CN114091710B - National maintenance technology supporting method and system - Google Patents
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Abstract
The invention provides a national maintenance technology support method and a system, which detects abnormal data at each time point across a plurality of regional networks; outputting the abnormal data and the binary value thereof, and storing the abnormal data and the binary value in a characteristic table in a characteristic value form; identifying an abnormal mode of each abnormal data by mapping the abnormal data higher than the threshold value and the historical behaviors of the abnormal characteristics of the categories to each other, and aggregating the abnormal modes into a corresponding category subset; the abnormal data lower than the threshold value is linked to one or more other types of abnormal events related to the abnormal data, service quality monitoring and maintenance technical support are carried out according to different abnormal data information, the sharing and sharing of maintenance technologies and resources are realized, various resource support and technical guidance are provided for national user units and all levels of maintenance bases, and a national linkage equipment maintenance service technical support system is formed.
Description
Technical Field
The invention belongs to the technical field of data operation maintenance management, and particularly relates to a national maintenance technical support method and a national maintenance technical support system.
Background
On the premise of rapid development of economy, office equipment plays a vital role in the process of economy. When the service life of one of the components of the office equipment or the device is over, the office equipment or the device can not work continuously. On one hand, maintenance personnel need to maintain, replace and the like the office equipment or the device after power failure, and further the whole production line is suspended, so that the production rate of products is greatly reduced, and the processing and production time of the products is increased, on the other hand, when the service life of one office equipment or one part of the device is over, the office equipment or the device is likely to be jammed, stopped, damaged and the like due to the part, the office equipment or the device is greatly damaged, and the service life of the office equipment or the device is likely to be shortened. Therefore, daily maintenance and repair of office equipment are the foundation for ensuring normal operation of the equipment.
The management of the maintenance base is more and more trended towards information management, and the customer information management is also transited from the traditional report type management to the information management. The customer management system is an advanced management mode, and needs to have strong technical and tool support for successful implementation, and is an essential technical and tool integrated support platform for implementing customer relationship management. The client management system can realize seamless connection of different functional departments based on information technologies such as network, communication, computer and the like, and can assist managers to better complete client relationship management.
However, the maintenance technical support system in the prior art still has many disadvantages, for example, in patent document CN113379224A, an intelligent service platform for dispatching after-sale installation and maintenance of an intelligent device is proposed, and particularly relates to the field of after-sale installation and maintenance of an intelligent device, including a user terminal, a warranty module, a customer service terminal, a technical worker terminal, a maintenance module and a cloud database; and the user terminal is connected with the cloud database and used for registering and verifying the identity information of the user and sending the registered identity information of the user to the cloud database for storage. The maintenance module is convenient for a user to install and maintain for reporting, customer service staff classify installation and maintenance application forms of the reporting according to the classification module and distribute the installation and maintenance application forms to technicians of corresponding grades according to corresponding grades, and the technicians can receive worker distribution information through logging in the technician terminal, so that the technicians of corresponding grades can be distributed according to installation and maintenance difficulty. However, in the technical scheme, a large gap exists in the aspect of data processing, and the implementation of the technical scheme completely depends on a hardware system, so that the system load is too heavy.
For another example, patent document CN110704443A proposes an intelligent positioning system and method for vehicle-mounted maintenance equipment. The system comprises a health management combination device, a portable positioning terminal and one or more spare part cabinets. The health management combination device comprises a fault part maintenance positioning module and a maintenance equipment record management module; and the fault part maintenance positioning module is used for acquiring information of the replacement spare parts and tools and sending the information to the portable positioning terminal. The portable positioning terminal is used for generating required test equipment and tool information and required supplementary maintenance equipment and tool information, receiving replacement spare part and tool information acquired by the fault part maintenance positioning module, and converting the replacement spare part and tool information into maintenance equipment information and running state information; the spare part cabinet positions the storage position of the maintenance equipment according to the information of the maintenance equipment, and a user executes the retrieval operation of the maintenance equipment at the storage position of the maintenance equipment according to the running state information. However, the technical scheme is single in system, only aims at the maintenance equipment ledger management and pushing, and completely does not relate to fault information sharing and the maintenance support of different levels of technologies provided according to fault information in a classified manner.
Disclosure of Invention
In order to solve the technical problems, the invention provides a national maintenance technology support method and a national maintenance technology support system, provides a support system for the national maintenance technology, realizes the sharing and sharing of maintenance technology and resources, provides various resource supports and technical guidance for national user units and various maintenance bases, and forms a national linkage equipment maintenance service technology support system.
A national maintenance technical support method comprises the following steps:
s1, abnormal data are detected at each time point across a plurality of regional networks;
s2, representing whether the abnormal data is lower than a threshold value or not by using a binary value, wherein when the binary value is 1, the abnormal data is lower than the threshold value, and when the binary value is 0, the abnormal data is higher than the threshold value;
s3, outputting the abnormal data and the binary value thereof, and storing the abnormal data and the binary value in a characteristic table in the form of a characteristic value;
s4, mapping the abnormal data above the threshold value and the historical behaviors of the abnormal features of the category subsets to each other, thereby identifying the abnormal mode of each abnormal data and aggregating the abnormal modes into the corresponding category subsets;
and S5, linking the abnormal data below the threshold value into one or more other types of abnormal events associated with the abnormal data.
Further, the abnormal data and the historical behaviors of the category abnormal features of the category subset are mapped with each other, and the mapping is realized by adopting a confidence interval for judging whether the abnormal data belongs to the historical behaviors of the category abnormal features.
Further, prior information describing the historical behavior distribution of the category abnormal features is used for constructing fusion prior distribution by using the prior information and the heterogeneous times of the historical behavior samples of the category abnormal features, and the confidence degree of the abnormal data is calculated according to the fusion prior distribution
Then;
wherein E is a fusion prior distribution function,
representing a confidence parameter to be verified, n representing the number of isomerism of the historical behaviour sample, x representing prior information of the historical behaviour distribution,
is a density function;
and mapping according to whether the confidence coefficient is positioned in a confidence interval, wherein the upper boundary of the confidence interval is the left cut-off of the historical behavior distribution histogram of the category abnormal features, and the lower boundary of the confidence interval is the right cut-off of the historical behavior distribution histogram of the category abnormal features.
Further, in step S5, it is detected whether one or more associated other types of abnormal events occur within a specified time after the abnormal data is abnormal, if it is determined that one or more associated other types of abnormal events occur within the specified time after the abnormal data is abnormal, it is determined whether the frequency of occurrence of the one or more associated other types of abnormal events is higher than a frequency threshold, and if it is higher than the frequency threshold, the abnormal data lower than the threshold is linked to the one or more other types of abnormal events associated therewith.
Further, inside the category subsets, the abnormal data are converted, an integral time stamp is used for replacing original complex time representation data, a category number is allocated to each category subset, and classified storage is conducted.
Further, the sorted and stored data structure is a boolean data structure, the class number of the class subset is denoted as TID = { t1, … …, ti }, and the content of the class subset is denoted as D = { a, … …, K }, where the content of the class subset is labeled with 0 and 1.
Further, determining the weight of each content in the content D = { A, … …, K } of each class subset in the Boolean data structure, further calculating the comprehensive weight of each class subset in TID = { t1, … …, ti }, and giving priority to each class subset from high to low according to the comprehensive weight; and the category subsets with different category numbers are sorted according to the priority of the category subsets, and the contents of the category subsets are sequentially sent to the maintenance bases with different levels according to the order from high to low of the priority.
A support system for implementing the national repair and maintenance technology support method, comprising: a plurality of area networks, a processor, a communication unit connecting the processor and the area networks, and a plurality of maintenance bases;
the plurality of area networks are used for transmitting abnormal data uploaded by terminals of the area networks;
the communication unit is configured to detect abnormal data at each time point across a plurality of regional networks;
the processor is used for processing the abnormal data, aggregating the abnormal data higher than the threshold value into corresponding category subsets, sequencing the abnormal data according to the priority of the category subsets, and sequentially sending the abnormal data to the maintenance bases of different levels;
the plurality of maintenance bases have different levels, and the maintenance bases of different levels perform service quality monitoring and maintenance technical support according to the received abnormal data information in the category subsets.
Further, the processor links the anomaly data below the threshold value to one or more other types of anomaly events associated therewith and sends the anomaly data to a corresponding repair and maintenance base.
