37 results about "Statistical classification" patented technology

This invention provides a comprehensive digital and intelligent operation and maintenance management method and system for power distribution networks, relating to the field of computer data processing technology. The method includes: S1, obtaining pole and tower information collected by drones during line inspections, performing defect mining and analysis on the pole and tower information, and integrating the analyzed defect information with the pole and tower information to form a pole and tower information collection table; S2, selecting and extracting corresponding data from the pole and tower information collection table, and visualizing the data on a GIS map containing the lines obtained from the pole and tower information collection table using preset markers to generate an inspection status map; S3, statistically classifying the analyzed defect information and matching it through a big data platform to generate a defect repair and management summary table; S4, analyzing the faults using an AI model and a big data platform, combining the inspection status map and the defect repair and management summary table, obtaining priority-ranked repair tasks, and proposing corresponding solutions.

The invention discloses a unit state analysis early warning method applied to a centralized control system, and the method comprises the steps: obtaining original data of a data interface layer, carrying out the data preprocessing and parameter reconstruction of the original data after statistical classification, and calling an equipment three-dimensional model in a model layer to carry out digital twin modeling, the preprocessed data, the reconstructed feature parameters and the digital twinborn model are correspondingly output; multi-dimensional index analysis is carried out on the preprocessed data and the reconstructed characteristic parameters by calling an analysis model and a diagnosis model in the model layer, an independent scoring rule is constructed, and the instantaneous state and the risk trend of the centralized control system are comprehensively evaluated; and by calling an early warning model and an evaluation model in the model layer, dynamically analyzing unit overall risk output early warning information according to the multi-dimensional analysis result, and displaying a dynamic simulation result and an early warning result of a unit state through a digital twin model. The objective degree and standardization problems of centralized control system evaluation are effectively solved.

The application discloses an XCT image-based CFRP defect automatic identification and classification method and system, and belongs to the field of carbon fiber reinforced composite material manufacturing and processing quality detection.The application obtains the three-dimensional features of defects through binarization three-dimensional label statistics classification, obtains the two-dimensional features of defects through manual labeling based on the three-dimensional label of defects, and after fusing the three-dimensional features and the two-dimensional features of defects, combines the features with a deep learning method to obtain an efficient real defect identification and classification model, which is simple and efficient and effectively improves the CFRP defect identification precision.The application uses the three-dimensional morphological parameters of defects when labeling the data set, and avoids the unreliability introduced by directly labeling on two-dimensional slices.The application trains a semantic segmentation model for identifying and classifying CFRP defects based on a small amount of two-dimensional CT images, and the semantic segmentation model is used for predicting internal defects of similar materials, so that a large amount of tedious and time-consuming manual image processing for obtaining internal defect data of materials is avoided, and a large amount of labor cost is saved.

The invention discloses a flow control method for intelligent voice outbound, and the method comprises the steps: introducing a statistical classification algorithm in the aspect of an outbound flow control algorithm, taking various outbound flow control factors as events, and calculating the probability that the outbound flow reaches a certain level under a certain condition; flow upper limit calculation and flow adjustment dynamic decision are introduced for flow control of intelligent voice outbound; and an outbound traffic controller based on an outbound traffic control algorithm is introduced to complete a full-intelligent outbound system. According to the method, accurate prediction and intelligent adjustment of the flow of the intelligent outbound system are realized, dynamic balance between the outbound task flow and available resources of the outbound voice platform is realized, the resource utilization rate of the outbound voice platform is maximized, and the capacity of the intelligent outbound system is further released, the cost is reduced and the efficiency is improved on the premise that the outbound resources are kept unchanged.

The invention provides an inspection data processing method, device and equipment, belongs to the technical field of information processing, and solves the problems that the inspection data processing efficiency is low and the execution process is difficult to control. The method comprises the following steps: acquiring inspection target data, wherein the inspection target data comprises inspection time data, inspection execution equipment data and inspection object data; according to the inspection target data, performing traversal filling processing on the inspection form database to obtain inspection work order data; sending the inspection work order data to the client; receiving a plurality of inspection feedback data sent by the client, wherein the inspection feedback data is obtained by the client by generating an operation instruction according to the inspection work order data and executing the operation instruction; and performing statistical classification processing on the plurality of inspection result data to obtain inspection report data. According to the scheme, automatic triggering and accurate execution of the inspection task are realized.

