CN114595123A - Big data based intelligent application software monitoring system and method - Google Patents
Big data based intelligent application software monitoring system and method Download PDFInfo
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Abstract
The invention discloses an application software intelligent monitoring system and method based on big data, comprising the following steps: the method comprises the steps of using an information acquisition module, a database, a software installation monitoring module, a software operation monitoring module and a software application processing module, acquiring information before and after application software is used by using the information acquisition module, storing and managing all acquired data by the database, monitoring the software installation process by using the software installation monitoring module, monitoring the operation state in the software use process by using the software operation monitoring module, adjusting the software installation mode and the software use mode by using the software application processing module, searching and reminding installation error behaviors in time, popping prompt data at corresponding error steps when the software is reinstalled, helping a user save installation time, improving user experience, monitoring installation and use effects again after software performance is adjusted, helping enterprise relevant departments to further debug and perfect software functions, user churn is reduced.
Description
Technical Field
The invention relates to the technical field of application software monitoring, in particular to an application software intelligent monitoring system and method based on big data.
Background
The application software refers to partial software provided for meeting application requirements of different fields and different problems of users, the main purpose of monitoring the application software is to solve the problem that the experience of the application software facing terminal users of an enterprise cannot be tracked, help related departments of the enterprise to know the use conditions of the application software facing different users, and be beneficial to improving the operation and maintenance level of the application software and better optimizing the application software;
the existing method for monitoring the application software still has some problems: firstly, most of the existing methods pay attention to monitoring of the state of a user when the user uses application software, but the installation mode of part of the application software before use is complex, the user cannot mount the application software absolutely and correctly, the existing methods ignore the installation problem of the application software before use, cannot monitor the software installation through big data analysis, and may cause loss of part of users; secondly, part of users habitually ignore the situation that the software is closed but runs in the background when not using the software, the consumption of the electronic equipment used by the users can be influenced when part of the software runs in the background, the software cannot be continuously monitored after being closed, and the problem of abnormal running is not facilitated to be timely processed.
Therefore, a system and a method for intelligently monitoring application software based on big data are needed to solve the above problems.
Disclosure of Invention
The invention aims to provide an application software intelligent monitoring system and method based on big data, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: the utility model provides an application software intelligent monitoring system based on big data which characterized in that: the system comprises: the system comprises a use information acquisition module, a database, a software installation monitoring module, a software operation monitoring module and a software application processing module;
the use information acquisition module is used for acquiring information before and after the application software is used;
the database is used for storing and managing all the collected data;
the software installation monitoring module is used for monitoring the software installation process;
the software running monitoring module is used for monitoring the running state of the software;
the software application processing module is used for adjusting a software installation mode and a software use mode.
Furthermore, the use information acquisition module comprises an installation data acquisition unit and an operation data acquisition unit, wherein the installation data acquisition unit is used for acquiring operation data when a user installs different application software; the running data acquisition unit is used for acquiring running data of the application software in the using process and transmitting all the acquired data to the database.
Further, the software installation monitoring module comprises an operation path generating unit, an installation analyzing unit, an operation analyzing unit and an installation abnormity warning unit, wherein the operation path generating unit is used for generating a user installation path according to operation data of a user during software installation and generating an installation success path; the installation analysis unit is used for comparing the installation path with the successful installation path; the operation analysis unit is used for judging whether the user has wrong operation behavior when installing the software according to the comparison result; the installation abnormity warning unit is used for sending an installation abnormity warning signal to the software application processing module when the misoperation behavior exists.
Furthermore, the software operation monitoring module comprises an operation state monitoring unit, an operation behavior monitoring unit and an operation abnormity warning unit, wherein the operation state monitoring unit is used for monitoring the operation of the installed software; the operation behavior monitoring unit is used for analyzing the continuity of software used by the user according to the operation data of the user; the abnormal operation alarm unit is used for analyzing the influence of the software in a background operation state on equipment used by a user and sending an abnormal operation alarm signal to the software application processing module.
