94 results about "Spatial distribution pattern" patented technology

The invention provides an atmospheric fine particulate matter PM2.5 influence factor analysis method and device, and belongs to the technical field of data processing, and the method comprises the steps: obtaining multi-source heterogeneous data of a target region in a target time period, the multi-source heterogeneous data comprising spatial distribution data of atmospheric fine particulate matter PM2.5 concentration, natural environment data and social economic activity data; preprocessing the multi-source heterogeneous data to obtain grid data with a preset temporal-spatial resolution, the grid data including grid data of PM2.5 concentration and grid data of each influence factor of PM2.5; and inputting the grid data of the PM2.5 concentration and the grid data of each influence factor into a pre-trained influence factor analysis model to obtain a spatial distribution pattern of each influence factor output by the influence factor analysis model. According to the method, the nonlinear-dynamic effect of multiple factors can be fully considered, each influence factor is quantitatively analyzed, and the precision of PM2.5 influence factor analysis is improved.

The invention relates to the technical field of welding seam defect detection, and discloses a welding seam defect detection method, device and equipment based on three-dimensional holographic scanning. Coherent light is used for irradiating a welding seam to be detected, a reference state hologram sequence and a loading state hologram sequence are generated, a phase difference field is calculated, and the deformation gradient is analyzed; marking a first abnormal region and extracting a deformation concentration ratio and a spatial distribution mode, calculating a dominant spatial frequency through space-frequency domain joint analysis, fusing abnormal features, marking a second abnormal region and extracting amplitude distribution and spatial continuity parameters, and performing defect characterization analysis by combining information of the first and second abnormal regions to obtain weld defect detection information. The method has the advantages of high precision, non-contact and real-time monitoring, the accuracy and efficiency of weld defect detection can be effectively improved, and the problem that weld detection is not accurate enough in the prior art is solved.

The invention discloses an artificial fish reef pile distribution characteristic analysis method based on echo detection, and relates to the technical field of ecological environment monitoring, and the method comprises the following steps: data collection and preprocessing: selecting a multi-frequency echo detection device, and transmitting and receiving sound waves of different frequencies at the same time to adapt to detection demands of different water depths and fish reef types; all-directional cruise detection is carried out in a target sea area, it is ensured that all areas where artificial fish reefs possibly exist are covered, and echo signal data under different frequencies are recorded; according to the invention, an advanced signal processing algorithm is adopted, a multi-angle detection data synthesis technology is combined, a high-resolution three-dimensional model of the fish reef pile is constructed, the model is optimized and refined, and the characteristic parameters of the fish reef pile, such as echo intensity, reflection area and height difference, are extracted, so that the high-resolution three-dimensional model of the fish reef pile is obtained. The spatial distribution pattern of the fish reef pile, the relation between the fish reef pile and the marine environment and the dynamic change condition along with time can be deeply analyzed.

A method for automatically monitoring dynamic changes of water blooms based on remote sensing images comprises the following steps: step 1, acquiring remote sensing reflectivity data of a multi-source remote sensing satellite in a screened time sequence range, calculating a spectral index, automatically determining an extraction threshold through pixel gradient statistics and an Otsu algorithm, and accurately identifying a water bloom area; and 2, calculating the water bloom area based on the multi-temporal data, researching the seasonal change, the long-term trend and the spatial distribution rule of the water bloom area in combination with a time sequence analysis method, and evaluating the accuracy of water bloom inversion by comparing the actually measured chlorophyll-a concentration with a water bloom inversion result. The application can monitor the change of the algal bloom area in real time.

