183 results about "Relationship mining" patented technology

The invention relates to the technical field of information security data processing, and discloses an information security analysis method and system based on big data, and the method comprises the steps: S1, collecting multi-source heterogeneous security related data which comprises a business log, a user behavior track, network traffic, an application program interface calling record, an identity authentication log and a real-time security data flow, preprocessing the collected data to obtain standardized data; and S2, performing entity identification, event extraction and relationship mining based on the standardized data, and constructing a cross-modal threat knowledge graph containing security entity nodes and associated edges. The method solves the problem of monitoring blind areas caused by lack of dynamic association mining capability among data in a traditional method, and particularly aims at distributed, low-frequency and multi-stage hidden attacks, the scheme can accurately recover an attack chain and identify high-risk threats through dynamic matching and path reasoning of a knowledge graph, and the method has a good application prospect. And the detection coverage rate and accuracy in a complex attack scene are remarkably improved.

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal threat perception method and system based on a space-time diagram neural network, and the method comprises the steps: obtaining a multi-modal original data set in a vehicle insurance claim settlement link, and carrying out the business relation mining and space-time dynamic analysis, and obtaining an entity space-time relation diagram; inputting the entity space-time relation graph into a space-time graph neural network for space-time fusion to obtain a node threat embedding vector; performing graph contrast learning and cross-modal feature discrimination on the node threat embedding vector to obtain a vehicle insurance threat feature vector; and carrying out fraud space-time propagation modeling based on the vehicle insurance threat feature vector, and generating a vehicle insurance threat blocking strategy, the method can accurately predict a propagation path and an influence boundary of gang fraud in a vehicle insurance ecological network, and identifies potential threats and starts prevention measures before a fraud behavior is completely displayed.

The invention provides an operational research course knowledge graph construction method based on multi-source data fusion. The method comprises the following steps: firstly, discussing logical association of courses, majors and students, collecting data such as textbooks, exercises and teaching programs by taking operational research knowledge points as a core and utilizing technologies such as OCR (Optical Character Recognition) and crawlers, and carrying out preprocessing and manual labeling; then, an improved deep learning model is adopted for entity recognition and relation extraction, BERT + BiLSTM + CRF is adopted for entity recognition, and a dynamic context pooling enhancement model is fused to improve the capture ability of a complex knowledge boundary; bERT + BiLSTM is adopted for relation extraction, a multi-head attention mechanism is combined, and hidden logical relation mining is enhanced. And finally, constructing a multi-level knowledge network which takes knowledge points as nodes and logic relations as edges, and embedding the multi-level knowledge network into a Neo4j graph database for visualization. The map can optimize a teaching path, provides personalized learning recommendation, and is widely applied to the fields of wisdom education, knowledge retrieval and the like.

The invention discloses a knowledge graph fused IT operation and maintenance knowledge base intelligent construction method and system. The method comprises the steps of preprocessing collected original data; an initial entity set and a relation set of the knowledge graph are constructed based on the standardized operation and maintenance data set, entities comprise equipment, components, fault types and solutions, and relations comprise association between the equipment and the components and association between the fault types and the solutions; performing semantic analysis on text information in the operation and maintenance data set, extracting key information as attribute information of the knowledge graph, and associating the key information with corresponding entities or relationships; optimizing the knowledge graph according to a preset algorithm, including entity alignment, relation reasoning and knowledge fusion, so as to generate a complete IT operation and maintenance knowledge graph; and storing the constructed IT operation and maintenance knowledge graph in a knowledge base. According to the method, the quality and intelligence of the knowledge graph are effectively improved, the problems of entity redundancy, insufficient excavation of implicit relationships and knowledge repetition are solved, the construction efficiency of the IT operation and maintenance knowledge base and the quality of a basic structure are remarkably improved, and a solid foundation is laid for subsequent knowledge optimization, reasoning and application.

The invention discloses a fault diagnosis method and system based on a multi-source data association rule and a graph neural network. The method comprises the steps of extracting high-frequency operation data and low-frequency time sequence state data based on historical data, and establishing an equipment operation feature set; an Apriori algorithm is utilized to screen correlation characteristics to calculate a correlation relation, and a fault symptom set is constructed; and taking the association relationship of the features as an adjacent matrix embedded graph neural network, and training the constructed fuzzy graph neural network based on historical data to obtain a fault diagnosis model. The system comprises a data acquisition module, a preprocessing module, a feature extraction module, a feature screening module, a feature association relationship analysis module, a fault diagnosis model training module, a fault diagnosis module and a database storage module, and can perform multi-source data fusion analysis and training and updating of a fault diagnosis model. According to the method, the fault diagnosis model is constructed by combining multi-source data fusion, feature extraction, association relationship mining and the fuzzy graph neural network, so that more accurate and efficient fault diagnosis is realized.

