1138 results about "Cluster analysis" patented technology

The invention relates to the technical field of cable asset management and fault prediction, and discloses a cable fault positioning method based on deep learning clustering analysis test waveform characteristics, and the method comprises the steps: collecting waveform and environment data in a cable operation period, and constructing a historical feature library comprising waveform, environment and position features; a self-adaptive detection model is adopted, and parameters are dynamically adjusted to adapt to different working conditions; multi-dimensional feature fusion and matching analysis are combined; a fault point distance is calculated through a signal propagation model and a time difference positioning algorithm, precise positioning is realized by fusing environment compensation and multi-point cross validation, and a three-dimensional geographic coordinate is generated by combining a laying path; and after multiple verifications, a structured report containing a fault type, a risk level, a prediction position, confidence and operation and maintenance suggestions is generated. According to the system, intelligent monitoring, fault risk prediction, asset optimization management and operation and maintenance decision support of a cable operation state are realized, and scientificity and economy of cable management in a complex environment are improved.

The invention provides a patient vital sign anomaly detection method based on an artificial intelligence technology, and relates to the technical field of data processing, and the method comprises the steps: collecting original sign data of a patient; calculating multi-dimensional characteristic parameters; establishing an individual baseline model, and determining a comprehensive reference interval in the model; the vital sign features monitored in real time are constructed into multi-dimensional feature vectors, the multi-dimensional feature vectors are input into the individual baseline model, and the deviation degree of the real-time multi-dimensional feature vectors in the comprehensive reference interval is calculated; performing a clustering analysis to identify an anomalous aggregation region; performing trend analysis, calculating change direction consistency and continuous change amplitude of the multi-dimensional feature vector, and generating a trend analysis result; calculating an accumulated change index in a continuous time window according to a trend analysis result to obtain a dynamic confidence score; when the dynamic confidence score continuously exceeds an adaptive threshold value, determining that a vital sign abnormal event exists; the autonomy and accuracy of the method for detecting the vital sign abnormity of the patient are improved.

The invention relates to the technical field of electricity metering anomaly monitoring, and provides a distributed resource access-oriented lightweight electricity metering anomaly monitoring method, which comprises the following steps of: acquiring electrical data and environment associated data of distributed access points at an edge side, and constructing a fluctuation fingerprint feature vector; when it is detected that the fluctuation fingerprint feature vector meets a preset fluctuation triggering condition, based on Pearson correlation analysis between the electrical data and the environment associated data, performing decoupling and elimination on false anomalies caused by environmental factors to obtain a fluctuation fingerprint feature vector of non-environmental suspicious anomalies; based on historical normal operation data, constructing a normal working condition mode library by adopting a clustering analysis method, and determining a feature center and a dynamic safety radius of each normal working condition mode; and calculating the feature distance between the non-environmental suspicious abnormal fluctuation fingerprint feature vector and each normal working condition feature center at the edge side, and judging whether the electricity consumption metering is abnormal or not according to the relationship between the feature distance and the dynamic safety radius.

The invention relates to the technical field of virtual power plant resource configuration, and discloses a virtual power plant resource dynamic coordination configuration method and system, and the method comprises the following steps: collecting the operation data of various resources in a virtual power plant and the external power grid environment data in real time, and carrying out the dynamic coordination configuration of the resources based on the operation data; extracting operation characteristic parameters reflecting response speed differences of various resources; clustering analysis is carried out on the multiple resources to form resource clusters with different response speed levels, and the collaborative deployment demand intensity of the power grid in the current time period is evaluated; predicting a power grid load balance state in a future target time period, and determining the callable response capability of each resource cluster in the target time period; and carrying out multi-target optimization calculation meeting the stability constraint of the power grid, and generating a dynamic coordination configuration scheme of various resources in the target time period. According to the method, through the synergistic effect of multi-level technical characteristics, the matching problem between resource characteristic differences and dynamic requirements is fundamentally solved.

