CN115601009A - Fault disposal record analysis method and system, electronic equipment and storage medium - Google Patents
Fault disposal record analysis method and system, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN115601009A CN115601009A CN202211228211.1A CN202211228211A CN115601009A CN 115601009 A CN115601009 A CN 115601009A CN 202211228211 A CN202211228211 A CN 202211228211A CN 115601009 A CN115601009 A CN 115601009A
- Authority
- CN
- China
- Prior art keywords
- maintenance
- fault
- data
- time
- historical
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Game Theory and Decision Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a fault handling record analysis method and system, electronic equipment and a storage medium, wherein the method comprises the following steps: collecting historical fault data, and performing data integration and relationship combing; analyzing the historical fault data according to manufacturer level, abnormal maintenance, system emergency and maintenance time to obtain an analysis result; comparing the analysis result with the full-scale data, and obtaining a grading evaluation value by adopting a grading mechanism; wherein the scoring mechanism comprises: device scoring, system scoring, and maintenance scoring; the analysis strength of the abnormal conditions is increased, and the influence degree of the abnormal conditions on the maintenance scheme is improved; the customized maintenance scheme can be generated according to the fault occurrence condition, the situation which possibly occurs in the maintenance process can be explained through the customized maintenance scheme from multiple dimensions, meanwhile, due to background logic judgment, the influence of artificial judgment is reduced, and the timeliness and the effectiveness of historical fault analysis can be ensured.
Description
Technical Field
The invention belongs to the technical field of fault analysis, and particularly relates to a fault handling record analysis method and system, electronic equipment and a storage medium.
Background
At present, the demand of the enterprise for the IT equipment is increased day by day, and the larger the scale of the enterprise is, the larger the scale of the IT equipment is. But the technology of the IT basic equipment is not broken through at the operation and maintenance level. In the case where the failure rate is not changed, the larger the scale, the more the number of failures. On this basis, the development of a fault maintenance strategy for the equipment is also increasingly important. Since the impact of a single failure is unchanged. That is, the risk of destroying 10 devices on a small scale is consistent with the risk of destroying 10 devices on a large scale. In this case, the shorter the repair time for a single repair, the smaller the business impact. It is a very important part of maintenance work to make maintenance strategies and maintenance solutions reasonable and to inform about the maintenance risks.
The existing fault analysis is mainly carried out through big data and a statistical correlation algorithm, only historical fault quantity, fault line graphs and the like can be obtained, and then whether fault trends are normal or not is decided through artificial judgment, and maintenance strategies are modified on the basis of the fault trends.
However, in this way, because the decision-making step is performed manually, the part has high requirements for decision-makers and needs to define the meanings of various statistical values; meanwhile, different decision results are generated under the same condition due to the personal habits of decision-makers; each failure analysis needs manpower to be consumed for sorting, analyzing and adjusting strategies, wherein human influence factors exist, and various recent newly-generated failures cannot be analyzed in time.
Disclosure of Invention
In view of the above, an object of the present invention is to provide a fault handling record analysis method and system, an electronic device, and a storage medium, which are used to generate a customized maintenance scheme for a fault occurrence situation, and the customized maintenance scheme can explain a situation that may occur in a maintenance process from multiple dimensions.
The application discloses in a first aspect a method for analyzing a fault handling record, comprising:
collecting historical fault data, and performing data integration and relationship combing; wherein the historical fault data comprises: change information, current fault information, event information, external information and maintenance labels;
analyzing the historical fault data according to manufacturer level, abnormal maintenance, system emergency and maintenance time to obtain an analysis result;
comparing the analysis result with the full-scale data, and obtaining a grading evaluation value by adopting a grading mechanism; wherein the scoring mechanism comprises: device scoring, system scoring, and maintenance scoring.
Optionally, in the method for analyzing a fault handling record, the collecting historical fault data, and performing data integration and relationship combing includes:
collecting change information, current fault information, event information, external information and maintenance labels in each external data;
searching the position, the system, the product and the manufacturer of the equipment in the external information through the serial number of the equipment;
searching historical change information of the equipment in the change information through the serial number of the equipment; the change information includes: implementing people, rechecking people, changing time and relevant information of equipment;
searching a special label and a remark in a historical fault handling record through the equipment serial number;
finding a historical handling process in the event information through the change information, wherein the historical handling process comprises the following steps: time of occurrence, procedure of disposal, solution;
and searching related change information and event information in the change information according to the system/product dimension through the system/product which the external information belongs to.
Optionally, in the fault disposal record analysis method, the analyzing of the manufacturer level, the maintenance exception, the system emergency, and the maintenance time is performed on the historical fault data to obtain an analysis result, where the analyzing includes:
extracting vendor service data, current fault system and component data, handling data of a current fault system, and operation data of a current fault component from the historical fault data;
performing manufacturer level analysis on the manufacturer service data to obtain a manufacturer maintenance level;
performing maintenance abnormity analysis on the current fault system and the component data to obtain an abnormity handling condition of the current fault system in the maintenance;
performing system emergency analysis on the disposal data of the current fault system to obtain the maximum time length available for maintenance of the current fault system in the maintenance;
analyzing the maintenance time of the operation data responsible for each product of the current fault component to obtain the predicted maintenance time of the current maintenance;
and combining the manufacturer maintenance level, the abnormal handling condition of the current fault system, the maximum available maintenance time length of the current fault system and the predicted maintenance time length to serve as a final analysis result.
