CN116521105B - Data management method and system based on big data equipment - Google Patents
Data management method and system based on big data equipment Download PDFInfo
- Publication number
- CN116521105B CN116521105B CN202310800105.4A CN202310800105A CN116521105B CN 116521105 B CN116521105 B CN 116521105B CN 202310800105 A CN202310800105 A CN 202310800105A CN 116521105 B CN116521105 B CN 116521105B
- Authority
- CN
- China
- Prior art keywords
- operation data
- data
- user
- monitoring
- index
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 19
- 238000013523 data management Methods 0.000 title claims abstract description 18
- 238000012544 monitoring process Methods 0.000 claims abstract description 136
- 238000007405 data analysis Methods 0.000 claims abstract description 19
- 238000010276 construction Methods 0.000 claims abstract description 8
- 238000012216 screening Methods 0.000 claims abstract description 7
- 230000004044 response Effects 0.000 claims description 43
- 238000004458 analytical method Methods 0.000 claims description 24
- 230000008859 change Effects 0.000 claims description 17
- 238000012806 monitoring device Methods 0.000 claims description 13
- 238000004364 calculation method Methods 0.000 claims description 7
- 238000013500 data storage Methods 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 abstract description 2
- 238000007639 printing Methods 0.000 description 13
- 230000002159 abnormal effect Effects 0.000 description 10
- 230000009471 action Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000006399 behavior Effects 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/12—Digital output to print unit, e.g. line printer, chain printer
- G06F3/1201—Dedicated interfaces to print systems
- G06F3/1202—Dedicated interfaces to print systems specifically adapted to achieve a particular effect
- G06F3/121—Facilitating exception or error detection and recovery, e.g. fault, media or consumables depleted
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/12—Digital output to print unit, e.g. line printer, chain printer
- G06F3/1201—Dedicated interfaces to print systems
- G06F3/1202—Dedicated interfaces to print systems specifically adapted to achieve a particular effect
- G06F3/1203—Improving or facilitating administration, e.g. print management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/12—Digital output to print unit, e.g. line printer, chain printer
- G06F3/1201—Dedicated interfaces to print systems
- G06F3/1223—Dedicated interfaces to print systems specifically adapted to use a particular technique
- G06F3/1237—Print job management
- G06F3/1259—Print job monitoring, e.g. job status
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/12—Digital output to print unit, e.g. line printer, chain printer
- G06F3/1201—Dedicated interfaces to print systems
- G06F3/1278—Dedicated interfaces to print systems specifically adapted to adopt a particular infrastructure
- G06F3/1285—Remote printer device, e.g. being remote from client or server
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Human Computer Interaction (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Testing And Monitoring For Control Systems (AREA)
- Alarm Systems (AREA)
Abstract
The invention relates to the field of equipment data management and technology, in particular to a big data based equipment data management method and system, comprising a multi-source database construction module, an initial capture data analysis module, a target operation data analysis module, an optimal operation data determination module and an intelligent regulation module; the multi-source database construction module is used for acquiring a multi-source database stored in the cloud by monitoring equipment managed by public cloud SaaS and intelligent terminal AIoT; the initial capture data analysis module is used for determining user side operation data in a monitoring period after the error state code is generated as initial capture data; the target operation data analysis module is used for analyzing target operation data which enable the state of the monitoring equipment to be changed; the optimal operation data determining module is used for screening optimal operation data in the target operation data based on the identity index; the intelligent adjusting module is used for analyzing the adjustable characteristic value of the monitoring equipment for the optimal operation data and performing intelligent adjustment based on the adjustable characteristic value.
Description
Technical Field
The invention relates to the technical field of equipment data management, in particular to a method and a system for managing equipment data based on big data.
Background
The existing intelligent printing equipment is provided with a printing scanning device which cooperates with the intelligent terminal through public cloud, and printing of data transmission can be performed by connecting a login user side with the cloud without using a computer; the printing and scanning equipment does not need to use expensive special custom equipment, does not need to develop printer driving software and related systems by oneself, has lower maintenance cost, and has single-line expansion and no operation and maintenance cost; the intelligent printing equipment does not need to worry about leakage of printing scanning data in the public network, supports concurrent use of millions of equipment, and has stable and reliable performance;
however, because the intelligent device records a plurality of error status codes in the use process, each error status code is reflected in the front end and different situations and forms of the user end, when a user unfamiliar with the intelligent printing device encounters the situation, false touch or other error behaviors are likely to occur, so that further loss of the device is caused; meanwhile, when the problem occurs, the intelligent operation is low through manual trial operation, and the error state of the current equipment cannot be changed rapidly and effectively.