Furthermore, the software application processing module comprises an installation prompt setting unit, an exception debugging unit and a processing result testing unit, wherein the installation prompt setting unit is used for setting installation prompt data when a user needs to reinstall software after receiving an installation exception alarm signal: popping up installation prompt information at the error installation step; the abnormal debugging unit is used for debugging the software state after receiving the operation alarm signal, and setting the automatic software shutdown time when the software is not used for a user; the processing result testing unit is used for testing the installation and use effects of the software after setting the installation prompt and debugging the software state.
An intelligent application software monitoring method based on big data is characterized in that: the method comprises the following steps:
s1: collecting application software installation information and use information;
s2: monitoring the operation behavior of a user in the software installation process;
s3: monitoring the running state of the software;
s4: judging whether the user needs to reinstall the software: if so, judging the step of the user with the installation error, and popping up installation mode prompt data for the user at the corresponding step when the user reloads the software; if not, go to step S5;
s5: and setting the automatic closing time of the software and testing the installation and use effects of the software.
Further, in steps S1-S2: acquiring a point coordinate set which needs to be clicked in sequence in the previous n steps before the application software is successfully installed as (X, Y) { (X1, Y1), (X2, Y2),. and (Xn, Yn) }, acquiring a point coordinate set which is influenced by the corresponding installation step on the successful installation of the application software as w { (w 1, w2,. and wn }, monitoring that the point coordinate set which is clicked in sequence when the corresponding software is installed in the previous n steps by a random user is (X, Y) { (X1, Y1), (X2, Y2),. and (Xn, Yn) }, respectively connecting the points which need to be clicked in sequence when the software is successfully installed and the points which are clicked in sequence when the corresponding software is installed by the user, generating a successful installation path and a user installation path, and respectively fitting two paths to obtain a successful installation curve function:
the user installation curve function is:
judging whether the first n steps of the user are installed correctly according to the following formula:
wherein F '(X) and F' (X) respectively represent first-order derivatives of functions F (X) and F (X), F "(X) and F" (X) respectively represent second-order derivatives of functions F (X) and F (X), s represents a difference value of areas enclosed by the two curves and an X axis, if s is 0 and the two curves are not centered on a straight line (X-X1)/(xn-X1) (y-y1)/(yn-y1), the user installation curve is coincident with a successful installation curve, and the installation software of the previous n steps of the user is judged to be correct; if s is not equal to 0, it is indicated that a user installation curve is not coincident with a successful installation curve, it is judged that incorrect operation behaviors exist in the software installation process of the first n steps of the user, an abnormal installation alarm signal is sent to a software application processing module, operation data of the user are analyzed through big data, whether the user is blocked when the user installs the application software is judged, an installation path is generated according to the operation data of the first n steps of the user, a Bezier curve is generated by fitting the path, accuracy of path fitting is improved, an area difference formed by the fact that the curve subjected to comparison and the x axis are surrounded is calculated through an integral mode, the purpose is to judge whether the first n steps of the user are installed correctly or not integrally, the operation data do not need to be compared one by one, searching and reminding of installation error behaviors in time is facilitated, and time wasted in installing the software by the user is reduced.
Further, in step S3: monitoring software operation after software installation, and acquiring the time of opening corresponding software after the software installation by a user and the time set of interval time of opening the software for the first time as t ═ t { (t)1，t2，...，tmThe total times of software opening is m +1, the electricity quantity reduction percentage set of equipment used by a user is q ═ q1, q2,.., qm } in a time period when the software is not opened by the user and the software is not operated in the background, the electricity quantity reduction percentage set is q '═ q 1', q2 ', qk' }, wherein k represents the number of time periods when the software is not opened by the user and the software is not operated in the background, and the influence degree of the corresponding software operation on the equipment used by the user is calculated and judged according to the following formula:
wherein the content of the first and second substances,
tjand tj+1Respectively representing the time interval between two random continuous software opening times and the time interval between the first software opening times, qi and qi' respectively representing the power reduction percentage of equipment used by a user in a random time period when the software is not opened by the user and the software is not operated in the background, Q representing an influence coefficient, and setting an influence coefficient threshold value QThreshold(s)Comparing Q with QThreshold(s): if Q is less than or equal to QThreshold(s)The influence coefficient does not exceed the threshold value; if Q>QThreshold(s)And when the influence coefficient exceeds the threshold value, sending an abnormal operation alarm signal to a software application processing module, considering that different application software has different influences on equipment, judging the continuity and compactness of the software used by a user in a standard deviation calculation mode by acquiring the time interval time of the software used by the user, solving the problem that the software is operated in a background because the software is required to be continuously used by the user in the conventional mode, and eliminating the influences of the other software except the corresponding software on the equipment in the background operation by combining with the analysis of the influences of the software on the equipment in the background operation mode so as to improve the accuracy of a judgment result.
Further, in step S4: comparing the point coordinate clicked by the user with the point coordinate clicked for successful installation, if the coordinates are different, judging that the corresponding installation step is wrong, and according to a formula
Judging whether the user needs to reinstall the software, wherein wi' represents the influence degree of the random one-time wrong installation steps on the successful installation of the application software: if W>40%, the influence degree of the corresponding installation step on the application software is large, the software is judged to be required to be installed again by the user, and when the user reinstalls the software, installation mode prompt data pop up at the corresponding step for the user; if W is less than or equal to 40%, the influence degree of the corresponding installation step on the application software is small, the software does not need to be reinstalled by a user, and the operation behaviors of installing part of the software are wideAnd different operation modes are allowed in part of the steps, so that the influence of operation change of the corresponding step on successful software installation is small, whether the software needs to be reinstalled is judged by analyzing the influence degree of the corresponding step on the successful installation after the error step is found out, and prompt data is popped up at the corresponding error step when the software is reinstalled, so that the method is beneficial to helping a user to save installation time, improving user experience and reducing user loss.
Further, in step S5: when receiving an abnormal operation alarm signal, setting software automatic closing time for a user: the automatic closing time is after the software runs in the background
After the time is long, after the prompt data of the installation mode is popped up and the automatic closing time is set, the data is updated, the installation and use effect of the software is retested, the software background operation closing time is set when the user does not continuously use the software, the fluency of the user using the electronic equipment is favorably improved, the installation and use effect is monitored again, and the further debugging and the improvement of the software function of related departments of an enterprise are favorably realized.
Compared with the prior art, the invention has the following beneficial effects:
according to the method, the operation data of the user is acquired and analyzed through big data, whether the user has wrong operation behavior when the user installs the application software is judged, the user is reminded of reinstalling the software in time, the phenomenon that the user finds that the software needs reinstalling when the user installs the application software in the last step is avoided, a Bezier curve is generated by fitting the path, the accuracy of path fitting is improved, the area difference formed by the curve for comparison and the x axis is calculated in an integral mode, whether the previous n steps of the user are installed correctly or not is judged quickly on the whole, the operation data do not need to be compared one by one, the user is helped to search and remind of installing wrong behavior in time, and the time wasted by the user for installing the software is reduced; the method has the advantages that the influence of the background operation of the software on the equipment is analyzed by combining with the use of the software by a user at intervals, the influence of the background operation of other software except corresponding software on the equipment is eliminated, whether the software needs to be reloaded or not is judged in advance, prompt data pop-up at corresponding error steps is carried out when the software is reloaded, the user is helped to save installation time, user experience is improved, the software background operation closing time is set, the fluency of the user in using the electronic equipment is improved, the installation and use effects are monitored again, further debugging and perfecting of software functions of related departments of an enterprise are facilitated, and the user loss is reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a block diagram of an intelligent monitoring system for big data-based application software according to the present invention;
FIG. 2 is a flow chart of an intelligent application software monitoring method based on big data according to the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it should be understood that they are presented herein only to illustrate and explain the present invention and not to limit the present invention.
Referring to fig. 1-2, the present invention provides a technical solution: the utility model provides an application software intelligent monitoring system based on big data which characterized in that: the system comprises: the system comprises a use information acquisition module, a database, a software installation monitoring module, a software operation monitoring module and a software application processing module;
the use information acquisition module is used for acquiring information before and after the application software is used;
the database is used for storing and managing all the acquired data;
the software installation monitoring module is used for monitoring the software installation process;
the software running monitoring module is used for monitoring the running state of the software;
the software application processing module is used for adjusting a software installation mode and a software use mode.
The use information acquisition module comprises an installation data acquisition unit and an operation data acquisition unit, and the installation data acquisition unit is used for acquiring operation data when a user installs different application software; the operation data acquisition unit is used for acquiring operation data of the application software in the using process and transmitting all the acquired data to the database.
The software installation monitoring module comprises an operation path generating unit, an installation analyzing unit, an operation analyzing unit and an installation abnormity warning unit, wherein the operation path generating unit is used for generating a user installation path according to operation data of a user during software installation and generating an installation success path; the installation analysis unit is used for comparing the installation path with the successful installation path; the operation analysis unit is used for judging whether the user has wrong operation behavior when installing the software according to the comparison result; the installation abnormity warning unit is used for sending an installation abnormity warning signal to the software application processing module when the misoperation behavior exists.
The software operation monitoring module comprises an operation state monitoring unit, an operation behavior monitoring unit and an operation abnormity warning unit, wherein the operation state monitoring unit is used for monitoring the operation of the installed software; the operation behavior monitoring unit is used for analyzing the continuity of the software used by the user according to the operation data of the user; and the abnormal operation alarm unit is used for analyzing the influence of the software in a background operation state on equipment used by a user and sending an abnormal operation alarm signal to the software application processing module.
The software application processing module comprises an installation prompt setting unit, an exception debugging unit and a processing result testing unit, wherein the installation prompt setting unit is used for setting installation prompt data after receiving an installation exception alarm signal when a user needs to reinstall software: popping up installation prompt information at the error installation step; the abnormal debugging unit is used for debugging the software state after receiving the operation alarm signal, and setting the automatic software shutdown time when the software is not used for a user; the processing result testing unit is used for testing the installation and use effects of the software after setting the installation prompt and debugging the software state.
An intelligent application software monitoring method based on big data is characterized in that: the method comprises the following steps:
s1: collecting application software installation information and use information;
s2: monitoring the operation behavior of a user in the software installation process;
s3: monitoring the running state of the software;
s4: judging whether the user needs to reinstall the software: if so, judging the step of the user with the installation error, and popping up installation mode prompt data for the user at the corresponding step when the user reloads the software; if not, go to step S5;
s5: and setting the automatic closing time of the software and testing the installation and use effects of the software.
In steps S1-S2: acquiring a point coordinate set which needs to be clicked in sequence in the previous n steps before the application software is successfully installed as (X, Y) { (X1, Y1), (X2, Y2),. and (Xn, Yn) }, acquiring a point coordinate set which is influenced by the corresponding installation step on the successful installation of the application software as w { (w 1, w2,. and wn }, monitoring that the point coordinate set which is clicked in sequence when the corresponding software is installed in the previous n steps by a random user is (X, Y) { (X1, Y1), (X2, Y2),. and (Xn, Yn) }, respectively connecting the points which need to be clicked in sequence when the software is successfully installed and the points which are clicked in sequence when the corresponding software is installed by the user, generating a successful installation path and a user installation path, and respectively fitting two paths to obtain a successful installation curve function:
the user installation curve function is:
judging whether the first n steps of the user are installed correctly according to the following formula:
wherein F '(X) and F' (X) respectively represent first-order derivatives of functions F (X) and F (X), F "(X) and F" (X) respectively represent second-order derivatives of functions F (X) and F (X), s represents a difference value of areas enclosed by the two curves and an X axis, if s is 0 and the two curves are not centered on a straight line (X-X1)/(xn-X1) (y-y1)/(yn-y1), the user installation curve is coincident with a successful installation curve, and installation software in the first n steps of the user is judged to be correct; if s is not equal to 0, the user installation curve is not coincident with the successful installation curve, incorrect operation behaviors existing in the software installation process of the previous n steps of the user are judged, an installation abnormity alarm signal is sent to the software application processing module, the installation error behaviors are conveniently and timely searched and reminded, and the time wasted by the user in installing the software is effectively reduced.
