920 results about "Parameter analysis" patented technology

The invention discloses a 3D intelligent simulation generation method for clothing styles based on a model library, and relates to the technical field of digital twinning, and the method comprises the steps: carrying out the entity relation extraction and semantic mapping of multi-source clothing data, constructing a cross-modal knowledge graph, and extracting a physical constraint rule of the cross-modal knowledge graph; performing association reasoning and cross validation on the physical constraint rule and the version library to obtain a basic version template set; inputting the basic model template set into an intelligent clothing model, performing structured feature decoupling by the parameter analysis layer, dynamically adjusting a topological structure by the geometric generation layer, and generating 3D simulated clothing; and carrying out digital modeling on the basic model template set through a digital twinning algorithm, constructing a digital twinning body, carrying out physical simulation and optimization verification on the 3D simulation clothing by using the digital twinning body, and outputting a clothing style 3D simulation scheme. According to the invention, by constructing the pattern library and the intelligent garment model, the efficiency, accuracy and individuation level of garment 3D simulation generation are improved.

The invention provides a performance optimization and regulation method for a high-temperature sintering furnace, and belongs to the technical field of intelligent regulation, and the performance optimization and regulation method comprises the following steps: arranging sensors in each heating area of the high-temperature sintering furnace, and monitoring the heating areas in real time; constructing a heating area operation state database, comparing the collected real-time data with a preset sintering process parameter range, and analyzing to obtain a performance analysis set of each heating area; establishing a three-dimensional heat conduction model, simulating heat flow distribution of each heating area under different heating powers and temperature settings, determining a thermal coupling effect of each heating area in different stages, and determining a heat transfer coefficient between adjacent heating areas; and an initial strategy of the high-temperature sintering furnace is determined, the initial strategy is optimized based on the auxiliary parameters of all the heating areas, and all the heating areas of the high-temperature sintering furnace are regulated and controlled according to the optimization strategy. The problems of inaccurate temperature control, single parameter analysis, insufficient thermal coupling effect processing and the like in the traditional regulation and control technology are effectively solved.

The invention provides a railway anomaly monitoring method and system based on unmanned aerial vehicle routing inspection, and relates to the field of data processing, the method comprises the following steps: obtaining railway routing inspection data collected by an unmanned aerial vehicle, extracting a monitoring instruction in the railway routing inspection data, carrying out parameter analysis on the monitoring instruction, and obtaining an anomaly monitoring parameter set corresponding to the monitoring instruction; performing spatio-temporal feature extraction processing on the plurality of spatio-temporal frame sequences, generating a spatial feature vector and a time feature vector of each spatio-temporal frame sequence, screening out at least one abnormal region from the plurality of spatio-temporal frame sequences based on a dynamic matching relationship between the abnormal monitoring parameter set and the spatial feature vector and the time feature vector, and performing abnormal region extraction processing on the abnormal region. And calling an anomaly identification model to perform conjoint analysis on the spatial-temporal characteristics of the abnormal region, generating a railway anomaly monitoring result, and mapping the railway anomaly monitoring result to a physical coordinate system of the railway line to generate an anomaly positioning report. According to the invention, the accuracy and intelligent decision of railway inspection anomaly detection can be improved.

The invention discloses a CAD automatic modeling method and system based on parameter driving. The method comprises the steps that model parameter information needing modeling is obtained; generating a CAD (Computer Aided Design) model on the basis of parameter analysis, geometric construction and surface recognition schemes; performing reverse reconstruction of the three-dimensional CAD model based on topology analysis, geometric feature extraction, pattern recognition and parameter relation inference; modeling, optimization and feedback evolution are carried out based on model analysis, feature learning and generation of parametric modeling rules; and according to the obtained data information, CAD automatic modeling based on parameter driving is completed. According to the method, CAD automatic modeling based on parameter driving is achieved, the reliability is higher, the accuracy is better, and the efficiency is higher.

According to the remote sensing image green roof intelligent identification method and system based on the improved Mask RCNN algorithm, the feature extraction capability of the algorithm is enhanced by introducing an attention mechanism, and the edge segmentation precision of a target is improved by constructing a joint loss function, so that precise detection of a green roof in a complex built environment is realized. According to the invention, the morphological parameter analysis module is synchronously integrated, geometric feature parameters such as the area, the perimeter and the shape index of the green roof can be automatically extracted, and integrated processing of green roof identification and feature parameter extraction is realized. Compared with a traditional method, the method can effectively solve the problems that target examples are difficult to distinguish, and environmental adaptability is poor, and provides a reliable technical means for green roof monitoring and ecological environment benefit evaluation.

The invention discloses a pigsty environment regulation and control optimization method and system, and belongs to the technical field of breeding environment management. The system comprises an intelligent sensing module, an environment analysis module, a parameter optimization module and a control management module. The intelligent sensing module is used for collecting pig house environment parameters and live pig physiological parameters through a multi-mode sensor; the environment analysis module is used for constructing a behavior recognition model, analyzing behavior characteristics based on physiological parameters and dividing abnormal regions; the parameter optimization module is used for establishing a physiological environment control model and setting an optimal optimization scheme for each abnormal region; and the control management module is used for generating an environment control instruction according to the optimal optimization scheme and controlling equipment execution. The pig house environment and the pig physiological parameters are collected and intelligently analyzed through the multi-mode sensor, automatic and precise environment optimization management is achieved, and the comfort level and the health level of the pigs are improved.

The invention discloses a network equipment abnormity monitoring and early warning method and system based on AI, and belongs to the technical field of network equipment monitoring, and the system module comprises a multi-source data acquisition module which obtains various characteristic parameters of network equipment; the data transmission and processing module is used for transmitting and preprocessing the characteristic parameters; the intelligent analysis module performs comprehensive analysis based on the characteristic parameters and judges whether the network equipment is abnormal or not; and the early warning response module performs hierarchical response based on a judgment result of the parameter analysis module. According to the invention, under the normal operation condition of the network equipment, the potential abnormal risk of the network equipment can be judged according to the influence condition of the environment where the network equipment is located and the relative deviation condition of each piece of network equipment, so that the potential abnormal risk can be handled in time, thereby reducing the occurrence of subsequent abnormal situations and improving the safety of the network equipment. And normal and stable operation of network equipment is ensured.

The invention provides an automatic history fitting method for gas drive numerical reservoir simulation, which comprises the following steps of: 1, establishing a gas drive numerical simulation mathematical model, and correcting the gas drive numerical simulation mathematical model by using a gas drive PVT phase state fitting experiment; 2, solving the corrected gas drive numerical simulation mathematical model obtained in the step 1, performing parameter analysis, and screening main control factors influencing accumulated oil production; 3, generating a numerical simulation training sample set based on an orthogonal experiment, and obtaining the main control factor parameter initial value constraint screened out in the step 2 through a machine learning algorithm; step 4, establishing a multi-parameter fitting objective function for realizing historical fitting; and step 5, using the main control factor parameter initial value constraint obtained in the step 3, performing iterative calculation through a projection gradient method, fitting the target function established in the step 4, and terminating when a convergence condition is satisfied. According to the method provided by the invention, the constraint parameter values can be adaptively calculated, automatic historical fitting is completed, and the definition of the fitting parameter range does not depend on empirical and experimental determination any more.

The invention relates to the field of water quality parameter detection, in particular to a full-spectrum water quality multi-parameter analysis detector which comprises a full-spectrum acquisition module, a preprocessing module, an information processing module and an application module. The full-spectrum acquisition module is used for transmitting a full-spectrum signal to a detection sample, detecting transmission light and scattered light signals and generating original spectrum data; the preprocessing module is used for preprocessing the original spectrum data by adopting an edge calculation mode to obtain a standardized spectrum matrix; the information processing module is used for calculating the actual concentration value of each impurity in the detection sample according to the standardized spectrum matrix; according to the method, the influence relation between different types of impurities is introduced, compared with a common prediction method, the prediction precision of the concentration of each impurity is higher, whether early warning is needed or not is judged according to the prediction precision, and the situation of misjudgment is avoided.

