CN116881834A - Stage load monitoring and early warning method based on stage deformation analysis - Google Patents
Stage load monitoring and early warning method based on stage deformation analysis Download PDFInfo
- Publication number
- CN116881834A CN116881834A CN202311154197.XA CN202311154197A CN116881834A CN 116881834 A CN116881834 A CN 116881834A CN 202311154197 A CN202311154197 A CN 202311154197A CN 116881834 A CN116881834 A CN 116881834A
- Authority
- CN
- China
- Prior art keywords
- stage
- data
- stress
- points
- early warning
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 53
- 238000004458 analytical method Methods 0.000 title claims abstract description 40
- 238000012544 monitoring process Methods 0.000 title claims abstract description 26
- 230000002159 abnormal effect Effects 0.000 claims abstract description 36
- 238000007781 pre-processing Methods 0.000 claims abstract description 9
- 230000005856 abnormality Effects 0.000 claims abstract description 5
- 238000012216 screening Methods 0.000 claims abstract description 4
- 238000004422 calculation algorithm Methods 0.000 claims description 44
- 238000005259 measurement Methods 0.000 claims description 34
- 238000009826 distribution Methods 0.000 claims description 23
- 238000011156 evaluation Methods 0.000 claims description 18
- 238000003064 k means clustering Methods 0.000 claims description 10
- 238000013450 outlier detection Methods 0.000 claims description 10
- 239000013598 vector Substances 0.000 claims description 9
- 238000012549 training Methods 0.000 claims description 8
- 206010000117 Abnormal behaviour Diseases 0.000 claims description 6
- 238000004519 manufacturing process Methods 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 6
- 238000009412 basement excavation Methods 0.000 claims description 5
- 238000007621 cluster analysis Methods 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 5
- 230000011218 segmentation Effects 0.000 claims description 5
- 238000012360 testing method Methods 0.000 claims description 5
- 238000012423 maintenance Methods 0.000 claims description 4
- 238000001914 filtration Methods 0.000 claims description 3
- 238000009499 grossing Methods 0.000 claims description 3
- 238000007689 inspection Methods 0.000 claims description 3
- 238000005304 joining Methods 0.000 claims description 3
- 238000012847 principal component analysis method Methods 0.000 claims description 3
- 230000001629 suppression Effects 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 2
- 238000005065 mining Methods 0.000 claims 1
- 230000000694 effects Effects 0.000 description 5
- 238000006073 displacement reaction Methods 0.000 description 4
- 238000001514 detection method Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 230000035945 sensitivity Effects 0.000 description 2
- 230000001960 triggered effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L5/00—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/10—Pre-processing; Data cleansing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/211—Selection of the most significant subset of features
- G06F18/2113—Selection of the most significant subset of features by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B31/00—Predictive alarm systems characterised by extrapolation or other computation using updated historic data
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Emergency Management (AREA)
- Computing Systems (AREA)
- Business, Economics & Management (AREA)
- Probability & Statistics with Applications (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The invention discloses a stage load monitoring and early warning method based on stage deformation analysis, which relates to the technical field of stage safety, and comprises the following steps: the stress of the key structure position of the stage is monitored in real time by utilizing a plurality of stress sensors; and acquiring stage stress data monitored in real time, preprocessing, extracting features of the preprocessed data, analyzing the feature importance of the extracted stage stress features, and screening out the importance features with the greatest influence on stage load abnormality early warning judgment. According to the invention, through preprocessing the data, noise, abnormal values or other irrelevant information in the data can be removed, so that the follow-up analysis is based on high-quality data, and through characteristic importance analysis, the characteristic with the most influence on stage load abnormal early warning judgment can be identified, thereby providing support for early warning.
Description
Technical Field
The invention relates to the technical field of stage safety, in particular to a stage load monitoring and early warning method based on stage deformation analysis.
Background
At present, along with the progress of social culture industry, more and more small performances show burst situations, the performance places are often random, such as school playgrounds, community squares and commercial street sides, so the demand of movable stages is very considerable, along with the increasing richness of cultural life, the requirements of people on stage effects in performance activities are higher and higher, and modern various large-scale performance places and the like are provided, so that the performance art has unusual special effects, various types of stage mechanical equipment are installed, and the effects of drawing eyes and getting in the mind in the stage performances of the scenario are played.
The traditional movable stage is generally of a stepped structure formed by multiple layers of steps, each layer is provided with a plurality of folding seats for audiences to sit, the bottom of the movable stage is provided with movable rollers, the movable stage can move according to the arrangement requirements of places, the movable stage is generally designed into a drawer type structure for saving storage space and facilitating transportation, and the bottom of the movable stage is provided with motor rollers, so that the stage can be unfolded layer by layer. The movable stage often adopts a modularized design concept, and the stage is split into a plurality of modules capable of moving independently, so that the requirements of quick construction and transportation can be met, the integration of the stage is sacrificed, and the rationality of stage load distribution needs to be considered after repeated construction.
Traditional movable stages are inconvenient to monitor and early warn on stage loads, have large potential safety hazards, and small deformation or displacement may not be easily detected, and particularly under the influence of noise or other interference factors, the potential risks may be ignored, and the risk of casualties caused by collapse of the movable stage may be caused.
For the problems in the related art, no effective solution has been proposed at present.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a stage load monitoring and early warning method based on stage deformation analysis, which solves the problems that the prior movable stage is basically not provided with a related monitoring and early warning system in the prior art, has larger potential safety hazards, small deformation or displacement can not be easily detected, and particularly under the influence of noise or other interference factors, the potential risks can be ignored, and the risk of casualties caused by collapse of the movable stage can be caused.
In order to achieve the above purpose, the invention is realized by the following technical scheme:
a stage load monitoring and early warning method based on stage deformation analysis comprises the following steps:
S1, monitoring stress of a key structure position of a stage in real time by using a plurality of stress sensors;
s2, acquiring stage stress data monitored in real time, preprocessing, extracting features of the preprocessed data, analyzing the feature importance of the extracted stage stress features, and screening out importance features with the greatest influence on stage load abnormality early warning judgment;
s3, based on the screened importance characteristics, performing cluster analysis on the stage by utilizing a stage deformation quantity analysis method, marking a stage area with abnormal stress change as a potential problem area, and performing segmentation processing on the potential problem area to determine a measurement target;
s4, accurately detecting and positioning edge points of different areas on the segmented contour by using a caliper tool method, fitting the edge points to obtain size parameters of key structure positions of the stage, and storing the size parameters into a database, wherein the size parameters are used as stress distribution and change trend of the different areas;
s5, carrying out deep excavation and analysis on the obtained size parameters of the key structure position of the stage and preset standard size parameters through a fuzzy isolated forest algorithm, judging whether the stage is at risk of overload or fatigue damage, and triggering stage overload or fatigue damage early warning if the stage is at risk;
And S6, performing detailed inspection and maintenance on the stage area triggering the early warning, and eliminating potential safety hazards.