The invention provides a multi-crop interplanting planting scheme determination method and system based on big data analysis, and relates to the technical field of digital agriculture, and the method comprises the steps: obtaining first information which comprises soil data, meteorological data, crop growth data and field management data of pepper-potato planting; screening the crop growth key influence factors based on the first information, performing dimension reduction processing on the screened data, and establishing a pepper-potato crop interaction relation model based on the pepper-potato growth data feature set after dimension reduction; optimizing a preset spatial distribution mode of a plurality of planting schemes based on the crop interaction relationship model, and deducing to obtain a preliminary planting scheme of pepper-potato interplanting; a growth dynamic change model is established based on the preliminary planting scheme of pepper-potato interplanting, and a crop growth curve is optimized and adjusted. The crop yield and quality can be improved, the water and fertilizer utilization efficiency is optimized, and the expandability of the planting mode is enhanced.

The invention relates to the field of forest ecological monitoring and operation management, and provides a method for quantifying the complexity of a three-dimensional structure of a forest canopy based on LiDAR and fractal dimension. Aiming at the key bottleneck that traditional canopy structure quantitative indexes are difficult to represent cross-scale three-dimensional space heterogeneity, high-precision three-dimensional point cloud data are acquired by using foundation and tower footing LiDAR, and precise individual tree segmentation is realized in combination with a machine learning clustering algorithm and tree structure features. According to the method, the three-dimensional void rate of the forest stand canopy is calculated, and the fractal dimension of a single tree is calculated by adopting a box counting method. The weight of a single tree is determined by comprehensively considering a single tree structure and a spatial distribution pattern of the single tree structure, the fractal dimension of a stand scale is calculated, a canopy structure complexity index is constructed in combination with a stand three-dimensional void rate, and scale-free quantification of the forest canopy three-dimensional structure complexity is achieved. According to the method, typical temperate zone secondary forest monitoring data is used for simulation and verification, and technical support is provided for forest accurate operation (cutting and complementary planting), carbon sink evaluation, biodiversity protection and the like.

The invention discloses an ice lake terrain estimation method, system and device and a storage medium, and belongs to the technical field of ice lake terrain measurement and estimation, and the method comprises the steps: obtaining the basic data of an ice lake; extracting morphological parameters from the basic data; obtaining a volume-area fitting formula of the ice lake; obtaining the theoretical volume of the glacial lake; a volume-area fitting formula of the glacial lake and the theoretical volume of the glacial lake are combined, and the relation between the scaling factor and the maximum depth of the glacial lake and the area of the glacial lake is obtained; generating an ice lake isobath; and generating a glacial lake terrain. According to the method, the complex modeling process of the ice lake terrain is simplified, the terrain estimation efficiency is improved under the condition that high precision is reserved, and technical support is provided for regional ice lake risk management and monitoring system construction; the method fully considers the characteristics of long and short axes, center coordinates, volume and area relation and the like of the ice lake, is beneficial to revealing the spatial distribution rule of the terrain of the ice lake, reflects the terrain details, and provides support for regional ice lake risk management and monitoring system construction.

The invention discloses a gas leakage position identification method based on fusion of images and sensing data and related equipment. According to the method, environment, gas and image data are collected through a sensor and a camera, gas data are corrected through temperature and humidity, and gas concentration dynamic change characteristics are analyzed and excavated through a sliding window to determine the concentration abnormal position. And carrying out space coordinate mapping on the gas concentration abnormal position and the image data, extracting optical features from a corresponding image region, carrying out threshold segmentation to obtain an optical abnormal region, and determining the position of a gas leakage point by utilizing edge detection and multi-source data fusion. By implementing the technical scheme provided by the invention, the image data and the gas data are effectively associated, the diffusion trend and the spatial distribution rule of gas leakage are clearly presented, and the precision and the reliability of gas leakage position identification are comprehensively improved.

The invention relates to the technical field of nursing data management, and particularly discloses a nursing data management optimization method and system for a brain tumor patient, which is used for performing structured conversion and FHIR format standardization processing based on a multi-dimensional rule on vital sign data and electronic medical record data of the brain tumor patient. Therefore, the consistency of data formats is ensured. For medical image data of a patient, a large model technology is particularly introduced to carry out automatic structured description on a medical image, through image feature extraction and semantic segmentation based on deep learning, morphological features and a spatial distribution mode of a focus are identified, a structured image description text is generated, and FHIR format conversion is further completed. Finally, unified access, storage, query and analysis of vital sign data, electronic medical record records and medical image reports are achieved by deploying an integrated server meeting the FHIR standard. According to the method, the integration level of nursing data can be remarkably improved, information islands are broken, and data interoperability is improved.