The invention relates to the technical field of document intelligent analysis, and discloses a document content self-adaptive analysis method and system based on a large model. The method comprises the following steps: acquiring original data flow of a to-be-analyzed document, wherein the original data flow comprises a text coding sequence, a layout structure mark and a multimedia embedding feature; the data stream is input into a pre-trained multi-modal large model, and a document semantic graph structure, a concept-containing node set, a relation edge weight matrix and a cross-modal alignment index are generated through context sensing analysis; performing dynamic hierarchical clustering on the semantic graph structure to obtain a hierarchical topic tree containing core topic branches, secondary topic branches and leaf node association strength; extracting a document logic framework containing chapter division suggestions, key information positioning coordinates and a cross reference mapping table according to the topic tree; a result is generated based on an adaptive analysis strategy optimization framework, and the strategy adjusts clustering granularity and relation mining depth according to document type features.

The invention discloses an AI-driven digital publication content and online derivative resource performance prediction system, which belongs to the technical field of digital publication, and comprises a multi-modal data acquisition layer for capturing publication content metadata, user reading tracks and social media UGC in real time through an API (Application Program Interface); the feature engineering engine comprises a text feature engine, a visual feature engine and a time sequence feature engine; the prediction model cluster comprises a basic prediction module, a generation enhancement module and a dynamic feedback system; and a content understanding and semantic analysis module. According to the method, a multi-modal data source and a deep semantic analysis technology are integrated, a full-dimensional analysis framework covering text, vision and time sequence features is constructed, internal association between content themes and audience emotions can be accurately captured based on entity relationship mining of a knowledge graph and a cross-modal alignment mechanism, and the market demand model of dynamic evolution is combined to obtain a new market demand model. And quantitative evaluation from content quality to market potential is realized.

The invention discloses a hidden group relationship mining method and device based on a social platform and a medium, and relates to the technical field of big data. The method comprises the steps of collecting to-be-mined tweet data description information; respectively calculating weights corresponding to the current tweet release time, the current tweet source, the current tweet forwarding information, the current comment information and the current mention user information through a multi-dimensional weight calculation method, and generating a target multi-dimensional fusion weight in combination with a multi-dimensional weight fusion calculation method; obtaining a target weighted graph network through a graph network construction method according to the target multi-dimensional fusion weight; and performing community division processing on the target weighted graph network, and determining a hidden group relationship mining result in combination with the current tweet content. The problems that static data analysis is relied on, multi-dimensional user behavior characteristics cannot be fully considered, and hidden relations or coordination behaviors among users cannot be revealed are solved, and the accuracy and interpretability of hidden group relation mining results are improved.

The invention belongs to the technical field of recommendation algorithms, and discloses a personalized learning path recommendation method based on knowledge relation mining and graph embedding driving, and the method comprises the following specific steps: 1, mining a knowledge point dependency relation, constructing a weighted directed graph, carrying out the data analysis on public data sets ASSISTments and Junyi, and carrying out the data analysis on the public data sets ASSISTments and Junyi; the implicit dependency relationship among knowledge points is mined through an improved Apriori algorithm, time dynamics and sequential dependency characteristics in the learning process are fully considered, and a weighted directed graph among the knowledge points is constructed according to the mined dependency relationship and the weight of the mined dependency relationship. According to the method, the implicit dependency relationship between the knowledge points is mined through an innovative method, the weighted directed graph is constructed, and a graph embedding technology is integrated into a recommendation model in combination with a unique self-defined embedding layer, so that compared with an existing recommendation method based on a simple association rule or a shallow network, the method has the advantages that the recommendation efficiency is improved; the mining and expression ability of the complex logic relation between the knowledge points is remarkably improved, and the recommendation omission rate of the sparse knowledge points can be effectively reduced.

