Electronic product maintenance remote monitoring and management system based on Internet of Things
The IoT remote monitoring system solves the problems of identifying redundant parts and tracing oversights in electronic product repair, enabling efficient repair quality traceability and rational resource allocation, and improving the controllability and resource utilization of the repair process.
Patent Information
- Application Number
- CN202510944854.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-09
- Publication Date
- 2025-11-21
AI Technical Summary
In existing technologies, it is difficult to accurately identify and trace redundant parts during the repair of electronic products, especially the distinction between new and old parts, which makes it difficult to trace the quality of repairs and to accurately locate the faulty links.
An IoT-based remote monitoring system is adopted, which uses a part type identification module, a fault summary module, a fault correlation analysis module, and a key monitoring correlation module to identify the type of redundant parts, trace fault links, and perform correlation analysis, construct a key monitoring time period sequence, and carry out targeted remote monitoring.
It enables accurate identification of redundant parts and precise tracing of oversights, improves the traceability of maintenance quality, allows for timely detection of problems and implementation of improvement measures, and rationally allocates remote monitoring resources, thereby enhancing the controllability and resource utilization of the maintenance process.
Smart Images
Figure CN120996773A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application belongs to the technical field of monitoring management, and in particular relates to an electronic product maintenance remote monitoring and management system based on the Internet of Things. BACKGROUND
[0002] With the rapid development of the Internet of Things technology, the electronic product maintenance industry is gradually transforming towards intelligence and remote. The electronic product maintenance remote monitoring and management system based on the Internet of Things emerges as the times require. Such a system aims to realize remote monitoring and efficient management of the electronic product maintenance process through real-time data collection, transmission and analysis, improve maintenance efficiency and quality, and reduce maintenance costs.
[0003] In the prior art, during the maintenance of electronic products, maintenance personnel may be negligent or unskilled, resulting in excess parts after installation. However, there is often a lack of effective means to accurately identify and classify these excess parts, especially it is difficult to distinguish between new parts that are originally redundant and parts that are not installed due to maintenance operation errors. This makes it impossible to accurately locate the specific maintenance installation link where the flaw occurs when tracing the maintenance quality, and it is also difficult to comprehensively trace the part information used in the maintenance process.
[0004] Therefore, the present application provides an electronic product maintenance remote monitoring and management system based on the Internet of Things. SUMMARY
[0005] In order to make up for the deficiencies of the prior art and solve at least one technical problem raised in the background art.
[0006] The technical scheme adopted by the present application to solve its technical problems is:
[0007] An electronic product maintenance remote monitoring and management system based on the Internet of Things, comprising:
[0008] A part type identification module: identifying the type of excess parts after installation of electronic product maintenance in a historical remote monitoring period, and screening out excess new parts;
[0009] A flaw link induction module: tracing the maintenance installation link of the excess new parts screened out after installation of electronic product maintenance in each historical remote monitoring period, identifying stable flaw links, and integrating them to construct a stable flaw link sequence;
[0010] A flaw correlation analysis module: performing correlation analysis on each stable flaw link in the stable flaw link sequence to obtain a link correlation degree, and identifying a flaw correlation link according to the link correlation degree;
[0011] Key monitoring association module: for the loophole association link, obtain the key monitoring period, and obtain the monitoring management priority value corresponding to the key monitoring period, construct the key monitoring period sequence according to the monitoring management priority value, and carry out key remote monitoring on the maintenance installation period.
[0012] As a further scheme of the application, the type of the redundant parts after the maintenance and installation of the electronic product is identified, and the target comparison contour line and the target reference contour line are obtained as follows:
[0013] The redundant part model and the maintenance new part model are constructed respectively; the contour lines are extracted from the redundant part model and the maintenance new part model respectively as the redundant part contour line and the maintenance new part contour line;
[0014] An arbitrary redundant part contour line is selected as the target comparison contour line, and a maintenance new part contour line in the same position as the target comparison contour line is selected as the target reference contour line.
[0015] As a further scheme of the application, the screening process of the redundant new parts is as follows:
[0016] The target comparison contour line and the target reference contour line are respectively input into the two-dimensional coordinate system to construct a coincidence comparison two-dimensional model; all coordinate points on the target comparison contour line and all coordinate points on the target reference contour line are extracted in the coincidence comparison two-dimensional model to obtain target comparison coordinate points and target reference coordinate points; the target comparison coordinate points and the target reference coordinate points are combined in the order of the start coordinate to the end coordinate on the contour line to obtain a target analysis group; and each target analysis group is input into the Euclidean distance formula in the order of the start coordinate to the end coordinate on the contour line to output a target contour difference value;
[0017] If the target contour difference value is equal to the target contour difference threshold, the analyzed target comparison contour line is marked as a target coincidence contour line;
[0018] If multiple target comparison contour lines on a redundant part are all target coincidence contour lines, the analyzed redundant part is marked as a redundant new part.
