CN116149236B - Application operation and maintenance management intelligent monitoring system and method based on block chain - Google Patents
Application operation and maintenance management intelligent monitoring system and method based on block chain Download PDFInfo
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- CN116149236B CN116149236B CN202310347488.4A CN202310347488A CN116149236B CN 116149236 B CN116149236 B CN 116149236B CN 202310347488 A CN202310347488 A CN 202310347488A CN 116149236 B CN116149236 B CN 116149236B
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- 238000012423 maintenance Methods 0.000 title claims abstract description 98
- 238000012544 monitoring process Methods 0.000 title claims abstract description 88
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000004458 analytical method Methods 0.000 claims abstract description 38
- 230000004044 response Effects 0.000 claims description 119
- 238000007726 management method Methods 0.000 claims description 26
- 206010041349 Somnolence Diseases 0.000 claims description 23
- 238000004364 calculation method Methods 0.000 claims description 23
- 208000032140 Sleepiness Diseases 0.000 claims description 20
- 230000037321 sleepiness Effects 0.000 claims description 20
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
- G05B19/0428—Safety, monitoring
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24024—Safety, surveillance
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention relates to the technical field of intelligent monitoring of application operation and maintenance management, in particular to an intelligent monitoring system and method of application operation and maintenance management based on a blockchain, wherein the intelligent monitoring system comprises a related data acquisition module, an application operation and maintenance early warning index analysis module, an early warning group value analysis module, a threshold judgment early warning module and a feedback monitoring module; the related data acquisition module is used for acquiring related data of intelligent equipment control performed by a user based on intelligent application; the application operation and maintenance early warning index analysis module is used for analyzing application operation and maintenance early warning indexes of the intelligent equipment associated application; the early warning group value analysis module is used for extracting application operation and maintenance early warning indexes of different users with the same application in the blockchain; the threshold judgment early warning module is used for setting an early warning group value threshold of the intelligent equipment association application; the feedback monitoring module is used for re-inputting the user in the monitoring period to acquire the related data after the intelligent application maintenance management is completed, and transferring to the operation and maintenance early warning index analysis module.
Description
Technical Field
The invention relates to the technical field of application operation and maintenance management intelligent monitoring, in particular to an application operation and maintenance management intelligent monitoring system and method based on a block chain.
Background
At present, intelligent home gradually goes into the field of vision of the public, more and more users can experience the convenience of people's daily life and modern life feeling brought by the intelligent home, and the intelligent home can integrate the life habits of different users to formulate personalized settings which are unique to the users;
however, the application of the smart home also has some drawbacks, the smart home used in the market often establishes smart application apps integrated with the smart home, the operation interfaces and the operation functions of the application apps are different, but the application apps are set at all, which are better auxiliary users for operating the smart home, but due to too complex or unreasonable settings, a plurality of problems exist in the process of using the application by some users, so that the efficiency of the application of the smart home is low, the receiving degree of the user group on the smart home is reduced, and the development of the application does not have a beneficial and praise effect; and for the feedback of the process, a lot of operation and maintenance operators of the intelligent home application app can not quickly and effectively extract useful information from huge user use data behind the application app to judge whether the user use experience applied to the user and the design of the application app have problems, and the like.
Disclosure of Invention
The invention aims to provide an application operation and maintenance management intelligent monitoring system and method based on a blockchain so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an intelligent monitoring method for application operation and maintenance management based on a blockchain comprises the following analysis steps:
step S1: acquiring relevant data of intelligent equipment control by a user based on intelligent application, wherein the relevant data comprises intelligent equipment response data and user control data; the intelligent equipment response data refers to a response mode of the intelligent equipment for recording user operation, wherein the response mode comprises a remote network control mode and a first control mode, and the first control mode refers to one-to-one control operation of the intelligent equipment by a user;
step S2: based on the related data in the step S1, analyzing an application operation and maintenance early warning index of the intelligent equipment associated application;
step S3: based on the application operation and maintenance early warning indexes of the step S2, extracting application operation and maintenance early warning indexes of different users, which are recorded in the blockchain and use the same application, and analyzing early warning group values of intelligent equipment associated intelligent application;
step S4: based on the early warning group value of the intelligent application in the step S3, an early warning group value threshold of the intelligent equipment associated application is set, and when the early warning group value is greater than or equal to the early warning group value threshold, an early warning signal is transmitted to maintain and manage the intelligent application associated with the intelligent equipment; and (3) after the intelligent application maintenance management is finished, the user re-invests the relevant data in the monitoring period to obtain the relevant data in the step (S1), and the step (S2) is shifted until the early warning group value is smaller than the early warning group value threshold value, and an application maintenance success signal is transmitted.
