119 results about "Region analysis" patented technology

The invention relates to the technical field of remote sensing geological prediction, in particular to a slope support stability prediction method and system based on remote sensing data, and the method comprises the steps: obtaining a multispectral remote sensing image of a target slope region, and generating a vegetation mask through employing a normalized vegetation index; removing interference pixels of a vegetation coverage area in combination with morphological filtering and connected area analysis, then identifying support structure features through an improved edge detection algorithm, delimiting an influence area by adopting a region growing algorithm based on machine learning, inputting an area image into a convolutional neural network model, and obtaining an image of the support structure; a deformation probability graph is output by using a data enhancement technology and a weight optimization layer, finally, a threshold value is determined according to historical data, slope support stability categories are divided in combination with spatial neighborhood information and a voting mechanism, vegetation interference is effectively eliminated, a support structure is accurately recognized, prediction precision is improved, slope support stability can be accurately judged in time, and the method is suitable for popularization and application. And a reliable basis is provided for early warning and protection of slope disasters.

The invention discloses a VSG virtual inertia and damping coefficient feasible region analysis, design and application method and device, and relates to the field of power system analysis processing, and the method comprises the steps: employing a VSG frequency stability analysis model to determine a frequency stability index, a virtual synchronous generator power constraint and an energy index constraint based on physical parameters, depicting a basic feasible region, and determining a frequency dynamic damping ratio and a time constraint; depicting a feasible region based on the basic feasible region in combination with a frequency dynamic damping ratio and a time constraint; and based on the customized dynamic performance requirement, determining whether the feasible region meets the feasible region existence condition, if so, taking the feasible region as a finally designed feasible region, and if not, adjusting the physical parameters, and carrying out feasible region analysis and design again. According to the invention, the problem that the fixed inverter parameters cannot meet the dynamic requirements of the system can be solved, and the problem that the system stability is poor after the static power generation unit is connected to the power grid can be effectively solved.

The invention relates to an unsupervised region representation learning method and system for learning urban region embedding, and belongs to the technical field of urban calculation and intelligent systems. The method specifically comprises the following steps: S1, constructing a regional mixed graph network, and capturing pairing and grouping relationships among regions by using multi-modal data; s2, graph and hypergraph comparative learning is executed, and discriminative representation of region nodes and a high-order relation between regions are learned from graph and hypergraph structures through a parallel comparative learning module; s3, cross-module comparative learning is carried out, information exchange between graph and hypergraph node representation is promoted through a controller assembly, and the learning ability of the model is enhanced; and S4, optimizing model parameters, and generating more effective region embedding through joint optimization of graph loss, hypergraph loss and cross-module loss so as to support various downstream tasks. According to the technical scheme provided by the invention, the accuracy and efficiency of urban area analysis are improved, and the capturing capability of the model on complex interaction between the areas is enhanced.

The invention provides an image feature detection method based on flexible connection, relates to the technical field of image analysis and processing, and aims to solve the defects of an existing image feature detection method in the aspects of accuracy, processing speed, detection sensitivity, false detection rate and the like. The method comprises the following steps: firstly, acquiring an input image, converting the input image into a grayscale image, carrying out Gaussian filtering processing, then calculating a gradient magnitude and direction, and carrying out non-maximum suppression and dual-threshold processing to obtain a preliminary edge image and a final edge image; and then connecting the weak edge pixels, extracting image features through a connected region analysis algorithm, calculating parameters such as a geometric center, a perimeter and an area, judging whether the features meet requirements or not according to a preset rule, and finally outputting the image features meeting the requirements. According to the method, parameters can be flexibly adjusted, different images can be adapted, the method is excellent in the fields of object recognition, medical image analysis, industrial detection and the like, the detection accuracy and efficiency are improved, the false detection rate is reduced, and the method has wide application prospects and practical value.

