235 results about "Multi dimensional analysis" patented technology

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data processing method and device based on factor weight optimization, equipment and a medium. Comprising the following steps: receiving structured data and unstructured data, and extracting a basic key factor set and monitoring object information in the data to generate a data feature set; executing multi-dimensional analysis and identification value determination based on the data feature set, and generating an analysis level and an identification value parameter; and performing weight correction processing on the basic key factor set, generating an optimized factor coefficient matrix, fusing the optimized factor coefficient matrix with associated equipment source data, and outputting a processing result. According to the method, structured and unstructured data are processed in a unified manner, so that the data integration capability is enhanced; multi-dimensional elements are extracted in combination with a pre-training model, and the key factor recognition effect is improved; and the analysis level and the identification value parameter are combined to drive weight correction and fuse the optimization matrix and the equipment source data to generate a processing result, so that the processing efficiency and the result accuracy are improved.

The invention relates to the technical field of ring main unit monitoring, and discloses a ring main unit online monitoring system based on multi-state quantity sensing. The system comprises an acquisition module used for acquiring a temperature signal and a partial discharge signal and obtaining state quantity data; the calibration module is electrically connected with the acquisition module and is used for preprocessing and calibrating the state quantity data; the analysis module is electrically connected with the calibration module and is used for performing multi-dimensional analysis on temperature and discharge characteristics; the transmission module is electrically connected with the analysis module and is used for data transmission and fusion processing; the early warning module is electrically connected with the transmission module and is used for performing multi-stage early warning analysis; and the adjusting module is electrically connected with the early warning module and is used for dynamically adjusting the monitoring parameters. According to the invention, accurate evaluation and timely early warning of the equipment operation state are realized, and the technical problems of low accuracy and poor adaptability of a single state quantity monitoring method are solved.

The invention relates to the technical field of data processing, and discloses a comprehensive management method and system for a smart park, and a storage medium. The method comprises the steps that environment, energy consumption and people flow data are collected through the Internet of Things, and a basic data set is obtained through edge node processing and semantic alignment; constructing a digital twin system by using a spatio-temporal data cube and a graphical mode; constructing a land digital identity system by adopting space-time coding and block chain technologies; performing anomaly identification and risk analysis on the activity data to obtain supervision early warning data; performing multi-dimensional analysis on the enterprise operation data to form an enterprise portrait; and presenting resource distribution through mixed reality, and analyzing cross-department data to obtain operation efficiency data. According to the invention, holographic perception, intelligent supervision and cooperative service of park elements are realized.

The invention discloses an operation and maintenance delivery abnormal event early warning method and system based on a cloud platform, and relates to the technical field of event early warning. The method comprises the steps of collecting a real-time operation data set of an operation and maintenance delivery link, performing multi-dimensional analysis based on the real-time operation data set, and constructing a multi-dimensional monitoring data set; performing context correlation analysis on the multi-dimensional monitoring data set, and constructing dynamic baseline parameters; deviation degree calculation is carried out on the real-time operation data set, a data deviation value is generated to trigger the cloud platform to carry out abnormal event judgment of operation and maintenance delivery, and an abnormal judgment result is obtained; performing event association according to an abnormality judgment result, determining an abnormality risk level, synchronizing the abnormality risk level to a cloud platform for tracing, generating a multi-level early warning instruction, and pushing the multi-level early warning instruction to a target terminal for graded warning. The technical problem that early warning of the operation and maintenance delivery event is not accurate and timely enough in the prior art is solved, and the technical effect of improving the accuracy and timeliness of early warning of the abnormal event is achieved.

