111 results about "Association mining" patented technology

The invention discloses an enterprise big data mining method and system based on artificial intelligence, and the method comprises the steps: carrying out the dynamic mode alignment through employing a multi-mode hypergraph neural network according to enterprise multi-source heterogeneous data, and generating a time-space consistent multi-mode joint embedded tensor; inputting the multi-modal joint embedding tensor into an orthogonal adversarial manifold learning module, and generating a low-dimensional compact semantic embedding vector with enhanced category separability; performing space-time causal association mining on the semantic embedding vector, and outputting a space-time causal meta-path map containing the recessive commercial logic; and inputting the space-time causal element path map into a dynamic game adversarial interpretation framework, and finally outputting an enterprise-level intelligent decision map with anti-factual robustness. By utilizing the embodiment of the invention, the multi-modal data can be efficiently integrated and intelligently analyzed, and the accuracy and effectiveness of the mining result are improved.

The invention provides a lightweight digital human lesson preparation system based on an intelligent agent, and belongs to the field of intelligent teaching. Through collaborative operation of four core modules of knowledge graph construction and reasoning, multi-modal cognitive agent, lightweight digital human generation and intelligent teaching plan assistance, the problems of low efficiency of resource integration, teaching content homogenization, insufficient digital human interaction experience and the like in traditional lesson preparation are solved. The knowledge graph construction and reasoning module is used for constructing a structured knowledge graph and realizing knowledge point association mining and teaching logic reasoning; the multi-modal cognitive agent module is used for generating personalized explanation content according with a teaching target by fusing multi-modal courseware analysis, semantic understanding and lecture style dynamic adaptation functions; the lightweight digital human generation module is combined with model pruning and emotion modeling technologies to synchronously output natural voice and a high-simulation digital human image; the intelligent teaching plan auxiliary module helps the teacher to intelligently generate a teaching plan and a teaching outline according to the courseware content based on the knowledge graph.

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 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 belongs to the technical field of data processing, discloses a statistical data association mining platform and method based on a knowledge graph, and aims to provide a comprehensive platform for extracting, integrating, analyzing and explaining complex economic data from various heterogeneous data sources so as to support more effective decision making. Collecting original data from various heterogeneous data sources, and preprocessing the original data to generate a comprehensive data set; secondly, mapping the comprehensive data set into a predefined knowledge graph to form a network structure of nodes and edges, and updating the knowledge graph in real time; then, feature extraction is carried out on the updated knowledge graph, and a comprehensive feature vector is generated; then, the comprehensive feature vector is used as input, a potential mode in the data is obtained, and statistical association is mined through the potential mode; and finally, generating an interpretation report, displaying the potential mode in the knowledge graph and the mined statistical association, and increasing the transparency of mode interpretation.

The invention discloses a digital archive online management system based on big data. The digital archive online management system comprises a digital acquisition layer which is used for carrying out semantic perception and structured extraction on heterogeneous archives; and the intelligent classification layer is used for mapping the collected archive entities, attributes and relationships to a dynamically expanded domain knowledge graph based on a knowledge graph construction technology and a graph neural network, realizing association mining and dynamic classification of cross-modal archives through node embedding and link prediction, adapting to evolution requirements of archive themes in combination with a weak supervised learning mechanism, and realizing dynamic classification of the cross-modal archives. A classification system with causal interpretation is formed; the storage retrieval layer is used for encoding the generation time, the space trajectory and the event causal chain of the archive into space-time causal metadata; the archive utilization layer is used for actively pushing associated archives and generating a personalized analysis report by analyzing user behavior preferences and scene requirements; and the backup layer is used for dynamically sensing the threat type and calling an evolutionary algorithm to adjust the backup frequency, the storage position and the recovery path.