The invention relates to the technical field of iron wire electrogalvanizing passivation monitoring, and discloses an iron wire electrogalvanizing passivation intelligent monitoring system with self-adaptive coating thickness control, and the system comprises a coating parameter analysis module which can analyze the matching degree of the coating thickness and the passivation effect according to the equipment operation state information, and generates a parameter analysis parameter set; based on the parameter set, the passivation feature extraction module extracts a space feature value and a distribution variable quantity of a passivation film on the surface of the iron wire to generate an optimized parameter set; the thickness correlation modeling module analyzes the correlation between the coating thickness and the passivation characteristics and generates a dynamic regulation and control result; the abnormal intervention adjustment module extracts a real-time feature matching rate and the like to generate an intervention adjustment data set; and the dynamic feedback regulation and control module analyzes the linkage demand and generates a feedback data table. The system can monitor the iron wire electrogalvanizing passivation process in a self-adaptive mode, the coating thickness is accurately regulated and controlled, and the passivation effect stability and the production efficiency are improved.

The invention discloses a bridge structure health state intelligent sensing and early warning system, relates to the technical field of bridge monitoring, and provides the following scheme that the bridge structure health state intelligent sensing and early warning system comprises a sensing layer, a transmission layer, a data layer, an analysis layer and an application layer which are connected in sequence and work cooperatively: the sensing layer is used for comprehensively collecting health state parameters of a bridge structure; according to the method, the comprehensiveness of monitoring parameters is guaranteed, the effectiveness and transmission efficiency of collected data are improved, redundant data are prevented from occupying resources, deep mining is achieved, a more scientific basis is provided for health status assessment and disease diagnosis, misjudgment caused by single parameter analysis is reduced, the diagnosis precision is improved, and the method is suitable for popularization and application. The intelligent optimization and simulation evaluation of the maintenance scheme are realized, the limitation of the traditional empirical scheme is avoided, the maintenance cost is reduced, the construction period is shortened, the influence on traffic is reduced, the performability of the scheme is improved, the stable and safe transmission of monitoring data in a complex environment is ensured, and the data loss or leakage is avoided.

The invention relates to the technical field of power transmission line analysis, and discloses an intelligent power transmission line analysis system based on feature recognition. The system comprises a line working condition feature library construction module, a spatial distribution analysis module, a dynamic risk assessment module, an abnormal mode recognition module and a risk decision output module. The line working condition feature library construction module obtains multiple types of data, associates equipment identifiers and calculates working condition feature score values to generate a feature library; the spatial distribution analysis module extracts related data, and marks consistent and conflict areas to form a partition mark set; the dynamic risk assessment module is used for generating a load fluctuation influence superposition assessment result in combination with multi-parameter analysis for consistent area equipment nodes; an abnormal mode identification module screens abnormal points and establishes an abnormal equipment node set; and the risk decision output module marks risk diffusion path nodes and outputs operation state detection and risk early warning results. The system realizes comprehensive intelligent analysis of the power transmission line.

The invention relates to the technical field of automobile coating, in particular to an automobile paint simulation method and system supporting dynamic forward and reverse two-way rendering, and the method comprises the steps: forward rendering: inputting automobile paint formula detail information, and analyzing paint layer rendering parameters and color master rendering parameters to output a rendering result in an automobile paint renderer; reverse rendering: inputting the original measured data, and performing reverse parameter analysis and parameter optimization to obtain vehicle paint physical parameters; forward and reverse fusion: taking the vehicle paint physical parameters as an analysis result of vehicle paint formula detail information newly input in forward rendering, and taking a rendering result as a prior reference in reverse rendering to constrain the range of the vehicle paint physical parameters; performing positive and negative consistency verification based on the rendering result and the original measured data to calculate a rendering deviation; and performing back propagation based on the rendering deviation to iteratively optimize the forward rendering and the reverse rendering until the rendering deviation is less than a threshold value. According to the invention, the precision and effect of vehicle paint rendering can be improved.