The invention belongs to the technical field of atomized liquid component analysis, and discloses an intelligent analysis system and method based on atomized liquid components, and the method comprises the following steps: carrying out multi-parameter sampling on atomized liquid to obtain pyrolysis data; performing temperature gradient analysis to generate a pyrolysis spectrogram; identifying trace markers to generate a feature library; analyzing the component evolution track to generate a dynamic evolution spectrum; carrying out partition demarcation processing to generate an atomization characteristic spectrum; performing thermodynamic analysis to generate a reaction map; analyzing component diffusion to generate quantized data; constructing a sensory response prediction engine; performing intelligent analysis to generate response feature data; evaluating and generating a quality report; and performing optimization processing to generate formula suggestions and executing accurate blending. According to the method, accurate analysis and evaluation of atomized liquid components are achieved through multi-dimensional analysis, a scientific basis is provided for product optimization, and safety and taste experience are effectively balanced.

The invention relates to the technical field of image recognition, and provides a video target recognition method and device based on artificial intelligence and a storage medium, and the method comprises the steps: obtaining a video frame data sequence of a target video, carrying out the multi-dimensional analysis of the video frame data sequence, obtaining global video frame information and local region-of-interest information, and generating a target feature map based on the global video frame information and the local region-of-interest information, then carrying out time sequence mode analysis to obtain time sequence evolution features, combining the generated semantic representation vector, inputting the semantic representation vector into a preset adaptive Transform model to carry out target recognition, and obtaining a target recognition result. A semantic representation vector is generated through feature fusion, and a self-adaptive Transform model is used for target recognition, so that the target recognition precision in a complex scene is improved, and the problems of low detection precision and insufficient time sequence information utilization during complex scene processing, dynamic change and long-time sequence analysis are solved.

The invention discloses a multi-dimensional real-time operation monitoring and alarm linkage system for an urban direct drinking water plant station, and relates to the technical field of intelligent water affairs. Water quality, water quantity and equipment operation state data are collected in real time and fused to form a multi-dimensional data set, and a threshold adjustment accuracy evaluation model is established based on an instantaneous flow fluctuation rate and a daily variation deviation value; a machine learning method is utilized to judge and correct an inaccurate threshold adjustment action, and a self-adaptive genetic algorithm is adopted to continuously optimize a multi-dimensional analysis model and an adjustment strategy, so that the recognition capability of the system on serious abnormities such as pipe burst and water leakage is effectively improved, the continuity and safety of direct drinking water supply are guaranteed, and the system is suitable for popularization and application. And the stability and the intelligent level of the whole operation safety guarantee system are obviously improved.

The invention discloses an information acquisition and analysis method, device and equipment, a medium and a product, and relates to the technical field of cloud computing. According to the scheme, through the gateway with a multi-protocol identification function and the dynamic flow processing platform, the language model calling request transmitted by multiple data sources is monitored, unified management of LLM calling data is realized, and multiple protocols are adapted; when a language model calling request is received, forwarding the language model calling request to a language model service cluster, generating corresponding question and answer data, and obtaining user behavior data generated by a target data source according to the question and answer data, so as to carry out full-link multi-dimensional analysis based on the question and answer data and the corresponding user behavior data; the analysis process not only pays attention to the call request and response, but also considers the corresponding user behavior, so that the generated call analysis result is more accurate, and the credibility of LLM application effect analysis is improved.

The invention relates to the field of power system planning, in particular to a micro-grid boundary quantitative evaluation method and system for multi-dimensional analysis and dynamic verification. The method comprises the steps that power distribution network and micro-grid scheme data are acquired, and scene recognition and decoupling modeling are carried out after standardization processing; the method comprises the following steps: extracting end supply-preserving scene data, analyzing cost elements, and generating a critical cost threshold table through normalization processing and a threshold approximation algorithm; performing multi-dimensional parameter correlation analysis and integrated learning training based on the table, and constructing a multi-dimensional boundary index model; performing benefit matching calculation and green value accounting according to the result to generate an economic benefit decomposition structure; real-time streaming data processing and stability verification are combined to generate an economical efficiency boundary index set; and finally, generating a standardized evaluation file through matrix mapping and weight dynamic adjustment. According to the method, dynamic quantitative evaluation of the economy boundary of the micro-grid is realized, the capacity substitution benefit and the green value are effectively integrated, and an accurate basis is provided for planning decision.

The invention belongs to the technical field of display design evaluation, and particularly relates to an exhibition display layout design effect analysis and evaluation method, which comprises the following steps of: constructing a dynamic spatial topology network by collecting audience moving tracks, exhibit information and spatial structure data in real time; the problem of insufficient attention on space utilization influenced by dynamic factors such as human traffic fluctuation is solved. And dynamic changes in the space are converted into quantifiable network nodes and connection relationships, so that the space utilization state is changed from static design to dynamic monitoring, accurate capture of the real-time use condition of the exhibition space is realized, a comprehensive and real-time data basis is provided for subsequent analysis, and the real-time monitoring of the exhibition space is realized. Meanwhile, the dynamic space topology network is analyzed by means of spatial-temporal feature fusion to determine the edge weight and the exhibition area correlation degree, the spatial efficiency index and the dynamic line rationality index are generated in combination with an analytic hierarchy process and multi-dimensional analysis, and the defect that a space utilization rate core evaluation index is not established in the prior art is overcome.

The present disclosure relates to a system (102) and method (500) for analyzing network performance parameters. A user interface module (212) enables reception of comprehensive network speed test requests that specify multiple network performance parameters for analysis. A test execution module (214) triggers coordinated operation of multiple testing units (302a, 302b, 302c) to perform simultaneous network speed tests across different platforms. A data collection module (216) systematically aggregates the generated performance information from all testing units. One or more processors (202) transform the collected information through advanced processing algorithms to generate standardized performance data for each network parameter. An analyzing module (218) performs multi-dimensional analysis of the processed data based on specific attributes to provide comprehensive network performance insights. The integrated approach enables automated cross-platform testing, unified data collection, and sophisticated analysis of network performance characteristics, offering significant advantages over conventional single-platform testing methods.

The invention provides a multi-dimensional medical data analysis and auxiliary decision-making system and method. The system comprises a data acquisition module, a preprocessing module, a feature extraction module, a model training module, a multi-dimensional analysis module, a decision-making generation module and a feedback optimization module. The data acquisition module acquires data from multiple sources; the preprocessing module performs data cleaning and standardization; the feature extraction module extracts multi-dimensional features; the model training module trains an adaptive decision model; according to the invention, scientificity, accuracy and reliability of medical decision making can be improved, and powerful support is provided for clinical decision making.

The invention relates to an intelligent archival repository RFID archive processing system, and relates to the technical field of archive data processing, and the system comprises a state generation module, a semantic clustering module, a personnel label clustering module, a frequency statistics module and an archiving configuration module. Wherein the state generation module is used for acquiring RFID tag position information and generating a file taking state; the semantic clustering module 12 is used for performing semantic clustering based on the archive content identification to form first-level classification; the personnel label clustering module 13 is used for re-clustering the primary classification result according to the archive personnel identifier to form secondary classification; the frequency statistics module 14 is used for counting the taking frequency of each file in the secondary classification result; and the archiving configuration module 15 is used for determining an optimal archiving position according to the characteristics and the access frequency of the archives to be stored. The archives are archived according to the multi-dimensional analysis of the archive content, the archive personnel and the archive taking frequency, the archive archiving reasonability is improved, and then the archive retrieval and management efficiency is improved.

The invention relates to the technical field of voice recognition, in particular to a voice interaction system and method for customer service based on artificial intelligence, and the system comprises a voice input processing module, an intention classification and routing module, a context dynamic adjustment module, a user behavior learning module, a multi-level intention fusion module and a final result module. According to the method, a multi-dimensional feature system is constructed by extracting tone intensity, speech speed frequency and emotional fluctuation amplitude, intention categories, priority weights and confidence scores are generated to realize accurate acquisition of appeals, and dialogue history, context and emotional change dynamic reconstruction path nodes, switching rules and response time sequences are tracked during interaction. Historical behavior mining preference features, habit fusion intention relevance, emergency calculation of an optimal strategy, construction of service steps, resource allocation schemes and execution timelines, adjustment of an interactive interface, a service process and a feedback mechanism according to multi-dimensional analysis, guarantee of differentiated service experience, and improvement of response accuracy and user satisfaction.

