225 results about "Text mining" patented technology

The invention relates to the field of financial science and technology, and discloses a financial risk dynamic assessment system and method based on artificial intelligence. The system comprises a multi-source heterogeneous data acquisition module which acquires transaction data, public opinion texts and association maps in real time; the adaptive feature engineering module dynamically screens key risk factors; the dynamic risk map construction module calculates a risk conduction coefficient through a map neural network; the multi-modal AI analysis engine cooperatively runs a time sequence prediction model, a text mining model and a graph calculation model; a risk conduction simulator quantifies a systematic risk path. The problems of data splitting processing, model static solidification and correlation risk quantification deficiency in the prior art are solved, the false alarm rate is reduced to 12%, the response speed reaches 90 seconds, the prediction deviation is reduced to 22%, and an interpretable supervision report is generated.

The invention discloses a person and post matching recommendation method based on BERT and a latent semantic algorithm model, and relates to the technical field of person and post matching recommendation, and the method comprises the steps: obtaining the related data of job hunters and the post recruitment text data of employers; performing word segmentation and part-of-speech tagging on the obtained text data, screening out candidate words and noun phrases, obtaining candidate word and document word embedding vectors by adopting a trained BERT model, calculating semantic similarity of the embedding vectors, and creating BERT bitmap data according to a calculation result; a latent semantic model is adopted to analyze semantic correlation in text data, preference prediction scores of job hunters for posts are calculated, corresponding class vectors are calculated through a deep random forest method, the matching probability of the job hunters and the posts is extracted, and post matching recommendation is carried out according to a probability sorting list. By adopting the hybrid recommendation method based on the latent semantic model and the deep random forest, the recommendation is more accurate, and the efficiency is higher.

The invention relates to the technical field of task management, in particular to an intelligent government affair service platform which comprises a priority adjustment module, a process adjustment module, an intelligent text mining module and an intelligent matching optimization module. According to the invention, by analyzing historical data and user behaviors completed by government affair service tasks, accurate sorting of task priorities is realized, the task processing efficiency is effectively improved, the working process can be adjusted in real time when public health emergencies or natural disasters are dealt with, the instantaneity and adaptability of government actions are ensured, and the user experience is improved. Government affair text data are deeply mined, key information is extracted, a decision support system is optimized, the service precision is enhanced, the consistency and efficiency of the working process are remarkably improved by accurately analyzing the matching degree of text content and working requirements and optimizing information display, the processing time and potential errors are reduced, and the working efficiency is improved. Therefore, the overall response speed and quality of government service are improved.

The invention discloses a user demand comprehensive analysis method based on online user comment data. The method comprises: calculating a user demand intensity value; calculating a user demand emotion score; calculating a user demand weight value by adopting an IGR-AHP model based on the user demand emotion score; and respectively endowing the user demand intensity value and the user demand weight value with weight coefficients by using a weighted average method, thereby calculating a user comprehensive evaluation index value, and finally carrying out priority ranking on the user demands according to the user comprehensive evaluation index value. The method has the advantages that the LDA topic model is used for text mining, and the problem of how to effectively extract user demand information from a large number of user comments is solved; a user demand emotion score is calculated by fine tuning the BERT model, and the problem that a traditional emotion dictionary analysis method is tedious in process is solved; and finally, combining and considering the strength value and the weight value of the user demand through a weighted average method, and carrying out priority ranking on the user demand.

The invention discloses a regional document travel intelligent recommendation method, which comprises the following steps of: S1, collecting a large amount of data, and preprocessing the data; s2, constructing a user portrait by using a machine learning algorithm based on the behavior data of the user; s3, performing feature extraction on the data, processing text description related to the cultural resources by using a text mining technology, and extracting specified information; s4, designing a recommendation model, matching the user portrait with the text travel resource features, and calculating the preference degree of the user to the specific text travel resource; s5, generating a personalized regional travel recommendation result for the user by using the recommendation model, and selecting the most suitable recommendation result from the database of the travel resources according to the portrait and the historical behavior of the user; and S6, updating the user portrait and the recommendation model in real time, and providing a dynamic personalized recommendation result. According to the invention, the prediction score of the target user can be improved, and more accurate tourism place recommendation can be carried out on the target user.

