1043 results about "Data point" patented technology

Hierarchical modelling and advanced feature engineering discover abnormalities in time series data with irregular trends. Data is collected in real time to ensure temporal integrity in the invention. Extraction filters and isolates useful data. Data cleansing removes noise and extraneous data after preliminary analysis identifies patterns and abnormalities. Feature engineering organizes cleansed data for machine learning algorithms. Primary storage stores this data for fast retrieval and extensive trend analysis. Holidays and weekends provide unique patterns in trend analysis. These trends are used to cluster data and create hierarchical predictive models, starting with a first-order model for general trends and increasing in order to refine residuals. Serializing these models improves storage and retrieval. Trend clusters are created from new data points, and algorithms detect pattern deviations. Statistical tests and machine learning classifiers identify anomalies and create alerts and remedial measures. The system monitors and analyzes incoming data to detect anomalies.

The invention discloses a multi-system collaborative data cross validation management system, and relates to the technical field of logistics data management, and the system comprises an initial credibility grading module which is used for obtaining a data stream and a preset static source reliability factor, and calculating an initial credibility score; the evidence weight dynamic quantification module is used for calculating a timeliness attenuation factor, matching a current service scene, distributing a service rule adjustment factor and determining a final evidence weight score of the data point; the gold record automatic judgment module is used for sequencing the data points; obtaining a highest score and a second high score; and the judgment confidence evaluation module is used for calculating a judgment confidence margin between the highest score and the second highest score, and automatically routing the conflict data set to a manual auditing queue or automatically confirming a golden record. Through quantitative evaluation, dynamic weighting and automatic judgment, the credible gold record is efficiently generated, and refined risk control and non-tampering decision traceability are realized.

Apparatus and method for generating an output using an AI-PII model. The apparatus includes at least a processor and memory communicatively connected to the at least a processor. The memory instructs the processor receive personally identifiable information (PII) data, receive one or more model constraints, map, using an AI-PII model, the PII data to at least a data schema as a function of the one or more model constraints by identifying at least a PII datum of the PII data, categorizing the at least a PII datum to one or more categories of a plurality of categories, and mapping the PII data to the at least a data schema, modify the data schema based on a refinement datum, wherein the refinement datum is generated based on a temporal datum of the one or more model constraints, and generate an output as a function of the refinement datum and data schema.

The invention relates to the technical field of three-dimensional modeling, in particular to a dynamic modeling method for a geological structure three-dimensional model. The method comprises the steps that multi-source data are acquired and preprocessed, a voxel semantic fusion algorithm based on variational optimization is introduced, after semantic information is extracted from the preprocessed multi-source data, the preprocessed multi-source data are mapped to a target three-dimensional space grid, and an optimal semantic fusion vector is obtained; based on the optimal semantic fusion vector, generating a standard voxel data pool, and constructing a geological structure three-dimensional model; monitoring data change, calculating the position of a newly added data point, combining the standard voxel data pool to obtain a space updating area, and modeling the space updating area to realize model updating; and after the modeling of the space updating region is completed, optimizing the boundary continuity. The problems that multi-source geological data cannot be directly used for structure construction and semantic fusion of a three-dimensional model, dynamic response to newly-added data is lacked, and geometric discontinuity and structural logic discontinuity exist at the boundary are solved.

The invention relates to the technical field of electric digital data processing, in particular to a radiator working state monitoring method and system based on data analysis, and the method comprises the steps: collecting multi-dimensional parameter data of a radiator at different moments, and taking the multi-dimensional parameter data of the radiator at the same moment as a data point; clustering all the data points to obtain a plurality of clusters; the abnormal degree of any cluster is calculated, the abnormal degree is in inverse correlation with the number of the data points in each cluster and is in positive correlation with the standard deviation of the data points in each cluster, the data points in the cluster with the abnormal degree larger than a set threshold value are responded to be abnormal data points, and it is judged that the radiator is abnormal at the moment corresponding to the abnormal data points. The problem that the monitoring efficiency and accuracy are not high in the process of monitoring the abnormality of the radiator is solved.