The invention relates to the technical field of twin models, in particular to a subway station flood prevention simulation method and system based on a three-dimensional model. According to the method, local important evaluation indexes are obtained through placename approximate analysis and concavity-convexity analysis of structures of point cloud data in a neighborhood space, and structural important evaluation indexes of single point cloud data are determined by combining and analyzing density distribution complexity in the neighborhood space; clustering the point cloud data and dividing the point cloud data into different regions; analyzing depth distribution of all the point cloud data in the regions; combining structural importance evaluation, determining an attention rate of each region, performing differential modeling and semantic processing to obtain a semantic three-dimensional model; and combining a hydrodynamic model to obtain a digital twinborn model. According to the method, differentiated three-dimensional modeling is carried out by evaluating the importance of each point cloud data in subway station flood prevention simulation, and flood prevention analysis is carried out more comprehensively and accurately by combining a digital twin model with a hydrodynamic model, so that the protection capability of subway flood prevention is improved.

The invention discloses an individual tree segmentation method based on machine learning, and particularly relates to the field of individual tree segmentation. The method comprises the following steps: respectively acquiring forest point cloud data through a ground scanner and an unmanned aerial vehicle, registering the forest point cloud data into a complete data set, preprocessing the point cloud data, extracting geometric, reflective and spatial distribution multi-dimensional features, and fusing the geometric, reflective and spatial distribution multi-dimensional features into a comprehensive feature vector; training a model by using a machine learning method based on the labeled data, taking the feature vector as input, and outputting whether the point belongs to a tree or not; predicting a point category by using the trained model, performing individual tree instance segmentation by using a connected component clustering and graph segmentation method through CRF post-processing optimization, constructing a 3D model and calculating key parameters; and finally, performing post-processing on a preliminary segmentation result, filtering noise by using connected region analysis, filling the deficiency by using a three-dimensional hole, optimizing a boundary based on graph segmentation, and optimizing a block seam by using an ICP (Inductively Coupled Plasma) algorithm.

The invention relates to the technical field of passenger flow information acquisition, and discloses a passenger flow information acquisition system and method based on multiple scenes, and the method comprises the steps of data acquisition, user classification, real-time analysis, regional analysis and information display.In the aspect of crowd flow analysis, mobile signaling data are accurately acquired, and timestamps and base station information are combined, so that the crowd flow analysis efficiency is improved; high-temporal-spatial-resolution analysis is realized, a crowd real-time flow diagram can be accurately generated, and urban traffic planning, large-scale activity security and the like are assisted; and in area evaluation, key inflow and normal flow areas are determined, a key area map is generated, and a basis is provided for commercial layout and public facility construction. User classification is accurate, and tourism management and local public service optimization are facilitated. An analysis result is visually presented through visual display, scientific decision making is assisted, and the method is suitable for multiple fields such as urban emergency management; a real-time updating mechanism ensures the timeliness of data, and improves the flexibility of city management and the capability of dealing with emergencies.

The invention relates to the technical field of vehicle damage detection and identification based on computer vision, and discloses a vehicle damage detection and identification method based on computer vision. The method comprises the steps of image acquisition, polarization image acquisition, Stokes parameter calculation, polarization feature extraction, anomaly evaluation, connected region analysis and the like. A polarization statistical model is established by setting an image coordinate system, collecting a multi-angle polarization diagram and calculating the polarization degree and the polarization angle of each pixel and taking an artificially selected nondestructive area as a reference, so that anomaly measurement and adaptive threshold segmentation are performed on the pixels, and a structured vehicle damage detection result is output in combination with corrosion, expansion and connected domain analysis. Physical interpretability and detection precision are enhanced by using polarization characteristics, recesses or scratches can be accurately identified even in a vehicle body area with a smooth surface or complex reflection, a standardized report containing position information and abnormality is provided, and automation and accuracy of vehicle damage assessment are effectively improved.