The invention discloses a marine ecological protection and restoration project comprehensive evaluation method based on multi-dimensional analysis, and relates to the field of marine ecological analysis, and the method comprises the steps: obtaining multi-dimensional monitoring data through constructing a target region map model and setting a monitoring point, and generating an ecological feature vector; calculating a relative distance and a deviation between the monitoring point and the comparison feature vector to obtain a repair expected goodness of fit; screening monitoring points with low goodness of fit and integrating the ecological data set; training and generating an optimized simulation feature vector based on a GAN model; and analyzing the simulation parameters and evaluating the ecological evolution trend to complete comprehensive evaluation. According to the method, multi-dimensional and high-precision ecological assessment is realized, the accuracy of restoration effect prediction and the scientificity of an assessment result are improved, reliable support is provided for a marine ecological protection restoration decision, and the problems of one-sided monitoring, insufficient prediction and the like of an existing method are solved.

The invention relates to the technical field of data processing, in particular to an engineering geological survey data processing method and system, and the method comprises the following steps: obtaining hole site space positioning and lithologic boundary information, collecting a structure segment sequence, extracting resistance change direction classification disturbance behaviors, recognizing node sequence conflicts, and judging hydrological turning positions. Identifying the corresponding relation between the structural section and the water level track, and generating a structural section water level merging mapping table. According to the method, the spatial information, the lithology identification, the sampling level and the penetration resistance of the drilling structure sections are collected and serialized, disturbance identification and non-sampling section classification are performed based on the resistance change trend between the sections, and node conflicts are identified in combination with the numbering relation and the lithology combination; continuous modeling and classified merging of geological information on the spatial level are achieved, the linkage adaptability of data in a multi-dimensional analysis scene is enhanced, misjudgment interference caused by information isolation is avoided, and dynamic recognition and hydrological response analysis of a geological structure are supported.

The invention discloses a film strength detection method and related equipment, and the method comprises the steps: carrying out the multi-angle broadband scanning sampling of the whole process of an incident light beam penetrating through a lens, recording the transmission light intensity, group phase and scattering power, and generating a reference transmission spectrum matrix; applying low-amplitude periodic ultrasonic vibration disturbance to the lens according to the reference matrix, synchronously obtaining a transmission spectrum time change curve, and performing time domain analysis to obtain a stress response characteristic matrix; determining a detection path by integrating the two matrixes, performing incremental load scratch disturbance along the path, recording data of transmission light intensity and scattering power changing along with the load in real time, and forming a critical damage response data set; performing fine grid transmission light scanning on the scratch and the neighborhood, acquiring multi-angle scattering distribution, and constructing optical characteristic distribution data; and performing multi-dimensional analysis on the data, and outputting a result. According to the scheme, early-stage accurate identification and evaluation of microcracks in a lens film layer and interface hidden defects are realized, and the use reliability of the lens is improved.

The invention provides a multispectral-based early fire point identification method and system, and the method comprises the steps: carrying out the multi-dimensional data collection of an early fire point monitoring region, obtaining a fire point monitoring original data set, carrying out the data preprocessing, obtaining a fire point monitoring data set, carrying out the feature extraction of the fire point monitoring data set, and obtaining a multi-dimensional fire point feature data set; early fire point recognition is carried out based on the multi-dimensional collaborative fire point recognition strategy and the multi-dimensional fire point feature data set, an early fire point recognition result is obtained, multi-dimensional analysis is carried out on early fire points by introducing multi-dimensional data, the early fire point recognition accuracy is improved, an early fire point verification decision is executed according to the early fire point recognition result, and the accuracy of early fire point recognition is improved. And performing early fire point verification based on the early fire point verification decision, obtaining an early fire point verification result, and optimizing the multi-dimensional collaborative fire point identification strategy according to the early fire point verification result, thereby improving the accuracy and stability of early fire point identification.