Further, the step of obtaining stage stress data monitored in real time, preprocessing the stage stress data, and extracting features of the preprocessed data comprises the following steps:
s21, collecting repeated data, missing values and abnormal values of stage stress data obtained by each stress sensor, and denoising, filtering and smoothing the repeated data, the missing values and the abnormal values;
s22, connecting unprocessed data rows in stage stress data collected by each stress sensor to generate a new data table, correlating different data tables through external key values to generate a complete data table, and obtaining an accurate data set;
s23, determining external key relations among different data sets, connecting data in different data tables with each other according to requirements, creating a new data table, and associating through specified external key values;
s24, connecting the data tables to be joined together through the JOIN operators in the SQL sentences, and ensuring that the integrity constraint of the data is satisfied when the joining is carried out;
s25, after connection is completed, inserting test data to check whether a connection result is correct, ensuring that the connection result can be correctly identified and associated, and obtaining accurate data of stage stress acquired by each stress sensor;
S26, fusing accurate data of the stage stress acquired by each stress sensor into the same data set by using a principal component analysis method;
s27, extracting relevant features from the fused data set to obtain feature data of stage stress, wherein the feature data at least comprise abnormal sudden increase of stress, continuous high stress, abnormal difference of stress variation and abnormal stress fluctuation;
s28, selecting a characteristic evaluation index for judging the stress abnormal behavior, and calculating the association degree between each stress data characteristic and the stress abnormal behavior based on the selected characteristic evaluation index;
and S29, sorting each stress data feature based on the calculated association degree, wherein the higher the association degree is, the more important the stress data feature is, and the stress data feature with the highest association degree is taken as an important stress feature.
Further, based on the selected importance features, performing cluster analysis on the stage by using a stage deformation amount analysis method, marking a stage area with abnormal stress change as a potential problem area, and performing segmentation processing on the potential problem area, wherein the step of determining a measurement target comprises the following steps:
s31, taking the feature quantity of the importance stress features as the quantity of clustering centers, clustering all stage areas by utilizing an improved K-Means clustering algorithm, and dividing the stage areas into a plurality of groups;
S32, extracting data points from a plurality of groups, calculating local outlier factors of the data points by using a local outlier detection algorithm, and judging whether the data points are outliers;
s33, counting the number of outliers in each group, and calculating the proportion of the outliers;
s34, if the proportion of the outliers in the group exceeds a preset threshold, judging that the group has stress variation, and marking all stage areas in the group as potential problem areas;
s35, taking the potential problem area as the overall outline of the stage, and dividing the overall outline by using an outline dividing method to determine a measurement target.
Further, the step of using the feature number of the importance stress features as the number of clustering centers, and using the improved K-Means clustering algorithm to cluster all the stage areas, and dividing the stage areas into a plurality of groups includes the following steps:
s311, calculating noise measurement indexes of the data objects for the clustered stage stress data sets;
s312, regarding the data object, if the noise measurement index is larger than a preset threshold value, taking the data object as an isolated point of the data set;
s313, deleting the isolated points or leading out the isolated points to an abnormal value list to obtain a new stage stress data set X;
S314, randomly selecting K data objects from the new stage stress data set X as initial clustering centers C1, C2, … and C k ；
S315, calculating the distance between the data object and each cluster center according to the initial cluster center, and distributing the data object to the class where the nearest cluster center is located;
s316, distributing the data objects to the nearest class, and calculating the average position of the data objects as a new clustering center;
s317, if the new cluster center is the same as the cluster center in the step S316, ending the algorithm, otherwise, replacing the old cluster center with the new cluster center, and repeating the steps S315-S316;
s318, presetting an iteration number threshold, stopping iteration when the iteration number reaches the threshold, and outputting a final clustering result.
Further, the calculation formula of the improved K-Means clustering algorithm is as follows:
wherein ,nrepresenting the number of data points in the cluster;
dexpressed as a number of data point features;
V{S，Hdenoted as noise measure;
Sirepresent the firstiClustering;
X ih expressed in clustersS i Middle (f)hFeature vectors of data points;
X jh representing all data points in the whole data set at the firsthAverage over individual features;
Hrepresented as a threshold;
jrepresenting a data point in the data 。
Further, the extracting the data points from the groups, calculating local outlier factors of the data points by using a local outlier detection algorithm, and judging whether the data points are outliers comprises the following steps:
s321, extracting all data points in the group;
s322, calculating a distance neighborhood of all data points, wherein the distance neighborhood is other data points with a certain data point nearest to the data point;
s323, calculating the reachable distances and the reachable distance ratio of all the data points relative to the distance neighborhood, wherein the reachable distance ratio is the reachable distance from the neighborhood divided by the average distance from other data points;
s324, calculating local outlier factors of all data points, wherein the local outlier factors are obtained by subtracting a preset threshold value from the distance reachable ratio;
s325, if the local outlier factor of the data point is greater than zero, judging the data point as an outlier.
Further, the step of taking the potential problem area as the overall contour of the stage, and dividing the overall contour by using a contour dividing method to determine a measurement target includes the following steps:
s351, setting fixed threshold parameters;
s352, circularly traversing points on the outline to obtain the total number M;
s353, selecting a reference point as a starting point, and connecting the starting point and the M/2 th point as adjacent bus segments;
S354, calculating the distance between the point on the contour line and the adjacent bus segment, and if the distance between the point on the contour line and the adjacent bus segment is larger than a fixed threshold parameter, connecting the maximum distance point with the starting point and the end point of the adjacent bus segment to form two new adjacent line segments I and II to replace the adjacent bus segment;
s355, continuing to iteratively calculate the distance between the contour points until all the line segment distances are smaller than a fixed threshold parameter;
s356, if the point on a certain section of the contour meets the linear equation, dividing the contour into straight lines, and if the point does not meet the linear division, sequentially comparing all adjacent line segments in the contour, and replacing the adjacent line segments by using an arc;
s357, if the maximum error of the arc approximation is smaller than the average error of the adjacent line segments, replacing the adjacent line segments with the arc, dividing the contour into the arc, and if the arc is a closed polygon, dividing the contour into the circle;
s358, determining measurement target size parameters according to the obtained parameters of the straight line, the circular arc and the circle, and taking the measurement target size parameters as stress distribution and variation trend of different areas.