Disclosure of Invention
The invention aims to provide a data management method and system based on big data equipment, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: the data management method based on the big data equipment comprises the following analysis steps:
step S1: acquiring a multisource database stored in a cloud by monitoring equipment managed by public cloud SaaS and intelligent terminal AIoT; the multi-source database records user side operation data logged in the binding monitoring equipment and response data recorded at the rear end of the monitoring equipment; the response data includes an error status code and valid response data; determining user side operation data in a monitoring period after error state codes are generated as initial captured data;
the printing scanning equipment utilizing public cloud SaaS+intelligent terminal AIoT adopts a distributed elastic architecture, can meet various applications of mass equipment connection, high-load printing and scanning scenes, and provides functions of equipment management, remote control, monitoring and the like through a REST API;
the analysis of the data corresponding to the error state codes can effectively analyze the behavior dynamics of the operation of the user side, so as to label different error state codes;
further, in step S1, it is determined that the user operation data in the monitoring period after the error status code is generated is initial captured data, including the following analysis steps:
step S11: the effective response data refers to response indexes experienced by the state of the monitoring equipment from the non-working state corresponding to the error state code to the working state; marking the moment when the error state code is stored in the multi-source database as the initial moment, taking the initial moment as the retrieval starting point, retrieving the user side operation data recorded by the monitoring equipment, and feeding back the user operation data to the storage moment when the effective response data is recorded at the rear end of the monitoring equipment; the user side operation data comprise data of the user operating by using the mobile side and physical operation data executed by the user;
step S12: taking the starting time to the storage time of the effective influence data as an event period; and extracting the user side operation data recorded in the event period and the error state code of the monitoring event to which the corresponding event period belongs as initial captured data. The initial capture of data is to obtain the data executed by the user after the error status code of the monitoring device is abnormal, and based on the data, the data is resolved.
Further, analyzing the target operation data that causes the monitoring device state to change includes the following analysis steps:
step S21: acquiring m times of user operation data of a monitoring event corresponding to the same error state code, wherein the m times of user operation data are all the same user; when the m times of user operation data record data are the same and unique, outputting user operation data corresponding to the monitoring event as target operation data;
step S22: when the m times of user operation data are not unique, acquiring the proportion A of the monitoring equipment where the ith user operation data are located to record the non-monitoring event of the user i And monitoring the proportion of events B i The non-monitoring event refers to an execution event without error status codes, and the execution event refers to an operation event recorded from starting to closing of the device by a user on the monitoring device; using the formula:
Q i =k 1 *(B i /A i )+k 2 *(1/T i )
calculating a driving index of the ith user operation data; wherein A is i =a i1 /a 2 ，a i1 Representing the number of non-monitored events recording the ith user operation data，a 2 Representing the total number of recorded user non-monitoring events; b (B) i =b i1 /b 2 ，b i1 Representing the number of monitoring events recording the ith user operation data, b 2 Representing the total number of recorded user monitoring events; t (T) i Representing the time length of the ith user operation data from the effective response data storage time; k (k) 1 Representing the scale-influencing coefficient, k 2 Representing a time influence coefficient, 0<k1、k2<1；
Calculating a driving index to analyze which operation data among different user operation data is target operation data of the change monitoring device; the analysis value for each operation data can be effectively obtained from the response time of the distance state change and the corresponding proportional difference value in different events;
step S23: outputting the maximum value max [ Q ] in the n driving indexes i ]The corresponding user operation data are target operation data, i is not less than n is not more than m, and n represents the total number of types of the user operation data.
Step S2: analyzing target operation data that causes a state of the monitoring device to change based on the initial captured data of the monitoring period; monitoring the change of the equipment state refers to changing the corresponding equipment state when the error state code is generated into the corresponding equipment state when the effective response data is generated;
step S3: judging operation identity indexes of different user terminals under the same state replacement based on target operation data; the error state code and the valid response data in the state change of the monitoring equipment are the same under the same state change; screening optimal operation data in the target operation data based on the identity index;
further, step S3 includes the following analysis steps:
step S31: acquiring the number d of user operation data types of different users under the same state replacement 1 Calculating an operational identity index D, d=d 1 D, d represents the number of different users recorded under the same state replacement; the user operation type of each user represents a target operation type;
step S32: setting an operation identity index D 0 When D<D 0 When the user operation data is stored, outputting the user operation data with the maximum number as the optimal operation data;
when D is greater than or equal to D 0 When the user terminal is in the same state, calculating the difference index P of the j-th user operation data under the condition of recording different state changes j ，P j =u j /v j Wherein u is j Indicating the number of the monitoring events recorded under the condition that j-th user operation data have different state changes, v j Representing the total number of monitoring events under different state replacement recorded by the jth user; the smaller the difference index is, the more the user operation data can represent the characteristic user operation under the current state replacement;
step S33: outputting user operation data corresponding to a minimum value min [ Pj ] in w differential indexes as optimal operation data, wherein j is less than or equal to w, and w represents the total number of the differential indexes.
