CN115278742A - Equipment alarm-based equipment base station health assessment system and method - Google Patents
Equipment alarm-based equipment base station health assessment system and method Download PDFInfo
- Publication number
- CN115278742A CN115278742A CN202210810929.5A CN202210810929A CN115278742A CN 115278742 A CN115278742 A CN 115278742A CN 202210810929 A CN202210810929 A CN 202210810929A CN 115278742 A CN115278742 A CN 115278742A
- Authority
- CN
- China
- Prior art keywords
- equipment
- base station
- line
- index
- influence
- 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
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/04—Arrangements for maintaining operational condition
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0805—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability
- H04L43/0817—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability by checking functioning
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/16—Threshold monitoring
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Environmental & Geological Engineering (AREA)
- Quality & Reliability (AREA)
- Mobile Radio Communication Systems (AREA)
- Alarm Systems (AREA)
Abstract
The invention discloses a health evaluation system and a method of an equipment base station based on equipment alarm, which comprises an alarm data analysis module, an overhaul condition analysis module, an influence index analysis module and a health state index analysis module; the alarm data analysis module is used for analyzing alarm data; the maintenance condition analysis module is used for analyzing and acquiring the maintenance condition of the base station equipment when the alarm occurs and analyzing the maintenance condition; the influence index analysis module is used for analyzing the influence indexes of the line arrangement among different base station devices when the overhaul object is the line among the devices; the health state index analysis module is used for comprehensively analyzing health state indexes of different base stations by synthesizing influence indexes of line arrangement among the equipment and fault risk indexes of the equipment; the invention analyzes the equipment fault rule or distribution condition of the alarm quantity and the alarm state of each equipment in the equipment base station, thereby evaluating the health state of the equipment base station.
Description
Technical Field
The invention relates to the technical field of base station equipment health assessment, in particular to equipment base station health assessment system and method based on equipment alarm.
Background
The base station comprises a base station body, a plurality of base stations are arranged on the base station body, equipment with different quantities can exist in different base stations, and when the base station operates, some abnormal equipment problems often occur, some abnormal equipment problems are caused by faults of the equipment and need to be maintained by professional maintenance engineers according to different conditions, some abnormal equipment conditions except for the fault reason of the equipment due to circuit influences among the equipment in the base station, when the second condition influences the equipment, the influence degree of the circuit faults on the equipment and the health condition of the circuit are difficult to determine, meanwhile, due to the difference caused by different equipment quantities and base station spaces in a plurality of different base stations, the circuits of the base stations are difficult to be checked and maintained one to one, and the working pressure of base station maintenance personnel is greatly increased.
Disclosure of Invention
The present invention is directed to a system and a method for evaluating health of a device base station based on device alarm, so as to solve the problems set forth in the background art.
In order to solve the technical problems, the invention provides the following technical scheme: the equipment alarm-based equipment base station health assessment method comprises the following processes:
acquiring the times of alarming of base station equipment in a monitoring period and the state of the equipment when alarming; distinguishing fault conditions of the equipment according to the state when the equipment gives an alarm, wherein the fault conditions comprise an internal source fault of the equipment and a line fault between the equipment, and acquiring event occurrence frequencies Yk1 and Yk2 of the internal source fault and the line fault between the equipment in a kth equipment base station;
acquiring the maintenance condition of base station equipment when an alarm occurs, analyzing maintenance objects of the base station during maintenance and calculating maintenance frequency indexes aiming at different maintenance objects, wherein the maintenance objects comprise the equipment and lines among the equipment;
when the overhaul object is a line between equipment, acquiring a distribution planning diagram of the line between different base station equipment, and analyzing an influence index of line arrangement between different base station equipment according to the distribution planning diagram of the line between the equipment and an overhaul frequency index of the line between the equipment;
comprehensively analyzing health state indexes of equipment of different base stations according to influence indexes of line arrangement among the equipment and fault risk indexes of the equipment; when the health state index is less than or equal to the preset health state index threshold value, the equipment of the base station is overhauled again, and the line between the equipment is replaced; and when the health state index is larger than the preset health state index threshold value, the base station is continuously monitored.
Further, analyzing the overhaul object of the base station during overhaul and calculating an overhaul frequency index for different overhaul objects includes the following processes:
acquiring an original image Pk0 of a circuit which is primarily arranged in a kth equipment base station equipment and a target image Pkj of the circuit when the kth equipment base station equipment alarms for overhauling for the jth time, sequentially carrying out gray value analysis on n (Pkj) target images Pkj and the original image Pk0, judging an overhauling object according to the gray value, wherein n (Pkj) represents the maximum value of the number of the target images Pkj in the kth equipment base station, and j is less than or equal to n (Pkj);
when the overhaul object is an inter-equipment line, recording that the gray value analysis of the target image Pkj in the kth equipment base station meets the condition that the number of the images of the line with the overhaul object as the inter-equipment is N (Pkj), and then the gray value analysis of the target image Pkj in the kth equipment base station meets the condition that the number of the images with the overhaul object as the equipment per se is N (Pkj) -N (Pkj);
then, the device repair frequency index of the kth device base station is calculated to be Rk1= N (Pkj) -N (Pkj)/N (Pkj), and the device link repair frequency index is calculated to be Rk2= N (Pkj)/N (Pkj).
