17 results about "Abnormal distribution" patented technology

The invention provides a physical examination data exception association analysis method and system based on a knowledge graph, and relates to the technical field of exception association analysis, and the method comprises the steps: determining all physical examination items of a target physical examination center; aiming at a plurality of selectable physical examination indexes, executing abnormal generality association and generality confidence degree analysis among the indexes, and establishing an abnormal generality association map; receiving a physical examination report; executing abnormal generality association verification based on the abnormal generality association graph; performing abnormal distribution characteristic analysis in a preset period for the plurality of selectable physical examination indexes, and performing distribution abnormality verification on the physical examination report; and performing joint abnormity verification according to the distribution abnormity verification result and the generality verification result, then performing abnormal physical examination item positioning, and sending the abnormal physical examination item positioning to a target physical examination center for reminding. The technical problem that potential health risks are difficult to recognize in time and the accuracy of physical examination results is affected in the prior art can be solved, and the technical effect of improving the accuracy of physical examination anomaly detection is achieved.

The invention provides a value-added service abnormal distribution monitoring method and device, equipment and a medium, and the method comprises the steps: obtaining the request information of a target user for a target value-added service, and generating a target user portrait of the target user according to the request information; determining a first matching result of the portrait of the target user and a preset portrait library; when the first matching result indicates that the abnormal user portrait similar to the target user portrait does not exist in the preset portrait library, determining a service request attribute of the target user; determining a second matching result of the service request attribute and a preset early warning rule; and generating early warning information of abnormal distribution of the value-added service according to the second matching result. Therefore, automatic monitoring and early warning of abnormal distribution of the value-added service can be realized, so that the timeliness and accuracy of processing abnormal distribution of the value-added service are effectively improved.

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 application discloses a network abnormal data security early warning evaluation processing system, comprising a risk feature acquisition module, a first profiling module, a second profiling module and a distribution correlation module; the first profiling module can evaluate the abnormal risk of traffic through a network neural model to obtain a first risk profile of encrypted traffic; the second profiling module can obtain the frequency abnormality of distribution detection features through global frequency abnormal distribution and local frequency abnormal distribution of the distribution detection features; the distribution correlation module can obtain distribution correlation features associated with the distribution detection features of the frequency abnormality, evaluate the degree of frequency abnormality in combination with the deviation degree of the distribution correlation features, and obtain a second risk profile of encrypted traffic corresponding to the distribution detection features; and the first risk profile is adjusted according to the obtained second risk profile. The application increases the monitoring accuracy of traffic risk and improves network security.

The application discloses a kind of detection method and system of inductor, define the abnormal part of the inductor at different assembly stages based on the identification of detection image of different assembly stages;Based on a plurality of abnormal parts and the corresponding position of inductor, construct the abnormal set of the inductor, and construct the abnormal distribution map of inductor according to the abnormal set and the factory image of inductor after production is completed;Further, according to the abnormal distribution map of inductor and the production batch of inductor, determine the process to be optimized of the production batch;For the abnormal problem corresponding to the process to be optimized, and output the corresponding optimization strategy for abnormal problem;According to the process to be optimized and the corresponding optimization strategy, output the corresponding optimization report, define the optimization degree of abnormal part in inductor based on each optimization report and the abnormal distribution map of inductor, ensure the accuracy of the optimization degree of abnormal part.

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 belongs to the field of image defect intelligent identification, and more particularly to a screen defect detection method and system based on a cooperative neural network. A trained feature relationship discrimination network can be used to obtain target defect distribution feature maps corresponding to each defect type according to target fusion defect feature maps and target defect features corresponding to each defect type, so that the distribution of each type of defect in the target feature map can be preliminarily obtained using a small number of samples of each type of defect image, and the generalization ability of detection and recognition is improved. The cosine similarity between each block of the target feature map and the defect-free feature map is calculated to obtain an abnormal distribution feature map that can accurately reflect the distribution of each abnormal block in the target feature map. Then, the overlap of the anchor boxes in the abnormal distribution feature map and the target defect distribution feature map corresponding to each defect type can be used to accurately obtain the actual detection and recognition result of the target feature map for multiple defect types in a double-channel joint determination manner.

The embodiment of the application provides a kind of merchant early warning monitoring method, system and terminal equipment based on multidimensional data fusion, belong to data fusion technical field.The method comprises: obtaining the historical operation data and user evaluation text of historical merchant, and the first abnormal data and the first abnormal distribution are obtained by carrying out abnormal detection to historical operation data;First description text is determined according to first abnormal data and first abnormal distribution;Second abnormal data and second abnormal distribution are obtained by clustering analysis to user evaluation text;Second description text is determined according to second abnormal data and second abnormal distribution;The historical correlation under historical operation data and user evaluation text is determined according to first description text and second description text;Risk prediction model is established according to historical operation data, user evaluation text in combination with historical correlation;According to risk prediction model, current operation data and current evaluation text are combined, and target merchant is early warning monitored to obtain target monitoring result.

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 application discloses a power plant thermal system multi-parameter correlation anomaly detection and positioning method, and belongs to the technical field of industrial process state monitoring and fault diagnosis. By acquiring historical normal operation data, a multi-source fusion graph is constructed according to a physical connection relationship and statistical correlation; a graph neural network is trained by using normal working condition data to obtain a health benchmark model. Real-time data are acquired, input into the health benchmark model to calculate reconstruction error, and it is determined whether the system is abnormal. When it is determined that the system is abnormal, a real-time fine graph representing a current abnormal state is subjected to multi-stage graph coarsening, coarse graph sequences of different resolutions are generated, abnormal distribution is analyzed layer by layer to position a fault starting node from coarse to fine, and in combination with the correlation change characteristics of the fault starting node and neighbor nodes, the fault type is determined. The application solves the problem that multi-parameter correlation anomalies are difficult to quickly and accurately position.

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 discloses a novel electric power system safety prediction and early warning method based on AI, and relates to the technical field of electronic power. Obtaining a contribution value range of the power abnormal condition to the action index associated with the power abnormal condition when the power abnormal condition exists, and taking the contribution value range as an abnormal contribution range; forming a weight coefficient of the power abnormal condition relative to the associated action index; when the real-time data of the action index exceeds the normal value range of the action index, taking the action index as an abnormal action index; obtaining an exception distribution value of the power exception condition; obtaining an abnormal prediction distribution value of the power abnormal condition; and if yes, giving out early warning. By forming the weight coefficient of the power abnormal condition for the action index, performing abnormal decomposition on the real-time data of the abnormal action index and performing joint distribution verification on the abnormal prediction distribution value of the power abnormal condition, the accuracy of abnormal prediction can be improved, and then targeted early restoration can be performed on the abnormality.

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.

The invention provides a coal mine equipment abnormal operation mode detection and identification method comprising the following steps: collecting multi-dimensional operation data of each coal mine equipment in a preset period to establish an operation data set; abnormal distribution detection is carried out on the operation data set, and when it is detected that an abnormal data set exists in the operation data set, according to the distribution difference and similarity between the current data of each coal mine device in the abnormal data set and the reference current data in the normal operation time period, the current data in the abnormal data set are acquired; the method comprises the following steps: respectively identifying whether each coal mine device belongs to an abnormal operation mode of external current manufacturing startup illusion, repeated analog current accessing to forgery startup and device idling according to the data of each coal mine device, the service effect of each coal mine device in a normal operation time period and the startup service state of each coal mine device. Therefore, by collecting the multi-dimensional operation data of the coal mine equipment, abnormal distribution detection and abnormal operation mode analysis are carried out, and accurate, efficient and traceable detection and identification of the abnormal operation mode of the coal mine equipment are realized.

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.