Step S4: and analyzing the adjustable characteristic value of the monitoring equipment for the optimal operation data, and performing intelligent adjustment based on the adjustable characteristic value.
Further, step S4 includes the following analysis steps:
acquiring an error state code of a monitoring event where the optimal operation data are located; when the effective response data in the corresponding monitoring event of the error state code record is identical to the optimal operation data in state replacement, outputting an adjustable characteristic value of 1;
when the state replacement of the effective response data in the monitoring event corresponding to the error state code record is the same as that of the optimal operation data, outputting an adjustable characteristic value of 0;
when the adjustable characteristic value is 1, the monitoring equipment transmits optimal operation data to the rear end for early warning adjustment when an error state code occurs;
when the adjustable characteristic value is 0, the monitoring equipment transmits an early warning signal to the user side to remind the user of executing the optimal operation data when the error state code occurs.
According to the invention, by analyzing the optimal operation data and the corresponding adjustable data, when the printing equipment developed by the cloud system is used by the user side and has an abnormal event, the optimal adjustment can be intelligently performed based on the executable of the equipment according to the optimal operation data, so that the damage caused by the error operation of the user under the premise of not knowing the equipment is avoided, and the current abnormal problem can be solved in the fastest and most convenient way.
The equipment data management system comprises a multi-source database construction module, an initial capture data analysis module, a target operation data analysis module, an optimal operation data determination module and an intelligent regulation module;
the multi-source database construction module is used for acquiring a multi-source database stored in the cloud by monitoring equipment managed by public cloud SaaS and intelligent terminal AIoT;
the initial capture data analysis module is used for determining user side operation data in a monitoring period after the error state code is generated as initial capture data;
the target operation data analysis module is used for analyzing target operation data which enable the state of the monitoring equipment to be changed;
the optimal operation data determining module is used for judging operation identity indexes of different user ends under the same state replacement and screening optimal operation data in the target operation data based on the identity indexes;
the intelligent adjusting module is used for analyzing the adjustable characteristic value of the monitoring equipment for the optimal operation data and performing intelligent adjustment based on the adjustable characteristic value.
Further, the target operation data analysis module comprises a data record analysis unit, a driving index calculation unit and a target operation data output unit;
the data record distinguishing unit is used for distinguishing whether the user operation data record data are the same and unique;
the driving index calculation unit is used for acquiring the proportion of the non-monitoring events of the recorded user and the proportion of the monitoring events when the user operation data are not unique, and calculating the driving index of the user operation data;
the target operation data output unit is used for outputting the user operation data corresponding to the monitoring event as target operation data when the user operation data record data are the same and unique, and outputting the user operation data corresponding to the maximum value in the driving index as target operation data when the user operation data are not unique.
Further, the optimal operation data determining module comprises an identity index calculating unit and an optimal operation data outputting unit;
the identity index calculating unit is used for obtaining the number of user operation data types of different users recorded under the same state replacement and calculating an operation identity index;
the optimal operation data output unit is used for comparing the identity operation index with the identity operation index threshold, and outputting the user operation data with the maximum number as the optimal operation data when the identity operation index is smaller than the identity operation index threshold; when the identity operation index is greater than or equal to the identity operation index threshold, calculating a difference index, and outputting user operation data corresponding to the minimum value in the difference index as optimal operation data based on the difference index.
Further, the intelligent adjusting module comprises an adjustable characteristic value determining unit and an intelligent adjusting and analyzing unit;
the adjustable characteristic value determining unit is used for acquiring an error state code of a monitoring event where the optimal operation data are located; when the effective response data in the monitoring event corresponding to the error state code record is identical to the optimal operation data state replacement, outputting an adjustable characteristic value of 1, and when the effective response data in the monitoring event corresponding to the error state code record is not identical to the optimal operation data state replacement, outputting an adjustable characteristic value of 0;
the intelligent regulation analysis unit is used for transmitting optimal operation data to the rear end for early warning regulation when the error state code occurs when the adjustable characteristic value is 1; when the adjustable characteristic value is 0, the monitoring equipment transmits an early warning signal to the user side to remind the user of executing the optimal operation data when the error state code occurs.
Compared with the prior art, the invention has the following beneficial effects: according to the method, abnormal events of error status codes in the historical data record are monitored, user operation data corresponding to different monitoring events are analyzed, layer-by-layer determination is carried out based on the user operation data, and optimal operation data in the monitoring events are output; meanwhile, whether the equipment can be actively executed or not is analyzed when the optimal operation data are determined, priority processing is carried out when the equipment can be actively executed, and early warning is carried out on the user side when the equipment cannot be actively executed; the method and the device give the user terminal the processing and adjusting direction of the abnormal event and the operation flow of the intelligent control equipment in the abnormal event, realize the maximized quick and efficient solution of the abnormal event, and avoid the damage caused by the error operation of the user under the premise of not knowing the equipment.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
fig. 1 is a schematic structural diagram of a data management system based on big data equipment according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides the following technical solutions: the data management method based on the big data equipment comprises the following analysis steps:
step S1: acquiring a multisource database stored in a cloud by monitoring equipment managed by public cloud SaaS and intelligent terminal AIoT; the multi-source database records user side operation data logged in the binding monitoring equipment and response data recorded at the rear end of the monitoring equipment; the response data includes an error status code and valid response data; determining user side operation data in a monitoring period after error state codes are generated as initial captured data;
as shown in the examples: the printing scanning equipment utilizing public cloud SaaS+intelligent terminal AIoT adopts a distributed elastic architecture, can meet various applications of mass equipment connection, high-load printing and scanning scenes, and provides functions of equipment management, remote control, monitoring and the like through a REST API;
step S2: analyzing target operation data that causes a state of the monitoring device to change based on the initial captured data of the monitoring period; monitoring the change of the equipment state refers to changing the corresponding equipment state when the error state code is generated into the corresponding equipment state when the effective response data is generated;
step S3: judging operation identity indexes of different user terminals under the same state replacement based on target operation data; the error state code and the valid response data in the state change of the monitoring equipment are the same under the same state change; screening optimal operation data in the target operation data based on the identity index;
step S4: and analyzing the adjustable characteristic value of the monitoring equipment for the optimal operation data, and performing intelligent adjustment based on the adjustable characteristic value.
In step S1, it is determined that the user operation data in the monitoring period after the error status code is generated is initial captured data, including the following analysis steps:
step S11: the effective response data refers to response indexes experienced by the state of the monitoring equipment from the non-working state corresponding to the error state code to the working state; marking the moment when the error state code is stored in the multi-source database as the initial moment, taking the initial moment as the retrieval starting point, retrieving the user side operation data recorded by the monitoring equipment, and feeding back the user operation data to the storage moment when the effective response data is recorded at the rear end of the monitoring equipment; the user side operation data comprise data of the user operating by using the mobile side and physical operation data executed by the user; physical operation data such as finger key monitoring devices;
step S12: taking the starting time to the storage time of the effective influence data as an event period; and extracting the user side operation data recorded in the event period and the error state code of the monitoring event to which the corresponding event period belongs as initial captured data. The initial capture of data is to obtain the data executed by the user after the error status code of the monitoring device is abnormal, and based on the data, the data is resolved. The monitoring period is much greater than the event period.
Analyzing the target operational data that causes the monitoring device state to change includes the following analysis steps:
step S21: acquiring m times of user operation data of a monitoring event corresponding to the same error state code, wherein the m times of user operation data are all the same user; when the m times of user operation data record data are the same and unique, outputting user operation data corresponding to the monitoring event as target operation data;
step S22: when the m times of user operation data are not unique, acquiring the proportion A of the monitoring equipment where the ith user operation data are located to record the non-monitoring event of the user i And monitoring the proportion of events B i The non-monitoring event refers to an execution event without error status codes, and the execution event refers to an operation event recorded from starting to closing of the device by a user on the monitoring device; using the formula:
Q i =k 1 *(B i /A i )+k 2 *(1/T i )
calculating a driving index of the ith user operation data; wherein A is i =a i1 /a 2 ，a i1 Representing the number of non-monitored events recording the ith user operation data, a 2 Representing the total number of recorded user non-monitoring events; b (B) i =b i1 /b 2 ，b i1 Representing the number of monitoring events recording the ith user operation data, b 2 Representing the total number of recorded user monitoring events; t (T) i Representing the time length of the ith user operation data from the effective response data storage time; k (k) 1 Representing the scale-influencing coefficient, k 2 Representing a time influence coefficient, 0<k1、k2<1；
Calculating a driving index to analyze which operation data among different user operation data is target operation data of the change monitoring device; the analysis value for each operation data can be effectively obtained from the response time of the distance state change and the corresponding proportional difference value in different events;
step S23: output n kinds of drivesMaximum value max [ Q ] in dynamic index i ]The corresponding user operation data are target operation data, i is not less than n is not more than m, and n represents the total number of types of the user operation data.
If "file content parsing fails" occurs when using the intelligent printer, the corresponding error status code is "30312"; the recording event period is that the file content analysis is successfully recovered from the back end recording of the device '30312', and user operation data including 'refreshing' and 'closing and re-uploading' are recorded in the period;
the total number of non-monitoring events is 5, and the total number of monitoring events is 4;
the number of the corresponding non-monitoring events of the refreshing is 3, and the number of the corresponding monitoring events is 2; the time length from the storage time is 3s;
regarding "close and re-upload" the number of corresponding presence non-monitoring events is 1 and the number of corresponding presence monitoring events is 3; the time length from the storage time is 1s; let k be 1 =0.4，k 2 =0.5；
The drive index corresponding to these two operations is calculated separately:
Q 1 =0.4*[(2/4)/(3/5)]+0.5*(1/3)=0.33+0.15=0.48；
Q 2 =0.4*[(3/4)/(1/5)]+0.5*(1/1)=1.5+0.5=2；
2>0.48；
then the "close and re-upload" is output as the target operational data.
