relationship analysis algorithm, a hierarchical clustering method and a neural network are utilized to realize conventional meteorological scene typing and extreme weather small sample amplification, so that the defects caused by fuzzy prediction scene, low sensitivity to meteorological changes and insufficient consideration to extreme conditions in traditional wind power prediction can be overcome; and moreover, the precision of the wind power short-term prediction model can be improved, and the problems that the prediction model cannot cope with the influence of extreme weather and the power prediction result still has large deviation are solved.

The invention provides an unknown encrypted traffic identification method and system based on small sample incremental learning, and a storage medium, and the method comprises the steps: 1, carrying out the fine-grained classification of known traffic: extracting the features of each level of original encrypted traffic, building a respective variational auto-encoder for each type of known traffic, generating a potential representation, and carrying out the fine-grained classification of the known traffic; inputting into a classifier to classify known attacks in a fine-grained manner; step 2, specific label distribution of unknown traffic: judging whether the sample is a drift sample or an unknown sample by adopting a scoring function, and performing hierarchical clustering on the samples according to each hierarchical feature to realize label distribution of the unknown traffic; and step 3, dynamically updating the classification model: training a new classifier by adopting a new sample, connecting other classifiers to form a classification graph, and updating nodes of the classifiers by adopting a graph attention network to realize small sample incremental learning. The method has the beneficial effects that low-sample incremental modeling of a new class is effectively supported, and the fine-grained recognition capability and the model generalization adaptability are remarkably improved.

The present invention discloses a prediction application based on an adaptive algorithm in student portrait data, which belongs to the technical field of personalized learning in education, including S1, collecting multi-dimensional data of students, and performing data cleaning and standardization preprocessing; S2, using a hierarchical clustering chameleon algorithm to perform cluster analysis on student data to form different student group portraits, and establishing a linear regression dynamic model based on these portraits to describe the student's behavior and grades, and using the recursive least squares method to identify the model parameters; S3, using the AGPC model to perform multi-step prediction, and according to the prediction results, using optimization technology to calculate the control input to achieve the expected learning effect. At the same time, through an adaptive mechanism, the model parameters and control strategies are dynamically adjusted according to actual feedback information to adapt to the changes of students; S4, applying the calculated control input to the actual teaching process to adjust the teaching content, difficulty or method, thereby optimizing teaching decisions and improving teaching quality.

The invention relates to a bio-inspired large-scale bee colony networking and formation control method, which comprises the following steps of: constructing a hierarchical clustering network by using a method based on maximum connectivity, and constructing routes in clusters and among the clusters; a hierarchical clustering network and grouping formation control are fused, and a message format is designed; the method comprises the following steps: constructing formation groups by network clustering; realizing pigeon flock formation simulation control in the groups; updating pigeon flock social levels by utilizing cluster heads; realizing starling formation simulation control among the groups; on the grouping level, a starling group mode, an obstacle avoidance mode and a following mode are realized. Compared with the prior art, the hierarchical clustering network architecture effectively reduces the routing overhead of the large-scale unmanned aerial vehicle cluster, and meanwhile, the control complexity and the control communication overhead of the large-scale unmanned aerial vehicle cluster are effectively reduced through the pigeon flock formation simulation control method and the starling formation simulation control method and the distributed architecture thereof.

The present application relates to the technical field of network routing, and discloses a method and system for adaptively adjusting network routing, the method comprising: performing detection data packet transmission and hierarchical clustering on a network interface to obtain a path cluster set; performing double-circular matrix encoding on the path cluster set to obtain a routing path encoding set; constructing a spatiotemporal feature tensor based on the routing path encoding set, and performing tensor decomposition and feature pattern extraction to obtain a time-varying weight coefficient; performing sparse-aware proportional iterative optimization calculation based on the spatiotemporal feature tensor and the time-varying weight coefficient to obtain an optimal routing configuration scheme, thereby achieving differentiated processing of different business flows and improving network resource utilization efficiency.