A dynamic statistics and result analysis system for watershed river and lake shoreline development and utilization comprises a remote sensing data acquisition module, a data preprocessing and classification module, a database construction module, a change trend analysis module, a river and lake directory and association retrieval module and an interaction management module. The data preprocessing and classification module is used for data preprocessing and statistical classification, the database construction unit is used for constructing a system database, the variation trend analysis module is used for variation trend analysis and statistical report generation, and the river and lake directory and association retrieval module is used for directory maintenance and result association. According to the dynamic statistics and result analysis system for watershed river and lake shoreline development and utilization, a remote sensing data statistical classification algorithm based on a neural network is proposed to classify remote sensing data; and a river-lake shoreline development and utilization change trend analysis algorithm based on deep learning is proposed to analyze the change trend.

The application provides a data collection method, device, equipment, medium and computer product, and relates to the technical field of flow collection. The method comprises the following steps: grouping the to-be-collected data according to statistical information obtained by statistical classification according to preset elements, obtaining at least one feature flow data and a corresponding analysis window; wherein each group of to-be-collected data corresponds to one feature flow data; meanwhile, each analysis window is moved from a first moving time node, and the first step length of the at least one feature flow data is obtained; and the data of each feature flow data in the time period corresponding to the analysis window is collected to obtain collected data. By moving the analysis window according to the step length of the feature flow data, the data of different feature flow data in the time period corresponding to the corresponding analysis window is collected, so that the system burden caused by a large amount of redundant data due to full collection can be avoided, and the collection and analysis resources can be dynamically allocated according to different flows.

The invention discloses a hot pot bottom boiling identification method based on time sequence image analysis, and the method comprises the following steps: S1, obtaining image data, and obtaining a pot bottom region image; s2, an image classification algorithm; s3, converting a grey-scale map; s4, performing frame difference; s5, carrying out binarization; s6, performing open operation denoising; s7, counting the number of pixels; s8, performing moving average denoising, repeating the steps S1-S7, and when t is less than or equal to 1000, obtaining a category queue C = {c1, c2,..., ck} and a difference value queue N = {num1, num2,..., numk}, the upper limit of the queue C and the upper limit of the queue N being 1000; when t is greater than 1000, executing the steps S1-S7, storing the obtained results into the tail part of the queue, and meanwhile, rejecting the data at the head part; s9, counting a classification result, calculating an average value At of the queue N, counting a classification number in the queue C which is a label number count of pot bottom boiling, and obtaining a spectrogram through At and count; and S10, performing fusion judgment. According to the method, the pot bottom boiling degree is quantified by analyzing the time sequence change during pot bottom boiling in combination with a traditional image processing technology and a data analysis technology, and the result accuracy is enhanced in combination with an image classification algorithm.

This invention relates to the field of oil and gas drilling engineering technology, specifically to a rapid directional drilling method for deep coal and gas horizontal wells based on real-time logging data, comprising: real-time acquisition of multi-source logging data; establishment of the functional relationship between idle friction torque and well depth; real-time calculation of effective rock-breaking torque of the drill bit; and MSE (Mean Separation of Forms). b Model building and real-time calculation; establishment and dynamic correction of lithology identification threshold: using logging and well logging data from adjacent wells, a statistical classification model for lithology identification is established; real-time guidance decision-making and trajectory adjustment: this application realizes real-time lithology identification, formation early warning and working condition judgment, thereby guiding precise adjustment of wellbore trajectory. This solution does not require additional equipment, is adaptable to complex drilling conditions, completely solves the data lag problem, significantly improves reservoir encounter rate and drilling efficiency, reduces operating costs and risks, and provides reliable technical support for the efficient development of deep coal and gas formations.

The invention relates to the technical field of power electronic components, and discloses a high-frequency transformer winding coil testing method, which comprises the following steps of: 1, measuring the initial temperature of a coil to be tested, measuring the initial high-frequency impedance of the coil to be tested under a specific high frequency, and acquiring an initial state reference; 2, collecting long-period operation data of the high-frequency transformer in practical application, carrying out statistical classification on the operation data, and constructing a compression working condition test sequence containing a high-load working condition and a light-load working condition; and 3, based on the compression working condition test sequence, applying the compression working condition test sequence to the to-be-tested coil. The technical scheme of constructing and applying the compression working condition test sequence from the real working condition is adopted, the technical effect of truly reproducing the composite working state of the coil in the test stage is achieved, and compared with the technical scheme of testing under the static or low-frequency condition in the prior art, the defect that the test result is seriously disjointed from the actual service performance is overcome.

The application provides a rock mass discontinuity three-dimensional Gaussian mixture model statistical classification and recognition method, and belongs to the field of mining engineering. On the basis of drawing the density mapping of the three-dimensional polar projection of the rock mass, the application utilizes a fast decision density peak value algorithm to automatically decide the optimal grouping number of the rock mass discontinuity. Then, the application utilizes a three-dimensional Gaussian mixture model to statistically analyze the size of the posterior probability of each point belonging to each three-dimensional Gaussian, and through a soft clustering mode, each point is distributed to the three-dimensional Gaussian mixture model with the maximum posterior probability, so as to realize the optimal soft clustering recognition of the number of the three-dimensional Gaussian mixture model, that is, the optimal grouping number, thereby realizing the rock mass discontinuity three-dimensional Gaussian mixture model statistical classification and recognition. The application is simple to realize, has a remarkable effect, and meets the application requirements.

The invention outlined here is an innovative approach to increasing the accuracy of survey responses by combining novel classification of inaccurate survey responses as noise with state-of-the-art statistical techniques. This invention innovatively combines 1) a novel method to quantify inaccurate survey responses, with 2) statistical distribution assessment of variability to quantify bounds of classification, and 3) statistical classification of responses into at least 3 categories of inaccuracy. This invention is implemented by a computer and will generate estimates of variability, which are subsequently utilized in classification. These estimates can be effectively used to classify field responses as either signal, noise, or indeterminate and be used to probabilistically adjust numerical calculations of field response in surveys.

The invention belongs to the technical field of business modeling, and particularly discloses a rapid business modeling and skeleton code generation method and system based on a manufacturing domain, and the method comprises the steps: carrying out the statistical classification of all stages, roles and activities in a business process, and dividing primary and secondary business fields; dividing a business field into a plurality of business sub-fields, and realizing process modeling; the method comprises the following steps: acquiring data elements and business entities involved in each business node, carrying out business modeling, drawing a logic data model, converting the logic data model of a business object and attributes thereof into a physical data model, carrying out service modeling according to a business function of each business node, and determining a required API service; defining request and response formats for each API service; and determining a software architecture mode based on the API service, and generating a business skeleton code. By adopting the technical scheme, common internet terms are used, the learning cost and the implementation difficulty are greatly reduced, and the skeleton code is automatically generated based on API management.

This invention relates to the field of data recognition technology, specifically to a data classification and management system based on core features of data statistical comparison. It includes a contract scanning unit, an image digitization unit, a text feature recognition unit, a statistical classification unit, and a fusion storage unit. The invention uses the contract scanning unit to scan paper contracts into image files, facilitating preprocessing of these image files before the text feature recognition unit identifies the text and extracts the contract data. This avoids the time-consuming and laborious process of manual data entry by staff, improving convenience. Then, the statistical classification unit categorizes the contract data, creating datasets that correspond one-to-one with each contract. Finally, the fusion storage unit stores these datasets in a mesh storage device, enabling quick and easy retrieval of company data based on customers and categories, further enhancing management convenience.

The invention relates to the field of natural language processing, and particularly provides an ICD coding prediction method and device based on a large language model, and the method comprises the following steps: S1, an ICD knowledge base module organizes the content in international statistical classification according to a tree structure; s2, performing disease mode prediction; s3, constructing a specific diagnosis prediction module; s4, an accurate diagnosis prediction module; s5, performing coding check; and S6, carrying out primary and secondary identification. Compared with the prior art, coding prediction can become rational and evidence, great convenience is brought to manual auditing, and the diagnosis and treatment process is more efficient.

The invention relates to a method and a system for detecting a flat region in a lunar permanent shadow region. The method comprises the following steps: acquiring a to-be-detected SAR image and DEM data; inputting the SAR image into a target detection algorithm model, and obtaining detection results of a meteorite crater region and a region in which the meteorite crater is not detected; performing Kmeans detection on the SAR image to obtain a statistical classification result that each pixel point in the SAR image is a rough area or a non-rough area; converting the DEM data into an inclination angle graph, and determining classification results of a flat region and a slope region in the inclination angle graph through a threshold extraction algorithm; and registering and fusing the detection results of the meteorite crater region and the region without the meteorite crater, the statistical classification results of the rough region and the non-rough region and the classification results of the flat region and the slope region to obtain a final flat region detection result. Compared with the prior art, the method has the advantages of being capable of rapidly, accurately and safely detecting the flat area in the permanent shadow area of the moon and the like.

This invention concerns an innovative method and device for recording and analyzing road surface smoothness, implemented via a telematics device-main unit (1) installed on a transport vehicle [e.g., truck, bus, etc.] (7) performing independent tasks, such as waste collection, goods transportation, passenger transport, or surveillance. The telematics device - main unit (1) records acceleration [vertical - longitudinal - lateral] (8) and angular velocity through the integrated three-axis accelerometer (2), gyroscope (3), and GPS receiver. Subsequently, the processor-controller (6) correlates the data with geographic coordinates and transmits them via the cellular communication module (5) to the cloud-based computing platform (9), where data is visualized on a GIS map with color-coded smoothness indicators, alert algorithm output, and administrative classification [Municipality, Region, Highway] (10). The device automatically calculates the vertical axis when the vehicle is stationary, regardless of the mounting orientation. It further applies corrections in cases of high longitudinal or lateral acceleration by filtering out vibrations unrelated to road surface degradation. The platform displays the latest measurements for each point, maintains historical records, and activates alerts only when new wear or damage is detected. The data is categorized into geographic units (municipalities, regions, highways), enabling the production of statistical and comparative reports per area, thereby supporting evidence-based planning and prioritization of maintenance interventions.

The present invention relates to the field of data processing technology, and provides a data enhancement method, apparatus, device, and storage medium. The data enhancement method includes: obtaining a data set to be enhanced and a sample database; the data set to be enhanced includes point cloud data and label information of M types of objects, and the sample database includes point cloud data and label information of N types of objects; determining the proportion of each type of object in the N types of objects under multiple statistical categories based on the sample database; wherein the multiple statistical categories are determined based on parameters in the label information; determining the target objects and the target number of target objects to be added to the data set to be enhanced; obtaining target data from the sample database based on the proportion of the target objects under multiple statistical categories, the target data including point cloud data and label information of a target number of target objects. The data enhancement method provided by the present invention makes the enhanced data distribution more balanced, thereby improving the data enhancement effect.

The embodiment of the invention discloses an intelligent statistical analysis method and device for traditional Chinese medicine clinical research, equipment and a medium, and the method comprises the steps: cleaning traditional Chinese medicine clinical research data, and obtaining normalized clinical research data; performing identification processing on the standardized clinical research data to obtain standardized clinical research data; classifying and labeling the standardized clinical research data to obtain labeled clinical research data; inputting the labeled clinical research data into a statistical classification model for processing to obtain a multi-task statistical analysis result; performing code generation and calling processing based on the multi-task statistical analysis result to obtain an executed statistical analysis code; performing multi-task cooperative processing on the executed statistical analysis result to obtain an integrated cooperative analysis result; and performing visual display processing on the integrated collaborative analysis result through a traditional Chinese medicine clinical research statistical data interaction platform.

The invention provides a DoS attack-oriented cloud control system modeling and event triggering prediction method under an interval type-2 T-S fuzzy model framework, and the method specifically comprises the steps: S1, monitoring the conditions of complex information transmission and the like of a cloud end and a physical end in a cloud control network system framework; establishing a cloud control system difference equation taking the interval type-2 T-S fuzzy model as a description language; s2, performing statistics, classification and linearization on the state of the fuzzy cloud control system by adopting a type-2 fuzzy set theory and a time division segmentation analysis method, and giving an attack model and regional parameters; s3, designing a dynamic event triggering mechanism with an exponential decay characteristic, reducing the data transmission quantity, and improving the bandwidth and energy utilization rate; s4, proposing an event type fuzzy prediction controller, and establishing a prediction model, a performance index and a constraint condition; s5, constructing a switching control law and a fuzzy optimization algorithm, and obtaining optimal parameters; and S6, the prediction stability of the interval type-2 T-S fuzzy system is ensured, and time-zone-division modeling and on-demand control are realized.

The present invention discloses a data table association relationship classification method and device based on graph search technology. The method relies on the statistical classification results of data table fields, constructs an association relationship set of association data tables based on a directed graph, and displays the association relationships between association data tables in a visual graph. The method uses graph search technology to construct an association relationship set of association data tables, and based on the set main table, constructs an association relationship classification for association data table aggregation. The method supports the inspection of the full connectivity of the data table and realizes the automatic generation of association relationships of association data tables. At the same time, the method can automatically locate the main table position and determine the connection fields of the association data table aggregation. The main innovation of the method is to use a directed graph to represent the association relationships between association data tables, and to use a graph search algorithm to find the association relationships between association data tables.

The invention provides a method for identifying and eliminating pollutants during quantification of microbe cfDNA in peripheral blood, which comprises the following steps: S1, screening common bacteria of peripheral blood and esophageal-gastric junction tumor, and extracting bacterial nucleic acid; s2, designing a corresponding primer probe combination according to the specific nucleic acid sequence of the bacteria, and carrying out multiple amplification on the bacterial nucleic acid by utilizing fluorescent quantitative PCR (Polymerase Chain Reaction); s3, carrying out statistical classification on the amplified specific nucleic acid sequence in the step S2 by utilizing R statistical software decontam to obtain a statistical classification program; the method comprises the following steps: adding artificially synthesized exogenous DNA during preparation of a sample of microorganism cfDNA in peripheral blood, and setting an identification sample confusion method; and S4, identifying and eliminating pollutants through a statistical classification program, and quantifying the microbe cfDNA in the peripheral blood. The method disclosed by the invention can be used for accurately, quickly and conveniently detecting the bacterial nucleic acid in the peripheral blood, identifying and eliminating polluted samples at the same time, and avoiding false positive during detection.

This invention discloses a morphology-based method and system for classifying pores in tight reservoir porous media, comprising: acquiring microstructural images of rock tight reservoir samples using field emission scanning electron microscopy; labeling the SEM images with two types of samples, pores and matrix / background, as training sets, and training a segmentation model for rock samples; segmenting the sample SEM images using the trained segmentation model to generate binary images that identify the background and pores; automatically measuring pore morphology parameters based on the binary images to obtain pore roundness and aspect ratio; classifying pore shapes according to preset quantitative classification thresholds for pore roundness and aspect ratio, and outputting statistical classification results; this invention achieves a systematic and objective characterization of pore cross-sectional shape, transforming the traditional subjective judgment-dependent pore morphology description into a quantifiable and comparable structural index, solving the problem that traditional pore morphology classification relies on subjective judgment and lacks unified quantitative standards.

The invention provides a data acquisition method, device and equipment, a medium and a computer product, and relates to the technical field of flow acquisition. The method comprises the steps of grouping to-be-collected data according to statistical information obtained after statistical classification is performed according to preset elements, and obtaining at least one piece of feature stream data and analysis windows corresponding to the feature stream data; wherein each group of to-be-collected data corresponds to one feature stream data; meanwhile, each analysis window is made to move from the first moving time node, a first step length is obtained according to at least one piece of feature flow data, data of each piece of feature flow data in a time period corresponding to the analysis window are collected, and collected data are obtained. According to the method, the analysis window is moved according to the changed step length of the feature flow data, and data acquisition is performed on different feature flow data in the time period corresponding to the corresponding analysis window, so that the situation that the system burden is increased due to a large amount of redundant data caused by full-amount acquisition can be avoided, and acquisition and analysis resources can be dynamically allocated for different flows.

The present technology relates to image signal processing. One aspect of the present technology involves analyzing reference imagery (e.g., depicting a conveyor belt) gathered by a camera system to determine which parts of an image frame offer higher probabilities of—relative to other image parts—containing content items. Image blocks representing localized content items can be provided to a signal detector to decode a signal.

The present application relates to the field of wheat image analysis, and specifically discloses a kind of wintering period wheat seedling distinguishing method based on remote sensing image data, for solving the existing wintering period winter wheat seedling identification method more depends on artificial investigation or single color, vegetation index threshold, difficult to fuse multidimensional key parameters, and easily be disturbed by shadow, bare soil and frost residue, leading to strong and weak seedling recognition unstable, partition is not continuous, risk positioning is not accurate Problem, including sample plot layout and remote sensing data acquisition, remote sensing image radiation geometry correction and pixel level registration, vegetation mask extraction and single plant object segmentation, single plant multidimensional characterization extraction and strong seedling potential value construction and sample plot statistical classification and global extrapolation output;The present application constructs seedling potential value and classifies extrapolation by multi-source remote sensing single plant segmentation and C, V, H, L, B fusion, and stably outputs global strong seedling partition and vigorous seedling partition, weak seedling risk area.

Methods, apparatus, systems and articles of manufacture to determine demographic classifications for census level impression counts and unique audience sizes are disclosed. In an example, the apparatus includes media tag format circuitry to generate a reformatted media tag corresponding to an impression request. The example apparatus also includes model execution circuitry to execute a machine learning model based on the reformatted media tag to generate outputs, the outputs including at least a value representative of a probability of an occurrence of a demographic classification. The example apparatus further includes audience counting circuitry to assign an identification of ones of audience members in a group to the demographic classification based at least on the outputs.

The invention belongs to the technical field of intelligent detection, and provides an anti-vibration intelligent detection system and method for precision equipment, and the system comprises a data collection module, a data communication module and an intelligent processing module. The data acquisition module is used for acquiring vibration data and environment data in the operation environment of the precision equipment, and sending the data to the intelligent processing module through the data communication module by using an encryption channel; the intelligent processing module is used for carrying out space-time alignment on the vibration data and the environment data, constructing a fusion data set containing environment correlation features, establishing a statistical classification model and a deep learning model, respectively inputting the fusion data set into the statistical classification model and the deep learning model to obtain a first anomaly detection result and a second anomaly detection result, and outputting the first anomaly detection result and the second anomaly detection result; and fusing the first abnormal detection result and the second abnormal detection result to obtain a vibration detection result. According to the scheme, the accuracy, the high efficiency and the convenience of a vibration detection link are improved according to the vibration data and the environment data and in combination with various analysis models.

The invention provides a power distribution network digital intelligent operation and maintenance comprehensive treatment method and system, and relates to the related technical field of computer data processing, and the method comprises the steps: S1, obtaining the tower information collected by an unmanned aerial vehicle during the line inspection, carrying out the defect mining and analysis of the tower information, and carrying out the detection of the tower information; integrating the defect information obtained by analysis with the tower information to form a tower information acquisition table; s2, selecting and extracting corresponding data from the tower information acquisition table, and visualizing the data on a GIS map containing a line obtained through the tower information acquisition table through a preset mark so as to generate an inspection condition diagram; s3, performing statistical classification on the defect information obtained by analysis, and generating a defect maintenance management summary sheet through big data platform matching; and S4, performing fault analysis through an AI model and big data platform analysis in combination with an inspection condition diagram and a defect maintenance management summary sheet to obtain a maintenance task with priority ranking, and providing a corresponding solution.