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 model intelligent verification and parameter correction method, which comprises the following steps of 1, building an unmanned ship task high-value verification point set based on expert experience and a large model collaboratively, screening boundary points and extreme points, and performing simulation-actual measurement data consistency verification; if the normalization error of the simulation data and the actual measurement data exceeds a threshold value, triggering correction; step 2, constructing a sensitive factor set according to global sensitivity analysis and a parameter association relationship mining result, screening high-sensitivity parameters as a priority correction target, and avoiding redundancy optimization; and step 3, optimizing the high-sensitivity parameters, and forming a verification-correction-update closed loop by verifying and iteratively updating the priority of the factors to realize the intelligent correction of the parameters of the unmanned ship model.

The invention provides a digital platform for driving an industrial supply chain by an industrial supply chain, which comprises the steps of policy recommendation strategy: policy tagging processing: performing word segmentation, part-of-speech tagging and syntactic analysis on a policy file by using an NLP technology in policy text analysis, and extracting key elements; enterprise portrait modeling: constructing enterprise feature vectors through the collected data; and an intelligent recommendation algorithm. Through the above model system, full-process digital management from policy intelligent matching, industrial chain relation mining to supply and demand dynamic scheduling is realized, a closed-loop ecological system of data driving-model decision-scene landing is formed, and the fusion efficiency of a department and wound supply chain and an industrial supply chain is effectively improved.

The invention discloses an Internet of Things distributed monitoring data synchronization method based on port machinery equipment state, which comprises the following steps: taking a fault sign as a node, and if the co-occurrence frequency between two nodes is greater than a preset value, constructing an undirected edge; constructing a fault group according to each connected component; acquiring image data through a plurality of information acquisition terminals, and analyzing whether a fault sign exists or not; obtaining residual fault signs in the corresponding fault group, generating a to-be-detected task, and broadcasting the to-be-detected task in the information acquisition terminal group; the information acquisition terminal calculates the spatial distance between the information acquisition terminal and each to-be-detected task, and the information acquisition terminal corresponding to the minimum value is used as a task execution terminal to confirm residual fault signs; summarizing residual fault sign confirmation conditions, generating a fault confidence coefficient, and forming a recheck message; according to the method, isolated alarm is converted into group early warning, the fault advanced sensing capability is remarkably improved, and the calculation amount of a data center is reduced through co-occurrence relation mining and group division of fault signs.

The invention discloses a multi-modal data association relationship mining method based on potential feature perception, which belongs to the field of multi-modal data analysis and feature association modeling in artificial intelligence and data mining technologies, and comprises the steps of multi-modal data acquisition and preprocessing, multi-modal feature mining based on potential semantic alignment, multi-modal data analysis and feature association modeling. Performing multi-stage feature fusion and time sequence association representation learning, and constructing a cross-modal semantic association graph. According to the method, under the conditions of noise interference, unbalanced sample distribution and weak semantic association of the multi-modal data, robust fusion and semantic consistency expression of the multi-modal features in a potential space can be realized through adaptive anomaly correction and a multi-level feature alignment mechanism, mismatching caused by noise pollution and shallow association is avoided, and the robustness of the multi-modal features is improved. And accurate mining of the high-order potential semantic relationship is realized. Meanwhile, the semantic edge and the time sequence edge can be subjected to separation modeling according to the internal structure of the multi-modal data under the conditions of modal isomerism and time sequence overlapping, and meanwhile, a unified cross-modal association graph is constructed. Furthermore, in order to improve the accuracy of time sequence relation modeling, time sequence comparative learning and dynamic consistency constraint are utilized, effective distinguishing between real time sequence dependence and multi-mode repeated representation is achieved, and the precision and robustness of multi-mode correlation analysis are remarkably improved.

The invention discloses a science and technology big data service system and method for promoting transformation of science and technology achievements, and relates to the technical field of science and technology services and big data, and the system comprises a data collection and integration module, a data analysis and mining module, an intelligent matching and recommendation module, and a transformation service support module. The data acquisition and integration module is used for acquiring data from multiple data sources and cleaning, preprocessing, integrating and storing the data; and the data analysis and mining module is used for carrying out feature extraction, market demand analysis and association relationship mining on the data. According to the science and technology big data service system and method for promoting the transformation of the science and technology achievements, the science and technology achievements and related information can be comprehensively and accurately collected through the multi-source data collection and integration module, the problem that the information is dispersed and incomplete is solved, and a solid data basis is provided for subsequent analysis and service. And the data cleaning and preprocessing unit ensures the data quality and improves the accuracy and reliability of data analysis.

