1701results about "Structured data browsing" patented technology

The invention discloses a computer big data information processing system, which comprises a data acquisition layer, a data processing layer and a data processing layer, wherein the data acquisition layer is used for accessing structured, unstructured and streaming data by using a multi-source adapter and Apache NiFi, executing format standardization, and extracting basic metadata and semantic tags through a rule engine and an NLP model; the metadata intelligent management layer integrates four modules, namely a federal learning framework for realizing cross-domain dynamic classification labels, an intelligent contract for real-time uplink storage evidence blood relationship change, a Neo4j combined graph neural network for constructing a knowledge graph for mining implicit association, and a reinforcement learning engine for optimizing a storage strategy based on frequency and risk indexes; the distributed storage calculation layer is used for processing batch and real-time metadata by adopting a Cassander + MinIO mixed framework and Spark / Flink, and dynamic partition balance performance is realized; and the application service layer is used for outputting functions of blood relationship query, classified browsing, compliance report and the like through a Vue.js portal and a Spring Cloud micro-service API (Application Program Interface) to form a full-link closed loop.

The invention relates to the field of environment monitoring, discloses an ecological protection red line holographic dynamic monitoring and early warning method and system, and aims to solve the defects of an existing monitoring and early warning mechanism in the aspects of timeliness, data integration and intelligent analysis. According to the method and the system, multi-source heterogeneous data are acquired and fused, a space bottom line digital model is constructed, space-time situation awareness analysis and change abnormity identification are performed, risk assessment early warning triggering is performed, and early warning issuing and processing feedback are managed. According to the invention, high-precision holographic acquisition and fusion of multi-source data can be realized, the accuracy and timeliness of change abnormity identification are improved, passive early warning is changed into active early warning, a monitoring disposal closed loop is formed, supervision intelligence and treatment efficiency are improved, and ecological safety is guaranteed.

The invention discloses a project performance evaluation and risk early warning method for multi-source heterogeneous scientific research data, and relates to the technical field of scientific research project management, and the method comprises the steps: collecting the multi-source heterogeneous scientific research data, cleaning, converting, filling missing values, and storing in a unified format; extracting performance and risk features, and fusing to obtain a fused feature set; combining an analytic hierarchy process, an entropy weight method and the like to construct a dynamic weight performance evaluation model; constructing a multi-modal risk early warning model by adopting multiple algorithms and optimizing a threshold value; collecting data in real time to update a feature set, and dynamically adjusting the model; and visually outputting a result, generating an improvement suggestion, and forming closed-loop feedback. According to the method, the multi-source data processing efficiency and evaluation accuracy are improved, the risk early warning timeliness and adaptability are enhanced, a management closed loop is formed, and the problems of difficult data integration, evaluation lagging, insufficient early warning and the like of a traditional method are solved.

The invention relates to the technical field of data integration, and particularly discloses a perfluoro / polyfluoroalkyl substance multi-source exposure data integration risk assessment system, which is characterized in that environmental exposure data of perfluoro / polyfluoroalkyl substances is acquired through a multi-source environmental sensor array, and a PFAS multi-mode exposure feature database is established; carrying out pollution source isotope fingerprint analysis, and obtaining source contribution rate distribution maps of three pollution sources of industrial emission, surface runoff and atmospheric settlement through a nonlinear source analysis algorithm; constructing a three-dimensional geographic information dynamic migration model according to the source contribution rate distribution map, and generating a multi-medium dynamic migration flux matrix; a composite risk assessment model is established based on the multi-medium dynamic migration flux matrix, probability risk assessment is executed in combination with an ecological toxicity threshold database, and a space gridding risk grade map is output; the method not only fills the blank of the prior art in the aspects of multi-medium dynamic modeling and nonlinear source analysis, but also provides powerful technical support for environmental pollution control and ecological risk prevention and control.

The invention discloses a data analysis method and system based on a large model. The method comprises the steps that natural language query of a user is received; performing semantic coding and intention analysis on the natural language query based on a pre-trained large language model; based on metadata retrieval of vector similarity, obtaining metadata related to natural language query semantics from a vector database; generating corresponding structured query parameters through the large language model in combination with the obtained metadata and the natural language query; according to the structured query parameter, generating an executable structured query language statement; and executing the structured query language statement to obtain a data query result. According to the method, the problems of insufficient semantic comprehension ability, low metadata retrieval precision and poor query generation controllability in the prior art are solved.