Further, the gray value analysis includes the following processes:
performing gray value processing on an original image and a target image of an equipment base station by the same means to obtain an original gray image and a target gray image, analyzing line regions on the original gray image and the target gray image, calibrating a gray value corresponding to the line region on the original gray image to obtain a line region gray value set A = { A11, A12, A13., auv }, wherein Auv represents a line region line-th row gray value corresponding to the original gray image, calibrating a line region corresponding gray value on the target gray image to obtain a line region gray value set B = { B11, B12, B13., buv }, and Buv represents a line region line-th row gray value corresponding to the target gray image;
using the formula:
I＝I1·g1+I2·g2+I3·g3
calculating a gray value comprehensive deviation index I, wherein I1 represents a gray value integral deviation index, I2 represents a transverse adjacent gray value similarity index, I3 represents a longitudinal adjacent gray value similarity index, g1, g2 and g3 are reference coefficients, and g1 is more than 0 and less than or equal to g2= g3; the reason for analyzing the two adjacent gray values in the same image is that the two adjacent gray values change synchronously along with the influence of dust along with the increase of time, and the comparison of the adjacent gray values has higher reliability when a part of dust in the image is rubbed off, so that the result that the target gray value is directly compared with the original gray value and the difference value is larger due to the influence of long-time dust accumulation is avoided;
when the gray value comprehensive deviation index is greater than or equal to a preset gray value comprehensive deviation index threshold, the overhaul object of the base station during overhaul is a line between the devices; and when the gray value comprehensive deviation index is smaller than a preset gray value comprehensive deviation index threshold, the overhaul object of the base station during overhaul is the equipment. The difference between an original image and a target image is comprehensively analyzed by utilizing the gray value deviation of the whole line region, the deviation of two transversely adjacent gray value and the deviation of two longitudinally adjacent gray value in the formula, and because the line touches the line when the line is overhauled and dust is accumulated when the line is placed in an external environment, but the dust can be rubbed to a certain extent after the line is touched, the possibility that the line region is touched is analyzed according to the whole and local gray value changes.
Further, analyzing an influence index of line arrangement between different base station devices according to a distribution planning diagram of lines between devices and a maintenance frequency index of the lines between devices, comprising the following steps:
setting base stations containing the same number of devices as a comparison base station group, and recording the number of the comparison base station group as M; obtaining influence factors in the equipment base station, wherein the influence factors comprise the number Qxk of the inflection points of the circuit in the kth equipment base station of the xth contrast base station group and the length Lxk of the circuit between the adjacent inflection points, and the adjacent inflection points are the inflection points existing when the number of the inflection points contained in one circuit between two pieces of equipment is not less than two; the comparison base station group is set because the complexity of lines among equipment is naturally different due to different equipment capacities of different base stations, and the data result obtained by performing comparison analysis on the base stations with the same scale is more convincing;
using the formula:
calculating a line deviation coefficient of the xth reference base station group, wherein bk represents the number of lines between adjacent inflection points in the kth equipment base station, mx represents the number of equipment base stations in the xth reference base station group, and k is less than or equal to m; comparing the line deviation coefficients of the comparison base station groups with a preset deviation threshold respectively to obtain w which is the number of the comparison base station groups with the line deviation coefficients less than or equal to the deviation threshold;
calculating the similarity ratio Tx of the equipment line according to the line deviation coefficient:
Tx＝w/M
when the similarity ratio Tx of the equipment lines is less than or equal to 50%, outputting an influence index of the line arrangement among the equipment to be 0;
and when the similarity ratio Tx of the equipment line is more than 50%, acquiring an equipment base station maintenance frequency index in the comparison base station group, judging whether the influence factor is a main influence factor, and analyzing the influence index of line arrangement among different base station equipment according to the proportion of the main influence factor.
Further, judging whether the influence factors are main influence factors or not, and analyzing influence indexes of line arrangement among different base station equipment according to the proportion of the main influence factors; comprehensively analyzing the health state indexes of the equipment of different base stations according to the influence indexes of the influence factors among the equipment lines and the fault risk indexes of the equipment; the method comprises the following steps:
acquiring an equipment base station maintenance frequency index meeting the condition of similarity proportion, calculating the similarity of the maintenance frequency indexes among all base stations in a comparison base station group, and outputting an influence index of line arrangement among equipment to be 0 if the similarity is smaller than a preset similarity threshold; if the similarity is greater than or equal to the preset similarity threshold, outputting the number of turning points of the line and the length of the line as main influence factors;
using the formula:
calculating an influence index S of the line arrangement, wherein the sigma mx is the total number of equipment base stations; the reason why the occupation ratio of the line inflection point among the devices in the base station is analyzed is that when the line inflection point and the line length are determined to be main influence factors, the health and safety of the base station devices are influenced more greatly when the line inflection point and the line length are heavier;
according to the influence index S of the line arrangement, the health state index of the kth equipment base station is calculated by using a formula: fk = S · Yk2+ Rk1 · Yk1.