The invention relates to the technical field of enterprise panoramic data visualization, and discloses a panoramic data visualization method and system based on multi-source heterogeneous data, and the method comprises the steps: firstly extracting enterprise data and industry data, forming an enterprise-level data set, carrying out the data cleaning of the enterprise-level data set, and carrying out the data cleaning of the enterprise-level data set; the method comprises the steps that a structured enterprise-level data set is obtained, relation mining is conducted on the structured enterprise-level data set according to a graph theory algorithm and machine learning, an incidence relation graph is constructed, and finally a dynamic interaction visual interface is generated based on the incidence relation graph and used for displaying enterprise data information to a user. According to the method provided by the embodiment of the invention, the association relationship between the heterogeneous data is quickly and deeply mined according to the graph theory algorithm and machine learning, and the association relationship is dynamically displayed to the user, so that the readability of the data is effectively improved, and richer information display is provided for the user.

The invention discloses a course recommendation method and system based on learner multi-behavior relation mining. The method comprises the following steps: firstly, constructing a learner multi-behavior heterogeneous graph; extracting a plurality of single-behavior sub-graphs from the multi-behavior heterogeneous graph of the learner; based on each single behavior sub-graph, fusing the embedded representation of the behavior into message transmission of GCN, and learning the embedded representation of the learner node and the embedded representation of the course node to obtain the embedded representation of the learner node and the embedded representation of the course node under each behavior; then, performing multi-behavior generality fusion on the embedded representations of the learner node and the course node based on the meta-path, and further performing multi-behavior generality enhancement on the embedded representations of the learner node and the course node to obtain the enhanced embedded representations of the learner node and the course node; and finally, calculating a correlation score of the learner-course pair, and recommending a course to the learner according to the correlation score. The behaviors are integrated into the embedded representation learning of the learner and the course nodes, so that the recommendation accuracy and the user satisfaction are remarkably improved.

The invention provides an intelligent Internet of Things safety monitoring method and system, and the method comprises the steps: obtaining an equipment operation record and a behavior operation record, carrying out the event serialization coding of the equipment operation record, obtaining a sensor event sequence vector, carrying out the behavior mode vectorization processing of the behavior operation record, obtaining a behavior event sequence vector, and carrying out the monitoring of the behavior event sequence vector. Inputting the sensor event sequence vector and the behavior event sequence vector into a pre-trained association analysis model, carrying out cross-sequence causal relationship mining processing, generating an abnormal event association graph, and based on event node attributes and association edge weight values in the abnormal event association graph, executing risk conduction path analysis to obtain a risk conduction path; and obtaining a key conduction path set and a risk accumulation intensity value of the house safety risk, and generating a safety monitoring result containing the risk level identifier and the risk source positioning information according to the key conduction path set and the risk accumulation intensity value. According to the invention, the accuracy and comprehensiveness of intelligent internet-of-things safety monitoring are effectively improved.

The invention provides a project contract construction period management method and system based on causal relationship modeling, and the method comprises the steps: firstly obtaining a construction period element set which comprises a contract agreed process execution sequence, a resource configuration scheme and historical similar project construction period influence records; the method comprises the steps of obtaining a causal dependency relationship chain among process nodes, generating a dynamic construction period intervention scheme based on the causal dependency relationship chain and process execution state data collected in real time, updating a reference construction period plan of a project contract according to the dynamic construction period intervention scheme, and obtaining an adjusted construction period plan. And comparing and verifying the adjusted construction period plan with the contract construction period constraint condition, and generating a construction period management report, thereby improving scientificity, accuracy and flexibility of project contract construction period management, and reducing construction period delay risk.

The invention discloses a smart farm knowledge warehouse establishment method and system. The smart farm knowledge warehouse establishment method comprises the steps of obtaining farm basic data and an agricultural field ontology library; preprocessing the farm basic data to obtain preprocessed data; acquiring data with traceability information according to the preprocessed data; generating a semantic enhanced association rule base; and generating a multi-modal agricultural knowledge representation library, wherein the multi-modal agricultural knowledge representation library comprises a feature layer, a semantic layer and a rule layer. According to the semantic enhanced association rule base constructed by the method, an agricultural field ontology base and a machine learning algorithm are fused, and rule dynamic updating and implicit relation mining are realized. The mechanism can automatically adapt to climatic change, variety improvement and other scenes, for example, irrigation rules are adjusted based on real-time environment data, and decision accuracy is improved.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an AI-based file data content accurate and deep analysis and interpretation method, which comprises the following steps: acquiring multi-format file data and file meta-information, constructing an AI analysis network, extracting text semantic vectors, image visual features, table structure information and document layout features, and constructing a multi-dimensional semantic map. According to a user query intention, semantic extension is performed in combination with a domain knowledge base, an enhanced semantic description vector is generated through a graph attention mechanism, semantic reasoning and relation mining are performed by adopting an improved knowledge distillation Transform model, and a deep analysis conclusion is generated through a multi-hop reasoning model in combination with file data complexity, information density and user requirements. And generating a personalized interpretation report in combination with a user role and a task scene, and outputting an analysis result through a visual interface. Therefore, the problems of poor understanding ability, poor file adaptability and the like in the prior art are solved.

The invention discloses a big data relation mining analysis method based on a graph neural network, which comprises the following steps: S1, acquiring multi-source data, extracting data features, constructing a data relation graph, and mapping the data relation graph into a low-dimensional vector as a basic data structure; s2, inputting a low-dimensional vector through the graph neural network model, outputting a prediction result, calculating an error by using a loss function based on a real relation label, adjusting parameters of the graph neural network model, and obtaining a target data model; s3, obtaining a data relationship prediction result through the target data model, mining a data potential relationship, and mining a data relationship result through the data potential relationship; and S4, combining a data relationship result with domain knowledge and business rules, constructing a relationship knowledge base, and displaying the data relationship through a visualization technology. According to the method, efficient and accurate big data relationship mining is realized, and the accuracy and practicability of potential relationship discovery in a complex data environment are improved.

The invention discloses an industrial Internet of Things equipment collaborative management and control system based on data and knowledge driving. The system comprises six units including a heterogeneous multi-source data fusion interaction unit, an improved knowledge graph construction reasoning unit and an optimized hypergraph neural network feature extraction unit. Heterogeneous data acquisition and fusion are realized through a customized industrial-grade data interface protocol and a self-adaptive coding conversion mechanism; constructing a multi-level knowledge graph by using a semantic relationship mining algorithm and performing logical reasoning; deeply extracting data features by means of an optimized hypergraph neural network; combining equipment collaborative parameter generation, executing and dynamically adjusting a strategy; and monitoring the running state of the system in real time and performing feedback optimization. The method corresponds to six steps of the system, and equipment collaborative full-process management and control are achieved. The problems that a traditional system is difficult in data fusion, low in strategy making and execution efficiency and the like are effectively solved, the industrial Internet of Things equipment collaboration efficiency and production benefits are remarkably improved, and the system is suitable for various industrial production scenes.

The new energy ship fault causal relationship construction method provided by the invention comprises the following steps: when a fault occurs, fusing multi-modal heterogeneous data and screening to obtain a core feature set; obtaining an environment invariant feature matrix through invariant risk minimization learning, evaluating causal edge strength among variable features based on mutual information to obtain a causal edge strength matrix, and mining a core fault causal skeleton in combination with a causal edge strength threshold and a condition independence test; based on the skeleton and the hierarchical node system, constructing an initial hierarchical fault causal graph, fusing a causal edge strength matrix to determine an initial causal edge weight, and dynamically updating by using a meta-learning model to obtain a target hierarchical fault causal graph; and after the target hierarchical fault causal graph is corrected through an anti-fact sample, a reasoning path is optimized, and then the fault causal relationship of the new energy ship is obtained through structured reasoning and combined with a large language model to mine an implicit causal relationship. Therefore, stable and self-adaptive fault causal relationship mining under a complex dynamic working condition is realized.

The invention relates to the technical field of artificial intelligence model interpretation, in particular to a multivariable time sequence prediction interpretation method and system based on an information theory and causal reasoning. The method comprises the following steps: acquiring original multivariable time sequence data; performing data preprocessing on the obtained original multivariable time sequence data; performing causal information reasoning on the preprocessed multivariable time sequence data; based on causal information reasoning, calculating a dynamic feature weight of the multivariable time sequence data; performing local fitting and interpretation generation based on the dynamic feature weight; and a time-feature two-dimensional thermodynamic diagram is obtained. Through multi-dimensional feature relationship mining, an optimized causal relationship reasoning algorithm, dynamic weight calculation and an intuitive time-feature two-dimensional thermodynamic diagram generation mechanism, time continuity is effectively considered, prediction interpretation is presented in an intuitive and understandable mode, and a user can quickly understand an artificial intelligence model decision process.

The invention provides an enterprise cooperation relationship mining model training method and a friend recommendation method and system, and the method comprises the steps: firstly integrating multi-source data, constructing a structured enterprise label through optical character recognition and a natural language processing technology, and constructing an enterprise relationship graph with an enterprise as a node and a plurality of business relationships as edges on the basis of the structured enterprise label; then, node pairs with business exchange are selected from the atlas as positive samples, node pairs which do not exchange but meet specific conditions are selected as negative samples, and a training set is formed; a model based on an inductive graph neural network encoder, a multi-relation graph attention layer, a cross-relation fusion layer and a link prediction layer is adopted for training, and parameters are optimized through a marginal contrast loss function. During application, a trained model is utilized to calculate the cooperation probability between a target enterprise and an unknown enterprise, and potential cooperation partners are recommended to a user through instant messaging according to the cooperation probability. According to the invention, the efficiency and the intelligent level of business expansion of enterprises are obviously improved.

The invention discloses a relay protection defect diagnosis method and device based on a dynamic knowledge graph, and belongs to the field of relay protection defect diagnos.The method comprises the steps that a first data set in an electric power system is collected in real time, standardization processing is conducted on the first data set, and a second data set is generated; the second data set is input into a MacBERT-BiLSTM-CRF model for entity extraction, and a plurality of entities are obtained; updating graph structure data according to the second data set and each entity, and inputting the graph structure data into a GNN model for relationship mining to obtain a plurality of entity relationships; updating a knowledge graph according to each entity and each entity relationship, performing rule reasoning based on the knowledge graph and real-time data of the target relay protection device, and performing probabilistic reasoning through a Bayesian network after a first diagnosis result is obtained to obtain a second diagnosis result; based on the second diagnosis result and the real-time data, grey correlation degree analysis is conducted, a consistency evaluation result is obtained, and when the consistency evaluation result is larger than a first threshold value, a diagnosis report is generated and output.

The invention belongs to the technical field of wind power plant fire assessment, and discloses a multi-modal fusion wind power plant fire hazard multi-source data space-time synchronous assessment method and system, and the method is characterized in that a reference calibration module builds a digital twin simulation, double closed-loop dynamic calibration and credibility quantification three-in-one mechanism; the time synchronization adopts a triple strategy of GPS time service, local clock compensation and transmission delay prediction, and the transmission delay is compensated in advance in combination with an LSTM network; the space calibration depends on a three-dimensional digital twin model of the fan, and mounting deviation and vibration drift are corrected through visual identification and coordinate matching; the quality grading module is used for constructing a three-dimensional quality model, distributing weights according to data quality grading, and reducing evaluation deviation caused by data heterogeneity; the feature fusion module adopts a spatial-temporal feature, modal feature and quality weight cross fusion mechanism; and capturing data time sequence association through an overlapped time window, and mining spatial association in combination with a digital twin spatial topological relation.

The present application relates to the technical field of complex equipment component repair, in particular to a device component performance and unit body maintenance level relationship mining method capable of effectively improving the use efficiency of an aero-engine, which first carries out expansion processing of repair samples, and then selects a support vector machine regression method which is better in the condition of small sample problems to solve the mapping relationship between the component performance before repair, unit body maintenance level and component performance after repair. Since a component is generally composed of multiple unit bodies, each component has multiple maintenance levels, and the mapping relationship between the component performance before repair, unit body maintenance level and component performance after repair is a many-to-one mapping relationship. In order to improve the accuracy of support vector machine regression, a hybrid kernel function method is used to optimize it, and a particle swarm algorithm is used to optimize the related parameters.

The invention relates to an alarm information processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: receiving a plurality of original alarm events of a target system; aggregating the plurality of original alarm events based on the alarm time to obtain an aggregated alarm event; performing alarm text semantic analysis on each aggregated alarm event to obtain an alarm object related to the aggregated alarm event and at least one alarm index item corresponding to the alarm object; obtaining historical value data of each alarm index item, and performing association relationship mining on a change condition of the historical value data to obtain an index value association relationship between the alarm index items; determining an alarm causal relationship between the aggregated alarm events according to the index value association relationship; and merging the aggregated alarm events according to the alarm causal relationship to obtain a target alarm event corresponding to the target system. By adopting the method, the alarm analysis efficiency and accuracy can be improved.