Optionally, in the method for analyzing the fault handling record, performing vendor level analysis on the vendor service data to obtain a vendor maintenance level includes:
obtaining the abnormal probability of the abnormal occurrence through the abnormal times in the vendor service data;
obtaining average secondary maintenance time according to each abnormal reason in the historical fault handling record;
obtaining the repair time of the manufacturer according to the abnormal probability and the secondary repair time;
and determining the maintenance level of the manufacturer according to the repair time of the manufacturer.
Optionally, in the method for analyzing the fault handling record, performing maintenance exception analysis on the current fault system and the component data to obtain an exception handling condition of the current fault system in the current maintenance, where the method includes:
determining a fault maintenance mode of the current fault system according to historical discovery time and starting time in the current fault system;
determining the probability of the current fault system being abnormal through the fault maintenance mode;
determining the handling time of the fault and the fault condition of the current fault system according to the starting time and the ending time of the fault;
and determining the abnormal handling condition comprising the abnormal handling time length of the fault according to the historical maintenance time length, the abnormal occurrence probability and the historical maintenance mode of the current type equipment.
Optionally, in the method for analyzing the fault disposal record, performing system emergency analysis on disposal data of the current fault system to obtain a maximum available maintenance duration of the current fault system in the current maintenance, where the method includes:
determining a maintenance mode of the current fault system according to the level of the current fault system and a historical maintenance time window;
judging the probability of the current fault system being abnormal according to the maintenance records of preset times and the emergency times;
determining the time when the current fault system generates influence according to the maintenance mode;
determining the average maintenance duration of the current fault system according to the current fault system abnormal probability and a historical maintenance time window;
and determining the maximum time length available for maintenance of the current fault system according to the time point of the influence confirmation of the current fault system, the time point of service recovery and the time length of the emergency treatment process.
Optionally, in the fault disposal record analysis method, analyzing the maintenance time of the operation data responsible for each product of the current faulty component to obtain the predicted maintenance duration of the current maintenance includes:
determining the processing duration of the hardware level, the system level and the product/application level according to the probability and the operation time of each operation in the hardware level, the system level and the product/application level;
and summing the processing time lengths of the hardware level, the system level and the product/application level to obtain the predicted maintenance time length of the maintenance.
A second aspect of the present application discloses a fault handling record analysis system, including:
the data collection module is used for collecting historical fault data and performing data integration and relationship combing; wherein the historical fault data comprises: change information, current fault information, event information, external information and maintenance labels;
the analysis module is used for analyzing the manufacturer level, abnormal maintenance, system emergency and maintenance time of the historical fault data to obtain an analysis result;
the scoring module is used for comparing the analysis result with the full data and obtaining a scoring estimated value by adopting a scoring mechanism; wherein the scoring mechanism comprises: device scoring, system scoring, and maintenance scoring.
A third aspect of the present application discloses an electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement a method of fault handling record analysis as claimed in any one of the first aspects of the application.
A fourth aspect of the present application discloses a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a fault handling record analysis method as defined in any of the first aspects of the present application.
As can be seen from the foregoing technical solutions, the method for analyzing a fault handling record provided by the present invention includes: collecting historical fault data, and performing data integration and relationship combing; analyzing the historical fault data according to manufacturer level, abnormal maintenance, system emergency and maintenance time to obtain an analysis result; comparing the analysis result with the full-scale data, and obtaining a grading evaluation value by adopting a grading mechanism; wherein the scoring mechanism comprises: device scoring, system scoring, and maintenance scoring; the analysis strength of the abnormal conditions is increased, and the influence degree of the abnormal conditions on the maintenance scheme is improved; the customized maintenance scheme can be generated according to the fault occurrence condition, the customized maintenance scheme can explain the possible occurrence condition in the maintenance process from multiple dimensions, meanwhile, due to background logic judgment, the influence of manual judgment is reduced, and the timeliness and the effectiveness of historical fault analysis can be guaranteed.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1-11 are flowcharts illustrating a method for analyzing a fault handling record according to an embodiment of the present invention;
FIG. 12 is a schematic diagram of a fault handling record analysis system provided by an embodiment of the invention;
fig. 13 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In this application, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
Interpretation of terms:
and (3) changing: the change is a collection of information records for recording the operation such as the operation name, the operation time, and the operation content, in which an implementer or a reviewer actively accepts external information and arranges maintenance. All operations related to maintenance performed by the user can be accurately acquired by changing the information.
Event: the event is used for recording the whole process of recovering from the abnormal occurrence to the abnormal handling, wherein the whole process comprises a final solution, each personnel handling process, information related to the abnormal occurrence and the like. The phenomenon record of the whole process from the occurrence of the abnormal phenomenon to the end of the treatment and the simple record of the relevant treatment information can be accurately obtained through the event information.
Labeling: the label is used for distinguishing various abnormal characteristics, and can be customized according to conditions, such as: significant impact, lengthy time, etc. A large amount of fault information can be distinguished according to the conditions required to be acquired through the labels.
Exception: the abnormal condition refers to the condition that the equipment cannot be normally used due to external reasons in the normal use process, wherein the external reasons comprise various reasons such as bottom hardware, system middleware, upper application and the like.
The embodiment of the application provides a fault handling record analysis method, which is used for solving the problem that in the prior art, decision making steps are carried out manually, so that the part has higher requirements on decision makers and needs to make clear the meanings of various statistical values; meanwhile, different decision results are generated under the same condition due to personal habits of decision-makers; each failure analysis needs to consume manpower to arrange, analyze and adjust strategies, and the problems that human influence factors exist and various recent newly-generated failures cannot be analyzed in time exist.