The invention discloses a deep rock mass fracture imaging method and system based on micro-seismic monitoring, and belongs to the technical field of geophysical exploration, and the method comprises the steps: collecting multi-channel micro-seismic event waveform data, obtaining a main seismic phase direct waveform and a scattering tail waveform through wave field separation, and carrying out the imaging of a deep rock mass fracture. Determining a three-dimensional space coordinate and an energy distribution form of the seismic source through joint inversion, carrying out clustering analysis, and constructing an initial three-dimensional space distribution form; each seismic source is regarded as an active excitation source, a scattering tail wave waveform is regarded as a reflection signal, scatterer three-dimensional point cloud data is generated through reverse time migration processing, analysis is carried out in combination with an initial three-dimensional space distribution form, and an enhanced fracture structure is formed; and finally, through topology analysis and orientation clustering, discrete scatterers are eliminated, and a fracture development map is generated. According to the method, a technical path of combining a main seismic phase direct wave inversion seismic source and a scattering tail wave inversion scatterer is adopted, high-precision and high-fidelity imaging of a deep rock mass movable fracture can be realized, and physical activity is revealed.

The embodiment of the invention provides an automobile gateway dynamic priority routing method and system. The method comprises the following steps: acquiring associated data of a running vehicle in real time; on the basis of driving data clustering analysis, feature sets of different driving scenes and scene conversion rules among different driving scenes are determined, the feature vectors and the feature sets are compared, and the driving scene and decision priority of the vehicle are determined on the basis of the operation confidence of a comparison result; detecting whether the current feature vector meets a scene conversion rule or not during vehicle driving, if so, and if the real-time confidence of the scene conversion rule reaches the standard, performing driving scene conversion, and determining a decision priority after conversion; and converting into a corresponding execution command based on the decision priority, continuously monitoring an execution performance index of the execution command, and carrying out feedback adjustment on feature sets of different driving scenes and scene conversion rules among the different driving scenes based on the execution performance index.

The invention discloses a film processing quality evaluation method and system, and the method comprises the steps: obtaining and preprocessing a processed film collection image, and obtaining a film gray image; determining a gray value of each pixel point in the film gray image, and performing clustering analysis on the pixel points based on the gray value to obtain a plurality of abnormal image areas; determining multi-dimensional image features in each abnormal image region, and analyzing abnormal conditions of each abnormal image region based on the multi-dimensional image features to obtain an abnormal coefficient; determining the number of the abnormal image areas, and determining a comprehensive quality evaluation value of film processing based on the number of the abnormal image areas and the abnormal coefficient; and determining a quality evaluation grade of thin film processing based on the comprehensive quality evaluation value, and evaluating the processing quality of the thin film according to the quality evaluation grade. According to the invention, comprehensive quantitative evaluation of the processing quality of the film can be realized, the production efficiency of the film is improved, the quality risk is reduced, and the production of high-quality film products meeting standards and customer requirements is ensured.

The invention relates to the technical field of molecular detection, and particularly discloses a liquid chip for identifying opisthopapus taihangensis and Hebei province as well as a preparation method and application of the liquid chip. Genotyping sites of the liquid chip comprise 1140 SNP sites; the physical positions of the 1140 SNP loci are determined based on comparison of a whole genome sequence of a bovine reference genome, and the version number of the whole genome sequence of the bovine reference genome is ARSUCD 1.2; wherein the physical position information of the 1140 SNP loci is as shown in a table 1. The liquid chip can realize genetic typing of the opisthopapus taihangensis and the Hebei province cattle, has the advantage of high detection rate, and can obviously cluster the opisthopapus taihangensis and the Hebei province cattle into two classes when clustering analysis is carried out on the opisthopapus taihangensis and the Hebei province cattle, so that a marker combination of the liquid chip can represent whole genome re-sequencing genes, assists in distinguishing the opisthopapus taihangensis and the Hebei province cattle, and is high in accuracy and high in accuracy. Therefore, the variety identification of the opisthopapus taihangensis and the Hebei Hebei is realized.