Further, the method for precisely detecting and positioning edge points of different areas on the segmented contour by using a caliper tool method, fitting the edge points to obtain dimension parameters of key structure positions of the stage, storing the dimension parameters into a database, and taking the dimension parameters as stress distribution and variation trend of the different areas, wherein the method comprises the following steps:
S41, acquiring basic information of a target to be measured, wherein the basic information comprises the diameter and center coordinates of a circle and two end point coordinates of a straight line;
s42, equidistant and equal-sized measurement rectangles are generated on the straight line or the circular outline and are used for sequentially detecting the positions of the positioning edge points;
s43, determining the optimal edge point detected by the measurement rectangle;
s44, calculating and measuring gradient amplitude values and directions of pixel points in the rectangle;
s45, determining the pixel points according to the non-maximum value suppression method as the optimal edge points;
s46, fitting the optimal edge points based on a Tukey algorithm to obtain the dimension parameters of the key structure positions of the stage as stress distribution and change trend of different areas.
Further, the step of obtaining the size parameter of the key structure position of the stage as the stress distribution and the variation trend of different areas by fitting the optimal edge points based on the Tukey algorithm comprises the following steps:
s461, at the beginning of the iteration, all edge points are given the same weight, i.e. W 1 ；
S462, fitting all edge points by using a least square method to obtain a standard straight line;
s463, calculating the distance from all edge points to the straight line;
s464, if the distance from an edge point to a straight line is smaller than the preset value, the weight in the next iteration is still set to W 1 If the distance is greater than the preset value, the weight is set to W 0 ；
S465, updating the weight of each point in each iteration, and gradually eliminating outliers;
s466, repeating the steps S461-S465 until the weight values of all the points are stabilized, and obtaining the clipping factors;
s467, fitting by using a Tukey weight function according to the weight and the clipping factor to obtain a final stage stress distribution model;
s468, converting the stage stress distribution model into size parameters of key structure positions of the stage through a calibration method, and storing the size parameters into a database.
Further, the depth excavation and analysis are performed on the obtained size parameters of the key structure position of the stage and the preset standard size parameters through a fuzzy isolated forest algorithm, whether the stage is at risk of overload or fatigue damage is judged, and if the stage is at risk, stage overload or fatigue damage early warning is triggered, wherein the step of triggering comprises the following steps:
s51, acquiring the dimension parameters of the key structure positions of the stage and the preset standard dimension parameters from a database;
s52, according to the analysis of the size manufacturing requirements of the stage, determining a related factor set influencing the size manufacturing requirements, and setting corresponding evaluation levels;
S53, training the size parameters of the extracted key structure positions of the stage and preset standard size parameters by using an isolated forest algorithm, calculating abnormal scores, and judging whether the key structure positions of the stage have the risk of overload or fatigue damage or not;
s54, normalizing the abnormal score value obtained by using an isolated forest algorithm, and calculating a fuzzy set;
s55, scoring each factor on different evaluation levels through professional evaluation to form a fuzzy relation matrix;
s56, calculating the fuzzy set and the fuzzy relation matrix by using a fuzzy operator to obtain a fuzzy comprehensive evaluation result vector;
s57, according to the rank summation of the component values and the grades in the vector, obtaining the relative position of the object to be evaluated, and judging the size overload or fatigue damage condition of the key structure position of the stage;
s58, judging whether the size of the critical structure position of the stage is within an allowable tolerance range, if so, judging that the critical structure position of the stage is qualified, and if not, judging that the risk of overload or fatigue damage exists;
and S59, triggering stage overload or fatigue damage early warning if the risk of overload or fatigue damage exists.
The beneficial effects of the invention are as follows:
1. according to the invention, through preprocessing the data, noise, abnormal values or other irrelevant information in the data can be removed, so that the follow-up analysis is based on high-quality data, and through characteristic importance analysis, the characteristic with the most influence on stage load abnormality early warning judgment can be identified, the attention is focused on the most critical information, and the interference caused by a large amount of irrelevant or secondary data is avoided, thereby not only providing support for early warning, but also providing valuable insight for stage design, maintenance and management, and helping related personnel to know the key stress points and potential risk areas of the stage.
2. According to the invention, by utilizing the local outlier detection algorithm, the data points which are abnormal in the local neighborhood of the stage can be identified, so that the tiny deformation or displacement which is possibly ignored can be detected, and the accuracy, sensitivity and robustness of the stage load monitoring and early warning method based on stage deformation analysis can be enhanced, thereby ensuring the safe and stable operation of the stage.
3. According to the invention, the edge points are detected through the caliper tool, so that the edge point with the largest gradient amplitude perpendicular to the rectangle is detected by generating the measurement rectangles with the same size and distance, the optimal edge points are sequentially obtained by using the set number of the measurement rectangles, and finally, the edge contour can be obtained more accurately by fitting all the detected edge points, so that the traversing time is reduced, and the detection efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a stage load monitoring and early warning method based on stage deformation analysis according to an embodiment of the present invention.
Description of the embodiments
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
In the description of the present invention, unless otherwise indicated, the meaning of "a plurality" is two or more. Furthermore, the terms "first," "second," "third," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
According to the embodiment of the invention, a stage load monitoring and early warning method based on stage deformation analysis is provided.
The invention will be further described with reference to the accompanying drawings and the specific embodiments, as shown in fig. 1, a stage load monitoring and early warning method based on stage deformation analysis according to an embodiment of the invention, the monitoring and early warning method includes the following steps:
s1, monitoring stress of a key structure position of a stage in real time by using a plurality of stress sensors;
specifically, the key structural locations include support structures, spandrel girders, connectors, moving parts, and the like.
