CN116521490B - PC system health degree self-checking method, self-checking device, equipment and medium - Google Patents

PC system health degree self-checking method, self-checking device, equipment and medium Download PDF

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Publication number
CN116521490B
CN116521490B CN202310809338.0A CN202310809338A CN116521490B CN 116521490 B CN116521490 B CN 116521490B CN 202310809338 A CN202310809338 A CN 202310809338A CN 116521490 B CN116521490 B CN 116521490B
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log
health
score
abnormal
current
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CN116521490A (en
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刘程程
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Suzhou Inspur Intelligent Technology Co Ltd
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Suzhou Inspur Intelligent Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3055—Monitoring arrangements for monitoring the status of the computing system or of the computing system component, e.g. monitoring if the computing system is on, off, available, not available
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3065—Monitoring arrangements determined by the means or processing involved in reporting the monitored data
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Abstract

The invention relates to a self-checking method, a self-checking device, equipment and a medium for PC system health degree, wherein the self-checking method comprises the following steps: acquiring a component detection score according to a component detection result of the PC system; setting corresponding log weights for various logs of the PC system respectively, and acquiring log detection scores according to all the log weights and log detection results of the PC system; setting the sum of the component detection score and the log detection score as the current health score of the PC system. By the technical scheme, the problems that the conventional personal computer PC cannot perform health degree self-checking, and the personnel maintenance time and the resource cost are high can be solved.

Description

PC system health degree self-checking method, self-checking device, equipment and medium
Technical Field
The invention relates to the technical field of personal computers, in particular to a self-checking method, a self-checking device, equipment and a medium for the health degree of a PC system.
Background
Under the current large environment, in order to guarantee service expansion and data security under extreme conditions, more and more domestic PCs (i.e., personal computers) are being silently popularized. As the use of domestic PCs tends to be daily, various problems due to component or system anomalies are typically encountered.
At present, in most cases, the user does not have the relevant technical knowledge reserves in the aspect of domestic PC, and cannot acquire the root cause of the abnormal problem, so that maintenance personnel spend a great deal of time and resources on troubleshooting the problem. Meanwhile, the domestic PC system lacks a self-checking means of daily health degree, which is used for realizing daily health degree detection of equipment, thereby reducing the time and resource consumption of maintenance work.
Disclosure of Invention
In order to solve the technical problems, the invention provides a self-checking method, a self-checking device, equipment and a medium for the health degree of a PC system, wherein the self-checking method for the health degree of the PC system is used for solving the problems that the PC of the prior personal computer cannot perform the self-checking for the health degree, and the personnel maintenance time and the resource cost are high.
In order to achieve the above object, the present invention provides a method for self-checking the health of a PC system, comprising:
acquiring a component detection score according to a component detection result of the PC system;
setting corresponding log weights for various logs of the PC system respectively, and acquiring log detection scores according to all the log weights and log detection results of the PC system;
setting the sum of the component detection score and the log detection score as the current health score of the PC system.
Further, the self-checking method further comprises:
judging whether the current log file has the same abnormal keywords as the log classification detail table corresponding to the cloud model library or not;
if the abnormal keyword exists, comparing the context of the abnormal keyword in the current log file with the context text segment content of the abnormal term in the cloud model library to obtain a matching degree value;
if the matching degree value is larger than the lower matching degree limit value but smaller than the upper matching degree limit value, marking the marking information in the current log file as suspected abnormality; if the matching degree value is larger than the matching degree upper limit value, marking the marking information in the current log file as abnormal confirmation; and if the matching degree value is smaller than the lower limit value of the matching degree, marking the marking information in the current log file as no abnormality.
Further, setting corresponding log weights for various logs of the PC system respectively, specifically including:
setting 7 corresponding log weights for a starting log, a system log, a kernel log, a user log, a display log, a kernel buffer log and other types of logs in the PC system respectively, so that the sum of the 7 corresponding log weights is 1;
The self-checking method further comprises the following steps:
and sequentially retrieving the log files of the corresponding types according to the sequence of the starting log, the system log, the kernel log, the user log, the display log, the kernel buffer log and the other types of logs, and judging whether the log files of the corresponding types have abnormal keywords which are the same as the log classification detail list corresponding to the cloud model library.
Further, the self-checking method further comprises:
when the current detection time is an idle time in a user idle time table, performing component detection on hardware components of the PC system, and acquiring various parameter values of a hard disk of the PC system, memory pressure measurement abnormal item information and equipment SN;
obtaining a component detection score according to a component detection result of the PC system, wherein the component detection score specifically comprises:
if the equipment component or the system information of the PC system is not matched with the data in the cloud model library and no corresponding change record exists, marking the state information of the corresponding equipment component as abnormal;
if the data of each parameter value of the hard disk exceeds the threshold value in the cloud model library, marking the current parameter item information of the corresponding hard disk as abnormal;
if the memory pressure measurement abnormal item information is abnormal, marking the corresponding pressure measurement item as abnormal;
And acquiring the component detection score according to the total number of the component detection items and the number of the component detection abnormal items of the PC system.
Further, the self-checking method further comprises:
creating a cloud model library and a local model library for the PC system;
creating at least one of the following in the cloud model library: a device model table, a device model detail table, a log information table, a log classification detail table and a health detection detail table;
creating in the local model library at least one of: user behavior analysis table and user idle time slot table.
Further, the self-checking method further comprises:
uploading the current health score of the PC system to the cloud model library, writing the health detection detail table, and updating the current health score to the equipment model detail table.
Further, the self-checking method further comprises:
acquiring total occupation information of the PC system in the current period, acquiring a total occupation data value of the PC system according to the total occupation information, and writing the total occupation data value into a total occupation field corresponding to the current period in the user behavior analysis table;
and acquiring the lowest occupation value time period in all the total occupation field records, and updating the lowest occupation value time period to an idle time period field of the idle time period table of the user.
Further, the obtaining the total occupation information of the PC system in the current period specifically includes:
respectively acquiring occupied data average values corresponding to the occupancy rate of the current part at a plurality of time period points, and acquiring the occupancy rate average value of the current part according to all occupied data average values; wherein the current component occupancy comprises at least one of: the processor occupancy rate, the memory occupancy rate, the display card occupancy rate, the hard disk read-write occupancy rate and the network card uplink and downlink occupancy rate of the PC system;
setting corresponding occupancy weights for the occupancy components of the PC system respectively, and acquiring total occupancy data of the current period of the PC system according to all the occupancy weights and the occupancy average value of all the current components.
Further, the component detection score is obtained according to the total number of component detection items and the number of component detection abnormal items of the PC system, and specifically includes:
setting the total health score of the PC system to beThe component detection health total score is set asThe log detection health total score is set asSo that；
Setting the total number of the component detection items to beThe number of the abnormal items detected by the component is set asCalculating the component detection score as: 。
Further, corresponding log weights are respectively set for various logs of the PC system, and log detection scores are obtained according to all log weights and log detection results of the PC system, and specifically include:
setting 7 corresponding log weights of the starting log, the system log, the kernel log, the user log, the display log, the kernel buffer log and the other types of logs as respectivelyAnd meet the following；
Setting log abnormal items asThe weight of the starting log isThe weight of the current abnormal item isThe lower limit of the matching degree isThe method comprises the steps of carrying out a first treatment on the surface of the When (when)When the log is suspected to be abnormal, log abnormal items are calculatedThe deduction score of (2) is:the method comprises the steps of carrying out a first treatment on the surface of the When (when)To confirm the exception, log exception entries are computedThe deduction score of (2) is:；
setting upThe total number of abnormal items detected for the weight overlapping set and the log of various types of logs isThe number of suspected abnormal items isConfirming the number of abnormal items asThe method comprises the steps of carrying out a first treatment on the surface of the An abnormal item is detected for any one log,is set to satisfy the matching degree of more than or equal toIs a collection of abnormal items; the log detection score is calculated as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein the method comprises the steps of。
Further, setting the sum of the component detection score and the log detection score as the current health score of the PC system specifically includes:
Calculating the current health score of the PC system as:
。
further, the obtaining the total occupation information of the PC system in the current period specifically includes:
the processor occupancy rate, the memory occupancy rate, the display card occupancy rate, the hard disk read-write occupancy rate and the network card uplink of the PC system are processedThe downlink occupancy rates are respectively set as、、、、；
Respectively obtaining occupied data average values corresponding to the current component occupancy rate at 12 time period points, and calculating the current component occupancy rate average value according to all occupied data average values as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein the current component occupancy comprises at least one of: the processor occupancy rate, the memory occupancy rate, the display card occupancy rate, the hard disk read-write occupancy rate and the network card uplink and downlink occupancy rate of the PC system;
the processor occupancy rate, the memory occupancy rate, the display card occupancy rate, the hard disk read-write occupancy rate and the corresponding occupancy rate weights of the network card uplink and downlink occupancy rates of the PC system are respectively set as、、、、The total occupancy rate data of the current period of the PC system is calculated as follows:wherein, the liquid crystal display device comprises a liquid crystal display device,。
further, setting corresponding occupancy weights for the occupancy components of the PC system respectively, specifically includes:
when the PC system performs equipment self-checking, setting the occupancy weights as follows: ，，。
Further, the self-checking method further comprises:
acquiring the current health score of the PC system in real time through the cloud model library;
and when the equipment corresponding to the PC system is not used any more, changing the equipment state field in the cloud model library into scrapped equipment.
Further, creating at least one of the following in the cloud model library specifically includes:
respectively creating log classification detail tables corresponding to each log type according to the log information tables; wherein the log categorization details table comprises at least one of: and starting a log detail table and a system log detail table.
Further, obtaining values of various parameters of a hard disk of the PC system, abnormal information of memory pressure measurement, and device SN, specifically includes:
detecting a hard disk of the PC system by using an S.M.A.R.T tool to obtain various parameter values of the hard disk;
performing pressure measurement on the memory of the PC system by using a pressure measurement tool, and acquiring the abnormal pressure measurement item information of the memory;
and comparing the equipment SN of the PC system with equipment initialization information in the cloud model library, and judging the matching degree.
The invention also provides a self-checking device for the health degree of the PC system, which is used for realizing the self-checking method for the health degree of the PC system, and comprises the following steps:
A component detection score acquisition unit configured to acquire a component detection score according to a component detection result of the PC system;
the log detection score acquisition unit is used for respectively setting corresponding log weights for various logs of the PC system and acquiring log detection scores according to all the log weights and log detection results of the PC system;
and the current health degree score obtaining unit is used for setting the sum of the component detection score and the log detection score as the current health degree score of the PC system.
The invention also provides a computer device, which comprises a memory, a processor and a computer program, wherein the computer program is stored in the memory and can run on the processor, and the steps of the PC system health self-checking method are realized when the processor executes the computer program.
The present invention further provides a computer readable storage medium storing a computer program which when executed by a processor implements the steps of the aforementioned method for self-checking the health of a PC system.
Compared with the prior art, the technical scheme provided by the invention has the following technical effects:
in the invention, the current health degree score of the PC system is set as the sum of a component detection score and a log detection score;
Wherein the component detection score is obtainable based on a component detection result of the PC system;
the PC system comprises a plurality of types of system logs, corresponding log weights are respectively set for the system logs of each type, and the overall log detection score of the PC system is obtained based on all log detection results and the corresponding log weights;
therefore, the self-checking method for the health degree of the PC system carries out distinguishing detection on software detection and hardware detection at the same time, and finally combines the software detection and the hardware detection, so that the health degree detection result has a reference value;
in addition, when calculating the health degree, different weights are given to different detection items; on one hand, the flexibility of adjustment is increased, and on the other hand, the key problem of the detection item can be better highlighted.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for self-checking the health of a PC system according to an embodiment of the invention;
FIG. 2 is a schematic overall flow chart of a health self-checking method according to an embodiment of the present invention;
FIG. 3 is a block diagram of a health self-checking device of a PC system in a second embodiment of the present invention;
fig. 4 is an internal structure diagram of a computer device in the second embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one:
as shown in fig. 1, an embodiment of the present invention provides a method for self-checking the health degree of a PC system, including:
s41, acquiring a component detection score according to a component detection result of the PC system;
s42, respectively setting corresponding log weights for various logs of the PC system, and acquiring log detection scores according to all the log weights and log detection results of the PC system;
s43 sets the sum of the component detection score and the log detection score as the current health score of the PC system.
In a specific embodiment, the current health score of the PC system is set as the sum of the component detection score and the log detection score;
wherein the component detection score is obtainable based on a component detection result of the PC system;
the PC system comprises a plurality of types of system logs, corresponding log weights are respectively set for the system logs of each type, and the overall log detection score of the PC system is obtained based on all log detection results and the corresponding log weights;
therefore, the self-checking method for the health degree of the PC system carries out distinguishing detection on software detection and hardware detection at the same time, and finally combines the software detection and the hardware detection, so that the health degree detection result has a reference value;
in addition, when calculating the health degree, different weights are given to different detection items; on one hand, the flexibility of adjustment is increased, and on the other hand, the key problem of the detection item can be better highlighted.
In practice, SN, i.e. the serial number of the serial number is generally fixed when shipped from the factory and is used as a unique identifier of the device;
the domestic PC mainly refers to a letter creation PC, adopts domestic components and carries equipment of a domestic operating system;
NLP, natural Language Processing, refers to natural language processing.
In a preferred embodiment, the self-test method further comprises:
s321, judging whether the current log file contains abnormal keywords which are the same as those in the log classification detail table corresponding to the cloud model library;
s322, if so, comparing the context of the abnormal keyword in the current log file with the context text segment content of the abnormal item in the cloud model library to obtain a matching degree value;
s323, if the matching degree value is larger than the lower limit value of the matching degree but smaller than the upper limit value of the matching degree, marking the marking information in the current log file as suspected abnormality; if the matching degree value is larger than the matching degree upper limit value, marking the marking information in the current log file as abnormal confirmation; and if the matching degree value is smaller than the lower limit value of the matching degree, marking the marking information in the current log file as no abnormality.
In a preferred embodiment, the setting of the corresponding log weights for each type of log of the PC system specifically includes:
setting 7 corresponding log weights for a starting log, a system log, a kernel log, a user log, a display log, a kernel buffer log and other types of logs in the PC system respectively, so that the sum of the 7 corresponding log weights is 1;
the self-checking method further comprises the following steps:
And sequentially retrieving the log files of the corresponding types according to the sequence of the starting log, the system log, the kernel log, the user log, the display log, the kernel buffer log and the other types of logs, and judging whether the log files of the corresponding types have abnormal keywords which are the same as the log classification detail list corresponding to the cloud model library.
In an actual embodiment, the method for self-checking the health degree of the PC system can refine log detection; the method is divided into seven items firstly and then divided into a plurality of small items, and the matching degree of the context locking is added while the key words are locked, so that the problem that the description of the same abnormal item is inconsistent under different hardware of different systems is solved.
In a preferred embodiment, the self-test method further comprises:
s311, when the current detection time is an idle time period in a user idle time period table, performing component detection on hardware components of the PC system, and acquiring various parameter values of a hard disk of the PC system, memory pressure measurement abnormal item information and equipment SN;
acquiring the component detection score according to the component detection result of the PC system, specifically comprising:
if the equipment component or the system information of the PC system is not matched with the data in the cloud model library and no corresponding change record exists, marking the state information of the corresponding equipment component as abnormal;
If the data of each parameter value of the hard disk exceeds the threshold value in the cloud model library, marking the current parameter item information of the corresponding hard disk as abnormal;
if the information of the abnormal pressure measurement item in the memory is abnormal, marking the corresponding pressure measurement item as abnormal;
and obtaining the component detection score according to the total number of the component detection items and the number of the component detection abnormal items of the PC system.
In an actual embodiment, the method for self-checking the health degree of the PC system adds matching detection of factory information and current equipment information in component detection, so that problems caused by compatibility of domestic components and systems can be easily eliminated.
In a preferred embodiment, the self-test method further comprises:
s11, creating a cloud model library and a local model library for a PC system;
s12, creating at least one of the following in a cloud model library: a device model table, a device model detail table, a log information table, a log classification detail table and a health detection detail table;
s13, creating at least one of the following in a local model library: user behavior analysis table and user idle time slot table.
In a preferred embodiment, at least one of the following is created in the cloud model library, specifically including:
respectively creating log classification detail tables corresponding to each log type according to the log information tables; wherein the log categorization details table comprises at least one of: and starting a log detail table and a system log detail table.
In an actual embodiment, the PC system health degree self-checking method creates a local model library and a cloud model library; the cloud model library can normalize equipment information, self-checking rules and detection results, and the data of the cloud model library are solidification data; the local model library analyzes the user behavior, determines self-checking time, and the data of the local model library is personalized data; the two phases are combined to complement each other, so that the data accuracy is ensured, and the usability is improved.
In a preferred embodiment, the self-test method further comprises:
s5, uploading the current health score of the PC system to a cloud model library, writing the current health score into a health detection detail table, and updating the current health score into an equipment model detail table.
In an actual embodiment, the method for self-checking the health degree of the PC system can upload and record the detection result in the cloud model library, can realize the health detection operation of the whole service period from delivery to scrapping of the equipment, ensures the overall controllability of the equipment, and also greatly shortens the problem checking period of maintenance personnel.
In a preferred embodiment, the self-test method further comprises:
s21, acquiring total occupation information of the PC system in the current period, acquiring a total occupation data value of the PC system according to the total occupation information, and writing the total occupation data value into a total occupation field corresponding to the current period in a user behavior analysis table;
S22, acquiring the lowest occupation value time period in all the total occupation field records, and updating the lowest occupation value time period to an idle time period field of the idle time period table of the user.
In a preferred embodiment, the method for obtaining the parameter values of the hard disk of the PC system, the information of the abnormal memory pressure measurement item and the device SN specifically includes:
detecting a hard disk of a PC system by adopting an S.M.A.R.T tool to obtain various parameter values of the hard disk;
performing pressure measurement on a memory of a PC system by using a pressure measurement tool, and acquiring abnormal item information of the memory pressure measurement;
and comparing the equipment SN of the PC system with the equipment initialization information in the cloud model library, and judging the matching degree.
In an actual embodiment, the self-checking method for the health degree of the PC system can start a component detection module to detect the current time; and when the current time is judged to be the idle time period in the idle time period table of the user, starting the component detection module.
In a preferred embodiment, the method for acquiring the total occupation information of the current period of the PC system specifically includes:
s211, respectively acquiring occupied data average values corresponding to the occupancy rate of the current part at a plurality of time period points, and acquiring the occupancy rate average value of the current part according to all occupied data average values; wherein the current component occupancy comprises at least one of: processor occupancy rate, memory occupancy rate, display card occupancy rate, hard disk read-write occupancy rate and network card uplink and downlink occupancy rate of the PC system;
S212, setting corresponding occupancy weights for the occupancy components of the PC system respectively, and acquiring total occupancy data of the current period of the PC system according to all the occupancy weights and the occupancy average value of all the current components.
In a preferred embodiment, the method for acquiring the total occupation information of the current period of the PC system specifically includes:
respectively setting the processor occupancy rate, the memory occupancy rate, the display card occupancy rate, the hard disk read-write occupancy rate and the network card uplink and downlink occupancy rate of the PC system as、、、、；
Respectively obtaining occupied data average values corresponding to the occupancy rate of the current component at 12 time period points, and calculating the occupancy rate average value of the current component according to all occupied data average values as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein the current component occupancy comprises at least one of: processor occupancy rate, memory occupancy rate, display card occupancy rate, hard disk read-write occupancy rate and network card uplink and downlink occupancy rate of the PC system;
the processor occupancy rate of the PC system,The corresponding occupancy weights of the memory occupancy rate, the display card occupancy rate, the hard disk read-write occupancy rate and the network card uplink and downlink occupancy rate are respectively set as、、、、The total occupancy rate data of the current period of the PC system is calculated as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,。
in a preferred embodiment, the setting of the corresponding occupancy weights for the occupancy components of the PC system respectively specifically includes:
When the PC system performs equipment self-checking, the occupancy weights are set as follows:，，。
in a preferred embodiment, the component detection score is obtained according to the total number of component detection items and the number of component detection abnormal items of the PC system, and specifically includes:
setting the total health score of the PC system to bePart(s)The piece inspection health total score is set toThe log detection health total score is set asSo that；
Setting the total number of the component detection items to beThe number of abnormal items detected by the component is set asThe calculation unit detects the fraction as:。
in a preferred embodiment, corresponding log weights are set for various logs of the PC system, and log detection scores are obtained according to all log weights and log detection results of the PC system, which specifically includes:
setting 7 corresponding log weights of a starting log, a system log, a kernel log, a user log, a display log, a kernel buffer log and other types of logs as respectivelyAnd meet the following；
Setting log abnormal items asThe weight of the starting log isThe weight of the current abnormal item isThe lower limit of the matching degree isThe method comprises the steps of carrying out a first treatment on the surface of the When (when)When the log is suspected to be abnormal, log abnormal items are calculatedThe deduction score of (2) is:the method comprises the steps of carrying out a first treatment on the surface of the When (when) To confirm the exception, log exception entries are computedThe deduction score of (2) is:；
setting upThe total number of abnormal items detected for the weight overlapping set and the log of various types of logs isThe number of suspected abnormal items isConfirming the number of abnormal items asThe method comprises the steps of carrying out a first treatment on the surface of the An abnormal item is detected for any one log,is set to satisfy the matching degree of more than or equal toIs a collection of abnormal items; the log detection score is calculated as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein the method comprises the steps of。
In a preferred embodiment, the sum of the component detection score and the log detection score is set as the current health score of the PC system, which specifically includes:
the current health score of the PC system is calculated as follows:。
in a preferred embodiment, the self-test method further comprises:
acquiring the current health score of the PC system in real time through a cloud model library;
when the equipment corresponding to the PC system is not used any more, the equipment state field in the cloud model library is changed to be scrapped.
In an actual embodiment, the method for self-checking the health degree of the PC system provided by the embodiment of the invention can be implemented through the following six parts of modules: model creation, behavior analysis, component detection, log detection, health degree calculation, result pushing and display;
the function of each module is as follows:
Model creation: the method is used for creating a model, a component, log analysis and health record model of the domestic PC. The method is mainly divided into two parts: a cloud model library and a local model library;
the cloud model library mainly comprises: a device model table, a device model detail table, a log information classification detail table and a health detection detail table;
the local model library mainly comprises: user behavior analysis table and user idle time slot table.
Behavioral analysis: and automatically recording the current common time period of the user according to the habit of the user, and screening the idle time period so as to detect in the idle time period.
And (3) detecting parts: the method comprises the steps of acquiring the component information of the current machine type from the cloud according to the current machine type, comparing the component information with the component information of the local equipment, and mainly checking compatibility problems possibly caused by component replacement. In addition, the memory is subjected to memory pressure measurement, and meanwhile, the S.M.A.R.T. detection is performed on the hard disk.
And (3) log detection: the detection of the components is to detect hardware information of a domestic PC; the log detection is to detect software problems under the system, the most used domestic systems are Kylin and UOS at present, and the main system logs are under the conditions of/var/log;
the logs are divided into seven categories here: a boot log (boot. Log), a system log (syslog, message), a kernel log (kern. Log), a user log (user. Log), a display log (xorg. Log), kernel buffer information (dmesg), other logs;
For different logs, respectively establishing a log classification detail table, which comprises: anomaly description, anomaly keywords, anomaly context, anomaly weights, and the like.
And (3) calculating the health degree: and calculating the health degree score of the current equipment according to the results of the component detection and the log detection.
Pushing and displaying results: and reporting the health score and the detection details to a cloud model database.
The user can check the last health detection result locally on the equipment and also check the historical health detection result of the equipment. The cloud end can always store the historical health result of the equipment in use, and the related health information of the current equipment cannot be deleted until the current equipment is set to be scrapped by maintenance personnel. The cloud end can track the health state and information of the current equipment all the time from the delivery of the equipment to the arrival of the maintenance age or the rejection of the equipment, and the state controllability of the whole service cycle of the equipment is ensured.
As shown in fig. 2, the specific implementation process is as follows:
stage one:
1) And starting a model creation module, wherein a model library is mainly divided into a cloud model and a local model.
Creating the following list in the cloud model library:
creating a device model table comprising: model number, model name, model number field, initializing model number and model name;
Creating a device model detail table comprising: SN, processor, memory, graphics card, motherboard, display, hard disk s.m.a.r.t. parameters and threshold, network card, system, change log, health score, equipment status field;
initializing hardware information and system versions of all devices under the current model, so that: the health score is initialized to 100; the equipment state is normal; when the device hardware and system are not changed, the change record field remains empty.
If the device has part information change, a change record is reserved, and table contents are updated.
Creating a log information table comprising: classification number, classification name, classification weight;
initializing classification data at creation time as: the system comprises seven types of logs including a start log (boot. Log), a system log (syslog, message), a kernel log (kern. Log), a user log (user. Log), a display log (xorg. Log), a kernel buffer log (dmesg) and other logs, and weights are respectively given.
Creating a log classification detail table, and referring to classification data in the log information table, respectively creating log detail tables corresponding to respective types;
for example: creating a starting log detail table, wherein the fields are as follows: numbering, anomaly description, recognition keywords, recognition context, lower limit of matching degree, upper limit of matching degree and anomaly weight;
Other classified log detail fields should be consistent with the current log table field.
Meanwhile, initializing a common log abnormality judgment method in each table;
for example: recording numbers (such as 1), exception descriptions (such as read-write errors), identification keywords (such as I/OError), identification contexts (such as Disk read inode block/Disk read block/Disk read error/Disk write error), lower matching degree limits (such as 50%), upper matching degree limits (such as 80%) and exception weights (such as 0.1) in a system log detail table;
here, it should be noted that the sum of the anomaly weights in the current table should be guaranteed to be 1.
Creating a health detection details table comprising: device SN, time of detection, health score, detection result detail field.
Creating the following list in the local model library:
creating a user behavior analysis table comprising: time period (0 time period is recorded in the unit of hours from 0 time to 1 time, and so on to 23 time periods), processor occupation, memory occupation, display card occupation, hard disk read-write occupation, network card uplink and downlink occupation and total occupation fields;
creating a user idle period table comprising: user number, user name, idle period, processor weight, memory weight, graphics card weight, hard disk weight, network card weight.
Stage two:
2) Starting a behavior analysis module, and writing the current login user name of the system into a user idle time period table;
meanwhile, in the current period, processor occupation (such as through a top command), memory occupation (such as through a free command), display card occupation (such as through cat/proc/gpunafo), hard disk read-write occupation (such as through vmstat-d) and network card uplink and downlink occupation (such as through cat/proc/net/dev) under the system are acquired every 5 minutes.
3) Setting the corresponding parameters of processor occupation, memory occupation, display card occupation, hard disk read-write occupation and network card uplink and downlink occupation as respectively、、、、；
Acquiring the average value of 12 acquisition points in the current period occupied by the processor, and obtaining the average value occupied by the processor as follows:the other components are the same.
Respectively setting weight parameters of a processor, a memory, a display card, a hard disk and a network card as、、、、The total system occupancy for the current time period can be obtained as:
wherein, the liquid crystal display device comprises a liquid crystal display device,；
if the device performs self-checking, the dependence on the processor and the memory is higher, the hard disk is inferior, the display card and the network card are inferior, and the corresponding weight value can be set as，，。
4) And obtaining the total occupation of the current time period, and recording the total occupation of the current time period in a user behavior analysis table.
If the old total occupation data exist in each time period in the user behavior analysis table, adding the old numerical value with the new numerical value obtained in the previous step to obtain a mean value, and writing the obtained result into the total occupation field.
And then acquiring the lowest time period in the total occupied field record, and updating the idle time period field of the idle time period table of the user.
Stage three:
5) Starting a component detection module to detect the current time; when the current time is judged to be the idle time in the idle time table of the user, starting a component detection module to acquire the SN, the processor, the memory, the display card, the main board, the display, the hard disk, the network card and the system information of the current equipment.
After the information detection is completed, an S.M.A.R.T tool (for example: smartctl) is used for detecting the hard disk, and the values of various parameters of the hard disk are obtained. And using a pressure measurement tool (memtester) to measure pressure of the memory, and acquiring a pressure measurement abnormal item in the memory. And comparing the SN with the equipment initialization information in the cloud model library.
6) After the detection of the component is completed, starting a log detection module;
under the system/var/log/catalog, the log files are respectively searched according to the sequence of a starting log (boot. Log), a system log (syslog, message), a kernel log (kern. Log), a user log (user. Log), a display log (xorg. Log), a kernel buffer log (dmesg) and other logs, and whether the logs generated on the same day have abnormal keywords which are the same as the log classification detail list corresponding to the cloud model library or not is searched.
7) If the same keyword exists, the context of the keyword appears in the search log file, and then the context is compared with the context text segment content of the abnormal item in the cloud model library to obtain the matching degree (the judgment of the matching degree can be realized by adopting but not limited to Chinese and English word segmentation technology of NLP).
If the matching degree is greater than the lower limit of the matching degree but less than the upper limit of the matching degree, marking the matching degree as suspected abnormality; if the value is greater than the upper limit, marking as abnormal confirmation; if the matching degree is smaller than the lower limit of the matching degree, marking that no abnormality exists.
Stage four:
8) Setting the total health score of domestic PC asTotal part health score ofThe log detection health total score isSatisfies the following conditions；
If it isCan be set to，。
9) Starting a health degree calculation module to obtain a component detection result;
if the situation that the equipment component or the system information is not matched with the data in the cloud model library and the corresponding change record does not exist, marking the component as abnormal;
if the data in the S.M.A.R.T parameter detection of the hard disk exceeds the threshold value in the cloud model library, marking the current parameter item of the hard disk as abnormal;
if the memory pressure is abnormal, the pressure measurement item is marked as abnormal.
Setting the total number of the detection items of the component as The number of abnormal items detected by the component isThe available component detection scores were:the method comprises the steps of carrying out a first treatment on the surface of the Wherein the method comprises the steps of。
10 Obtaining the result of the log detection item, and setting the log abnormal item as；
If the log belongs to the starting log, the starting log has the weight ofThe weight of the current abnormal item isThe lower limit of the matching degree isCan obtain abnormal log itemsThe deduction score of (2) is:
(L is suspected abnormality)
(L is confirmation of abnormality)
11 The classification weights of the start, the system, the kernel, the user, the display, the kernel buffer and other logs are respectively set as followsAnd meet the following；
Is provided withFor the weight overlapping set of various logs, the total number of abnormal log detection items isThe number of suspected abnormal items isConfirming the number of abnormal items asThe method comprises the steps of carrying out a first treatment on the surface of the An abnormal item is detected for any one log,is set to satisfy the matching degree of more than or equal toIs a collection of abnormal items; the available log detection scores are:wherein the method comprises the steps of；
12 According to the results of the previous step and step (9), the health score of the current device may be the sum of the component detection score and the log detection score, that is, the health score of the current device is:
stage five:
13 Starting a result pushing and displaying module, combining the results of abnormal component detection items and abnormal log detection items, uploading the equipment SN, the detection time, the detection results and the health scores to a cloud model library respectively, and writing the results into a health detection detail table.
And updating the latest health score to the equipment model detail table.
14 The health score of the current equipment can be obtained from the cloud model library in real time, and the historical health detection result can be checked.
15 If the equipment is not used any more, the equipment status field in the cloud model library can be changed into scrapped state, and the model creation module can remove the health detection history of the equipment together.
In conclusion, the self-checking method for the health degree of the PC system realizes self-checking of the health degree of the equipment in a user noninductive state; meanwhile, the health information of the equipment is stored in the cloud database, so that the time for a user to check the problem is shortened, and the maintenance efficiency of operation and maintenance personnel is greatly improved;
it has the following advantages:
1) Two model libraries are created, the cloud model library is used for standardizing equipment information, setting self-checking rules and storing detection results, and the local model library is used for analyzing user behavior information and determining self-checking time.
2) The logs are divided in a refined mode, and the problems caused by inconsistent parts and systems can be ignored when the abnormal items are determined accurately by adopting a keyword, context, matching degree and weight, so that the log detection has higher universality.
3) After the compatibility problem is checked through hardware information matching, the detection result is uploaded to the cloud for storage, so that the health state of the whole equipment is in a tracking state, and the problems can be found and checked in time.
It should be noted that, although the steps in the flowchart are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least a portion of the steps in the flowcharts may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order in which the sub-steps or stages are performed is not necessarily sequential, and may be performed in turn or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Embodiment two:
as shown in fig. 3, the embodiment of the present invention further provides a self-checking device for PC system health, configured to implement the foregoing self-checking method for PC system health, where the self-checking device includes:
a component detection score acquisition unit for acquiring a component detection score based on a component detection result of the PC system;
the system comprises a log detection score acquisition unit, a log detection score acquisition unit and a log detection unit, wherein the log detection score acquisition unit is used for respectively setting corresponding log weights for various logs of the PC system and acquiring log detection scores according to all the log weights and log detection results of the PC system;
The current health degree score obtaining unit is used for setting the sum of the component detection score and the log detection score as the current health degree score of the PC system.
In a preferred embodiment, the self-test device further comprises:
an abnormal keyword judgment unit configured to: judging whether the current log file has the same abnormal keywords as the log classification detail table corresponding to the cloud model library or not;
a matching degree value obtaining unit, configured to:
if the abnormal keyword exists, comparing the context of the abnormal keyword in the current log file with the context text segment content of the abnormal term in the cloud model library to obtain a matching degree value;
an abnormality marking unit configured to:
if the matching degree value is larger than the lower limit value of the matching degree but smaller than the upper limit value of the matching degree, marking the marking information in the current log file as suspected abnormality;
if the matching degree value is larger than the matching degree upper limit value, marking the marking information in the current log file as abnormal confirmation;
and if the matching degree value is smaller than the lower limit value of the matching degree, marking the marking information in the current log file as no abnormality.
In a preferred embodiment, the log detection score acquisition unit includes a log weight setting unit for:
Setting 7 corresponding log weights for a starting log, a system log, a kernel log, a user log, a display log, a kernel buffer log and other types of logs in the PC system respectively, so that the sum of the 7 corresponding log weights is 1;
the self-checking device further comprises a log file retrieving unit for:
and sequentially retrieving the log files of the corresponding types according to the sequence of the starting log, the system log, the kernel log, the user log, the display log, the kernel buffer log and the other types of logs, and judging whether the log files of the corresponding types have abnormal keywords which are the same as the log classification detail list corresponding to the cloud model library.
In a preferred embodiment, the self-test device further comprises a component detection information acquisition unit for:
when the current detection time is an idle time in a user idle time table, performing component detection on hardware components of the PC system, and acquiring various parameter values of a hard disk of the PC system, memory pressure measurement abnormal item information and equipment SN;
the component detection score acquisition unit is further configured to:
if the equipment component or the system information of the PC system is not matched with the data in the cloud model library and no corresponding change record exists, marking the state information of the corresponding equipment component as abnormal;
If the data of each parameter value of the hard disk exceeds the threshold value in the cloud model library, marking the current parameter item information of the corresponding hard disk as abnormal;
if the information of the abnormal pressure measurement item in the memory is abnormal, marking the corresponding pressure measurement item as abnormal;
and obtaining the component detection score according to the total number of the component detection items and the number of the component detection abnormal items of the PC system.
In a preferred embodiment, the self-test device further comprises a model library creation unit for:
creating a cloud model library and a local model library for the PC system;
creating at least one of the following in a cloud model library: a device model table, a device model detail table, a log information table, a log classification detail table and a health detection detail table;
creating in a local model library at least one of: user behavior analysis table and user idle time slot table.
In a preferred embodiment, the self-test device further comprises a current health score updating unit for:
uploading the current health score of the PC system to a cloud model library, writing the current health score into a health detection detail table, and updating the current health score into an equipment model detail table.
In a preferred embodiment, the self-checking device further comprises a user idle period acquisition unit for:
Acquiring total occupation information of the PC system in the current period, acquiring a total occupation data value of the PC system according to the total occupation information, and writing the total occupation data value into a total occupation field corresponding to the current period in a user behavior analysis table;
and acquiring the lowest occupation value time period in all the total occupation field records, and updating the lowest occupation value time period to an idle time period field of the idle time period table of the user.
In a preferred embodiment, the user idle period acquisition unit is further configured to:
respectively acquiring occupied data average values corresponding to the occupancy rate of the current part at a plurality of time period points, and acquiring the occupancy rate average value of the current part according to all occupied data average values; wherein the current component occupancy comprises at least one of: processor occupancy rate, memory occupancy rate, display card occupancy rate, hard disk read-write occupancy rate and network card uplink and downlink occupancy rate of the PC system;
setting corresponding occupancy weights for the occupancy components of the PC system respectively, and acquiring total occupancy data of the current period of the PC system according to all the occupancy weights and the occupancy average value of all the current components.
In a preferred embodiment, the component detection score acquisition unit is further configured to:
setting the total health score of the PC system to be The component detection health total score is set asThe log detection health total score is set asSo that；
Setting the total number of the component detection items to beThe number of abnormal items detected by the component is set asThe calculation unit detects the fraction as:。
in a preferred embodiment, the log detection score acquisition unit is further configured to:
setting 7 corresponding log weights of a starting log, a system log, a kernel log, a user log, a display log, a kernel buffer log and other types of logs as respectivelyAnd meet the following；
Setting the weight of the current abnormal item asThe lower limit of the matching degree isSetting upThe total number of abnormal items detected for the weight overlapping set and the log of various types of logs isThe number of suspected abnormal items isConfirming abnormalityThe number of items isThe method comprises the steps of carrying out a first treatment on the surface of the An abnormal item is detected for any one log,is set to satisfy the matching degree of more than or equal toIs a collection of abnormal items;
the log detection score is calculated as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein the method comprises the steps of。
In a preferred embodiment, the current health score acquisition unit is further configured to:
the current health score of the PC system is calculated as follows:。
in a preferred embodiment, the user idle period acquisition unit is further configured to:
respectively setting the processor occupancy rate, the memory occupancy rate, the display card occupancy rate, the hard disk read-write occupancy rate and the network card uplink and downlink occupancy rate of the PC system as 、、、、；
Respectively obtaining the occupancy rate of the current part to be 12The occupied data average value corresponding to the time period point is calculated as the occupied rate average value of the current component according to all occupied data average values, and the occupied rate average value is:the method comprises the steps of carrying out a first treatment on the surface of the Wherein the current component occupancy comprises at least one of: processor occupancy rate, memory occupancy rate, display card occupancy rate, hard disk read-write occupancy rate and network card uplink and downlink occupancy rate of the PC system;
the processor occupancy rate, the memory occupancy rate, the display card occupancy rate, the hard disk read-write occupancy rate and the occupancy rate weights corresponding to the network card uplink and downlink occupancy rates of the PC system are respectively set as、、、、The total occupancy rate data of the current period of the PC system is calculated as follows:wherein, the liquid crystal display device comprises a liquid crystal display device,。
in a preferred embodiment, the user idle period acquisition unit is further configured to:
when the PC system performs equipment self-checking, the occupancy weights are set as follows:，，。
in a preferred embodiment, the self-test device further comprises a discard state setting unit for:
acquiring the current health score of the PC system in real time through a cloud model library;
when the equipment corresponding to the PC system is not used any more, the equipment state field in the cloud model library is changed to be scrapped.
In a preferred embodiment, the model library creating unit is further configured to:
Respectively creating log classification detail tables corresponding to each log type according to the log information tables; wherein the log categorization details table comprises at least one of: and starting a log detail table and a system log detail table.
In a preferred embodiment, the component detection information acquisition unit is further configured to:
detecting a hard disk of a PC system by adopting an S.M.A.R.T tool to obtain various parameter values of the hard disk;
performing pressure measurement on a memory of a PC system by using a pressure measurement tool, and acquiring abnormal item information of the memory pressure measurement;
and comparing the equipment SN of the PC system with the equipment initialization information in the cloud model library, and judging the matching degree.
For specific limitations of the above apparatus, reference may be made to the limitations of the method described above, which are not repeated here.
Each of the modules in the above apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware, or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
The computer device may be a terminal, as shown in fig. 4, which includes a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It is to be understood that the structures shown in the above figures are merely block diagrams of some of the structures associated with the present invention and are not limiting of the computer devices to which the present invention may be applied, and that a particular computer device may include more or less components than those shown, or may combine some of the components, or have a different arrangement of components.
Implementation of all or part of the flow in the above-described embodiment methods may be accomplished by a computer program that instructs related hardware, and the computer program may be stored in a non-volatile computer readable storage medium, and the computer program may include the flow in the above-described embodiment methods when executed.
Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It should be noted that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.

Claims (19)

1. The self-checking method for the health degree of the PC system is characterized by comprising the following steps of:
acquiring a component detection score according to a component detection result of the PC system;
setting corresponding log weights for various logs of the PC system respectively, and acquiring log detection scores according to all the log weights and log detection results of the PC system;
setting the sum of the component detection score and the log detection score as the current health score of the PC system; corresponding log weights are respectively set for various logs of the PC system, and log detection scores are obtained according to all the log weights and log detection results of the PC system, and specifically comprise the following steps:
Setting the total health score of the PC system as S and the total health score of the component detection as S 1 The log detection health total score is set as S 2 So that S 1 +S 2 ＝S；
Setting 7 corresponding log weights of a starting log, a system log, a kernel log, a user log, a display log, a kernel buffer log and other types of logs as l respectively 1 、l 2 、l 3 、l 4 、l 5 、l 6 、l 7 And satisfy l 1 +l 2 +l 3 +l 4 +l 5 +l 6 +l 7 ＝1；
Setting log abnormal item as L and the weight of the starting log as L 1 The weight of the current abnormal item is w L The lower limit of the matching degree is m L The method comprises the steps of carrying out a first treatment on the surface of the When L is suspected abnormal, calculating deduction score of log abnormal item L as follows: f (f) L ＝S 2 *l 1 *w L *m L The method comprises the steps of carrying out a first treatment on the surface of the When L is the confirmed exception, the deduction score of the log exception item L is calculated as follows: f (f) L ＝S 2 *l 1 *w L ；
Setting j= { l 1 ，l 2 ，...，l 7 The number of the suspected abnormal items is x, and the number of the abnormal items is confirmed to be y; for any one log detection anomaly, Γ (L) is set to satisfy a matching degree of m or more L Is a collection of abnormal items; the log detection score is calculated as follows: wherein J is E J,)>
2. The method for self-checking the health of a PC system according to claim 1, further comprising:
judging whether the current log file has the same abnormal keywords as the log classification detail table corresponding to the cloud model library or not;
If the abnormal keyword exists, comparing the context of the abnormal keyword in the current log file with the context text segment content of the abnormal term in the cloud model library to obtain a matching degree value;
if the matching degree value is larger than the lower matching degree limit value but smaller than the upper matching degree limit value, marking the marking information in the current log file as suspected abnormality; if the matching degree value is larger than the matching degree upper limit value, marking the marking information in the current log file as abnormal confirmation; and if the matching degree value is smaller than the lower limit value of the matching degree, marking the marking information in the current log file as no abnormality.
3. The method for self-checking the health of a PC system according to claim 2, wherein,
the self-checking method further comprises the following steps:
and sequentially retrieving the log files of the corresponding types according to the sequence of the starting log, the system log, the kernel log, the user log, the display log, the kernel buffer log and the other types of logs, and judging whether the log files of the corresponding types have abnormal keywords which are the same as the log classification detail list corresponding to the cloud model library.
4. The method for self-checking the health of a PC system according to claim 3, further comprising:
When the current detection time is an idle time in a user idle time table, performing component detection on hardware components of the PC system, and acquiring various parameter values of a hard disk of the PC system, memory pressure measurement abnormal item information and equipment SN;
obtaining a component detection score according to a component detection result of the PC system, wherein the component detection score specifically comprises:
if the equipment component or the system information of the PC system is not matched with the data in the cloud model library and no corresponding change record exists, marking the state information of the corresponding equipment component as abnormal;
if the data of each parameter value of the hard disk exceeds the threshold value in the cloud model library, marking the current parameter item information of the corresponding hard disk as abnormal;
if the memory pressure measurement abnormal item information is abnormal, marking the corresponding pressure measurement item as abnormal;
and acquiring the component detection score according to the total number of the component detection items and the number of the component detection abnormal items of the PC system.
5. The method for self-checking the health of a PC system according to claim 4, further comprising:
creating a cloud model library and a local model library for the PC system;
creating at least one of the following in the cloud model library: a device model table, a device model detail table, a log information table, a log classification detail table and a health detection detail table;
Creating in the local model library at least one of: user behavior analysis table and user idle time slot table.
6. The method for self-checking the health of a PC system according to claim 5, further comprising:
uploading the current health score of the PC system to the cloud model library, writing the health detection detail table, and updating the current health score to the equipment model detail table.
7. The method for self-checking the health of a PC system according to claim 6, further comprising:
acquiring total occupation information of the PC system in the current period, acquiring a total occupation data value of the PC system according to the total occupation information, and writing the total occupation data value into a total occupation field corresponding to the current period in the user behavior analysis table;
and acquiring the lowest occupation value time period in all the total occupation field records, and updating the lowest occupation value time period to an idle time period field of the idle time period table of the user.
8. The method for self-checking the health of a PC system according to claim 7, wherein the obtaining the total occupancy information of the PC system in the current period of time specifically includes:
Respectively acquiring occupied data average values corresponding to the occupancy rate of the current part at a plurality of time period points, and acquiring the occupancy rate average value of the current part according to all occupied data average values; wherein the current component occupancy comprises at least one of: the processor occupancy rate, the memory occupancy rate, the display card occupancy rate, the hard disk read-write occupancy rate and the network card uplink and downlink occupancy rate of the PC system;
setting corresponding occupancy weights for the occupancy components of the PC system respectively, and acquiring total occupancy data of the current period of the PC system according to all the occupancy weights and the occupancy average value of all the current components.
9. The method for self-checking the health of a PC system according to claim 4 or 8, wherein the obtaining the component detection score according to the total number of component detection items and the number of component detection abnormal items of the PC system specifically includes:
setting the total number of the component detection items as u, setting the number of the component detection abnormal items as v, and calculating the component detection score as follows:
10. the method for self-checking the health of a PC system according to claim 1, wherein the sum of the component detection score and the log detection score is set as a current health score of the PC system, specifically comprising:
Calculating the current health score of the PC system as:
11. the method for self-checking the health of a PC system according to claim 10, wherein the obtaining the total occupancy information of the PC system in the current period of time specifically includes:
the processor occupancy rate, the memory occupancy rate, the display card occupancy rate, the hard disk read-write occupancy rate and the network of the PC systemThe occupancy rate of the uplink and the downlink of the card is respectively set as O c 、O m 、O g 、O d 、O n ；
Respectively obtaining occupied data average values corresponding to the occupancy rate of the current component at 12 time period points, and calculating the occupancy rate average value of the current component according to all occupied data average values as follows:wherein the current component occupancy comprises at least one of: the processor occupancy rate, the memory occupancy rate, the display card occupancy rate, the hard disk read-write occupancy rate and the network card uplink and downlink occupancy rate of the PC system;
the processor occupancy rate, the memory occupancy rate, the display card occupancy rate, the hard disk read-write occupancy rate and the occupancy rate weights corresponding to the network card uplink and downlink occupancy rates of the PC system are respectively set as w c 、w m 、w g 、w d 、w n The total occupancy rate data of the current period of the PC system is calculated as follows:
wherein w is c +w m +w g +w d +w n ＝1。
12. The method for self-checking the health of a PC system according to claim 11, wherein the setting of the corresponding occupancy weights for the occupied parts of the PC system respectively comprises:
When the PC system performs equipment self-checking, setting the occupancy weights as follows: w (w) c ＝w m ＝0.3，w d ＝0.2，w g ＝w n ＝0.1。
13. The method for self-checking the health of a PC system according to claim 2, further comprising:
acquiring the current health score of the PC system in real time through the cloud model library;
and when the equipment corresponding to the PC system is not used any more, changing the equipment state field in the cloud model library into scrapped equipment.
14. The method for self-checking the health of a PC system according to claim 13, wherein at least one of the following is created in the cloud model library, specifically including:
respectively creating log classification detail tables corresponding to each log type according to the log information tables; wherein the log categorization details table comprises at least one of: and starting a log detail table and a system log detail table.
15. The method for self-checking the health of a PC system according to claim 14, wherein obtaining the values of various parameters of a hard disk of the PC system, the information of abnormal memory pressure measurement items, and the device SN specifically includes:
detecting a hard disk of the PC system by using an S.M.A.R.T tool to obtain various parameter values of the hard disk;
Using a pressure testing tool to press-test the memory of the PC system and acquiring abnormal item information of the memory pressure test;
and comparing the equipment SN of the PC system with equipment initialization information in the cloud model library, and judging the matching degree.
16. A self-checking device for the health degree of a PC system, which is used for implementing a self-checking method for the health degree of a PC system according to any one of claims 1 to 15, and comprises:
a component detection score acquisition unit configured to acquire a component detection score according to a component detection result of the PC system;
the log detection score acquisition unit is used for respectively setting corresponding log weights for various logs of the PC system and acquiring log detection scores according to all the log weights and log detection results of the PC system;
a current health score obtaining unit, configured to set a sum of the component detection score and the log detection score as a current health score of the PC system;
wherein the log detection score acquisition unit is further configured to:
setting the total health score of the PC system as S and the total health score of the component detection as S 1 The log detection health total score is set as S 2 So that S 1 +S 2 ＝S；
Setting 7 corresponding log weights of a starting log, a system log, a kernel log, a user log, a display log, a kernel buffer log and other types of logs as l respectively 1 、l 2 、l 3 、l 4 、l 5 、l 6 、l 7 And satisfy l 1 +l 2 +l 3 +l 4 +l 5 +l 6 +l 7 ＝1；
Setting log abnormal item as L and the weight of the starting log as L 1 The weight of the current abnormal item is w L The lower limit of the matching degree is m L The method comprises the steps of carrying out a first treatment on the surface of the When L is suspected abnormal, calculating deduction score of log abnormal item L as follows: f (f) L ＝S 2 *l 1 *w L *m L The method comprises the steps of carrying out a first treatment on the surface of the When L is the confirmed exception, the deduction score of the log exception item L is calculated as follows: f (f) L ＝S 2 *l 1 *w L ；
Setting j= { l 1 ，l 2 ，...，l 7 The number of the suspected abnormal items is x, and the number of the abnormal items is confirmed to be y; for any one log detection anomaly, Γ (L) is set to satisfy a matching degree of m or more L Is a collection of abnormal items; the log detection score is calculated as follows: wherein J is E J,)>
17. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the PC system health self-test method according to any one of claims 1-15 when the computer program is executed.
18. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the PC system health self-checking method according to any one of claims 1-15.
19. A PC system comprising a domestic PC that implements a health self-test by a PC system health self-test method as claimed in any one of claims 1 to 15.
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