In step S3: monitoring software operation after software installation, and acquiring the time of opening corresponding software after the software installation by a user and the time set of interval time of opening the software for the first time as t ═ t { (t)1，t2，...，tmThe total times of software opening is m +1, the electricity quantity reduction percentage set of equipment used by a user is q ═ q1, q2,.., qm } in a time period when the software is not opened by the user and the software is not operated in the background, the electricity quantity reduction percentage set is q '═ q 1', q2 ', qk' }, wherein k represents the number of time periods when the software is not opened by the user and the software is not operated in the background, and the influence degree of the corresponding software operation on the equipment used by the user is calculated and judged according to the following formula:
wherein the content of the first and second substances,
tjand tj+1Respectively representing the time interval between two random continuous software opening times and the time interval between the first software opening times, qi and qi' respectively representing the power reduction percentage of equipment used by a user in a random time period when the software is not opened by the user and the software is not operated in the background, Q representing an influence coefficient, and setting an influence coefficient threshold value QThreshold(s)Comparing Q with QThreshold(s): if Q is less than or equal to QThreshold(s)Indicating that the influence coefficient does not exceed the threshold; if Q>QThreshold(s)If the influence coefficient exceeds the threshold value, sending an abnormal operation alarm signal to the computerThe software application processing module solves the problem that the software is operated in the background because the user needs to continuously use the software cannot be accurately judged by calculating the average interval time in the prior art, eliminates the influence of the operation of other software except the corresponding software in the background on equipment, and improves the accuracy of the judgment result of the influence degree.
In step S4: comparing the point coordinate clicked by the user with the point coordinate clicked for successful installation, if the coordinates are different, judging that the corresponding installation step is wrong, and according to a formula
Judging whether the user needs to reinstall the software, wherein wi' represents the influence degree of the random one-time wrong installation steps on the successful installation of the application software: if W>40%, the influence degree of the corresponding installation step on the application software is large, the software is judged to be required to be installed again by the user, and when the user reinstalls the software, installation mode prompt data pop up at the corresponding step for the user; if W is less than or equal to 40%, the influence degree of the corresponding installation steps on the application software is small, and the software does not need to be installed again by the user, so that the user can be helped to save the installation time, the user experience is improved, and the user loss is reduced.
In step S5: when receiving an abnormal operation alarm signal, setting software automatic closing time for a user: the automatic closing time is after software runs in the background
After the time is long, after the installation mode prompt data is popped up and the automatic closing time is set, the data is updated, the installation and use effects of the software are retested, the fluency of using the electronic equipment by a user is improved conveniently, and related departments of an enterprise are helped to further debug and perfect the software functions.
The first embodiment is as follows: acquiring a point coordinate set (x, y) { (x1, y1), (x2, y2), (x3, y3) } { (1, 2), (3, 3), (5, 6) } which needs to be clicked in sequence in the first 3 steps before the successful installation of the application software, acquiring a set of influence degrees of the corresponding installation steps on the successful installation of the application software, namely w { w1, w2, w3} {5, 7, 6}, and monitoringWhen a random user installs corresponding software in the first 3 steps, the coordinate set of points clicked in sequence is (X, Y) { (X1, Y1), (X2, Y2), (X3, Y3) } { (1, 2), (3, 3), (2, 4) }, the points clicked in sequence when the software installation succeeds and the points clicked in sequence when the user installs corresponding software are connected respectively, a successful installation path and a user installation path are generated, and two paths are fitted respectively to obtain a successful installation curve function:
the user installation curve function is:
according to the formula
Judging whether the first 3 steps of the user are installed correctly: s is not equal to 0, judging that incorrect operation behaviors exist in the software installation process in the first 3 steps of the user, sending an abnormal installation alarm signal, comparing the point coordinate clicked by the user with the point coordinate clicked for successful installation, judging the error in the third installation step, and judging the error in the third installation step according to a formula
Judging whether the user needs to reinstall the software: w is approximately equal to 33%<40%, the influence degree of the corresponding installation step on the application software is small, and the user is judged not to need to reinstall the software;
example two: the method comprises the steps of collecting the time of opening corresponding software after software is installed by a user and the time set of the interval between the first time of opening the software, namely t ═ t { (t) } t1，t2，t310, 20, 50, in units of: in minutes, the set of percentage charge reduction of the device used by the user is q ═ { q1, q2, q3} { 10%, 6%, 8% } during the time period when the software is not turned on by the user, and is q ' ═ { q1 ', q2 ' } { 3%, 5% } during the time period when the software is not turned on by the user and the software is not running in the background, according to the formula
Calculating and judging the influence degree of the corresponding software operation on the user equipment, and setting an influence coefficient threshold QThreshold(s)Compare Q and Q50%Threshold(s)：Q≈37.7％，Q≤QThreshold(s)The influence coefficient does not exceed the threshold value, and the automatic closing time is not set.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The utility model provides an application software intelligent monitoring system based on big data which characterized in that: the system comprises: the system comprises a use information acquisition module, a database, a software installation monitoring module, a software operation monitoring module and a software application processing module;
the use information acquisition module is used for acquiring information before and after the application software is used;
the database is used for storing and managing all collected data;
the software installation monitoring module is used for monitoring the software installation process;
the software running monitoring module is used for monitoring the running state of the software;
the software application processing module is used for adjusting a software installation mode and a software use mode.
2. The intelligent application software monitoring system based on big data as claimed in claim 1, characterized in that: the use information acquisition module comprises an installation data acquisition unit and an operation data acquisition unit, and the installation data acquisition unit is used for acquiring operation data when a user installs different application software; the running data acquisition unit is used for acquiring running data of the application software in the using process and transmitting all the acquired data to the database.
3. The intelligent application software monitoring system based on big data as claimed in claim 1, characterized in that: the software installation monitoring module comprises an operation path generating unit, an installation analyzing unit, an operation analyzing unit and an installation abnormity warning unit, wherein the operation path generating unit is used for generating a user installation path according to operation data of a user during software installation and generating an installation success path; the installation analysis unit is used for comparing the installation path with the successful installation path; the operation analysis unit is used for judging whether the user has wrong operation behavior when installing the software according to the comparison result; the installation abnormity warning unit is used for sending an installation abnormity warning signal to the software application processing module when an error operation behavior exists.
4. The intelligent application software monitoring system based on big data as claimed in claim 1, characterized in that: the software operation monitoring module comprises an operation state monitoring unit, an operation behavior monitoring unit and an operation abnormity warning unit, wherein the operation state monitoring unit is used for monitoring the operation of the installed software; the operation behavior monitoring unit is used for analyzing the continuity of software used by the user according to the operation data of the user; the abnormal operation alarm unit is used for analyzing the influence of the software in a background operation state on equipment used by a user and sending an abnormal operation alarm signal to the software application processing module.
5. The intelligent application software monitoring system based on big data as claimed in claim 1, characterized in that: the software application processing module comprises an installation prompt setting unit, an exception debugging unit and a processing result testing unit, wherein the installation prompt setting unit is used for setting installation prompt data when a user needs to reinstall software after receiving an installation exception alarm signal: popping up installation prompt information at the error installation step; the abnormal debugging unit is used for debugging the software state after receiving the operation alarm signal, and setting the automatic software shutdown time when the software is not used for a user; the processing result testing unit is used for testing the installation and use effects of the software after setting the installation prompt and debugging the software state.
6. An intelligent application software monitoring method based on big data is characterized in that: the method comprises the following steps:
s1: collecting application software installation information and use information;
s2: monitoring the operation behavior of a user in the software installation process;
s3: monitoring the running state of the software;
s4: judging whether the user needs to reinstall the software: if so, judging the step of the user with the installation error, and popping up installation mode prompt data for the user at the corresponding step when the user reloads the software; if not, go to step S5;
s5: and setting the automatic closing time of the software and testing the installation and use effects of the software.
7. The intelligent application software monitoring method based on big data as claimed in claim 6, characterized in that: in steps S1-S2: acquiring a point coordinate set which needs to be clicked in sequence in the previous n steps before the application software is successfully installed as (X, Y) { (X1, Y1), (X2, Y2),. and (Xn, Yn) }, acquiring a point coordinate set which is influenced by the corresponding installation step on the successful installation of the application software as w { (w 1, w2,. and wn }, monitoring that the point coordinate set which is clicked in sequence when the corresponding software is installed in the previous n steps by a random user is (X, Y) { (X1, Y1), (X2, Y2),. and (Xn, Yn) }, respectively connecting the points which need to be clicked in sequence when the software is successfully installed and the points which are clicked in sequence when the corresponding software is installed by the user, generating a successful installation path and a user installation path, and respectively fitting two paths to obtain a successful installation curve function:
the user installation curve function is:
judging whether the first n steps of the user are installed correctly according to the following formula:
wherein F '(X) and F' (X) respectively represent first-order derivatives of functions F (X) and F (X), F "(X) and F" (X) respectively represent second-order derivatives of functions F (X) and F (X), s represents a difference value of areas enclosed by the two curves and an X axis, if s is 0 and the two curves are not centered on a straight line (X-X1)/(xn-X1) (y-y1)/(yn-y1), the user installation curve is coincident with a successful installation curve, and the installation software of the previous n steps of the user is judged to be correct; if s is not equal to 0, the user installation curve is not coincident with the successful installation curve, the fact that incorrect operation behaviors exist in the software installation process of the previous n steps of the user is judged, and an abnormal installation alarm signal is sent to the software application processing module.
8. The intelligent application software monitoring method based on big data as claimed in claim 6, characterized in that: in step S3: monitoring software operation after software installation, and collecting the time of opening corresponding software after software installation by a user and the time interval set of first software opening as t ═ t { (t)1，t2，...，tmThe total times of software opening is m +1, the electricity quantity reduction percentage set of equipment used by a user is q ═ q1, q2,.., qm } in a time period when the software is not opened by the user and the software is not operated in the background, the electricity quantity reduction percentage set is q '═ q 1', q2 ', qk' }, wherein k represents the number of time periods when the software is not opened by the user and the software is not operated in the background, and the influence degree of the corresponding software operation on the equipment used by the user is calculated and judged according to the following formula:
wherein the content of the first and second substances,
tjand tj+1Respectively representing the time interval between two random continuous software opening times and the time interval between the first software opening times, qi and qi' respectively representing the power reduction percentage of equipment used by a user in a random time period when the software is not opened by the user and the software is not operated in the background, Q representing an influence coefficient, and setting an influence coefficient threshold value QThreshold(s)Comparing Q with QThreshold(s): if Q is less than or equal to QThreshold(s)Indicating that the influence coefficient does not exceed the threshold; if Q>QThreshold(s)And sending an abnormal operation alarm signal to the software application processing module when the influence coefficient exceeds the threshold value.
9. The intelligent application software monitoring method based on big data as claimed in claim 7, characterized in that: in step S4: comparing the point coordinate clicked by the user with the point coordinate clicked for successful installation, if the coordinates are different, judging that the corresponding installation step is wrong, and according to a formula
Judging whether the user needs to reinstall the software, wherein wi' represents the influence degree of the random one-time wrong installation steps on the successful installation of the application software: if W>40%, the influence degree of the corresponding installation step on the application software is large, the software is judged to be required to be installed again by the user, and when the user reinstalls the software, installation mode prompt data pop up at the corresponding step for the user; if W is less than or equal to 40%, the influence degree of the corresponding installation step on the application software is small, and the user is judged not to need to install the software again.
10. The intelligent application software monitoring method based on big data as claimed in claim 8, characterized in that: in step S5: upon receiving the abnormal operation alarm signal, isThe user sets the automatic closing time of the software: the automatic closing time is after the software runs in the background
After the time is long, after the installation mode prompt data is popped up and the automatic closing time is set, the data is updated, and the installation and use effects of the software are retested.
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| CN116707940A (en) * | 2023-06-26 | 2023-09-05 | 邯郸市乡年网络科技有限公司 | Data security visual analysis method and system based on big data |
| CN117173613A (en) * | 2023-09-15 | 2023-12-05 | 中国铁路广州局集团有限公司 | Intelligent management system and method for whole process informatization of engineering construction project |
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| CN116707940A (en) * | 2023-06-26 | 2023-09-05 | 邯郸市乡年网络科技有限公司 | Data security visual analysis method and system based on big data |
| CN116707940B (en) * | 2023-06-26 | 2024-02-13 | 天翼安全科技有限公司 | Data security visual analysis method and system based on big data |
| CN117173613A (en) * | 2023-09-15 | 2023-12-05 | 中国铁路广州局集团有限公司 | Intelligent management system and method for whole process informatization of engineering construction project |
| CN117173613B (en) * | 2023-09-15 | 2024-03-29 | 中国铁路广州局集团有限公司 | Intelligent management system and method for whole process informatization of engineering construction project |
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