S2, acquiring stage stress data monitored in real time, preprocessing, extracting features of the preprocessed data, analyzing the feature importance of the extracted stage stress features, and screening out importance features with the greatest influence on stage load abnormality early warning judgment;
s3, based on the screened importance characteristics, performing cluster analysis on the stage by utilizing a stage deformation quantity analysis method, marking a stage area with abnormal stress change as a potential problem area, and performing segmentation processing on the potential problem area to determine a measurement target;
s4, accurately detecting and positioning edge points of different areas on the segmented contour by using a caliper tool method, fitting the edge points to obtain size parameters of key structure positions of the stage, and storing the size parameters into a database, wherein the size parameters are used as stress distribution and change trend of the different areas;
Specifically, it should be noted that the size parameters of the critical structural positions of the stage reflect the sizes and shapes of the different areas, and these parameters can be combined with the stress distribution of the different areas to better understand the stress variation condition of each area. For example, a larger area region may have a larger range of stress variation because the region is more affected by the load.
S5, carrying out deep excavation and analysis on the obtained size parameters of the key structure position of the stage and preset standard size parameters through a fuzzy isolated forest algorithm, judging whether the stage is at risk of overload or fatigue damage, and triggering stage overload or fatigue damage early warning if the stage is at risk;
and S6, performing detailed inspection and maintenance on the stage area triggering the early warning, and eliminating potential safety hazards.
In one embodiment, the step of acquiring stage stress data monitored in real time, preprocessing the stage stress data, and extracting features from the preprocessed data includes the following steps:
s21, collecting repeated data, missing values and abnormal values of stage stress data obtained by each stress sensor, and denoising, filtering and smoothing the repeated data, the missing values and the abnormal values;
S22, connecting unprocessed data rows in stage stress data collected by each stress sensor to generate a new data table, correlating different data tables through external key values to generate a complete data table, and obtaining an accurate data set;
s23, determining external key relations among different data sets, connecting data in different data tables with each other according to requirements, creating a new data table, and associating through specified external key values;
s24, connecting the data tables to be joined together through the JOIN operators in the SQL sentences, and ensuring that the integrity constraint of the data is satisfied when the joining is carried out;
s25, after connection is completed, inserting test data to check whether a connection result is correct, ensuring that the connection result can be correctly identified and associated, and obtaining accurate data of stage stress acquired by each stress sensor;
s26, fusing accurate data of the stage stress acquired by each stress sensor into the same data set by using a principal component analysis method;
s27, extracting relevant features from the fused data set to obtain feature data of stage stress, wherein the feature data at least comprise abnormal sudden increase of stress, continuous high stress, abnormal difference of stress variation and abnormal stress fluctuation;
S28, selecting a characteristic evaluation index for judging the stress abnormal behavior, and calculating the association degree between each stress data characteristic and the stress abnormal behavior based on the selected characteristic evaluation index;
and S29, sorting each stress data feature based on the calculated association degree, wherein the higher the association degree is, the more important the stress data feature is, and the stress data feature with the highest association degree is taken as an important stress feature.
In one embodiment, based on the selected importance features, a stage deformation analysis method is used for performing cluster analysis on a stage, a stage area with abnormal stress change is marked as a potential problem area, segmentation processing is performed on the potential problem area, and a measurement target is determined, wherein the method comprises the following steps:
s31, taking the feature quantity of the importance stress features as the quantity of clustering centers, clustering all stage areas by utilizing an improved K-Means clustering algorithm, and dividing the stage areas into a plurality of groups;
s32, extracting data points from a plurality of groups, calculating local outlier factors of the data points by using a local outlier detection algorithm, and judging whether the data points are outliers;
in particular, it should be noted that the local outlier detection algorithm (Local Outlier Factor, abbreviated as LOF) is an algorithm for anomaly detection, unlike many other outlier detection algorithms, the LOF considers the local characteristics of neighboring points, so it can more accurately identify outliers in various density areas, and in the LOF algorithm, the LOF score of an object is greater than 1, indicating that the object is more likely to be an outlier than its neighbors.
S33, counting the number of outliers in each group, and calculating the proportion of the outliers;
specifically, the proportion of outliers calculated is obtained by dividing the number of outliers by the total number of points in the cluster.
S34, if the proportion of the outliers in the group exceeds a preset threshold, judging that the group has stress variation, and marking all stage areas in the group as potential problem areas;
s35, taking the potential problem area as the overall outline of the stage, and dividing the overall outline by using an outline dividing method to determine a measurement target.
In one embodiment, the step of using the feature number of the importance stress features as the number of clustering centers and using the improved K-Means clustering algorithm to cluster all stage areas, and dividing the stage areas into a plurality of groups includes the following steps:
s311, calculating noise measurement indexes of the data objects for the clustered stage stress data sets;
s312, regarding the data object, if the noise measurement index is larger than a preset threshold value, taking the data object as an isolated point of the data set;
s313, deleting the isolated points or leading out the isolated points to an abnormal value list to obtain a new stage stress data set X;
S314, randomly selecting K data objects from the new stage stress data set X as initial clustering centers C1, C2 and …，C k ；
S315, calculating the distance between the data object and each cluster center according to the initial cluster center, and distributing the data object to the class where the nearest cluster center is located;
s316, distributing the data objects to the nearest class, and calculating the average position of the data objects as a new clustering center;
s317, if the new cluster center is the same as the cluster center in the step S316, ending the algorithm, otherwise, replacing the old cluster center with the new cluster center, and repeating the steps S315-S316;
s318, presetting an iteration number threshold, stopping iteration when the iteration number reaches the threshold, and outputting a final clustering result.
In one embodiment, the improved K-Means clustering algorithm is calculated as:
wherein ,nrepresenting the number of data points in the cluster;
dexpressed as a number of data point features;
V{S，Hdenoted as noise measure;
Sirepresent the firstiClustering;
X ih expressed in clustersS i Middle (f)hFeature vectors of data points;
X jh representing all data points in the whole data set at the firsthAverage over individual features;
Hrepresented as a threshold;
jRepresenting a data point in the data.
Specifically, a noise metricV{S，HNoise or isolated point data are prone to interfere with the clustering effect, so that noise metrics are added to improve the original K-Means clustering algorithm。
In one embodiment, the extracting data points from the groups, calculating local outlier factors of the data points by using a local outlier detection algorithm, and judging whether the data points are outliers comprises the following steps:
s321, extracting all data points in the group;
s322, calculating a distance neighborhood of all data points, wherein the distance neighborhood is other data points with a certain data point nearest to the data point;
specifically, it should be noted that, it is a very common operation to calculate a distance neighborhood of each data point, that is, a distance neighborhood, where a certain data point is closest to other data points under a certain distance measure, a common distance measure method is: euclidean distance, manhattan distance, chebyshev distance, mahalanobis distance, and the like.
