308 results about "Multi dimensional analysis" patented technology

The present invention discloses a digital production operation management method for data quality assessment and analysis, including: step 1: constructing a multi-level distributed data acquisition system; step 2: designing a multimodal data fusion framework, combining graph neural network and time series data processing technology to realize data fusion and multidimensional analysis; step 3: constructing an intelligent scheduling engine to realize dynamic adaptive scheduling strategy to cope with real-time changes in the production environment; step 4: designing a decentralized self-organizing production network to realize flexible adjustment and optimization of the production process; step 5: realizing the coordinated optimization of product design and equipment maintenance through topology optimization, multi-objective optimization and machine learning algorithms; step 6: constructing a virtual production network to realize real-time monitoring, dynamic prediction and optimization decision support of the production system. Through the present invention, enterprises can break the limitations of traditional production models, improve production efficiency, flexibly respond to changes in market demand, and optimize resource allocation.

The invention discloses a data-driven material conveyor fault early warning method and system, and relates to the field related to data early warning, and the method comprises the steps: arranging a plurality of sensors for real-time collection, obtaining a real-time operation data set of a material conveyor for multi-dimensional analysis, determining multi-dimensional operation data for time series decomposition, extracting a plurality of operation features, and carrying out the early warning of the faults of the material conveyor; learning training is carried out according to the historical operation record log to obtain a fault prediction model, the real-time operation data set is synchronized to the fault prediction model for prediction calculation, and the operation health score of the material conveyor is determined to carry out fault judgment on the material conveyor. And determining a fault risk level, generating an early warning signal, and sending the early warning signal to a remote terminal for intelligent early warning feedback of the fault. The technical problem that intelligent maintenance is difficult to achieve due to the fact that fault monitoring of the material conveyor is not timely and the early warning precision is low is solved, real-time monitoring and intelligent feedback of the equipment state are achieved through data-driven fault prediction and real-time early warning, and the technical effect of improving the operation and maintenance efficiency is achieved.

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 discloses a project-level visualization display method and system for highway multi-source data fusion, and the method comprises the following steps: 1, constructing a multi-source heterogeneous data exchange and integration frame, and carrying out the seamless connection and mapping of a BIM model, GIS space geographic information and an oblique photography three-dimensional point cloud model; 2, formulating a standardized interface specification, and performing specification processing on data from different sources through a data analysis and conversion engine; 3, dynamically and synchronously updating the multi-source engineering data and the project progress in real time; 4, gathering engineering data to construct a digital twinborn model of the expressway project to realize centralized management, multi-dimensional analysis and dynamic presentation in a visual scene; 5, carding and extracting management business logic to construct a flexibly expanded professional application module so as to realize intelligent analysis of the engineering project; and step 6, performing visual immersive display on the multi-source data fusion project by adopting visual man-machine interaction. According to the invention, visual comprehensive management and traceability of each element of the project are realized.

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 discloses a knowledge graph-driven sales data multi-dimensional analysis and visualization method and system. The method comprises the following steps: constructing a sales domain knowledge graph containing products, customers and transaction entities; on the basis of the knowledge graph, preprocessing the original sales data to obtain preprocessed sales data; according to a knowledge graph and the preprocessed sales data, realizing time sequence adaptive multi-view collaborative graph embedding, and obtaining graph embedding features; fusing the graph embedding feature with the traditional sales data feature to obtain a fused feature; performing multi-dimensional analysis based on the fusion features to obtain an analysis result; performing visual presentation on the analysis result to generate visual data; on the basis of visual data, an interactive exploration interface is provided, deep analysis and insight of data are achieved, by introducing the knowledge graph technology, the complex relation between entities can be effectively captured and utilized, the association rule mining effect is remarkably improved, and the association rule mining efficiency is improved by 77.8% compared with a traditional method.

The invention provides an auxiliary decision-making method and an auxiliary decision-making system for construction project site selection in urban planning, and relates to the technical field of urban planning, and the method comprises the steps: obtaining a project site selection auxiliary decision-making system according to a whole chain of urban planning construction project site selection work demands; loading the geographic data; performing plot screening on the geographic data through a plot screening unit to obtain an intentional region plot set; performing multi-dimensional integrated analysis on the intentional region plot set according to a scheme analysis unit to obtain plot scheme multi-dimensional analysis information; and performing split-screen comparison and consultation and review decision on the plot scheme multi-dimensional analysis information and the intention area plot set based on a consultation and review unit, and determining a project site selection decision scheme. According to the method, the technical problems of low building project site selection data collection and analysis efficiency and lack of scientific land parcel evaluation means at present are solved, the working efficiency, scientificity and accuracy of building project site selection are remarkably improved, and sustainable development of urban construction is promoted.

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 user-defined multi-dimensional analysis configuration method, system and equipment based on a constellation model and a medium, belongs to the technical field of data analysis, and aims to solve the technical problem that basic data such as dimensions, indexes and data models in multi-dimensional analysis are complicated to construct. According to the technical scheme, according to the method, the dimensionality, the index and the incidence relation between the fact table and the dimensionality table configured by the model table are imported, the mapping between the dimensionality tables is automatically imported into the correlation table, the attribute and the model relation, the model maintenance cost is reduced, and the model maintenance time is shortened; ordered model construction is realized through dimensions, indexes, fact tables, dimension tables, data models and self-defined multi-dimensional query, multi-fact-table dimensions screen application configuration scenes of reverse mapping of property of the fact tables, required indexes are flexibly selected from the fact tables through dragging operation, and according to a cascade mode of the indexes and the dimensions, the application configuration scenes of reverse mapping of property of the property of the fact tables are selected to be matched with the property of the property of the property of the property of the property of the property of the property of the property of the property of the property of the property of the property. And automatically screening out the generality dimension for display.

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 discloses a method and system for flexibly formulating a product carbon footprint model based on LCA, and relates to the technical field of environmental sciences, and the method comprises the steps: determining a carbon footprint evaluation demand, and constructing an evaluation framework according to the demand; dividing the LCA data into data sets according to life cycle stages; collecting carbon emission data in a life cycle stage in real time; constructing an LSTM model for predicting the carbon emission trends of the product in different life cycle stages; and carrying out carbon footprint accounting, hotspot analysis, sensitivity analysis and uncertainty evaluation based on the LSTM model. According to the method for flexibly formulating the product carbon footprint model based on the LCA provided by the invention, flexible carbon footprint evaluation is realized through a modularized data platform and an adjustable system boundary; by combining the Internet of Things and the AI algorithm, the data acquisition accuracy and analysis efficiency are improved, manual intervention is reduced, carbon emission trend prediction and multi-dimensional analysis are realized, and the evaluation precision and decision support capability are improved.

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 relates to a carbon emission measuring and calculating method and system based on urban public transport. The method comprises the following steps: step 1, constructing an urban public transport carbon emission calculation model; step 2, collecting data related to urban public transport carbon emission; step 3, acquiring public database data; step 4, data cleaning and preprocessing; 5, calculating the carbon emission of the urban public transport: calculating the carbon emission in the urban public transport system according to the data in the step 4, and outputting specific emission data; step 6, carbon emission data aggregation and multi-dimensional analysis; step 7, carbon emission trend prediction; and step 8, strategy optimization. According to the method, the accuracy of carbon emission calculation is improved, the carbon emission condition in the public traffic operation process is reflected in real time, and two scientific carbon emission data supports are provided for traffic management departments, enterprises and the public.

The invention relates to data analysis, in particular to an intelligent dependency graph construction method based on multi-dimensional analysis and self-adaptive optimization, which comprises the following steps: reading module data, and carrying out preliminary analysis on a dependency relationship among modules to construct a preliminary dependency graph; performing multi-dimensional analysis on a dependency relationship between each pair of modules in the preliminary dependency graph, and calculating a comprehensive score of each pair of modules according to a multi-dimensional analysis result; adjusting the preliminary dependency graph according to the comprehensive score of each pair of modules to obtain an adjusted dependency graph; performing cyclic dependency detection on the adjustment dependency graph, and merging static dependency data and dynamic dependency data of each module; self-adaptive optimization is carried out according to the complexity of the adjusted dependency graph, a final dependency graph is generated, and the final dependency graph is stored and output; according to the technical scheme provided by the invention, the defect that the software module dependency graph is difficult to accurately and efficiently analyze and construct in the prior art can be effectively overcome.

The invention discloses a Transform-based multi-scene fraud phone recognition system, relates to the technical field of fraud phone recognition, and solves the problems that in the prior art, abnormal numbers are screened out by performing multi-dimensional analysis on calling numbers, a fraud can easily avoid a blacklist without considering that the fraud frequently changes the numbers or uses false numbers, and the fraud time is shortened. The technical problem that all potential fraud sources cannot be covered possibly can be solved; the method comprises the steps of preprocessing a call recording sample; stop words in the call text are removed; converting the call text into a text vector by using a word embedding technology; constructing a Transform model by using a sequence modeling capability and a self-attention mechanism, obtaining a fraud probability corresponding to the call recording sample, and training the Transform model by using the text vector and the fraud probability; by dynamically adjusting hyper-parameters and the number of layers in the model, specific field vocabulary embedding is introduced, so that the model adapts to multiple types of fraud scenes.

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.

Aspects of the present invention include setting task management and project management parameters; utilizing a queuing theory calculator for a task and a project management based on various types of multi-dimensional visualization; generating a plurality of results of the task management and the project management based on utilizing the queueing theory calculator; analyzing the results with at least one machine learning algorithm; outputting a multi-dimensional analysis of the results based on the analysis of the results with the at least one machine learning algorithm; analyzing the results using a magic square analysis; comparing the results with a plurality of requirements based on the analysis of the results with the at least one machine learning algorithm and the analysis of the results using the magic square analysis; and determining a subset of the plurality of results that meet a threshold of a comparison of the requirements.

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.