The equipment base station health evaluation system based on equipment alarm comprises an alarm data analysis module, a maintenance condition analysis module, an influence index analysis module and a health state index analysis module;
the alarm data analysis module is used for analyzing alarm data and comprises an alarm data acquisition module and an event occurrence frequency calculation module; the alarm data acquisition module is used for acquiring the times of alarm occurrence and the state of equipment alarm in a monitoring period of the base station equipment; the event occurrence frequency calculation module is used for calculating the respective time occurrence frequencies corresponding to the two faults in the equipment base station;
the maintenance condition analysis module is used for analyzing and acquiring the maintenance condition of the base station equipment when the alarm occurs and analyzing the maintenance condition;
the influence index analysis module is used for analyzing the influence indexes of the line arrangement among different base station devices when the overhaul object is the line among the devices;
and the health state index analysis module is used for comprehensively analyzing the health state indexes of the equipment by synthesizing the influence indexes of the line arrangement among the equipment and the fault risk indexes of the equipment per se on the equipment of different base stations.
Furthermore, the maintenance condition analysis module comprises a maintenance object judgment module and a maintenance frequency index calculation module;
the overhaul object judgment module is used for judging whether an overhaul object of the equipment base station is an equipment or a line between the equipment; the maintenance object judgment module comprises a gray value acquisition module, a gray value comprehensive deviation index calculation module and a gray value judgment module;
the gray value acquisition module is used for acquiring a gray value of an original image of the equipment base station and a gray value of a corresponding line area on a target image, wherein the original image is an image of a line which is initially arranged on the equipment base station, and the target image is an image of the line when the equipment alarm is maintained; the gray value comprehensive deviation index calculation module is used for calculating a gray value comprehensive deviation index of the original image and the target image; the gray value judgment module analyzes the object to be overhauled during alarming according to the gray value comprehensive deviation index;
and the maintenance frequency index calculation module is used for calculating the frequency index when the maintenance object is the equipment and the frequency index when the maintenance object is the line between the equipment.
Further, the influence index meter analysis module comprises an influence factor determination module and an influence index calculation module;
the influence factor determining module is used for determining whether the influence factors of the lines among the devices are main influence factors or not, wherein the influence factors comprise the number of turns of the lines and the length of the lines; the influence factor determination module comprises a comparison base station group setting module, a line deviation coefficient calculation module and a line similarity proportion calculation module;
the comparison base station group setting module is used for setting a comparison base station group, the line deviation coefficient calculation module is used for calculating the line deviation coefficient of the base stations in the same comparison base station group, and the line similarity proportion calculation module is used for analyzing the line deviation coefficient to obtain a line similarity proportion;
and the influence index calculation module is used for calculating the influence index of the line arrangement when the influence factor of the line between the devices is determined as the main influence factor.
Compared with the prior art, the invention has the following beneficial effects: the method comprises the steps of analyzing the equipment fault rule or distribution condition of the alarm quantity and the alarm state of each equipment in the equipment base station, evaluating the health state of the equipment base station, respectively analyzing and calculating the base stations with different specifications in the analysis process, extracting common influence factors, further integrally analyzing whether the influence factors have differences with different base station specifications or not, determining the main influence factors of the equipment base station according to the differences, and finally integrally judging the health state of the equipment by combining the abnormity of the base station and the maintenance frequency; the working pressure of the maintainers is reduced, and the work of the maintainers is more targeted according to the health state of the base station.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of a health assessment system of a base station of a device based on device alarm according to the present invention;
FIG. 2 is a layout diagram of a device base station circuit according to the device alarm-based device base station health assessment method of the present invention;
FIG. 3 is a layout diagram of the equipment base station line according to the equipment alarm-based equipment base station health assessment method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1-3, the present invention provides a technical solution: the equipment base station health assessment method based on the equipment alarm comprises the following processes:
acquiring the times of alarming of base station equipment in a monitoring period and the state of the equipment when alarming; distinguishing fault conditions of the equipment according to the state of the equipment when alarming, wherein the fault conditions comprise endogenous faults of the equipment and line faults among the equipment, and acquiring event occurrence frequencies Yk1 and Yk2 corresponding to the endogenous faults and the line faults among the equipment in a kth equipment base station;
acquiring the maintenance condition of base station equipment when an alarm occurs, analyzing a maintenance object of the base station during maintenance, and calculating a maintenance frequency index aiming at different maintenance objects, wherein the maintenance object comprises the equipment and a line between the equipment;
when the overhaul object is a line between equipment, acquiring a distribution planning diagram of the line between different base station equipment, and analyzing an influence index of line arrangement between different base station equipment according to the distribution planning diagram of the line between the equipment and an overhaul frequency index of the line between the equipment;
comprehensively analyzing health state indexes of the equipment of different base stations according to influence indexes of line arrangement among the equipment and fault risk indexes of the equipment; when the health state index is less than or equal to the preset health state index threshold value, the equipment of the base station is overhauled again, and lines among the equipment are replaced; and when the health state index is larger than the preset health state index threshold value, the base station is continuously monitored.
The method for analyzing the overhaul object of the base station during overhaul and calculating the overhaul frequency index aiming at different overhaul objects comprises the following processes:
acquiring an original image Pk0 of a circuit which is primarily arranged in a kth equipment base station equipment and a target image Pkj of the circuit when the kth equipment base station equipment alarms for overhauling for the jth time, sequentially carrying out gray value analysis on n (Pkj) target images Pkj and the original image Pk0, judging an overhauling object according to the gray value, wherein n (Pkj) represents the maximum value of the number of the target images Pkj in the kth equipment base station, and j is less than or equal to n (Pkj);
when the overhaul object is an inter-equipment line, recording that the gray value analysis of the target image Pkj in the kth equipment base station meets the condition that the number of the images of the line with the overhaul object as the inter-equipment is N (Pkj), and then the gray value analysis of the target image Pkj in the kth equipment base station meets the condition that the number of the images with the overhaul object as the equipment per se is N (Pkj) -N (Pkj);
then, the device repair frequency index of the kth device base station is calculated to be Rk1= N (Pkj) -N (Pkj)/N (Pkj), and the inter-device line repair frequency index is calculated to be Rk2= N (Pkj)/N (Pkj).
The grey value analysis comprises the following processes:
performing gray value processing on an original image and a target image of an equipment base station by the same means to obtain an original gray image and a target gray image, analyzing line regions on the original gray image and the target gray image, calibrating a gray value corresponding to the line region on the original gray image to obtain a line region gray value set A = { A11, A12, A13., auv }, wherein Auv represents a line region line-th row gray value corresponding to the original gray image, calibrating a line region corresponding gray value on the target gray image to obtain a line region gray value set B = { B11, B12, B13., buv }, and Buv represents a line region line-th row gray value corresponding to the target gray image;
using the formula:
I＝I1·g1+I2·g2+I3·g3
calculating a gray value comprehensive deviation index I, wherein I1 represents a gray value integral deviation index, I2 represents a transverse adjacent gray value similarity index, I3 represents a longitudinal adjacent gray value similarity index, g1, g2 and g3 are reference coefficients, and g1 is greater than 0 and is equal to or less than g2= g3; the reason for analyzing the two adjacent gray values in the same image is that the two adjacent gray values change synchronously along with the influence of dust along with the increase of time, and the comparison of the adjacent gray values has higher reliability when a part of dust in the image is rubbed off, so that the problem that the target gray value is directly compared with the original gray value and the difference is larger due to the influence of long-time dust accumulation is avoided;
when the gray value comprehensive deviation index is greater than or equal to a preset gray value comprehensive deviation index threshold, the overhaul object of the base station during overhaul is a line between the devices; and when the gray value comprehensive deviation index is smaller than a preset gray value comprehensive deviation index threshold, the overhaul object of the base station during overhaul is the equipment. The difference between an original image and a target image is comprehensively analyzed by utilizing the gray value deviation of the whole line region, the deviation of two transversely adjacent gray value and the deviation of two longitudinally adjacent gray value in the formula, and because the line touches the line when the line is overhauled and dust is accumulated when the line is placed in an external environment, but the dust can be rubbed to a certain extent after the line is touched, the possibility that the line region is touched is analyzed according to the whole and local gray value changes.
For example: calibrating the line region corresponding to the gray-scale value on the original gray-scale image to obtain a line region gray-scale value set A = { A11, A12, A13, A21, A22, A23, A31, A32, A33}, and calibrating the line region corresponding to the gray-scale value on the target gray-scale image to obtain a line region gray-scale value set B = { B11, B12, B13, B21, B22, B23, B31, B32, B33};
and performing difference on the horizontal adjacent gray values and the vertical adjacent gray values of the set B, so that the difference between the two gray values of { B22, B32} and the adjacent gray values is larger, and the calculated similarity index of the horizontal adjacent gray values and the similarity index of the vertical adjacent gray values can reflect whether the line between the devices has the evidence of overhaul to a certain extent.
Analyzing influence indexes of line arrangement among different base station equipment according to a distribution planning diagram of the lines among the equipment and a maintenance frequency index of the lines among the equipment, and comprising the following processes:
setting base stations containing the same number of devices as a comparison base station group, and recording the number of the comparison base station group as M; obtaining influence factors in the equipment base station, wherein the influence factors comprise the number Qxk of the inflection points of the circuit in the kth equipment base station of the xth contrast base station group and the length Lxk of the circuit between the adjacent inflection points, and the adjacent inflection points are the inflection points existing when the number of the inflection points contained in one circuit between two pieces of equipment is not less than two; the comparison base station group is set because the complexity of lines among equipment is naturally different due to different equipment capacities of different base stations, and the data result obtained by performing comparison analysis on the base stations with the same scale is more convincing;
using the formula:
calculating a line deviation coefficient of the xth reference base station group, wherein bk represents the number of lines between adjacent inflection points in the kth equipment base station, mx represents the number of equipment base stations in the xth reference base station group, and k is less than or equal to m; comparing the line deviation coefficients of the comparison base station groups with a preset deviation threshold respectively to obtain w which is the number of the comparison base station groups with the line deviation coefficients less than or equal to the deviation threshold;
calculating the similarity ratio Tx of the equipment line according to the line deviation coefficient:
Tx＝w/M
when the similarity ratio Tx of the equipment lines is less than or equal to 50%, outputting an influence index of the line arrangement among the equipment to be 0;
and when the similarity ratio Tx of the equipment line is more than 50%, acquiring an equipment base station maintenance frequency index in the comparison base station group, judging whether the influence factor is a main influence factor, and analyzing the influence index of line arrangement among different base station equipment according to the proportion of the main influence factor.
For example: there were two control base station groups X1 and X2, i.e. M =2;
in X1: the control base station group X1 includes two device base stations of the same specification, i.e., m1=2,
the first device base station includes 5 devices as the boxes in fig. 2, the connection lines between the devices are as in fig. 2, and 8 inflection points are as the circles in fig. 2, i.e., Q11=8
The line between 3 adjacent inflection points is as a dotted line in fig. 2, i.e., L11= {2m,2.5m,2m }, and b1=3;
the second device base station includes 5 devices as the boxes in fig. 3, the connection lines between the devices are as in fig. 3, and 7 inflection points are as shown by the circles in fig. 3, that is, Q12=7
The lines between 2 adjacent inflection points are shown by the dotted lines in fig. 3, i.e., L12= {5m,2.5m }, b2=2; then:
t1＝(26.5-20)/23.12＝0.27；
the same calculation is performed in X2, where X2 includes 3 device base stations with the same specification, and m2=3, and each device base station includes ten devices; calculating the maximum value and the minimum value of the three equipment base stations to be 80 and 54, wherein if the average value is 65.15, t2=0.52;
if the preset deviation threshold is 0.4, the control base station group smaller than the deviation threshold is X1, that is, w =1, and Tx =1/2.
Judging whether the influence factors are main influence factors or not, and analyzing influence indexes of line arrangement among different base station equipment according to the proportion of the main influence factors; comprehensively analyzing health state indexes of the equipment of different base stations according to the influence indexes of the influence factors among the equipment lines and the fault risk indexes of the equipment; the method comprises the following steps:
acquiring an equipment base station maintenance frequency index meeting the condition of similarity proportion, calculating the similarity of the maintenance frequency indexes among all base stations in a comparison base station group, and outputting an influence index of line arrangement among equipment to be 0 if the similarity is smaller than a preset similarity threshold; if the similarity is greater than or equal to a preset similarity threshold, outputting the number of turns of the line and the length of the line as main influence factors;
using the formula:
calculating an influence index S of the line arrangement, wherein the sigma mx is the total number of equipment base stations; the reason why the occupation ratio of the line inflection point among the devices in the base station is analyzed is that when the line inflection point and the line length are determined to be main influence factors, the health and safety of the base station devices are influenced more greatly when the line inflection point and the line length are heavier;
according to the influence index S of the line arrangement, the health state index of the kth equipment base station is calculated by using a formula: fk = S · Yk2+ Rk1 · Yk1.
The equipment base station health evaluation system based on equipment alarm comprises an alarm data analysis module, a maintenance condition analysis module, an influence index analysis module and a health state index analysis module;
the alarm data analysis module is used for analyzing alarm data and comprises an alarm data acquisition module and an event occurrence frequency calculation module; the alarm data acquisition module is used for acquiring the times of alarm generation of the base station equipment in a monitoring period and the state of the equipment when alarming; the event occurrence frequency calculation module is used for calculating respective time occurrence frequencies corresponding to the two faults in the equipment base station;
the maintenance condition analysis module is used for analyzing and acquiring the maintenance condition of the base station equipment when the alarm occurs and analyzing the maintenance condition;
the influence index analysis module is used for analyzing the influence indexes of the line arrangement among different base station devices when the overhaul object is the line among the devices;
and the health state index analysis module is used for comprehensively analyzing the health state indexes of the equipment by synthesizing the influence indexes of the line arrangement among the equipment and the fault risk indexes of the equipment per se on the equipment of different base stations.
The maintenance condition analysis module comprises a maintenance object judgment module and a maintenance frequency index calculation module;
the overhaul object judgment module is used for judging whether an overhaul object of the equipment base station is an equipment or a line between the equipment; the overhaul object judgment module comprises a gray value acquisition module, a gray value comprehensive deviation index calculation module and a gray value judgment module;
the gray value acquisition module is used for acquiring a gray value of a corresponding line area on an original image and a target image of the equipment base station, wherein the original image is an image of a line which is initially arranged on the equipment base station, and the target image is an image of the line when the equipment is alarmed to overhaul; the gray value comprehensive deviation index calculation module is used for calculating a gray value comprehensive deviation index of the original image and the target image; the gray value judgment module analyzes the object to be overhauled during alarming according to the gray value comprehensive deviation index;
and the maintenance frequency index calculation module is used for calculating the frequency index when the maintenance object is the equipment and the frequency index when the maintenance object is the line between the equipment.