The invention discloses a method for researching a Tarim red deer population spatial distribution pattern in the field of animal ecology. The method comprises the following steps: step 1, analyzing Tarim red deer wild population distribution characteristics; 2, analyzing forest environment characteristics of the Tarim red deer wild population habitat; 3, analyzing the spatial correlation between the Tarim red deer wild population distribution and the biological environment factors; step 4, tracking and analyzing the motion trail of the Tarim red deer; 5, analyzing the influence of habitat space distribution and abiotic factors on Tarim red deer wild population distribution; and step 6, evaluating habitat suitability of the Tarim red deer wild population. The method is reliable and accurate, and provides data support for construction of a Tarim red deer wild population protected area in the future by analyzing a Tarim red deer wild population space distribution pattern and habitat forest environment characteristics, using the unmanned aerial vehicle to track a motion trail of a red deer group, evaluating habitat suitability of a habitat and evaluating a potential suitable area of the habitat.

The invention relates to the technical field of ecological space evaluation, in particular to an urban agglomeration ecological space recreation utilization suitability evaluation method, which takes an urban agglomeration as a research area and comprehensively considers the influence of regional natural environment, social and economic factors on ecological space recreation utilization suitability. An evaluation index system is constructed on the basis of ecological suitability, recreation potential and social economic support conditions, global scale ecological space recreation utilization suitability is empirically evaluated in combination with a GIS and an entropy weight method, and through comparison of an existing ecological recreation space distribution pattern and a recreation utilization suitability horizontal distribution pattern, the recreation utilization suitability is evaluated. The future optimization direction of the urban agglomeration ecological space recreation utilization pattern is provided, and effective support is provided for territorial space planning and layout.

The invention provides a multi-source open data-based urban carbon emission hectometer spatial distribution gridding method. The method comprises the steps of obtaining multi-source open big data of a target city, constructing a hectometer grid multi-dimensional information index and calculating the total amount of urban carbon emission; initial hectometer grid carbon emission is obtained according to the hectometer grid multi-dimensional information index and the total urban carbon emission, and initial district-level total carbon emission and initial town scale total carbon emission are obtained according to the initial hectometer grid carbon emission; obtaining a district-level reference total carbon emission amount and a town scale reference total carbon emission amount, constructing a fitness function according to the district-level reference total carbon emission amount, the town scale reference total carbon emission amount, the initial district-level total carbon emission amount and the initial town scale total carbon emission amount, and carrying out iterative solution on the fitness function by using a multi-agent genetic algorithm, and obtaining the final urban hectometer grid carbon emission corresponding to the minimum fitness function. According to the method, urban information can be comprehensively integrated to calculate the carbon emission spatial distribution pattern, and the grid precision of the carbon emission spatial distribution pattern is effectively improved.

The invention provides an optical fiber detection method and system for an optical time domain reflectometer, and relates to the technical field of power grids. According to the invention, the multi-wavelength backscattering signal of the optical fiber link to be detected is acquired, preprocessed and converted into the sample to be detected containing the multi-wavelength one-dimensional waveform data and the multi-wavelength two-dimensional diagram data at the same time, so that the sequence characteristic of the light intensity of each wavelength changing along with time is reserved; and spatial features of correlation and structural information between wavelengths can be reflected through data conversion construction, and multi-dimensional and structured representation of optical fiber events is realized. And combined analysis and recognition are carried out by combining a fault recognition model, and a time sequence rule in a one-dimensional waveform and a spatial distribution mode in a two-dimensional diagram are cooperatively mined, so that deep fusion and intelligent analysis of optical fiber fault spatial-temporal characteristics are realized, and the recognition capability of a complex fault mode is improved. The technical problem of low accuracy of optical fiber fault detection with a single wavelength at present is solved, and the accuracy and reliability of optical cable line fault detection are improved.

