CN111367773B - Method, system, equipment and medium for detecting server network card - Google Patents
Method, system, equipment and medium for detecting server network card Download PDFInfo
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Abstract
The invention discloses a method, a system, equipment and a storage medium for detecting a network card of a server, wherein the method comprises the following steps: selecting a standard sample detected by a server network card, and taking a historical signal characteristic value in the standard sample and a detection result corresponding to the historical signal characteristic value as a training set; inputting the training set into a BP neural network for training to obtain a BP neural network detection model; collecting signals of metal terminals in a network card slot of each server, generating detection signals with preset frequency and sending the detection signals to a BP neural network detection model for detection; and displaying the corresponding detection result based on the identification code of each server network card. The method, the system, the equipment and the medium for detecting the network card of the server detect the signals of the metal terminals in the network card slot by establishing the BP neural network detection model, not only can display the state of the network card of the server in time, but also can predict the state of the network card, and improve the performance of the server.
Description
Technical Field
The present invention relates to the field of network cards, and more particularly, to a method, a system, a computer device, and a readable medium for detecting a network card of a server.
Background
At present, in the process of detecting the server network card, because the complexity of the server state needs to be detected in a manual mode, the working state of the server and the possible fault prevention are determined, however, the complex disassembly and assembly work needs to be carried out manually in the detection process, serious inconvenience is brought to the detection, the efficiency of manual detection is too low, and great influence is brought to the timely prevention of the server fault.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, a system, a computer device, and a computer readable storage medium for detecting a server network card, where a BP neural network detection model is established to detect a signal of a metal terminal in a network card slot, so that not only a state of the server network card can be displayed in time, but also the state of the network card can be predicted, and performance of the server is improved.
Based on the above object, an aspect of the embodiments of the present invention provides a method for detecting a network card of a server, including the following steps: selecting a standard sample detected by a server network card, and taking a historical signal characteristic value in the standard sample and a detection result corresponding to the historical signal characteristic value as a training set; inputting the training set into a BP neural network for training to obtain a BP neural network detection model; collecting signals of metal terminals in each server network card slot, generating detection signals with preset frequency and sending the detection signals to the BP neural network detection model for detection; and displaying the corresponding detection result based on the identification code of each server network card.
In some embodiments, the inputting the training set into a BP neural network for training, and obtaining a BP neural network detection model includes: initializing a neural network, and endowing a first value and a second value of each neuron with random values; obtaining errors of output layers based on the training set, and obtaining errors of each neuron of each layer through back propagation based on the errors of the output layers; and updating the first value and the second value based on the error of the neuron.
In some embodiments, the deriving the error of the output layer based on the training set comprises: and inputting the training set, carrying out forward propagation to obtain output values of all neurons of an output layer, and obtaining errors of the output layer based on the output values.
In some embodiments, the updating the first value and the second value based on the error of the neuron comprises: different weights are assigned to the errors of the neurons of each layer based on the contribution rate of each layer to the influence of the errors.
In another aspect of the embodiments of the present invention, a system for detecting a network card of a server is further provided, including: the selection module is configured for selecting a standard sample for server network card detection, and taking a historical signal characteristic value in the standard sample and a detection result corresponding to the historical signal characteristic value as a training set; the training module is configured to input the training set into a BP neural network for training to obtain a BP neural network detection model; the detection module is configured to collect signals of the metal terminals in the network card slots of each server, generate detection signals with preset frequency and send the detection signals to the BP neural network detection model for detection; and the display module is configured to display the corresponding detection result based on the identification code of each server network card.
In some embodiments, the training module is further configured to: initializing a neural network, and giving a random value to the first value and the second value of each neuron; obtaining errors of output layers based on the training set, and obtaining errors of each neuron of each layer through back propagation based on the errors of the output layers; and updating the first value and the second value based on the error of the neuron.
In some embodiments, the training module is further configured to: and inputting the training set, carrying out forward propagation to obtain output values of all neurons of an output layer, and obtaining errors of the output layer based on the output values.
In some embodiments, the training module is further configured to: different weights are assigned to the errors of the neurons of each layer based on the contribution rate of each layer to the influence of the errors.
In another aspect of the embodiments of the present invention, there is also provided a computer device, including: at least one processor; and a memory storing computer instructions executable on the processor, the instructions when executed by the processor implementing the steps of the method as above.
In a further aspect of the embodiments of the present invention, a computer-readable storage medium is also provided, in which a computer program for implementing the above method steps is stored when the computer program is executed by a processor.