S323, calculating the reachable distances and the reachable distance ratio of all the data points relative to the distance neighborhood, wherein the reachable distance ratio is the reachable distance from the neighborhood divided by the average distance from other data points;
S324, calculating local outlier factors of all data points, wherein the local outlier factors are obtained by subtracting a preset threshold value from the distance reachable ratio;
s325, if the local outlier factor of the data point is greater than zero, judging the data point as an outlier.
In one embodiment, the step of taking the potential problem area as the whole outline of the stage, and dividing the whole outline by using an outline dividing method to determine the measurement target comprises the following steps:
s351, setting fixed threshold parameters;
s352, circularly traversing points on the outline to obtain the total number M;
s353, selecting a reference point as a starting point, and connecting the starting point and the M/2 th point as adjacent bus segments;
s354, calculating the distance between the point on the contour line and the adjacent bus segment, and if the distance between the point on the contour line and the adjacent bus segment is larger than a fixed threshold parameter, connecting the maximum distance point with the starting point and the end point of the adjacent bus segment to form two new adjacent line segments I and II to replace the adjacent bus segment;
s355, continuing to iteratively calculate the distance between the contour points until all the line segment distances are smaller than a fixed threshold parameter;
s356, if the point on a certain section of the contour meets the linear equation, dividing the contour into straight lines, and if the point does not meet the linear division, sequentially comparing all adjacent line segments in the contour, and replacing the adjacent line segments by using an arc;
S357, if the maximum error of the arc approximation is smaller than the average error of the adjacent line segments, replacing the adjacent line segments with the arc, dividing the contour into the arc, and if the arc is a closed polygon, dividing the contour into the circle;
s358, determining measurement target size parameters according to the obtained parameters of the straight line, the circular arc and the circle, and taking the measurement target size parameters as stress distribution and variation trend of different areas.
In one embodiment, the method for precisely detecting and positioning edge points of different areas on the segmented contour by using a caliper tool method, fitting the edge points to obtain the dimension parameters of the key structure position of the stage, storing the dimension parameters into a database, and taking the dimension parameters as stress distribution and variation trend of the different areas, wherein the method comprises the following steps:
s41, acquiring basic information of a target to be measured, wherein the basic information comprises the diameter and center coordinates of a circle and two end point coordinates of a straight line;
s42, equidistant and equal-sized measurement rectangles are generated on the straight line or the circular outline and are used for sequentially detecting the positions of the positioning edge points;
s43, determining the optimal edge point detected by the measurement rectangle;
s44, calculating and measuring gradient amplitude values and directions of pixel points in the rectangle;
s45, determining the pixel points according to the non-maximum value suppression method as the optimal edge points;
S46, fitting the optimal edge points based on a Tukey algorithm to obtain the dimension parameters of the key structure positions of the stage as stress distribution and change trend of different areas.
In one embodiment, the fitting the optimal edge points based on Tukey algorithm to obtain the dimension parameters of the critical structural positions of the stage as the stress distribution and the variation trend of different areas includes the following steps:
s461, at the beginning of the iteration, all edge points are given the same weight, i.e. W 1 ；
S462, fitting all edge points by using a least square method to obtain a standard straight line;
s463, calculating the distance from all edge points to the straight line;
s464, if the distance from an edge point to a straight line is smaller than the preset value, the weight in the next iteration is still set to W 1 If the distance is greater than the preset value, the weight is set to W 0 ；
S465, updating the weight of each point in each iteration, and gradually eliminating outliers;
s466, repeating the steps S461-S465 until the weight values of all the points are stabilized, and obtaining the clipping factors;
s467, fitting by using a Tukey weight function according to the weight and the clipping factor to obtain a final stage stress distribution model;
S468, converting the stage stress distribution model into size parameters of key structure positions of the stage through a calibration method, and storing the size parameters into a database.
In one embodiment, the depth excavation and analysis are performed on the obtained size parameter of the key structure position of the stage and the preset standard size parameter through a fuzzy isolated forest algorithm, whether the stage is at risk of overload or fatigue damage is judged, and if the stage is at risk, stage overload or fatigue damage early warning is triggered, wherein the step of triggering comprises the following steps:
s51, acquiring the dimension parameters of the key structure positions of the stage and the preset standard dimension parameters from a database;
s52, according to the analysis of the size manufacturing requirements of the stage, determining a related factor set influencing the size manufacturing requirements, and setting corresponding evaluation levels;
s53, training the size parameters of the extracted key structure positions of the stage and preset standard size parameters by using an isolated forest algorithm, calculating abnormal scores, and judging whether the key structure positions of the stage have the risk of overload or fatigue damage or not;
specifically, it should be noted that training the size parameter of the extracted critical structural position of the stage and the preset standard size parameter by using an isolated forest algorithm, calculating an anomaly score, and judging whether the critical structural position of the stage has an overload or fatigue damage risk further includes:
Dividing the size parameters of the extracted key structure position of the stage and preset standard size parameters into a training set and a testing set;
training an isolated forest model by randomly selecting split points in the range of the characteristics and the characteristic values by using training set data;
constructing a plurality of isolated trees to form an isolated forest model;
calculating an average path length of each data point in the test set from the root node to the leaf node by using the isolated forest model;
calculating abnormal scores of size parameters of key structure positions of the stage and preset standard size parameters based on the average path length of the data points;
and judging whether each data point has size overload or fatigue damage according to the anomaly score and the set threshold value.
Specifically, during the training process, the data points are divided into two subsets (one of which contains the data points smaller than or equal to the characteristic value and the other contains the data points larger than the characteristic value) according to the selected characteristic value, and a splitting operation is performed; this process is repeated recursively for each subset until a stop condition is met (e.g., subset size reaches a predetermined threshold, tree depth reaches a maximum).
S54, normalizing the abnormal score value obtained by using an isolated forest algorithm, and calculating a fuzzy set;
S55, scoring each factor on different evaluation levels through professional evaluation to form a fuzzy relation matrix;
s56, calculating the fuzzy set and the fuzzy relation matrix by using a fuzzy operator to obtain a fuzzy comprehensive evaluation result vector;
s57, according to the rank summation of the component values and the grades in the vector, obtaining the relative position of the object to be evaluated, and judging the size overload or fatigue damage condition of the key structure position of the stage;
s58, judging whether the size of the critical structure position of the stage is within an allowable tolerance range, if so, judging that the critical structure position of the stage is qualified, and if not, judging that the risk of overload or fatigue damage exists;
and S59, triggering stage overload or fatigue damage early warning if the risk of overload or fatigue damage exists.