The invention belongs to the technical field of economic value evaluation, and discloses an economic value estimation method and system based on a natural resource asset package, and the method comprises the steps: processing natural resource data through employing a natural language processing technology; identifying spatial distribution modes of different natural resource data by applying a geographic information technology, and defining a spatial range of an asset package; integrating the space range of the asset package and the physical quantity data by using geographic information processing software, establishing a physical quantity data set, and constructing an asset package evaluation model in combination with a machine learning algorithm; and evaluating the asset package by using the asset package evaluation model to obtain an asset package score, and establishing an asset package price system based on the natural resource type to obtain the economic value of the asset package. According to the method, the natural resource data of various sources are acquired and input, and the spatial range of the asset package is precisely defined by using the geographic information technology, so that the integrity and accuracy of the data are ensured, and a basis is provided for subsequent resource management and planning.

The invention discloses a mining area vegetation degradation identification method based on a remote sensing spectrum. The method comprises the following steps: collecting multispectral image data of a mining area; based on the multispectral image data of the mining area, main spectral feature vectors are extracted by adopting a principal component analysis method, and a vegetation spectral feature set is determined; based on the vegetation spectral feature set, performing health state division on vegetation pixels by adopting a support vector machine classifier to obtain a vegetation degradation classification map; extracting a spatial distribution pattern from the vegetation degradation classification map, calculating an area proportion and a position coordinate of each type of pixels, and determining a spatial distribution feature of a degradation degree; acquiring a multi-temporal remote sensing image sequence according to the spatial distribution characteristics of the degradation degree, analyzing and tracking the change of a classification graph through time sequence difference, and judging a dynamic change trend; driving mechanism indexes are integrated from the dynamic change trend, and a comprehensive monitoring and recognition report is obtained and used for revealing the trend of vegetation degradation in the mining area.

The invention relates to a global land comprehensive improvement monitoring method based on artificial intelligence and remote sensing images, and belongs to the technical field of environment management. According to the method, multi-band high-resolution remote sensing images of a target area in two time phases before and after improvement are obtained for radiation and atmospheric correction to ensure data quality, and land coverage classification maps of the two time phases are generated respectively by using a pre-trained deep convolutional neural network land coverage classification model. Through pixel-level change detection and confidence analysis, a reliable land coverage change area is identified, vectorization and spatial filtering are carried out, multi-dimensional change features are extracted, ecological influence and a spatial distribution mode of the multi-dimensional change features are comprehensively evaluated, and a comprehensive monitoring result including a spatial distribution diagram, a statistical report and trend prediction is generated. And the information is integrated to an interactive geographic information system platform for visual display and early warning. According to the invention, automatic, precise and systematic monitoring and evaluation of the global land comprehensive remediation process are realized.

The invention discloses a regional prospecting method and system based on multi-element space coupling of a metallogenic system, and belongs to the technical field of mineral resource prospecting prediction and comprehensive modeling of the metallogenic system. The method comprises the following steps: acquiring multi-source spatial data covering a mine concentration area; according to a process mechanism of a metallogenic system, converting and constructing the multi-source spatial data into a background domain, a transmission domain, an ore containing domain and a target domain to indicate spatial distribution indexes; constructing a comprehensive metallogenic potential index for representing a spatial distribution pattern of the regional metallogenic potential; an uncertainty prediction index is constructed based on the consistency deviation degree between the domain index responses and the local difference, and synchronous credibility quantification is carried out on the comprehensive mineralization potential evaluation result; and in combination with a joint discriminant relationship between the mineralization potential index and the uncertainty index, performing classified screening and target region delineation on the regional mineralization abnormal unit, so as to realize continuous quantitative prediction of the regional scale mineralization potential and optimization of the prospecting target region, and provide a mineral geological exploration service developed by using a high technology.