The invention has the following beneficial technical effects: the BP neural network detection model is established to detect the signals of the metal terminals in the network card slot, so that the state of the network card of the server can be displayed in time, the state of the network card can be predicted, and the performance of the server is improved.
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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 diagram illustrating an embodiment of a method for detecting a network card of a server according to the present invention;
fig. 2 is a schematic diagram of a hardware structure of a computer device for detecting a server network card 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 only used for convenience of expression and should not be construed as a limitation to the embodiments of the present invention, and no description is given in the following embodiments.
Based on the above purpose, a first aspect of the embodiments of the present invention provides an embodiment of a method for detecting a network card of a server. Fig. 1 is a schematic diagram illustrating an embodiment of a method for detecting a server network card according to the present invention. As shown in fig. 1, the embodiment of the present invention includes the following steps:
s1, selecting a standard sample detected by a server network card, and taking a historical signal characteristic value in the standard sample and a detection result corresponding to the historical signal characteristic value as a training set;
s2, inputting the training set into a BP neural network for training to obtain a BP neural network detection model;
s3, collecting signals of metal terminals in each server network card slot, generating detection signals with preset frequency and sending the detection signals to a BP neural network detection model for detection; and
and S4, displaying a corresponding detection result based on the identification code of each server network card.
The BP (back propagation) neural network is a multi-layer feedforward neural network trained according to an error back propagation algorithm. The BP neural network includes an input layer, a hidden layer, and an output layer.
Detecting the state of each network card of the server by using a receiving and sending device, and sending a detection signal to a processor for analysis; the processor automatically identifies the signal by adopting a BP neural network and sends the identified state information to the display terminal; and the display terminal displays the identified state information.
Selecting a standard sample detected by a server network card, and taking a historical signal characteristic value in the standard sample and a detection result corresponding to the historical signal characteristic value as a training set.
And inputting the training set into a BP neural network for training to obtain a BP neural network detection model. In some embodiments, the inputting the training set into a BP neural network for training, and obtaining a BP neural network detection model includes: initializing a neural network, and giving a random value to the first value and the second value of each neuron; obtaining errors of output layers based on the training set, and obtaining errors of each neuron of each layer through back propagation based on the errors of the output layers; and updating the first value and the second value based on the error of the neuron.
The specific steps can be as follows:
a1: initializing a neural network, and endowing w and b of each neuron with random values;
a2: inputting a training sample set, inputting each sample into an input layer of a neural network respectively, and carrying out forward propagation once to obtain output values of each neuron of an output layer;
a3: solving the error of the output layer, and then backwards solving the error of each neuron of each layer through a back propagation algorithm;
a4: the error can be used to obtain the value of each neuron
Multiplying the negative learning rate-eta to obtain delta w and delta b, and multiplying each timeAnd updating w and b of each neuron into w + delta w and b + delta b, thereby completing the training of the BP neural network model.
wherein, y k Representing the output value, T, of the kth node of the neuron output layer k M is the number of nodes of the output layer;
the calculation formulas of the delta w and the delta b are as follows:
in some embodiments, the deriving the error of the output layer based on the training set comprises: and inputting the training set, carrying out forward propagation to obtain output values of each neuron of an output layer, and obtaining errors of the output layer based on the output values.
In some embodiments, the updating the first and second values based on the error of the neuron comprises: different weights are assigned to the errors of the neurons of each layer based on the contribution rate of each layer to the influence of the errors.
And collecting signals of metal terminals in the network card slot of each server, generating detection signals with preset frequency and sending the detection signals to the BP neural network detection model for detection. Firstly, marking ID { C1, C2, C3, \ 8230;, CN } of each network card, wherein N represents the number of the network cards; collecting signals of each network card, and extracting a signal characteristic value { U ] through set state parameters 1 ，U 2 ，U 3 ，…，U N }。
And displaying the corresponding detection result based on the identification code of each server network card. And associating the result with the identification code of the network card, arranging the result in sequence from small to large according to the identification code of the server network card, and displaying the detection result at the corresponding position of the network card. And repairing or replacing the abnormal network card, predicting the network card which is not abnormal, and checking the network card which is abnormal in a short time.
It should be noted that, the steps in the embodiments of the method for detecting a server network card described above may be mutually intersected, replaced, added, or deleted, and therefore, these methods for detecting a server network card that are reasonably transformed in permutation and combination also belong to the scope of the present invention, and the scope of the present invention should not be limited to the embodiments.