Further, the maintenance base comprises an abnormal management terminal, a component management terminal, a warehouse system, a central processing unit and a repair service site;
the exception management terminal is used for recording and storing the received exception data information;
the abnormal management terminal sends a maintenance order to the repair service site, and requests a technician to provide maintenance service to a client in a terminal shop with abnormal data;
the repair service site appoints a technician for providing support according to the content of the maintenance and repair order;
the component management terminal extracts information of components needing to be repaired or replaced in the maintenance process and stores the information in a management database;
the central processing unit determines the replacement rate or the loss rate of each part when the abnormal data is maintained according to the latest and historical repaired or replaced part information, forms a replaced part list according to the priority and sends the replaced part list to the warehouse system;
the warehouse system performs spares according to the priority order of the replacement parts list for extraction by the technician.
Drawings
FIG. 1 is a schematic illustration of a national repair and maintenance technology support system of the present invention;
FIG. 2 is a schematic diagram of a maintenance base according to the present invention;
FIG. 3 is a schematic diagram of a Boolean data structure according to the present invention;
FIG. 4 is a histogram of the historical behavior distribution of the class anomaly feature of the present invention;
FIG. 5 is a flow chart of a national repair and maintenance technology support method of the present invention.
Detailed Description
The technical scheme of the invention is specifically explained below with reference to the accompanying drawings. It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1, a schematic structural diagram of the national repair and maintenance technology support system of the present invention includes: a plurality of area networks 100, a processor 200, a communication unit 300 connecting the processor 200 and the area networks, and a plurality of maintenance bases 400.
The regional network 100 is configured to transmit abnormal data uploaded at different time points by terminals disposed in each regional network; the customer transmits abnormal data to the communication unit 300 via the area network 100 using the maintenance menu installed in the terminal of the shop, and the communication unit 300 detects abnormal data at various points in time across a plurality of area networks 100.
The processor 200 is configured to process abnormal data at each time point detected by the communication unit 300, and use a binary value to indicate whether the abnormal data is lower than a threshold, where when the binary value is 1, the abnormal data is lower than the threshold, and when the binary value is 0, the abnormal data is higher than the threshold; storing the abnormal data and the binary value thereof in a characteristic table in a characteristic value form, and aggregating the abnormal data higher than a threshold value, namely the abnormal data with the binary value of 0, into a corresponding category subset; exception data below the threshold, i.e., binary value 1, is linked to one or more other types of exception events associated therewith.
And converting the abnormal data inside the class subsets, replacing original complex time representation data with an integral time stamp, assigning a class number to each class subset, and classifying and storing each class subset. As shown in fig. 3, the data structure of all the sorted and stored category subsets is embodied by a boolean data structure.
Where the class number of the class subset is denoted TID = { t1, … …, ti }, and the content of each class subset is denoted D = { a, … …, K }, where the content of the class subset is labeled with 0 and 1. The content D = { a, … …, K } of the subset of categories may include: A. an event code for uniquely identifying an exception and a code attached when the exception occurs; B. a client code for identifying a code of a user; C. an alarm code for indicating the occurrence of an abnormal content; D. and a time code for indicating the occurrence time of the occurred abnormal content, and the like.
Determining the weight of each content in the content D = { A, … …, K } of each category subset in the sorted and stored Boolean data structure, further calculating the comprehensive weight of each category subset in TID = { t1, … …, ti }, and giving priority to each category subset according to the sequence of the comprehensive weight from high to low.
The category subsets with different category numbers are sorted according to the priority of the category subsets, and the contents of the category subsets are sequentially sent to the maintenance bases with different levels according to the priority from high to low, for example, the contents of the category subset with high priority are sent to the maintenance base with high level, and the contents of the category subset with low priority are sent to the maintenance base with low level. The technical support capability of the high-level maintenance base is stronger, and the technical support capability of the low-level maintenance base is lower.
The abnormal data linked to the abnormal events of other types and lower than the threshold value is sent to the maintenance bases corresponding to the abnormal events of other types and the maintenance bases of the levels corresponding to the abnormal events of other types.
In a preferred embodiment, the exception data is associated with a class exception attribute of a subset of classesAnd mapping the historical behaviors of the features with each other, wherein the mapping is realized by adopting a confidence interval for judging whether the abnormal data belongs to the historical behaviors of the class abnormal features. Specifically, the prior information describing the historical behavior distribution of the category abnormal features is utilized, the heterogeneous times of the prior information and the historical behavior samples of the category abnormal features are utilized to construct fusion prior distribution, and the confidence degree of the abnormal data is calculated according to the fusion prior distribution
Then;
wherein E is a fusion prior distribution function,
representing a confidence parameter to be verified, n representing the number of isomerism of the historical behaviour sample, x representing prior information of the historical behaviour distribution,
is a density function;
and mapping according to whether the confidence degree is in a confidence interval, wherein the upper bound of the confidence interval is the left cut-off of the historical behavior distribution histogram of the class abnormal features, the lower bound of the confidence interval is the right cut-off of the historical behavior distribution histogram of the class abnormal features, as shown in fig. 4, the X axis represents the quantized value of the historical behavior feature of the class abnormal features, and the Y axis represents the occurrence frequency of the quantized value of the historical behavior feature of the class abnormal features.