The invention discloses a rock mass mechanical parameter determination method and system for a rock slope blasting excavation damage area, particularly relates to the technical field of rock mass engineering stability evaluation, and is used for solving the problem of misalignment of parameter characterization caused by neglecting a damage area rock bridge and fracture zone composite structure in the prior art. The method comprises the following steps: arranging a point load test in a damage area and identifying non-unimodal distribution data; combining fracture catalog data, fusing penetrability fracture density segmentation capability and a dominant inclination angle direction failure effect to generate a collaborative isolation parameter, and dividing a residual rock bridge and fracture fracture zone spatial distribution mode according to the collaborative isolation parameter; accurately classifying the intensity data into two types of subsets based on space mapping; and finally, macro-mechanical parameters are generated through weighted fusion of the area proportion and the double-source strength statistical value, the rock mass strength prediction precision of the blasting damage area is remarkably improved, and a reliable basis is provided for slope stability analysis and support design.

The invention discloses a crop disease severity dynamic estimation method and system based on multi-scale fusion, provides a spatial stratified sampling strategy, realizes refined partitioning based on growth potential difference and disease risk clustering, ensures that sampling points cover field key areas, and meanwhile, improves the accuracy of crop disease severity estimation. The dynamic time compensation sampling strategy is combined with the field disease development situation and environmental factor self-adaptive adjustment sampling to provide high-quality and dynamic data support for estimation; multi-source features such as scab quantity and edge roughness are introduced into the leaf scale to describe scab morphology and development difference in a fine granularity manner, and the plant scale is fused with weights such as leaf space position to highlight contribution of functional leaves and key leaves to the health of the whole plant; the spatial distribution pattern of the field diseases is effectively reflected by combining the field scale with the growth potential partition weight, the risk partition weight, the health degree and the like of the plants, and finally, the constructed dynamic time integral model is fused with the propagation rate and the fertility sensitivity to obtain a dynamic comprehensive estimation result of the accumulated influence of the diseases in the monitoring period.

This invention discloses a GIS-based risk management system and method for pollutant migration in mining area watersheds, relating to the technical field of simulating pollutant migration patterns. It includes a 3D GIS functional module, a hydrodynamic-water quality model coupling module, and a mining watershed pollution risk management module. Based on watershed hydrology theory and pollutant migration and diffusion mechanisms, it fully considers the migration of pollutants in the storage area after rainfall via surface microrunoff. By studying the formation patterns of microrunoff, it simulates the watershed migration process of pollutants in the mining area under different rainfall intensities, thereby more accurately calculating pollutant migration and diffusion fluxes and managing the risk of pollutant migration in the watershed. Based on historical and real-time data and models, it uses a 3D model to display the spatial distribution patterns and development trends of pollutants, achieving accurate pollutant identification and source tracing. It can simulate pollution control measures in mining area watersheds, predict development trends, and provide early warnings for pollution events.

The present invention provides a structural geochemical combination anomaly identification method for concealed ore detection, comprising: collecting a series of structural geochemical samples at the scale of a ore deposit or a mining area; determining the types and numbers of a series of elements based on expert knowledge for different types of hydrothermal deposits, and testing and analyzing their content values; determining the leading edge halo indicator elements, the near-ore halo indicator elements, and the tail halo indicator elements based on the axial zonation characteristics of the primary halo of the hydrothermal deposit; calculating the combined anomalies of the leading edge halo indicator elements, the near-ore halo indicator elements, and the tail halo indicator elements through orthogonal basis transformation, and drawing a spatial distribution pattern diagram of the three combined anomalies; revealing the deep mineralization characteristics of the hydrothermal deposit and the spatial positioning law of the ore body by studying the intensity, shape, size, and superposition relationship of the three combined anomalies. On this basis, determine the center of the anomaly, predict the occurrence space of the blind ore body, and delineate the prospecting target area.

This invention belongs to the field of ferroalloy smelting technology and industrial process control technology. Specifically, it discloses an intelligent smelting method for high-carbon ferrochrome alloys based on a submerged arc furnace. The method includes: generating a comprehensive material surface state map by collecting material surface state and temperature data; analyzing the overall spatial distribution pattern of grid cells based on the state map to diagnose the overall furnace condition; triggering corresponding global control commands if abnormalities are found; and comparing the material surface sinking height, raw material sinking speed, current temperature, and temperature change rate of each grid cell with preset evaluation criteria to diagnose local furnace conditions if the overall furnace condition is healthy. For healthy cells, precise quantitative material replenishment is calculated when the sinking height meets the standard, and the replenishment azimuth angle is determined through polar coordinate transformation. For abnormal cells, differentiated local control commands are triggered based on their abnormal characteristics. This invention achieves closed-loop intelligent control of the submerged arc furnace smelting process from global perception to precise local control, effectively improving smelting efficiency.

This invention discloses a method for compiling mineralization center distribution maps based on the spatial distribution patterns of anomalous elements. The method involves collecting stream sediment data from the work area, performing principal component analysis, and extracting principal components with eigenvalues ​​greater than 1. It also distinguishes between medium-high temperature element combinations and medium-low temperature element combinations; calculates the mineralization center factor M; and calculates the outlier value M of the mineralization center factor. c Import the mineralization center factor M into the ZScape software and draw a contour map of the mineralization center factor in the working area; use the outlier value M of the mineralization center factor. c Create a mineralization center distribution map for the boundary; M≥M c The region is represented as the mineralization center; M≤M c The region is represented as the mineralization edge; based on the relationship between elemental zoning and temperature in stream sediments, this invention constructs mineralization center factors for magmatic-hydrothermal related deposits, realizes the quantitative compilation of mineralization center distribution maps, and innovates a new model for map compilation.

The present application discloses a method for recognizing architectural patterns, which is based on multiple spatial proximity graph distances and architectural attribute features, and is used to identify architectural complexes with complex nonlinear spatial distribution patterns. The present application constructs three graph sequences to capture the spatial adjacent structures and non-spatial features of different feature views, and then obtains three improved graphs through a graph diffusion process, and fuses them into a joint graph shared by all views, which can depict the mutual relationships between them and produce the final clustering results. The recognition method of the present application uses two distance metrics, contour distance and centroid distance, to make full use of the complementary information of spatial structure and non-spatial attributes, and to determine architectural patterns by combining spatial proximity relationships and non-spatial similarities, providing technical support for aspects such as map making synthesis and urban scene semantic segmentation, and representing geographical reality as realistically as possible within the limitations of map scale.

This invention provides a trend analysis method (MBE) based on the identification results of microorganisms in production environments. The MBE platform manages the metadata (including room, cleanroom level, sampling method, and planned category) and identification results of microbial samples detected in pharmaceutical, food, and cosmetic production environments. It aggregates data according to specified time units (year, month, day, and batch), analyzes the genus and species ratios of environmental microbial flora and the distribution of metadata within each classification within a given time range, and analyzes temporal changes in the flora from four perspectives: phenotypic classification, genus and species level, bacteria of concern, and community diversity. Frequency analysis is used to analyze the temporal and spatial distribution patterns of the flora, and multivariate statistical analysis is used to analyze correlations between different classifications and between different genera and species. This invention can help identify microorganisms that may have a significant impact on product quality and safety, assess and control microbial contamination risks, and ensure quality and safety.

The invention discloses a reservoir classification evaluation method, device, equipment and medium based on multiple types of geological elements, and relates to the technical field of oil and gas field development geology, and the method comprises the steps: constructing a fault distance attribute plane graph, constructing a fracture density distribution graph according to fracture analysis data and imaging logging fracture interpretation data, constructing a matrix storage property parameter plane graph and a fluidity parameter plane graph by utilizing matrix rock core analysis data; standardizing the parameters of the target atlas, and determining the weight coefficient of each geological element according to the control effect of each geological element in the reservoir; the target image set comprises a fault distance attribute plane graph, a fracture density distribution graph and a matrix storage property parameter and fluidity parameter plane graph; and superposing the processed image sets based on the weight coefficient to obtain a coupling strength index graph, and classifying the target research area according to the indexes in the coupling strength index graph and the spatial distribution pattern of the coupling area corresponding to the indexes to obtain a reservoir classification evaluation result. And efficient and accurate reservoir classification is realized.

This invention discloses a method and system for anomaly detection of elevated facilities based on environmental feature fusion. The method includes: acquiring video streams, GPS location data, and timestamp information to obtain inspection data; identifying and determining specific elevated facilities and their components, and defining the target inspection area; performing dual-channel detection on specific elevated facilities based on a dynamically learned individual appearance benchmark model and an individual spatial relationship model to identify potential problems; comparing the material types and standard appearance characteristics of specific elevated facilities in each road segment with the dynamically learned individual appearance benchmark model to obtain appearance inspection results; tracking and recording the continuous spatial distribution patterns of specific elevated facilities in each road segment based on the dynamically learned individual spatial relationship model; integrating the continuous spatial distribution patterns of specific elevated facilities in each road segment with the appearance inspection results to identify potential problems; and generating an anomaly report. This invention improves the accuracy and adaptability of elevated facility detection.

The present application relates to the technical field of image processing, and more particularly to an endoscope fault image feature extraction method based on artificial intelligence, which comprises: bad point detection to determine abnormal pixels and coordinates; density ratio and cluster analysis to determine spatial distribution patterns; identification of fault types to determine the timing evolution trend; extraction of fingerprint feature vectors; prediction of development trend to determine the maintenance time window; and positioning of the physical location of the fault. The present application locates abnormal pixels through bad point detection, determines whether the bad point distribution belongs to edge priority or linear arrangement through density ratio and cluster analysis, and further determines the fault type of sealing failure or cable breakage. Meanwhile, the feature vector is extracted through the fusion of CNN and Transformer, the development trend is predicted in combination with the timing evolution to determine the maintenance window, and finally the physical location is located through coordinate mapping when the maintenance conditions are met, thereby improving the efficiency and accuracy of endoscope fault diagnosis.

The invention provides a lithofacies paleogeography mapping method and system based on tectonic-sedimentary differentiation research. The method comprises the following steps: analyzing and determining key tectonic event occurrence and evolution processes of different key times based on a basin to be processed; geologic structure types and spatial distribution rules of different regions are analyzed, and a geologic structure partition map is comprehensively compiled; a stratum and a stratum internal structure are divided by combining logging, rock cores, rock debris and seismic data; further analyzing a structure-deposition differentiation type based on internal characteristics of the stratum, establishing a non-Wartt facies deposition-structure mode, determining a filling sequence interface division scheme, establishing a sequence stratigraphic well-connecting framework, and analyzing a stratum filling rule; main control factors are researched on the basis of the divided sedimentary facies law units, the spatial distribution law of the mapping units is analyzed, and a lithofacies paleogeography map is compiled and formed. By adopting the scheme, the problems of limited mapping mode and insufficient accuracy in the prior art can be solved, the structure development evolution condition of the sedimentary basin is reliably represented, and the map accuracy is improved.

The invention belongs to the technical field of computers, and particularly relates to a real-time fault positioning method for a drainage system, which comprises the following steps of: constructing a directed network model and a node-pipe section incidence matrix which reflect the complex topology of a pipe network according to a pipe network geographic information system and sensor data; calculating the flow unbalance degree of each node based on the real-time flow data and the incidence matrix, and dynamically checking and updating the network direction by using the real-time pressure or flow data; reversely tracing an upstream suspicious pipe section area in the network by taking the unbalance degree over-limit node as a starting point, and performing downstream influence propagation analysis; and finally, combining the suspicious region, the influence range and the whole network unbalance degree space distribution mode, and cooperatively judging the fault type and the accurate position. According to the method, the problems of false alarm and inaccurate positioning caused by model simplification are solved, and accurate and rapid traceability and diagnosis of complex drainage pipe network faults are realized.