Based on the above object, a second aspect of the embodiments of the present invention provides a system for detecting a network card of a server, including: the selection module is configured for selecting a standard sample for server network card detection, and taking a historical signal characteristic value in the standard sample and a detection result corresponding to the historical signal characteristic value as a training set; the training module is configured to input the training set into a BP neural network for training to obtain a BP neural network detection model; the detection module is configured to collect signals of the metal terminals in the network card slots of each server, generate detection signals with preset frequency and send the detection signals to the BP neural network detection model for detection; and the display module is configured to display the corresponding detection result based on the identification code of each server network card.
In some embodiments, the training module is further configured to: initializing a neural network, and giving a random value to the first value and the second value of each neuron; obtaining errors of output layers based on the training set, and obtaining errors of each neuron of each layer through back propagation based on the errors of the output layers; and updating the first value and the second value based on an error of the neuron.
In some embodiments, the training module is further configured to: and inputting the training set, carrying out forward propagation to obtain output values of each neuron of an output layer, and obtaining errors of the output layer based on the output values.
In some embodiments, the training module is further configured to: different weights are assigned to the errors of the neurons of each layer based on the contribution rate of each layer to the influence of the errors.
In view of the above object, a third aspect of an embodiment of the present invention provides a computer device, including: at least one processor; and a memory storing computer instructions executable on the processor, the instructions being executable by the processor to perform the steps of: s1, selecting a standard sample detected by a server network card, and taking a historical signal characteristic value in the standard sample and a detection result corresponding to the historical signal characteristic value as a training set; s2, inputting the training set into a BP neural network for training to obtain a BP neural network detection model; s3, collecting signals of metal terminals in each server network card slot, generating detection signals with preset frequency and sending the detection signals to a BP neural network detection model for detection; and S4, displaying the corresponding detection result based on the identification code of each server network card.
In some embodiments, the inputting the training set into a BP neural network for training, and obtaining a BP neural network detection model includes: initializing a neural network, and giving a random value to the first value and the second value of each neuron; obtaining errors of output layers based on the training set, and obtaining errors of each neuron of each layer through back propagation based on the errors of the output layers; and updating the first value and the second value based on the error of the neuron.
In some embodiments, the deriving the error of the output layer based on the training set comprises: and inputting the training set, carrying out forward propagation to obtain output values of all neurons of an output layer, and obtaining errors of the output layer based on the output values.
In some embodiments, the updating the first value and the second value based on the error of the neuron comprises: different weights are assigned to the errors of the neurons of each layer based on the contribution rate of each layer to the influence of the errors.
Fig. 2 is a schematic diagram of a hardware structure of an embodiment of the computer device for detecting a server network card provided by the present invention.
Taking the apparatus shown in fig. 2 as an example, the apparatus includes a processor 301 and a memory 302, and may further include: an input device 303 and an output device 304.
The processor 301, the memory 302, the input device 303 and the output device 304 may be connected by a bus or other means, and fig. 2 illustrates the connection by a bus as an example.
The memory 302 is used as a non-volatile computer-readable storage medium, and can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as program instructions/modules corresponding to the method for detecting a server network card in the embodiment of the present application. The processor 301 executes various functional applications and data processing of the server by running the nonvolatile software program, instructions and modules stored in the memory 302, that is, implements the method for detecting the network card of the server according to the foregoing method embodiment.
The memory 302 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the method of detecting the server network card, and the like. Further, the memory 302 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, memory 302 optionally includes memory located remotely from processor 301, which may be connected to a local module via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 303 may receive information such as a user name and a password that are input. The output means 304 may comprise a display device such as a display screen.
Program instructions/modules corresponding to one or more methods for detecting a server network card are stored in the memory 302, and when executed by the processor 301, perform the method for detecting a server network card in any of the above-described method embodiments.
Any embodiment of the computer device executing the method for detecting the network card of the server can achieve the same or similar effects as any corresponding embodiment of the method.
The invention also provides a computer readable storage medium storing a computer program which, when executed by a processor, performs the method as above.
Finally, it should be noted that, as one of ordinary skill in the art can appreciate, all or part of the processes in the methods of the foregoing embodiments may be implemented by instructing relevant hardware through a computer program, and the program of the method for detecting a server network card may be stored in a computer-readable storage medium, and when executed, may include the processes of the foregoing embodiments of the methods. The storage medium of the program may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a Random Access Memory (RAM), or the like. The embodiments of the computer program may achieve the same or similar effects as any of the above-described method embodiments.