The invention provides an intelligent environment damage questionnaire generation and semantic analysis method and system, and the method comprises the steps: obtaining structured environment data through a government department interface, and capturing network public data in combination with an AI search engine; performing cleaning and normalization processing on the data, and constructing a standardized environment database; constructing a semantic model based on an NLP technology, dynamically generating a customized investigation problem according to an interview object type and an event scene classifier, and optimizing the priority of the problem; extracting a key entity from the interview text by adopting a pre-training model; mapping the entity to a preset pollution category through the environmental damage ontology library, and generating a dynamic causal association map; and verifying the integrity and consistency of the atlas by using a logic verification rule, and automatically outputting a structured survey report. According to the method, the defects that a traditional questionnaire is rigid in design, fragmented in information and insufficient in implicit association mining are overcome, the accuracy and efficiency of environmental damage investigation are remarkably improved, and intelligent support is provided for pollution traceability and responsibility definition.

The invention relates to the field of resource association analysis, and discloses a knowledge graph-based archive decision support and association analysis method, which comprises the following steps of: firstly, acquiring structured and unstructured archive data; preprocessing is carried out through data cleaning, duplicate removal and format conversion, semantic disambiguation is carried out on historical fuzzy words in unstructured data, and entity normalization processing is carried out on multi-category names of the same character; secondly, based on the preprocessed structured data, entities, relationships and attributes are extracted, and entity association evidences are supplemented through a cross-modal alignment technology; performing consistency verification, and storing the verified entity relationship network into a graph database to form an archive knowledge graph; then, in combination with a graph traversal and time sequence analysis algorithm, a hidden association path is mined, and trend prediction is carried out; and finally, generating a decision evidence chain according to an association mining result. And establishing a knowledge graph dynamic updating mechanism, and automatically reconstructing the entity association network when a file or a historical research result is newly added.

The invention discloses a method and a system for generating an SQL (Structured Query Language) query based on a natural language question. The method comprises the following steps: converting mode information of a target database and the natural language question into semantic vector representation; based on the semantic vector representation, a simplified database mode set related to SQL query is screened out through an attention mechanism; based on the natural language problem, the simplified database mode set and preset database constraint information, generating an SQL structural skeleton, and filling specific elements of the SQL structural skeleton to form a preliminary SQL query; and performing dynamic correction and verification on the initial SQL query by utilizing a large language model, and outputting a final SQL query. According to the method, association mining between user query and a database mode is effectively enhanced by utilizing a context-aware cross-encoder mechanism, and an implicit corresponding relation between a natural language problem and a database table / column can be more accurately identified and utilized.

The invention discloses a multi-modal data processing method and device based on a large model, equipment and a medium. Performing feature extraction and association mining on the multi-modal data to obtain multi-modal data features; according to a retrieval sequence of the multiple text matching methods, sequentially using the multiple text matching methods to perform text retrieval on the multi-modal data features in a pre-constructed knowledge base to obtain a first associated sub-graph; based on the semantic similarity between the multi-modal data features and multi-modal data in a pre-constructed knowledge base and a similarity threshold value, determining a second associated sub-graph; performing standard evaluation on the plurality of candidate results in the first associated sub-graph and the second associated sub-graph, and sorting the plurality of candidate results according to an evaluation result to obtain a retrieval result; inputting the retrieval result and the multi-modal data features into a large model for information analysis to obtain prompt information and a target tool; and sending the prompt information and the target tool to the terminal, and receiving a data processing result of the terminal.

The invention provides an online value evaluation system based on real estate, and belongs to the technical field of online evaluation, and the system comprises an online data obtaining module which is used for obtaining a basic feature vector and multi-modal evaluation data of a target real estate online; the feature extraction and mining module is used for carrying out space mapping on the explicit features and the defect features and carrying out potential association mining on the implicit features, and obtaining high-association implicit description in combination with the basic feature vector; the evaluation processing module is used for identifying real evaluation, false evaluation and fuzzy evaluation, and correcting the fuzzy evaluation after preliminary analysis according to a spatial mapping result and high-correlation implicit description in combination with social platform subject discussion data; and the value evaluation module is used for obtaining a comprehensive value according to the basic value reference line, the real evaluation and the correction evaluation, and outputting a value evaluation report. The problems that real estate evaluation is difficult to distinguish virtuality and reality and is fuzzy and difficult to solve are effectively solved, and the evaluation result can be comprehensive and accurate.