Step S3 comprises the following analysis steps:
step S31: acquiring the number d of user operation data types of different users under the same state replacement 1 Calculating an operational identity index D, d=d 1 D, d represents the number of different users recorded under the same state replacement; the user operation type of each user represents a target operation type;
step S32: setting an operation identity index D 0 When D<D 0 When the user operation data is stored, outputting the user operation data with the maximum number as the optimal operation data;
when D is greater than or equal to D 0 When the user terminal is in the same state, calculating the difference index P of the j-th user operation data under the condition of recording different state changes j ，P j =u j /v j Wherein u is j Indicating the number of the monitoring events recorded under the condition that j-th user operation data have different state changes, v j Representing the total number of monitoring events under different state replacement recorded by the jth user; the smaller the difference index is, the more the user operation data can represent the characteristic user operation under the current state replacement;
step S33: outputting user operation data corresponding to a minimum value min [ Pj ] in w differential indexes as optimal operation data, wherein j is less than or equal to w, and w represents the total number of the differential indexes.
Step S4 comprises the following analysis steps:
acquiring an error state code of a monitoring event where the optimal operation data are located; when the effective response data in the corresponding monitoring event of the error state code record is identical to the optimal operation data in state replacement, outputting an adjustable characteristic value of 1; the valid response data and the optimal operation data are identical in state replacement, namely that a user changes the wrong working state of the equipment by transmitting the valid response data through own operation;
when the state replacement of the effective response data in the monitoring event corresponding to the error state code record is the same as that of the optimal operation data, outputting an adjustable characteristic value of 0;
when the adjustable characteristic value is 1, the monitoring equipment transmits optimal operation data to the rear end for early warning adjustment when an error state code occurs;
when the adjustable characteristic value is 0, the monitoring equipment transmits an early warning signal to the user side to remind the user of executing the optimal operation data when the error state code occurs.
According to the invention, by analyzing the optimal operation data and the corresponding adjustable data, when the printing equipment developed by the cloud system is used by the user side and has an abnormal event, the optimal adjustment can be intelligently performed based on the executable of the equipment according to the optimal operation data, so that the damage caused by the error operation of the user under the premise of not knowing the equipment is avoided, and the current abnormal problem can be solved in the fastest and most convenient way.
The equipment data management system comprises a multi-source database construction module, an initial capture data analysis module, a target operation data analysis module, an optimal operation data determination module and an intelligent regulation module;
the multi-source database construction module is used for acquiring a multi-source database stored in the cloud by monitoring equipment managed by public cloud SaaS and intelligent terminal AIoT;
the initial capture data analysis module is used for determining user side operation data in a monitoring period after the error state code is generated as initial capture data;
the target operation data analysis module is used for analyzing target operation data which enable the state of the monitoring equipment to be changed;
the optimal operation data determining module is used for judging operation identity indexes of different user ends under the same state replacement and screening optimal operation data in the target operation data based on the identity indexes;
the intelligent adjusting module is used for analyzing the adjustable characteristic value of the monitoring equipment for the optimal operation data and performing intelligent adjustment based on the adjustable characteristic value.
The target operation data analysis module comprises a data record analysis unit, a driving index calculation unit and a target operation data output unit;
the data record distinguishing unit is used for distinguishing whether the user operation data record data are the same and unique;
the driving index calculation unit is used for acquiring the proportion of the non-monitoring events of the recorded user and the proportion of the monitoring events when the user operation data are not unique, and calculating the driving index of the user operation data;
the target operation data output unit is used for outputting the user operation data corresponding to the monitoring event as target operation data when the user operation data record data are the same and unique, and outputting the user operation data corresponding to the maximum value in the driving index as target operation data when the user operation data are not unique.
The optimal operation data determining module comprises an identity index calculating unit and an optimal operation data outputting unit;
the identity index calculating unit is used for obtaining the number of user operation data types of different users recorded under the same state replacement and calculating an operation identity index;
the optimal operation data output unit is used for comparing the identity operation index with the identity operation index threshold, and outputting the user operation data with the maximum number as the optimal operation data when the identity operation index is smaller than the identity operation index threshold; when the identity operation index is greater than or equal to the identity operation index threshold, calculating a difference index, and outputting user operation data corresponding to the minimum value in the difference index as optimal operation data based on the difference index.
The intelligent adjusting module comprises an adjustable characteristic value determining unit and an intelligent adjusting and analyzing unit;
the adjustable characteristic value determining unit is used for acquiring an error state code of a monitoring event where the optimal operation data are located; when the effective response data in the monitoring event corresponding to the error state code record is identical to the optimal operation data state replacement, outputting an adjustable characteristic value of 1, and when the effective response data in the monitoring event corresponding to the error state code record is not identical to the optimal operation data state replacement, outputting an adjustable characteristic value of 0;
the intelligent regulation analysis unit is used for transmitting optimal operation data to the rear end for early warning regulation when the error state code occurs when the adjustable characteristic value is 1; when the adjustable characteristic value is 0, the monitoring equipment transmits an early warning signal to the user side to remind the user of executing the optimal operation data when the error state code occurs.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. The data management method based on the big data equipment is characterized by comprising the following analysis steps:
step S1: acquiring a multisource database stored in a cloud by monitoring equipment managed by public cloud SaaS and intelligent terminal AIoT; the multisource database records user side operation data logged in the binding monitoring equipment and response data recorded at the rear end of the monitoring equipment; the response data comprises an error status code and valid response data; determining user side operation data in a monitoring period after error state codes are generated as initial captured data;
in the step S1, it is determined that the user operation data in the monitoring period after the error status code is generated is initial captured data, and the method includes the following analysis steps:
step S11: the effective response data refers to response indexes experienced by the state of the monitoring equipment from the non-working state corresponding to the error state code to the working state; marking the moment when the error state code is stored in the multi-source database as the initial moment, taking the initial moment as the retrieval starting point, retrieving the user side operation data recorded by the monitoring equipment, and feeding back the user operation data to the storage moment when the effective response data is recorded at the rear end of the monitoring equipment; the user side operation data comprise data of the user operation by using the mobile side and physical operation data executed by the user;
step S12: taking the starting time to the storage time of the effective influence data as an event period; extracting user side operation data recorded in an event period and error state codes of monitoring events to which the corresponding event period belongs as initial capturing data;
step S2: analyzing target operation data that causes a state of the monitoring device to change based on the initial captured data of the monitoring period; the monitoring equipment state change refers to that the corresponding equipment state is changed into the corresponding equipment state when effective response data are generated when an error state code is generated;
the analyzing the target operational data such that the status of the monitoring device changes includes the steps of:
step S21: acquiring m times of user operation data of a monitoring event corresponding to the same error state code, wherein the m times of user operation data are all the same user; when the m times of user operation data record data are the same and unique, outputting the user operation data corresponding to the monitoring event as target operation data;
step S22: when the m times of user operation data are not unique, acquiring the proportion A of the monitoring equipment where the ith user operation data are located to record the non-monitoring event of the user i And monitoring the proportion of events B i The non-monitoring event refers to an execution event without an error status code, and the execution event refers to an operation event recorded from starting to closing the equipment by a user on the monitoring equipment; using the formula:
Q i =k 1 *(B i /A i )+k 2 *(1/T i )
calculating a driving index of the ith user operation data; wherein A is i =a i1 /a 2 ，a i1 Representing the number of non-monitored events recording the ith user operation data, a 2 Representing the total number of recorded user non-monitoring events; b (B) i =b i1 /b 2 ，b i1 Representing the number of monitoring events recording the ith user operation data, b 2 Representing the total number of recorded user monitoring events; t (T) i Representing the time length of the ith user operation data from the effective response data storage time; k (k) 1 Representing the scale-influencing coefficient, k 2 Representing a time influence coefficient, 0<k1、k2<1；
Step S23: outputting the maximum value max [ Q ] in the n driving indexes i ]The corresponding user operation data are target operation data, i is not less than n and not more than m, and n represents the total number of types of the user operation data;
step S3: judging operation identity indexes of different user terminals under the same state replacement based on target operation data; the error state code and the effective response data in the state change of the monitoring equipment are the same under the same state replacement; screening optimal operation data in the target operation data based on the identity index;
step S4: and analyzing the adjustable characteristic value of the monitoring equipment for the optimal operation data, and performing intelligent adjustment based on the adjustable characteristic value.
2. The big data equipment based data management method according to claim 1, wherein: the step S3 includes the following analysis steps:
step S31: acquiring the number d of user operation data types of different users under the same state replacement 1 Calculating an operational identity index D, d=d 1 D, d represents the number of different users recorded under the same state replacement; the user operation type of each user represents a target operation type;
step S32: setting an operation identity index D 0 When D<D 0 When the user operation data is stored, outputting the user operation data with the maximum number as the optimal operation data;
when D is greater than or equal to D 0 When the user terminal is in the same state, calculating the difference index P of the j-th user operation data under the condition of recording different state changes j ，P j =u j /v j Wherein u is j Indicating the number of the monitoring events recorded under the condition that j-th user operation data have different state changes, v j Representing the total number of monitoring events under different state replacement recorded by the jth user;
step S33: outputting user operation data corresponding to a minimum value min [ Pj ] in w differential indexes as optimal operation data, wherein j is less than or equal to w, and w represents the total number of the differential indexes.
3. The big data equipment based data management method according to claim 2, wherein: the step S4 includes the following analysis steps:
acquiring an error state code of a monitoring event where the optimal operation data are located; when the effective response data in the corresponding monitoring event of the error state code record is identical to the optimal operation data in state replacement, outputting an adjustable characteristic value of 1;
when the state replacement of the effective response data in the monitoring event corresponding to the error state code record is the same as that of the optimal operation data, outputting an adjustable characteristic value of 0;
when the adjustable characteristic value is 1, the monitoring equipment transmits optimal operation data to the rear end for early warning adjustment when an error state code occurs;
when the adjustable characteristic value is 0, the monitoring equipment transmits an early warning signal to the user side to remind the user of executing the optimal operation data when the error state code occurs.
4. A device data management system based on a big data device data management method according to any one of claims 1-3, comprising a multi-source database construction module, an initial capture data analysis module, a target operation data analysis module, an optimal operation data determination module and an intelligent regulation module;
the multi-source database construction module is used for acquiring a multi-source database stored in a cloud by monitoring equipment managed by public cloud SaaS and intelligent terminal AIoT;
the initial capture data analysis module is used for determining user side operation data in a monitoring period after the error state code is generated as initial capture data;
the target operation data analysis module is used for analyzing target operation data for changing the state of the monitoring equipment;
the target operation data analysis module comprises a data record analysis unit, a driving index calculation unit and a target operation data output unit;
the data record distinguishing unit is used for distinguishing whether the user operation data record data are the same and unique;
the driving index calculation unit is used for acquiring and recording the proportion of non-monitoring events of the user and the proportion of monitoring events when the user operation data are not unique, and calculating the driving index of the user operation data;
the target operation data output unit is used for outputting the user operation data corresponding to the monitoring event as target operation data when the user operation data record data are the same and unique, and outputting the user operation data corresponding to the maximum value in the driving index as target operation data when the user operation data are not unique;
the optimal operation data determining module is used for judging operation identity indexes of different user ends under the same state replacement and screening optimal operation data in target operation data based on the identity indexes;
the intelligent adjusting module is used for analyzing the adjustable characteristic value of the monitoring equipment for the optimal operation data and performing intelligent adjustment based on the adjustable characteristic value.
5. The device data management system of claim 4, wherein: the optimal operation data determining module comprises an identity index calculating unit and an optimal operation data outputting unit;
the identity index calculation unit is used for obtaining the number of user operation data types recorded by different users under the same state replacement and calculating an operation identity index;
the optimal operation data output unit is used for comparing the identity operation index with the identity operation index threshold, and outputting the user operation data with the maximum number as the optimal operation data when the identity operation index is smaller than the identity operation index threshold; when the identity operation index is greater than or equal to the identity operation index threshold, calculating a difference index, and outputting user operation data corresponding to the minimum value in the difference index as optimal operation data based on the difference index.
6. The device data management system of claim 5, wherein: the intelligent adjusting module comprises an adjustable characteristic value determining unit and an intelligent adjusting and analyzing unit;
the adjustable characteristic value determining unit is used for obtaining an error state code of a monitoring event where the optimal operation data are located; when the effective response data in the monitoring event corresponding to the error state code record is identical to the optimal operation data state replacement, outputting an adjustable characteristic value of 1, and when the effective response data in the monitoring event corresponding to the error state code record is not identical to the optimal operation data state replacement, outputting an adjustable characteristic value of 0;
the intelligent regulation analysis unit is used for transmitting optimal operation data to the rear end for early warning regulation when the error state code occurs when the adjustable characteristic value is 1; when the adjustable characteristic value is 0, the monitoring equipment transmits an early warning signal to the user side to remind the user of executing the optimal operation data when the error state code occurs.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310800105.4A CN116521105B (en) | 2023-07-03 | 2023-07-03 | Data management method and system based on big data equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310800105.4A CN116521105B (en) | 2023-07-03 | 2023-07-03 | Data management method and system based on big data equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116521105A CN116521105A (en) | 2023-08-01 |