Disclosed in the present invention is an unmanned aerial vehicle flight trajectory anomaly tracing method based on a fusion clustering algorithm. A hierarchical clustering algorithm is applied to classify and organize complex and sensitive spatio-temporal data features to identify a potential anomaly point; then, weights of various data features obtained by using a Lasso regression algorithm are used to calculate the sum of products of the potential anomaly point and the weights of the various data features; a system detects an anomaly point in a flight trajectory of an unmanned aerial vehicle to ensure flight safety and stability; the anomaly point is confirmed; and the system instantly calls an improved KD tree algorithm to quickly notify nearest on-duty personnel to go to the site, thereby comprehensively inspecting and thoroughly verifying factors interfering with the normal flight of the unmanned aerial vehicle, quickly and accurately processing anomaly conditions of the flight of the unmanned aerial vehicle, ensuring the flight safety and data integrity of the unmanned aerial vehicle, and providing reliable guarantee and support for a flight system of the unmanned aerial vehicle.

A disease marker structure evolution characteristic change point determination method belongs to the field of disease markers, and comprises the steps of obtaining and preprocessing time sequence structure characteristic data of a disease marker, constructing a characteristic transformation image and calculating characteristic intensity distribution, establishing a structure characteristic fitting model and calculating a time evolution coefficient and an intensity evolution coefficient, determining a structural feature evolution trajectory and calculating a correlation index; establishing a structural feature piecewise function and identifying a feature mutation point; calculating a structural feature contribution value and generating a feature evolution matrix; calculating an evolution stability index and determining a structural feature change point; hierarchical clustering is carried out, main structural feature change points and secondary structural feature change points are determined, a feature weight distribution diagram is constructed, a change point time sequence table is generated, a time sequence corresponding relation is established, and a structural feature change point determination result is output; and fine analysis and accurate description of structural evolution characteristics of complex disease markers are realized.

The invention discloses a code repository question and answer method based on a hierarchical code knowledge graph, which comprises the following steps: collecting all files of a target code repository, and preprocessing to obtain code files and text files; generating an abstract syntax tree from the code file, and extracting related entities; carrying out block segmentation on the text file, and extracting to obtain text blocks; constructing a code knowledge graph representing a code structure based on the extracted entities and text blocks; generating a hierarchical code knowledge graph by adopting a community detection algorithm and a hierarchical clustering method, and generating a functional abstract for each community in combination with a large language model; a Monte Carlo tree search-based code warehouse question and answer agent is used for performing question and answer path optimization, an optimal query track is dynamically planned through the steps of node selection, expansion, simulation, reward evaluation and the like, and a path with the highest accumulated reward is selected to generate a final question and answer result. The code knowledge modeling quality and the reasoning accuracy and efficiency of the question answering system can be effectively improved.

The invention discloses an internal medicine drug curative effect evaluation method based on big data analysis of an electronic health record, and the method comprises the steps: extracting basic health data, diagnosis and treatment time sequence data and drug intervention data from the electronic health record, and carrying out the time-space alignment to generate a dynamic feature set; subgroups are obtained based on disease typing standard hierarchical clustering, and historical data and real world data are fused through transfer learning to construct a subgroup curative effect reference matrix; collecting data after medication in real time, and generating an evaluation vector containing short-term physiological response, middle-term symptom improvement and long-term prognosis risk through deep learning; dynamically matching the evaluation vector with the reference matrix, and introducing an individual weight coefficient to correct deviation; taking the deviation correction value as input, constructing a self-adaptive evaluation model through reinforcement learning, and performing iterative optimization; and generating an individualized report containing the curative effect level, the medication suggestion and the risk early warning, and quantifying the curative effect level through a fuzzy comprehensive evaluation method. According to the method, individual differences are accurately captured, full-cycle dynamic evaluation is realized, and the curative effect evaluation accuracy and the clinical decision-making efficiency are improved.