Referring to fig. 1, the fault handling record analysis method includes:
s101, collecting historical fault data, and performing data integration and relationship combing.
Wherein the historical fault data includes: change information, current fault information, event information, external information and maintenance labels.
The step is mainly responsible for collecting related data from different systems and data sources and dividing and sorting the data according to the relationship among the data.
Specifically, each system may be an external system or an internal system, as long as relevant data can be collected through the system, which is not described herein any more, and is within the protection scope of the present application depending on the actual situation. The same data sources are all within the protection scope of the present application.
Because the operation of the emergency at the earlier stage is performed by different personnel, the operation records of the emergency are inconsistent with the data sources according to which the emergency is searched. Therefore, most of the time, the current scene is dictated temporarily according to the emergency requirement, and various data are searched again and arranged into a temporary file for assisting in explaining the emergency situation.
The function of this step is to retrieve data from the data storage units. The function firstly needs to extract the information of the fault, mainly including an equipment serial number, a fault part and a maintenance engineer. Then, information such as change information, event information, external information, and maintenance labels is extracted.
The historical fault information may also include other information, which is not described in detail herein, and is within the scope of the present application as appropriate.
And S102, analyzing the historical fault data according to the manufacturer level, abnormal maintenance, system emergency and maintenance time to obtain an analysis result.
That is, the historical failure data is analyzed in multiple aspects, and the analysis results in multiple aspects are used together as the final analysis result.
In addition, historical fault data may be subjected to relevant data extraction, and then relevant analysis may be performed. Extracting data related to the manufacturer level from historical fault data to perform manufacturer level analysis; extracting data related to maintenance abnormity from historical fault data to perform maintenance abnormity analysis; extracting system emergency related data from historical fault data to perform system emergency analysis; and extracting data related to maintenance time from the historical fault data to perform maintenance time analysis.
S103, comparing the analysis result with the full-scale data, and obtaining a grading evaluation value by adopting a grading mechanism; wherein the scoring mechanism comprises: device scoring, system scoring, and maintenance scoring.
That is, the analysis result is evaluated in multiple directions, and then the evaluation values of different angles can be obtained.
The evaluation value can be a single value or a plurality of values, which respectively represent different angles. The specific usage of the scoring estimation is not described herein any more, and it is only required according to the actual situation, and all of them are within the scope of the present application.
The scoring estimated value can be displayed to an engineer, so that the engineer can know the scoring estimated value of the current fault system and further judge whether to execute maintenance actions and the like.
That is to say, the application is mainly divided into three layers, wherein the first layer is bottom layer data collection and is mainly responsible for collecting relevant data from each external data to carry out integration and relationship combing. The second layer is each model analysis module, which is mainly responsible for carrying out targeted analysis on the collected data to draw a conclusion, and relates to four analysis modules of manufacturer level, abnormal maintenance, system emergency and maintenance time. The third layer is a scoring mechanism and is mainly responsible for comparing the analysis result in the second layer with the full data once, and a scoring value is calculated according to a formula, so that an engineer can more intuitively know the risk of the maintenance through the scoring value.
In the embodiment, historical fault data is collected, and data integration and relationship combing are performed; analyzing the historical fault data according to manufacturer level, abnormal maintenance, system emergency and maintenance time to obtain an analysis result; comparing the analysis result with the full data, and obtaining a scoring estimated value by adopting a scoring mechanism; wherein the scoring mechanism comprises: device scoring, system scoring, and maintenance scoring; the analysis strength of the abnormal conditions is increased, and the influence degree of the abnormal conditions on the maintenance scheme is improved; the customized maintenance scheme can be generated according to the fault occurrence condition, the customized maintenance scheme can explain the possible occurrence condition in the maintenance process from multiple dimensions, meanwhile, due to background logic judgment, the influence of manual judgment is reduced, and the timeliness and the effectiveness of historical fault analysis can be guaranteed.
It should be noted that, in the prior art, the development of maintenance strategies, maintenance schemes and the determination of maintenance risks are more based on predicted risks during the construction of equipment and periodic fault analysis of long-term maintenance. The failure analysis mainly aims at analyzing a large amount of data, and is insufficient for analyzing a small amount of abnormal conditions. The abnormal conditions are very important for the equipment to establish a maintenance scheme; the maintenance strategy is mainly established for a large range, such as a certain manufacturer, a certain type of equipment and the like, and the maintenance suggestions cannot be analyzed and generated carefully for abnormal situations. Meanwhile, the established maintenance strategy can only be used for execution, the historical disposal records cannot be checked, and the historical fault disposal records cannot be effectively utilized; therefore, on one hand, the timeliness of the relevant maintenance strategy formulation is insufficient, and the analysis is performed only once in a passive way under the condition that the service has a great influence, and on the other hand, the normal treatment and the abnormal treatment are analyzed as equivalent data.
In the embodiment, historical fault handling records can be more effectively utilized, the analysis strength on abnormal conditions is increased, and the influence degree of the abnormal conditions on the maintenance scheme is improved. By the method, a customized maintenance scheme can be generated according to the fault occurrence condition, and the customized maintenance scheme can explain the possible occurrence condition in the maintenance process from multiple dimensions.
In practical applications, see fig. 2, step S101, collecting historical fault data, and data integration and relationship combing are carried out, comprising:
s201, collecting change information, current fault information, event information, external information and maintenance labels in each external data.
Wherein, this fault data includes: equipment serial number, faulty components, maintenance engineer, etc.
