289 results about "Relationship mining" patented technology

The invention provides a cross-modal similarity-based text mining data query method and system, relates to the technical field of data mining, comprises a cross-modal similarity-based text mining data query method and system, and belongs to the technical field of natural language processing. The method comprises the following steps: constructing a multi-level feature extraction network to respectively obtain text, image and time sequence feature vectors; feature fusion is realized by using an adaptive cross-modal contrast learning network, and a cross-modal similarity matrix is constructed; performing hierarchical screening on the target text by adopting a multi-level screening model to obtain a candidate text set; a text relation reasoning module is constructed based on a graph neural network, and text clustering and relation prediction are completed through a multi-task learning framework. According to the method, the accuracy of cross-modal text data query is improved, the adaptivity of feature fusion is enhanced, and efficient text relationship mining is realized.

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 relates to the technical field of medical diagnosis systems, in particular to an early gonitis diagnosis system and method based on multi-modal deep learning, a data collection module is used for collecting and preprocessing multi-modal data including knee joint X-ray image data, MRI (Magnetic Resonance Imaging) image data and clinical evaluation data; the model construction module constructs a deep learning model by combining a convolutional neural network and a Transform architecture, realizes mapping from multi-modal data to a clinical assessment phenotype label by adopting a feature automatic learning module and a potential relationship mining module, and integrates a Grad-CAM interpretability method for understanding a model prediction basis; diagnostic data are continuously collected to be fed back to the data collection module for model optimization, the convolutional network is good at capturing local image features, the Transform architecture is good at modeling a long-range dependency relationship, and the feature extraction and relationship modeling capabilities of the model are greatly improved through combination of the convolutional network and the Transform architecture.

The invention relates to an association method, system and device for electric power safety knowledge and a storage medium, and the method comprises the following steps: obtaining multi-dimensional data of the electric power safety knowledge, and carrying out the fusion of the multi-dimensional data, and obtaining the fusion data of the electric power safety knowledge; performing graph modeling based on the electric power safety knowledge fusion data to obtain an electric power safety knowledge graph; acquiring power fault information, and performing fault correlation analysis on the power fault information based on the power safety knowledge graph to obtain a power fault propagation path; performing causal relationship mining on the power failure propagation path to obtain power failure equipment and an induction mechanism corresponding to the power failure equipment; performing influence range analysis on the power grid equipment based on the induction mechanism to obtain an influence power grid region range, and formulating a management strategy corresponding to the equipment in the influence power grid region range; the technical problem that comprehensive analysis and prediction cannot be carried out timely and accurately in the face of complex power faults is solved.

The invention discloses a chain drugstore purchasing scheme generation method and system based on big data, and relates to the field of purchasing management. The method comprises the following steps: firstly, collecting historical sales data, real-time inventory data and external environment data, and constructing a multi-dimensional database; performing standardization processing on the data, and constructing a medicine classification matrix through a data mining model; identifying key factors influencing drug requirements by using a causal analysis method, and establishing a requirement influence factor weight matrix; based on the weight matrix, calculating purchase parameters such as a safe inventory level, an economic purchase batch, a purchase trigger point and a purchase period; and finally, in combination with a supplier evaluation system, generating a purchasing scheme including a purchasing medicine list, a purchasing quantity, purchasing time and supplier selection. According to the method, through multi-dimensional data analysis and causal relationship mining, scientization and accuracy of purchasing decision are realized, the purchasing efficiency is improved, and the inventory cost is reduced.

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 discloses an intelligent biological reaction monitoring system and method, and belongs to the field of intelligent monitoring, and the system comprises a reaction stage dividing unit which is used for dividing and determining multiple reaction stages; the variable relation mining unit is used for determining a dominant variable and an auxiliary variable, mining a variable linear relation and a collaborative insensitive loss function, and supervising and training the soft monitoring module; the continuous monitoring acquisition unit is used for configuring a monitoring equipment group, performing continuous monitoring and determining reaction monitoring data; the data analyzing and screening unit is used for preprocessing the data, performing linear analysis and abnormal reaction positioning and determining effective monitoring data; the game equilibrium decision-making unit is used for performing game equilibrium decision-making on the abnormal reaction identifier and determining a reaction adjustment strategy; and the reaction monitoring management unit is used for responding to the adjustment strategy, carrying out feedback monitoring analysis and reaction monitoring management, improving the biological reaction monitoring precision, realizing real-time online monitoring and carrying out intelligent regulation and control aiming at abnormal conditions.

The invention discloses a root cause association method and device under the condition of multiple alarms between root cause analysis devices, and the method comprises the steps: S01, obtaining historical alarm information and device topological information, obtaining the historical alarm information, carrying out the modeling through employing the alarm information, obtaining the topological relation data of a target region device, and carrying out the modeling through employing the topological relation data; the topological relation data describes the connection relation between the devices; s02, historical alarm information processing: processing the historical alarm information one by one and modeling; s03, modeling a causal relationship of historical alarm information; and S04, reasoning a root cause relationship in real time by using a causal relationship model. According to the invention, many-to-many root cause relationship mining between device alarms is solved, and many-to-many causal relationships between different device alarms are obtained through a neural network modeling mode, thereby helping to judge a root cause path.

The invention discloses an enterprise actual holder identification and risk conduction relation mining method and equipment. The method comprises the following steps: performing data acquisition from a data source, preprocessing the acquired data, and performing entity identification and relationship extraction according to the acquired data; according to the identified entity and the extracted relationship, constructing a relationship knowledge graph; deducing the relation knowledge graph based on a graph neural network model to mine a potential relation of an undominant record; analyzing a risk conduction path and quantifying conduction influence; and a visual interaction interface is provided, and result display and model optimization are realized. According to the method, an AI large model is taken as a core, knowledge graph construction and graph neural network reasoning technologies are combined, enterprise actual holder identification and risk conduction relation mining are completed through a multi-step process, diffusion paths and intensity changes of risks in a multi-level stockholder network can be simulated, key risk nodes and high-influence paths can be identified, and the risk prediction accuracy is improved. And risk conduction prediction and early warning capabilities are provided for a supervision mechanism.

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 provides an equity penetration data association analysis and hidden relationship mining method, which comprises the following steps of: performing semantic analysis on an equity opinion text through a natural language processing method, extracting sentences containing keywords, and judging whether an equity voting right entrusting behavior exists in the opinion or not, the keywords comprising entrusting, agency and authorization; calculating the voting weight of each shareholder according to the stock holding proportion and the voting right entrusting proportion of each shareholder in the entrusting chain, tracing the final stock holding proportion of the hidden controller, and determining the actual control force of each shareholder on the target company; and extracting an association network centered on the hidden control person in the equity relationship network, processing multiple types of nodes and edge relationships in the equity network by using a graph neural network, and quantifying the influence and connectivity of entities in the hidden control person association party network.

The invention discloses an agricultural multi-source knowledge migration method based on a graph model, and belongs to the technical field of agricultural information, and the method comprises the steps: carrying out the reduction, feature selection and feature fusion of agricultural literature data; the data processing efficiency and accuracy are improved by utilizing agricultural data reduction based on self-organizing mapping and feature selection and fusion based on spatial mapping; constructing a knowledge migration network based on a graph model, and performing knowledge semantic extraction and knowledge migration relationship mining; agricultural multi-carrier meta-knowledge acquisition and information feedback are adjusted, and meta-knowledge acquisition and network weight adjustment are optimized. According to the method, large-scale and heterogeneous agricultural data can be effectively processed, effective migration of knowledge is realized, the performance of agricultural learning tasks is greatly improved, and the method is of great significance to improvement of agricultural production efficiency.

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 relates to the technical field of people relationship mining, in particular to a multi-dimensional people relationship discovery method based on multi-modal information fusion, which comprises the following steps: extracting text, visual and audio modal features from a video; according to the Euclidean distance of the modal features, optimal alignment path optimization and nonlinear time adjustment are carried out, and the modal features of time step alignment are obtained; performing self-attention and cross-attention mechanism processing on the modal features to obtain multi-modal representations of texts, vision and audios; multi-modal representation of texts, vision and audios is carried out, and a graph structure network of a social relation, a standing relation and a co-occurrence relation is established; determining the social relationship closeness degree, the standing consistency degree and the co-occurrence strength of each person in the video according to the graph structures of the social relationship, the standing relationship and the co-occurrence relationship; according to the method, the understanding accuracy of the multi-dimensional character relationship can be improved.

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 discloses a meta-learning recommendation algorithm based on construction of user historical interaction information, and the algorithm comprises the steps: recognizing a plurality of similar users according to the attributes of a user and the user interaction information, carrying out the feature fusion of the plurality of similar users and the user, and enhancing the feature representation of the user; the user codes and the item codes are aggregated, the relation between user features and items is learned through N layers of full-connection layers according to the aggregated new user-item codes, and the user features and preferences are mined; in addition, the algorithm adopts a meta-learning framework to solve the problem of poor recommendation effect under the condition of sparse interaction data of new users and users; finally, experimental results on a public data set MovieLens-1m and an intelligent joint recruitment data set prove that accurate recommendation can be provided for new users and users who are not registered for a long time, and the cold start problem in recommendation is effectively relieved.

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.