The invention discloses a document proofreading system and method based on artificial intelligence, and relates to the technical field of natural language processing, and the method comprises the steps: analyzing a document, dividing semantic units, and obtaining a document with a context label based on context type library labeling; performing initial error detection by using the corresponding rule set, and comparing the format of the document and the text quality characteristics with a standard sample vector space to obtain an initial error set; constructing a defect conduction chain network, and locating root cause error nodes causing a plurality of secondary errors by tracing the defect conduction chain network; generating an intelligent proofreading report; receiving a new-version document revised by the user, comparing the new-version document with the original-version document, positioning a change area, and analyzing a quality difference of the change area on a related quality dimension; based on the quality difference, an incremental proofreading report is output, and the incremental proofreading report comprises newly introduced errors and unsolved root cause errors.

The invention provides a deep learning method and system based on a station-city fusion space energy consumption model, and the method comprises the steps: analyzing a core energy consumption system of a core space region in a station-city fusion three-dimensional network space through employing a space region analysis method, and constructing a station-city fusion three-dimensional network energy consumption model of the core energy consumption system; constructing a carbon emission mechanism model through a station-city fusion three-dimensional network energy consumption model, defining a loss function of the carbon emission mechanism model according to a deep learning neural network structure of an initial input variable and an output variable in the carbon emission mechanism model, and performing iterative training on the loss function by adopting an iterative optimization function to obtain a carbon emission mechanism model; and outputting a deep learning neural network agent model until the loss value is lower than an allowable value, and finally evaluating the green operation performance of the station-city fusion three-dimensional network space by adopting the deep learning neural network agent model according to specification requirements to obtain an evaluation result. Compared with the prior art, the method is higher in evaluation efficiency and higher in reliability.

The invention discloses a chip aging test method based on thermal migration analysis. The method comprises the following steps: acquiring power consumption distribution data, material attribute data and manufacturing process data; discrete mapping and dynamic adjustment are carried out on the power consumption distribution data by adopting finite element network nodes to obtain an initial heat distribution diagram; processing the initial thermal distribution diagram based on a thermal imaging technology to generate a temperature mapping diagram of the surface of the chip; performing network division based on the temperature mapping graph, and generating a heat transfer path graph in combination with a gradient tracking algorithm; based on the heat transfer path diagram, screening a potential area through a preset temperature threshold value, and generating a potential heat distribution diagram through clustering analysis; performing microstructure analysis on the abnormal area according to the potential thermal distribution diagram to generate an abnormal analysis report; and solving high-temperature region distribution according to the manufacturing process data and the anomaly analysis report, and performing region analysis to generate a thermal identification report. According to the method, the problem that the heat transfer path cannot be accurately captured and the hot spot area cannot be identified under the non-destructive test can be solved.

The invention discloses a lung three-dimensional reconstruction method and system for intraoperative positioning, and the method comprises the steps: processing a lung CT image in a DICOM format through an improved lung parenchyma segmentation algorithm, precisely extracting a lung structure through a global threshold method, connected region analysis and mathematical morphology processing, and then processing a mask generated through segmentation through a real-time smooth interpolation method, and through a dynamic parameter adaptation technology, accurate matching of a segmentation result and a gray value range of modeling data is ensured. According to the method, the problems of artifacts, redundant surfaces and gray level jump of the lung parenchyma area caused by separation of segmentation and reconstruction in a traditional method are avoided, full-process automation from data reading and lung parenchyma segmentation to three-dimensional modeling and visualization is achieved, meanwhile, a user-friendly interaction interface is designed, and a convenient tool is provided for intraoperative navigation.

The invention provides an image processing method, device and equipment based on hardware perception and dynamic rarefaction, and relates to the technical field of image processing, and the method comprises the steps: inputting hardware operation state data into a preset PPO strategy network, so as to obtain the sparse rate of each convolution layer; reserving a specified number of channels as effective calculation channels; performing saliency region analysis on an input to-be-processed image to generate a pixel-level saliency map corresponding to the to-be-processed image; dividing the to-be-processed image into a key target area and a background area; allocating a first preset precision to the key target area for image processing, and allocating a second preset precision to the background area for image processing; according to the sparse rate of each convolutional layer, the first preset precision allocated to the key target region and the second preset precision allocated to the background region, collaborative image processing is carried out on the to-be-processed image; according to the invention, the large-scale landing capability and continuous optimization performance of the edge intelligent vision system can be improved.

The invention relates to the technical field of heat exchangers, and discloses a control method and system for a spiral wound tube type heat exchanger based on the Internet of Things. The method comprises the following steps: acquiring flow velocity distribution data in a pipe in real time through a multi-point sensor to form a first data set, performing regional analysis to obtain a flow velocity deviation value, evaluating heat exchange abnormality, positioning an abnormal position, and generating an abnormal region distribution map; a dynamic parameter adjustment scheme is calculated in combination with a heat exchange efficiency target, a first adjustment instruction set is generated, a second flow rate data set is obtained after execution, and the matching degree is evaluated through a fuzzy control algorithm; if the preset standard is not met, a second adjusting instruction set is generated in combination with working condition requirements, and finally an operation state report is output. The method has the following effects that the flow velocity distribution in the spiral wound tube type heat exchanger is accurately controlled, the problems of local overheating and uneven heat exchange are effectively solved, the heat exchange efficiency is improved, the purpose of balancing energy consumption is achieved, and stable operation of the system is guaranteed.

The invention discloses a building data intelligent management system and method based on artificial intelligence, and relates to the technical field of building data intelligent management, and the system comprises a building digital twin module, an Internet of Things intelligent service module, a regional analysis module, a historical data selection module and an intelligent sequence output module. The output end of the building digital twinning module is connected with the input end of the Internet of Things intelligent service module; the output end of the Internet of Things intelligent service module is connected with the input end of the regional analysis module; the output end of the regional analysis module is connected with the input end of the historical data selection module; and the output end of the historical data selection module is connected with the input end of the intelligent sequence output module. According to the invention, the construction efficiency can be improved, and monitoring and optimal management of energy consumption are realized through effective resource allocation and energy-saving measures; and in the aspect of improving integration, data pre-judgment is realized, and the probability of repeated operation and invalid construction is reduced.

The application relates to the technical field of product copying, in particular to a product copying software system for three-dimensional model import and conversion, and discloses the following scheme: a primary three-dimensional model is generated by acquiring a main view image and a sub-view image, extracting a first characteristic parameter and a second characteristic parameter; target parameter adjustment is carried out based on the bidirectional mapping relationship between the target parameter and an inferred parameter, and a parameter combination is generated; the response coefficient of the inferred parameter is corrected through reverse deduction, a bidirectional correlation matrix is optimized, and the consistency of the target parameter and the inferred parameter is ensured. Through shadow area analysis and surface fitting technology of multi-view images, the problem of bottom area data loss is solved, and accurate three-dimensional reconstruction of the object surface and the bottom area is realized.

The invention relates to the technical field of image processing, in particular to a geochemical element layer distinguishing method and system.The method comprises the steps that a copper element layer is subjected to grid division, the copper element color concentration change rate of adjacent grid points is calculated, and the grid points exceeding a set change rate threshold value are marked as mutation areas; and analyzing the copper element content difference of adjacent grid points in the abrupt change area, marking boundary points, and obtaining the distribution information of the copper element boundary points. According to the method, the mutation region is accurately recognized by calculating the copper element concentration change rate, and the problem of boundary fuzziness caused by data sparsity is reduced. A connection mode is optimized based on boundary curvature change, boundary information is ensured to be continuous and complete, and space expression precision is improved. The sampling point density calculation is clear in high and low density areas, the interpolation calculation is optimized, and errors caused by non-uniform data are reduced. The color concentration gradient of the abrupt change area is analyzed, the interpolation mode is adjusted, element transition is smoother, and error accumulation is reduced.

The invention relates to the technical field of image processing, and relates to a remote sensing image cutting method and system based on a multiband remote sensing image, and the method comprises the steps: carrying out the local superpixel region segmentation of an initial remote sensing image; performing region identification on the plurality of local superpixel regions to obtain at least one region of interest; according to the edge curve corresponding to each local superpixel region, performing region analysis on the at least one region of interest to obtain an edge detail disturbance coefficient corresponding to each region of interest; acquiring an edge diffusion coefficient corresponding to each region of interest; determining a cutting scale corresponding to each region of interest according to the edge detail disturbance coefficient corresponding to each region of interest and the edge diffusion coefficient corresponding to each region of interest; cutting each region of interest according to the cutting scale corresponding to each region of interest to obtain each cut region of interest; and splicing each cut region of interest to obtain a target remote sensing image. According to the invention, a high-quality target remote sensing image can be obtained.

The invention provides a non-local mean filtering and adaptive difference-based chip defect detection method, which comprises the following steps of: firstly, collecting image data of a chip to be detected through an industrial camera, and preprocessing to improve the image quality; and then, calculating a local entropy of each pixel to dynamically adjust a smoothing parameter of non-local mean filtering, and carrying out image denoising through the non-local mean filtering. Thirdly, a background image is obtained through a Gaussian filter, and a difference image is obtained through difference operation; and dynamically adjusting a difference threshold based on local frequency information, carrying out binarization processing on the difference image, and extracting a defect area. And finally, marking and classifying the detected defects through connected region analysis and contour detection. According to the method, by combining the self-adaptive non-local mean filtering of the local entropy and the self-adaptive difference threshold value method based on the local frequency, the denoising effect is improved, and the accuracy and robustness of defect detection are remarkably enhanced. According to the method, the limitation of a traditional chip defect detection method in a complex scene is overcome, and effective technical support is provided for chip quality control in actual industrial production.

The present invention relates to the technical field of road construction, in particular to a big data analysis-based real-time prediction system and method for degree of road compaction. The system comprises a compaction region interference intervention analysis module, the compaction region interference intervention analysis module acquiring a road surface grid region passed by a compaction apparatus during the advancing process of the apparatus, which is denoted by a first grid region, acquiring a related affected grid region around the first grid region, which is denoted by a second grid region, and then analyzing the degree-of-compaction intervening interference coefficients of the first grid region respectively corresponding to different road surface grid regions in the second grid region during the execution process of the compaction apparatus. The present invention not only considers more diversified factors in the prediction process, but also takes into account the pressing effect of the road surface region passed by the compaction apparatus on the peripheral regions in the advancing process of the apparatus, thus achieving real-time calibration of the degree of compaction corresponding to the peripheral regions around the road surface region passed by the apparatus, and ensuring the accuracy of prediction results on the degree of road compaction.

The invention provides an end-to-end real-time analysis method for an endoscope image fluorescence brightness area based on double deep learning model joint training and a self-group iteration upgrading device. The end-to-end real-time analysis method comprises the following steps: acquiring an image of a biological tissue and processing the image by using a deep learning algorithm to obtain a training data set; sequentially constructing and training an image preprocessing deep learning model and an unsupervised image segmentation deep learning model based on the training data set; inputting image data acquired by the endoscope into an image preprocessing deep learning model to obtain a first output image; inputting the first output image into an unsupervised image segmentation deep learning model to obtain a second output fluorescence image; and carrying out brightness analysis on the second output fluorescence image, and visually outputting and storing the second output fluorescence image. The real-time performance and the accuracy of fluorescence region analysis can be improved. According to the self-group iteration upgrading device, model training iteration upgrading can be carried out in multiple modes, and the performance of the device is continuously improved.

The embodiment of the invention relates to the technical field of machine learning, in particular to a method and device for removing noisy points of a scanned document image, computing equipment and a storage medium. The method comprises the following steps: acquiring a scanned document image; performing preprocessing operation on the document image to obtain a preprocessed image; performing connected region analysis on the preprocessed image, for a connected region with the area smaller than a threshold value T, taking the gravity center of the connected region as the center, intercepting an image block on the preprocessed image, then inputting the image block into a classification model, and outputting the probability that the connected region is a noisy point as the output of the classification model; if the output value indicates that the connected region is a noisy point, removing the connected region from the image I; and finally, performing post-processing operation on the document image to obtain a processing result image of the noisy point removal method. According to the scheme, text-independent noisy points introduced in the scanning process can be removed, meanwhile, related contents such as texts and tables in the document image cannot be damaged, and the attractiveness of the document image and the performance of follow-up document analysis and recognition are improved.

The invention discloses a risk number management and control method and electronic equipment, and belongs to the technical field of communication, and the method comprises the steps: determining a plurality of regions, and calculating a region risk score corresponding to each region; determining a regional risk score threshold, and determining a regional risk level corresponding to each region according to the regional analysis score and the regional risk score threshold; determining a silent remote starting risk area, and detecting a target number in the silent remote starting risk area in real time according to a silent remote starting model; determining a track risk coefficient of the target number through a historical region of the target number; judging a number risk level corresponding to the target number through the track risk coefficient; and performing different management and control according to different number risk levels corresponding to the target number. According to the invention, the accuracy and timeliness of data analysis are improved, so that the technical effect of effectively preventing risks is achieved.

The invention provides a degraded equipment quantization process parameter feasible region analysis method under predictive performance constraint, which belongs to the technical field of reliability engineering, and comprises the following steps of: describing a quantization error caused by digital-to-analog conversion in a data transmission engineering by adopting a uniform quantizer; a random degradation process based on a Wiener process is constructed, and life prediction solving is realized; establishing a performance measurement criterion for measuring the difference between equipment life distribution and equipment estimated life distribution, and solving a feasible region of quantization parameters in an equipment degradation process under the condition of a given life prediction allowable range; aiming at equipment monitoring data which generally have measurement errors and quantization errors in an actual measurement process, the method effectively improves decision conservative property caused by directly using the monitoring data, ensures the precision of a life prediction result and a reliability evaluation result, provides a powerful theoretical basis for management decisions such as spare part ordering and optimal replacement of equipment, and has a good application prospect. And efficient and reasonable equipment management is realized.

The application discloses a kind of area energy storage configuration methods for improving power distribution network carrying capacity under source-load coupling scene.First, the source-load comprehensive carrying capacity coefficient calculation model of power distribution network area is constructed, for calculating the source-load comprehensive carrying capacity coefficient of each typical day of area;Then, the source-load comprehensive carrying capacity feasible region analysis model of power distribution network area is established, for calculating the source-load comprehensive carrying capacity feasible region area of area;Finally, based on the source-load comprehensive carrying capacity coefficient and source-load comprehensive carrying capacity feasible region area of each typical day of area, double-layer optimization model is constructed, and double-layer optimization model is solved using hierarchical solving method, and the optimal energy storage configuration scheme is obtained.The method considers source-load coupling phenomenon, the carrying capacity bottleneck of area is characterized by source-load comprehensive carrying capacity coefficient, the source-load comprehensive carrying capacity of area is quantitatively evaluated by source-load comprehensive carrying capacity feasible region area, and the problem that traditional method is not accurate for power distribution network carrying capacity evaluation is solved.

The invention relates to the technical field of low-frequency sonar systems, in particular to an active azimuth history graph suspected target prompting method based on image segmentation, which comprises the following steps: firstly, carrying out binarization processing on an active azimuth history graph through a maximum between-class variance method, then, carrying out morphological processing and connected region analysis on a binary image to realize 8 connected region marking, and finally, carrying out image segmentation on the image; secondly, calculating the area and the mass center of each connected region, filtering the region with the large area to obtain a discrete target region mask image, multiplying the discrete target region mask image with an original binary image to obtain a discrete target region image, and finally, carrying out BFS search on the mass center of each discrete target region to obtain a discrete target region image; and calculating the sum of the Euclidean distances between the active azimuth history graph and five nearest connected regions, marking the mass centers of five discrete target regions with the maximum Euclidean distance sum on the active azimuth history graph, and realizing isolated bright spot positioning, thereby realizing automatic prompting of suspected targets in the active azimuth history graph of the sonar system so as to reduce the difficulty of target identification of a worker.

The embodiment of the invention provides a pop-up window processing method and device and a storage medium. In the embodiment of the invention, based on a collaborative architecture of two AI models, pop-up window processing is divided into two stages of potential pop-up window area analysis and pop-up window feature recognition, a potential pop-up window area in a screen image is analyzed by using a first AI model, and a sub-image of the potential pop-up window area is cut out from the screen image; and performing multi-dimensional feature analysis and popup window feature identification based on the sub-images by using the second AI model, positioning position information of closing options according to the multi-dimensional feature analysis and the popup window feature identification, executing popup window closing operation based on the position information, cutting out the sub-images of potential popup window areas, converting global analysis into local analysis, reducing calculation overhead of irrelevant areas, and on the basis, improving the popup window closing efficiency. Model reasoning is used for analyzing the cut sub-images, the efficiency and accuracy of pop-up window detection and closing are effectively improved, then the probability that automatic testing is interrupted due to pop-up window interference is reduced, and the testing stability and continuity are improved.

The invention discloses an aerodynamic simulation analysis method and system based on thermodynamic coupling, and relates to the technical field of simulation analysis, and the method comprises the steps: calculating a sensitive index and an uncertain index of each reference parameter, and fully considering the characteristics of the reference parameters to accurately enhance the robustness for the description of a simulation model. According to the method, the geometric features of each region, the reference parameter features and the sensitive indexes of the reference parameters are counted to determine the corresponding required grid density of the region, and the grid densities of different regions of the geometric three-dimensional model are specifically set according to different features of the regions and the reference parameter features, so that the analysis and calculation requirements of different regions are met. According to the uncertain indexes of the reference parameters, the reference parameters in the simulation model are endowed with credibility, ship turbine equipment is subjected to simulation analysis through the simulation model, the calculation precision of different areas is reasonably distributed, and the complex and changeable working conditions of the equipment are met. The multi-physics coupling condition is effectively captured, and the accuracy and adaptability of simulation analysis are improved.

This invention relates to the field of marine engineering safety early warning and intelligent prediction, and in particular to a point-to-area short-term wave height prediction method and system for offshore construction early warning. The invention takes historical pattern wave height, historical reference monitoring wave height, and future pattern wave height of a measurement point as inputs. It constructs a short-term prediction model based on a Transformer encoder and introduces a joint loss function including absolute error, large value underestimation penalty, trend asymmetry, residual, and peak time terms to automatically output the future correction results for each measurement point. Then, based on the mapping relationship between the measurement point and the corresponding coverage area, the correction results of the measurement point are extended to the entire target area to form a corrected wave height field. Combined with construction thresholds, it automatically determines the levels of normal, attention, alert, and work stoppage. Without requiring continuous manual interpretation, it forms a complete closed loop from data acquisition, regional analysis, prediction, regional correction to graded early warning and result storage and push.

The invention discloses a power distribution cabinet operation state intelligent monitoring method and system, and belongs to the technical field of power distribution cabinet state monitoring, and the method comprises the steps: carrying out the real-time analysis of each power distribution cabinet needing to be subjected to state monitoring, determining a state target, needing to be subjected to timestamp alignment monitoring, of each power distribution cabinet, and marking the state target as a time monitoring target; performing real-time simulation analysis on the time monitoring target, determining an alignment optimization mode of the time monitoring target, and adjusting a corresponding timestamp alignment mode in the corresponding power distribution cabinet according to the alignment optimization mode; monitoring the power distribution cabinet in real time to obtain monitoring data; analyzing the monitoring data to obtain state analysis data of the power distribution cabinet; carrying out regional analysis on the state analysis data of the power distribution cabinet, obtaining the state analysis data in real time, carrying out regional influence evaluation on the state analysis data, and determining whether the state analysis data has a regional risk; and when the regional risk is judged to exist, carrying out regional risk early warning.