S202, searching the device position, the system, the product and the device manufacturer in the external information through the device serial number.
S203, the change information of the equipment history is searched in the change information through the equipment serial number.
The change information includes: implementation person, review person, change time, device related information.
And S204, searching for special labels and remarks in the historical fault handling record through the equipment serial number.
That is, the maintenance tag includes: whether the information is abnormal or not, whether the information has influence or not, maintenance risk, whether protection setting is effective or not, remarks and the like.
And S205, finding the history handling process in the event information through the change information.
Wherein the history handling process comprises: time of occurrence, procedure of disposal, solution.
S206, searching related change information and event information in the change information according to the system/product dimension through the system/product to which the external information belongs.
That is, the device location, the belonging system, the belonging product, and the device manufacturer are searched in the external information by the device serial number.
And searching the historical change information of the equipment in the change information through the serial number of the equipment, wherein the historical change information comprises an implementer, a rechecker, change time and equipment related information.
And searching for a special label and remarks in the historical fault handling record through the equipment serial number, wherein the part of information is mainly used for judging whether an abnormal condition occurs in the historical maintenance.
The event information is changed to find out the historical handling process including the occurrence time, the handling process and the solution.
And searching related change information and event information in the change information according to the system/product dimension through the system/product to which the external information belongs.
Through the steps, the information of the whole process of the historical fault of the equipment, the equipment information, the maintenance label, the whole maintenance record of the system to which the equipment belongs and the like can be obtained according to the fault information. The information also serves as a data analysis basis to provide data support for upper-layer analysis, and meanwhile, the data is used for displaying at the front end and is responsible for the function of acquiring the chart data.
In practical application, referring to fig. 3, step S102, analyzing the historical fault data according to manufacturer level, abnormal maintenance, system emergency, and maintenance time to obtain an analysis result, including:
s301, extracting manufacturer service data, current fault system and component data, handling data of the current fault system and operation data of the current fault component from historical fault data.
The operational data of the currently failed component may be operational data of each product principal of the currently failed component.
The specific extraction process can be obtained by classifying and then extracting according to data classification, data source and other modes; of course, other modes may be adopted, which are not described herein in detail, and are all within the scope of the present application as appropriate.
And S302, carrying out manufacturer level analysis on the manufacturer service data to obtain the manufacturer maintenance level.
And extracting information related to the manufacturer service from the historical fault data, calculating according to the incidence relation and the formula among the information related to the manufacturer service, and finally obtaining the related information such as the possible condition of the maintenance, thereby determining the maintenance level of the manufacturer.
It should be noted that, in the following description, the judgment standard of the manufacturer maintenance level is mainly based on abnormal conditions. Therefore, the probability of the occurrence of the anomaly needs to be calculated through the number of anomalies. And then calculating the average secondary maintenance time according to different abnormal reasons recorded in the historical fault handling record of the equipment. Meanwhile, the abnormal probability and the secondary maintenance time can be calculated to generate the expected time of the maintenance; the maintenance duration is only the repair time of the manufacturer, and is also used as an evaluation standard of the maintenance level of the manufacturer.
In practical applications, referring to fig. 4, in step S302, performing vendor level analysis on vendor service data to obtain a vendor maintenance level, including:
and S401, obtaining the abnormal probability of the abnormal occurrence through the abnormal times in the manufacturer service data.
S402, obtaining average secondary maintenance time according to each abnormal reason in the historical fault handling record.
The calculation formula adopted by the average secondary maintenance time is as follows: time S ＝∑P i ×Time i 。
Wherein, time S For a second maintenance duration, P i For the probability of occurrence of different abnormal situations, time i The maintenance time required when different abnormal conditions occur.
And S403, obtaining the repair time of the manufacturer according to the abnormal probability and the secondary repair time.
The formula adopted by the expected manufacturer for the time of this maintenance is as follows:
Time y time for the manufacturer's Time of this maintenance 1 As the standard first repair Time, time s For secondary maintenance time, P s Is the probability of an anomaly occurring.
Since the risk increases exponentially every time an abnormality occurs, the influence of the occurrence of the abnormality is increased by calculating the abnormality probability to the third power.
In addition, the probability of the occurrence of the abnormality is related to different abnormality causes; the reasons for the abnormality include: spare part DOA, double failure, misjudgment, etc.
Spare part DOA is newly lost and needs to be replaced again with the same component.
Double failure is the occurrence of other failures based on the original failure, requiring the simultaneous replacement of two components.
The judgment error is the original fault judgment error, and actually, other fault parts need to be replaced.
Specifically, as shown in fig. 8, the anomaly probability is determined according to the anomaly number; determining the time length of secondary maintenance according to different abnormal reasons, such as spare part DOA, double faults and judgment errors; and determining the predicted maintenance time length according to the secondary maintenance time length and the abnormal probability.
And S404, determining the maintenance level of the manufacturer according to the repair time of the manufacturer.
Of course, the manufacturer maintenance level may also be determined in other manners, which is not described herein any more, and all of which are within the scope of the present application, depending on the actual situation.
And S303, performing maintenance abnormity analysis on the current fault system and the component data to obtain the abnormity handling condition of the current fault system in the maintenance.
Namely, information related to a current fault system and a fault component is collected and extracted from historical fault data; calculating according to the incidence relation and formula of the information to finally obtain the related information such as the possible situations of the type components in the system during the maintenance; the exception handling of the system is determined by the conditions that may occur for that type of component within the system.
Specifically, the criterion for determining the maintenance abnormality mainly takes the maintenance time as a basis.