Further, step S2 includes the following analysis steps:
the user control data comprises remote network control data and first control data; the remote network control data is application operation data recorded when the user uses the intelligent application, and the application operation data comprises operation items and operation instructions; the first control data are first data of one-to-one control operation records of the intelligent equipment by a user;
acquiring operation time t corresponding to the jth operation item of the ith response stage of the user recorded by the application program in the monitoring period ij 1 The method comprises the steps of carrying out a first treatment on the surface of the And generating a kth response time t corresponding to the remote network control mode through instruction transmission after implementing the operation item ik 2 K is less than or equal to n, n represents the total number of times the device generates a response; executing each operation item does not necessarily result in an instruction transmission, but the instruction transmission must result from the operation item being implemented; obtaining the maximum value T of the corresponding time intervals of m response phases in the monitoring period, wherein T=max { T } i M is less than or equal to i, the time interval T i Refers to the operation time t corresponding to the 1 st operation item in the same response stage i1 1 And the last response time t of the remote network control mode generated by instruction transmission after the operation project is implemented in 2 Time interval of (T) i =t in 2 -t i1 1 The method comprises the steps of carrying out a first treatment on the surface of the The response phase refers to a period from when the user opens the intelligent application to when the intelligent application is closed; the last response time indicates that the user achieves the purpose of responding to the intelligent equipment through the intelligent application;
extracting operation data recorded after a response phase corresponding to the maximum value T of the time interval as target operation data, wherein a response period corresponding to the target operation data is a target monitoring period, and utilizing a formula:
ρ=(∑t 0 )/T 0
calculating user operation density for a target monitoring periodWherein t is 0 Representing the interval duration of user control data corresponding to the adjacent remote network control mode and the first control mode in the target monitoring period, T 0 Representing the total duration of the target monitoring period; the user operation density represents the selection tendency of the user for two different operation modes in the monitoring process; the higher the user operation density, the less the user's tendency to select to use the smart application;
analyzing the time interval maximum to determine a time range that is least familiar to the user with operation when using the application; the subsequent operations can reflect the use of the intelligent application by the user with minimal error, i.e. without regard to the familiarity of the user with the application;
acquiring total times r of first control data records in target monitoring period 1 Total number of response phases r corresponding to remote network control data 2 The method comprises the steps of carrying out a first treatment on the surface of the Using the formula:
Y=0.55*[r 1 /(r 1 +r 2 )]+0.45*ρ
and calculating the intimacy Y of the user to the intelligent device association application. The greater affinity indicates that the user has less usage of the smart device associated application or less probability of successfully implementing control of the device through the application, because the user rarely reselects other ways to control when the user can effectively use the smart application to achieve manipulation of the smart device, and when there are other ways to manipulate, it is stated that the smart application is not effective and fully implements the value and benefits of the application settings.
Further, step S2 further includes the following analysis steps:
acquiring the item number g of operation items in the user click guide bar in the ith response stage i To monitor the circumference ofDrawing the abscissa of the response phase in the period according to the time sequence, establishing a coordinate system of the number of the corresponding items of the response phase, and drawing the number g of the items with the sequence pairs as the response phase i Forming a first fitting curve; the operation items in the guide bar reflect different functions of application development in the intelligent application; obtaining the number g of the items in the fitting curve i Maximum value maxg of i The corresponding response phase is an initial response phase, and the last response phase in the monitoring period is a final response phase; the coordinates in the coordinate system corresponding to the connection start response stage and the termination response stage form a line segment I;
when the maximum value is not the same, selecting a response phase corresponding to the maximum value which appears first according to the time sequence as an initial response phase;
acquiring the number f of sequence pairs on one side of line segment, which is close to the abscissa 1 And the number f of sequence pairs on one side of the line segment away from the abscissa 2 Using the formula:
X=(maxg i -g 0 )*(f 1 -f 2 )
computing user's drowsiness X for smart device associated applications, where g 0 Representing the number of items in the corresponding sequence pair of the termination response phase; (maxg) i -g 0 ) Reflecting the interest change trend of the user for different operation items in the application guide bar after a period of use, (f) 1 -f 2 ) Reflecting dynamic fluctuation of a user during use; the smaller the user's sleepiness, the more successful the development of the application, the stronger the sustainability of the user's use of the application;
the operation and maintenance early warning index w=0.45x+0.55y is applied.