The invention relates to the technical field of forest fire management, in particular to an integrated forest fire intelligent analysis method and system. The system comprises a background service system, a central intelligent analysis system and a mobile intelligent analysis system. The background service system is deployed in a cloud computing server cluster and is used for uniformly storing personnel information, fire scene information and geographic information data and realizing multi-source data fusion and authority management through a standardized API (Application Program Interface); the central intelligent analysis system is deployed in a high-performance computer, integrates a forest fire danger auxiliary decision-making model, a forest fire spreading trend prediction model, a force distribution dynamic display module and a collaborative plotting module, and supports fire spreading prediction and real-time decision-making instruction issuing based on multi-dimensional data such as weather, terrain and vegetation; the mobile intelligent analysis system realizes fire spreading trend analysis, mobile plotting and instruction receiving in an offline environment, supports offline data caching and calling in an emergency scene, and effectively improves forest fire emergency response efficiency and resource scheduling accuracy.

The invention relates to a land space planning and surveying and mapping engineering collaborative measurement method, which comprises the following steps: collecting multi-source surveying and mapping data of a target area, and constructing a live-action three-dimensional semantic model bearing ground feature semantics through fusion processing; key planning elements are extracted based on the model, the spatial relation of the key planning elements is quantified, and a spatial relation graph with geometric entities as nodes and the spatial relation as edges is generated; the planning management and control terms are formalized into computable logic assertions, and a machine executable planning rule base is constructed; performing collaborative traversal and logical reasoning on the spatial relation graph based on the rule base, automatically comparing and identifying violation situations, and generating a planning conflict report; and carrying out fusion mapping on the report and the three-dimensional model to generate a three-dimensional visual review layer for intuitively indicating the position, type and degree of the violation. According to the method, deep fusion of surveying and mapping data and planning rules is realized, planning conformity review can be automatically and intelligently completed, and the problems that the prior art depends on manpower, and is low in efficiency and different in standard are effectively solved.

The invention discloses an ecological restoration effect evaluation method for a land and space comprehensive improvement area, and relates to the technical field of ecological environment monitoring. A terrain structure data set and a moisture dynamic data set are obtained after data analysis and image feature extraction; the method comprises the following steps: carrying out terrain disturbance evaluation by constructing a terrain disturbance response index Rtz, carrying out hydrological back-up state evaluation by constructing a hydrological back-up trend index Gsh when the terrain has an unsteady disturbance region, carrying out ecological adaptation evaluation by constructing an ecological adaptation comprehensive index SSZ when the hydrological back-up meets the standard, and generating a landform base map of a renovation region by calling DEM (Digital Elevation Model) data. And each evaluation result is subjected to visual partition display through GIS space rendering, and a repair effect report is automatically generated. According to the method, multi-dimensional quantitative evaluation of terrain disturbance, hydrological refilling and ecological adaptability of the renovation area is realized.

The invention relates to the technical field of computer application, and particularly discloses a chemical industry park safety risk assessment method based on an agent model. The method comprises the following steps: constructing a multi-agent collaborative perception framework, and fusing multi-source heterogeneous data of equipment, environment, materials and personnel to form a park panoramic view under a unified space-time reference; establishing an agent behavior model library embedded with the chemical process mechanism and the safety interlocking logic; an intelligent agent decision strategy is optimized through deep reinforcement learning, and multi-target training is carried out by taking a safety index as a reward function; executing dynamic risk deduction and accident chain simulation in a digital twin environment, and quantifying risk probability and consequences in combination with Monte Carlo sampling; and finally, a visual thermodynamic diagram and an interpretable risk assessment report are generated, weak links are identified, and intervention suggestions are provided. According to the technical scheme, dynamic, accurate and prospective evaluation of the safety risk of the chemical industry park can be realized, and the early warning timeliness and the evaluation reliability are remarkably improved.

The invention provides an intelligent data consanguinity tracking and visualization method based on graph calculation, and the method comprises the steps: carrying out the data structure analysis and metadata extraction of original data assets, so as to generate a standardized data asset package; performing graph node attribute definition on the data entities in the standardized data asset package, and performing graph edge attribute definition on the association relationship between the data entities to generate a first data blood relationship model; performing blood relationship path mining on graph nodes and graph edges in the first data blood relationship graph model to obtain a basic blood relationship path set, and performing quantitative calculation and feature labeling on path association strength in the basic blood relationship path set to generate a second data blood relationship graph model; and performing visual rule mapping on graph node attributes and graph edge attributes in the second data blood relationship graph model to construct a standardized visual data set and generate a data blood relationship visual interaction interface according to the standardized visual data set.