Furthermore, the methods disclosed according to embodiments of the present invention may also be implemented as a computer program executed by a processor, which may be stored in a computer-readable storage medium. Which when executed by a processor performs the above-described functions as defined in the method disclosed by an embodiment of the invention.
Further, the above method steps and system elements may also be implemented using a controller and a computer readable storage medium for storing a computer program for causing the controller to implement the functions of the above steps or elements.
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. By way of example, and not limitation, 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), which can act as external cache memory. By way of example and not limitation, RAM is available in a variety of forms such as synchronous RAM (DRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The storage devices of the disclosed aspects are intended to comprise, without being limited to, these and other suitable types of 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 various illustrative logical blocks, modules, and circuits described in connection with the disclosure herein may be implemented or performed with the following components designed to perform the functions herein: a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination of these components. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP, and/or any other such configuration.
The steps of a method or algorithm described in connection with the disclosure herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.
In one or more exemplary designs, the functions may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk and disc, as used herein, includes Compact Disc (CD), laser disc, optical disc, digital Versatile Disc (DVD), floppy disk, blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.
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 above embodiments of the present invention are merely for description, and do not represent the advantages or disadvantages 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 or scope 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 method for detecting a network card of a server is characterized by comprising the following steps:
selecting a standard sample detected by a server network card, and taking a historical signal characteristic value in the standard sample and a detection result corresponding to the historical signal characteristic value as a training set;
inputting the training set into a BP neural network for training to obtain a BP neural network detection model;
collecting signals of metal terminals in a network card slot of each server, generating detection signals with preset frequency and sending the detection signals to the BP neural network detection model for detection; and
and displaying the corresponding detection result based on the identification code of each server network card.
2. The method of claim 1, wherein inputting the training set to a BP neural network for training to obtain a BP neural network detection model comprises:
initializing a neural network, and giving a random value to the first value and the second value of each neuron;
obtaining errors of output layers based on the training set, and obtaining errors of each neuron of each layer through back propagation based on the errors of the output layers; and
updating the first value and the second value based on an error of the neuron.
3. The method of claim 2, wherein the deriving the error for the output layer based on the training set comprises:
and inputting the training set, carrying out forward propagation to obtain output values of all neurons of an output layer, and obtaining errors of the output layer based on the output values.
4. The method of claim 2, wherein the updating the first value and the second value based on the error of the neuron comprises:
different weights are assigned to the errors of the neurons of each layer based on the contribution rate of each layer to the influence of the errors.
5. A system for detecting a network card of a server, comprising:
the selection module is configured for selecting a standard sample for server network card detection, and taking a historical signal characteristic value in the standard sample and a detection result corresponding to the historical signal characteristic value as a training set;
the training module is configured to input the training set into a BP neural network for training to obtain a BP neural network detection model;
the detection module is configured to collect signals of the metal terminals in the network card slots of each server, generate detection signals with preset frequency and send the detection signals to the BP neural network detection model for detection; and
and the display module is configured to display the corresponding detection result based on the identification code of each server network card.
6. The system of claim 5, wherein the training module is further configured to:
initializing a neural network, and giving a random value to the first value and the second value of each neuron;
obtaining errors of output layers based on the training set, and obtaining errors of each neuron of each layer through back propagation based on the errors of the output layers; and
updating the first value and the second value based on an error of the neuron.
7. The system of claim 6, wherein the training module is further configured to:
and inputting the training set, carrying out forward propagation to obtain output values of each neuron of an output layer, and obtaining errors of the output layer based on the output values.
8. The system of claim 6, wherein the training module is further configured to:
different weights are assigned to the errors of the neurons of each layer based on the contribution rate of each layer to the influence of the errors.
9. A computer device, comprising:
at least one processor; and
a memory storing computer instructions executable on the processor, the instructions when executed by the processor implementing the steps of the method of any one of claims 1 to 4.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 4.
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| CN112582068B (en) * | 2020-12-31 | 2022-09-20 | 山东省妇幼保健院 | Safety device for gestational eclampsia symptoms |
| CN113420779A (en) * | 2021-05-23 | 2021-09-21 | 济南浪潮数据技术有限公司 | Method, device, equipment and readable medium for web request anomaly detection |
| CN116627770B (en) * | 2023-07-18 | 2023-09-26 | 苏州浪潮智能科技有限公司 | Network card temperature prediction method and device, computer equipment and storage medium |
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