In practical application, referring to fig. 5, step S303 is to perform maintenance anomaly analysis on the current fault system and the component data to obtain an anomaly handling condition of the current fault system in the current maintenance, where the maintenance anomaly handling condition includes:
s501, determining a fault maintenance mode of the current fault system according to historical discovery time and starting time in the current fault system.
The fault repair mode may include: immediate maintenance and maintenance when the service is idle.
Specifically, the historical discovery time and the historical start time can be used for judging whether the fault maintenance mode is immediate maintenance or maintenance in idle service.
And if the mode of the starting time is close to the same time point and the time is found to be different time points, the maintenance window is proved to be the appointed service idle time point.
If the start time and the find interval time mode are both short, the repair time window is proven to be immediate maintenance.
S502, determining the probability of the abnormality of the current fault system through a fault maintenance mode.
The time length for analyzing the fault can be obtained by calculating the starting time and the finding time, and if the analyzing time length is too long, the abnormal condition is proved to have a larger probability. This step is differentiated for the criteria of immediate maintenance and off-the-shelf.
1) The class is maintained immediately.
The judgment standard of the immediate maintenance type is that the starting time minus the finding time is the analysis time, then the analysis time is divided by taking the hour as the dimension according to statistics, if the single analysis time is 2 times of the mode of the analysis time, the abnormality occurs with a larger probability, and meanwhile, the multiple and the probability are also in direct proportion, namely, the larger the multiple is, the higher the probability of the abnormality occurs.
2) Class maintenance when the service is idle.
The discovery time maintained when the service is idle is not consistent, but the start time is basically consistent. Therefore, the division is carried out by taking days as dimensions, and if the starting time minus the occurrence time is more than 24 hours, the fault on the same day is proved to fail to be repaired on the same day, and abnormal risks may exist. Similarly, if the excess time is 48 hours, the probability of abnormality is increased. Namely, the analysis time days are in direct proportion to the probability, and the larger the days are, the higher the probability of abnormality occurrence is.
And S503, determining the handling time of the fault and the fault condition of the current fault system according to the starting time and the ending time of the fault.
The starting time and the ending time can be used for judging the maintenance time, the maintenance time of the same component is basically consistent, and if the maintenance time exceeds the historical maintenance time mode, the possibility of abnormity exists. That is, the more the repair duration exceeds the mode, the greater the probability of the occurrence of an abnormality. Exceeding 1.5 times the duration may indicate the presence of an abnormal condition.
And S504, determining the abnormal handling condition comprising the abnormal handling time of the fault according to the historical maintenance time, the abnormal probability and the historical maintenance mode of the type of equipment.
The historical abnormal handling time length can be calculated and obtained through the historical maintenance time length, the abnormal probability and the historical maintenance mode of the equipment through a formula.
Time s1 For the expected disposal time after the abnormality of this maintenance, P i Is the probability of an abnormality in a certain situation, time i Time, the Time for handling an abnormal condition 1 Is routine maintenance time.
Specifically, as shown in fig. 9, an analysis duration and a maintenance duration are determined according to the discovery time, the start time, and the end time, an abnormality occurrence probability is determined according to the analysis duration, and an abnormality disposal duration is determined according to whether the maintenance type is immediate maintenance or business idle maintenance, the abnormality occurrence probability, and the maintenance duration.
S304, performing system emergency analysis on the disposal data of the current fault system to obtain the maximum time length available for maintenance of the current fault system in the maintenance.
The system emergency analysis module is mainly responsible for extracting disposal records related to the current fault system from the data collection module and calculating according to the incidence relation and the formula, and finally obtaining information such as possible situations of the system equipment in the current maintenance.
Since the system may have a configuration change, the long-term maintenance records are not meaningful. Therefore, only 30 maintenance records need to be acquired when acquiring the maintenance-related information. Of course, the number of times of maintenance can be recorded, which is not described in detail herein, and is within the scope of the present application depending on the actual situation.
In practical application, referring to fig. 6, step S304 is to perform system emergency analysis on the disposition data of the current fault system to obtain the maximum time length available for maintenance of the current fault system in the current maintenance, and includes:
s601, determining a maintenance mode of the current fault system according to the level of the current fault system and a historical maintenance time window.
Specifically, whether the system is an immediate maintenance system or not can be judged through the system level and the historical maintenance time window, the judgment standard is consistent with the rule in the maintenance anomaly analysis module, and the details refer to the step S501.
And S602, judging the probability of the current fault system occurring abnormity according to the maintenance records of preset times and the occurring emergency times.
The probability of the occurrence of an abnormality can be judged by only 30 service records and the number of emergencies.
And S603, determining the time when the current fault system influences according to the maintenance mode.
The time of the system influence can be judged according to the maintenance mode.
If the emergency service is maintained immediately, the time point of generating the service influence can be judged according to the time point of triggering the emergency at the later stage and the emergency record.
If the maintenance is idle, the time point of the service influence can be confirmed according to the maintenance time window.
S604, determining the average maintenance duration of the current fault system according to the abnormal probability of the current fault system and the historical maintenance time window.
The average system and treatment duration can be calculated according to the system anomaly probability and the historical maintenance time window.
S605, determining the maximum time length available for maintenance of the current fault system according to the time point of the influence confirmation of the current fault system, the time point of service recovery and the time length of the emergency disposal process.
Calculating the maximum time length available for maintenance of the system according to the time point for confirming the influence, the time point for recovering the service and the time length of the emergency disposal process; i.e. how long the maintenance takes, no disposable business impact will occur.