The invention discloses an automatic mural protection and repair area identification method and system based on image analysis, and the method comprises the steps: generating degradation states of a mural at different time nodes through an image simulation algorithm, converting a two-dimensional damage image into a three-dimensional space model through the combination of a three-dimensional mapping technology, and precisely calibrating a high-risk damage point. The clustering analysis algorithm further integrates and disperses damage points, the property and spatial features of a damage group are extracted, and the curve feature detection algorithm automatically identifies damage boundaries by analyzing texture continuity and line trend. Dynamic sequence analysis tracks inter-frame differential change, a degradation extension track is disclosed, damage information is efficiently compressed and stored by a coding technology, and a matched degradation mode is mined and a potential damage area is updated by historical comparison analysis. According to the method, three-dimensional space modeling is taken as a core, multi-dimensional analysis and efficient storage are fused, accurate positioning, dynamic monitoring and preventive protection of mural damage are realized, and the scientificity and efficiency of cultural relic protection are remarkably improved.

The invention provides a privacy protection advertisement monitoring method, system and device and a medium, and is used for solving the problem of effectively monitoring dynamically generated advertisement contents on the premise of protecting user dialogue context privacy in a generative artificial intelligence application. The method comprises the steps that firstly, an advertisement text and context information are obtained, and a first vector is obtained through semantic embedding processing; performing privacy protection conversion on the vector by adopting a random projection method to obtain a second vector, wherein the conversion can reduce the dimension and scatter semantic distribution; inputting the processed vectors into a pre-trained multi-dimensional analysis model to obtain real-time analysis results of content compliance, content correctness, emotional tendency, creativity quality and the like; and finally summarizing analysis results according to advertisement activities and outputting a report. According to the method, differential privacy noise can be selectively added, and a delivery decision is generated based on a multi-threshold judgment model. According to the invention, the privacy of the user is protected, and the advertisement content is efficiently monitored in real time.

The invention discloses a non-natural earthquake phase identification method based on image processing, and relates to the technical field of image processing, and the method comprises the following steps: capturing a continuous signal with the amplitude changing with time in a seismic wave propagation process in real time based on a sensor distributed by an earthquake monitoring station network, forming an original amplitude sequence, and carrying out the calculation of the original amplitude sequence; collected continuous signals are converted into three-channel images, and the image brightness is automatically adjusted according to the seismic wave data dynamic range. According to the method, a seismic wave multi-dimensional analysis system is constructed through a three-channel construction strategy, the first channel clearly shows the time domain difference between natural seismic progressive fluctuation and non-natural seismic pulse type sudden change, and the second channel accurately captures microsecond-level sudden change which is easy to neglect in traditional time domain analysis; the third channel clearly distinguishes natural earthquake low-frequency energy concentration and non-natural earthquake intermediate-frequency characteristic spectral lines, and the adaptive brightness adjustment technology aims at the intensity difference of multi-source earthquake signals, improves the distinguishing degree of non-natural and natural earthquake edges, and builds a recognition foundation firmly.

The present invention belongs to the technical field of atomized liquid component analysis. The present invention discloses a system and method based on intelligent analysis of atomized liquid components, including: multi-parameter sampling of atomized liquid to obtain pyrolysis data; temperature gradient analysis to generate a pyrolysis spectrum; identification of trace markers to generate a feature library; analysis of component evolution trajectories to generate a dynamic evolution spectrum; partitioning and demarcation processing to generate an atomization characteristic spectrum; thermodynamic analysis to generate a reaction spectrum; analysis of component diffusion to generate quantitative data; construction of a sensory response prediction engine; intelligent analysis to generate response characteristic data; evaluation to generate a quality report; optimization processing to generate recipe suggestions and implement precise blending. This method achieves precise analysis and evaluation of atomized liquid components through multi-dimensional analysis, providing a scientific basis for product optimization and effectively balancing safety and taste experience.

The invention relates to the technical field of production quality control, and discloses a production data hierarchical processing and quality tracing system. The system comprises a production data acquisition module, a data hierarchical processing module, a quality fluctuation index module, a multi-dimensional quality difference analysis module and an abnormal source positioning module. The production data acquisition module acquires a real-time parameter data sequence of each production link; the data layering processing module is used for layering the data into a plurality of data hierarchies; the quality fluctuation index module obtains a quality fluctuation index based on the fluctuation condition of the data hierarchy; the multi-dimensional quality difference analysis module analyzes the multi-dimensional quality difference based on the index; and the abnormal source positioning module positions a production abnormal source based on the multi-dimensional quality difference. According to the system, through hierarchical data processing, quality fluctuation quantification and multi-dimensional difference analysis, accurate positioning of a production abnormal source is realized, and systematicness and accuracy of production data processing and quality tracing are improved.

The invention relates to the technical field of large model service tracking, and discloses a large model service chain type calling tracking system and a root cause analysis method, and the method comprises the steps: carrying out the semantic coding of the input and output of a large model, generating a final fingerprint, and carrying out the cross-Span fingerprint inheritance and extension; semantic uncertainty is calculated and analyzed, and the sampling rate is dynamically adjusted; based on sampling data acquired by a sampling rate, fusing a call chain topological structure and a time decay factor, constructing a dynamic space-time atlas, optimizing an atlas index, constructing a multi-dimensional analysis framework, and realizing positioning from surface anomaly to deep root cause in combination with an anomaly propagation contribution degree obtained by calculation; carrying out statistics on distribution parameters of local indexes and carrying out differential privacy processing, and calculating a global threshold value of the distribution parameters and carrying out issuing execution; and based on a final positioning root cause obtained by global threshold analysis, evaluating a root cause repair effect and performing model iteration, and converting historical root cause data into a driving force for system optimization.

The invention discloses an AI-based piano practice assisting method, system and equipment and a medium, and the method specifically comprises the steps: carrying out the denoising and segmentation of sound data through employing a signal processing technology, extracting the finger key time sequence characteristics of motion data through employing a motion recognition algorithm, and obtaining a sound feature set and a motion feature set; based on the sound feature set and the action feature set, performing multi-dimensional analysis by adopting a bimodal Transform evaluation network to obtain a first evaluation score; acquiring emotional expression data of the learner during piano playing, and analyzing and processing the emotional expression data according to a preset emotional analysis model to obtain an emotional analysis result; and according to the first sentiment analysis result and the first evaluation score, a deep learning algorithm is adopted to classify playing styles and weak links of the learner, and a personalized improvement direction and a priority sequence are determined. According to the invention, comprehensive playing evaluation and sentiment analysis are provided for learners, and the efficiency and pertinence of piano practice are effectively improved.

The invention particularly relates to a spatial analysis method based on large model agent driving. According to the spatial analysis method based on large model agent driving, syntactic analysis and intention recognition are carried out on a spatial problem described by a natural language through a large model, and spatial-temporal clustering, network analysis and a field simulation algorithm module are dynamically matched; packaging a heterogeneous algorithm into a standardized service component based on a model context protocol (MCP), and automatically constructing an analysis process chain; and integrating a GIS engine and a visual template library, generating a dynamic thematic map according to the processed spatio-temporal data through the visual engine, and superposing multi-dimensional analysis annotations. According to the spatial analysis method based on large model agent driving, the full-chain intelligent capability from data processing to decision support is constructed, the efficiency and precision of spatio-temporal evolution modeling are remarkably improved, meanwhile, the system integration and maintenance cost is reduced, the method is suitable for high-complexity scenes needing real-time spatio-temporal data analysis, and the method is suitable for popularization and application. And the method has remarkable technical innovation and industrial application value.

The invention relates to the technical field of operation index analysis, in particular to an operation index multi-dimensional analysis optimization method based on AI large model dynamic weight adjustment, which comprises the following steps: acquiring a market fluctuation index, an operation efficiency index, a financial health index and a supply chain elasticity index of an enterprise; four-dimensional index data of enterprise operation are input into a hypergraph tensor decomposer, the hypergraph tensor decomposer maps four-dimensional indexes into mutually orthogonal hypergraph nodes through orthogonally constrained Tucker decomposition, a decoupling hypergraph is generated, the decoupling hypergraph is injected into a dynamic weight modeling model, and an optimization strategy chain is generated based on a real-time event stream; and the orthogonal constraint condition of the decoupling hypergraph is corrected through tensor gradient back propagation after execution of the optimization strategy, and closed-loop optimization is formed. According to the method, the optimal response action with business semantics and timeliness can be generated according to unexpected situations such as market abnormal fluctuation, supply chain breakage or financial pressure.

The invention provides a dam safety monitoring data analysis method and system based on a multi-modal knowledge graph and a large language model, and belongs to the technical field of calculation models. The method comprises the following steps: collecting multi-modal data of dam monitoring, and preprocessing the multi-modal data; constructing a dam safety analysis knowledge graph by adopting a water conservancy object multi-modal knowledge graph construction mode; an intelligent positioning function is used for finding an associated object for the monitored abnormal signals, information of the associated object is obtained, and a dam analysis model is used for association analysis; generating an abnormal associated sub-graph; and forming a multi-dimensional analysis report conclusion by using a pre-trained water conservancy professional knowledge large language model in combination with cue words and user questions. Multi-aspect information generated in the dam safety management process is integrated by utilizing the knowledge graph to form a comprehensive information network, efficient query and reasoning are realized by virtue of a graph computing technology, and a monitoring analysis result for explaining the dam is clearly, intuitively, accurately and comprehensively presented.

The invention relates to the technical field of steel structure assessment, and discloses a steel structure connection risk assessment system and method based on industrial data analysis, and the system comprises a multi-dimensional collection module and an intelligent analysis module. According to the steel structure connection risk assessment system and method based on industrial data analysis, management data of all components of a steel structure, inspection data of all joints and sensing data of all environmental factors are acquired through a multi-dimensional acquisition module and are classified to form a data set, and an intelligent analysis module analyzes the assemblability of each component and generates a performance index; the weight distribution proportion can be adjusted according to actual engineering requirements, multi-dimensional analysis is flexible and high in applicability, and an intelligent analysis module analyzes the erosion effect of environmental factors on the performance of the steel structure, generates attenuation coefficients, sets threshold values in a fixed range and evaluates whether the steel structure has potential risks or not; and targeted suggestions are provided, preventive maintenance is realized, and the intelligent evaluation precision is high.

The invention relates to the technical field of platform drainage and promotion, in particular to a large factory cross-platform user drainage and growth promotion system based on artificial intelligence, and provides a basis for subsequent data analysis through behavior time axis alignment and interest dimension unification and attention attenuation node labeling; the interest of the low-activity platform user is effectively activated by constructing an interest energy conduction network and performing directional path planning to optimize resource allocation; by generating a three-dimensional tensor field, the spatial-temporal change and theme relevance of interest energy is captured, and a multi-dimensional analysis framework is provided for a complex interest scene; user interest energy ecological niche features are quantified based on a three-dimensional tensor field, multi-level dynamic clustering is achieved, and the user layering operation efficiency and the conversion effect are improved; by defining platform interest energy ecological niche indexes, identifying dominant platforms and complementary combinations, and optimizing platform ecological layout and synergistic effect; by generating the interest energy guide chain, the viscidity and activeness of the user to the platform are improved.