The invention provides a photovoltaic module surface defect positioning method based on image processing, and the method comprises the steps: carrying out the preprocessing of an original image, obtaining a gray image, obtaining a second abnormal seed point through a multi-level abnormal seed point screening strategy, including global statistical judgment, gray deviation analysis and neighborhood aggregation optimization, and obtaining a second abnormal seed point; and taking the second abnormal seed point as a starting point, realizing accurate extraction of a real defect area of the photovoltaic module through a self-adaptive multi-threshold iterative expansion algorithm in combination with weighted distance measurement of gray, gradient and texture, and finally performing characteristic parameter analysis on the defect area to realize identification and classified output of real defects. The method has the advantages of being high in positioning precision, high in robustness and suitable for various defects, and is suitable for automatic detection of the surface defects of the photovoltaic module.

The invention belongs to the technical field of bridge engineering, and aims to eliminate the problem of systematic underestimation of web bearing capacity under the condition of standard effective loading length. The invention provides a method for correcting a local pressure bearing capacity calculation formula of a steel beam web, which comprises the following steps of: identifying and determining a key parameter ratio and an engineering application range thereof by carrying out sensitivity parameter analysis on a bending reduction coefficient; on the basis of the reference engineering case, fixed values are taken from part of parameters, and other geometric quantities are inversely calculated; based on a preset variable sequence, finite element models are established in Abaqus in batches, and the ultimate bearing capacity and the flange longitudinal stress level of each model are extracted; the standard calculation value is corrected according to a finite element result, the corrected ultimate bearing capacity of the steel beam web is calculated, and a corrected bending reduction coefficient is obtained accordingly; and finally, fitting a corrected formula about the regularized slenderness ratio on the correction coefficient by taking the specification parameter as an independent variable to obtain an expression capable of directly replacing the original specification. The method can significantly improve the calculation precision.

The invention provides a multi-dimensional operation parameter analysis fault detection method and system, and relates to the technical field of fault detection, and the method comprises the steps: building an inspection task system and a fault triggering system, and forming a task basic data set; generating an inspection task instance object; analyzing to obtain monitoring index data; effective fault report information is extracted, and a software system inspection result set is formed; operating parameters of the hardware system are collected, threshold detection and fluctuation analysis are executed, and a hardware system inspection result set is generated; and extracting multi-dimensional features, inputting a fault scoring function, calculating a comprehensive fault score of each device or system, and automatically generating an inspection report and a fault report sheet. According to the method and the device, the problems that fault evaluation depends on manpower and judgment standards are not uniform in the prior art can be solved, automatic risk grading and result generation based on comprehensive scores are realized, and thus the accuracy and the diagnosis efficiency of fault detection are improved.

The invention discloses a three-dimensional point cloud building automatic modeling system based on deep learning. The system comprises a point cloud acquisition and input module, a point cloud preprocessing module, a depth feature extraction module, a semantic segmentation and component identification module, a structure parameter analysis module and a parameterized model generation module. According to the invention, through deep fusion of the point cloud deep learning network and the parametric modeling engine, full-process automation of building three-dimensional modeling is realized; the depth feature extraction module is used, so that the recognition accuracy of structures such as doors, windows and roof edges can be effectively improved, the precision of a building model is ensured, and the depth feature extraction module can continuously improve the recognition accuracy along with continuous use of the system; and by using the semantic segmentation and component identification module, the irregularity of the building point cloud, the complexity of the structure and the shielding noise can be identified and distinguished, and the problem that semantic annotation and topological repair need to be manually performed in the prior art is solved.