Specifically, referring to fig. 10, it is determined whether it is an immediate maintenance type according to the system level and the maintenance time window, and the influence time is confirmed; and determining the abnormal probability and the emergency disposal duration according to the maintenance time window and the emergency times, and determining the maintainable duration according to the influence time, the service recovery time point and the emergency disposal duration.
S305, analyzing the maintenance time of the operation data responsible for each product of the current fault component to obtain the estimated maintenance time of the current maintenance.
And extracting operation records responsible for each product related to the current fault component from historical fault data, and then obtaining the predicted maintenance duration of the maintenance through statistical correlation calculation according to the records.
In practical application, referring to fig. 7, in step S305, performing maintenance time analysis on operation data responsible for each product of a current faulty component to obtain a predicted maintenance duration of the current maintenance, including:
s701, determining the handling duration of the hardware level, the system level and the product/application level according to the probability and the operation time of each operation in the hardware level, the system level and the product/application level.
1. Hardware level: there are three types of commonly used abnormalities:
a) Secondary replacement: often, the spare parts are replaced again in a mode of replacing new spare parts under the condition that the spare parts cannot be identified by the spare parts DOA.
b) Full replacement: it often happens that after replacement of spare parts, recovery is not possible and all parts associated with the failure are replaced.
c) Redeployment: and under the condition that the first two steps cannot be repaired, replacing the old machine with a new machine, and redeploying the machine according to the construction rules.
2. And (3) system level: there are two types of commonly used exception handling:
a) File repair: after maintenance, the file is confirmed to be damaged, and the related file content is restored through the backup of the system level.
b) Reinstalling the system: and when the operating system cannot be recovered due to the fact that the system files cannot be recovered after maintenance, reinstalling the operating system according to the construction rules.
3. Product/application level: there are three types of commonly used abnormalities:
a) And (3) data restoration: after maintenance, data damage is confirmed but repair can be carried out, data repair can be carried out through commands, and repair time is short.
b) And (3) recovering the total amount: after maintenance, it is confirmed that data is lost and cannot be repaired, and all data needs to be restored from other backups, so that the repair time is long.
c) Redeployment: and after the product or the application cannot be used after the maintenance, redeploying the product or the application according to the construction rule, and then performing data recovery according to the operation of full recovery after the redeployment.
4. The average treatment duration of the product can be calculated according to the probability of occurrence of each operation and the operation time.
S702, summing the treatment duration of the hardware level, the system level and the product/application level to obtain the predicted maintenance duration of the maintenance.
The sum of the treatment time of the hardware, the system and the application is the estimated maintenance time of the maintenance.
Specifically, referring to fig. 11, the processing method is divided into three levels of hardware, system and product application according to the processing mode; carrying out secondary replacement, total replacement and redeployment on the hardware, and determining the average duration of hardware maintenance; carrying out file repair and system reinstallation on the system, and determining the average maintenance duration of the system; performing data restoration, total recovery and redeployment on the product application, and determining the average duration of maintenance of the product application; and determining the predicted maintenance time according to the average time of hardware, the average time of a system and the average time of product application.
S306, combining the maintenance level of a manufacturer, the abnormal handling condition of the current fault system, the maximum available maintenance time length of the current fault system and the predicted maintenance time length as a final analysis result.
And S103, comparing the analysis result with the full-scale data, and obtaining a grading evaluation value by adopting a grading mechanism. The specific process of scoring the equipment, scoring the system and scoring the maintenance comprises the following steps:
(1) A device scoring mechanism.
Device scoring requires the addition of two-dimensional scores.
Vendor service scoring:
Num A scoring for vendor services:
wherein, the base is divided into 10 points and Time s Predicting exception handling Time, for the device 1 Handling Time, for the device component regularly S-Max The time value N with the longest exception handling time s The number of occurrences of an anomaly.
The score of this formula is a value in the middle of 0 to 10. The lower the value, the lower the representative service level for that vendor.
The longer the single exception handling time in the formula, the lower the score. The higher the number of anomalies, the faster the score will decrease.
The lowest score is 0, which indicates that the manufacturer has no facility to maintain.
And (3) equipment abnormality scoring:
Num B device anomalies are scored.
Wherein the base is divided into 10 points, P i Probability, time, to trigger exception handling i Time required to perform exception handling 1 The time is routinely handled for this equipment component.
The exception handling related to the equipment does not comprise exception handling of a system and an application.
The score of this formula is a value in the range of 0 to 10. The lower the value, the longer the time to represent this type of device exception handling.
Different anomalies in the formula will have a greater impact on the score for longer times due to different treatment times.
The lowest score of 0 represents that the device does not have the condition to evaluate the treatment time.
(2) And (4) a system scoring mechanism.
The system score needs to take the sum of the scores of the two aspects.
And (3) scoring the system fault:
Num A and scoring the system fault.
Wherein the base is divided into 10 points and P i Probability, time, to trigger exception handling i The time required to perform exception handling. There is no conventional disposal time as the system conventionally does not need disposal.
The score of this formula is a value in the range of 0 to 10. The lower the value, the more problematic the system is represented.
Different anomalies in the formula will have a greater impact on the score for longer times due to different treatment times.
The lowest score is 0, which represents that the system has no possibility of routine maintenance.
Systematic impact scoring:
Num B device anomalies are scored.
Wherein the base is divided into 1 point, and P is the probability of generating service influence.
The exception handling related to the system does not comprise exception handling of hardware and application.