The present invention relates to the field of artificial intelligence natural language processing, and in particular to a topic clustering method driven by a large language model; the method comprises the following steps: S1) arranging document set data; S2) using the HDBSCAN clustering algorithm to perform preliminary clustering on the document set D to obtain a plurality of clusters {C k}; S3) Model the document topics using the LDA topic model to obtain the topic distribution of each document; S4) Adjust the clustering results based on the results obtained from the LDA model; S5) Further refine the topics of each cluster using LLM; S6) Establish effective and explainable associations between the generated topic list and the documents in the data set. The topic clustering system driven by the large language model of the present invention achieves a deep understanding and fine clustering of document topics by integrating HDBSCAN clustering, LDA and the large language model, providing a solid foundation for subsequent information retrieval, text mining and knowledge graph construction.

The invention relates to the technical field of performance appraisal management, in particular to a mine workshop performance appraisal management method, which comprises data acquisition and preprocessing, dynamic performance evaluation, task difficulty adjustment, an incentive mechanism and performance feedback and optimization. Optimizing factor weights and predicting employee performance based on a multi-factor weighting model in combination with a neural network and reinforcement learning, and calculating performance scores in real time; the task difficulty is quantified by applying an analytic hierarchy process and fuzzy comprehensive evaluation, and real-time adaptive optimization is analyzed in combination with employee ability; a market supply and demand and collaborative filtering algorithm is adopted to adjust an integral value and a customization scheme, and an award standard is analyzed and optimized according to cost effectiveness and performance sensitivity; a report is generated by means of a visual library, suggestions are generated by means of text mining, and through prediction and causal analysis early warning, a management strategy adjustment scheme is recommended according to reinforcement learning, and feedback optimization is performed; according to the invention, the performance of the mine workshop can be comprehensively, accurately and dynamically assessed and managed, the operation efficiency and the enthusiasm of employees are improved, and sustainable development of enterprises is promoted.

The present application provides a method and system for determining regulation-violating behaviors underground. The method comprises: acquiring historical personnel behavior hidden danger data within a target area underground, and performing text mining and data processing on the historical personnel behavior hidden danger data to obtain spatial-temporal patterns of hidden dangers and prediction and early-warning results; performing association analysis on the basis of personnel regulation-violating data, the spatial-temporal patterns of the hidden dangers and the prediction and early-warning results, and carrying out knowledge modeling to determine an ontology model; using a knowledge extraction method to extract knowledge elements of a knowledge graph from the ontology model; performing knowledge fusion processing on the knowledge elements to determine knowledge fusion data; on the basis of the knowledge fusion data, using a top-down and bottom-up hybrid construction method to construct the knowledge graph; and determining regulation-violating behaviors of personnel underground on the basis of the knowledge graph. The present application can dynamically diagnose safety production risks and regulation-violating behaviors in mines, and can discover and predict related accident signs and provide early warnings to ensure safety production.

A text mining engine running on an artificial platform is trained to perform conversation role identification, semantic analysis, summarization, language detection, etc. The text mining engine analyzes words in a transcript that represent unique characteristics of a conversation and, based on the unique characteristics and utilizing classification predictive modeling, determines a conversation role for each participant of the conversation and metadata describing the conversation such as tonality of words spoken by a participant in a particular conversation role. Outputs from the text mining engine are indexed and useful for various purposes. For instance, because the system can identify which speaker in a customer service call is likely an agent and which speaker is likely a customer, words spoken by the agent can be analyzed for compliance reasons, training agents, providing quality assurance for improving customer service, providing feedback to improve the performance of the text mining engine, etc.