In summary, by means of the above technical solution of the present invention, by using the local outlier detection algorithm, the present invention can identify the data points that are abnormal in the local neighborhood thereof, so that the tiny deformation or displacement that may be ignored can be detected, and further the accuracy, sensitivity and robustness of the stage load monitoring and early warning method based on stage deformation analysis can be enhanced, thereby ensuring the safe and stable operation of the stage. According to the invention, the edge points are detected through the caliper tool, so that the edge point with the largest gradient amplitude perpendicular to the rectangle is detected by generating the measurement rectangles with the same size and distance, the optimal edge points are sequentially obtained by using the set number of the measurement rectangles, and finally, the edge contour can be obtained more accurately by fitting all the detected edge points, so that the traversing time is reduced, and the detection efficiency is improved.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (10)
1. The stage load monitoring and early warning method based on stage deformation analysis is characterized by comprising the following steps of:
s1, monitoring stress of a key structure position of a stage in real time by using a plurality of stress sensors;
s2, acquiring stage stress data monitored in real time, preprocessing, extracting features of the preprocessed data, analyzing the feature importance of the extracted stage stress features, and screening out importance features with the greatest influence on stage load abnormality early warning judgment;
s3, based on the screened importance characteristics, performing cluster analysis on the stage by utilizing a stage deformation quantity analysis method, marking a stage area with abnormal stress change as a potential problem area, and performing segmentation processing on the potential problem area to determine a measurement target;
s4, accurately detecting and positioning edge points of different areas on the segmented contour by using a caliper tool method, fitting the edge points to obtain size parameters of key structure positions of the stage, and storing the size parameters into a database, wherein the size parameters are used as stress distribution and change trend of the different areas;
S5, carrying out deep excavation and analysis on the obtained size parameters of the key structure position of the stage and preset standard size parameters through a fuzzy isolated forest algorithm, judging whether the stage is at risk of overload or fatigue damage, and triggering stage overload or fatigue damage early warning if the stage is at risk;
and S6, performing detailed inspection and maintenance on the stage area triggering the early warning, and eliminating potential safety hazards.
2. The stage load monitoring and early warning method based on stage deformation analysis according to claim 1, wherein the steps of acquiring stage stress data monitored in real time, preprocessing the data, and extracting features of the preprocessed data comprise the following steps:
s21, collecting repeated data, missing values and abnormal values of stage stress data obtained by each stress sensor, and denoising, filtering and smoothing the repeated data, the missing values and the abnormal values;
s22, connecting unprocessed data rows in stage stress data collected by each stress sensor to generate a new data table, correlating different data tables through external key values to generate a complete data table, and obtaining an accurate data set;
s23, determining external key relations among different data sets, connecting data in different data tables with each other according to requirements, creating a new data table, and associating through specified external key values;
S24, connecting the data tables to be joined together through the JOIN operators in the SQL sentences, and ensuring that the integrity constraint of the data is satisfied when the joining is carried out;
s25, after connection is completed, inserting test data to check whether a connection result is correct, ensuring that the connection result can be correctly identified and associated, and obtaining accurate data of stage stress acquired by each stress sensor;
s26, fusing accurate data of the stage stress acquired by each stress sensor into the same data set by using a principal component analysis method;
s27, extracting relevant features from the fused data set to obtain feature data of stage stress, wherein the feature data at least comprise abnormal sudden increase of stress, continuous high stress, abnormal difference of stress variation and abnormal stress fluctuation;
s28, selecting a characteristic evaluation index for judging the stress abnormal behavior, and calculating the association degree between each stress data characteristic and the stress abnormal behavior based on the selected characteristic evaluation index;
and S29, sorting each stress data feature based on the calculated association degree, wherein the higher the association degree is, the more important the stress data feature is, and the stress data feature with the highest association degree is taken as an important stress feature.
3. The stage load monitoring and early warning method based on stage deformation analysis according to claim 1, wherein the stage is clustered by stage deformation analysis based on the selected importance characteristics, the stage area with abnormal stress change is marked as a potential problem area, the potential problem area is segmented, and the determination of the measurement target comprises the following steps:
s31, taking the feature quantity of the importance stress features as the quantity of clustering centers, clustering all stage areas by utilizing an improved K-Means clustering algorithm, and dividing the stage areas into a plurality of groups;
s32, extracting data points from a plurality of groups, calculating local outlier factors of the data points by using a local outlier detection algorithm, and judging whether the data points are outliers;
s33, counting the number of outliers in each group, and calculating the proportion of the outliers;
s34, if the proportion of the outliers in the group exceeds a preset threshold, judging that the group has stress variation, and marking all stage areas in the group as potential problem areas;
s35, taking the potential problem area as the overall outline of the stage, and dividing the overall outline by using an outline dividing method to determine a measurement target.
4. The stage load monitoring and early warning method based on stage deformation analysis according to claim 3, wherein the step of clustering all stage areas by using the improved K-Means clustering algorithm and dividing the stage areas into a plurality of groups comprises the following steps:
s311, calculating noise measurement indexes of the data objects for the clustered stage stress data sets;
s312, regarding the data object, if the noise measurement index is larger than a preset threshold value, taking the data object as an isolated point of the data set;
s313, deleting the isolated points or leading out the isolated points to an abnormal value list to obtain a new stage stress data set X;
s314, randomly selecting K data objects from the new stage stress data set X as initial clustering centers C1, C2, … and C k ；
S315, calculating the distance between the data object and each cluster center according to the initial cluster center, and distributing the data object to the class where the nearest cluster center is located;
s316, distributing the data objects to the nearest class, and calculating the average position of the data objects as a new clustering center;
s317, if the new cluster center is the same as the cluster center in the step S316, ending the algorithm, otherwise, replacing the old cluster center with the new cluster center, and repeating the steps S315-S316;
S318, presetting an iteration number threshold, stopping iteration when the iteration number reaches the threshold, and outputting a final clustering result.
5. The stage load monitoring and early warning method based on stage deformation analysis according to claim 4, wherein the calculation formula of the improved K-Means clustering algorithm is as follows:
；
wherein ,nrepresenting the number of data points in the cluster;
dexpressed as a number of data point features;
V{S，Hdenoted as noise measure;
Sirepresent the firstiClustering;
X ih expressed in clustersS i Middle (f)hFeature vectors of data points;
X jh representing all data points in the whole data set at the firsthAverage over individual features;
Hrepresented as a threshold;
jrepresenting a data point in the data.