The invention relates to a data AI analysis management method and system based on a production element association map. The method comprises the steps that a multi-source heterogeneous production data flow covering the whole industrial production cycle is obtained; constructing a whole-process production element association map through an entity recognition and relationship extraction technology; actual operation indexes of the production nodes are calculated in real time and compared with a reference threshold value, and potential production bottleneck nodes are accurately recognized; upstream associated node data are backtracked, historical data in the same period are combined to be input into the bottleneck root cause diagnosis model, and core influence factors are accurately obtained; generating an optimal scheduling strategy according to the core influence factors and issuing the optimal scheduling strategy to a production execution system; according to the scheme, the multi-source heterogeneous data of the whole period of industrial production can be effectively analyzed and managed, accurate regulation and control of the production process are achieved, and the production efficiency and the product quality are improved.

The invention relates to the technical field of data processing, in particular to an intelligent supervision method and system based on a data visualization platform, and the method comprises the steps: collecting multi-source water conservancy data through dual-channel redundancy check, converting the multi-source water conservancy data into a standardized space-time matrix, carrying out the data cleaning through a containerization adaptation and variational auto-encoder, and marking the confidence; hierarchically storing the data based on a confidence threshold, and generating a routing strategy through reinforcement learning to distribute data fragments; calculating a flood peak evolution prediction result by using space-time diagram convolution, calculating an output risk assessment value by combining a Bayesian network and long and short-term memory, and activating federal learning parameter updating when the confidence coefficient is insufficient; the digital twin model loads physical constraint parameters to deduce a flood control scene, and a scheduling instruction set and a rehearsal animation are generated through multi-objective optimization; and dynamically rendering to generate an interactive visual interface, associating a causal deduction path, and triggering federal model parameter updating by user feedback, thereby realizing dynamic collaboration of the forecasting and early warning rehearsal plan function chain.

The invention relates to a port water area ship navigation risk distribution calculation and visualization method and device based on AIS and GIS data fusion, and the method comprises the following steps: 110, obtaining ship AIS data, and carrying out the preprocessing of the ship AIS data, thereby obtaining the preprocessed ship AIS data, and the preprocessing comprises the standardization, feature extraction and abnormal point detection; step 120, carrying out port water area grid construction, and carrying out grid mapping and legality screening on the preprocessed ship AIS data; step 130, performing port water area static risk assessment, port water area dynamic risk assessment and port water area propagation risk assessment on the port water area, and performing port water area comprehensive risk assessment on the basis to obtain an assessment result; and step 140, drawing a port water area risk thermodynamic diagram based on the evaluation result to realize risk distribution visualization. According to the invention, efficient and scientific decision support can be provided for port safety management.

The invention relates to the technical field of big data processing and analysis, in particular to a commodity traceability management method and system based on big data. The method comprises the steps of determining a data range and association logic of each type of events by constructing a semantic event system of a commodity life cycle, performing data acquisition, preprocessing and credibility evaluation at edge nodes and generating an evidence header, and further converting data into multilayer verifiable commodity fingerprints of an L1 edge abstract, L2 event level fusion and L3 full link fusion, the method comprises the following steps: realizing feature fusion and dynamic incremental updating, then performing distributed notarization and multi-party verification on fingerprints through a federated anchor point mechanism to ensure that a full link is traceable and cannot be tampered, and finally supporting full link traceability query and anomaly tracking based on a unique commodity identifier. And closed-loop traceable management is realized through visual display and multi-role authority management. According to the invention, the reliability, interpretability and cross-organization trust of commodity traceability are effectively improved.

The invention discloses an electric power equipment thermal anomaly behavioristics identification method and system based on artificial intelligence, and relates to the field of electric power system intelligent inspection, and the method comprises the steps: an electric power inspection robot terminal collects a temperature data sequence of the surface of electric power equipment along with the time according to a set period; performing correction processing on the temperature data sequence, extracting a periodic feature, a trend feature and a mutability feature based on the model correction temperature sequence, and combining to form a thermal behavior feature vector; inputting the model thermal behavior feature vector into a hybrid artificial intelligence recognition model of a convolutional neural network and a long-short-term memory network, performing abnormal fragment recognition in combination with an attention mechanism, and outputting a thermal abnormal behavior category and a corresponding confidence coefficient; storing an identification result in a behavior database to record a historical thermal behavior curve and a normal mode portrait of the equipment; and displaying the thermal anomaly behavior at the display end and labeling the category and trend. The method can provide powerful guarantee for safe and stable operation of the power system.

The invention discloses an automatic settlement path generation method fusing a knowledge graph and a tax rule, and relates to the technical field of financial science and technology and intelligent finance and tax compliance, and the method comprises the steps: S1, obtaining initial decision basis data and execution process details from a settlement path through a block chain network, employing a Hash chain structure to link each link record, s2, according to the obtained path chain, a graph database is adopted to store dynamic association attributes, and the dependency relationship of each node in the path is determined through knowledge graph reasoning, and S3, if an information fault is detected in the path chain, the matching degree of an execution result and a decision basis is compared through a timestamp verification mechanism, and if the information fault is detected in the path chain, the decision basis is determined. Judging a deviation position and marking a deviation point; according to the automatic settlement path generation method fusing the knowledge graph and the tax rules, the security and reliability of financial transactions and the tax management efficiency are effectively improved.

The invention provides an interactive multi-dimensional data visualization display sand table and method for business data analysis, and the method comprises the steps: carrying out the semantic mapping of state parameters in an interaction control operation when the interaction control operation is received, obtaining a plurality of pieces of visualization intention metadata, and generating a structured query instruction; based on the structured query instruction, extracting a target data subset rendered visually by the heterogeneous commercial data source from a multi-dimensional data cube of the commercial data source, performing visual optimization on the visually rendered chart type, the visual coding channel and the initial visual parameter through the cardinal number of the dimension field in the target data subset and the data distribution type and the numerical range of the measurement field to obtain a visual configuration set of the target data subset; and using the visualization configuration set to render and generate a multi-dimensional data visualization view, and outputting and displaying the data visualization view in a graphical user interface. Based on the scheme, automatic closed loop generation from heterogeneous data to interactive views can be realized.

The invention discloses an artificial intelligence-based agricultural waste carbon emission reduction scenario simulation method and system, and the method comprises the steps: obtaining multi-source geographic space data of a target region, uniformly fusing the multi-source geographic space data into a grid system with a preset resolution, and carrying out the calculation of the multi-source geographic space data based on a space weighted distribution model, the statistical yield of provincial agricultural products is distributed to each grid of a grid system, and the theoretical resource quantity and the high-precision spatial distribution diagram of the agricultural wastes are obtained in combination with a grass-grain ratio database. Method for determining carbon emission and carbon sink quantitative parameters of agricultural wastes under various utilization paths through dynamic accounting model, simulating carbon emission reduction potential and spatial distribution under different resource utilization scenes based on artificial intelligence model according to carbon emission and carbon sink quantitative parameters of agricultural wastes under various utilization paths, and planting front-end crops and structure and space layout is optimized. A quantifiable and visual scientific decision-making tool is provided for regional low-carbon agricultural planning, and the accuracy and feasibility of an emission reduction strategy are remarkably improved.

The invention belongs to the field of artificial intelligence and big data analysis, and particularly relates to an AI-based drive type data analysis method and system.The method comprises the steps that S1, on the basis of natural language query input by a user, candidate SQL and / or DSL statements are generated through a pre-trained big language model; s2, generating a parallel query plan and optimizing the parallel query plan based on the candidate SQL and / or DSL statements and the metadata knowledge graph; s3, extracting authorization data; s4, fusing the extracted data to generate an analysis feature wide table, and identifying key features, abnormal modes and text abstracts in the analysis feature wide table; s5, executing controlled retrieval in the business knowledge graph based on the key features and the abnormal mode, and generating a security evidence sub-graph; s6, inputting the text abstract of the analysis feature wide table and the security evidence sub-graph into an AI inference engine, and outputting a structured report; and S7, displaying the structured report. The problems of high technical threshold and weak safety and interpretability in a data analysis process are solved.

The invention discloses a natural resource big data off-line comprehensive analysis, annotation and proof system and method, and belongs to the technical field of natural resource informatization and geographic information processing. The system comprises the following components: a spatial data fusion module, which is used for realizing unified management and efficient processing of heterogeneous spatial data; the spatial index optimization module is used for realizing quick retrieval and high-performance query optimization of spatial data; the intelligent annotation and arrangement module is used for realizing low-code rapid configuration and business process automatic arrangement of different natural resource investigation scenes; the off-line analysis module is used for realizing buffer area analysis, space superposition and terrain analysis in an off-line environment; the real-time visualization module is used for realizing multi-level map display, track recording, vector element editing and multimedia evidence collection; and the data quality management module is used for realizing automatic quality inspection of geometry, topology and attributes and statistical analysis, version control and export management of annotation results.

The invention relates to the field of combat command and dispatch, in particular to a multistage linkage combat unit command and dispatch system. According to the invention, a basic-level minimum emergency unit element information base is constructed through a data acquisition module; generating an electronic map supporting 2D and 3D switching; an intelligent analysis early warning module identifies abnormal conditions and generates early warning information; a plurality of multi-level linkage combat unit topology networks are generated through the multi-level linkage command architecture module; generating a command data large screen for dynamically aggregating public security indexes and a global situation visualization electronic map through a global situation visualization module; an early warning area is circled and selected through the intelligent scheduling module; and associating the corresponding multi-stage linkage combat unit to generate a deployment instruction. According to the multi-level linkage combat unit command and dispatch system provided by the invention, the multi-level linkage combat unit is effectively combined with a dynamic visual geographical environment from data acquisition, analysis and early warning to command and dispatch through cooperation of multiple modules, so that the command and dispatch efficiency is remarkably improved.

The invention discloses an order-oriented cross-node production process automatic arrangement and traceability method and an order-oriented cross-node production process automatic arrangement and traceability system. The method comprises the following steps: analyzing order demand characteristics and generating production task specifications; discovering available production nodes based on an MCP protocol and evaluating node capability; automatically arranging a cross-node production process by adopting a multi-objective optimization algorithm; establishing a distributed traceability data chain to record full-process execution information; and constructing a visual traceability view to support quality traceability and analysis. The system comprises an order analysis module, a node discovery module, a process arrangement module, a traceability chain module and a visualization module. According to the invention, the problems that flow arrangement depends on artificial experience and quality tracing is difficult in a traditional production system are solved, and intelligent arrangement and full-life-cycle tracing of the production flow are realized.

The invention discloses a multi-source data integration display method and device, equipment and a storage medium, and the method comprises the steps: collecting multi-source power grid data in at least one power grid project, carrying out the data integration of the multi-source power grid data based on project identification information, and generating a full-life-cycle project data table corresponding to each power grid project; for the project data table, based on a business logic rule of a target power grid project, performing business logic verification on a business field with business association; and under the condition that the project data table passes the business logic verification, performing visual display on the project data table through at least one preset visual template. Through the technical scheme, the consistency of multi-source data and the automatic verification capability of logic integrity are improved.

The invention relates to the technical field of heat meter performance test and operation and maintenance management, in particular to a heat supply working condition multi-source parameter real-time acquisition heat meter performance intelligent analysis system, which comprises a working condition simulation subsystem, a multi-source parameter real-time acquisition module, an intelligent analysis engine and data management and visual interaction interface, and a digital twinning virtual calibration module. Multi-dimensional variable working conditions are constructed through the working condition simulation subsystem, the acquisition module realizes high-precision synchronous acquisition, the intelligent analysis engine integrates multiple algorithms to complete data fusion, performance prediction, health degree evaluation and fault early warning, and the data management module realizes whole-process closed-loop management. The heat meter test accuracy and the working condition coverage are improved, intelligent analysis and accurate calibration are achieved, the operation and maintenance cost is reduced, and stable and efficient operation of a heat supply system is guaranteed.

The invention discloses a site-level CCUS dynamic source-sink matching optimization method for an oil field enterprise, relates to the technical field of large-scale deployment of carbon capture, utilization and storage of the oil field enterprise, and particularly relates to the site-level CCUS dynamic source-sink matching optimization method for the oil field enterprise. Comprising the following steps: integrating a carbon source end full life cycle technical economy evaluation system and a storage target area'geology-potential-economy 'three-dimensional grading model to form a dynamic database; on the basis of the dynamic database, a mixed integer linear programming model fusing source sink dynamic priority coefficients is constructed, three scenes of cost minimization, oil displacement income maximization and carbon sink subsidy excitation are set, and a constraint system is coupled; according to the method, carbon source technology economic evaluation and storage target area three-dimensional grading are fused, the specific injection-production cycle, policy incentive and pipe network constraint of an oil field are converted into time-varying weight coefficients, traditional static optimization limitation is broken through, and site-level source-sink dynamic accurate matching is achieved.

The invention discloses an early-stage intelligent monitoring system for citrus reiculata Blanco canopy diseases and insect pests based on multispectral imaging, and belongs to the technical field of agricultural information. The system comprises a multispectral imaging module, a three-dimensional point cloud acquisition module, a data fusion module, a time sequence data analysis module, an intelligent identification module and a monitoring result output module. The method comprises the following steps: synchronously acquiring a multispectral image and laser radar point cloud data of a citrus reiculata tree canopy, and generating a point cloud model through spatial registration fusion; continuously recording model data of a plurality of time points, and extracting a time sequence feature vector; identifying disease and pest types and severity by using a deep learning model; and outputting a result to the user terminal. According to the invention, the problem that large-range and high-precision early monitoring of diseases and insect pests of citrus reiculata canopies is difficult to realize in the prior art is solved, early discovery, precise positioning and trend early warning of the diseases and insect pests are realized through air-space-ground integrated data fusion and intelligent analysis, and the intelligent level of orchard management and the disease and insect pest control efficiency are effectively improved.

The invention discloses a regional grid safety evaluation method and device based on smart gas and a medium, belongs to the technical field of gas safety supervision, and aims to solve the technical problem of how to overcome the defects of insufficient matching between grid division and gas facility characteristics and lagging leakage risk prediction in the prior art. In order to realize full-process intelligence from risk identification to closed-loop management and improve supervision efficiency and early warning precision, the technical scheme adopted by the invention is as follows: dividing a pressure sensitive grid: performing optimization division on a regional grid based on a gas pipeline pressure grade and facility distribution characteristics, and realizing accurate division of the regional grid where a pipe network is located by combining a GIS and an improved DBSCAN algorithm; acquiring three-level Internet of Things data: integrating sensor data of an unmanned aerial vehicle, a pipe network pressure device and a leakage monitoring device, and covering a full-link data system of a terminal sensing layer, an edge computing layer and a cloud storage layer; constructing a double-engine evaluation model; carrying out digital twinborn visualization; and scheduling the capability matrix.

The invention discloses a bacterial selenoprotein online resource platform, an application method, a terminal and a medium, and relates to the technical field of biological medicine, the online resource platform is deployed on a server, the cloud server is a Ubuntu cloud server, and Nginx, Waitpress and Flask are configured; the server side is in butt joint with a background resource, and the background resource is in butt joint with the constructed bacterial selenoprotein database; the server is in butt joint with a user interface of the front end, the user interface designs a corresponding interface framework and an interaction function based on static resources hosted by Nginx, and the interaction function is used for realizing query, analysis and use of relevant information of the bacterial selenoprotein. According to the method, a convenient online access channel of an integrated database and a real-time data analysis tool are provided for users in related fields, and important support is provided for accurate annotation of selenoprotein genes in a bacterial genome plan.

The invention relates to an expert question and answer auxiliary interaction method and system based on a large water affair model, and relates to the technical field of intelligent water affair. The method comprises the following steps: firstly, constructing a problem semantic vector fusing a current intention and a historical background by deeply analyzing user interaction data and a historical session; then, SCADA time sequence data, a static knowledge graph and a historical processing record are retrieved from an external multi-source data stream by taking the vector as an index, and are converted into a situation feature vector reflecting the running state constraint at the current moment through semantic mapping and time sequence feature engineering. By jointly inputting a problem semantic vector and a situation feature vector into a pre-trained water affair field large model, the system can perform verification reasoning under strict working condition constraints, and encapsulates a reasoning result into a structured expert reply based on a visual template. The technical problems that in the prior art, a general model is prone to generating illusion, the retrieval result accuracy is low, and end-to-end intelligent diagnosis cannot be provided are effectively solved.

The invention relates to the technical field of building construction intellectualization, in particular to a building construction intelligent monitoring system based on multi-source sensor data fusion. Comprising a data acquisition and storage unit; a data fusion processing unit; the construction state analysis unit uses an improved anomaly detection algorithm; a risk assessment early warning unit; and a visualization and decision-making unit. Through the dynamic adaptation module, the structural displacement feature weight is adjusted based on the construction stage parameters to preferentially extract the construction key stage structural features, and the structural material elastic deformation limit is corrected in combination with the material linear expansion coefficient and the temperature difference value; meanwhile, the dynamic critical generation module generates a risk critical judgment basis changing along with the construction process in combination with structural bearing capacity parameters and equipment mechanical stress limits in the construction stage, so that the problem that abnormal detection and risk judgment are lack of dynamic adaptability is solved; and the accuracy of personnel border crossing, equipment abnormity and structure abnormity identification and the adaptability of risk assessment are effectively improved.