Further, step S3 includes the following analysis steps:
acquiring application operation and maintenance indexes W recorded in a block chain and used for different users with the same application, and recording the total number N of the users, and removing the maximum value maxW and the minimum value minW of the application operation and maintenance indexes W corresponding to the different users; using the formula:
W 0 =[1/(N-2)]*[∑W-maxW-minW]
early warning group value W of intelligent equipment associated intelligent application is calculated 0 。
An application operation and maintenance management intelligent monitoring system based on a blockchain comprises a related data acquisition module, an application operation and maintenance early warning index analysis module, an early warning group value analysis module, a threshold judgment early warning module and a feedback monitoring module;
the related data acquisition module is used for acquiring related data of intelligent equipment control based on intelligent application by a user, wherein the related data comprises intelligent equipment response data and user control data;
the application operation and maintenance early warning index analysis module is used for analyzing application operation and maintenance early warning indexes of the intelligent equipment associated application;
the early warning group value analysis module is used for extracting application operation and maintenance early warning indexes of different users using the same application recorded in the blockchain and analyzing early warning group values of intelligent equipment associated intelligent application; when the early warning group value is greater than or equal to the early warning group value threshold value, transmitting an early warning signal to maintain and manage intelligent application associated with the intelligent equipment;
the threshold value judging and early warning module is used for setting an early warning group value threshold value of the intelligent equipment associated application, and transmitting an early warning signal to maintain and manage the intelligent equipment associated intelligent application when the early warning group value is greater than or equal to the early warning group value threshold value;
the feedback monitoring module is used for restarting the user in the monitoring period to acquire related data after the intelligent application maintenance management is completed, and the application operation and maintenance early warning index analysis module is transferred until the early warning group value is smaller than the early warning group value threshold value, and transmits an application maintenance success signal.
Further, the application operation and maintenance early warning index analysis module comprises a user control data dividing unit, a intimacy calculating unit, a sleepiness calculating unit and an application operation and maintenance early warning index calculating unit;
the user control data dividing unit is used for dividing remote network control data and first control data;
the affinity calculation unit is used for calculating the affinity of the user for the intelligent equipment association application;
the sleepiness calculating unit is used for the sleepiness of the user for the intelligent equipment associated application;
the application operation and maintenance early warning index calculation unit is used for calculating the application operation and maintenance early warning index based on the affinity of the affinity calculation unit and the sleepiness of the sleepiness calculation unit.
Further, the intimacy calculating unit includes a target period determining unit, a user operation density calculating unit;
the target period determining unit is used for determining a response period corresponding to the target operation data as a target monitoring period;
the user operation density calculation unit is used for calculating the user operation density of the target monitoring period based on the interval duration of the user control data corresponding to the adjacent remote network control mode and the first control mode in the target monitoring period and the total duration of the target monitoring period.
Further, the sleepiness calculating unit comprises a sequence pair acquiring unit, a fitting curve drawing unit and a data extracting unit;
the sequence pair acquisition unit is used for acquiring the number of items of operation items in the clicking guide bar of a user in a response stage, drawing an abscissa according to a time sequence in the response stage in a monitoring period, establishing a coordinate system of the number of the items corresponding to the response stage, and establishing a sequence pair (the number of the items in the response stage);
the fitting curve drawing unit is used for obtaining sequence pairs and establishing a fitting curve;
the data extraction unit is used for extracting the number of the sequence pairs on one side, close to the abscissa, of the line segment and the number of the sequence pairs on one side, far from the abscissa, of the line segment based on the fitting curve; the first line segment is a response phase corresponding to the maximum value of the number of the items in the first fitting curve, and is a starting response phase, and the last response phase in the monitoring period is a termination response phase; the coordinates in the coordinate system corresponding to the connection start response phase and the termination response phase form a line segment one.
Further, the early warning group value analysis module comprises a group data extraction unit and an early warning group value calculation unit;
the group data extraction unit is used for obtaining application operation and maintenance indexes of different users using the same application recorded in the block chain and recording the total number of the users;
the early warning group value calculation unit is used for removing the maximum value of the application operation and maintenance indexes and the minimum value of the application operation and maintenance indexes corresponding to different users, and acquiring the data in the group data extraction unit to calculate the early warning group value.
Compared with the prior art, the invention has the following beneficial effects: according to the intelligent home application management method, the user data of the same application program in the same monitoring period in the blockchain are monitored and obtained in real time, the use experience of the intelligent application developed by the intelligent device for the user is analyzed and inferred from the frequency of operating the intelligent application by the user on the intelligent device, the use intimacy and the exploratory property of the user on the development application in diversity and the interest expression of the user in the use process, so that the efficiency of the intelligent home application is determined, the receiving degree of a user group on the intelligent home application is determined, and the development of the application has a beneficial and drama effect; the invention can purposefully extract the user data, so that the system can effectively judge whether the user experience and the design of the application have problems based on the data, thereby realizing the purposes of quickly and effectively finding out the problems of the application and returning to the operation and maintenance center for upgrading and maintenance, and maximally reducing the loss of the user.
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 diagram of a block chain based application operation and maintenance management intelligent monitoring system.
Description of the embodiments
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: an intelligent monitoring method for application operation and maintenance management based on a blockchain comprises the following analysis steps:
step S1: acquiring relevant data of intelligent equipment control by a user based on intelligent application, wherein the relevant data comprises intelligent equipment response data and user control data; the intelligent equipment can be intelligent home equipment and other equipment capable of being associated with intelligent application to perform remote network control; the intelligent equipment response data refers to a response mode of the intelligent equipment for recording user operation, wherein the response mode comprises a remote network control mode and a first control mode, and the first control mode refers to one-to-one control operation of the intelligent equipment by a user; if a plurality of home devices exist in the intelligent home system, the remote network control means that the intelligent application program related to the intelligent home is utilized to control the plurality of home devices in a one-to-many manner, and the remote network control means that the intelligent application program is an application main body and the remote network control means that the intelligent application program is related to the plurality of home devices; the first control mode may be that a user controls a certain household device, such as a mode of manually turning off the lighting, and one-to-one refers to a result of turning off the lighting corresponding to the operation of turning off the lighting by hand, and the action and the household are one-to-one;
step S2: based on the related data in the step S1, analyzing an application operation and maintenance early warning index of the intelligent equipment associated application;
step S3: based on the application operation and maintenance early warning indexes of the step S2, extracting application operation and maintenance early warning indexes of different users, which are recorded in the blockchain and use the same application, and analyzing early warning group values of intelligent equipment associated intelligent application;
step S4: based on the early warning group value of the intelligent application in the step S3, an early warning group value threshold of the intelligent equipment associated application is set, and when the early warning group value is greater than or equal to the early warning group value threshold, an early warning signal is transmitted to maintain and manage the intelligent application associated with the intelligent equipment; the maintenance management refers to perfect updating or repairing of application development by research personnel and operation and maintenance personnel of the intelligent equipment; and (3) after the intelligent application maintenance management is finished, the user re-invests the relevant data in the monitoring period to obtain the relevant data in the step (S1), and the step (S2) is shifted until the early warning group value is smaller than the early warning group value threshold value, and an application maintenance success signal is transmitted.
Step S2 comprises the following analysis steps:
the user control data comprises remote network control data and first control data; the remote network control data is application operation data recorded when the user uses the intelligent application, and the application operation data comprises operation items and operation instructions; the first control data are first data of one-to-one control operation records of the intelligent equipment by a user; the remote network control means that the application app is used for controlling through wireless transmission devices such as mobile phones, tablets and the like;
acquiring operation time t corresponding to the jth operation item of the ith response stage of the user recorded by the application program in the monitoring period ij 1 The method comprises the steps of carrying out a first treatment on the surface of the The operation items can be options such as 'electric lamp', 'air conditioner' on the intelligent home application, and also can be operation items such as 'my home', 'mall', 'intelligent' in a taskbar; and generating a kth response time t corresponding to the remote network control mode through instruction transmission after implementing the operation item ik 2 K is less than or equal to n, n represents the total number of times the device generates a response; executing each operation item does not necessarily result in an instruction transmission, but the instruction transmission must result from the operation item being implemented; obtaining the maximum value T of the corresponding time intervals of m response phases in the monitoring period, wherein T=max { T } i M is less than or equal to i, the time interval T i Refers to the operation time t corresponding to the 1 st operation item in the same response stage i1 1 And the last response time t of the remote network control mode generated by instruction transmission after the operation project is implemented in 2 Time interval of (T) i =t in 2 -t i1 1 The method comprises the steps of carrying out a first treatment on the surface of the The response phase refers to a period from when the user opens the intelligent application to when the intelligent application is closed; the last response time indicates that the user achieves the purpose of responding to the intelligent equipment through the intelligent application;
extracting operation data recorded after a response phase corresponding to the maximum value T of the time interval as target operation data, wherein a response period corresponding to the target operation data is a target monitoring period, and utilizing a formula:
ρ=(∑t 0 )/T 0
calculating a user operation density ρ of the target monitoring period, where t 0 Representing the interval duration of user control data corresponding to the adjacent remote network control mode and the first control mode in the target monitoring period, T 0 Representing the total duration of the target monitoring period; the user operation density represents the selection tendency of the user for two different operation modes in the monitoring process; the higher the user operation density, the less the user's tendency to select to use the smart application;
analyzing the time interval maximum to determine a time range that is least familiar to the user with operation when using the application; the subsequent operations can reflect the use of the intelligent application by the user with minimal error, i.e. without regard to the familiarity of the user with the application;
acquiring total times r of first control data records in target monitoring period 1 Total number of response phases r corresponding to remote network control data 2 The method comprises the steps of carrying out a first treatment on the surface of the Using the formula:
Y=0.55*[r 1 /(r 1 +r 2 )]+0.45*ρ
and calculating the intimacy Y of the user to the intelligent device association application. The greater affinity indicates that the user has less usage of the smart device associated application or less probability of successfully implementing control of the device through the application, because the user rarely reselects other ways to control when the user can effectively use the smart application to achieve manipulation of the smart device, and when there are other ways to manipulate, it is stated that the smart application is not effective and fully implements the value and benefits of the application settings.
Step S2 further comprises the following analysis steps:
acquiring the item number g of operation items in the user click guide bar in the ith response stage i Drawing the abscissa according to the time sequence by using the response phase in the monitoring period, establishing a coordinate system of the number of the corresponding items of the response phase, and drawing the number g of the items by using the sequence pair as the response phase i Forming a first fitting curve; the operation items in the guide bar reflect different functions of application development in the intelligent application; obtaining the number g of the items in the fitting curve i Maximum value maxg of i The corresponding response phase is an initial response phase, and the last response phase in the monitoring period is a final response phase; the coordinates in the coordinate system corresponding to the connection start response stage and the termination response stage form a line segment I;
when the maximum value is not the same, selecting a response phase corresponding to the maximum value which appears first according to the time sequence as an initial response phase;
acquiring the number f of sequence pairs on one side of line segment, which is close to the abscissa 1 And the number f of sequence pairs on one side of the line segment away from the abscissa 2 Using the formula:
X=(maxg i -g 0 )*(f 1 -f 2 )
computing user's drowsiness X for smart device associated applications, where g 0 Representing the number of items in the corresponding sequence pair of the termination response phase; (maxg) i -g 0 ) Reflecting the interest change trend of the user for different operation items in the application guide bar after a period of use, (f) 1 -f 2 ) Reflecting dynamic fluctuation of a user during use; the smaller the user's sleepiness, the more successful the development of the application, the stronger the sustainability of the user's use of the application;
the operation and maintenance early warning index w=0.45x+0.55y is applied.
Step S3 comprises the following analysis steps:
acquiring application operation and maintenance indexes W recorded in a block chain and used for different users with the same application, and recording the total number N of the users, and removing the maximum value maxW and the minimum value minW of the application operation and maintenance indexes W corresponding to the different users; using the formula:
W 0 =[1/(N-2)]*[∑W-maxW-minW]
early warning group value W of intelligent equipment associated intelligent application is calculated 0 。
An application operation and maintenance management intelligent monitoring system based on a blockchain comprises a related data acquisition module, an application operation and maintenance early warning index analysis module, an early warning group value analysis module, a threshold judgment early warning module and a feedback monitoring module;
the related data acquisition module is used for acquiring related data of intelligent equipment control based on intelligent application by a user, wherein the related data comprises intelligent equipment response data and user control data;
the application operation and maintenance early warning index analysis module is used for analyzing application operation and maintenance early warning indexes of the intelligent equipment associated application;
the early warning group value analysis module is used for extracting application operation and maintenance early warning indexes of different users using the same application recorded in the blockchain and analyzing early warning group values of intelligent equipment associated intelligent application; when the early warning group value is greater than or equal to the early warning group value threshold value, transmitting an early warning signal to maintain and manage intelligent application associated with the intelligent equipment;
the threshold value judging and early warning module is used for setting an early warning group value threshold value of the intelligent equipment associated application, and transmitting an early warning signal to maintain and manage the intelligent equipment associated intelligent application when the early warning group value is greater than or equal to the early warning group value threshold value;
the feedback monitoring module is used for restarting the user in the monitoring period to acquire related data after the intelligent application maintenance management is completed, and the application operation and maintenance early warning index analysis module is transferred until the early warning group value is smaller than the early warning group value threshold value, and transmits an application maintenance success signal.
The application operation and maintenance early warning index analysis module comprises a user control data dividing unit, an affinity calculating unit, a sleepiness calculating unit and an application operation and maintenance early warning index calculating unit;
the user control data dividing unit is used for dividing remote network control data and first control data;
the affinity calculation unit is used for calculating the affinity of the user for the intelligent equipment association application;
the sleepiness calculating unit is used for the sleepiness of the user for the intelligent equipment associated application;
the application operation and maintenance early warning index calculation unit is used for calculating the application operation and maintenance early warning index based on the affinity of the affinity calculation unit and the sleepiness of the sleepiness calculation unit.
The affinity calculation unit comprises a target period determination unit and a user operation density calculation unit;
the target period determining unit is used for determining a response period corresponding to the target operation data as a target monitoring period;
the user operation density calculation unit is used for calculating the user operation density of the target monitoring period based on the interval duration of the user control data corresponding to the adjacent remote network control mode and the first control mode in the target monitoring period and the total duration of the target monitoring period.
The sleepiness calculating unit comprises a sequence pair acquiring unit, a fitting curve drawing unit and a data extracting unit;
the sequence pair acquisition unit is used for acquiring the number of items of operation items in the user click guide bar in the response stage, drawing the abscissa according to the time sequence in the monitoring period response stage, establishing a coordinate system of the number of the items corresponding to the response stage, and establishing sequence pairs of the response stage and the number of the items;
the fitting curve drawing unit is used for obtaining sequence pairs and establishing a fitting curve;
the data extraction unit is used for extracting the number of the sequence pairs on one side, close to the abscissa, of the line segment and the number of the sequence pairs on one side, far from the abscissa, of the line segment based on the fitting curve; the first line segment is a response phase corresponding to the maximum value of the number of the items in the first fitting curve, and is a starting response phase, and the last response phase in the monitoring period is a termination response phase; the coordinates in the coordinate system corresponding to the connection start response phase and the termination response phase form a line segment one.
The early warning group value analysis module comprises a group data extraction unit and an early warning group value calculation unit;
the group data extraction unit is used for obtaining application operation and maintenance indexes of different users using the same application recorded in the block chain and recording the total number of the users;
the early warning group value calculation unit is used for removing the maximum value of the application operation and maintenance indexes and the minimum value of the application operation and maintenance indexes corresponding to different users, and acquiring the data in the group data extraction unit to calculate the early warning group value.
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 (9)
1. The intelligent monitoring method for application operation and maintenance management based on the blockchain is characterized by comprising the following analysis steps:
step S1: acquiring related data of intelligent equipment control by a user based on intelligent application, wherein the related data comprises intelligent equipment response data and user control data; the intelligent equipment response data refers to a response mode of the intelligent equipment for recording user operation, wherein the response mode comprises a remote network control mode and a first control mode, and the first control mode refers to one-to-one control operation of the intelligent equipment by a user;
step S2: based on the related data in the step S1, analyzing an application operation and maintenance early warning index of the intelligent equipment associated application;
step S3: based on the application operation and maintenance early warning indexes of the step S2, extracting application operation and maintenance early warning indexes of different users, which are recorded in a blockchain and use the same application, and analyzing early warning group values of the intelligent equipment associated intelligent application;
step S4: based on the early warning group value of the intelligent application in the step S3, an early warning group value threshold of the intelligent equipment associated application is set, and when the early warning group value is greater than or equal to the early warning group value threshold, an early warning signal is transmitted to maintain and manage the intelligent application associated with the intelligent equipment; and (3) after the intelligent application maintenance management is finished, the user re-invests the relevant data in the monitoring period to obtain the relevant data in the step (S1), and the step (S2) is shifted until the early warning group value is smaller than the early warning group value threshold value, and an application maintenance success signal is transmitted.
2. The intelligent monitoring method for application operation and maintenance management based on block chain according to claim 1, wherein the intelligent monitoring method is characterized by comprising the following steps: the step S2 includes the following analysis steps:
the user control data comprises remote network control data and first control data; the remote network control data is application operation data recorded when a user uses the intelligent application, and the application operation data comprises operation items and operation instructions; the first control data are first data of one-to-one control operation records of the intelligent equipment by a user;
acquiring operation time t corresponding to the jth operation item of the ith response stage of the user recorded by the application program in the monitoring period ij 1 The method comprises the steps of carrying out a first treatment on the surface of the And generating a kth response time t corresponding to the remote network control mode through instruction transmission after implementing the operation item ik 2 K is less than or equal to n, n represents the total number of times the device generates a response; obtaining the maximum value T of the corresponding time intervals of m response phases in the monitoring period, wherein T=max { T } i I.ltoreq.m, said time interval T i Refers to the operation time t corresponding to the 1 st operation item in the same response stage i1 1 And the last response time t of the remote network control mode generated by instruction transmission after the operation project is implemented in 2 Time interval of (T) i =t in 2 -t i1 1 The method comprises the steps of carrying out a first treatment on the surface of the The response phase refers to a period from when a user opens the intelligent application to when the user closes the intelligent application;
extracting operation data recorded after a response phase corresponding to the maximum value T of the time interval as target operation data, wherein a response period corresponding to the target operation data is a target monitoring period, and utilizing a formula:
ρ=(∑t 0 )/T 0
calculating a user operation density ρ of the target monitoring period, where t 0 Representing the interval duration of user control data corresponding to the adjacent remote network control mode and the first control mode in the target monitoring period, T 0 Representing the total duration of the target monitoring period;
acquiring total times r of first control data records in target monitoring period 1 Total number of response phases r corresponding to remote network control data 2 The method comprises the steps of carrying out a first treatment on the surface of the Using the formula:
Y=0.55*[r 1 /(r 1 +r 2 )]+0.45*ρ
and calculating the intimacy Y of the user to the intelligent device association application.
3. The intelligent monitoring method for application operation and maintenance management based on the blockchain according to claim 2, wherein the intelligent monitoring method is characterized by comprising the following steps: the step S2 further comprises the following analysis steps:
acquiring the item number g of operation items in the user click guide bar in the ith response stage i Drawing the abscissa according to the time sequence by using the response phase in the monitoring period, establishing a coordinate system of the number of the corresponding items of the response phase, and drawing the number g of the items by using the sequence pair as the response phase i Forming a first fitting curve; obtaining the number g of the items in the fitting curve i Maximum value maxg of i The corresponding response phase is an initial response phase, and the last response phase in the monitoring period is a final response phase; the coordinates in the coordinate system corresponding to the connection start response stage and the termination response stage form a line segment I;
when the maximum value is not the same, selecting a response phase corresponding to the maximum value which appears first according to the time sequence as an initial response phase;
acquiring the number f of sequence pairs on one side of line segment, which is close to the abscissa 1 And the number f of sequence pairs on one side of the line segment away from the abscissa 2 Using the formula:
X=(maxg i -g 0 )*(f 1 -f 2 )
computing user's drowsiness X for smart device associated applications, where g 0 Representing the number of items in the corresponding sequence pair of the termination response phase;
the operation and maintenance early warning index w=0.45x+0.55y is applied.
4. The intelligent monitoring method for application operation and maintenance management based on block chain according to claim 3, wherein the intelligent monitoring method is characterized by comprising the following steps: the step S3 includes the following analysis steps:
acquiring application operation and maintenance indexes W recorded in a block chain and used for different users with the same application, and recording the total number N of the users, and removing the maximum value maxW and the minimum value minW of the application operation and maintenance indexes W corresponding to the different users; using the formula:
W 0 =[1/(N-2)]*[∑W-maxW-minW]
early warning group value W of intelligent equipment associated intelligent application is calculated 0 。
5. A blockchain-based application operation and maintenance management intelligent monitoring system applying the blockchain-based application operation and maintenance management intelligent monitoring method according to any one of claims 1-4, which is characterized by comprising a related data acquisition module, an application operation and maintenance early warning index analysis module, an early warning group value analysis module, a threshold judgment early warning module and a feedback monitoring module;
the related data acquisition module is used for acquiring related data of intelligent equipment control based on intelligent application by a user, wherein the related data comprises intelligent equipment response data and user control data;
the application operation and maintenance early warning index analysis module is used for analyzing application operation and maintenance early warning indexes of the intelligent equipment associated application;
the early warning group value analysis module is used for extracting application operation and maintenance early warning indexes of different users using the same application recorded in the blockchain and analyzing early warning group values of the intelligent equipment associated intelligent application; when the early warning group value is greater than or equal to the early warning group value threshold value, transmitting an early warning signal to maintain and manage intelligent application associated with the intelligent equipment;
the threshold value judging and early warning module is used for setting an early warning group value threshold value of the intelligent equipment associated application, and transmitting an early warning signal to maintain and manage the intelligent equipment associated intelligent application when the early warning group value is greater than or equal to the early warning group value threshold value;
the feedback monitoring module is used for restarting the user in the monitoring period to acquire related data after the intelligent application maintenance management is completed, and the application operation and maintenance early warning index analysis module is transferred until the early warning group value is smaller than the early warning group value threshold value, and transmits an application maintenance success signal.
6. The intelligent monitoring system for managing application operation and maintenance based on block chain as set forth in claim 5, wherein: the application operation and maintenance early warning index analysis module comprises a user control data dividing unit, a intimacy calculating unit, a sleepiness calculating unit and an application operation and maintenance early warning index calculating unit;
the user control data dividing unit is used for dividing remote network control data and first control data;
the affinity calculation unit is used for calculating the affinity of the user for the intelligent equipment associated application;
the sleepiness calculating unit is used for the sleepiness of the user for the intelligent equipment associated application;
the application operation and maintenance early warning index calculation unit is used for calculating the application operation and maintenance early warning index based on the affinity of the affinity calculation unit and the sleepiness of the sleepiness calculation unit.
7. The blockchain-based application operation and maintenance management intelligent monitoring system of claim 6, wherein: the intimacy calculating unit comprises a target period determining unit and a user operation density calculating unit;
the target period determining unit is used for determining a response period corresponding to the target operation data as a target monitoring period;
the user operation density calculation unit is used for calculating the user operation density of the target monitoring period based on the interval duration of the user control data corresponding to the adjacent remote network control mode and the first control mode in the target monitoring period and the total duration of the target monitoring period.
8. The blockchain-based application operation and maintenance management intelligent monitoring system of claim 7, wherein: the sleepiness calculating unit comprises a sequence pair acquiring unit, a fitting curve drawing unit and a data extracting unit;
the sequence pair acquisition unit is used for acquiring the number of items of operation items in the clicking guide bar of a user in a response stage, drawing an abscissa according to a time sequence in the response stage in a monitoring period, establishing a coordinate system of the number of items corresponding to the response stage, and establishing sequence pairs of the response stage and the number of the items;
the fitting curve drawing unit is used for obtaining sequence pairs and establishing a fitting curve;
the data extraction unit is used for extracting the number of the sequence pairs on one side, close to the abscissa, of the line segment and the number of the sequence pairs on one side, far from the abscissa, of the line segment based on the fitting curve; the first line segment is a response phase corresponding to the maximum value of the number of items in the first acquired fitting curve and is an initial response phase, and the last response phase in the monitoring period is a termination response phase; the coordinates in the coordinate system corresponding to the connection start response phase and the termination response phase form a line segment one.
9. The blockchain-based application operation and maintenance management intelligent monitoring system of claim 7, wherein: the early warning group value analysis module comprises a group data extraction unit and an early warning group value calculation unit;
the group data extraction unit is used for obtaining application operation and maintenance indexes of different users using the same application recorded in the blockchain and recording the total number of the users;
the early warning group value calculation unit is used for removing the maximum value of the application operation and maintenance indexes and the minimum value of the application operation and maintenance indexes corresponding to different users, and acquiring data in the group data extraction unit to calculate the early warning group value.
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