6. The stage load monitoring and early warning method based on stage deformation analysis according to claim 5, wherein the steps of extracting data points from a plurality of groups, calculating local outliers of the data points by using a local outlier detection algorithm, and judging whether the data points are outliers comprise the following steps:
s321, extracting all data points in the group;
s322, calculating a distance neighborhood of all data points, wherein the distance neighborhood is other data points with a certain data point nearest to the data point;
S323, calculating the reachable distances and the reachable distance ratio of all the data points relative to the distance neighborhood, wherein the reachable distance ratio is the reachable distance from the neighborhood divided by the average distance from other data points;
s324, calculating local outlier factors of all data points, wherein the local outlier factors are obtained by subtracting a preset threshold value from the distance reachable ratio;
s325, if the local outlier factor of the data point is greater than zero, judging the data point as an outlier.
7. The stage load monitoring and early warning method based on stage deformation analysis according to claim 6, wherein the step of taking the potential problem area as the overall contour of the stage, and dividing the overall contour by using a contour dividing method to determine the measurement target comprises the following steps:
s351, setting fixed threshold parameters;
s352, circularly traversing points on the outline to obtain the total number M;
s353, selecting a reference point as a starting point, and connecting the starting point and the M/2 th point as adjacent bus segments;
s354, calculating the distance between the point on the contour line and the adjacent bus segment, and if the distance between the point on the contour line and the adjacent bus segment is larger than a fixed threshold parameter, connecting the maximum distance point with the starting point and the end point of the adjacent bus segment to form two new adjacent line segments I and II to replace the adjacent bus segment;
S355, continuing to iteratively calculate the distance between the contour points until all the line segment distances are smaller than a fixed threshold parameter;
s356, if the point on a certain section of the contour meets the linear equation, dividing the contour into straight lines, and if the point does not meet the linear division, sequentially comparing all adjacent line segments in the contour, and replacing the adjacent line segments by using an arc;
s357, if the maximum error of the arc approximation is smaller than the average error of the adjacent line segments, replacing the adjacent line segments with the arc, dividing the contour into the arc, and if the arc is a closed polygon, dividing the contour into the circle;
s358, determining measurement target size parameters according to the obtained parameters of the straight line, the circular arc and the circle, and taking the measurement target size parameters as stress distribution and variation trend of different areas.
8. The stage load monitoring and early warning method based on stage deformation analysis according to claim 1, wherein the method is characterized in that edge points of different areas on the segmented contour are accurately detected and positioned by using a caliper tool method, the edge points are fitted to obtain size parameters of key structure positions of the stage, the size parameters are stored in a database, and the size parameters are used as stress distribution and change trend of the different areas, and the method comprises the following steps:
s41, acquiring basic information of a target to be measured, wherein the basic information comprises the diameter and center coordinates of a circle and two end point coordinates of a straight line;
S42, equidistant and equal-sized measurement rectangles are generated on the straight line or the circular outline and are used for sequentially detecting the positions of the positioning edge points;
s43, determining the optimal edge point detected by the measurement rectangle;
s44, calculating and measuring gradient amplitude values and directions of pixel points in the rectangle;
s45, determining the pixel points according to the non-maximum value suppression method as the optimal edge points;
s46, fitting the optimal edge points based on a Tukey algorithm to obtain the dimension parameters of the key structure positions of the stage as stress distribution and change trend of different areas.
9. The stage load monitoring and early warning method based on stage deformation analysis according to claim 8, wherein the step of obtaining the size parameters of the key structure position of the stage as the stress distribution and the variation trend of different areas by fitting the optimal edge points based on Tukey algorithm comprises the following steps:
s461, at the beginning of the iteration, all edge points are assigned to a phaseSame weight, i.e. W 1 ；
S462, fitting all edge points by using a least square method to obtain a standard straight line;
s463, calculating the distance from all edge points to the straight line;
s464, if the distance from an edge point to a straight line is smaller than the preset value, the weight in the next iteration is still set to W 1 If the distance is greater than the preset value, the weight is set to W 0 ；
S465, updating the weight of each point in each iteration, and gradually eliminating outliers;
s466, repeating the steps S461-S465 until the weight values of all the points are stabilized, and obtaining the clipping factors;
s467, fitting by using a Tukey weight function according to the weight and the clipping factor to obtain a final stage stress distribution model;
s468, converting the stage stress distribution model into size parameters of key structure positions of the stage through a calibration method, and storing the size parameters into a database.
10. The stage load monitoring and early warning method based on stage deformation analysis according to claim 1, wherein the step of carrying out deep mining and analysis on the obtained size parameter of the key structure position of the stage and the preset standard size parameter by a fuzzy isolated forest algorithm to judge whether the stage is at risk of overload or fatigue damage, and if so, triggering stage overload or fatigue damage early warning comprises the following steps:
s51, acquiring the dimension parameters of the key structure positions of the stage and the preset standard dimension parameters from a database;
s52, according to the analysis of the size manufacturing requirements of the stage, determining a related factor set influencing the size manufacturing requirements, and setting corresponding evaluation levels;
S53, training the size parameters of the extracted key structure positions of the stage and preset standard size parameters by using an isolated forest algorithm, calculating abnormal scores, and judging whether the key structure positions of the stage have the risk of overload or fatigue damage or not;
s54, normalizing the abnormal score value obtained by using an isolated forest algorithm, and calculating a fuzzy set;
s55, scoring each factor on different evaluation levels through professional evaluation to form a fuzzy relation matrix;
s56, calculating the fuzzy set and the fuzzy relation matrix by using a fuzzy operator to obtain a fuzzy comprehensive evaluation result vector;
s57, according to the rank summation of the component values and the grades in the vector, obtaining the relative position of the object to be evaluated, and judging the size overload or fatigue damage condition of the key structure position of the stage;
s58, judging whether the size of the critical structure position of the stage is within an allowable tolerance range, if so, judging that the critical structure position of the stage is qualified, and if not, judging that the risk of overload or fatigue damage exists;
and S59, triggering stage overload or fatigue damage early warning if the risk of overload or fatigue damage exists.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311154197.XA CN116881834B (en) | 2023-09-08 | 2023-09-08 | Stage load monitoring and early warning method based on stage deformation analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311154197.XA CN116881834B (en) | 2023-09-08 | 2023-09-08 | Stage load monitoring and early warning method based on stage deformation analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116881834A true CN116881834A (en) | 2023-10-13 |
| CN116881834B CN116881834B (en) | 2023-11-24 |
Family
ID=88266682
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311154197.XA Active CN116881834B (en) | 2023-09-08 | 2023-09-08 | Stage load monitoring and early warning method based on stage deformation analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116881834B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117076258A (en) * | 2023-10-15 | 2023-11-17 | 江苏全天软件有限公司 | Remote monitoring method and system based on Internet cloud |
| CN117173177A (en) * | 2023-11-02 | 2023-12-05 | 泰州市航宇电器有限公司 | Image-based metal shell optical size detection method and system |
| CN117308275A (en) * | 2023-11-28 | 2023-12-29 | 江苏嘉尚环保科技有限公司 | Temperature difference-based pipeline connection abnormality detection method and system |
| CN117610214A (en) * | 2024-01-23 | 2024-02-27 | 南方电网数字电网研究院股份有限公司 | Intelligent power distribution network wiring planning method based on dynamic geographic features |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20160019119A (en) * | 2014-08-07 | 2016-02-19 | 순환엔지니어링 주식회사 | Stage monitoring and diagnosis system, stage apparatus and manufacturing, measuring and inspecting equipment |
| CN109916326A (en) * | 2019-04-11 | 2019-06-21 | 杭州友邦演艺设备有限公司 | A kind of stage Payload Monitoring And Control method for early warning |
| CN110288196A (en) * | 2019-05-28 | 2019-09-27 | 国网河南省电力公司电力科学研究院 | Transmission line galloping grading forewarning system method and system |
| CN111236704A (en) * | 2020-01-22 | 2020-06-05 | 哈尔滨工业大学 | Panel, panel load real-time test system and panel load safety control system |
-
2023
- 2023-09-08 CN CN202311154197.XA patent/CN116881834B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20160019119A (en) * | 2014-08-07 | 2016-02-19 | 순환엔지니어링 주식회사 | Stage monitoring and diagnosis system, stage apparatus and manufacturing, measuring and inspecting equipment |
| CN109916326A (en) * | 2019-04-11 | 2019-06-21 | 杭州友邦演艺设备有限公司 | A kind of stage Payload Monitoring And Control method for early warning |
| CN110288196A (en) * | 2019-05-28 | 2019-09-27 | 国网河南省电力公司电力科学研究院 | Transmission line galloping grading forewarning system method and system |
| CN111236704A (en) * | 2020-01-22 | 2020-06-05 | 哈尔滨工业大学 | Panel, panel load real-time test system and panel load safety control system |
Non-Patent Citations (1)
| Title |
|---|
| 王帆;高文元;: "舞台吊杆的稳定性方案的确定", 甘肃科技, no. 03 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117076258A (en) * | 2023-10-15 | 2023-11-17 | 江苏全天软件有限公司 | Remote monitoring method and system based on Internet cloud |
| CN117173177A (en) * | 2023-11-02 | 2023-12-05 | 泰州市航宇电器有限公司 | Image-based metal shell optical size detection method and system |
| CN117308275A (en) * | 2023-11-28 | 2023-12-29 | 江苏嘉尚环保科技有限公司 | Temperature difference-based pipeline connection abnormality detection method and system |
| CN117308275B (en) * | 2023-11-28 | 2024-02-06 | 江苏嘉尚环保科技有限公司 | Temperature difference-based pipeline connection abnormality detection method and system |
| CN117610214A (en) * | 2024-01-23 | 2024-02-27 | 南方电网数字电网研究院股份有限公司 | Intelligent power distribution network wiring planning method based on dynamic geographic features |
| CN117610214B (en) * | 2024-01-23 | 2024-05-17 | 南方电网数字电网研究院股份有限公司 | Intelligent power distribution network wiring planning method based on dynamic geographic features |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116881834B (en) | 2023-11-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116881834B (en) | Stage load monitoring and early warning method based on stage deformation analysis | |
| CN106779200A (en) | Based on the Wind turbines trend prediction method for carrying out similarity in the historical data | |
| CN106092625B (en) | The industrial process fault detection method merged based on amendment type independent component analysis and Bayesian probability | |
| US20020188580A1 (en) | System and method for grating profile classification | |
| CN111401785A (en) | Power system equipment fault early warning method based on fuzzy association rule | |
| CN110807245B (en) | Automatic modeling method and system for equipment fault early warning | |
| CN109033615A (en) | Autocrane jib structure methods of risk assessment based on potential failure mode | |
| CN109214522B (en) | Equipment performance degradation evaluation method based on multiple attributes | |
| CN111027841A (en) | Low-voltage transformer area line loss calculation method based on gradient lifting decision tree | |
| CN114036452B (en) | Yield evaluation method applied to discrete production line | |
| CN114580940A (en) | Grouting effect fuzzy comprehensive evaluation method based on grey correlation degree analysis method | |
| JP2004110602A (en) | Method and program for extracting feature for diagnosis/monitoring of facility or inspection of defective/non-defective product from waveform pattern data | |
| CN117173177A (en) | Image-based metal shell optical size detection method and system | |
| JP4098687B2 (en) | Power quality evaluation system, power quality evaluation method, and power quality evaluation program | |
| CN111105168A (en) | Load model goodness-of-fit evaluation method based on cloud matter element theory | |
| JPH10253444A (en) | Detecting method for abnormal sound, mechanical abnormality judging method using its detected value, similarity detecting method for oscillatory wave, voice recognizing method using its detected value | |
| CN114741876B (en) | Intelligent inspection method for tower crane | |
| CN106709522B (en) | High-voltage cable construction defect classification method based on improved fuzzy trigonometric number | |
| KR101811962B1 (en) | Apparatus and method for evaluating class discrimination of nonlinear data | |
| JP7430271B2 (en) | Error factor estimation device and estimation method | |
| JP2007188471A (en) | Method and device for analyzing quality, computer program, and computer readable storage medium | |
| CN108241917A (en) | The appraisal procedure and device of part reliability | |
| CN112926861A (en) | Storage tank safety performance evaluation method | |
| CN113448840A (en) | Software quality evaluation method based on predicted defect rate and fuzzy comprehensive evaluation model | |
| CN110516750A (en) | A kind of steel-plate shape abnormal quality detection method based on CART decision tree |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |