CN115062277A - Computer software management system based on big data analysis - Google Patents
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Abstract
The invention discloses a computer software management system based on big data analysis, and relates to the technical field of computer software management. The invention is used for solving the technical problems that the computer software data can not be comprehensively collected in a classified manner, diversified analysis feedback is adopted aiming at the conditions of secret backup, memory management and updating off-line, and accurate management measures are adopted; the computer software management system collects operation and maintenance data and functional data of system software and application software in a classified manner, performs targeted software risk analysis, confidential backup analysis, memory management analysis and update offline analysis after the operation and maintenance data and the functional data which are comprehensively refined are sent in a classified manner, is convenient for rating feedback of the overall risk of the computer software and taking diversified analysis feedback and targeted management measures on the confidential backup condition, the memory management condition and the update offline condition of each system software and application software, and ensures stable and efficient operation of the computer software.
Description
Technical Field
The invention relates to the technical field of computer software management, in particular to a computer software management system based on big data analysis.
Background
Computer software is an interface between a user and hardware, and the user mainly communicates with the computer through the software. In order for computer software to have a high overall utility, it is necessary to take into account the combination of software and hardware, as well as the requirements of the user and the requirements of the software. The management method of computer software generally needs to cover the links of purchasing, developing, online, using, keeping, scrapping and reporting loss of the software, and has safety and reliability. The scale of data generated in the computer software management process is huge, the data cannot be captured, managed and processed within a certain time range, and useful information is extracted to form conclusions so as to study and summarize the data in detail.
The prior art (CN110543760A) discloses a software management system and a software protection method thereof, the software management system includes an analysis module, a reading module, a simulation sandbox module and a management module, the analysis module is used for analyzing a software installation package, so as to obtain the size and brief introduction information of software; the reading module is used for reading the analyzed software installation package and acquiring configuration information of the software; the simulation sandbox module is a created sandbox model which is completely isolated from the main system, and can prevent viruses, trojans and the like carried in software from invading the main system while the software is opened; the management module is used for carrying out operations such as uninstalling and updating on the installed software, and is convenient for centralized management. On the basis of existing software uninstalling updating and permission setting, a privacy setting module is added, and high-level permissions of software such as hiding, opening and rewriting are set by using account login, so that the management range of the software is widened, the management effect of the software is improved, and personal privacy is protected. The following technical problems are found through research: the method can not comprehensively collect the computer software data in a classified manner, and adopts diversified analysis feedback and accurate management measures aiming at the conditions of confidential backup, memory management and updating off-line.
A solution is now proposed to address the technical drawback in this respect.
Disclosure of Invention
The invention aims to provide a computer software management system based on big data analysis, which is used for solving the technical problems that the computer software data can not be comprehensively collected in a classified manner, diversified analysis feedback is adopted aiming at the conditions of confidential backup, memory management and updating offline, and accurate management measures are adopted in the prior art.
The operation and maintenance data of the system software and the application software are collected in a classified mode, the operation and maintenance data are subdivided into risk data, inner pipe data and updating data, various different subdivision data are collected according to the operation and maintenance functional characteristics of the system software and the application software, and the technical problem that the data of the computer software cannot be collected comprehensively in a classified mode is solved;
by carrying out software risk analysis on the risk data, carrying out confidential backup analysis on the functional data, carrying out memory management analysis on the managed data and carrying out update offline analysis on the updated data, the overall risk of the computer software is conveniently graded and fed back, and diversified analysis feedback measures are adopted for the confidential backup condition, the memory management condition and the update offline condition of each system software and each application software, so that the technical problem that diversified analysis feedback cannot be adopted for the confidential backup, the memory management and the update offline condition is solved;
after different signals output after the confidential backup analysis, the memory management analysis and the update offline analysis are received, targeted management measures are taken for the confidential backup condition, the memory condition and the update online and offline condition of system software and application software, and the technical problem that accurate management measures cannot be taken is solved.
The purpose of the invention can be realized by the following technical scheme:
the computer software management system based on big data analysis comprises a system software acquisition module, an application software acquisition module, a big data classification transceiving module, a big data analysis module, a security backup module, a memory management module, an update offline module and a monitoring alarm module;
the system software acquisition module is used for acquiring system software operation and maintenance data and system software function data in a fixed time period and sending the system software operation and maintenance data and the system software function data to the big data classification transceiving module; the system software operation and maintenance data comprises soft risk data, soft inner tube data and soft update data;
the application software acquisition module is used for acquiring application software operation and maintenance data and application software function data in a fixed time period and sending the application software operation and maintenance data and the application software function data to the big data classification transceiving module; the application software operation and maintenance data comprises soft risk data, soft inner tube data and soft update data;
the big data classification transceiving module is used for sending the system soft risk data and the strain soft risk data to the big data analysis module, sending the system software function data and the application software function data to the confidential backup module, sending the system soft inner tube data and the strain soft inner tube data to the memory management module, and sending the system soft update data and the strain soft update data to the update offline module;
the big data analysis module is used for carrying out software risk analysis on the soft risk data and the soft risk data to generate a primary risk signal, a secondary risk signal or a risk qualified signal and sending the primary risk signal and the secondary risk signal to the monitoring alarm module;
the confidential backup module is used for carrying out confidential backup analysis on the system software functional data and the application software functional data, generating a high-confidential real-time backup signal, a high-confidential delayed backup signal or a low-confidential cache signal, and sending the high-confidential real-time backup signal and the high-confidential delayed backup signal to the management execution module;
the memory management module is used for carrying out memory management analysis on the system soft inner tube data and the response soft inner tube data to generate an exclusive memory signal, an increase memory signal or a decrease memory signal and sending the exclusive memory signal, the increase memory signal or the decrease memory signal to the management execution module;
the updating offline module is used for performing updating offline analysis on the system soft updating data and the response soft updating data, generating an offline signal, an updating signal or a maintaining signal, and sending the offline signal and the updating signal to the management execution module;
the monitoring alarm module is used for generating a first-level alarm signal and restarting the computer after receiving the first-level risk signal; and after the secondary risk signal is received, generating a secondary alarm signal and restarting the system software acquisition module and the application software acquisition module.
Further, the system software risk data comprises the variation number of the system software, the document integrity, the use frequency and the average use time of each system software; the system soft inner tube data comprises the occupied memory and the occupied memory change rate of each system software; the system software updating data comprises the updating times and the latest updating interval time of each system software; the system software function data comprises the function perfection, the source code length and the source code memory of each system software; the function perfection comprises single item simplicity, multiple item complexity and function synthesis, and the document perfection comprises an annotation-free source code, an annotation code detail and correct document perfection content;
the soft risk data comprises the number change rate of the application software, the document integrity, the use frequency and the latest use interval time of each application software; the data of the software-in-software tube comprises the occupied memory and the change rate of the occupied memory of each application software; the data to be soft updated comprises the updating times and the updating frequency of each application software; the application software functional data comprises a design method, a source code length and a source code memory of each application software; the document integrity comprises an annotation-free source code, an annotation code detail and correct document complete content, and the design method comprises the steps of digesting and introducing secondary development, autonomous development and software engineering method development.
Further, the specific steps of the software risk analysis are as follows:
the method comprises the steps that firstly, soft risk data are obtained, the variable quantity of system software is marked as XB, the document integrity, the use frequency and the average use time of each system software are respectively marked as Xwi, XPi and XSi, i is 1. When the integrity of the document is respectively the source code without annotation, the detailed annotation code or the complete content of the document is correct, XWi assigns values of 0.6, 0.8 and 0.9 respectively, and the soft risk coefficient Xr is obtained through analysis;
step two, acquiring data of the risk to be softened, marking the change rate of the number of the application software as YB, and respectively marking the document integrity, the use frequency and the last use interval time of each application software as YWt, YPt and YSt, wherein t is 1. YWt are respectively assigned with value 0.6, 0.8 and 0.9 when the document integrity is the source code without annotation, the annotation code is detailed or the document integrity is correct; analyzing to obtain a soft risk coefficient Yr;
step three, multiplying the system soft risk coefficient XR and the strain soft risk coefficient YR to obtain a comprehensive risk coefficient XYr; comparing the comprehensive risk coefficient with a preset range of the comprehensive risk coefficient, and generating a primary risk signal and sending the primary risk signal to a monitoring alarm module when the comprehensive risk coefficient is larger than the maximum value of the preset range; when the comprehensive risk coefficient is within the preset range, generating a secondary risk signal and sending the secondary risk signal to the monitoring alarm module; and when the comprehensive risk coefficient is smaller than the minimum value of the preset range, generating a risk qualified signal.
Further, the specific steps of the secure backup analysis are as follows:
step one, acquiring system software function data, and respectively marking the function perfectness, the source code length and the source code memory of each system software as XGi, XCi and XNi, wherein i is 1. When the function perfection of the system software is single and simple, multiple functions are complex and the functions are integrated, XGi respectively assigns values of 0.6, 0.8 and 0.9, and a secret backup factor XMi of each system software is obtained through analysis;
step two, acquiring application software function data, and respectively marking a design method, a source code length and a source code memory of each application software as YGt, YCt and YNt, wherein t is 1. When the design method of the application software introduces secondary development, autonomous development and software engineering method development for digestion, YGt assigns values of 0.6, 0.8 and 0.9 respectively, and a secret backup factor YMt of each application software is obtained through analysis;
comparing the confidential backup factors of each system software and each application software with the preset range, and generating a high-confidential real-time backup signal and sending the high-confidential real-time backup signal to the management execution module when the confidential backup factors are larger than the maximum value of the preset range; when the secret backup factor is within the preset range, generating a high-secret delay backup signal and sending the high-secret delay backup signal to the management execution module; and when the secret backup factor is smaller than the minimum value of the preset range, generating a low-secret buffer signal.
Further, the specific steps of the memory management analysis are as follows:
acquiring system soft inner tube data, and respectively marking the occupied memory and the change rate of the occupied memory of each system software as XAi and XBI, wherein i is 1, n is a positive integer greater than 1; analyzing to obtain a memory management factor XHi of each system software;
step two, acquiring data of the corresponding soft inner tube, and respectively marking the occupied memory and the change rate of the occupied memory of each application software as YAt and YBt, wherein t is 1. Analyzing to obtain a memory management factor YHt of each application software;
comparing the memory management factors of each system software and each application software with the preset range, and generating an exclusive memory signal and sending the exclusive memory signal to the management execution module when the memory management factors are larger than the maximum value of the preset range; when the memory management factor is within the preset range, generating a memory increasing signal and sending the memory increasing signal to the management execution module; and when the memory management factor is smaller than the minimum value of the preset range, generating a memory reduction signal and sending the memory reduction signal to the management execution module.
Further, the specific steps of updating offline analysis are as follows:
acquiring system software updating data, and marking the updating times of each system software and the latest updating interval time as XDi and XEi, wherein i is 1. Analyzing to obtain an update factor XFi of each system software;
step two, acquiring data to be soft updated, and marking the updating times and the updating frequency of each application software as YDt and YEt, wherein t is 1. Analyzing to obtain an update factor YFt of each application software;
comparing the update factors of each system software and each application software with the preset range of the update factors, and generating an offline signal and sending the offline signal to the management execution module when the update factors are larger than the maximum value of the preset range; when the updating factor is within the preset range, generating an updating signal and sending the updating signal to the management execution module; when the update factor is less than the minimum value of its preset range, a sustain signal is generated.
Furthermore, the management execution module is used for calling system software and application software corresponding to the high-security real-time backup signal and performing high-security and real-time backup operation on the system software and the application software, and calling system software and application software corresponding to the high-security delayed backup signal and performing high-security and delayed backup operation on the system software and the application software; calling system software and application software corresponding to the exclusive memory signals and carrying out operation of exclusive sharing a CPU and a memory, calling system software and application software corresponding to the increased memory signals and carrying out operation of increasing the shared memory capacity, calling system software and application software corresponding to the reduced memory signals and carrying out operation of reducing the shared memory capacity; and calling the system software or the application software corresponding to the offline signal and carrying out uninstalling operation on the system software or the application software, and calling the system software or the application software corresponding to the updating signal and carrying out networking updating operation on the system software or the application software.
The invention has the following beneficial effects:
1. the invention collects the operation and maintenance data and functional data of the system software and the application software in a classified manner, performs targeted software risk analysis, confidential backup analysis, memory management analysis and update offline analysis after the operation and maintenance data and the functional data which are comprehensively refined are sent in a classified manner, and is convenient for rating feedback of the overall risk of the computer software and taking diversified analysis feedback measures on the confidential backup condition, the memory management condition and the update offline condition of each system software and each application software.
2. According to the method, during software risk analysis, the system soft risk coefficient and the response soft risk coefficient are integrated and analyzed to obtain a comprehensive risk coefficient reflecting the fault risk in the using and running processes of the computer software, and the comprehensive risk coefficient is compared with a preset range to generate risk signals of different levels, so that different control methods can be adopted for the risk signals of different levels in the follow-up process; different signals are generated for each system software and each application software during the confidential backup analysis, the memory management analysis and the updating offline analysis, so that different management methods can be adopted pertinently, and the accuracy of the confidential backup management, the memory management and the updating offline management is improved.
3. After different signals output after the confidential backup analysis, the memory management analysis and the update offline analysis are received, the method adopts targeted management measures for the confidential backup condition, the memory condition and the update online and offline condition of system software and application software, ensures the stable and efficient operation of computer software, and improves the safety and reliability of computer software management.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block diagram of a big data analysis-based computer software management system according to the present invention;
FIG. 2 is a flow chart of a computer software management method based on big data analysis according to the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
As shown in fig. 1, the present embodiment provides a computer software management system based on big data analysis, which is suitable for integrated management of a computer installed with various system software and application software, and includes a system software acquisition module, an application software acquisition module, a big data classification transceiver module, a big data analysis module, a secret backup module, a memory management module, an update offline module, and a monitoring alarm module. Computer software is generally divided into two major types, namely system software and application software according to rules and standards, wherein the system software generally refers to a type of software which can provide support for development and operation of the application software for effectively using a computer system, or can provide convenience for a user to manage and use a computer, such as a basic input/output system (BIOS), an operating system windows, a programming language processing system (C) language compiler and a database management system (ORACLE); the application software generally refers to software specially used for solving various specific application problems, such as word processing software, information retrieval software, game software, media playing software and network communication software.
Specifically, the system software acquisition module is used for acquiring system software operation and maintenance data and system software function data in a fixed time period and sending the system software operation and maintenance data and the system software function data to the big data classification transceiving module; wherein the fixed time period is selected from 24 hours, and the system software operation and maintenance data comprises soft risk data, soft inner tube data and soft update data; the soft risk data comprises the change number of the system software, the document integrity, the use frequency and the average use time of each system software; the system soft inner tube data comprises the occupied memory and the occupied memory change rate of each system software; the system software updating data comprises the updating times and the latest updating interval time of each system software; the system software functional data comprises the functional perfection, the source code length and the source code memory of each system software. The function perfectness comprises single item simplicity, multiple item complexity and function synthesis, and the document perfectness comprises an annotation-free source code, an annotation code detail and correct document perfection content; the function perfection is manually divided according to the functions of system software, and other data are acquired by corresponding sensors and performance monitors.
The application software acquisition module is used for acquiring application software operation and maintenance data and application software function data in a fixed time period and sending the application software operation and maintenance data and the application software function data to the big data classification transceiving module; the application software operation and maintenance data comprises soft risk data, soft inner tube data and soft update data; the soft risk data comprises the quantity change rate of the application software, the document integrity of each application software, the use frequency and the latest use interval time; the data of the software-in-software tube comprises the occupied memory and the change rate of the occupied memory of each application software; the software-to-be-updated data comprises the updating times and the updating frequency of each application software; the application software functional data comprises a design method, a source code length and a source code memory of each application software. The document integrity comprises an annotation-free source code, an annotation code detail and correct document complete content, and the design method comprises the steps of digesting and introducing secondary development, autonomous development and software engineering method development; the design method is divided artificially according to the development mode of application software, and other data are acquired by corresponding sensors and performance monitors.
The big data classification transceiving module is used for sending the system soft risk data and the response soft risk data to the big data analysis module, sending the system software function data and the application software function data to the confidential backup module, sending the system soft inner tube data and the response soft inner tube data to the memory management module, and sending the system soft update data and the response soft update data to the update offline module.
By classifying and collecting operation and maintenance data and functional data of system software and application software, wherein the operation and maintenance data comprises risk data, inner tube data and update data, after the operation and maintenance data and the functional data which are comprehensively refined are classified and sent, targeted software risk analysis, confidential backup analysis, memory management analysis and update offline analysis are carried out, so that the whole risk of the computer software can be conveniently graded and fed back, and diversified analysis feedback measures can be adopted for the confidential backup condition, the memory management condition and the update offline condition of each system software and each application software.
The big data analysis module is used for carrying out software risk analysis on the soft risk data and the soft risk data, generating a first-level risk signal, a second-level risk signal or a risk qualified signal, and sending the first-level risk signal and the second-level risk signal to the monitoring alarm module.
The software risk analysis comprises the following specific steps:
step one, acquiring system soft risk data, marking the change quantity of system software as XB, and marking the document of each system softwareThe integrity, the frequency of use and the average time of use of each time are respectively marked as XWi, XPi and XSi, i is 1. When the document integrity is the source code without annotation, the annotation code is detailed or the document integrity is correct, XWi assigns values of 0.6, 0.8 and 0.9 respectively according to the formula
Obtaining a system soft risk coefficient Xr; wherein a1, a2, a3 and a4 are all preset weight coefficients, a2 is more than a3 and more than a4 is more than a1 and more than a1+ a2+ a3+ a4 is 6.587; it should be noted that the larger the apparent value of the soft risk coefficient is, the higher the risk of failure in the use and operation process of the system software is;
step two, acquiring the data of the risk of responding to soft, marking the change rate of the number of the application software as YB, and respectively marking the document integrity, the use frequency and the last use interval time of each application software as YWt, YPt and YSt, wherein t is 1. YWt are respectively assigned with value 0.6, 0.8 and 0.9 when the document integrity is the source code without annotation, the annotation code is detailed or the document integrity is correct; according to the formula
Obtaining the coefficient Yr of the soft risk; b1, b2, b3 and b4 are all preset weight coefficients, b2 is more than b3 is more than b4 is more than b1 is more than 0, and b1+ b2+ b3+ b4 is 4.851; it should be noted that the larger the apparent value of the soft risk coefficient is, the higher the risk of failure during the use and operation of the application software is;
step three, multiplying the system soft risk coefficient XR and the strain soft risk coefficient YR to obtain a comprehensive risk coefficient XYr; comparing the comprehensive risk coefficient with a preset range of the comprehensive risk coefficient, and generating a primary risk signal and sending the primary risk signal to a monitoring alarm module when the comprehensive risk coefficient is larger than the maximum value of the preset range; when the comprehensive risk coefficient is within the preset range, generating a secondary risk signal and sending the secondary risk signal to the monitoring alarm module; and when the comprehensive risk coefficient is smaller than the minimum value of the preset range, generating a risk qualified signal.
And when the software risk analysis is carried out, the system soft risk data are analyzed to obtain a system soft risk coefficient, the system soft risk data are analyzed to obtain a soft risk coefficient, the system soft risk coefficient and the soft risk coefficient are analyzed to obtain a comprehensive risk coefficient reflecting the fault risk in the use and operation processes of the computer software, and the comprehensive risk coefficient is compared with a preset range to generate risk signals of different levels, so that different control methods can be adopted for the risk signals of different levels in the follow-up process.
The security backup module is used for carrying out security backup analysis on the system software functional data and the application software functional data, generating a high-security real-time backup signal, a high-security delay backup signal or a low-security cache signal, and sending the high-security real-time backup signal and the high-security delay backup signal to the management execution module.
The specific steps of the secure backup analysis are as follows:
step one, acquiring system software function data, and respectively marking the function perfectness, the source code length and the source code memory of each system software as XGi, XCi and XNi, wherein i is 1. When the function perfection of system software is simple, complex and comprehensive, XGi is respectively assigned with values of 0.6, 0.8 and 0.9 according to a formula
Obtaining a secret backup factor XMi for each system software; wherein c1, c2 and c3 are all preset weight coefficients, c1 > c3 > c2 > 0, and c1+ c2+ c3 ═ 2.589; it should be noted that, the higher the apparent value of the secret backup factor of the system software is, the higher the requirement of the secret backup of the system software is;
step two, acquiring application software function data, and respectively marking a design method, a source code length and a source code memory of each application software as YGt, YCt and YNt, wherein t is 1. When the design method of the application software introduces secondary development, autonomous development and software engineering method development for digestion, YGt assigns values of 0.6, 0.8 and 0.9 respectively according to the formula
Obtaining a secret backup factor YMt for each application; d1, d2 and d3 are preset weight coefficients, d1 is more than d3 is more than d2 is more than 0, and d1+ d2+ d3 is 2.876; it should be noted that, the higher the apparent value of the secret backup factor of the application software is, the higher the requirement of the secret backup of the application software is;
comparing the confidential backup factor of each system software and each application software with a preset range, and generating a high-confidentiality real-time backup signal and sending the high-confidentiality real-time backup signal to the management execution module when the confidential backup factor is larger than the maximum value of the preset range; when the secret backup factor is within the preset range, generating a high-secret delay backup signal and sending the high-secret delay backup signal to the management execution module; and when the secret backup factor is smaller than the minimum value of the preset range, generating a low-secret buffer signal.
The method comprises the steps of analyzing functional data of each system software to obtain a confidential backup factor of each system software during confidential backup analysis, analyzing the functional data of each application software to obtain the confidential backup factor of each application software, comparing the confidential backup factors with a preset range of the confidential backup factors, and outputting three different signals, so that different management methods can be adopted for encryption and backup conditions of the system software and the application software according to different signals subsequently, and the accuracy of software confidential backup management is improved.
The memory management module is used for performing memory management analysis on the system soft inner tube data and the system soft inner tube data to generate an exclusive memory signal, an increase memory signal or a decrease memory signal, and sending the exclusive memory signal, the increase memory signal or the decrease memory signal to the management execution module.
The specific steps of memory management analysis are as follows:
step one, acquiring system software inner tube data, and respectively marking the occupied memory and the change rate of the occupied memory of each system software as XAi and XBI, wherein i is 1. According to the formula
Obtaining XHi memory management factors for each system software; wherein e1 and e2 are both preset weight coefficients, e2 > e1 > 0, and e1+ e2 is 1.289；
Step two, acquiring data of the corresponding soft inner tube, and respectively marking the occupied memory and the change rate of the occupied memory of each application software as YAt and YBt, wherein t is 1. According to the formula
Obtaining YHt memory management factors for each application software; wherein f1 and f2 are both preset weight coefficients, f2 > f1 > 0, and f1+ f2 is 1.648;
comparing the memory management factors of each system software and each application software with the preset range, and generating an exclusive memory signal and sending the exclusive memory signal to the management execution module when the memory management factors are larger than the maximum value of the preset range; when the memory management factor is within the preset range, generating a memory increasing signal and sending the memory increasing signal to the management execution module; and when the memory management factor is smaller than the minimum value of the preset range, generating a memory reduction signal and sending the memory reduction signal to the management execution module.
When the memory management is analyzed, the inner tube data of each system software is analyzed to obtain the memory management factor of each system software, the inner tube data of each application software is analyzed to obtain the memory management factor of each application software, the memory management factors are compared with the preset range of the memory management factors, then three different signals are output, different management methods can be conveniently adopted for the memory conditions of the system software and the application software according to different signals, and the accuracy of software memory management is improved.
The updating offline module is used for performing updating offline analysis on the soft update data and the soft update data, generating an offline signal, an update signal or a maintenance signal, and sending the offline signal and the update signal to the management execution module.
The specific steps for updating the offline analysis are as follows:
step one, acquiring system software updating data, and marking the updating times of each system software and the latest updating interval time as XDi and XEi, wherein i is 1. According to the formula
Obtaining an update factor XFi for each system software; wherein g1 and g2 are both preset weight coefficients, g1 is greater than g2 is greater than 0, and g1+ g2 is 7.826;
step two, acquiring data to be soft updated, and marking the updating times and the updating frequency of each application software as YDt and YEt, wherein t is 1. According to the formula
Obtaining an update factor YFt for each application software; wherein h1 and h2 are both preset weight coefficients, h1 is more than h2 is more than 0, and h1+ h2 is 2.843;
comparing the update factors of each system software and each application software with the preset range, and generating an offline signal and sending the offline signal to the management execution module when the update factors are larger than the maximum value of the preset range; when the updating factor is within the preset range, generating an updating signal and sending the updating signal to the management execution module; when the update factor is less than the minimum value of its preset range, a sustain signal is generated.
When the off-line analysis is carried out, the updating data of each system software is analyzed to obtain the updating factor of each system software, the updating data of each application software is analyzed to obtain the updating factor of each application software, the updating management factor is compared with the preset range of the updating management factor, and then three different signals are output, so that different management methods can be adopted for the subsequent updating and on-line and off-line conditions of the system software and the application software according to different signals, and the accuracy of software updating and on-line and off-line management is improved.
The monitoring alarm module is used for generating a first-level alarm signal and restarting the computer after receiving the first-level risk signal; and after the secondary risk signal is received, generating a secondary alarm signal and restarting the system software acquisition module and the application software acquisition module. The monitoring alarm module generates alarm signals of different levels according to the received risk signals of different levels and takes corresponding management measures, so that the safety of software risk management is improved.
The management execution module is used for calling system software and application software corresponding to the high-security real-time backup signal and carrying out high-encryption and real-time backup operation on the system software and the application software, and calling system software and application software corresponding to the high-security delayed backup signal and carrying out high-encryption and delayed backup operation on the system software and the application software; calling system software and application software corresponding to the exclusive memory signal and carrying out operation of exclusive sharing of a CPU and a memory on the system software and the application software, calling system software and application software corresponding to the increased memory signal and carrying out operation of increasing shared memory capacity on the system software and the application software, calling system software and application software corresponding to the decreased memory signal and carrying out operation of decreasing shared memory capacity on the system software and the application software; and calling the system software or the application software corresponding to the offline signal and carrying out uninstalling operation on the system software or the application software, and calling the system software or the application software corresponding to the updating signal and carrying out networking updating operation on the system software or the application software.
After receiving different signals output after the confidential backup analysis, the memory management analysis and the update offline analysis, the management execution module takes targeted management measures on the confidential backup condition, the memory condition and the update online and offline condition of the system software and the application software, ensures the stable and efficient operation of the computer software, and improves the safety and reliability of the computer software management.
The preset weight coefficient is used for balancing the proportion weight of each item of data in formula calculation, so that the accuracy of the calculation result is promoted; the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and a corresponding weight factor coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relationship between the parameters and the quantized values is not affected.
The above formulas are obtained by collecting a large amount of data and performing software simulation, and the coefficients in the formulas are set by those skilled in the art according to actual conditions.
Example 2
As shown in fig. 2, the present embodiment provides a computer software management method based on big data analysis, which is applicable to the computer software management system of embodiment 1, and includes the following steps:
s1, collecting computer software data: collecting system software operation and maintenance data and system software function data in a fixed time period, and collecting application software operation and maintenance data and application software function data in the fixed time period; the system software operation and maintenance data comprises soft risk data, soft inner tube data and soft update data; the application software operation and maintenance data comprises soft risk data, soft inner tube data and soft update data;
s2, big data classification transceiving: sending the system soft risk data and the strain soft risk data to a big data analysis module, sending system software function data and application software function data to a confidential backup module, sending the system soft inner tube data and the strain soft inner tube data to a memory management module, and sending the system soft update data and the strain soft update data to an update offline module;
s3, software risk analysis: performing software risk analysis on the soft risk data and the soft risk data to generate a primary risk signal, a secondary risk signal or a risk qualified signal, and sending the primary risk signal and the secondary risk signal to a monitoring alarm module;
s4, secret backup analysis: carrying out confidential backup analysis on system software functional data and application software functional data to generate a high-confidential real-time backup signal, a high-confidential delayed backup signal or a low-confidential cache signal, and sending the high-confidential real-time backup signal and the high-confidential delayed backup signal to a management execution module;
s5, memory management analysis: performing memory management analysis on the system soft inner tube data and the system soft inner tube data to generate an exclusive memory signal, an increase memory signal or a decrease memory signal, and sending the exclusive memory signal, the increase memory signal or the decrease memory signal to a management execution module;
s6, updating offline analysis: performing update offline analysis on the system soft update data and the strain soft update data to generate an offline signal, an update signal or a maintenance signal, and sending the offline signal and the update signal to a management execution module;
s7, monitoring and alarming: after receiving the first-level risk signal, generating a first-level alarm signal and restarting the computer; after receiving the secondary risk signal, generating a secondary alarm signal and restarting a system software acquisition module and an application software acquisition module;
s8, management and execution: the method comprises the steps of calling system software and application software corresponding to a high-security real-time backup signal and carrying out high-security and real-time backup operation on the system software and the application software, and calling system software and application software corresponding to a high-security delayed backup signal and carrying out high-security and delayed backup operation on the system software and the application software; calling system software and application software corresponding to the exclusive memory signal and carrying out operation of exclusive sharing of a CPU and a memory on the system software and the application software, calling system software and application software corresponding to the increased memory signal and carrying out operation of increasing shared memory capacity on the system software and the application software, calling system software and application software corresponding to the decreased memory signal and carrying out operation of decreasing shared memory capacity on the system software and the application software; and calling the system software or the application software corresponding to the offline signal and carrying out uninstalling operation on the system software or the application software, and calling the system software or the application software corresponding to the updating signal and carrying out networking updating operation on the system software or the application software.
The computer software management method of the embodiment collects operation and maintenance data and functional data of system software and application software in a classified manner, performs software risk analysis, confidential backup analysis, memory management analysis and update offline analysis after the operation and maintenance data and the functional data which are comprehensively refined are sent in a classified manner, and takes targeted management measures on the confidential backup condition, the memory condition and the update online and offline condition of the system software and the application software according to different signals obtained by analysis, thereby ensuring the stable and efficient operation of the computer software and improving the safety and reliability of the computer software management.
The foregoing is merely illustrative and explanatory of the present invention, and various modifications, additions or substitutions as would be apparent to one skilled in the art to the specific embodiments described are possible without departing from the invention as claimed herein or beyond the scope thereof.
In the description herein, references to the description of "one embodiment," "an example," "a specific example," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (7)
1. The computer software management system based on big data analysis is characterized by comprising a system software acquisition module, an application software acquisition module, a big data classification transceiving module, a big data analysis module, a secret backup module, a memory management module, an update offline module and a monitoring alarm module;
the system software acquisition module is used for acquiring system software operation and maintenance data and system software function data in a fixed time period and sending the system software operation and maintenance data and the system software function data to the big data classification transceiving module; the system software operation and maintenance data comprises soft risk data, soft inner tube data and soft update data;
the application software acquisition module is used for acquiring application software operation and maintenance data and application software function data in a fixed time period and sending the application software operation and maintenance data and the application software function data to the big data classification transceiving module; the application software operation and maintenance data comprises soft risk data, soft inner tube data and soft update data;
the big data classification transceiving module is used for sending the system soft risk data and the strain soft risk data to the big data analysis module, sending the system software function data and the application software function data to the confidential backup module, sending the system soft inner tube data and the strain soft inner tube data to the memory management module, and sending the system soft update data and the strain soft update data to the update offline module;
the big data analysis module is used for carrying out software risk analysis on the soft risk data and the soft risk data to generate a primary risk signal, a secondary risk signal or a risk qualified signal and sending the primary risk signal and the secondary risk signal to the monitoring alarm module;
the confidential backup module is used for carrying out confidential backup analysis on the system software functional data and the application software functional data, generating a high-confidential real-time backup signal, a high-confidential delayed backup signal or a low-confidential cache signal, and sending the high-confidential real-time backup signal and the high-confidential delayed backup signal to the management execution module;
the memory management module is used for performing memory management analysis on the system soft inner tube data and the system soft inner tube data to generate an exclusive memory signal, an increase memory signal or a decrease memory signal and sending the exclusive memory signal, the increase memory signal or the decrease memory signal to the management execution module;
the updating offline module is used for performing updating offline analysis on the system soft updating data and the strain soft updating data, generating an offline signal, an updating signal or a maintaining signal, and sending the offline signal and the updating signal to the management execution module;
the monitoring alarm module is used for generating a first-level alarm signal and restarting the computer after receiving the first-level risk signal; and after the secondary risk signal is received, generating a secondary alarm signal and restarting the system software acquisition module and the application software acquisition module.
2. The big data analysis-based computer software management system according to claim 1, wherein the soft risk data comprises the number of system software changes and the document integrity, usage frequency, and average usage time per system software; the system soft inner tube data comprises the occupied memory and the occupied memory change rate of each system software; the system software updating data comprises the updating times and the latest updating interval time of each system software; the system software function data comprises the function perfection, the source code length and the source code memory of each system software; the function perfectness comprises single item simplicity, multiple item complexity and function synthesis, and the document perfectness comprises an annotation-free source code, an annotation code detail and correct document perfection content;
the soft risk data comprises the number change rate of the application software, the document integrity, the use frequency and the latest use interval time of each application software; the data of the software-in-software tube comprises the occupied memory and the change rate of the occupied memory of each application software; the data to be soft updated comprises the updating times and the updating frequency of each application software; the application software functional data comprises a design method, a source code length and a source code memory of each application software; the document integrity comprises an annotation-free source code, an annotation code detail and correct document complete content, and the design method comprises the steps of digesting and introducing secondary development, autonomous development and software engineering method development.
3. The computer software management system based on big data analysis according to claim 2, characterized in that the software risk analysis comprises the following specific steps:
the method comprises the steps that firstly, soft risk data are obtained, the variable quantity of system software is marked as XB, the document integrity, the use frequency and the average use time of each system software are respectively marked as Xwi, XPi and XSi, i is 1. When the integrity of the document is respectively the source code without annotation, the detailed annotation code or the complete content of the document is correct, XWi assigns values of 0.6, 0.8 and 0.9 respectively, and the soft risk coefficient Xr is obtained through analysis;
step two, acquiring data of the risk to be softened, marking the change rate of the number of the application software as YB, and respectively marking the document integrity, the use frequency and the last use interval time of each application software as YWt, YPt and YSt, wherein t is 1. YWt are respectively assigned with value 0.6, 0.8 and 0.9 when the document integrity is the source code without annotation, the annotation code is detailed or the document integrity is correct; analyzing to obtain a soft risk coefficient Yr;
step three, multiplying the system soft risk coefficient XR and the strain soft risk coefficient YR to obtain a comprehensive risk coefficient XYr; comparing the comprehensive risk coefficient with a preset range of the comprehensive risk coefficient, and generating a primary risk signal and sending the primary risk signal to a monitoring alarm module when the comprehensive risk coefficient is larger than the maximum value of the preset range; when the comprehensive risk coefficient is within the preset range, generating a secondary risk signal and sending the secondary risk signal to the monitoring alarm module; and when the comprehensive risk coefficient is smaller than the minimum value of the preset range, generating a risk qualified signal.
4. The computer software management system based on big data analysis of claim 2, wherein the confidential backup analysis comprises the following specific steps:
step one, acquiring system software function data, and respectively marking the function perfectness, the source code length and the source code memory of each system software as XGi, XCi and XNi, wherein i is 1. When the function perfection of the system software is single and simple, multiple functions are complex and the functions are integrated, XGi respectively assigns values of 0.6, 0.8 and 0.9, and a secret backup factor XMi of each system software is obtained through analysis;
step two, acquiring application software function data, and respectively marking a design method, a source code length and a source code memory of each application software as YGt, YCt and YNt, wherein t is 1. When the design method of the application software introduces secondary development, autonomous development and software engineering method development for digestion, YGt assigns values of 0.6, 0.8 and 0.9 respectively, and a secret backup factor YMt of each application software is obtained through analysis;
comparing the confidential backup factor of each system software and each application software with a preset range, and generating a high-confidentiality real-time backup signal and sending the high-confidentiality real-time backup signal to the management execution module when the confidential backup factor is larger than the maximum value of the preset range; when the secret backup factor is within the preset range, generating a high-secret delay backup signal and sending the high-secret delay backup signal to the management execution module; and when the secret backup factor is smaller than the minimum value of the preset range, generating a low-secret cache signal.
5. The computer software management system based on big data analysis according to claim 2, wherein the specific steps of the memory management analysis are as follows:
step one, acquiring system software inner tube data, and respectively marking the occupied memory and the change rate of the occupied memory of each system software as XAi and XBI, wherein i is 1. Analyzing to obtain a memory management factor XHi of each system software;
step two, acquiring data of the corresponding soft inner tube, and respectively marking the occupied memory and the change rate of the occupied memory of each application software as YAt and YBt, wherein t is 1. Analyzing to obtain a memory management factor YHt of each application software;
comparing the memory management factors of each system software and each application software with the preset range, and generating an exclusive memory signal and sending the exclusive memory signal to the management execution module when the memory management factors are larger than the maximum value of the preset range; when the memory management factor is within the preset range, generating a memory increasing signal and sending the memory increasing signal to the management execution module; and when the memory management factor is smaller than the minimum value of the preset range, generating a memory reduction signal and sending the memory reduction signal to the management execution module.
6. The big data analysis-based computer software management system according to claim 2, wherein the specific steps of updating the offline analysis are as follows:
step one, acquiring system software updating data, and marking the updating times of each system software and the latest updating interval time as XDi and XEi, wherein i is 1. Analyzing to obtain an update factor XFi of each system software;
step two, acquiring data to be soft updated, and marking the updating times and the updating frequency of each application software as YDt and YEt, wherein t is 1. Analyzing to obtain an update factor YFt of each application software;
comparing the update factors of each system software and each application software with the preset range of the update factors, and generating an offline signal and sending the offline signal to the management execution module when the update factors are larger than the maximum value of the preset range; when the updating factor is within the preset range, generating an updating signal and sending the updating signal to the management execution module; when the update factor is less than the minimum value of its preset range, a sustain signal is generated.
7. The computer software management system based on big data analysis according to claim 1, wherein the management execution module is configured to invoke system software and application software corresponding to the high-security real-time backup signal and perform high-encryption and real-time backup operations thereon, and invoke system software and application software corresponding to the high-security delayed backup signal and perform high-encryption and delayed backup operations thereon; calling system software and application software corresponding to the exclusive memory signal and carrying out operation of exclusive sharing of a CPU and a memory on the system software and the application software, calling system software and application software corresponding to the increased memory signal and carrying out operation of increasing shared memory capacity on the system software and the application software, calling system software and application software corresponding to the decreased memory signal and carrying out operation of decreasing shared memory capacity on the system software and the application software; and calling the system software or the application software corresponding to the offline signal and carrying out uninstalling operation on the system software or the application software, and calling the system software or the application software corresponding to the updating signal and carrying out networking updating operation on the system software or the application software.
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