In a preferred embodiment, whether one or more associated other types of abnormal events occur within a specified time after the abnormal data is abnormal is detected, if it is determined that one or more associated other types of abnormal events occur within the specified time after the abnormal data is abnormal, whether the frequency of occurrence of one or more associated other types of abnormal events is higher than a frequency threshold is determined, and if the frequency is higher than the frequency threshold, the one or more associated other types of abnormal events are associated with the abnormal data in a feature table.
For example, one record of abnormal data is "part a, time a, replacement", and another record of abnormal data is "part B, time B, replacement". In the historical repair data, the abnormality of the component a occurs at time a, and the abnormality of the component B occurs at time B after time a, it is determined that the abnormality of the component B is likely to be imminent within Δ T after time a, Δ T = time B-time a. The specific value of Δ T may be manually set to detect whether one or more associated other types of abnormal events occurred within a specified time after the occurrence of the abnormality of component a. Predicting to end if it is determined that one or more associated other types of exception events have not occurred within a specified time after the component a exception occurred; if it is determined that one or more associated other types of abnormal events have occurred within a specified time after the occurrence of the abnormality of component a, it may be determined whether the frequency of the occurrence of the abnormality of the one or more associated other types of abnormal events is above a frequency threshold, and if so, the one or more associated other types of abnormal events are associated with the abnormal data in the feature table.
And supporting engineers of a plurality of maintenance bases carry out service quality monitoring and maintenance technical support according to the received abnormal data information.
As shown in the schematic configuration of the maintenance base in fig. 2, the maintenance base 400 includes an administrative abnormality management terminal 401, a component management terminal 402, a warehouse system 403, a central processor 405, and a repair service site 404.
The anomaly management terminal 401 is configured to record the received anomaly data and store the anomaly data in a technical database of the anomaly management terminal 401.
The abnormality management terminal 401 transmits a maintenance order to the repair service site 404, requests a technician to provide a maintenance service to a customer in a terminal shop where abnormality data occurs, and the repair service site 404 assigns a technician for support based on the contents of the maintenance order, the contents of which include the abnormality data.
The component management terminal 402 extracts information on components that need to be repaired or replaced during the maintenance process from the repair and maintenance order transmitted from the abnormality management terminal 401, and stores the information in the management database of the component management terminal 402, the management database storing not only the latest information on components that need to be repaired or replaced, but also information on components that need to be repaired or replaced corresponding to the past repair and maintenance order.
The central processor 405 determines the replacement rate of each relevant component at the time of repair maintenance of the abnormal data or the component loss rate at the time of causing the abnormal content due to a functional problem of the relevant component, based on the latest and historical repair or replacement component information stored in the management database of the component management terminal 402; the produced list of related components is prioritized in order of high replacement rate or low wear rate, and sent to the warehouse system 403.
The warehouse system 403 performs the replacement of the parts in accordance with the received prioritized list of parts that need replacement and the maintenance spare parts list for retrieval by support technicians assigned to the repair service site 404.
As shown in fig. 4, a flowchart of a method implemented by the national service and maintenance technology support system of the present invention includes the following steps:
And 2, representing whether the abnormal data is lower than a threshold value or not by using a binary value, wherein when the binary value is 1, the abnormal data is lower than the threshold value, and when the binary value is 0, the abnormal data is higher than the threshold value.
And 3, outputting the abnormal data and the binary value thereof, and storing the abnormal data and the binary value in a characteristic table in a characteristic value form.
And 4, mapping the abnormal data with the binary value of 0 and the historical behaviors of the abnormal characteristics of the categories to each other, thereby identifying the abnormal mode of each abnormal data and aggregating the abnormal modes into the corresponding category subset.
In a preferred embodiment, the abnormal data and the historical behaviors of the category abnormal features of the category subset are mapped with each other, and the mapping is realized by adopting a confidence interval for judging whether the abnormal data belongs to the historical behaviors of the category abnormal features. Specifically, the prior information of the historical behavior distribution of the category abnormal features is described, the fusion prior distribution is constructed by utilizing the prior information and the heterogeneous times of the historical behavior samples, and the confidence coefficient of the abnormal data is calculated according to the fusion prior distribution
. And mapping according to whether the confidence degree is located in a confidence interval, wherein the upper bound of the confidence interval is the left cut-off of the historical behavior distribution histogram of the class abnormal feature, and the lower bound of the confidence interval is the right cut-off of the historical behavior distribution histogram of the class abnormal feature.
And step 5, linking the abnormal data which is lower than the threshold value into one or more other types of abnormal events which are associated with the abnormal data. Specifically, whether one or more associated other types of abnormal events occur within a specified time after the abnormal data is abnormal is detected, if it is determined that one or more associated other types of abnormal events occur within the specified time after the abnormal data is abnormal, whether the frequency of the one or more associated other types of abnormal events is higher than a frequency threshold value is determined, and if the frequency is higher than the frequency threshold value, the abnormal data lower than the threshold value is linked to one or more other types of abnormal events associated with the abnormal data.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (10)
1. A national maintenance technical support method is characterized by comprising the following steps:
s1, abnormal data are detected at each time point across a plurality of regional networks;
s2, representing whether the abnormal data is lower than a threshold value or not by using a binary value, wherein when the binary value is 1, the abnormal data is lower than the threshold value, and when the binary value is 0, the abnormal data is higher than the threshold value;
s3, outputting the abnormal data and the binary value thereof, and storing the abnormal data and the binary value in a characteristic table in the form of a characteristic value;
s4, mapping the abnormal data higher than the threshold value with the historical behaviors of the abnormal characteristics of the categories of the category subsets, thereby identifying the abnormal mode of each abnormal data, aggregating the abnormal modes into the corresponding category subsets, sequencing according to the priority of the category subsets, and sequentially sending the abnormal modes to the maintenance bases of different levels, wherein the maintenance bases of different levels monitor the service quality and support the maintenance technology according to the received abnormal data information in the category subsets;
and S5, linking the abnormal data below the threshold value into one or more other types of abnormal events associated with the abnormal data and sending the abnormal data to a corresponding repair and maintenance base.
2. The national repair and maintenance technology support method as claimed in claim 1, wherein the anomaly data is mapped with historical behavior of category anomaly features of a subset of categories, and the mapping is performed using a confidence interval that determines whether the anomaly data belongs to the historical behavior of the category anomaly features.
3. The national repair and maintenance technical support method of claim 2, whichIs characterized in that prior information describing the historical behavior distribution of the category abnormal features is utilized, fused prior distribution is constructed by utilizing the prior information and the heterogeneous times of the historical behavior samples of the category abnormal features, and the confidence coefficient of the abnormal data is calculated according to the fused prior distribution
Then;
wherein E is a fusion prior distribution function,
representing a confidence parameter to be verified, n representing the number of isomerism of the historical behaviour sample, x representing prior information of the historical behaviour distribution,
is a density function;
and mapping according to whether the confidence coefficient is positioned in a confidence interval, wherein the upper boundary of the confidence interval is the left cut-off of the historical behavior distribution histogram of the category abnormal features, and the lower boundary of the confidence interval is the right cut-off of the historical behavior distribution histogram of the category abnormal features.
4. The national repair and maintenance technical support method according to claim 1, wherein in the step S5, it is detected whether one or more associated other types of abnormal events have occurred within a specified time after the abnormal data is abnormal, if it is determined that one or more associated other types of abnormal events have occurred within the specified time after the abnormal data is abnormal, it is determined whether the frequency of occurrence of the one or more associated other types of abnormal events is higher than a frequency threshold, and if it is higher than the frequency threshold, the abnormal data lower than the threshold is linked to the one or more other types of abnormal events associated therewith.
5. The national repair and maintenance technical support method according to claim 1, wherein the abnormal data is converted inside the category subsets, an integer time stamp is used to replace the original complex time representation data, and each category subset is assigned a category number for classified storage.
6. The national repair and maintenance technology support method according to claim 5, wherein the sorted and stored data structure is a Boolean data structure, wherein the class number of the class subset is denoted TID = { t1, … …, ti }, and the content of the class subset is denoted D = { A, … …, K }, and wherein the content of the class subset is labeled with 0 and 1.
7. The national maintenance technology support method of claim 6, wherein the weight of each of the contents D = { A, … …, K } for each subset of categories in the Boolean data structure is determined, and further the composite weight for each subset of categories TID = { t1, … …, ti } is calculated and prioritized from high to low for each subset of categories in the composite weight; and the category subsets with different category numbers are sorted according to the priority of the category subsets, and the contents of the category subsets are sequentially sent to the maintenance bases with different levels according to the priority from high to low.
8. A support system for implementing the national repair and maintenance technology support method of any one of claims 1 to 7, comprising: a plurality of area networks, a processor, a communication unit connecting the processor and the area networks, and a plurality of maintenance bases;
the plurality of area networks are used for transmitting abnormal data uploaded by terminals of the area networks;
the communication unit is configured to detect abnormal data at each time point across a plurality of regional networks;
the processor is used for processing the abnormal data, aggregating the abnormal data higher than the threshold value into corresponding category subsets, sequencing the abnormal data according to the priority of the category subsets, and sequentially sending the abnormal data to the maintenance bases of different levels;
the plurality of maintenance bases have different levels, and the maintenance bases of different levels perform service quality monitoring and maintenance technical support according to the received abnormal data information in the category subsets.
9. The national repair and maintenance technology support system of claim 8, wherein the processor links the below-threshold anomaly data into one or more other types of anomaly events associated therewith and sends to a corresponding repair and maintenance base.
10. The national service maintenance technical support system of claim 8, wherein the service maintenance base comprises a management anomaly management terminal, a component management terminal, a warehouse system, a central processor, and a repair service site;
the exception management terminal is used for recording and storing the received exception data information;
the abnormal management terminal sends a maintenance order to the repair service site, and requests a technician to provide maintenance service to a client in a terminal shop with abnormal data;
the repair service site appoints a technician for providing support according to the content of the maintenance and repair order;
the component management terminal extracts information of components needing to be repaired or replaced in the maintenance process and stores the information in a management database;
the central processing unit determines the replacement rate or the loss rate of each part when the abnormal data is maintained according to the latest and historical repaired or replaced part information, forms a replaced part list according to the priority and sends the replaced part list to the warehouse system;
the warehouse system performs spares according to the priority order of the replacement parts list for extraction by the technician.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101267362A (en) * | 2008-05-16 | 2008-09-17 | 亿阳信通股份有限公司 | A dynamic identification method and its device for normal fluctuation range of performance normal value |
| CN101634851A (en) * | 2009-08-25 | 2010-01-27 | 西安交通大学 | Method based on cause-and-effect relation of variables for diagnosing failures in process industry |
| CN103810104A (en) * | 2014-03-04 | 2014-05-21 | 中国人民解放军63863部队 | Method and system for optimizing software test case |
| CN104614623A (en) * | 2015-02-13 | 2015-05-13 | 河海大学常州校区 | Remote fault positioning and field polling prompting device for railway annunciator |
| CN113708949A (en) * | 2020-05-22 | 2021-11-26 | 亚信科技（中国）有限公司 | Alarm root cause positioning method and device |
-
2022
- 2022-01-20 CN CN202210066459.6A patent/CN114091710B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101267362A (en) * | 2008-05-16 | 2008-09-17 | 亿阳信通股份有限公司 | A dynamic identification method and its device for normal fluctuation range of performance normal value |
| CN101634851A (en) * | 2009-08-25 | 2010-01-27 | 西安交通大学 | Method based on cause-and-effect relation of variables for diagnosing failures in process industry |
| CN103810104A (en) * | 2014-03-04 | 2014-05-21 | 中国人民解放军63863部队 | Method and system for optimizing software test case |
| CN104614623A (en) * | 2015-02-13 | 2015-05-13 | 河海大学常州校区 | Remote fault positioning and field polling prompting device for railway annunciator |
| CN113708949A (en) * | 2020-05-22 | 2021-11-26 | 亚信科技（中国）有限公司 | Alarm root cause positioning method and device |
Non-Patent Citations (2)
| Title |
|---|
| 光储微电网内部线路智能故障诊断的研究;陈月;《中国优秀硕士学位论文全文数据库-工程科技Ⅱ辑》;20200115(第1期);C042-2586 * |
| 电力监控网络安全态势智能感知方法研究;张之刚;《中国博士学位论文全文数据库-信息科技辑》;20200229(第2期);I139-10 * |
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