The invention discloses a power grid big data closed-loop monitoring system and a risk assessment method, and relates to the technical field of industrial data management. The power grid big data closed-loop monitoring system and risk assessment method comprises the following steps: S1, preprocessing physical quantity time sequence data, operation state data and static configuration data; s2, performing complete fidelity of the physical quantity time series data and allocation of cache resources according to the health measurement and the cache evaluation result; s3, redundant data screening, abnormal fragment repairing and high-confidence data reconstruction are executed according to an index confidence fusion analysis result; s4, carrying out multi-level risk and clustering analysis, judging a risk state, and carrying out linkage regulation and control response; and S5, integrating to health visualization and operation and maintenance linkage processing according to a global health situation analysis result. The problems of monitoring result distortion and risk closed-loop chain scission caused by the problems of data redundancy superposition and risk time period information loss due to imbalance of time slot scheduling and a cache mechanism are solved.

The invention relates to the field of image analysis, in particular to a colon cancer lymph node metastasis risk prediction method and system based on image analysis. The method comprises the following steps: acquiring a pathological section scanning image, carrying out layer-by-layer downsampling and feature reconstruction, and generating pathological section topological features; carrying out adaptive clustering analysis and multi-parameter fusion evaluation on the basis of the pathological section topological features to obtain independent migration evaluation values of individual tumor cells; performing tumor metastasis branch analysis according to the independent migration evaluation value, and constructing a metastasis branch path distribution diagram; extracting an immunohistochemical staining image of a patient to obtain immune cell space distribution data; and performing multi-region lymphatic metastasis simulation and metastasis risk situation prediction based on the immune cell spatial distribution data and the metastasis branch path distribution diagram to obtain a final metastasis risk assessment result. According to the method, the lymph node metastasis risk is accurately and efficiently analyzed, and the credibility of a pathological analysis result is improved.

The invention relates to the technical field of wiring robot system fault detection, in particular to a transformer wiring robot system fault intelligent detection method. The method comprises the following steps: acquiring an image in a wiring process and graying the image to obtain a grayscale image; obtaining candidate pixel points in the grayscale image and a pixel point sequence of the candidate pixel points; acquiring a local gradient amplitude change feature value and a local gradient direction change feature value of each neighbor pixel point, and further acquiring initial edge feature credibility of the candidate pixel points; obtaining the final edge feature credibility based on the initial edge feature credibility of each marked pixel point of one candidate pixel point; performing clustering analysis based on the final edge feature credibility of each candidate pixel point in a gray level image to obtain a low threshold value and a high threshold value of the gray level image; and carrying out edge detection on the grayscale image by using the low threshold and the high threshold, and identifying the abnormity of the wiring robot. According to the invention, the wiring abnormity of the wiring robot can be effectively processed.

The invention discloses an enterprise type identification method fusing multi-source data. The method comprises the following steps: acquiring map POI point of interest information, enterprise registration information and enterprise business brief introduction information; matching the POI name with the enterprise registration name to obtain matched enterprise data; cleaning the brief introduction information of the enterprise business and the matched enterprise data to obtain cleaned data; performing semantic information extraction on brief introduction information in the cleaned data to obtain extracted data; fusing the enterprise data in the cleaned data with the extracted data to form a fused data set; and clustering the fused data set to obtain theme distribution of each enterprise. According to the method, semantic mining, LDA topic modeling, keyword recognition and regional clustering analysis can be combined, so that accurate subdivision of enterprise types and intelligent recognition of regional dominant industries are realized, and regional industry strategy making and industrial park planning layout are supported.

The invention discloses a storage battery online equalization method and system, and the method comprises the steps: collecting the operation data of a storage battery pack, stripping polarization voltage interference, extracting steady-state deviation characteristics, and constructing a single state matrix in combination with a capacity attenuation index; secondly, identifying high-energy monomers and low-energy monomers through clustering analysis, analyzing an energy mismatch degree according to position distribution and current distribution conditions of the monomers, and delimiting a layered equilibrium domain; the deterioration evolution rule of outlier monomers is mined to form a consistent deterioration trajectory, an internal resistance compensation coefficient is generated in combination with internal resistance change characteristics to determine an equilibrium critical position, and a charge equilibrium association table is established; then planning a low-loss energy transfer path to formulate a basic equilibrium list; and finally, forming an equalization sequence table according to the unbalance degree and the thermal constraint condition, determining an equalization starting threshold and outputting an equalization execution command. According to the method, the real state difference and transient fluctuation of the single bodies can be accurately distinguished, the equalization demand is pre-judged in advance, the equalization operation is scheduled in order, and the equalization efficiency and the operation reliability of the storage battery pack are improved.

The invention discloses a smart city public facility supervision method based on the Internet of Things, and the method comprises the steps: carrying out the multispectral collection and space marking of a surface block of a to-be-detected circuit board, and achieving the standardized preprocessing and physical feature extraction of multi-label high-dimensional pixel data; according to the method, material spectral features and surface texture data are combined, a clustering analysis and supervision discrimination model is used for accurately discriminating the type of a block material, material attributes and multi-spectral features are fused, and pixel-level thickness prediction and dynamic adaptive correction are realized through a machine learning model. The system can automatically monitor drifting of the material and the mapping relation, and performs real-time optimization on the mapping model based on periodic calibration data.

The invention discloses an intelligent monitoring and early warning method and system for a dangerous large project on a building construction site, and the method comprises the steps: carrying out the fusion processing of multi-source data, such as displacement, inclination and axial force, collected by edge Internet of Things equipment, extracting the characteristics of structural deformation and load change, and constructing a comprehensive situation representation; a high-risk area and a safety area of a construction site are identified by adopting clustering analysis, a spatial proximity relationship between the areas is established, a risk coupling degree is calculated, and a differentiated monitoring strategy is delimited according to the spatial proximity relationship; performing time sequence density analysis on the abnormal event data, fitting a risk evolution curve, positioning a situation mutation node, and establishing association mapping of construction conditions and risk steps; according to the method, a transmission path is tracked based on a risk attenuation rule, a preliminary early warning sequence is generated, an early warning trigger threshold value is determined through adaptive grading and boundary matching, a graded early warning instruction is output after gradient verification, and timely and accurate early warning information can be provided for safety management and control of dangerous engineering.

A load feature optimization clustering method based on a one-dimensional convolution auto-encoder comprises the following steps: acquiring load types including daily load curve data as a data set, and performing preprocessing; the method comprises the following steps: constructing a one-dimensional convolution auto-encoder model which comprises an encoder and a decoder, pre-training load data by taking minimization of reconstruction loss as a target, extracting load features, and storing model parameters obtained by pre-training at the same time; the method comprises the following steps: firstly, decoding a network part, removing a decoding network part, retaining an encoding part of feature extraction, constructing a PCA-Kmeans space conversion model, transmitting training data in an encoder, and converting a potential space into a low-dimensional space by using a PCA algorithm to carry out K-means clustering analysis; the encoder is finely adjusted, the encoder is trained by taking minimization of a clustering loss function as a target, and after the encoder is trained for one epoch, K-means clustering is carried out on a newly generated potential space; new clustering distribution is obtained, and power load mode extraction is achieved. Compared with other traditional clustering methods, the method has the advantages that the application complexity is simplified, and the classification efficiency is improved.

The invention provides a photovoltaic typical scene generation method and device based on source-load time sequence decomposition, and the method comprises the steps: collecting historical photovoltaic output time sequence data of a region, and carrying out the decomposition based on an empirical mode; the method comprises the following steps: acquiring load time sequence data of a region, and decomposing the load time sequence data based on a simplified autoregressive moving average model; performing feature extraction on the photovoltaic output periodic component, the photovoltaic output random component and the photovoltaic output trend component to obtain photovoltaic periodic features, photovoltaic random features and photovoltaic trend features; performing feature extraction on the load periodic component, the load random component and the load trend component to obtain a load periodic feature, a load random feature and a load basic feature; carrying out clustering analysis according to the photovoltaic periodic characteristics, the photovoltaic random characteristics, the photovoltaic trend characteristics, the load basic characteristics, the load periodic characteristics and the load random characteristics to obtain a photovoltaic typical scene; according to the method, the description of the rural source load characteristics is more accurate.

The invention belongs to the technical field of tumor clustering analysis, and particularly relates to a tumor clustering analysis system and method based on big data. Through deep fusion and space-time alignment processing of multi-dimensional biological data, the limitation of single-dimension or simple data integration in a traditional method is overcome, complex biological characteristics and space-time evolution laws in the tumor can be captured more comprehensively, and the tumor can be rapidly and accurately captured by constructing a low-dimensional association and independent characteristic space and combining a nonlinear dimension reduction technology. According to the method, low-dimensional feature vectors with biomarker features are effectively extracted, a high-quality data basis is provided for subsequent clustering analysis, meanwhile, based on sliding window density estimation and determination of a dynamic neighborhood radius, accurate spatial adjacency relation construction of the low-dimensional feature vectors is achieved, and the accuracy of the spatial adjacency relation is improved. And the potential clustering center of the tumor subtype is extracted through the multi-scale characteristic spectrum, so that the generated classification label can more accurately reflect the real distribution of the tumor subtype.

The invention discloses an acoustic emission activity monitoring method and an acoustic emission activity monitoring system for early warning of internal damage of concrete, particularly relates to the technical field of acoustic emission monitoring, and is used for solving the problems of large positioning result deviation and low reliability caused by non-uniformity of a medium in the concrete in an existing acoustic emission positioning method. Spatial coherence characteristics are generated by analyzing spatial coherence of acoustic emission signals among acceleration sensor arrays, preliminary positioning is carried out in combination with time difference of arrival and sensor coordinates, and multi-dimensional characteristic vectors containing the spatial coherence characteristics and preliminary positioning results are extracted; and carrying out clustering analysis on the current damage event feature vector and the historical event feature vector to determine a reliability level, and carrying out weighted correction on a preliminary positioning result based on the reliability level to finally obtain an accurate damage source space coordinate.

The invention relates to the technical field of battery management, and discloses a battery fault diagnosis method and system for a CCS module, and the method comprises the steps: obtaining a multi-dimensional data sequence of the voltage, current and temperature of a battery pack, carrying out the abnormality detection, and obtaining a potential abnormal time period and a multi-dimensional data subset; performing clustering analysis on the multi-dimensional data subset to obtain an abnormal feature clustering center, and performing dynamic association analysis to obtain an association mode vector set; calculating the similarity of each single data and a fault propagation path, and matching the propagation path with a preset mode to obtain a fault propagation score; fusing the clustering center and the propagation score, positioning a fault monomer through a support vector machine regression model, and determining a fault monomer identifier; and carrying out segmented clustering and logic judgment on the data corresponding to the identifier, and determining a fault type. According to the invention, accurate positioning and type determination of fault monomers can be realized.

The invention relates to the technical field of multi-view clustering analysis, and discloses an incomplete multi-view clustering method and system, a storage medium and equipment. In order to solve the problems that a small amount of easy-to-obtain supervision information is not utilized in an existing method, and the clustering performance is deteriorated due to low view interpolation quality under high missing degree, the invention provides a technical scheme of combining paired constraint weighted interpolation and double-level feature fusion. The method comprises the following steps: firstly, screening similar samples by using pairwise constraint information, and reconstructing a missing view through similarity weighting; then extracting feature mean values and standard deviations of all views based on an encoder, aligning features through view hierarchy self-adaptive comparison learning and reserving private information, and optimizing feature distribution in combination with sample hierarchy semi-supervised loss; and finally, completing clustering through a Gaussian mixture model. According to the method, the pairwise constraint information is effectively utilized, the view recovery quality and the feature complementarity under the high-missing scene are improved, the clustering performance is remarkably improved, and the method is suitable for scenes such as data analysis of multi-view data missing and the like.

The invention discloses a BIM model reconstruction method based on multi-source data fusion and semantic recognition, and the method comprises the steps: automatically generating an orthogonal projection snapshot image, and carrying out the component recognition and segmentation through a visual large language model, thereby effectively reducing the manual intervention, and remarkably improving the modeling efficiency; meanwhile, point cloud data and a model output result are combined, an orthogonal mapping algorithm and space intersection and clustering analysis are utilized, rapid conversion from a two-dimensional image to a three-dimensional model is achieved, the modeling process is further accelerated, accurate mapping of pixel points is ensured through the orthogonal mapping algorithm, and the geometric accuracy of the model is guaranteed; in addition, the visual large language model improves the accuracy of component recognition and segmentation, reduces human errors, and enhances semantic precision. In addition, multi-source data fusion and semantic recognition enable modeling to be more intelligent, different building requirements can be automatically met, the whole process from data acquisition and processing to model generation is automatic, the error rate is reduced, and the high standard of modern building engineering for precision and efficiency is fully met.

The invention discloses a shield tail sealing leakage detection method and system based on multi-sensor fusion, and belongs to the field of intelligent detection.The method comprises the following steps that a real-time multi-physical-field data set is obtained according to seepage, stress and temperature multi-physical-field data collected by a sensor array deployed in a shield tail sealing area; carrying out fusion processing on the real-time multi-physical field data set by adopting a data fusion method, extracting correlation characteristics of a seepage effect, stress distribution and temperature change, and generating a coupling characteristic vector; performing classification processing on the coupling feature vector based on a mixed model of a graph convolutional network and a support vector machine to obtain an abnormal classification result; a geological environment dynamic change trend is extracted according to the anomaly classification result, and a quantitative index sequence is generated; performing mode grouping on the quantitative index sequence by adopting a clustering analysis method, and determining leakage risk level distribution; and triggering a real-time alarm mechanism based on the leakage risk level distribution to obtain a leakage detection confirmation signal.

The invention relates to the technical field of power system analysis, in particular to a distribution network grounding fault information research and judgment method and system, computer equipment and a medium. The method comprises the following steps: acquiring multi-source fault data in a distribution network system and preprocessing the multi-source fault data to obtain a standardized fault data set; extracting fault feature information from the standardized fault data set through a time sequence pattern recognition algorithm; based on the fault feature information, performing clustering analysis based on a topological relation on the fault feature information through a multi-source data fusion research and judgment algorithm, and generating a fault section positioning result; and generating distribution network grounding fault information according to the topological relation identifier and the electrical parameter characteristics in the fault section positioning result. By means of the mode, the technical problem that an existing ground fault research and judgment technology is insufficient in positioning precision in a power distribution network with the high reliability requirement is solved, and the automation and intelligence level of fault research and judgment and the accuracy and usability of a research and judgment conclusion are improved.

The invention discloses a civil construction intelligent management and control method and system based on the Internet of Things, and relates to the technical field of buildings, through selecting a to-be-tracked reference point set on site and constructing a drift record sequence and a static credible domain, the time continuity stability of reference points in the construction process can be quantified in real time, and the construction efficiency is improved. And the omission of early drift symptom in single measurement is avoided. The validity of the reference point is judged in combination with the static credible domain, and an error propagation command is triggered through a drift trend, so that the system can intervene and dynamically correct an effective point set and the credible domain when the error is still in an early stage, deviation accumulation and upward propagation are prevented, and the risk of staggered layers of the whole structure is reduced. And constructing a full-field error vector field by utilizing clustering analysis, identifying a high-risk area and a high-risk offset component, and uploading the high-risk area and the high-risk offset component to a construction main control system to generate a retest and re-setting-out task, so that automatic identification and priority correction of the risk component are realized.

The invention relates to the technical field of data processing, in particular to an analysis decision-making method, device and equipment based on coal quality data and a medium. According to the method, the coal quality time sequence knowledge graph fused with the time attribute is constructed, so that traditionally isolated test, production and purchase data are systematically integrated, and the fundamental transformation of coal quality management from scattered information to associated knowledge is realized. On the basis, a stable or fluctuating coal quality group is automatically identified through clustering analysis, and an influence path of process parameters on quality is accurately positioned by utilizing association mining, so that a direct basis is provided for process optimization. Meanwhile, the system can carry out dynamic early warning and intelligent matching recommendation based on the atlas, the core targets of improving the coal quality stability, optimizing the production cost and assisting scientific decision making are finally achieved, and the comprehensive benefits of coal production and utilization are comprehensively improved.

The invention provides an underwater terrain inversion method based on a height measurement satellite, belongs to the technical field of remote sensing geoscience application, and constructs an automatic processing flow integrating space screening, elevation correction, signal denoising, double-strategy surface and bottom recognition and physical correction. The method is suitable for underwater topography generation in complex water body environments such as lakes and reservoirs, and the efficiency of underwater topography data acquisition is greatly improved. A strategy of combining adaptive kernel density estimation and clustering analysis is adopted to remarkably improve the water surface and water bottom signal recognition capability, and a multi-temporal collaborative analysis and refractive index physical correction model is introduced to realize high-precision underwater terrain extraction of water bodies such as inland reservoirs. The method has obvious advantages in the aspects of automation degree, application range and result stability, and has important engineering application and popularization value.

The invention relates to a method for calculating equivalent parameters of a data-driven permanent magnet direct drive wind power plant, which belongs to the technical field of analog computation of a power system and comprises the following steps of: identifying a wind turbine generator group with similar dynamic behaviors by carrying out clustering analysis on a transient current time sequence of an outlet of a wind turbine generator; a neural network model is constructed, key electrical parameters such as operation condition parameters and voltage drop serve as input, a clustering result serves as output, and automatic classification and dynamic response feature extraction of the wind turbine generator are achieved by training a neural network; calculating unit equivalent parameters and current collection line parameters to obtain an equivalent model; and multi-operation scene simulation verification of the large-scale permanent magnet direct-driven wind power plant is passed. According to the method, equivalent modeling of the large-scale permanent-magnet direct-drive wind power plant is realized, and the modeling efficiency and the adaptability to complex operation scenes are remarkably improved.

The invention provides a vehicle-mounted log uploading method and system, vehicle-mounted equipment and a storage medium, and relates to the technical field of data processing.The method comprises the steps that log data of a target vehicle is obtained and subjected to structured processing, and structured log data comprising multiple structured log texts are obtained; based on a preset keyword library, performing keyword extraction on each structured log text through a keyword extraction algorithm to obtain a plurality of keywords; based on a natural language processing algorithm, determining the semantic similarity of any two keywords; based on the semantic similarities, performing clustering analysis on the plurality of structured log texts to obtain a plurality of log clusters; obtaining an abnormal score of each log cluster according to a preset scoring factor corresponding to each log cluster; according to each abnormal score, screening out at least one target log cluster from the plurality of log clusters, the target log cluster comprising at least one target log; and uploading each target log to a cloud server. According to the invention, the uploading efficiency of the vehicle-mounted log is improved.

The invention discloses a water conservancy monitoring method and system for automatic early warning of water conservancy dangerous cases, and belongs to the technical field of water conservancy monitoring, and the method specifically comprises the steps: carrying out the data collection according to dynamically divided water channel sections through distributed pressure sensors and flow meters disposed along a water channel; performing multi-dimensional comparison on the flow data of each section and a historical operation data set to generate a comprehensive difference index; spatial clustering analysis is carried out on the comprehensive difference indexes of the adjacent sections, an abnormal conduction model based on a water channel topological structure is constructed, and the position of an initial section causing water flow abnormity is recognized; according to the positioning result of the abnormal section, a combined dispatching instruction containing an upstream shunting strategy and a downstream compensation strategy is automatically generated, and water flow dynamic balance of the abnormal section is achieved by adjusting the opening degree of a water channel gate and cooperative control over the power of a pump station; according to the invention, the accuracy, high efficiency and intelligent level of canal operation scheduling in intelligent water affairs are improved.