| CN116521105B true CN116521105B (en) | 2023-09-08 |
Family
ID=87394462
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310800105.4A Active CN116521105B (en) | 2023-07-03 | 2023-07-03 | Data management method and system based on big data equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116521105B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002196917A (en) * | 2000-12-26 | 2002-07-12 | Canon Inc | Trouble shooting system, server, and method of controlling them |
| JP2005165774A (en) * | 2003-12-03 | 2005-06-23 | Canon Inc | Proxy printing system, information processor, control method, and control program |
| CN108933716A (en) * | 2018-06-20 | 2018-12-04 | 山东大学 | Cloud interactive gateway, communication means and smart home system based on OpenWrt |
| CN110045934A (en) * | 2019-05-23 | 2019-07-23 | 宜春宜联科技有限公司 | Printer fault handling method and device |
| JP2019153194A (en) * | 2018-03-06 | 2019-09-12 | 京セラドキュメントソリューションズ株式会社 | Equipment management system, and equipment management program |
| CN110275967A (en) * | 2019-06-24 | 2019-09-24 | 广东虹瑞智能设备股份有限公司 | A kind of visual punching machine failure auxiliary maintaining method, system and terminal |
| KR20200014655A (en) * | 2018-08-01 | 2020-02-11 | 주식회사 빅솔론 | System and method for controlling print device |
| CN113954542A (en) * | 2021-11-18 | 2022-01-21 | 苏州忧企印信息科技有限公司 | Printer fault detection system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130016377A1 (en) * | 2011-07-15 | 2013-01-17 | Somasundaram Meiyappan | System and method for automatic recovery of cloud print jobs after printer error |
-
2023
- 2023-07-03 CN CN202310800105.4A patent/CN116521105B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002196917A (en) * | 2000-12-26 | 2002-07-12 | Canon Inc | Trouble shooting system, server, and method of controlling them |
| JP2005165774A (en) * | 2003-12-03 | 2005-06-23 | Canon Inc | Proxy printing system, information processor, control method, and control program |
| JP2019153194A (en) * | 2018-03-06 | 2019-09-12 | 京セラドキュメントソリューションズ株式会社 | Equipment management system, and equipment management program |
| CN108933716A (en) * | 2018-06-20 | 2018-12-04 | 山东大学 | Cloud interactive gateway, communication means and smart home system based on OpenWrt |
| KR20200014655A (en) * | 2018-08-01 | 2020-02-11 | 주식회사 빅솔론 | System and method for controlling print device |
| CN110045934A (en) * | 2019-05-23 | 2019-07-23 | 宜春宜联科技有限公司 | Printer fault handling method and device |
| CN110275967A (en) * | 2019-06-24 | 2019-09-24 | 广东虹瑞智能设备股份有限公司 | A kind of visual punching machine failure auxiliary maintaining method, system and terminal |
| CN113954542A (en) * | 2021-11-18 | 2022-01-21 | 苏州忧企印信息科技有限公司 | Printer fault detection system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116521105A (en) | 2023-08-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CA3098860C (en) | Alarm log compression method, apparatus, and system, and storage medium | |
| US9077610B2 (en) | Performing call stack sampling | |
| CN110493348A (en) | A kind of intelligent monitoring and alarming system based on Internet of Things | |
| EP2874064B1 (en) | Adaptive metric collection, storage, and alert thresholds | |
| CN107566163A (en) | A kind of alarm method and device of user behavior analysis association | |
| CN104598367A (en) | System and method for automatically managing fault events of data center | |
| KR102432284B1 (en) | A system that automatically responds to event alarms or failures in IT management in real time and its operation method | |
| CN103761173A (en) | Log based computer system fault diagnosis method and device | |
| CN110933115B (en) | Analysis object behavior abnormity detection method and device based on dynamic session | |
| US20220318118A1 (en) | Detecting changes in application behavior using anomaly corroboration | |
| US20070071081A1 (en) | Communication analysis apparatus and method and storage medium storing communication analysis program, and organization rigidification analysis apparatus and method and storage medium storing organization rigidification analysis program | |
| CN104899130A (en) | Application program management method and system | |
| WO2021147220A1 (en) | Page access duration acquisition method, device, medium, and electronic apparatus | |
| CN103746829A (en) | Cluster-based fault perception system and method thereof | |
| CN103488793A (en) | User behavior monitoring method based on information retrieval | |
| CN111078513A (en) | Log processing method, device, equipment, storage medium and log alarm system | |
| US20150325108A1 (en) | Determining alert criteria in a network environment | |
| CN116233902B (en) | Wireless communication network anomaly identification system and method based on big data | |
| CN112416872A (en) | Cloud platform log management system based on big data | |
| CN116521105B (en) | Data management method and system based on big data equipment | |
| CN112257546B (en) | Event early warning method and device, electronic equipment and storage medium | |
| CN112583842A (en) | Network security situation awareness system platform based on data stream processing | |
| CN116956282B (en) | Abnormality detection system based on network asset memory time sequence multi-feature data | |
| US9645877B2 (en) | Monitoring apparatus, monitoring method, and recording medium | |
| CN112699048A (en) | Program fault processing method, device and equipment based on artificial intelligence 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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |