CN113806171A - Server health assessment method, system, equipment and medium - Google Patents
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Abstract
The invention discloses a server health assessment method, which comprises the following steps: acquiring a plurality of configured evaluation parameters; acquiring corresponding historical data according to the plurality of evaluation parameters; generating a corresponding evaluation model according to the corresponding historical data; acquiring current real-time data corresponding to the evaluation parameters of the server to be evaluated; and inputting the real-time data into the evaluation model to evaluate the health degree of the server to be evaluated. The invention also discloses a system, a computer device and a readable storage medium. According to the technical scheme provided by the invention, the health state calculation method suitable for a specific user scene is defined through the emphasis configuration of the server health state index items, the health state prediction is carried out by combining the performance monitoring characteristic index item data of the corresponding server, the preparation of the server health state in the specific scene can be effectively improved, and meanwhile, the server equipment with potential fault risk is screened and early warned.
Description
Technical Field
The invention relates to the field of servers, in particular to a server health assessment method, a system, equipment and a storage medium.
Background
With the development of information technology, the equipment scale of a data center is larger and larger, the operation and maintenance difficulty of the equipment is also larger and larger, when the server generates an alarm, the server needs to be checked and maintained aiming at the alarm, but for part of clients, under a specific scene, some alarms do not affect the use of the clients, and some slight alarms are concerned by the clients; in a common monitoring system, the alarms are only divided into different levels, so that whether the alarms are false alarms or not cannot be effectively distinguished, and meanwhile, the related performance monitoring indexes of the server are only used as display type data and are not effectively utilized. In an actual data center scene, there is no accurate definition of the health state of the server, so that whether the server equipment has hidden danger or not cannot be accurately judged.
Disclosure of Invention
In view of the above, in order to overcome at least one aspect of the above problems, an embodiment of the present invention provides a server health assessment method, including:
acquiring a plurality of configured evaluation parameters;
acquiring corresponding historical data according to the plurality of evaluation parameters;
generating a corresponding evaluation model according to the corresponding historical data;
acquiring current real-time data corresponding to the evaluation parameters of the server to be evaluated;
and inputting the real-time data into the evaluation model to evaluate the health degree of the server to be evaluated.
In some embodiments, generating a corresponding assessment model from the historical data further comprises:
dividing the historical data into a training set and a test set;
and training the evaluation model by using the training set and testing the evaluation model by using the testing set.
In some embodiments, further comprising:
acquiring all evaluation parameters;
acquiring corresponding data according to all the evaluation parameters;
and cleaning the corresponding data and then storing the cleaned data as historical data.
In some embodiments, further comprising:
and responding to the situation that the health degree of the server to be evaluated is smaller than a threshold value, performing differential display and early warning through a preset way.
Based on the same inventive concept, according to another aspect of the present invention, an embodiment of the present invention further provides a server health assessment system, including:
the configuration module is configured to acquire a plurality of configured evaluation parameters;
the first acquisition module is configured to acquire corresponding historical data according to a plurality of evaluation parameters;
a generation module configured to generate a corresponding evaluation model from the corresponding historical data;
the acquisition module is configured to acquire the current real-time data corresponding to the evaluation parameters of the server to be evaluated;
and the evaluation module is configured to input the real-time data into the evaluation model so as to evaluate the health degree of the server to be evaluated.
In some embodiments, the generation module is further configured to:
dividing the historical data into a training set and a test set;
and training the evaluation model by using the training set and testing the evaluation model by using the testing set.
In some embodiments, the system further comprises a second obtaining module configured to obtain all the evaluation parameters;
acquiring corresponding data according to all the evaluation parameters;
and cleaning the corresponding data and then storing the cleaned data as historical data.
In some embodiments, further comprising a notification module configured to:
and responding to the situation that the health degree of the server to be evaluated is smaller than a threshold value, performing differential display and early warning through a preset way.
Based on the same inventive concept, according to another aspect of the present invention, an embodiment of the present invention further provides a computer apparatus, including:
at least one processor; and
a memory storing a computer program operable on the processor, wherein the processor executes the program to perform the steps of:
acquiring a plurality of configured evaluation parameters;
acquiring corresponding historical data according to the plurality of evaluation parameters;
generating a corresponding evaluation model according to the corresponding historical data;
acquiring current real-time data corresponding to the evaluation parameters of the server to be evaluated;
and inputting the real-time data into the evaluation model to evaluate the health degree of the server to be evaluated.
In some embodiments, generating a corresponding evaluation model from the historical data further comprises:
dividing the historical data into a training set and a test set;
and training the evaluation model by using the training set and testing the evaluation model by using the testing set.
In some embodiments, further comprising:
acquiring all evaluation parameters;
acquiring corresponding data according to all the evaluation parameters;
and cleaning the corresponding data and then storing the cleaned data as historical data.
In some embodiments, further comprising:
and responding to the situation that the health degree of the server to be evaluated is smaller than a threshold value, performing differential display and early warning through a preset way.
Based on the same inventive concept, according to another aspect of the present invention, an embodiment of the present invention further provides a computer-readable storage medium storing a computer program which, when executed by a processor, performs the steps of:
acquiring a plurality of configured evaluation parameters;
acquiring corresponding historical data according to the plurality of evaluation parameters;
generating a corresponding evaluation model according to the corresponding historical data;
acquiring current real-time data corresponding to the evaluation parameters of the server to be evaluated;
and inputting the real-time data into the evaluation model to evaluate the health degree of the server to be evaluated.
In some embodiments, generating a corresponding evaluation model from the historical data further comprises:
dividing the historical data into a training set and a test set;
and training the evaluation model by using the training set and testing the evaluation model by using the testing set.
In some embodiments, further comprising:
acquiring all evaluation parameters;
acquiring corresponding data according to all the evaluation parameters;
and cleaning the corresponding data and then storing the cleaned data as historical data.
In some embodiments, further comprising:
and responding to the situation that the health degree of the server to be evaluated is smaller than a threshold value, performing differential display and early warning through a preset way.
The invention has one of the following beneficial technical effects: according to the scheme provided by the invention, the health state calculation method suitable for a specific user scene is defined through the emphasis configuration of the server health state index item, and the health state prediction is carried out by combining the performance monitoring characteristic index item data of the corresponding server, so that the preparation of the server health state in the specific scene can be effectively improved, and meanwhile, the server equipment with potential fault risk is screened and early warned.
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, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other embodiments can be obtained by using the drawings without creative efforts.
FIG. 1 is a schematic flow chart illustrating a server health assessment method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a server health assessment apparatus according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a server health assessment system according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a computer device provided in an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a computer-readable storage medium according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following embodiments of the present invention are described in further detail with reference to the accompanying drawings.
It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are used for distinguishing two entities with the same name but different names or different parameters, and it should be noted that "first" and "second" are merely for convenience of description and should not be construed as limitations of the embodiments of the present invention, and they are not described in any more detail in the following embodiments.
According to an aspect of the present invention, an embodiment of the present invention provides a server health assessment method, as shown in fig. 1, which may include the steps of:
s1, acquiring a plurality of configured evaluation parameters;
s2, acquiring corresponding historical data according to the plurality of evaluation parameters;
s3, generating a corresponding evaluation model according to the corresponding historical data;
s4, acquiring the current real-time data corresponding to the evaluation parameters of the server to be evaluated;
and S5, inputting the real-time data into the evaluation model to evaluate the health degree of the server to be evaluated.
In some embodiments, generating a corresponding evaluation model from the historical data further comprises:
dividing the historical data into a training set and a test set;
and training the evaluation model by using the training set and testing the evaluation model by using the testing set.
In some embodiments, further comprising:
acquiring all evaluation parameters;
acquiring corresponding data according to all the evaluation parameters;
and cleaning the corresponding data and then storing the cleaned data as historical data.
In some embodiments, further comprising:
and responding to the situation that the health degree of the server to be evaluated is smaller than a threshold value, performing differential display and early warning through a preset way.
The technical scheme provided by the invention can be used for automatically configuring the health state characteristic index item of the server according to the requirements of a user on a specific scene by acquiring the data information of the current equipment performance monitoring and other relevant health state characteristics of the server, combining the current fault information, combining the historical performance monitoring information and the fault information, training and constructing a decision tree prediction model according to the historical sample data of the configured characteristic index item, predicting the health state of the current server equipment through the established decision tree prediction model, marking the equipment with abnormal predicted health state, identifying the equipment with abnormal health state, reminding operation and maintenance personnel of which equipment has fault risk, and carrying out early detection, investigation and maintenance on the equipment with the fault risk, thereby reducing the equipment fault rate.
It should be particularly noted that, the steps in the embodiments of the server health assessment method described above may be mutually intersected, replaced, added, or deleted, and therefore, these reasonable permutation and combination transformations should also belong to the scope of the present invention, and should not limit the scope of the present invention to the embodiments.
In some embodiments, as shown in fig. 2, the server health assessment method provided by the present invention may be implemented by a data collection module, a health status configuration module, a decision tree model generation module, a health status analysis module, a marker early warning module, and a feature storage module.
In some embodiments, the feature data collection module includes a data collection and data cleansing function, the data collection is used to collect feature quantities related to the health status of the server, the server health status feature quantities are feature quantities corresponding to node types that can be used as a server health status prediction model based on a decision tree algorithm, and include, but are not limited to, the following performance, monitoring, and alarm data: CPU temperature, CPU utilization rate, memory utilization rate, fan rotation speed, power supply real-time power, hard disk IOPS, network card transceiving rate, voltage, current, Trap alarm and the like. The data cleaning is used for cleaning a large amount of characteristic data and filtering out some abnormal data. The acquisition module is used for acquiring health state characteristic data such as server performance monitoring and the like, and the characteristic storage module is used for storing the server performance data.
Therefore, the characteristic data acquisition module provides a data acquisition function to acquire the characteristic quantity related to the health state of the server, and the characteristic quantity of the health state of the server is the characteristic quantity corresponding to the node type of the server health state prediction model based on the decision tree algorithm.
In some embodiments, the feature storage module may be configured to store feature quantities of server health phases and may provide an efficient feature data query service. The characteristic storage module is a device for information reserve persistence, can be understood as a program with a local cache and a database capable of persisting, or a service with the function, the cache layer can provide efficient query for characteristic data query, and the persistence layer persists the characteristic data and the prediction result.
In some embodiments, the health status configuration module classifies and counts the currently collected data index items and provides the user with a focus on custom configuration index items to adjust the health status calculation. The method includes the steps that currently acquired index items are classified and counted through a feature storage module, the fault rate of each index item when the index items are abnormal is counted, reference is provided for configuration, meanwhile, a health state index item configuration function is provided, and support is provided for a decision tree prediction generation module through matching of feature index items and index item weights influencing the health state of a current specific scene.
The health state configuration module is a health state index management module and can perform classified statistics on health state characteristic value data stored by the characteristic storage module, calculate the fault rate when each index item is abnormal and provide reference for configuration; and meanwhile, an index item configuration function influencing the health state is provided, so that the emphasis adjustment of the index item is performed according to certain specific scenes.
In some embodiments, the decision tree model generation module, in combination with the configured health status indicator, establishes a server health status prediction model based on a decision tree algorithm using historically collected corresponding health status feature indicator data.
In some embodiments, the health state analysis module may call the health state prediction model to obtain a server health state prediction result according to the acquired data as input data of the prediction model, deliver the health state prediction result to the storage module for persistent storage, and input the prediction result to the mark early warning module for subsequent operations.
In some embodiments, the mark early warning module includes an abnormal display function and an early warning notification function, receives the prediction analysis result data of the health state analysis module, is used for performing differential display on the server in the abnormal health state to distinguish server devices in different health states, and can notify and early warn operation and maintenance staff for abnormal information by configuring a notification template.
According to the scheme provided by the embodiment of the invention, the health state calculation method suitable for a specific user scene is defined through the weighted configuration of the server health state index items, the health state prediction is carried out by combining the performance monitoring characteristic index item data of the corresponding server, the preparation of the server health state in the specific scene can be effectively improved, and meanwhile, the server equipment with potential fault risk is screened and early warned.
Based on the same inventive concept, according to another aspect of the present invention, an embodiment of the present invention further provides a server health assessment system 400, as shown in fig. 3, including:
a configuration module 401 configured to obtain a plurality of configured evaluation parameters;
a first obtaining module 402 configured to obtain corresponding historical data according to a plurality of evaluation parameters;
a generating module 403 configured to generate a corresponding evaluation model according to the corresponding historical data;
the acquisition module 404 is configured to acquire the current real-time data corresponding to the evaluation parameters of the server to be evaluated;
an evaluation module 405 configured to input the real-time data into the evaluation model to evaluate the health of the server to be evaluated.
In some embodiments, the generation module 403 is further configured to:
dividing the historical data into a training set and a test set;
and training the evaluation model by using the training set and testing the evaluation model by using the testing set.
In some embodiments, the system further comprises a second acquisition module configured to
Acquiring all evaluation parameters;
acquiring corresponding data according to all the evaluation parameters;
and cleaning the corresponding data and then storing the cleaned data as historical data.
In some embodiments, further comprising a notification module configured to:
and responding to the situation that the health degree of the server to be evaluated is smaller than a threshold value, performing differential display and early warning through a preset way.
The technical scheme provided by the invention can be used for automatically configuring the health state characteristic index item of the server according to the requirements of a user on a specific scene by acquiring the data information of the current equipment performance monitoring and other relevant health state characteristics of the server, combining the current fault information, combining the historical performance monitoring information and the fault information, training and constructing a decision tree prediction model according to the historical sample data of the configured characteristic index item, predicting the health state of the current server equipment through the established decision tree prediction model, marking the equipment with abnormal predicted health state, identifying the equipment with abnormal health state, reminding operation and maintenance personnel of which equipment has fault risk, and carrying out early detection, investigation and maintenance on the equipment with the fault risk, thereby reducing the equipment fault rate.
Based on the same inventive concept, according to another aspect of the present invention, as shown in fig. 4, an embodiment of the present invention further provides a computer apparatus 501, including:
at least one processor 520; and
a memory 510, the memory 510 storing a computer program 511 executable on the processor, the processor 520 executing the program to perform the steps of:
s1, acquiring a plurality of configured evaluation parameters;
s2, acquiring corresponding historical data according to the plurality of evaluation parameters;
s3, generating a corresponding evaluation model according to the corresponding historical data;
s4, acquiring the current real-time data corresponding to the evaluation parameters of the server to be evaluated;
and S5, inputting the real-time data into the evaluation model to evaluate the health degree of the server to be evaluated.
In some embodiments, generating a corresponding evaluation model from the historical data further comprises:
dividing the historical data into a training set and a test set;
and training the evaluation model by using the training set and testing the evaluation model by using the testing set.
In some embodiments, further comprising:
acquiring all evaluation parameters;
acquiring corresponding data according to all the evaluation parameters;
and cleaning the corresponding data and then storing the cleaned data as historical data.
In some embodiments, further comprising:
and responding to the situation that the health degree of the server to be evaluated is smaller than a threshold value, performing differential display and early warning through a preset way.
The technical scheme provided by the invention can be used for automatically configuring the health state characteristic index item of the server according to the requirements of a user on a specific scene by acquiring the data information of the current equipment performance monitoring and other relevant health state characteristics of the server, combining the current fault information, combining the historical performance monitoring information and the fault information, training and constructing a decision tree prediction model according to the historical sample data of the configured characteristic index item, predicting the health state of the current server equipment through the established decision tree prediction model, marking the equipment with abnormal predicted health state, identifying the equipment with abnormal health state, reminding operation and maintenance personnel of which equipment has fault risk, and carrying out early detection, investigation and maintenance on the equipment with the fault risk, thereby reducing the equipment fault rate.
In some embodiments, as shown in fig. 2, the server health assessment method provided by the present invention may be implemented by a data collection module, a health status configuration module, a decision tree model generation module, a health status analysis module, a marker early warning module, and a feature storage module.
In some embodiments, the feature data collection module includes a data collection and data cleansing function, the data collection is used to collect feature quantities related to the health status of the server, the server health status feature quantities are feature quantities corresponding to node types that can be used as a server health status prediction model based on a decision tree algorithm, and include, but are not limited to, the following performance, monitoring, and alarm data: CPU temperature, CPU utilization rate, memory utilization rate, fan rotation speed, power supply real-time power, hard disk IOPS, network card transceiving rate, voltage, current, Trap alarm and the like. The data cleaning is used for cleaning a large amount of characteristic data and filtering out some abnormal data. The acquisition module is used for acquiring health state characteristic data such as server performance monitoring and the like, and the characteristic storage module is used for storing the server performance data.
Therefore, the characteristic data acquisition module provides a data acquisition function to acquire the characteristic quantity related to the health state of the server, and the characteristic quantity of the health state of the server is the characteristic quantity corresponding to the node type of the server health state prediction model based on the decision tree algorithm.
In some embodiments, the feature storage module may be configured to store feature quantities of server health phases and may provide an efficient feature data query service. The characteristic storage module is a device for information reserve persistence, can be understood as a program with a local cache and a database capable of persisting, or a service with the function, the cache layer can provide efficient query for characteristic data query, and the persistence layer persists the characteristic data and the prediction result.
In some embodiments, the health status configuration module classifies and counts the currently collected data index items and provides the user with a focus on custom configuration index items to adjust the health status calculation. The method includes the steps that currently acquired index items are classified and counted through a feature storage module, the fault rate of each index item when the index items are abnormal is counted, reference is provided for configuration, meanwhile, a health state index item configuration function is provided, and support is provided for a decision tree prediction generation module through matching of feature index items and index item weights influencing the health state of a current specific scene.
The health state configuration module is a health state index management module and can perform classified statistics on health state characteristic value data stored by the characteristic storage module, calculate the fault rate when each index item is abnormal and provide reference for configuration; and meanwhile, an index item configuration function influencing the health state is provided, so that the emphasis adjustment of the index item is performed according to certain specific scenes.
In some embodiments, the decision tree model generation module, in combination with the configured health status indicator, establishes a server health status prediction model based on a decision tree algorithm using historically collected corresponding health status feature indicator data.
In some embodiments, the health state analysis module may call the health state prediction model to obtain a server health state prediction result according to the acquired data as input data of the prediction model, deliver the health state prediction result to the storage module for persistent storage, and input the prediction result to the mark early warning module for subsequent operations.
In some embodiments, the mark early warning module includes an abnormal display function and an early warning notification function, receives the prediction analysis result data of the health state analysis module, is used for performing differential display on the server in the abnormal health state to distinguish server devices in different health states, and can notify and early warn operation and maintenance staff for abnormal information by configuring a notification template.
According to the scheme provided by the embodiment of the invention, the health state calculation method suitable for a specific user scene is defined through the weighted configuration of the server health state index items, the health state prediction is carried out by combining the performance monitoring characteristic index item data of the corresponding server, the preparation of the server health state in the specific scene can be effectively improved, and meanwhile, the server equipment with potential fault risk is screened and early warned.
Based on the same inventive concept, according to another aspect of the present invention, as shown in fig. 5, an embodiment of the present invention further provides a computer-readable storage medium 601, where the computer-readable storage medium 601 stores computer program instructions 610, and the computer program instructions 610, when executed by a processor, perform the following steps:
s1, acquiring a plurality of configured evaluation parameters;
s2, acquiring corresponding historical data according to the plurality of evaluation parameters;
s3, generating a corresponding evaluation model according to the corresponding historical data;
s4, acquiring the current real-time data corresponding to the evaluation parameters of the server to be evaluated;
and S5, inputting the real-time data into the evaluation model to evaluate the health degree of the server to be evaluated.
In some embodiments, generating a corresponding evaluation model from the historical data further comprises:
dividing the historical data into a training set and a test set;
and training the evaluation model by using the training set and testing the evaluation model by using the testing set.
In some embodiments, further comprising:
acquiring all evaluation parameters;
acquiring corresponding data according to all the evaluation parameters;
and cleaning the corresponding data and then storing the cleaned data as historical data.
In some embodiments, further comprising:
and responding to the situation that the health degree of the server to be evaluated is smaller than a threshold value, performing differential display and early warning through a preset way.
The technical scheme provided by the invention can be used for automatically configuring the health state characteristic index item of the server according to the requirements of a user on a specific scene by acquiring the data information of the current equipment performance monitoring and other relevant health state characteristics of the server, combining the current fault information, combining the historical performance monitoring information and the fault information, training and constructing a decision tree prediction model according to the historical sample data of the configured characteristic index item, predicting the health state of the current server equipment through the established decision tree prediction model, marking the equipment with abnormal predicted health state, identifying the equipment with abnormal health state, reminding operation and maintenance personnel of which equipment has fault risk, and carrying out early detection, investigation and maintenance on the equipment with the fault risk, thereby reducing the equipment fault rate.
Finally, it should be noted that, as will be understood by those skilled in the art, all or part of the processes of the methods of the above embodiments may be implemented by a computer program, which may be stored in a computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above.
Further, it should be appreciated that the computer-readable storage media (e.g., memory) herein can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as software or hardware depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the disclosed embodiments of the present invention.
The foregoing is an exemplary embodiment of the present disclosure, but it should be noted that various changes and modifications could be made herein without departing from the scope of the present disclosure as defined by the appended claims. The functions, steps and/or actions of the method claims in accordance with the disclosed embodiments described herein need not be performed in any particular order. Furthermore, although elements of the disclosed embodiments of the invention may be described or claimed in the singular, the plural is contemplated unless limitation to the singular is explicitly stated.
It should be understood that, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly supports the exception. It should also be understood that "and/or" as used herein is meant to include any and all possible combinations of one or more of the associated listed items.
The numbers of the embodiments disclosed in the embodiments of the present invention are merely for description, and do not represent the merits of the embodiments.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, of embodiments of the invention is limited to these examples; within the idea of an embodiment of the invention, also technical features in the above embodiment or in different embodiments may be combined and there are many other variations of the different aspects of the embodiments of the invention as described above, which are not provided in detail for the sake of brevity. Therefore, any omissions, modifications, substitutions, improvements, and the like that may be made without departing from the spirit and principles of the embodiments of the present invention are intended to be included within the scope of the embodiments of the present invention.
Claims (10)
1. A server health assessment method is characterized by comprising the following steps:
acquiring a plurality of configured evaluation parameters;
acquiring corresponding historical data according to the plurality of evaluation parameters;
generating a corresponding evaluation model according to the corresponding historical data;
acquiring current real-time data corresponding to the evaluation parameters of the server to be evaluated;
and inputting the real-time data into the evaluation model to evaluate the health degree of the server to be evaluated.
2. The method of claim 1, wherein generating a corresponding assessment model from the historical data further comprises:
dividing the historical data into a training set and a test set;
and training the evaluation model by using the training set and testing the evaluation model by using the testing set.
3. The method of claim 1, further comprising:
acquiring all evaluation parameters;
acquiring corresponding data according to all the evaluation parameters;
and cleaning the corresponding data and then storing the cleaned data as historical data.
4. The method of claim 1, further comprising:
and responding to the situation that the health degree of the server to be evaluated is smaller than a threshold value, performing differential display and early warning through a preset way.
5. A server health assessment system, comprising:
the configuration module is configured to acquire a plurality of configured evaluation parameters;
the first acquisition module is configured to acquire corresponding historical data according to a plurality of evaluation parameters;
a generation module configured to generate a corresponding evaluation model from the corresponding historical data;
the acquisition module is configured to acquire the current real-time data corresponding to the evaluation parameters of the server to be evaluated;
and the evaluation module is configured to input the real-time data into the evaluation model so as to evaluate the health degree of the server to be evaluated.
6. The system of claim 5, wherein the generation module is further configured to:
dividing the historical data into a training set and a test set;
and training the evaluation model by using the training set and testing the evaluation model by using the testing set.
7. The system of claim 5, further comprising a second acquisition module configured to
Acquiring all evaluation parameters;
acquiring corresponding data according to all the evaluation parameters;
and cleaning the corresponding data and then storing the cleaned data as historical data.
8. The system of claim 5, further comprising a notification module configured to:
and responding to the situation that the health degree of the server to be evaluated is smaller than a threshold value, performing differential display and early warning through a preset way.
9. A computer device, comprising:
at least one processor; and
memory storing a computer program operable on the processor, characterized in that the processor executes the program to perform the steps of the method according to any of claims 1-4.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, is adapted to carry out the steps of the method according to any one of claims 1-4.
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| CN202111065323.5A CN113806171A (en) | 2021-09-12 | 2021-09-12 | Server health assessment method, system, equipment and medium |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202111065323.5A CN113806171A (en) | 2021-09-12 | 2021-09-12 | Server health assessment method, system, equipment and medium |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114493116A (en) * | 2021-12-25 | 2022-05-13 | 南京移腾电力技术有限公司 | Power distribution network low-voltage circuit breaker state evaluation method based on cart algorithm |
| CN115190039A (en) * | 2022-07-31 | 2022-10-14 | 苏州浪潮智能科技有限公司 | Equipment health evaluation method, system, equipment and storage medium |
| CN116070963A (en) * | 2023-03-06 | 2023-05-05 | 华安证券股份有限公司 | Online customer service system health degree detection method based on big data |
| WO2023221587A1 (en) * | 2022-05-16 | 2023-11-23 | 深圳市道通合创数字能源有限公司 | Method for determining state of health of power battery of electric vehicle, and server |
| CN118190443A (en) * | 2024-02-28 | 2024-06-14 | 武汉万曦智能科技有限公司 | Comprehensive field vehicle detection system and detection method |
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2021
- 2021-09-12 CN CN202111065323.5A patent/CN113806171A/en not_active Withdrawn
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114493116A (en) * | 2021-12-25 | 2022-05-13 | 南京移腾电力技术有限公司 | Power distribution network low-voltage circuit breaker state evaluation method based on cart algorithm |
| WO2023221587A1 (en) * | 2022-05-16 | 2023-11-23 | 深圳市道通合创数字能源有限公司 | Method for determining state of health of power battery of electric vehicle, and server |
| CN115190039A (en) * | 2022-07-31 | 2022-10-14 | 苏州浪潮智能科技有限公司 | Equipment health evaluation method, system, equipment and storage medium |
| CN115190039B (en) * | 2022-07-31 | 2023-08-08 | 苏州浪潮智能科技有限公司 | Equipment health evaluation method, system, equipment and storage medium |
| CN116070963A (en) * | 2023-03-06 | 2023-05-05 | 华安证券股份有限公司 | Online customer service system health degree detection method based on big data |
| CN118190443A (en) * | 2024-02-28 | 2024-06-14 | 武汉万曦智能科技有限公司 | Comprehensive field vehicle detection system and detection method |
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