[0019] As a further scheme of the application, the maintenance and installation link tracing process of the redundant new parts screened after each maintenance and installation of the electronic product is as follows:
[0020] The redundant new parts screened after each maintenance and installation of the electronic product are identified according to the new part types on the part maintenance list each time, and the installation link is extracted according to the identified new part types as a target installation link;
[0021] In the redundant new parts screened out after each electronic product maintenance installation, the number of redundant new parts belonging to the same target installation link is extracted, the proportion of the total number of redundant new parts screened out after each electronic product maintenance installation is obtained, and the number ratio of the same unit link is obtained.
[0022] The unit same link quantity ratio corresponding to each historical remote monitoring period is obtained, standard deviation calculation and mean value calculation are performed respectively, and the same link quantity ratio standard deviation and the same link quantity ratio mean value are output and input into the variation coefficient calculation formula, and the target link analysis value is output.
[0023] As a further scheme of the application, the process of identifying stable leak links and constructing a stable leak link sequence is as follows:
[0024] If the target link analysis is less than or equal to the target link analysis threshold, it means that the unit same link quantity ratio of the target installation link in each historical remote monitoring period has a small fluctuation degree, and is a stable leak link.
[0025] The target link analysis value corresponding to each stable leak link is extracted, and sorted from small to large according to the target link analysis value, and a stable leak link sequence is constructed.
[0026] As a further scheme of the application, the process of performing correlation analysis on each stable leak link in the stable leak link sequence is as follows:
[0027] Each stable leak link in the stable leak link sequence is combined into a link correlation analysis group in any two groups, and a target correlation analysis group is obtained, and the front stable leak link and the rear stable leak link in the target correlation analysis group are extracted.
[0028] The maintenance installation period corresponding to the front stable leak link and the rear stable leak link in the target correlation analysis group in the historical remote monitoring period is extracted as the front stable leak link period and the rear stable leak link period, and the end time point corresponding to the front stable leak link period and the start time point corresponding to the rear stable leak link period are obtained as the front end time point and the rear start time point.
[0029] As a further scheme of the application, the process of obtaining the link correlation degree is as follows:
[0030] The time interval between the front end time point and the rear start time point is obtained, and the proportion of the total time length of the historical remote monitoring period is calculated, and the link correlation interval length is obtained, and the historical remote monitoring period in which the target correlation analysis group exists is extracted as the target correlation analysis period.
[0031] The link correlation interval duration of the target correlation analysis group in each target correlation analysis period is input into a Euclidean calculation formula according to the time sequence corresponding to the target correlation analysis period, and a link correlation stability value is output.
[0032] The link correlation interval duration of the target correlation analysis group in each target correlation analysis period is input into a Euclidean calculation formula according to the time sequence corresponding to the target correlation analysis period, and a link correlation stability value is output.
[0033] As a further scheme of the application, the identification process of the loophole correlation link is as follows:
[0034] If the link correlation degree is less than or equal to the link correlation degree threshold value, the two stable loophole links in the analyzed target correlation analysis group are identified as loophole correlation links.
[0035] As a further scheme of the application, the acquisition process of the key monitoring period is as follows:
[0036] The front stable loophole link in all target correlation analysis groups is extracted, the corresponding maintenance installation period is acquired, the earliest maintenance installation period in the historical remote monitoring period is selected, and the corresponding target correlation analysis group is taken as the first target analysis group.
[0037] The front stable loophole link in the remaining target correlation analysis group except the first target analysis group is extracted, and is combined with the rear stable loophole link in the first target analysis group to obtain a combined correlation analysis group.
[0038] If the link correlation degree is less than or equal to the link correlation degree threshold value, it indicates that the correlation degree of the two stable loophole links in the analyzed target correlation analysis group is relatively close, the two stable loophole links in the analyzed combined correlation analysis group are identified as loophole correlation links, and the analyzed target correlation analysis group is combined with the first target analysis group to obtain a combined management group.
[0039] The link correlation degree corresponding to the combined correlation analysis group is acquired, and if the link correlation degree is greater than the link correlation degree threshold value, it indicates that the correlation degree of the two stable loophole links in the analyzed target correlation analysis group is relatively distant, the two stable loophole links in the analyzed combined correlation analysis group are identified as non-loophole correlation links, and other target correlation analysis groups are recombined and analyzed according to the combination operation of the combined management group to obtain a combined management group.
[0040] The front stable loophole link in each combined management group is extracted, and the corresponding maintenance installation period is acquired as a key monitoring period.
[0041] As a further scheme of the present application, the process of obtaining the monitoring management priority value and constructing the key monitoring time period sequence is as follows:
[0042] The proportion of the number of target correlation analysis groups in each combined management group in the total number of target correlation analysis groups is obtained to obtain the monitoring management priority value.
[0043] According to the size of the monitoring management priority value, the key monitoring time periods are sorted in descending order to construct the key monitoring time period sequence.
[0044] The beneficial effects of the present application are as follows:
[0045] 1. The present application identifies the types of redundant parts after the repair and installation of electronic products in the historical remote monitoring period, and screens out redundant new parts, which helps to screen out the repair and installation links with operation flaws, provides traceability basis for the repair quality of electronic products, and can also trace the part information used in the repair process, timely find quality problems and take improvement measures, and in multiple historical remote monitoring periods, the redundant new parts screened out after the repair and installation of electronic products are traced back to the repair and installation links, stable flaw links are identified and integrated, and a stable flaw link sequence is constructed, so that not only can a link with low average value be identified as a stable flaw link due to low target link analysis value, and hidden hazard repair and installation links are avoided, but also the risk level of the link is distinguished, the monitoring frequency of the front link in the sequence is increased, the monitoring intensity of the rear link in the sequence is reduced, and the remote monitoring resources are reasonably distributed.
[0046] 2. The present application performs correlation analysis on each stable flaw link in the stable flaw link sequence to obtain a link correlation degree, identifies a flaw correlation link according to the link correlation degree, obtains a monitoring management priority value for the flaw correlation link, and constructs a key monitoring time period sequence according to the monitoring management priority value, so that the importance of each time period is clear, the monitoring personnel can prioritize the high-risk time period for real-time monitoring, the working time and task allocation of the monitoring personnel are reasonably arranged, the main effort is placed on the key monitoring time period, the utilization rate of the remote monitoring resources for electronic product repair is improved, strict monitoring of the key monitoring time period can timely find non-standard operation and potential problems in the repair process, timely correct and prevent, and the controllability of the entire repair process is enhanced. BRIEF DESCRIPTION OF DRAWINGS
[0047] The present application will be further described below with reference to the accompanying drawings.
[0048] Fig. 1 is a schematic diagram of an internal module of an electronic product repair remote monitoring and management system based on the Internet of Things;
[0049] Fig. 2 This is a flowchart of the steps corresponding to the Internet of Things-based remote monitoring and management system for electronic product repair according to the present invention.
[0050] Fig. 3 This is a flowchart illustrating the decision-making process of a module within an IoT-based remote monitoring and management system for electronic product repair. Detailed Implementation
[0051] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.
[0052] Example 1
[0053] Please see Figs. 1-3 As shown in the embodiment of the present invention, a remote monitoring and management system for electronic product repair based on the Internet of Things includes the following steps:
[0054] Step 1: During the historical remote monitoring period, identify the types of redundant parts after the repair and installation of electronic products and filter out redundant new parts;
[0055] It should be noted that the outline of any extra parts after installation is a regular shape;
[0056] In some embodiments, computer vision technology is used to capture images of redundant parts after the repair and installation of electronic products using a camera, and the captured images are processed using image processing and machine learning algorithms to extract feature information of the redundant parts after the repair and installation of electronic products from the images and construct a redundant parts model.
[0057] It should be noted that the feature information includes: edges, corners, contours, etc.
[0058] For example, the contour lines on the redundant part model are extracted as the redundant part contour lines;
[0059] Arbitrarily select the outline of an extra part as the target comparison outline;
[0060] Similarly, obtain the feature information of the new parts to be repaired from the parts repair list, and construct a model for repairing the new parts;
[0061] It should be noted that the construction method for the new repair part model is the same as that for the redundant part model, and the feature information includes: edges, corners, contours, etc.
[0062] Extract the outline from the model of the new repair part and use it as the outline of the new repair part;
[0063] Select the contour line of the new part to be repaired at the same position as the target contour line as the target reference contour line;
[0064] It should be noted that the target reference profile line and the target comparison profile line are on the same type of part;
[0065] The target comparison profile line and the target reference profile line are superimposed and compared, and the process is as follows:
[0066] The target comparison profile line and the target reference profile line are respectively input into a two-dimensional coordinate system to construct a superimposed comparison two-dimensional model;
[0067] In the superimposed comparison two-dimensional model, all coordinate points on the target comparison profile line and all coordinate points on the target reference profile line are extracted to obtain target comparison coordinate points and target reference coordinate points;
[0068] Among them, the target comparison coordinate points include the start point coordinate and the end point coordinate on the target comparison profile line, and the target reference coordinate points include the start point coordinate and the end point coordinate on the target reference profile line;
[0069] The target comparison coordinate points and the target reference coordinate points are combined in the order of the start point coordinate→the end point coordinate on the profile line to obtain a target analysis group;
[0070] For example, all coordinate points on the target comparison profile line are , , , and , and all coordinate points on the target reference profile line are , , , and ;
[0071] Among them, and are the start point coordinate and the end point coordinate on the target comparison profile line, and are the start point coordinate and the end point coordinate on the target reference profile line;
[0072] The target comparison coordinate points and the target reference coordinate points are combined, such as and are combined to form a target analysis group;
[0073] The target comparison coordinate points and the target reference coordinate points are combined, such as and are combined to form a target analysis group;
[0074] The target comparison coordinate points and the target reference coordinate points are combined, such as and The target comparison coordinate points and the target reference coordinate points are combined, such as
[0075] The target comparison coordinate points and the target reference coordinate points are combined, such as and The target comparison coordinate points and the target reference coordinate points are combined, such as
[0076] The target comparison coordinate points and the target reference coordinate points are combined, such as and The target comparison coordinate points and the target reference coordinate points are combined, such as
[0077] Each target analysis group is input into the Euclidean distance formula in the order of the starting point coordinate→the ending point coordinate on the contour line, and the target contour difference value is output
[0078] Specifically, the Euclidean distance formula is: wherein R represents the total number of target analysis groups, represents the e-th target comparison coordinate point, represents the e-th target reference coordinate point.
[0079] The target contour difference value is compared with the target contour difference threshold value, and the process is as follows:
[0080] If the target contour difference value is not equal to the target contour difference threshold value, it indicates that the target comparison contour line and the target reference contour line have a large difference, and the analyzed target comparison contour line is marked as a target difference contour line.
[0081] If the target contour difference value is equal to the target contour difference threshold value, it indicates that the target comparison contour line and the target reference contour line coincide, and the analyzed target comparison contour line is marked as a target coincidence contour line.
[0082] If multiple target comparison contour lines on one redundant part are all target coincidence contour lines, the analyzed redundant part is marked as a redundant new part.
[0083] If at least one target comparison contour line on one redundant part is a target difference contour line, the analyzed redundant part is marked as a redundant old part.
[0084] It should be noted that the purpose of screening the redundant new part is:
[0085] Purpose one: The screening of the redundant new part helps to distinguish new and old parts, provides a traceability basis for electronic product maintenance quality, and can trace the part information used in the maintenance process, timely find quality problems and take improvement measures.
[0086] Objective two: screening of redundant new parts is an important link in the maintenance process, which helps to screen out the maintenance installation link of maintenance operation flaws;
[0087] Step two: in a plurality of historical remote monitoring periods, the redundant new parts screened out after each electronic product maintenance installation are traced back to the maintenance installation link, the stable flaw link is identified and integrated, and the stable flaw link sequence is constructed;
[0088] It should be noted that each historical remote monitoring period corresponds to an electronic product maintenance installation operation;
[0089] In some embodiments, the redundant new parts screened out after each electronic product maintenance installation are identified according to the new part types on each part maintenance list, and the installation link is extracted according to the identified new part types as the target installation link;
[0090] It should be noted that the part maintenance list is provided by those skilled in the art, and each new part type on the part maintenance list corresponds to a maintenance installation link;
[0091] Among the redundant new parts screened out after each electronic product maintenance installation, the number of redundant new parts belonging to the same target installation link is extracted, the proportion of the total number of redundant new parts screened out after each electronic product maintenance installation is obtained, and the unit same link quantity ratio is obtained;
[0092] The unit same link quantity ratio corresponding to each historical remote monitoring period is obtained, and the standard deviation is calculated, and the same link quantity ratio standard deviation is output;
[0093] The unit same link quantity ratio corresponding to each historical remote monitoring period is calculated by mean value, and the same link quantity ratio mean value is output;
[0094] The same link quantity ratio standard deviation and the same link quantity ratio mean value are input into the coefficient of variation calculation formula, and the target link analysis value is output ;
[0095] Specifically, the coefficient of variation calculation formula is: , wherein, represents the same link quantity ratio standard deviation, represents the same link quantity ratio mean value;
[0096] It can be understood that the meaning represented by the target link analysis value is that the fluctuation degree of the target installation link in multiple historical periods is quantified by the ratio of the standard deviation to the mean value, which helps to identify the link with small fluctuation and frequent error, and is conducive to tilting the electronic product maintenance remote monitoring resources to the link with small fluctuation and frequent error, reducing the excessive monitoring of the link with large fluctuation and non-frequent error, and improving the utilization rate of the electronic product maintenance remote monitoring resources;
[0097] The target link analysis value is compared with the target link analysis threshold value, and the process is as follows:
[0098] If the target link analysis value is greater than the target link analysis threshold value, it means that the number of unit same links existing in the target installation link in each historical remote monitoring period is greater than the fluctuation degree, which is a random error link.
[0099] If the target link analysis value is less than or equal to the target link analysis threshold value, it means that the number of unit same links existing in the target installation link in each historical remote monitoring period is less than the fluctuation degree, which is a stable error link.
[0100] The target link analysis value corresponding to each stable error link is extracted, and the target link analysis value is sorted from small to large to construct a stable error link sequence.
[0101] It should be noted that the purpose of constructing the stable error link sequence is:
[0102] Purpose one: The stable error link sequence is sorted by CV value to distinguish the risk level of the link, to realize increasing the monitoring frequency of the front link (high priority) in the sequence and reducing the monitoring strength of the rear link in the sequence, which is helpful for the reasonable allocation of remote monitoring resources.
[0103] Purpose two: The sequence is constructed based on historical full data, and the fluctuation is quantified by CV value, so that even if the mean value of a certain link is low, it can still be identified as a stable error link due to the low target link analysis value, avoiding ignoring the hidden maintenance installation link.
[0104] The specific implementation of the embodiment is: in a historical remote monitoring period, type identification is performed on the redundant parts after the electronic product is repaired and installed, and new redundant parts are screened out, which helps to screen out the maintenance and installation link with operation flaws, provides a traceability basis for the repair quality of the electronic product, and can also trace the part information used in the repair process, timely find quality problems and take improvement measures, and in multiple historical remote monitoring periods, the new redundant parts screened out after the repair and installation of the electronic product each time are traced back to the repair and installation link, stable flaw links are identified, and are integrated, and a stable flaw link sequence is constructed, so that not only can a link with a low average value be identified as a stable flaw link even if the target link analysis value is low, and hidden danger repair and installation links are avoided, but also the risk level of the link is distinguished, the monitoring frequency of the front link in the sequence is increased, the monitoring strength of the rear link in the sequence is reduced, and the reasonable allocation of remote monitoring resources is facilitated.
[0105] Embodiment 2
[0106] Please refer to Figs. 1-3 The electronic product repair remote monitoring and management system based on the Internet of Things comprises the following steps
[0107] Step three: performing correlation analysis on each stable flaw link in the stable flaw link sequence to obtain a link correlation degree, and identifying a flaw correlation link according to the link correlation degree;
[0108] In some embodiments, each stable flaw link in the stable flaw link sequence is combined into a set of link correlation analysis groups in any two groups, and a plurality of sets of link correlation analysis groups are obtained;
[0109] A set of link correlation analysis groups is selected as a target correlation analysis group, and a front stable flaw link and a rear stable flaw link in the target correlation analysis group are extracted;
[0110] The front stable flaw link in the target correlation analysis group refers to the stable flaw link that is sorted in front in time sequence in the repair and installation process, and the rear stable flaw link in the target correlation analysis group refers to the stable flaw link that is sorted behind in time sequence in the repair and installation process;
[0111] The repair and installation time period corresponding to the front stable flaw link in the target correlation analysis group in the historical remote monitoring period is extracted as a front stable flaw link time period, and the terminal time point corresponding to the front stable flaw link time period is obtained as a front terminal time point;
[0112] Similarly, the repair and installation time period corresponding to the rear stable flaw link in the target correlation analysis group in the historical remote monitoring period is extracted as a rear stable flaw link time period, and the starting time point corresponding to the rear stable flaw link time period is obtained as a rear starting time point;
[0113] obtaining the time interval between the front termination time point and the rear starting time point, and calculating the proportion of the total length of the historical remote monitoring period, to obtain the link correlation interval length;
[0114] It should be noted that the link correlation interval length is not 0.
[0115] Extracting the historical remote monitoring period of the target correlation analysis group as the target correlation analysis period.
[0116] Obtaining the link correlation interval length of the target correlation analysis group in each target correlation analysis period, and inputting the link correlation interval length of the target correlation analysis group in each target correlation analysis period into the Euclidean calculation formula according to the time sequence corresponding to the target correlation analysis period, and outputting to obtain the link correlation stability value .
[0117] Specifically, the Euclidean distance formula is: , wherein, represents the total number of link correlation interval lengths, represents the link correlation interval length in the first target correlation analysis period, represents the link correlation interval length in the first target correlation analysis period;
[0118] The link correlation interval length of the target correlation analysis group in each target correlation analysis period is calculated by mean value, and the link correlation interval length mean value is output.
[0119] The link correlation stability value and the link correlation interval length mean value are input into the improved coefficient of variation calculation formula, and the link correlation degree is output.
[0120] Specifically, the improved coefficient of variation calculation formula is: , wherein, represents the link correlation interval length mean value;
[0121] It should be noted that the principle of the coefficient of variation calculation formula is to calculate the standard deviation and the mean value of a group of data, and then calculate the ratio of the standard deviation and the mean value. The above improved coefficient of variation calculation formula is obtained by the Euclidean calculation formula, and the data parameter calculated by the Euclidean calculation formula represents the distance between two coordinate points or two positions, which is helpful to understand the multiple parameters in a group of data. It can be understood as the gap (distance) between multiple parameters in a group of data, so the in the improved coefficient of variation calculation formula is relatively close.Compared with the standard deviation in the original coefficient of variation formula, the analysis accuracy of the environmental correlation degree of the two stable leakage links in the target correlation analysis group is improved, which is not affected by the mean value of multiple parameters in a set of data.
[0122] It can be understood that the meaning represented by the link correlation degree is: used to measure the correlation closeness between any two stable leakage links in the stable leakage link sequence, reflecting the degree of mutual influence and correlation of the two links in the maintenance and installation process. Not only can the remote monitoring resources be more targetedly allocated, with more emphasis on remote monitoring between links with high correlation degree and less emphasis on remote monitoring between links with low correlation degree, but also potential problems can be found in advance after the problem of the link in the maintenance and installation sequence appears, so as to timely correct and adjust to avoid affecting the problem of the link associated after it, thereby improving the identification accuracy of potential problem trends.
[0123] The link correlation degree is compared with the link correlation degree threshold value, and the process is as follows:
[0124] If the link correlation degree is greater than the link correlation degree threshold value, it means that the correlation degree of the two stable leakage links in the target correlation analysis group analyzed is relatively distant, and the two stable leakage links in the target correlation analysis group analyzed are identified as non-leakage correlation links.
[0125] If the link correlation degree is less than or equal to the link correlation degree threshold value, it means that the correlation degree of the two stable leakage links in the target correlation analysis group analyzed is relatively close, and the two stable leakage links in the target correlation analysis group analyzed are identified as leakage correlation links.
[0126] Step four: for the leakage correlation link, the key monitoring period is obtained, and the monitoring management priority value corresponding to the key monitoring period is obtained. According to the monitoring management priority value, a key monitoring period sequence is constructed, and the maintenance and installation period is monitored remotely in a targeted manner.
[0127] In some embodiments, for the leakage correlation link, the target correlation analysis group in which the leakage correlation link is located is extracted.
[0128] For example, the front stable leakage link in all target correlation analysis groups is extracted, the corresponding maintenance and installation period is obtained, the earliest maintenance and installation period in the historical remote monitoring period is selected, and the corresponding target correlation analysis group is taken as the first target analysis group.
[0129] The front stable leakage link in the remaining target correlation analysis group except the first target analysis group is extracted, and is combined with the rear stable leakage link in the first target analysis group to obtain a merged correlation analysis group.
[0130] If the link correlation degree is less than or equal to the link correlation degree threshold value, it is indicated that the two stable loophole link correlation degrees in the analyzed target correlation analysis group are relatively close, the two stable loophole links in the analyzed combined correlation analysis group are marked as loophole correlation links, and the analyzed target correlation analysis group and the first target analysis group are combined to obtain a combined management group;
[0131] If the link correlation degree is greater than the link correlation degree threshold value, it is indicated that the two stable loophole link correlation degrees in the analyzed target correlation analysis group are relatively distant, the two stable loophole links in the analyzed combined correlation analysis group are marked as non-loophole correlation links, and other target correlation analysis groups are recombined according to the combination operation of the combined management group to obtain a combined management group;
[0132] The front stable loophole link in each combined management group is extracted, and the corresponding maintenance installation period is obtained as a key monitoring period;
[0133] The proportion of the number of target correlation analysis groups in each combined management group in the total number of target correlation analysis groups is obtained to obtain a monitoring management priority value;
[0134] According to the size of the monitoring management priority value, the key monitoring periods are sorted in descending order to construct a key monitoring period sequence;
[0135] The purpose of constructing the key monitoring period sequence is:
[0136] Purpose one: The key monitoring period sequence clearly defines the importance of each period, and the monitoring personnel can prioritize real-time monitoring of high-risk periods according to the sequence, reasonably arrange the working hours and task allocation of the monitoring personnel, and focus on the key monitoring periods to improve the utilization rate of electronic product maintenance remote monitoring resources;
[0137] Purpose two: Strict monitoring of the key monitoring periods can timely discover non-standard operations and potential problems in the maintenance process, and timely correct and prevent, which helps to enhance the controllability of the entire maintenance process, ensures that the maintenance work is carried out according to the standard process, and improves the stability and reliability of the maintenance quality;
[0138] In the embodiment, each stable fault link in the stable fault link sequence is subjected to correlation analysis to obtain a link correlation degree, and a fault correlation link is identified according to the link correlation degree. A monitoring management priority value is obtained for the fault correlation link, and a key monitoring time period sequence is constructed according to the monitoring management priority value. Thus, the importance of each time period is determined, so that monitoring personnel can prioritize high-risk time periods for real-time monitoring, and the work time and task allocation of the monitoring personnel can be reasonably arranged, so that the monitoring personnel can focus on the key monitoring time periods, and the utilization rate of the remote monitoring resources for the electronic product maintenance can be improved. In addition, strict monitoring of the key monitoring time periods can help to find non-standard operations and potential problems in the maintenance process in a timely manner, so that the problems can be corrected and prevented in a timely manner, and the controllability of the entire maintenance process can be improved.
[0139] The basic principles, main features and advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited by the above embodiments, and the above embodiments and descriptions in the specification are only illustrative of the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the present application. The scope of protection of the present application is defined by the appended claims and their equivalents.
Claims
1. A remote monitoring and management system for electronic product repair based on the Internet of Things, characterized in that: include: Parts type identification module: During the historical remote monitoring period, the module identifies the types of redundant parts after the repair and installation of electronic products and filters out redundant new parts. Error tracking module: Within multiple historical remote monitoring cycles, the module traces the repair and installation process of redundant new parts selected after each electronic product repair and installation, identifies stable error tracking points, and summarizes and integrates them to construct a stable error tracking sequence. The error correlation analysis module performs correlation analysis on each stable error link in the stable error link sequence to obtain the link correlation degree, and identifies the error-related links based on the link correlation degree. Key monitoring and association module: For the links of oversight, key monitoring time periods are obtained, and the corresponding monitoring management priority values are obtained. Based on the monitoring management priority values, a key monitoring time period sequence is constructed, and key remote monitoring is carried out for maintenance and installation periods.
2. The remote monitoring and management system for electronic product repair based on the Internet of Things as described in claim 1, characterized in that: The process of identifying the type of excess parts after the repair and installation of electronic products, and obtaining the target comparison contour line and the target reference contour line, is as follows: Construct a redundant part model and a new repair part model respectively; extract contour lines from the redundant part model and the new repair part model respectively, as the contour lines of the redundant part and the new repair part. Arbitrarily select a redundant part outline as the target comparison outline, and select the outline of the new repair part at the same position as the part containing the target comparison outline as the target reference outline.
3. The remote monitoring and management system for electronic product repair based on the Internet of Things as described in claim 1, characterized in that: The process for screening excess parts is as follows: The target comparison contour line and the target reference contour line are respectively input into a two-dimensional coordinate system to construct a coincident comparison two-dimensional model. In the coincident comparison two-dimensional model, all coordinate points on the target comparison contour line and all coordinate points on the target reference contour line are extracted to obtain the target comparison coordinate points and the target reference coordinate points. The target comparison coordinate points and the target reference coordinate points are combined in the order from the start coordinates to the end coordinates on the contour line to obtain the target analysis group. Each target analysis group is input into the Euclidean distance formula in the order from the start coordinates to the end coordinates on the contour line to obtain the target contour difference value. If the target contour difference value is equal to the target contour difference threshold, then the analyzed target comparison contour line is marked as the target coincident contour line; If multiple target contour lines on a redundant part are all coincident contour lines, then the analyzed redundant part is marked as a new redundant part.
4. The remote monitoring and management system for electronic product repair based on the Internet of Things as described in claim 1, characterized in that: The process for tracing the repair and installation of surplus new parts identified after each electronic product repair and installation is as follows: After each electronic product repair and installation, the excess new parts are identified according to the type of new parts on the repair list for each repair, and the installation steps are extracted based on the identified new part types as the target installation steps. Among the excess new parts screened out after each electronic product repair and installation, the number of excess new parts belonging to the same target installation stage is extracted, and the proportion of the excess new parts to the total number of excess new parts screened out after each electronic product repair and installation is obtained to obtain the unit same stage quantity ratio. Obtain the ratio of the same type of component in each historical remote monitoring cycle, calculate the standard deviation and mean value respectively, and output the standard deviation and mean value of the ratio of the same type of component. Input these values into the formula for calculating the coefficient of variation to obtain the target component analysis value.
5. The remote monitoring and management system for electronic product repair based on the Internet of Things as described in claim 1, characterized in that: The process of identifying stable vulnerabilities and constructing a sequence of stable vulnerabilities is as follows: If the target component analysis is less than or equal to the target component analysis threshold, it indicates that the number of units of the same type in the target installation component fluctuates less in each historical remote monitoring cycle, and it is a stable defective component. Extract the target step analysis value corresponding to each stable flaw step, and sort them in ascending order of target step analysis value to construct a stable flaw step sequence.
6. The remote monitoring and management system for electronic product repair based on the Internet of Things as described in claim 1, characterized in that: Correlation analysis is performed on each stable flaw in the stable flaw sequence, as follows: Each stable vulnerability in the stable vulnerability sequence is randomly paired into a group of vulnerability association analysis groups to obtain the target association analysis group. The previous stable vulnerability and the subsequent stable vulnerability in the target association analysis group are then extracted. The maintenance and installation periods corresponding to the pre-stabilization and post-stabilization flaws within the target correlation analysis group are extracted separately within the historical remote monitoring cycle and used as the pre-stabilization flaw period and post-stabilization flaw period, respectively. The termination time point corresponding to the pre-stabilization flaw period and the start time point corresponding to the post-stabilization flaw period are obtained and used as the pre-termination time point and post-start time point.
7. The remote monitoring and management system for electronic product repair based on the Internet of Things as described in claim 6, characterized in that: The process of obtaining the correlation degree of each link is as follows: Obtain the time interval between the previous termination time point and the subsequent start time point, and calculate the proportion of the total duration of the historical remote monitoring cycle to obtain the link association interval duration. Extract the historical remote monitoring cycles that have target association analysis groups as the target association analysis cycle. Obtain the duration of the link association interval of the target association analysis group in each target association analysis period, and input the duration of the link association interval of the target association analysis group in each target association analysis period into the Euclidean calculation formula according to the time series corresponding to the target association analysis period, and output the link association stability value. The average duration of the link association interval in each target association analysis cycle is calculated and the average duration of the link association interval is output. This average duration of the link association interval is then input into the improved coefficient of variation calculation formula to obtain the link association degree.
8. The remote monitoring and management system for electronic product repair based on the Internet of Things as described in claim 1, characterized in that: The process for identifying the links that lead to errors is as follows: If the link correlation degree is less than or equal to the link correlation degree threshold, then the two stable flaw links within the target correlation analysis group will be identified as flaw-related links.
9. The remote monitoring and management system for electronic product repair based on the Internet of Things as described in claim 1, characterized in that: The process for obtaining key monitoring periods is as follows: Extract all pre-stability loopholes within the target association analysis group, obtain the corresponding maintenance and installation time periods, select the earliest maintenance and installation time period within the historical remote monitoring cycle, and use the corresponding target association analysis group as the first target analysis group; Extract the pre-stable loopholes in the remaining target association analysis groups excluding the first target analysis group, and combine them with the post-stable loopholes in the first target analysis group to obtain the merged association analysis group; If the correlation degree of the links is less than or equal to the correlation degree threshold, it means that the two stable flaw links in the target correlation analysis group are closely related. The two stable flaw links in the merged correlation analysis group are identified as flaw-related links, and the target correlation analysis group is merged with the first target analysis group to obtain the merged management group. Obtain the link correlation degree corresponding to the merged correlation analysis group. If the link correlation degree is greater than the link correlation degree threshold, it means that the two stable error links in the target correlation analysis group are relatively distant. Mark the two stable error links in the merged correlation analysis group as non-error related links, and re-merge and analyze the other target correlation analysis groups according to the merged management group to obtain the merged management group. Extract the pre-stability loopholes within each merged management group, identify the corresponding maintenance and installation periods, and designate these as key monitoring periods.
10. The remote monitoring and management system for electronic product repair based on the Internet of Things as described in claim 9, characterized in that: The process of obtaining monitoring and management priority values and constructing a sequence of key monitoring time periods is as follows: Obtain the proportion of the number of target association analysis groups within each merged management group to the total number of target association analysis groups, and then obtain the monitoring and management priority value. Based on the priority value of monitoring and management, the key monitoring time periods are sorted in descending order to construct a sequence of key monitoring time periods.