The invention relates to the technical field of trajectory analysis, in particular to a trajectory aiming trajectory analysis method which comprises the following steps: acquiring wind speed time-varying data and centroid parameters, calculating wind disturbance stress and screening a maximum value, judging a rotating direction trend, executing pitch angle consistency judgment, and calculating an angle adjustment rate to generate boundary parameters. The method comprises the steps of analyzing a target speed trend to judge delay, calculating a deviation trend difference value, extracting wind disturbance sudden change to mark a high-sensitivity zone, judging that deviation coincides with the high-sensitivity zone, and generating a dynamic correction instruction set. Modeling analysis is carried out on a target delay trend by combining a direction included angle and mode difference change in a target speed trend sequence, and a trajectory correction mapping relation is constructed by combining flight parameters and timestamps, so that the adjustability and striking precision of an overall hit path are effectively improved.

The invention provides a meter management method fusing remote parameter configuration and local response control, and the method comprises the steps: constructing a self-adaptive configuration parameter set comprising basic parameters, dynamic regulation factors and environment adaptation coefficients through a remote configuration center according to a diversified operation environment and equipment difference, and transmitting the self-adaptive configuration parameter set to a target meter; the meter completes parameter analysis and rule mapping fusion locally, a control instruction sequence containing control logic and threshold expression is generated, then real-time monitoring and control operation is executed, and a comprehensive operation data set is formed through collection; by analyzing operation data and an instruction response result, a control adaptation adjustment factor is calculated to reflect control deviation, local rule mapping is updated, a self-adaptation feedback data set is generated and transmitted back to a remote configuration center, and parameter configuration and closed-loop optimization of a control strategy are completed; the method improves configuration flexibility and local response precision, and is suitable for intelligent management of large-scale heterogeneous meters.

The invention discloses a substation primary and secondary equipment communication prefabricated optical cable online monitoring method and system, and belongs to the technical field of communication prefabricated optical cable monitoring. The system comprises an optical cable parameter acquisition module, a communication quality evaluation module, a physical parameter analysis module, a first-aid repair path planning module and a fault database. The method comprises the following steps: optical cable parameter acquisition: acquiring data in real time, and converting high-dimensional data into low-dimensional data through a principal component analysis algorithm; communication quality evaluation: identifying communication characteristics in the low-dimensional signal parameter matrix based on a fuzzy comprehensive evaluation algorithm, and outputting a communication quality score; the physical parameter analysis module extracts physical deep features of the optical cable by using a deep belief network DBN, and inputs an improved Bayesian classifier to output a state classification result; according to the first-aid repair path planning, OTDR detection parameters are dynamically adjusted, and an optimal first-aid repair path is planned in combination with the position of the inspection robot; according to the invention, optical cable communication quality evaluation, accurate physical state classification and efficient fault repair planning are realized.

The invention provides a battery cell fault real-time monitoring method and system based on multi-parameter analysis and calculation, and relates to the field of battery safety monitoring. The method comprises the following steps: synchronously acquiring voltage, temperature and internal resistance signals of a battery cell, and preprocessing to obtain original data; and performing voltage correlation analysis, internal resistance-temperature collaborative judgment and isolated forest anomaly detection on the original data in sequence to form a progressive anomaly judgment chain, and extracting multi-dimensional battery cell state features. And dynamically correcting the voltage protection threshold value based on the battery health state attenuation rate and the real-time temperature. And fusing multi-dimensional battery cell state characteristics through a fuzzy rule base, and calculating a comprehensive fault probability to divide a fault probability grade. And triggering a matched response strategy based on the fault probability level. The technical problems that single-parameter monitoring is low in sensitivity and high in false alarm rate, and the real-time monitoring requirement is difficult to meet by depending on historical data are solved.

The invention belongs to the field of laser processing and numerical simulation, and particularly relates to a finite element modeling and analysis method for a nanosecond pulse laser target material. The method comprises the following steps: establishing and loading a laser heat source model; establishing a target material finite element model and carrying out grid division; establishing and solving a transient heat conduction equation; carrying out multi-cycle iterative solution and post-processing; and performing model verification and parameter analysis. According to the method, the laser heat source model is established, the adaptive grid encryption strategy for temperature gradient optimization is adopted, and the transient solution algorithm is combined, so that compared with a traditional simplified analysis model or general commercial software simulation, the prediction precision of the target material temperature field spatio-temporal evolution law under the nanosecond pulse laser effect is remarkably improved. The method can accurately predict the temperature distribution and evolution law of the target material under the action of laser, has the advantages of high modeling precision, good calculation efficiency, wide application range and the like, and is suitable for engineering applications such as laser processing and damage evaluation of optical materials and composite materials.

The invention belongs to the technical field of testing, and particularly discloses a comprehensive evaluation method for rock damage and fracture acoustic emission events. The method comprises the following steps: collecting an acoustic emission full-waveform signal released by a rock sample in a damage and fracture process, and separating an independent acoustic emission event from a full waveform by utilizing peak identification time, impact identification time and impact locking time; respectively extracting a time domain characteristic parameter and a frequency domain spectrum parameter for each independent event; entropy weight quantitative analysis is carried out on various extracted parameters based on an information entropy theory, and a comprehensive evaluation index of the acoustic emission event is constructed on the basis. According to the method, synchronous extraction and fusion of multiple time domain and frequency domain parameters of the acoustic emission event are realized, objective weight distribution is performed in combination with an information entropy theory, and a rock damage and fracture comprehensive evaluation index capable of comprehensively representing global features of the acoustic emission event is established. The limitation that a traditional method only depends on single time domain or frequency domain parameter analysis is effectively solved.

The invention discloses a battery safety performance test system based on artificial intelligence. The system comprises a data acquisition module, an algorithm processing module, a multi-field coupling module, a parameter analysis module, an optimization regulation and control module and a result output module. The data acquisition module acquires multi-dimensional data, transmits the multi-dimensional data to the algorithm processing module, processes the multi-dimensional data through an SE-Res-LSTM algorithm, transmits the processed data to the multi-field coupling module, calculates the processed data through a coupled electrochemical-thermal-mechanical multi-field equation, transmits a result to the parameter analysis module, analyzes a safety performance parameter threshold value, and transmits the analyzed safety performance parameter threshold value to the optimization regulation and control module. And the working condition parameters are adjusted by a self-adaptive fluctuation optimization algorithm and are finally displayed by a result output module. The system overcomes the defect that traditional single physical field analysis and parameter adjustment depend on experience, comprehensive and dynamic evaluation is achieved, the test efficiency and accuracy are improved, and support is provided for battery safety protection.

The invention discloses a confidential evaluation compliance document automatic generation system and method based on strategic data management and control and full-link verification, and aims at solving the problems that traditional confidential evaluation document generation is low in efficiency, insufficient in safety protection, poor in expansibility and the like. According to the method, hard coding logic is replaced by a strategy engine center, a scene-template-strategy association mechanism is constructed, and full-process automatic generation of secret evaluation documents is achieved through scene configuration, intelligent template matching, parameter analysis, multi-dimensional data cleaning and desensitization, national secret standard automatic signing and full-process log management and control. The generation efficiency is remarkably improved, a sensitive data'identification-processing-protection 'safety closed loop is formed, the tamper-proof and auditing requirements of secret evaluation are met, a newly-added scene can be quickly accessed through strategy configuration, and the expansibility and reusability are excellent.

The invention relates to the technical field of engineering machinery, and discloses an engineering machinery active safety control method and device based on multi-parameter analysis, and the method comprises the steps: collecting the multi-modal data of an environment where the engineering machinery is located based on a multi-modal data collection device, and constructing a dynamic model of the engineering machinery and the environment according to the multi-modal data, analyzing the multi-modal data through a dynamic model to obtain an environment data analysis result and an equipment data analysis result, determining an operation risk level of the engineering machinery according to the environment data analysis result and the equipment data analysis result, and determining an optimal working path of the engineering machinery according to the multi-modal data. And generating a safety control strategy according to the operation risk level and the optimal working path. Therefore, by implementing the method, the evaluation accuracy of the operation risk can be improved, the probability of misoperation of operators is reduced, the operation accuracy and reliability of the engineering machinery are enhanced, and the operation efficiency, the operation stability and the operation safety of the engineering machinery are further improved.

The invention discloses an anomaly identification method and system combined with grading parameter analysis, and relates to the technical field of fault detection, and the method comprises the steps: extracting fault detection information of target semiconductor equipment; retrieving in a fault record database to determine a first sub-event set; key parameters are extracted, and a first sub-event key parameter value sequence set is obtained; carrying out parameter fluctuation duration integration analysis to obtain a first parameter fluctuation duration; extracting associated equipment to obtain K secondary fault component sets; obtaining K secondary operation parameter sets; obtaining an abnormal component set and an abnormal detection item set; and carrying out potential fault detection on the target semiconductor equipment. The technical problems that potential fault sources of semiconductor equipment with a complex structure are difficult to accurately predict and position and the detection efficiency is low in existing semiconductor equipment fault detection are solved, and the technical effect of improving the fault detection efficiency and accuracy of the semiconductor equipment is achieved.

The invention belongs to the field of electronic design automation, and discloses a multi-agent radio frequency circuit netlist generation method and system based on a large language model, and the method comprises the steps: preparing a data set, and constructing a knowledge base; the method comprises the following steps: receiving and analyzing a natural language demand of a user, combining a cue word project and an RAG mechanism, carrying out chained reasoning, introducing a radio frequency design rule, and converting the information into structured parameter information; combining RAG knowledge base retrieval and new topology generation, and fusing PDK process constraints to obtain candidate topologies and a simulation netlist; a simulation tool is called to execute parameter analysis, iterative optimization is conducted on parameters in combination with a closed-loop feedback mechanism, and a final netlist meeting user requirements is obtained. Automatic decomposition, parallel cooperation and high-speed iteration of design tasks are achieved through multi-agent cooperation and in combination with the radio frequency knowledge graph and the RAG; introducing physical constraints to enable the generated topological structure and parameters to meet the physical constraints; and a closed-loop simulation feedback mechanism is fused, so that the dependence on manpower is reduced.

The invention discloses a graphite equipment weld defect intelligent detection and positioning system based on multispectral imaging, and particularly relates to the field of welding, and the system is characterized in that a multiband image acquisition unit acquires spectral image data of multiple bands and transmits the spectral image data to a multispectral data storage library; the spectral parameter analysis unit extracts characteristic parameters of the weld joint area and establishes a spectral characteristic parameter library; a spectrum abnormity identification unit locates a suspected defect area of spectrum abnormity; the weld defect positioning unit marks defect boundaries and ranges; the defect identification and grading unit calculates a defect severity index; the element component analysis unit is used for detecting the suspected defect position sample and analyzing the types and contents of trace elements in the suspected defect position sample; the defect and component data are integrated through the weld quality comprehensive evaluation unit, the quality grade and the rectification suggestion are generated, the problem that the defect cause cannot be positioned in the prior art is solved, data support is provided for follow-up maintenance and process optimization, and potential safety hazards of equipment operation and maintenance are reduced.

The invention relates to the technical field of intelligent safety management and control of a thermal power plant, and discloses a safety management and control application method and system based on big data acquisition and analysis. The method comprises the following steps: constructing an equipment operation situation map by fusing real-time data streams, and realizing dynamic panoramic perception of a system operation state; key nodes are identified based on the atlas, a real-time safety margin index is calculated by using a safety evaluation model, a dynamic instruction set with priority is generated, and the inspection robot is driven to carry out self-adaptive accurate data acquisition; and the central processing unit updates the atlas by comparing and analyzing real-time and historical data, so as to realize risk re-assessment and early warning. According to the method, equipment monitoring is improved from isolated parameter analysis to global association cognition, a fixed inspection mode is converted into predictive intervention based on real-time risks, and the state perception depth, the early warning timeliness and the resource allocation efficiency are improved.