The influence index meter analysis module comprises an influence factor determination module and an influence index calculation module;
the influence factor determining module is used for determining whether the influence factors of the lines among the devices are main influence factors or not, wherein the influence factors comprise the number of turns of the lines and the length of the lines; the influence factor determination module comprises a comparison base station group setting module, a line deviation coefficient calculation module and a line similarity proportion calculation module;
the comparison base station group setting module is used for setting a comparison base station group, the line deviation coefficient calculating module is used for calculating the line deviation coefficient of the base stations in the same comparison base station group, and the line similarity proportion calculating module is used for analyzing the line deviation coefficient to obtain a line similarity proportion;
and the influence index calculation module is used for calculating the influence index of the line arrangement when the influence factor of the line between the devices is determined as the main influence factor.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. The equipment alarm-based equipment base station health assessment method is characterized by comprising the following processes:
acquiring the times of alarming of base station equipment in a monitoring period and the state of the equipment when alarming; distinguishing fault conditions of the equipment according to the state when the equipment gives an alarm, wherein the fault conditions comprise an internal source fault of the equipment and a line fault between the equipment, and acquiring event occurrence frequencies Yk1 and Yk2 of the internal source fault and the line fault between the equipment in a kth equipment base station;
acquiring the maintenance condition of base station equipment when an alarm occurs, analyzing maintenance objects of the base station during maintenance and calculating maintenance frequency indexes aiming at different maintenance objects, wherein the maintenance objects comprise the equipment and lines among the equipment;
when the overhaul object is a line between equipment, acquiring a distribution planning diagram of the line between different base station equipment, and analyzing influence indexes of line arrangement between different base station equipment according to the distribution planning diagram of the line between the equipment and the overhaul frequency index of the line between the equipment;
comprehensively analyzing health state indexes of equipment of different base stations according to influence indexes of line arrangement among the equipment and fault risk indexes of the equipment; when the health state index is less than or equal to the preset health state index threshold value, the equipment of the base station is overhauled again, and the line between the equipment is replaced; and when the health state index is larger than the preset health state index threshold value, the base station is continuously monitored.
2. The equipment alarm-based equipment base station health assessment method according to claim 1, wherein: the analyzing of the overhaul object of the base station during overhaul and the calculation of the overhaul frequency index for different overhaul objects comprises the following processes:
acquiring an original image Pk0 of a circuit which is primarily arranged in a kth equipment base station equipment and a target image Pkj of the circuit when the kth equipment base station gives an alarm for maintenance, sequentially performing gray value analysis on n (Pkj) target images Pkj and the original image Pk0, judging a maintenance object according to the gray value, wherein n (Pkj) represents the maximum value of the number of the target images Pkj in the kth equipment base station, and j is less than or equal to n (Pkj);
when the overhaul object is an inter-equipment line, recording that the gray value analysis of the target image Pkj in the kth equipment base station meets the condition that the number of the images of the line with the overhaul object as the inter-equipment is N (Pkj), and then the gray value analysis of the target image Pkj in the kth equipment base station meets the condition that the number of the images with the overhaul object as the equipment per se is N (Pkj) -N (Pkj);
then, the device repair frequency index of the kth device base station is calculated to be Rk1= N (Pkj) -N (Pkj)/N (Pkj), and the inter-device line repair frequency index is calculated to be Rk2= N (Pkj)/N (Pkj).
3. The equipment alarm-based equipment base station health assessment method according to claim 2, wherein: the grey value analysis comprises the following processes:
performing gray value processing on an original image and a target image of an equipment base station by the same means to obtain an original gray image and a target gray image, analyzing line regions on the original gray image and the target gray image, calibrating the gray values corresponding to the line regions on the original gray image to obtain a line region gray value set A = { A11, A12, A13,. Multidot.Auv }, wherein Auv represents the gray value of the u-th row and the v-th column of the line region corresponding to the original gray image, calibrating the gray values corresponding to the line regions on the target gray image to obtain a line region gray value set B = { B11, B12, B13,. Multidot.Buv }, and Buv represents the gray value of the u-th row and the v-th column of the line region corresponding to the target gray image;
using the formula:
I＝I1·g1+I2·g2+I3·g3
calculating a gray value comprehensive deviation index I, wherein I1 represents a gray value integral deviation index, I2 represents a transverse adjacent gray value similarity index, I3 represents a longitudinal adjacent gray value similarity index, g1, g2 and g3 are reference coefficients, and g1 is more than 0 and less than or equal to g2= g3;
when the gray value comprehensive deviation index is greater than or equal to a preset gray value comprehensive deviation index threshold, the overhaul object of the base station during overhaul is a line between the devices; and when the gray value comprehensive deviation index is smaller than a preset gray value comprehensive deviation index threshold, the overhaul object of the base station during overhaul is the equipment.
4. The equipment alarm based equipment base station health assessment method of claim 3, wherein: the method for analyzing the influence indexes of the line arrangement among different base station equipment according to the distribution planning diagram of the lines among the equipment and the overhaul frequency indexes of the lines among the equipment comprises the following steps:
setting base stations containing the same number of devices as a comparison base station group, and recording the number of the comparison base station group as M; obtaining influence factors in an equipment base station, wherein the influence factors comprise the number Qxk of line inflection points in the kth equipment base station of an xth contrast base station group and the line length Lxk between adjacent inflection points, and the adjacent inflection points are the inflection points existing when the number of the inflection points in a line between two pieces of equipment is not less than two;
using the formula:
calculating a line deviation coefficient of the xth reference base station group, wherein bk represents the number of lines between adjacent inflection points in the kth equipment base station, mx represents the number of equipment base stations in the xth reference base station group, and k is less than or equal to m; comparing the line deviation coefficients of the comparison base station groups with a preset deviation threshold respectively to obtain w which is the number of the comparison base station groups with the line deviation coefficients less than or equal to the deviation threshold;
calculating the similarity ratio Tx of the equipment line according to the line deviation coefficient:
Tx＝w/M
when the similarity ratio Tx of the equipment lines is less than or equal to 50%, outputting an influence index of the line arrangement among the equipment to be 0;
and when the similarity ratio Tx of the equipment line is more than 50%, acquiring an equipment base station maintenance frequency index in the comparison base station group, judging whether the influence factor is a main influence factor, and analyzing the influence index of line arrangement among different base station equipment according to the proportion of the main influence factor.
5. The equipment alarm-based equipment base station health assessment method according to claim 4, wherein: judging whether the influence factors are main influence factors or not, and analyzing influence indexes of line arrangement among different base station equipment according to the proportion of the main influence factors; comprehensively analyzing the health state indexes of the equipment of different base stations according to the influence indexes of the influence factors among the equipment lines and the fault risk indexes of the equipment; the method comprises the following steps:
acquiring an equipment base station maintenance frequency index meeting the condition of similarity proportion, calculating the similarity of the maintenance frequency indexes among all base stations in a comparison base station group, and outputting an influence index of line arrangement among equipment to be 0 if the similarity is smaller than a preset similarity threshold; if the similarity is greater than or equal to the preset similarity threshold, outputting the number of turning points of the line and the length of the line as main influence factors;
using the formula:
calculating an influence index S of the line arrangement, wherein sigma mx is the total number of equipment base stations;
according to the influence index S of the line arrangement, the health state index of the kth equipment base station is calculated by using a formula: fk = S · Yk2+ Rk1 · Yk1.
6. An equipment alarm-based equipment base station health assessment system applied to the equipment alarm-based equipment base station health assessment method according to any one of claims 1 to 5, characterized by comprising an alarm data analysis module, a maintenance condition analysis module, an influence index analysis module and a health status index analysis module;
the alarm data analysis module is used for analyzing alarm data and comprises an alarm data acquisition module and an event occurrence frequency calculation module; the alarm data acquisition module is used for acquiring the times of alarm occurrence of the base station equipment in a monitoring period and the state of the equipment when the equipment alarms; the event occurrence frequency calculation module is used for calculating the respective time occurrence frequencies corresponding to the two faults in the equipment base station;
the maintenance condition analysis module is used for analyzing and acquiring the maintenance condition of the base station equipment when the alarm occurs and analyzing the maintenance condition;
the influence index analysis module is used for analyzing the influence index of the line arrangement among different base station devices when the overhaul object is a line among the devices;
the health state index analysis module is used for comprehensively analyzing the health state indexes of different base stations by integrating the influence indexes of line arrangement among the devices and the fault risk indexes of the devices.
7. The equipment alarm based equipment base station health assessment system of claim 6, wherein: the maintenance condition analysis module comprises a maintenance object judgment module and a maintenance frequency index calculation module;
the maintenance object judgment module is used for judging whether the maintenance object of the equipment base station is the equipment or a line between the equipment; the overhaul object judgment module comprises a gray value acquisition module, a gray value comprehensive deviation index calculation module and a gray value judgment module;
the gray value acquisition module is used for acquiring a gray value of a corresponding line area on an original image and a target image of an equipment base station, wherein the original image is an image of a line which is initially arranged on the equipment base station, and the target image is an image of the line when equipment alarm is overhauled; the gray value comprehensive deviation index calculation module is used for calculating a gray value comprehensive deviation index of the original image and the target image; the gray value judgment module analyzes an object to be overhauled during alarming according to the gray value comprehensive deviation index;
the maintenance frequency index calculation module is used for calculating the frequency index when the maintenance object is the equipment and the frequency index when the maintenance object is the line between the equipment.
8. The equipment alarm based equipment base station health assessment system of claim 6, wherein: the influence index meter analysis module comprises an influence factor determination module and an influence index calculation module;
the influence factor determining module is used for determining whether the influence factors of the lines among the devices are main influence factors or not, wherein the influence factors comprise the number of turns of the lines and the length of the lines; the influence factor determination module comprises a comparison base station group setting module, a line deviation coefficient calculation module and a line similarity proportion calculation module;
the comparison base station group setting module is used for setting a comparison base station group, the line deviation coefficient calculating module is used for calculating the line deviation coefficient of the base stations in the same comparison base station group, and the line similarity proportion calculating module is used for analyzing the line deviation coefficient to obtain a line similarity proportion;
and the influence index calculation module is used for calculating the influence index of the line arrangement when the influence factor of the line between the devices is determined as the main influence factor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210810929.5A CN115278742B (en) | 2022-07-11 | 2022-07-11 | Equipment alarm-based equipment base station health assessment system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210810929.5A CN115278742B (en) | 2022-07-11 | 2022-07-11 | Equipment alarm-based equipment base station health assessment system and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115278742A true CN115278742A (en) | 2022-11-01 |
| CN115278742B CN115278742B (en) | 2023-04-14 |
Family
ID=83766016
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210810929.5A Active CN115278742B (en) | 2022-07-11 | 2022-07-11 | Equipment alarm-based equipment base station health assessment system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115278742B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7797367B1 (en) * | 1999-10-06 | 2010-09-14 | Gelvin David C | Apparatus for compact internetworked wireless integrated network sensors (WINS) |
| CN111160685A (en) * | 2019-09-23 | 2020-05-15 | 上海安恪企业管理咨询有限公司 | Maintenance decision method based on equipment comprehensive health condition analysis and management |
| CN112348277A (en) * | 2020-11-18 | 2021-02-09 | 国家电网有限公司 | Distribution network reliability assessment system considering maintenance strategy and equipment health index |
| CN114295940A (en) * | 2022-01-04 | 2022-04-08 | 珠海市鸿瑞信息技术股份有限公司 | Distribution network fault state monitoring system and method based on smart city |
| CN114696467A (en) * | 2022-05-31 | 2022-07-01 | 广东电网有限责任公司佛山供电局 | Method and system for analyzing and processing fault tripping event of high-voltage transmission long line |
-
2022
- 2022-07-11 CN CN202210810929.5A patent/CN115278742B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7797367B1 (en) * | 1999-10-06 | 2010-09-14 | Gelvin David C | Apparatus for compact internetworked wireless integrated network sensors (WINS) |
| CN111160685A (en) * | 2019-09-23 | 2020-05-15 | 上海安恪企业管理咨询有限公司 | Maintenance decision method based on equipment comprehensive health condition analysis and management |
| CN112348277A (en) * | 2020-11-18 | 2021-02-09 | 国家电网有限公司 | Distribution network reliability assessment system considering maintenance strategy and equipment health index |
| CN114295940A (en) * | 2022-01-04 | 2022-04-08 | 珠海市鸿瑞信息技术股份有限公司 | Distribution network fault state monitoring system and method based on smart city |
| CN114696467A (en) * | 2022-05-31 | 2022-07-01 | 广东电网有限责任公司佛山供电局 | Method and system for analyzing and processing fault tripping event of high-voltage transmission long line |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115278742B (en) | 2023-04-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CA2634328C (en) | Method and system for trend detection and analysis | |
| US20140365179A1 (en) | Method and Apparatus for Detecting and Identifying Faults in a Process | |
| CN113485268B (en) | Industrial Internet of things-based multi-scene distributed information control system | |
| CN118536817A (en) | A cable channel monitoring system based on sensor network | |
| CN118134700A (en) | Comprehensive intelligent maintenance decision method and system for bridge structure | |
| CN109711111A (en) | A Method for Axial Temperature Prediction Using Hierarchical Multiple Stepwise Regression Analysis | |
| CN115951606A (en) | Intelligent factory production environment early warning processing method | |
| CN117057955B (en) | Bridge deformation intelligent monitoring system based on big data | |
| CN118464914B (en) | A product quality inspection device and inspection method based on visual inspection | |
| CN115278742B (en) | Equipment alarm-based equipment base station health assessment system and method | |
| CN110598265A (en) | Bridge health data abnormity correction method and system based on wavelet analysis | |
| CN117472893A (en) | Method for systematically improving traffic flow data quality | |
| CN116401137A (en) | Core particle health state prediction method and device, electronic equipment and storage medium | |
| CN115698879B (en) | Abnormal modulation cause determination device, abnormal modulation cause determination method, and abnormal modulation cause determination program | |
| CN119046600B (en) | Cable-stayed bridge condition assessment method, system and storage medium based on inspection and monitoring fusion | |
| CN120180373A (en) | Intelligent fault early warning analysis system and method for electrical equipment based on artificial intelligence | |
| CN118247940B (en) | Electrical fire early warning system and method based on Internet of things | |
| CN118152994B (en) | Security factor influence calculation method | |
| JP6587950B2 (en) | Program, apparatus, and method capable of detecting time series change point by scalar feature | |
| CN113672658A (en) | A method for identifying faulty data in on-line monitoring of power equipment based on complex correlation coefficient | |
| CN117762716A (en) | Method and system for rapidly positioning memory bank abnormality on main board | |
| Shamsuzzaman et al. | Optimization design of X̅ &EWMA control chart for minimizing mean number of defective units per out-of-control case | |
| Asplund et al. | Assessment of the data quality of wayside wheel profile measurements. | |
| KR101098044B1 (en) | Quality Error Prediction System and Method of Railroad Vehicle Auxiliary Power Supply | |
| Cha et al. | Some probability functions in reliability and their applications |
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 |