The invention relates to the technical field of case analysis, and discloses a multi-case collision method based on file-related personnel archives, which comprises the following steps of: integrating multi-source heterogeneous data such as file-related personnel, time tracks, geographic positions and the like, constructing a three-dimensional digital twinborn body of a case through a hierarchical feature extraction and cross-modal alignment technology, and establishing a three-dimensional digital twinborn body of the case; causal association between case elements is discovered based on data driving and domain knowledge double channels, after an initial causal atlas is constructed, illegal logic connection is automatically filtered through a reality constraint library, and a trusted causal network conforming to law enforcement specifications is generated. According to the method, a constraint enhanced deduction framework is constructed, physical logic compliance guarantee of an anti-fact scene is realized on the basis of keeping case total-factor digital reconstruction and dynamic association mining capability, and a reconnaissance strategy scheme conforming to real law enforcement constraints and having tactical innovation is generated; and the performability and decision credibility of a deduction result in a complex actual combat environment are remarkably improved.

The invention discloses a lost customer behavior characteristic mode association mining method and device, a medium and a product, and relates to the technical field of financial intelligent prediction, and the method comprises the steps: carrying out the preprocessing of a preset lost customer data set, and obtaining a transaction data table, the lost customer data set comprises at least one group of lost customer behavior characteristics and at least one lost mode; determining a frequent item set according to the transaction data table, extracting association rules in the frequent item set, and determining a key association rule from the association rules; determining a mapping relationship between the lost customer behavior feature combination and the lost mode according to the key association rule; and querying a target contact loss mode corresponding to the to-be-predicted contact loss customer behavior feature combination in the mapping relationship, and generating a risk early warning strategy according to the target contact loss mode. According to the method, the complex association between the lost customer behavior characteristics and the lost mode can be analyzed.

The invention relates to a network fault root cause positioning system based on multi-modal learning and causal inference, and belongs to the technical field of network fault diagnosis and positioning. The system comprises a data processing and association mining module, an intelligent fault diagnosis and evaluation module, a root cause positioning module and a system optimization module which are connected in sequence. The data processing module collects data through edge nodes, constructs a hierarchical knowledge graph and outputs a feature matrix; the diagnosis module performs fault detection by adopting a multi-modal model of a fusion graph neural network, integrates a small sample learning mechanism and outputs a fault event with confidence; the positioning module constructs a causal graph based on a knowledge graph, fuses multi-source evidences and realizes root cause tracing through a random walk algorithm; and the optimization module adjusts diagnosis parameters by utilizing reinforcement learning, expands a sample set based on the generative adversarial network, and realizes continuous optimization of the model through an automatic assembly line. Closed-loop self-optimization from fault sensing to root cause positioning is realized, and the network fault management capability is improved.

The invention relates to the technical field of data management, in particular to a cross-platform advertisement putting and SEO collaborative optimization system which comprises a resource time sequence scheduling module, a keyword screening module, a putting path construction module, a data conflict comparison module and a content archiving and auditing module. According to the invention, through association mining of multi-platform advertisement data and user behaviors, real-time mapping of time sequence distribution of put resources and user group states, and dynamic integration of budget weights and audience portraits in a keyword selection process, a multi-parameter interactive screening mechanism is formed for advertisement paths; text similarity judgment and conflict source tracing are synchronously introduced into channel content, an archiving standard is jointly defined by content change frequency and source credibility, data in each link automatically adjusts an attribution boundary according to a change trend, multi-source data validity is accurately distinguished in an archiving process, and structure-level linkage is realized by advertisement circulation, content integration, path adaptation and conflict detection. And the consistency and uniqueness of data updating and archiving are guaranteed.

The invention relates to the technical field of big data mining, in particular to a big data text retrieval mining system and method.The method comprises the steps that an original text data set input by a user is obtained; performing association mining on the structured feature matrix based on a graph neural network to obtain a knowledge association graph; receiving a to-be-retrieved text; inputting the text to be retrieved into a pre-trained multi-mode encoder to obtain a composite query vector; performing association expansion on the candidate texts based on the knowledge association graph to obtain an expanded text set; and carrying out importance sorting on the extended text set to obtain a final retrieval result set. According to the method, through cross-modal feature mapping and multi-modal data fusion, different types of data can be comprehensively analyzed; according to the characteristics, the retrieval range can be widened, the accuracy of a retrieval result can be improved, and particularly, when other modal data except a text are processed, potential correlation among the data can be fully mined.

The embodiment of the invention provides a three-dimensional point cloud data analysis method and system based on artificial intelligence, and relates to the technical field of three-dimensional point cloud data analys.The method comprises the steps that firstly, a three-dimensional point cloud data set containing space coordinates, a collection time sequence and other space-time attributes is obtained, and then a three-dimensional point cloud fragment space-time association evolution structure is constructed; the spatial-temporal association degree is determined and dynamically updated through a spatial-temporal neighborhood cooperative propagation mechanism, then multi-level cross-fragment feature interaction enhancement processing is executed, multi-dimensional spatial-temporal features are extracted and fused to generate an enhanced cross-fragment feature interaction vector, then a pre-training model is called, multiple rounds of global association mining are performed through a spatial-temporal multi-layer interaction module, and the spatial-temporal association degree is obtained. And finally, the space-time structure association rule is analyzed to generate an application instruction, and the application instruction is sent to the target processing terminal, so that the three-dimensional point cloud data analysis efficiency is improved.

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 knowledge graph-based nursing science professional portrait construction method and system, belongs to the technical field of data processing, and solves the problem of insufficient visualization and interactivity of an existing method. The method comprises the following steps: adopting a top-down and bottom-up combined graph construction strategy, extracting a natural language question keyword, identifying and understanding a natural language question, triggering a graph retrieval instruction, and constructing a nursing science professional portrait based on a retrieval enhancement technology. According to the method, the knowledge graph is used as an input part of the large language model, and the knowledge graph knowledge is used for guiding or constraining the large language model to generate output texts of entities, relationships and path evidences, so that the interactivity of nursing science professional portraits is realized; the problems that in a traditional portrait construction method, knowledge association mining in the complex nursing science field is insufficient, and the dynamic updating ability is weak are effectively solved, and the interactive query and visual expression ability of the nursing science professional portrait is improved.

The invention relates to the technical field of artificial intelligence, and particularly provides an answer generation method, system and device based on an RAG technology and a storage medium, and the method comprises the steps: receiving a question text input by a user; based on the question text, performing semantic retrieval in a pre-constructed knowledge base, calculating semantic similarity between the question text and knowledge fragments in the knowledge base, and retrieving target knowledge fragments of which the semantic similarity meets a preset threshold value; performing splicing processing on the question text and the target knowledge fragment to form an input prompt word; and inputting the input prompt word into a pre-trained generative large language model, generating a corresponding answer text by the generative large language model, and outputting the answer text. According to the method, three breakthroughs of semantic understanding, association mining and result optimization are realized, and multi-modal retrieval is supported.

The invention provides an association mining method based on a large model. The association mining method based on the large model comprises the following steps: constructing a standard expression according to a predetermined text format; cleaning input original text data according to the standard expression to obtain output text data conforming to the standard expression; outputting a preset sentence pattern text from the output text data based on a model according to the constraint of a preset prompt word template; and taking the predetermined sentence pattern text as the input of the model for training to obtain a strong correlation model so as to realize correlation mining. The question and answer matching degree of the model is improved through the predetermined cue word template, and the technical problem that the trained model lacks accuracy in the prior art is solved.

The invention relates to a space-time alignment and semantic association modeling method for cross-platform geographic information data, and belongs to the field of big data and natural language processing. The method comprises the following steps: step 1, carrying out space-time uncertainty modeling and probabilistic representation, and quantifying time and space uncertainty of multi-source data; 2, combining an alignment decision mechanism with adaptive learning to complete multi-evidence intelligent flexible alignment fusion; step 3, carrying out deep semantic analysis and real-time association discovery based on multi-modal semantic understanding and association mining; and 4, dynamically updating and reasoning the knowledge graph, and performing real-time analysis and real-time reasoning. According to the method, for a sudden search task, a new data source can be rapidly integrated, the knowledge graph is enriched, global situation visual control is achieved, multilevel reasoning based on the knowledge graph is achieved, efficient and accurate geographic information data intelligence is supported, and the task response efficiency is improved.

The invention discloses an import and export commodity intelligent classification method based on knowledge graph metadata topology, and relates to the technical field of reinforcement learning, and the method comprises the steps: inputting an initial data packet into a dynamic interaction model, carrying out explicit association mining through a semantic enhancement layer, optimizing a rule matching path through a rule evolution layer, and constructing a dynamic commodity knowledge graph; performing topological structure derivation on the dynamic commodity knowledge graph to generate a graph topological analysis report and a metadata list, and performing knowledge reasoning integration on the graph topological analysis report and the metadata list to generate an intelligent navigation engine; calling an intelligent navigation engine to execute multi-path semantic query and rule verification on the dynamic knowledge graph to generate a candidate classification scheme set; and performing multi-target collaborative optimization on the candidate classification scheme set to generate a sorting scheme sequence, performing traceability packaging on the sorting scheme sequence, and outputting an intelligent classification scheme. According to the invention, through the dynamic interaction model and multi-target collaborative optimization, the rule adaptation efficiency in a complex scene is improved.

The embodiment of the invention provides a multi-modal data processing method and system based on a knowledge graph, and aims to solve the problems of semantic association deficiency and potential association mining difficulty in multi-source information processing. The method comprises the following steps: firstly, acquiring a multi-source information set containing different expression form information units and source identifiers, and performing entity mapping on the multi-source information set and a preset semantic association network to generate an information unit set with semantic association tags; performing cross-source feature association processing to obtain a comprehensive feature set; then, a semantic reasoning rule is called for association extension, and an extension feature set is generated; and finally, based on the extended feature set, generating an information processing result conforming to an application scene and feeding back the information processing result to a corresponding interface, thereby effectively integrating multi-source information and mining potential association.

The invention discloses an industrial equipment intelligent detection and monitoring cloud service platform, and relates to the technical field of industrial equipment detection and monitoring, and the platform comprises the following components: a data perception and transmission module, a data storage and management module, an association mining and causal analysis module, an anomaly detection and early warning module, and a prediction and decision support module. By integrating advanced data mining, correlation analysis and causal inference algorithms, potential laws and causal relationships in equipment operation data can be deeply mined, accurate assessment of the equipment health state is realized, and based on the analysis results, the platform can predict possible faults of the equipment in advance and provide early warning information for a user, so that the user experience is improved. An enterprise can take active maintenance measures, production interruption and loss caused by sudden equipment failures are avoided, and the reliability and the production efficiency of the equipment are remarkably improved.

The invention relates to the technical field of intelligent fire protection and big data analysis, and discloses a knowledge graph construction and association analysis method and system for fire protection supervision and communication global data. In order to solve the technical problems of heterogeneous fire-fighting data and single association, the invention provides a domain ontology-driven knowledge graph automatic construction and deep analysis method. The core innovation of the method is that a multi-source heterogeneous data fusion model is adopted to unify semantic representation of multi-modal data such as fire-fighting documents and Internet of Things perception; performing entity relationship joint extraction by utilizing the pre-training model, and efficiently constructing a fire-fighting knowledge graph of massive triples; and a graph neural network is introduced to carry out deep association mining and reasoning, a risk conduction path is quantitatively evaluated, and potential threats are predicted. According to the invention, discrete fire-fighting data is improved into a computable and reasonable knowledge system, and deep perception and intelligent decision-making of a fire-fighting safety situation are realized.

The invention relates to the technical field of financial data analysis, in particular to a financial risk prediction system based on artificial intelligence, which comprises a cross-market implicit association mining module, a high-frequency risk pulse detection module, an association path conduction simulation module, a composite risk value generation module and a chained early warning decision module. Wherein the cross-market implicit association mining module is used for outputting an associated network topology file; the association path conduction simulation module is used for generating a risk diffusion tree diagram; the composite risk value generation module is used for outputting a composite risk value list with a mechanism identifier; and the chain type early warning decision module is used for generating a chain type early warning instruction set from the first-level market to the derivative market. According to the invention, by constructing a cross-market risk identification and chained early warning linkage mechanism, collaborative linkage of high-frequency pulse identification, risk path simulation and composite risk value generation is realized, and the accuracy and response efficiency of systematic financial risk prediction are improved.

The invention discloses a search optimization system based on knowledge association of a knowledge base, which improves the correlation, comprehensiveness and depth of search results, and meets the high-quality knowledge retrieval requirements under the scenes of intelligent search, knowledge management and the like. According to the technical scheme, the system comprises a user interaction layer module used for achieving direct interaction between a user and the system, receiving query input and displaying optimized search results; the core function layer module is used for completing intention accurate analysis, deep association mining, quantitative extension retrieval and association dominant sorting by taking knowledge association as driving force; the knowledge association support layer module is used for providing underlying technical support of knowledge association mining, quantification and sorting; and the auxiliary data layer module is used for providing auxiliary data for field adaptation and user personalization so as to enhance the processing precision of the core function layer module.

The invention discloses an agricultural supply chain risk prediction method and system based on a knowledge graph, and the method comprises the steps: synchronously collecting business data, environment meteorological data and market public opinion data at each business node of an agricultural supply chain, and writing the data into an edge buffer region according to a unified time identifier; on the basis of the improved agricultural supply chain reference model, entity recognition, relation extraction and attribute standardization are performed on the collected data, and a heterogeneous instance knowledge graph is constructed; markov chain random walk path sampling is carried out on each node, low-dimensional representation learning is carried out on a sampling result by adopting a cold ice self-adaptive embedding algorithm, and a risk representation vector of each node is generated; and inputting a risk prediction model jointly constructed based on a grey theory and a Markov state transition theory, calculating a risk score of each supply chain node, and outputting a grading result. According to the method, the problems of dynamic analysis, deep association mining and real-time accurate prediction of the risk of each node of the agricultural supply chain are effectively solved.

The invention discloses a distribution and micro collaborative low-voltage diagnosis method and system based on digital twinborn and association mining. The method comprises the following steps: constructing a digital twinborn model of a distribution and micro collaborative power distribution network; analyzing low-voltage spatial-temporal distribution characteristics, screening a governance area, simulating the governance area by using a digital twinborn model, selecting a corresponding governance strategy to adjust the digital twinborn model for simulation again after mining a low-voltage cause, and verifying a governance effect of the selected governance strategy based on a simulation result; and for the governance strategy with the governance effect meeting the requirement, calculating a comprehensive score according to the corresponding simulation result, and taking the governance strategy with the highest comprehensive score as a low-voltage governance strategy. According to the method, the distribution micro-grid collaborative digital twin is constructed, diagnosis and treatment of the low-voltage problem are carried out based on the digital twin and association mining, and the voltage treatment level of the distribution micro-grid is improved.