The invention relates to the technical field of sewage treatment, and discloses a risk early warning method and system for a sewage treatment trusteeship operation project. The method comprises the following steps: acquiring multi-source operation data of sewage treatment equipment, and generating a time sequence data set after time synchronization processing; identifying abnormal data points based on the data set and generating an abnormal mark sequence; performing time sequence fluctuation and persistence analysis on the sequence, and extracting features to construct an abnormal trend feature vector; performing correlation analysis in combination with medicament dosage records, and determining a cost risk assessment result; generating a layered early warning signal according to the risk level, and matching a response strategy; and integrating related data to form a push data set, sending a notification to an operator and updating a processing record, and finally generating a traceable exception processing file. According to the invention, timely early warning and accurate response in the sewage treatment process can be realized, and the efficiency and safety of operation management and the accuracy of risk prevention and control are improved.

The invention relates to the technical field of industrial process control and automatic monitoring, in particular to a facility state anomaly diagnosis method and system based on robot inspection data, and the method comprises the steps: constructing a historical normal data set, and constructing a mapping model for mapping local density into neighborhood parameters based on the local density extreme value range of the data set; for the new inspection data points, determining adaptive neighborhood parameters by using a mapping model according to the real-time local density of the new inspection data points, determining a neighborhood set based on the parameters, and calculating local abnormal factors; a facility state is determined in response to a comparison of the local anomaly factor to an anomaly threshold. According to the method, through a self-adaptive adjustment mechanism of neighborhood parameters, the problems of false alarm and missing alarm caused by multi-working-condition density difference are solved, and the accuracy of facility state abnormity diagnosis is improved.

The invention discloses a small modular prismatic high-temperature gas cooled reactor core optimization design method and system based on data driving. The method comprises the following steps: constructing a training data set; a data-driven agent model is trained to replace and execute high-cost simulation calculation; and exploring a design space and generating a group of Pareto optimal candidate solutions by using a multi-objective evolution algorithm and taking the proxy model as a fitness function evaluator. The key feature of the method is a closed loop of iterative verification and model updating: verifying the candidate solution through high-fidelity simulation, if the prediction precision does not meet the convergence criterion, expanding the verified new data point to a training data set, and retraining the agent model. According to the method, the calculation cost can be remarkably reduced, a complex design space can be efficiently explored, and a physically reliable reactor design scheme with better performance can be obtained.

The invention provides a finite element analysis method and system for geometric design of a flow regulating valve, and relates to the technical field of data processing.The method comprises the steps that 1, spatial geometric feature analysis is conducted on each data point in a discrete contour coordinate dataset, the principal curvature value of each data point on the sealing surface of a metal diaphragm is solved, and the principal curvature value of each data point is calculated; determining a point with the maximum average curvature value on the sealing surface of the metal diaphragm as a core morphology reference point; step 2, establishing a local coordinate system by taking the core morphology reference point as an original point, screening four sealing state points from the discrete contour coordinate data set, solving a corresponding space plane equation, and calculating a space intersection line between the space plane and the sealing surface of the metal diaphragm; according to the method, through accurate positioning of the core reference point, definition of the analysis sub-domain, grid division, mechanical solution, leakage rate calculation and parameter optimization, the dynamic particulate matter problem is effectively improved, and the sealing performance and the micro flow adjusting precision are improved.

The invention relates to the field of hydraulic system monitoring, in particular to a leakage monitoring method and system for a hydraulic system, and the method comprises the steps: obtaining the pressure data of a valve element of a hydraulic servo system in a zero position state; constructing a local analysis window by taking each pressure data point as a center, and performing local mean decomposition processing on the pressure data to obtain a residual component, the local mean decomposition processing including using an adaptive sliding step length for the data points; calculating an average descent rate of the residual component; and generating an alarm signal in response to the fact that the average descent rate is greater than a set threshold. According to the invention, the sensitivity and the accuracy of the zero-position tiny internal leakage detection of the hydraulic valve core are improved, and false alarm and missing alarm are effectively avoided.

The invention provides an operation and maintenance performance assessment data management system based on dynamic weight optimization, and relates to the technical field of operation and maintenance management, and the system comprises a quantification module which is used for obtaining an original operation and maintenance data sequence through a data collection interface, and mapping the operation and maintenance data sequence to a multi-dimensional feature space; in the multi-dimensional feature space, quantitative analysis is carried out on spatial distribution of data points, and data density distribution characteristics of all areas are determined; the calculation module is used for executing unsupervised clustering operation based on the density distribution characteristics and the proximity relation of the data points to obtain a group of data sets with internal consistency; core statistical features are calculated for each generated data set, and a specific calibration coefficient is derived for the data set based on the core statistical features. According to the invention, precision, scenario and high efficiency of operation and maintenance performance assessment are realized.

The invention relates to the technical field of load prediction, and provides a power load prediction method and system based on time sequence decomposition and an attention mechanism, and the method comprises the steps: carrying out the adaptive time sequence decomposition of an obtained original load sequence, calculating the sample entropy of each decomposed component, and carrying out the clustering; constructing a group of encoder and decoder networks for each piece of clustered data, performing parallel encoding to extract features, performing serial decoding reconstruction on the features from low frequency to high frequency, and outputting prediction data from low frequency to high frequency step by step; the weight is initialized based on the sample entropy, the trained weight is obtained through optimization in the encoder and decoder network training process, and the predicted value of the power load is obtained through weighted fusion. According to the method, adaptive time sequence decomposition, a weight mechanism guided by sample entropy and an attention-enhanced encoder-decoder structure are introduced, so that multi-component collaborative modeling and cross-scale dynamic prediction are realized, and the prediction accuracy and stability in complex load data and small sample scenes are effectively improved.

The invention belongs to the technical field of spatial data processing, and particularly discloses a multi-source remote sensing data gridding construction and processing method and system, a terminal and a medium. Comprising the following steps: acquiring remote sensing image data, vector data, point cloud data and TIN data to form a standardized data set; performing three-dimensional layered mesh generation on the target area to generate an initial multi-level mesh set; performing geometric superposition and coverage judgment on the standardized data set to form an element-grid mapping set; performing double-engine coding and decoding on the grid units in the mapping set, generating a unique grid code, and establishing a code-space range mapping table; constructing a three-dimensional space topological structure; obtaining a space intersection result, a space union result and a space difference result; a multi-scale grid set is generated, and a unified gridding integrated data set is formed. Through gridding fusion and self-adaptive multi-scale processing of multi-source data, integrated expression and efficient organization of multi-source remote sensing data in a three-dimensional space are realized.

The invention provides a plant phenotype platform three-dimensional imaging method and device based on multi-sensor fusion, and the method comprises the steps: collecting the original three-dimensional point cloud data of a plant through a plurality of heterogeneous imaging sensors carried on a plant phenotype platform, and carrying out the time synchronization processing and space registration processing. And dynamically calculating the confidence coefficient weight of each data point according to the environment illumination condition, the measurement attribute of each data point in the registration point cloud set and the characteristics of the imaging sensor to which the data point belongs, and obtaining a point cloud set with a point-level confidence coefficient weight. Performing adaptive weighted fusion processing according to the spatial distribution of the point cloud set to generate a preliminary fusion point cloud model; and according to a quality index of the model and a real-time environment illumination condition, dynamically adjusting and calculating a weight parameter of a confidence coefficient weight, and finally outputting an optimized three-dimensional point cloud model after iterative optimization. The defect that a fusion result of an existing plant three-dimensional phenotype accurate acquisition scheme is insufficient in stability and precision in a complex and changeable field environment is overcome.

The invention relates to the field of sewage treatment, and particularly discloses a water quality prediction method, system and product based on time sequence decomposition and multi-component special attention, and the method comprises the following prediction model training steps: S1, obtaining historical time sequence data according to water quality parameters; s2, decomposing the time sequence data into different data components; s3, training a deep learning prediction module according to different data components, and constructing a corresponding attention mechanism; the method comprises the following prediction steps: S4, acquiring water quality parameter monitoring time sequence data, and decomposing the water quality parameter monitoring time sequence data into data components; s5, inputting each data component into a corresponding deep learning prediction module, outputting corresponding prediction time sequence data, and adding the data point by point to obtain total prediction time sequence data; and S6, adjusting process parameters of sewage treatment based on the total prediction time sequence data of the water quality parameters. According to the invention, multiple coincidence time sequence characteristics of water quality data can be effectively handled, and key impact load signals are accurately captured and predicted.

The invention discloses a water conservancy digital twin data fusion method, system and device based on multi-source heterogeneous data dynamic alignment and a medium, and belongs to the technical field of data fusion. Performing cleaning processing, format standardization processing and timestamp alignment processing based on a time window on the data to obtain preprocessed data; performing sliding window division processing on the preprocessed data, performing regional grid numbering processing according to a space division rule, and enabling each data point to correspond to a divided time window and a space grid to obtain time-space mapping data; and performing fusion processing on the data subjected to space-time mapping according to the data type. According to the method, the fusion strategy is designed by distinguishing the data types in the fusion stage, and it is guaranteed that the fusion result can still keep high reliability and stability when facing data of different qualities and different sources.

The invention discloses a six-dimensional sensor multi-source data fusion system based on dynamic weight, relates to the technical field of sensor data processing, and is used for improving fusion precision and robustness of measured data of a multi-source sensor. Firstly, a working condition modal identification module analyzes time-frequency characteristics output by a six-dimensional sensor, matches the time-frequency characteristics with a predefined working condition template, and identifies a current dominant working condition modal; then, a credible domain dynamic mapping module counts residual mean values and variances of single-point output and global fusion estimation values in a sliding time window, and adaptively adjusts a standard deviation multiple in combination with an instantaneous signal-to-noise ratio to form a dynamic credible domain boundary; the closed-loop weight optimization module distributes weights according to the relative positions of the data points and the credible domain boundary, punishes the abnormal measurement data points and generates a dynamic weight matrix; and finally, the fusion execution module performs federated filtering fusion by using the dynamic weight matrix, outputs global optimal estimation feedback to update residual statistics, and realizes multi-source data fusion closed-loop optimization.

A computer system obtains an initial dataset for rendering a data visualization. The initial dataset includes a plurality of data points, and each data point has a respective spatial location in the visualization. The system dynamically generates a data structure, including (i) recursively dividing the plurality of data points into a plurality of nodes until each node satisfies a set of criteria and (ii) allocating a respective subset of data points to each node according to a spatial location of a respective data point in the data visualization. For each node, the system recursively applies a linearization algorithm to an initial subset of data points to obtain a reduced subset of data points. The system obtains a reduced dataset, generates a data visualization according to data in the reduced dataset, and causes display of the visualization on a browser application.

The invention relates to the technical field of anomaly detection, in particular to an industrial Internet of Things equipment behavior pattern anomaly detection method. Clustering the data points according to the time domain features and the frequency domain features of the data points in the local time period, and obtaining an outlier degree and a target data point according to the number features of the data points in the data cluster and the distance features between the data points and the maximum data cluster; obtaining a first confidence coefficient according to the data difference of the target data point in other data points in a preset neighborhood range in the clustering space, the number ratio of other data clusters to which the other data points belong, and the discrete feature of the target data point; obtaining a second confidence coefficient according to the data difference between the target data point and the same historical moment, the trend difference between the target data point and the same local time period corresponding to the same historical moment, and the data fluctuation in the local time period of the target data point; and obtaining the abnormal degree of the target data point according to the comprehensive confidence coefficient and the outlier degree, and detecting the behavior state of the equipment, thereby improving the accuracy of anomaly detection.

The invention provides an eye pattern intersection point estimation method and system, relates to the technical field of communication link evaluation, and solves the problems of insufficient estimation precision and low efficiency caused by sparse data of an intersection point region in an existing eye pattern test. The method comprises the following steps: acquiring VNA frequency domain data, converting the VNA frequency domain data into time domain data, and performing convolution calculation by using a random sequence to generate a convolution sequence; according to a preset unit interval length, segmenting the convolution sequence into a plurality of eye pattern data units to form an eye pattern data set; screening out a rising edge data subset and a falling edge data subset based on the position relationship between the starting point and the ending point of each eye pattern data unit in the eye pattern data set and the middle position of the eye pattern; and respectively carrying out average processing to obtain rising edge data and falling edge data, and carrying out linear fitting to obtain an intersection point of the rising edge data and the falling edge data as an eye pattern intersection point. According to the method, calculation can be completed only through a small number of basic data points, the processing burden is greatly relieved, and the testing efficiency is improved.

Examples are directed to systems and methods that cause a user interface to be displayed that allows an end-user to combine, via assigning weights and bias corrections, sources that provide weather-related datapoints and emphasize the final combination of weather-related datapoints. The user interface has inputs at each datapoint of the weather-related datapoints and can receive user inputs at the inputs. The user inputs correspond to weights and bias corrections relating to the sources, and the manual or automatic combination of weighted, bias-corrected weather-related datapoints generates a customized weather forecast for a location or region

The invention provides a water body pollution spatial-temporal distribution dynamic monitoring system based on multi-source remote sensing data fusion and deep learning, and relates to the technical field of data processing, and the system comprises the steps: obtaining a satellite hyperspectral image, unmanned aerial vehicle multispectral data, and water quality parameter data collected in real time through ground Internet of Things equipment; the inversion module is used for performing pollutant concentration inversion on the satellite hyperspectral image to obtain pollutant concentration data; performing local pollution source positioning on the unmanned aerial vehicle multispectral data to obtain pollution source position data; acquiring real-time water quality parameter data from ground Internet of Things equipment; the spatial discretization module is used for carrying out spatial discretization processing on pollutant concentration data, pollution source position data and real-time water quality parameter data, establishing a quadtree index and carrying out recursion division on the quadtree index into sub-regions; and generating a dynamic space correction parameter according to the statistical characteristics of the data points in each sub-region. The method can effectively improve the water body pollution time-space distribution monitoring precision and the pollution early warning reliability.

The invention relates to the technical field of radars, in particular to a weather radar distance resolution calibration method and system. The method comprises the following steps: collecting oversampling original baseband I / Q data of a point target in a plurality of continuous pulse periods; determining a pulse signal-to-noise ratio, and screening out effective pulses according to a threshold value; carrying out sub-sampling stage time alignment on the effective pulses based on cross-correlation analysis, and carrying out incoherent averaging on power module values of all the aligned pulses to generate an average pulse power envelope; converting the average pulse power envelope to a logarithm domain, and selecting discrete data points in a main lobe region; according to the discrete data points, a nonlinear least square method is adopted to carry out iterative fitting on the generalized Gaussian mathematical model, and a continuous analytic function describing the pulse envelope is obtained; according to the continuous analytic function, determining a pulse time width when the power is reduced to a peak value of-6dB; and determining the real distance resolution according to the pulse time width. The measurement robustness is improved, and sub-meter calibration precision is achieved.

The invention provides a power grid multi-disaster coupling strength calculation method and system, and relates to the technical field of power emergency, and the method comprises the steps: collecting the weather forecast data of a target power transmission channel and the real-time operation parameters of a power grid; analyzing weather forecast data, and configuring virtual monitoring points at the midpoint of an ice-prone section of a line, the tower top of a tower in a thunder and lightning frequent area and the geometric center of a tower foundation in a soil frost heaving sensitive area; generating a dynamic feature point set based on the virtual monitoring points; analyzing real-time operation parameters of a power grid, and collecting an equipment state feature point set at monitoring points at four corners of a tower foundation platform, a joint of an insulator steel foot and a hanging point and an outlet of a wire strain clamp; and extracting a data point sequence with time-space continuous evolution characteristics in the dynamic characteristic point set as a core trajectory for constructing the three-dimensional dynamic ellipsoid. The method can improve the dynamic nature and accuracy of power grid risk assessment under the disaster chain effect, and guarantees the safe operation of a power transmission channel.

Systems and methods are disclosed for automated method parameter configuration for differential mobility separations. As non-limiting examples, various aspects of this disclosure provide receiving a sample in an open port interface; transferring the sample to an ionization source; ionizing the transferred sample; introducing the ionized sample into a mass spectrometer; mass analyzing the ionized sample to produce an initial mass analysis result; determining a peak width of the initial mass analysis result; and determining a dwell time for subsequent measurements based on the determined peak width, a pre-defined number of data points across subsequent mass analysis peak widths, and a number of different analytes to be assessed for the sample. The sample may be diluted and transferred to the ionization source by a sample introduction apparatus selected from a group including an acoustic droplet ejector (ADE), a pneumatic ejector, a piezoelectric ejector, and a hydraulic ejector.

The invention discloses an earthquake monitoring and early warning method and system based on an earthquake precursor, and relates to the technical field of earthquake forecasting, and the method comprises the steps: collecting the core data and auxiliary state data of the earthquake precursor, marking a sample label, and forming a training data set; establishing a core parameter screening model, and training the core parameter screening model by using the training data set to obtain core parameters; constructing a typical working condition library, calculating an Euclidean distance between a real-time data point and a historical working condition sample point, selecting first K neighbor points, and returning a working condition category with the highest frequency as a current operation working condition; corresponding anomaly prediction models are established according to the different operation conditions, and anomaly types under the different current operation conditions are predicted according to the anomaly prediction models; an EWI index is calculated through the core parameters, and when the EWI index is larger than an index threshold value, earthquake early warning is triggered; otherwise, continuously monitoring. According to the invention, real-time early warning of the earthquake precursor is realized under the condition that the equipment is abnormal.

The present invention is directed to a software-based technology system for managing and executing high volumes of work on complex projects across multiple organizations involving multiple users that results in superior outcomes at significantly reduced time and cost. Litigation is the initial application of this invention; each case is handled as a project. As applied to litigation, the system of the invention empowers a novel method of end-to-end case assessment, planning, execution and management by integrating tools, processes and procedures allowing for more informed decision-making and deliberate execution of all case-related actions as well as enhanced coordination and communication among all constituents, resulting in efficient achievement of targeted outcomes. The system provides intuitive, menu-driven functionality for inputting, processing and utilizing vast amounts of information, including company and case-specific information, attorney discretionary evaluation and input, document templates and monitoring and reporting requirements. A threat algorithm cross-correlates via multivariate analysis critical data points with customized company-specific information and business logic to generate a numeric threat score as an output. The system also predicts the expected settlement range with a high degree of precision for a specific case as the output of one or a combination of multiple artificial intelligence (AI) / machine learning algorithms. The system then generates a unique case strategy (which includes an expected outcome, including projected outcome, Case Plan and estimated attorneys' fees and costs). System logic learns from thousands of examples of similar cases and directs highly effective and efficient execution of a specific Case Plan providing each attorney and staff member assigned a role in a case with a prioritized task list that is updated in real time as other users complete tasks and litigation priorities change. For each user, the system integrates assigned tasks across multiple cases and multiple client portfolios into a single, prioritized task list. The execution features become increasingly valuable given the volume and complexity of projects across multiple organizations and users, allowing users to focus on their area of expertise and avoid the wasted time and distraction of trying to determine what they should be working on or recording notes to communicate what they have been doing since the system automatically performs these functions and directs the various users' next steps while monitoring their progress against the plan. Integration of administrative functions, including automated time tracking, billing and reporting without the need to rely on users to update this information, further increases quality and efficiency of legal services. Key objectives of the system of the present invention include eliminating gaps between business objectives and litigation strategy and optimizing communication and coordination of case-related tasks and priorities in a fully automated fashion, which are all achieved by leveraging data and technology to enable a fundamentally different approach that becomes smarter and more precise via machine learning and predictive modeling.

The invention belongs to the technical field of three-dimensional geometric measurement, and particularly relates to a three-coordinate detection data processing method and system for automobile parts, and the method comprises the steps: obtaining an actual point cloud and a standard point cloud; by calculating the maximum angle gap of the data points, screening to obtain actual and standard feature point sets; anti-noise geometric descriptors based on tangential weights are constructed for the feature points in the two sets of feature point sets respectively; matching the two groups of feature points based on the descriptors and generating an initial matching set; solving global optimal transformation from the initial matching set by adopting a random sampling consistency algorithm; and the transformation is applied to the actual point cloud, fine registration is carried out by using an iterative nearest point algorithm, and finally deviation analysis is completed. The technical problem that traditional registration depends on initial alignment and is prone to falling into local optimum is solved, and the robustness and accuracy of registration are improved.

A decentralized dynamic policy learning and implementation system automatically processes incoming report and generates real-time data based on pre-configured rules and uses a graph-based transformer to generate new policies. The generation of the new polices is based on new alerts or notifications indicating an error with existing policies. An intelligent auto-router detects new security issues and re-routes information to generate the new policy and initiate analysis via a digital twin based on decisioning data points. The digital twin system qualifies the new policy to determine effectiveness in overcoming the security issue. An extended reality environment automatically generates an extended reality environment based on a graph node structure of the policy.

The invention discloses a railway power distribution network fault information automatic diagnosis and positioning method and system, and belongs to the technical field of power distribution fault diagnosis and positioning, and the method comprises the steps: employing an auxiliary classification generation adversarial network, obtaining a class-balanced standardized training data set, and carrying out the preprocessing; mapping the preprocessed data in the standardized training data set into a snowflake-shaped SDP image under a two-dimensional polar coordinate system, inputting the snowflake-shaped SDP image into a fault diagnosis model based on a deep convolutional neural network, and outputting a fault section and a preliminary fault distance; constructing a physical circuit model of railway power distribution network distribution parameters, and calculating theoretical simulation voltage and simulation current by using a transmission line equation; and starting iterative correction based on the Pearson's correlation coefficient of each data, and outputting a diagnosis result. According to the method, high-precision, high-reliability and self-adaptive fault information automatic diagnosis and positioning are realized by fusing data enhancement, feature transformation, intelligent diagnosis, physical inversion check and topology self-adaptive adjustment.