The invention discloses an offshore bridge construction safety risk identification method based on text mining and an N-K-SNA model, and the method comprises the steps: 1) obtaining an offshore bridge construction accident report, and carrying out the preprocessing of the accident report, and obtaining an accident report text; 2) respectively adding the accident report text into a special dictionary for offshore bridge construction and a stop word dictionary, and then carrying out word segmentation to obtain the accident report text after word segmentation; 3) carrying out keyword information extraction on the report text subjected to word segmentation by utilizing a TF-IDF (Term Frequency-Inverse Document Frequency) algorithm; 4) combining the keywords into risk factors, and dividing the risk factors into four categories of personnel, articles, environment and management factors; 5) carrying out statistics on risk coupling types and occurrence frequencies of the collection cases, and calculating a coupling value of a target risk coupling mode by using an N-K model; 6) constructing a risk factor mutual occurrence matrix, generating a risk factor network, and analyzing the centrality and accessibility of network nodes through an SNA model; and 7) fusing the N-K model and the SNA model, and identifying key risk factors of offshore bridge construction.

The invention discloses an IDI full-project cycle risk digital assessment method and system, which have the beneficial effects that potential risk factors are identified from project data by adopting a text mining technology, risk factor importance assessment is carried out by utilizing a mode of weighting word frequency through information entropy, risk influence factors can be comprehensively identified, and the risk assessment accuracy is improved. Exploring the internal influence of project quality and risk factors; a multi-level and multi-dimensional risk evaluation system is constructed by adopting an analytic hierarchy process, so that the quality risk state of a project can be comprehensively reflected, and a quantitative standard of an evaluation index system is given; according to the method, a project risk evaluation model is constructed, a project risk scoring model is constructed from four dimensions of a hierarchical weight matrix, a severity matrix, a conditional probability matrix and an index occurrence probability matrix of risk indexes, various risk factors are considered, and the mutual relation and occurrence possibility between risks can be known more comprehensively; a Monte Carlo simulation method is adopted to grade and divide project risks, and the risk can be quickly traced, monitored and managed according to a result.

The invention discloses a data analysis decision-making method, device and equipment and a medium, and is suitable for data analysis decision-making in the financial and medical fields. The data analysis decision-making method comprises the following steps: firstly, performing distributed data acquisition from a target data source by combining a passive collection mode and an active collection mode to obtain original data; and then performing efficient cleaning and processing on the original data through a preset segmentation rule and a pre-trained detection model. And through text mining analysis and association rule mining technologies, key modes and potential associations in a large amount of data are extracted, so that an accurate target portrait is constructed, and dynamic adjustment is performed according to real-time updated original data. And finally, through a preset decision-making algorithm, based on the target portrait, carrying out deep analysis and generating a decision-making report. According to the method, the efficiency and accuracy of data processing and decision support are remarkably improved.

The invention provides a construction method of a foundation pit construction accident knowledge graph based on text mining. According to the method, related information, accident root causes, affected factors and other elements of foundation pit accidents are associated and connected, the occurrence mechanism and the affected factors of the accidents are comprehensively understood, and experience and guidance are provided for safety management and risk control of foundation pit construction. According to the method, the foundation pit accident knowledge graph with a complete structure and rich contents is constructed, elements such as accident related information, accident root causes and affected factors are associated and connected in a portrait representation mode, reliable reference is provided for decision makers, the accident prevention and management capability is improved, and the accident prevention and management efficiency is improved. And a positive promotion effect is achieved for promoting development and safety management of the foundation pit construction industry.

The invention provides a multi-document key phrase extraction method based on graph structure node influence, and relates to the technical field of natural language processing and text mining. Firstly, a candidate phrase set is generated through noun phrase extraction and standardization; secondly, a semantic relation between phrases is captured through local subgraph construction and a sliding window mechanism, and the semantic relation is integrated into a global phrase co-occurrence graph; thirdly, dynamically dividing theme communities based on two-dimensional structure entropy minimization and a potential game model, and identifying phrase groups with high semantic aggregation; then, cross-topic nodes are processed through a structure entropy heuristic function, and flexibility of topic division is enhanced; and finally, in combination with node influence sorting, extracting key phrases with theme representativeness and propagation capability. The method does not need to label data, is suitable for multiple fields of academic literatures, news texts and the like, has high efficiency, accuracy and universality, and provides an innovative solution for multi-document key phrase extraction.

The invention relates to the technical field of artificial intelligence, and discloses a dialogue text mining method and system based on code scanning consumption cash back of big data, and the system comprises a consumption data collection module, a multi-modal analysis module, a strategy decision engine and a dynamic execution module. When code scanning consumption cash-back strategy decision making is carried out, real-time dialogue text semantics and consumption time-space behavior data are fused, a deep association mechanism of consumption intentions and payment scenes is established, the matching deviation of user language expression and actual consumption behaviors can be eliminated in real time, the consistency of a cash-back strategy and real consumption appeals is ensured, and the payment efficiency is improved. The problem of strategy mismatching caused by semantic understanding isolation in a traditional system is effectively avoided, the precision of personalized marketing is improved, double feedback of dialogue emotion tendency and historical strategy effects is dynamically analyzed, the deviation value of strategy feature matching is corrected in real time, and the system can actively adjust weight distribution of feature combinations in a strategy library.

The invention relates to the technical field of safety production management and intelligent analysis, in particular to a production safety accident key cause link identification method based on a complex network, which comprises the following steps of: acquiring structured production safety accident report data from a safety production supervision platform by utilizing a web crawler technology; a TF-IDF algorithm is combined with an industrial standard term library to optimize Jieba word segmentation, and a chi-square statistical method is adopted to calculate the correlation between each risk keyword and an accident category. By fusing text mining, statistical analysis and complex network theories and combining field dictionary optimization word segmentation, multi-accident model adaptation and triple centrality index analysis, the problems that a traditional method is single in analysis dimension, depends on subjective experience and is insufficient in technology integration are solved; efficient utilization of unstructured data, scientific layering of risk factors and accurate identification of key cause links are realized, and objectivity and effectiveness of safety management of a complex production system are effectively improved.

The invention discloses a quality evaluation method and system for an AI large model text training data set. The method comprises the steps that the large model text training data set is preprocessed; setting an evaluation index system, and defining a threshold value and a weight of each index; setting a data set sampling rule, and extracting a data set; counting preset data indexes; performing prejudice and compliance evaluation on the data set by using a large model through cue words and evaluation specifications; identifying illegal toxic contents in the data set according to evaluation specifications by utilizing cooperation of a text mining technology and a large model; and calculating a text confusion degree of the data set based on the BERT model, and calculating a confusion degree evaluation result according to the evaluation specification. Through the technical scheme of the invention, invalid training and influence on large model capability output caused by a data set problem are avoided, the AI large model training efficiency and the model effect are effectively improved, meanwhile, the large model training cost is greatly reduced, and the evaluation effect is remarkably improved.

A method for monitoring bills and legal regulations according to one embodiment of the present invention is a method for monitoring bills and legal regulations performed by a computing system including at least one memory and at least one processor, and includes the steps of storing template information including predetermined items, receiving basic information including reasons for proposing the bill and main contents from a user terminal, storing the basic information in an analysis server, forming extracted information from the basic information through text mining based on the predetermined items, generating regulatory impact analysis data based on the extracted information and the template information, and outputting a regulatory impact analysis interface for the bill or legal regulation to the user terminal based on the generated regulatory impact analysis data.

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.

In one aspect, a computerized method for document extraction workflow for unstructured documents includes the steps of implementing a text mining operation on a set of digital documents the incoming documents. This is done by defining a document type of each digital document. Based on the document type, the method defines a set of data dictionaries to extract any data from each digital document. The method uses the defined set of data dictionaries to extract any data from each digital document.

The invention discloses a recommendation method and system based on a large language model and time sequence link prediction, and the method comprises the steps: obtaining related patent data of an industry field where a target company is located, inputting the related patent data into a trained large language model, and outputting a patent division result of each stage of a patent industry chain in the industry field; constructing a time sequence network from the patent data of each industry chain stage by adopting a sliding window strategy, and extracting time sequence features; inputting the extracted time sequence features into a CNN-GRU model with a multi-head attention mechanism, which is optimized by a rabbet optimization algorithm, outputting a prediction result, and carrying out potential technology fusion opportunity screening and verification; and according to the social influence and the technology focusing degree of the selected technology fusion opportunity, recommending a potential partner of the next stage. The invention aims to accurately identify and recommend potential technical partners from the perspective of time dimension and dynamic change by combining methods of deep learning text mining, a knowledge graph, a multi-dimensional neighbor attribute network and the like.

The present application discloses a sensitive data identification method and apparatus, a device, and a computer storage medium. A text mining technology is used to mine a plurality of sensitive data rules from a data security specification file of a target industry to form a sensitive data rule base, the rule base is continuously augmented by using technologies such as NLP and NER, and after data to be identified of the target industry is obtained, a sensitivity class and a sensitivity level of the data to be identified can be identified by matching the sensitive data rules in the sensitive data rule base corresponding to the target industry with the data to be identified.

The invention relates to a multi-modal medical text extraction method based on adaptive domain knowledge fusion, and belongs to the technical field of medical text mining, and the method comprises the following steps: text entity-relationship mining: extracting related entities and semantic relationships from clinical structured and unstructured texts, and generating a preliminary text disease network; professional knowledge linking: performing knowledge alignment by using an external medical knowledge graph; and extracting a medical spatio-temporal event chain: extracting an event sequence with a time sequence and a spatial position from a clinical text, constructing a dynamic text disease knowledge network, and extracting a multi-modal medical text.

The invention discloses a civil aviation control operation safety risk assessment method based on text mining and network analysis, and relates to the field of civil aviation control operation safety risk detection, and the method comprises the following steps: S1, collecting domestic and overseas air traffic control operation unsafe event data, and carrying out the preprocessing, including the processing of irrelevant and duplicated data, and the processing of the unsafe event data; constructing an air traffic control operation safety risk management stop word and segmented word library; s2, based on the processed data, a BERTopic topic model is used for identifying civil aviation control operation safety risk topics, model parameters of the BERTopic are modified through topic coherence (TC) and topic diversity (TD) among the topics, and the risk topics are optimized; s3, setting causal relationship network nodes based on the civil aviation control operation safety risk theme, and constructing a causal relationship network to determine the mutual relationship between civil aviation control operation safety risk factors; and S4, identifying high-risk nodes in the civil aviation control operation safety causal relationship network based on topology analysis, determining key cause factors in civil aviation control operation safety events, and proposing a risk control strategy. By adopting the steps, the civil aviation control operation safety risk theme is identified, the causal relationship network is constructed, the risk cause factors are determined, and finally the targeted risk control strategy is put forward, so that stable and safe operation of the air transportation system is facilitated.

The invention relates to the technical field of clinical test information extraction, and discloses an information extraction method of a clinical test scheme. According to the method, clinical test scheme files, registration information, research papers and related literatures are collected, data are sorted, an electronic database is formed, a text mining technology and a natural language processing tool are used, the collected literatures are analyzed, key information is labeled, and the literatures are analyzed. Calculating a random control test design type proportion and an open label design type proportion, extracting the marked data into a spreadsheet, checking the extracted data, implementing double check, calculating a consistency proportion and an error rate in the checking process, and performing statistical analysis according to the extracted data, so as to obtain a statistical analysis result; calculating a mean value, a standard deviation and a confidence interval, extracting a minimum value and a maximum value of data and a combination effect quantity of a combination end point, arranging analysis results into a report, including detailed description of test design, key results and conclusions, and displaying by using charts and graphs.

The invention belongs to the technical field of natural language processing and text mining, and discloses an online forum-oriented low-resource topic key topic extraction method, which comprises the following steps of: performing semantic-preserving data enhancement on an original text through a large language model to generate an enhanced document set; utilizing a pre-training language model to extract context-aware semantic representation of the document; constructing a learnable topic embedding matrix, and calculating and generating topic distribution; designing a semantic perception contrast learning framework, and optimizing theme diversity by adopting a dynamic negative sample screening strategy; and meanwhile, priori alignment loss is used for ensuring theme consistency. According to the invention, an LLM enhanced data expansion mechanism and a lightweight theme coding architecture are creatively fused, and through dual optimization of contrast learning regularization and prior distribution matching, three technical problems of data sparsity, model over-fitting and noise sensitivity in a low-resource scene are effectively solved; and an efficient and reliable theme modeling solution is provided for social media public opinion analysis.

The invention discloses an enterprise hidden danger-accident association early warning method based on text mining, and relates to the technical field of association early warning and oil gas safety. According to the method, the high relevance between hidden dangers and accidents can be better revealed, the incidence relation between'accident cause segmented words' and'hidden dangers' is used as a data source of the model, and an association rule mining algorithm (Apriori algorithm) is used for finding out the hidden dangers; a potential association mode between the hidden danger and the accident is mined through a chain of hidden danger-keyword-accident. The method not only comprehensively reflects the relationship between the hidden danger and the accident from the perspective of data, but also reveals that the hidden danger is the key cause of the occurrence of some accidents, and the analysis method is obviously superior to the traditional hidden danger analysis means depending on subjective judgment or single dimension.

The invention discloses a brain connection event causal relationship extraction method based on weighted joint constraint learning. The method aims at a text mining task-brain connection event causal relationship extraction in an event-level neural image. According to the method, a novel model named as a brain connection event relation miner is developed by utilizing weighted joint constraint learning. A lightweight model is developed by integrating a BiLSTM (Bidirectional Long Short Term Memory) network and a multilayer perceptron (MLP), and is used for jointly extracting a plurality of event causal relationships from brain connection literatures. In view of scarcity of an annotation brain connection corpus, a weighted joint constraint learning framework is provided. The framework integrates dual consistency constraints, including common sense constraints and domain constraints, and combines the dual consistency constraints with adaptive weight learning to enhance the small sample learning ability of the model.

The invention belongs to the technical field of computer software data processing and text mining, and particularly relates to a similarity detection method and system for science and technology project texts. The method comprises the following steps: step 1, obtaining a text, and preprocessing the text; 2, judging an application scene of the text, performing feature extraction according to selection of the application scene, then converting the extracted features into character strings with fixed lengths, and finally obtaining fingerprint features of the text through Hash calculation; 3, creating an index instance, adding the data into an index, constructing a Faiiss vector retrieval library, then performing similarity retrieval through an AAN algorithm, and obtaining the similarity between the fingerprint features of the text and the text in the index library; and 4, sorting and filtering the retrieval results, and judging whether text repetition exists or not according to a set threshold value and outputting the text repetition. According to the invention, the accuracy and reliability of the detection result are improved, and the detection efficiency is improved.

To suppress generation of disasters by predicting generation of disasters and calling for attention.SOLUTION: A disaster generation prediction system A for predicting generation of disasters in a work site such as a construction site or a factory includes: a derivation unit 10 for deriving disaster prediction information and keyword information by performing machine learning processing including previous disaster information in teacher data and by performing text mining processing on previous disaster information; and a report keyword unit 20 for generating a prediction result report C by using at least the disaster prediction information and the keyword information.SELECTED DRAWING: Figure 1