8 results about "Abnormal distribution" patented technology

The invention relates to the technical field of leprosy nerve injury grading evaluation, in particular to a leprosy nerve injury intelligent grading evaluation method based on high-frequency ultrasound multi-parameter fusion, which comprises the following steps: acquiring multiple frames of nerve images by adopting high-frequency ultrasound, analyzing a gray scale trend to construct a closed path, and screening out frames with abnormal distribution to generate a consistent sequence; and extracting a region coverage range, a brightness form and a texture arrangement feature, generating a change description according to a feature difference, matching a preset grading rule according to a description type, judging a damage level, and obtaining an intelligent grading evaluation result of the leprosy nerve injury. According to the method, the neural region is adaptively defined by using the image gray scale change, experience dependence is reduced, the consistency image sequence is screened, the spatial homology and stability of feature comparison are ensured, the single index interference is described and weakened based on the overall trend organization change, and the continuity and distinction degree of the evaluation result under the complex background are ensured. And the applicability of the grading conclusion in the dynamic observation scene is enhanced.

The invention relates to the technical field of wagon operation monitoring, in particular to an intelligent detection method for an unbalance loading state in the operation process of a railway wagon, which comprises the following steps of: acquiring an instantaneous load response track of each side axle box relative to a track in the operation process to form a left-right path differential response sequence; the method is used for reflecting wheel-rail contact abnormal distribution characteristics caused by unbalance loading. Inputting the left and right path differential response sequence into an abnormal disturbance code construction module, marking key load disturbance points by using a multi-window trajectory disturbance detection mechanism, and generating a corresponding adaptive disturbance code sequence; and outputting the unbalance loading state type of the current vehicle and the corresponding offset side direction. According to the method, the consistency judgment of the disturbance form and the physical configuration is realized, the unbalance loading grade and direction two-dimensional diagnosis result is finally output, and compared with a traditional threshold value method, the method has the advantages of low false alarm rate, high adaptability and good interpretability, and is suitable for various railway wagon marshalling structures and operation conditions.

The invention discloses an alarm signal management and control method, device and equipment and a storage medium, and relates to the technical field of information processing, and the method comprises the steps: obtaining a to-be-sent alarm signal; determining an abnormality judgment condition according to the index type corresponding to the alarm signal to be sent; determining anomaly distribution and a historical baseline value according to the anomaly judgment condition, the index type and the generation time of the alarm signal to be sent; and performing signal control on the alarm signal to be sent based on the abnormal distribution and the historical baseline value. According to the invention, by customizing the abnormality judgment condition according to the index type and combining the historical baseline value comparison, identification and filtering of redundant alarms of'frequent historical abnormality but no essential risk at present ', the interference of invalid notifications on operation and maintenance personnel is reduced, and only alarms of'significant difference between current abnormality and historical rules' are sent, so that the operation and maintenance efficiency is improved. The operation and maintenance personnel are ensured to focus on system risks really needing to be processed, and the situation that important problems caused by alarm flooding are ignored is avoided.

The application discloses a kind of equipment failure early warning detection methods based on digital radar holography scanning, it is related to the technical field of failure early warning, including, the abnormal propagation direction and parameter relationship transmission characteristics between the analysis secondary abnormal distribution set and primary abnormal distribution set, form abnormal evolution chain, extract abnormal structure mode of abnormal evolution chain by structure analysis, and execute fault fitting processing, generate multidimensional fault fitting set;Estimate the abnormal evolution trend of multidimensional fault fitting set, generate operation risk index set, execute abnormal evolution consistency determination to operation risk index set, generate failure early warning result set.The application deduces and constructs abnormal evolution chain by counterfactual state, realizes the explicit modeling to equipment "normal operation relationship mode", makes abnormal change from "discrete identification" to "structured evolution analysis", improves failure early warning foresight and reliability.

The invention relates to the field of stamping die control, and particularly discloses an intelligent control system and method for a precision hardware stamping die, and the method comprises the steps: drawing and preprocessing a force-stroke curve based on the stamping force and stroke data of an upper die measuring point; generating a standard force-stroke curve and a reference tolerance zone thereof based on the stable production data; calculating deviations of typical characteristics such as the peak force, the stroke position reaching the peak force, the curve slope of a plastic deformation area and the force value of a specific stroke section between each stroke-frequency curve and the standard curve; according to the characteristic deviation and the time sequence change trend, early warning is triggered, and an abnormal curve is recognized; and by analyzing the deformation consistency, the abnormal distribution range and the change mode of the abnormal curve, judging whether the abnormal source is a material problem or a mold problem. According to the method, force-stroke dynamic information is fused, early trend early warning and root diagnosis are realized, and the problems of single monitoring dimension, early warning lag and diagnosis deficiency in the prior art are solved.

The present application relates to the technical field of leprosy nerve injury grading evaluation, in particular to a leprosy nerve injury intelligent grading evaluation method based on high-frequency ultrasound multi-parameter fusion, which adopts high-frequency ultrasound to obtain multiple frames of nerve images, analyzes gray scale trends to construct a closed path, screens out abnormal distribution frames to generate a consistent sequence, extracts regional coverage, brightness morphology and texture arrangement features, generates change descriptions by comparing feature differences, matches preset grading rules according to description types, determines the injury level, and obtains the leprosy nerve injury intelligent grading evaluation result. The present application adaptively defines the nerve region by using the gray scale change of the image, reduces the experience dependence, ensures the spatial homology and stability of the feature comparison by screening the consistent image sequence, weakens the single index interference based on the overall trend of the change description, guarantees the coherence and distinguishability of the evaluation result under the complex background, and enhances the applicability of the grading conclusion in the dynamic observation scene.

The invention relates to a method and system for checking whether zero calibration of a spiral feeder is accurate or not. The method comprises the steps that the spiral rotating speed and the corresponding discharging amount of the spiral feeder in the normal working period are obtained; performing data cleaning on the spiral rotation speed of the spiral feeder and the corresponding blanking amount data to obtain data after data cleaning; carrying out normality verification and descriptive statistics on the spiral rotation speed in the same blanking amount interval; when the statistical result of the data is in abnormal distribution, data cleaning is carried out again; when the statistical result of the data is normal distribution, determining a normal operation range of the spiral rotation speed by using a probability density function of the normal distribution; and when the spiral rotation speed of the target spiral feeder continuously exceeds the normal operation range, an abnormal calibration alarm is given out. According to the method, the normal operation range of the spiral rotation speed is defined according to the statistical rule (normal distribution interval), and the operation reliability and the production stability of the spiral feeder can be improved.

An apparatus for evaluating the performance of an anomaly detection model, according to one embodiment of the present invention, comprises: a distribution calculation unit which uses an anomaly detection model so as to generate a normal distribution for preset normal learning data and which generates an abnormal distribution for preset abnormal learning data; an index value calculation unit which calculates a first index value for the normal distribution and the abnormal distribution on the basis of normality for the normal distribution and the abnormal distribution, and which calculates a second index value for areas of the normal distribution and the abnormal distribution; and a performance evaluation unit for evaluating the performance of the anomaly detection model on the basis of the first index value and the second index value.