The lowest score is 0, which represents that the system has no maintenance possibility.
(3) A scoring mechanism is maintained.
Maintenance scoring requires adding the scores of both aspects.
Component maintenance scoring:
Num A a score is maintained for the part.
Wherein the base is divided into 10 points and P i Probability, time, for triggering exception handling i Time required to perform exception handling 1 The time is routinely handled for this equipment component.
The score of this formula is a value in the range of 0 to 10. The lower the value, the more problematic the component replacement is represented.
Different anomalies in the formula will have a greater impact on the score for longer times due to different treatment times.
The lowest score, 0, represents the possibility that this type of part has not been routinely maintained.
System maintenance scoring:
Num B a score is maintained for the system.
Wherein the base is divided into 10 points, P i Probability, num, to trigger exception handling i The number of times an abnormality occurs.
The lowest score is 0, which represents that the system has no maintenance possibility.
In the embodiment, all recorded fault records can be analyzed in real time through automatic design, so that the timeliness of analysis is improved; the effectiveness of the data is improved by a mode of collecting various data in a centralized manner and performing multivariate analysis; influence factors of abnormal conditions are increased through a reasonable design scoring formula, and the accuracy of analysis is improved.
Another embodiment of the present application provides a fault handling record analysis system.
Referring to fig. 12, the fault handling record analysis system includes:
and the data collection module 101 is used for collecting historical fault data and performing data integration and relationship combing. Wherein the historical fault data comprises: change information, current fault information, event information, external information and maintenance labels.
And the analysis module 102 is used for analyzing the manufacturer level, abnormal maintenance, system emergency and maintenance time of the historical fault data to obtain an analysis result.
The scoring module 103 is used for comparing the analysis result with the full-scale data and obtaining a scoring estimated value by adopting a scoring mechanism; wherein the scoring mechanism comprises: device scoring, system scoring, and maintenance scoring.
Another embodiment of the present application provides a storage medium, on which a computer program is stored, wherein when being executed by a processor, the computer program implements the fault handling record analysis method according to any one of the above embodiments.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
Another embodiment of the present invention provides an electronic device, as shown in fig. 13, including:
one or more processors 201.
A storage device 202 on which one or more programs are stored.
The one or more programs, when executed by the one or more processors 201, cause the one or more processors 201 to implement a fault handling record analysis method as in any one of the above embodiments.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
While several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
It should be noted that the fault handling record analysis method and system, the electronic device, and the storage medium provided by the present invention can be used in the artificial intelligence field, the block chain field, the distributed field, the cloud computing field, the big data field, the internet of things field, the mobile internet field, the network security field, the chip field, the virtual reality field, the augmented reality field, the holographic technology field, the quantum computing field, the quantum communication field, the quantum measurement field, the digital twin field, or the financial field. The foregoing is merely an example, and does not limit the application fields of the fault handling record analysis method and system, the electronic device, and the storage medium provided by the present invention.
Features described in the embodiments in the present specification may be replaced or combined with each other, and the same and similar portions among the embodiments may be referred to each other, and each embodiment is described with emphasis on differences from other embodiments. In particular, the system or system embodiments are substantially similar to the method embodiments and therefore are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for related points. The above-described system and system embodiments are only illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method of fault handling record analysis, comprising:
collecting historical fault data, and performing data integration and relationship combing; wherein the historical fault data comprises: change information, current fault information, event information, external information and maintenance labels;
analyzing the historical fault data according to manufacturer level, abnormal maintenance, system emergency and maintenance time to obtain an analysis result;
comparing the analysis result with the full data, and obtaining a scoring estimated value by adopting a scoring mechanism; wherein the scoring mechanism comprises: device scoring, system scoring, and maintenance scoring.
2. The method of fault handling record analysis according to claim 1, wherein collecting historical fault data and performing data integration and relationship combing comprises:
collecting change information, current fault information, event information, external information and maintenance labels in each external data;
searching the position, the system, the product and the manufacturer of the equipment in the external information through the serial number of the equipment;
searching historical change information of the equipment in the change information through the serial number of the equipment; the change information includes: implementing people, rechecking people, changing time and relevant information of equipment;
searching a special label and a remark in a historical fault handling record through the equipment serial number;
finding a historical handling process in the event information through the change information, wherein the historical handling process comprises the following steps: time of occurrence, procedure of disposal, solution;
and searching related change information and event information in the change information according to the system/product dimension through the system/product which the external information belongs to.
3. The method for analyzing fault handling records according to claim 1, wherein analyzing the historical fault data according to manufacturer level, abnormal maintenance, system emergency and maintenance time to obtain an analysis result comprises:
extracting vendor service data, current fault system and component data, handling data of a current fault system, and operation data of a current fault component from the historical fault data;
performing manufacturer level analysis on the manufacturer service data to obtain a manufacturer maintenance level;
performing maintenance abnormity analysis on the current fault system and the component data to obtain an abnormity handling condition of the current fault system in the maintenance;
performing system emergency analysis on the disposal data of the current fault system to obtain the maximum time length available for maintenance of the current fault system in the maintenance;
analyzing the maintenance time of the operation data responsible for each product of the current fault component to obtain the predicted maintenance time of the current maintenance;
and combining the manufacturer maintenance level, the abnormal handling condition of the current fault system, the maximum available maintenance time length of the current fault system and the predicted maintenance time length to serve as a final analysis result.
4. The method of claim 3, wherein performing a vendor level analysis on the vendor service data to obtain a vendor repair level comprises:
obtaining the abnormal probability of the abnormal occurrence through the abnormal times in the vendor service data;
obtaining average secondary maintenance time according to each abnormal reason in the historical fault handling record;
obtaining the repair time of the manufacturer according to the abnormal probability and the secondary repair time;
and determining the maintenance level of the manufacturer according to the repair time of the manufacturer.
5. The method for analyzing the fault handling record according to claim 3, wherein performing maintenance anomaly analysis on the current fault system and the component data to obtain an anomaly handling condition of the current fault system in the current maintenance includes:
determining a fault maintenance mode of the current fault system according to historical discovery time and starting time in the current fault system;
determining the probability of the current fault system being abnormal through the fault maintenance mode;
determining the handling time of the fault and the fault condition of the current fault system according to the starting time and the ending time of the fault;
and determining the abnormal handling condition comprising the abnormal handling time length of the fault according to the historical maintenance time length, the abnormal occurrence probability and the historical maintenance mode of the current type equipment.
6. The method for analyzing the fault disposal record according to claim 3, wherein performing system emergency analysis on the disposal data of the current fault system to obtain the maximum available duration of the current fault system for maintenance in the current maintenance includes:
determining a maintenance mode of the current fault system according to the level of the current fault system and a historical maintenance time window;
judging the probability of the current fault system being abnormal according to the maintenance records with preset times and the emergency times;
determining the time when the current fault system generates influence according to the maintenance mode;
determining the average maintenance duration of the current fault system according to the current fault system abnormal probability and a historical maintenance time window;
and determining the maximum time length available for maintenance of the current fault system according to the time point of the influence confirmation of the current fault system, the time point of service recovery and the time length of the emergency treatment process.
7. The method according to claim 3, wherein analyzing the maintenance time of the operation data in charge of each product of the currently-failed component to obtain the estimated repair duration of the current repair comprises:
determining the processing duration of the hardware level, the system level and the product/application level according to the probability and the operation time of each operation in the hardware level, the system level and the product/application level;
and summing the processing time lengths of the hardware level, the system level and the product/application level to obtain the predicted maintenance time length of the maintenance.
8. A fault handling record analysis system, comprising:
the data collection module is used for collecting historical fault data and performing data integration and relationship combing; wherein the historical fault data comprises: change information, current fault information, event information, external information and maintenance labels;
the analysis module is used for analyzing the manufacturer level, abnormal maintenance, system emergency and maintenance time of the historical fault data to obtain an analysis result;
the scoring module is used for comparing the analysis result with the full data and obtaining a scoring estimated value by adopting a scoring mechanism; wherein the scoring mechanism comprises: device scoring, system scoring, and maintenance scoring.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the fault handling record analysis method of any of claims 1-7.
10. A storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the fault handling record analysis method according to any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211228211.1A CN115601009A (en) | 2022-10-08 | 2022-10-08 | Fault disposal record analysis method and system, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211228211.1A CN115601009A (en) | 2022-10-08 | 2022-10-08 | Fault disposal record analysis method and system, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115601009A true CN115601009A (en) | 2023-01-13 |
Family
ID=84846111
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211228211.1A Pending CN115601009A (en) | 2022-10-08 | 2022-10-08 | Fault disposal record analysis method and system, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115601009A (en) |
-
2022
- 2022-10-08 CN CN202211228211.1A patent/CN115601009A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR101856543B1 (en) | Failure prediction system based on artificial intelligence | |
| US20100082708A1 (en) | System and Method for Management of Performance Fault Using Statistical Analysis | |
| US11153144B2 (en) | System and method of automated fault correction in a network environment | |
| CA2389253A1 (en) | Method and system for remotely managing communication of data used for predicting malfunctions in a plurality of machines | |
| CN104346574A (en) | Automatic host computer security configuration vulnerability restoration method and system based on configuration specification | |
| CN111585799A (en) | Network fault prediction model establishing method and device | |
| JP2015164005A (en) | Monitoring apparatus, monitoring method, and program | |
| CN107301120A (en) | Method and device for handling unstructured daily record | |
| US11188409B2 (en) | Data lifecycle management | |
| CN113098715B (en) | Information processing method, device, system, medium and computing equipment | |
| CN111459692A (en) | Method, apparatus and computer program product for predicting drive failure | |
| US20160210580A1 (en) | Method of determining availability and reliability of facility equipment | |
| CN115601009A (en) | Fault disposal record analysis method and system, electronic equipment and storage medium | |
| CN110619572A (en) | Method for monitoring high fault tolerance growth of enterprise public data | |
| US20210027254A1 (en) | Maintenance management apparatus, system, method, and non-transitory computer readable medium | |
| CN116596510A (en) | Operation and maintenance fault management method, system, terminal equipment and storage medium | |
| CN111324516A (en) | Method and device for automatically recording abnormal event, storage medium and electronic equipment | |
| CN113537519A (en) | Method and device for identifying abnormal equipment | |
| US11749070B2 (en) | Identification of anomalies in an automatic teller machine (ATM) network | |
| JP2020135739A (en) | Failure sign detection system | |
| CN117541171A (en) | Information processing method and system based on block chain | |
| CN116701423A (en) | Method, device, equipment and storage medium for updating operation logic library | |
| CN118210648A (en) | Problem positioning method and device for abnormal system operation, electronic equipment and storage medium | |
| CN113849378A (en) | Resource monitoring device, resource monitoring system, resource monitoring method, electronic device, and medium | |
| CN114169896A (en) | Abnormal business processing method and device, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |