CN116340104A - Application data authentication system for computer diskless workstation - Google Patents
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Abstract
The invention relates to a computer diskless workstation application data authentication system, comprising: the proportion judging device is used for identifying the newly-increased occupation proportion of the operation kernel of the monitored diskless workstation after the application software is newly added at the current moment of the monitored diskless workstation; the traversal processing equipment is used for acquiring each newly-increased occupation proportion corresponding to each application software stored on the remote server; and the marking processing equipment is used for marking the application software of which the corresponding newly-added occupation proportion in each application software does not exceed the residual unoccupied proportion of the operation kernel at the current moment of the monitored diskless workstation as the safety application software. The invention can acquire the newly added operand duty ratio of each application software to the local operation core after running on the monitored diskless workstation in the current state, thereby identifying each safety application software which does not cause overload fault of the operation core and maintaining the unmanned management level of the monitored diskless workstation.
Description
Technical Field
The invention relates to the field of diskless workstations, in particular to a computer diskless workstation application data authentication system.
Background
As an important class of computer systems, diskless workstations refer to computers that have no floppy disk, no hard disk, and no optical drive connected to a lan. In the network system, the operating system and the application software used by the diskless workstation end are all placed on the server, and a system administrator only needs to complete management and maintenance on the server and only needs to configure once for upgrading and installing the software, so that all computers in the whole network can use new software. The diskless workstation has the advantages of cost saving, high system security, manageability, easy maintenance and the like, and has great attraction to network administrators.
The working principle of the diskless workstation is that a starting chip (Boot ROM) of a network card sends a starting request number to a server in different forms, after the starting request number is received by the server, starting data is sent to the workstation according to different mechanisms, after the workstation downloads the starting data, the control right of the system is transferred to certain specific areas in a memory by the Boot ROM, and an operating system is guided. More commonly diskless workstations can be classified into RPL and PXE according to different startup mechanisms.
As the diskless workstation always requires the most simple and convenient local unmanned management mode, the higher the unmanned management degree is, the more excellent the performance of the corresponding diskless workstation is. However, as the parameters of each local system of the diskless workstation are more, the communication link with the remote server is complex and changeable, and meanwhile, the data monitoring of the remote server is difficult, so that the local site volume of each application program which is pointed to the diskless workstation but stored in the remote server can not be determined, the phenomenon of overload of the local operation kernel of the diskless workstation is easily caused, and the unmanned management degree of the diskless workstation is seriously influenced.
Disclosure of Invention
In order to solve the above-mentioned defects in the prior art, the invention provides a computer diskless workstation application data identification system, which can acquire the new operand proportion of the monitored diskless workstation running in the current state to the local operation core aiming at each application software stored on a remote server bound by the monitored diskless workstation, thereby identifying each safety application software and each dangerous application software and reducing the risk of overload fault of the operation core occurring locally in the diskless workstation.
According to an aspect of the present invention, there is provided a computer diskless workstation application data qualification system, the system comprising:
the remote detection device is connected with a remote server network bound with the monitored diskless workstation and is used for acquiring the maximum communication bandwidth, the storage capacity and the operation rate of the remote server, and simultaneously, is also used for acquiring the total number of diskless workstations managed by the remote server, and is also used for acquiring ASCIL code values of software names of each application software stored on the remote server and assigned to the monitored diskless workstation;
the station body measuring device is connected with the monitored diskless workstation and is used for acquiring the maximum communication bandwidth of the monitored diskless workstation, the maximum capacity of the internal memory and the real-time calculation total consumed by each currently executed process;
the model learning device is used for executing fixed number of learning processes on the radial basis function neural network to obtain the radial basis function neural network after the learning process is completed and outputting the radial basis function neural network as an artificial intelligent model, wherein in each learning process, the real-time operation total amount consumed by each process executed by a monitored diskless workstation at a certain moment, the maximum communication bandwidth of the monitored diskless workstation, the maximum capacity of an internal memory and the ASCIL code value of the software name of newly added application software at a certain moment are taken as each input information of the artificial intelligent model, and the newly added occupation proportion of the operation core of the monitored diskless workstation after the application software is newly added at a certain moment of the monitored diskless workstation is taken as single output information of the artificial intelligent model to complete the learning process;
the proportion judging device is respectively connected with the model learning device, the station body measuring device and the remote detecting device and is used for parallelly inputting the real-time operation total amount consumed by each process executed by the monitored diskless workstation at the current moment, the maximum communication bandwidth of the monitored diskless workstation, the maximum capacity of the internal memory and the ASCIL code value of the software name of the newly-added application software at the current moment into the artificial intelligent model, and operating the artificial intelligent model to obtain the newly-added occupation proportion of the operation core of the monitored diskless workstation after the newly-added application software of the monitored diskless workstation is output by the artificial intelligent model and serve as the prediction increment proportion corresponding to the newly-added application software at the current moment;
the traversal processing equipment is connected with the proportion judging equipment and is used for inputting all ASCIL code values which are stored on a remote server and respectively correspond to all application software assigned to the monitored diskless workstation into the artificial intelligent model one by one so as to obtain all predicted increasing proportions respectively corresponding to all application software;
the marking processing device is connected with the traversing processing device and is used for marking the application software of which the corresponding predicted increment proportion in each application software does not exceed the residual unoccupied proportion of the operation kernel at the current moment of the monitored diskless workstation as safe application software.
It can be seen that the present invention has at least the following two important inventions:
firstly, intelligently judging the newly-increased occupation proportion of the operation kernel of the monitored diskless workstation after the application software is newly added at the current moment of the monitored diskless workstation based on the real-time operation total amount consumed by each process executed at the current moment of the monitored diskless workstation, the maximum communication bandwidth and the maximum capacity of an internal memory of the monitored diskless workstation and the ASCIL code value of the software name of the newly-added application software at the current moment, and taking the newly-increased occupation proportion as the prediction increment proportion corresponding to the newly-added application software at the current moment;
and secondly, inputting all ASCIL code values which are stored on a remote server and are respectively corresponding to all application software of the monitored diskless workstation one by one into an artificial intelligent model for executing intelligent judgment to obtain all prediction increasing ratios respectively corresponding to all application software, and marking the application software of which the corresponding prediction increasing ratio in all application software does not exceed the residual unoccupied ratio of the operation kernel of the monitored diskless workstation at the current moment as safe application software, thereby realizing real-time identification and classification of the application software types of the monitored diskless workstation and avoiding the phenomenon of overload of the operation kernel of the monitored diskless workstation.
Drawings
Embodiments of the present invention will be described below with reference to the accompanying drawings, in which:
fig. 1 is a block diagram illustrating a computer diskless workstation application data authentication system according to an embodiment of the present invention.
Fig. 2 is a block diagram illustrating a computer diskless workstation application data authentication system according to an embodiment of the present invention.
Fig. 3 is a block diagram illustrating a computer diskless workstation application data authentication system according to an embodiment of the present invention.
Detailed Description
Embodiments of the computer diskless workstation application data qualification system of the present invention will be described in detail below with reference to the accompanying drawings.
Example 1
FIG. 1 is a block diagram of a computer diskless workstation application data qualification system, according to an embodiment of the present invention, including:
the remote detection device is connected with a remote server network bound with the monitored diskless workstation and is used for acquiring the maximum communication bandwidth, the storage capacity and the operation rate of the remote server, and simultaneously, is also used for acquiring the total number of diskless workstations managed by the remote server, and is also used for acquiring ASCIL code values of software names of each application software stored on the remote server and assigned to the monitored diskless workstation;
illustratively, the ASCLL code value of the software name of each application software stored on the remote server and assigned to the monitored diskless workstation is a binary-valued representation pattern;
the station body measuring device is connected with the monitored diskless workstation and is used for acquiring the maximum communication bandwidth of the monitored diskless workstation, the maximum capacity of the internal memory and the real-time calculation total consumed by each currently executed process;
the station measuring device comprises a plurality of measuring units, wherein the measuring units are used for respectively acquiring the maximum communication bandwidth, the maximum capacity of an internal memory and the total real-time operation amount consumed by each currently executed process of the monitored diskless workstation;
the model learning device is used for executing fixed number of learning processes on the radial basis function neural network to obtain the radial basis function neural network after the learning process is completed and outputting the radial basis function neural network as an artificial intelligent model, wherein in each learning process, the real-time operation total amount consumed by each process executed by a monitored diskless workstation at a certain moment, the maximum communication bandwidth of the monitored diskless workstation, the maximum capacity of an internal memory and the ASCIL code value of the software name of newly added application software at a certain moment are taken as each input information of the artificial intelligent model, and the newly added occupation proportion of the operation core of the monitored diskless workstation after the application software is newly added at a certain moment of the monitored diskless workstation is taken as single output information of the artificial intelligent model to complete the learning process;
the proportion judging device is respectively connected with the model learning device, the station body measuring device and the remote detecting device and is used for parallelly inputting the real-time operation total amount consumed by each process executed by the monitored diskless workstation at the current moment, the maximum communication bandwidth of the monitored diskless workstation, the maximum capacity of the internal memory and the ASCIL code value of the software name of the newly-added application software at the current moment into the artificial intelligent model, and operating the artificial intelligent model to obtain the newly-added occupation proportion of the operation core of the monitored diskless workstation after the newly-added application software of the monitored diskless workstation is output by the artificial intelligent model and serve as the prediction increment proportion corresponding to the newly-added application software at the current moment;
the traversal processing equipment is connected with the proportion judging equipment and is used for inputting all ASCIL code values which are stored on a remote server and respectively correspond to all application software assigned to the monitored diskless workstation into the artificial intelligent model one by one so as to obtain all predicted increasing proportions respectively corresponding to all application software;
the marking processing device is connected with the traversing processing device and is used for marking the application software of which the corresponding predicted increment proportion in each application software does not exceed the residual unoccupied proportion of the operation kernel at the current moment of the monitored diskless workstation as safe application software.
Example two
Fig. 2 is a block diagram illustrating a computer diskless workstation application data authentication system according to an embodiment of the present invention.
Unlike FIG. 1, the computer diskless workstation application data qualification system shown in accordance with an embodiment of the present invention may further include:
the instant display device is connected with the mark processing device and arranged in the monitored diskless workstation and is used for carrying out blue low-brightness marks on each piece of safety application software in the application software;
the instant display device may be an LED display array, an LCD display array, or a liquid crystal display device, for example;
wherein the instant display device is further configured to highlight red for each of the respective applications.
Example III
Fig. 3 is a block diagram illustrating a computer diskless workstation application data authentication system according to an embodiment of the present invention.
Unlike FIG. 1, the computer diskless workstation application data qualification system shown in accordance with an embodiment of the present invention may further include:
the proportion storage device is connected with the marking processing device and is used for temporarily storing the residual unoccupied proportion of the operation kernel at the current moment of the monitored diskless workstation;
the quartz oscillation device is respectively connected with the proportion judging equipment, the model learning device and the remote detection device;
the quartz oscillation device is used for providing different reference clock pulses required by the proportion judging device, the model learning device and the remote detection device respectively.
Next, a further description of the specific structure of the computer diskless workstation application data authentication system of the present invention will be continued.
In a computer diskless workstation application data qualification system according to various embodiments of the present invention:
the marking processing device is further used for marking the application software with the corresponding predicted increment proportion in each application software exceeding or being equal to the residual unoccupied proportion of the operation kernel at the current moment of the monitored diskless workstation as excessive application software.
In a computer diskless workstation application data qualification system according to various embodiments of the present invention:
in each learning process, taking the real-time operation total amount consumed by each process executed by the monitored diskless workstation at a certain moment, the maximum communication bandwidth and the maximum capacity of an internal memory of the monitored diskless workstation and the ASCIL code value of the software name of the newly added application software at a certain moment as each input information of the artificial intelligent model, taking the newly added occupation ratio of the operation kernel of the monitored diskless workstation after the newly added application software of the monitored diskless workstation at a certain moment as each output information of the artificial intelligent model, and completing the learning process comprises the following steps: in each learning process, respectively carrying out normalization processing on the total real-time operation amount consumed by each process executed by the monitored diskless workstation at a certain moment, the maximum communication bandwidth of the monitored diskless workstation, the maximum capacity of an internal memory and the ASCIL code value of the software name of the newly added application software at a certain moment, and then inputting the normalized ASCIL code value into the artificial intelligent model in parallel;
in each learning process, taking the real-time operation total amount consumed by each process executed by the monitored diskless workstation at a certain moment, the maximum communication bandwidth and the maximum capacity of an internal memory of the monitored diskless workstation and the ASCIL code value of the software name of the newly added application software at a certain moment as each input information of the artificial intelligent model, taking the newly added occupation ratio of the operation kernel of the monitored diskless workstation after the newly added application software of the monitored diskless workstation at a certain moment as each output information of the artificial intelligent model, and completing the learning process comprises the following steps: and normalizing the newly increased occupation proportion of the operation kernel of the monitored diskless workstation after newly adding application software at a certain moment, and taking the newly increased occupation proportion as single output information of the artificial intelligent model.
In a computer diskless workstation application data qualification system according to various embodiments of the present invention:
inputting the total real-time operation amount consumed by each process executed by the monitored diskless workstation at the current moment, the maximum communication bandwidth of the monitored diskless workstation, the maximum capacity of an internal memory and the ASCIL code value of the software name of the newly added application software at the current moment into the artificial intelligent model in parallel, operating the artificial intelligent model to obtain the output newly added occupation proportion of the operation kernel of the monitored diskless workstation after the newly added application software of the monitored diskless workstation at the current moment, and taking the newly added occupation proportion as the prediction increment proportion corresponding to the newly added application software at the current moment, wherein the method comprises the following steps of: and respectively carrying out normalization processing on the total real-time operation amount consumed by each process executed by the monitored diskless workstation at the current moment, the maximum communication bandwidth of the monitored diskless workstation, the maximum capacity of the internal memory and the ASCIL code value of the software name of the newly added application software at the current moment, and then inputting the normalized ASCIL code value into the artificial intelligent model in parallel.
And in a computer diskless workstation application data qualification system according to various embodiments of the invention:
the remote detection device is connected with a remote server network bound with the monitored diskless workstation, and is used for acquiring the communication bandwidth, the storage capacity and the operation rate of the remote server, and simultaneously, the total number of diskless workstations managed by the remote server is also acquired, and the remote detection device comprises: the remote server is a big data server or a cloud server;
the remote detection device is connected with a remote server network bound with the monitored diskless workstation, and is used for acquiring the communication bandwidth, the storage capacity and the operation rate of the remote server, and simultaneously, the total number of diskless workstations managed by the remote server is also acquired, and the method comprises the following steps: the remote detection device is connected with a remote server network bound with the monitored diskless workstation through a wireless communication link based on a time division duplex communication mode;
and alternatively, the remote detecting device is connected with a remote server network bound with the monitored diskless workstation, and is used for acquiring the communication bandwidth, the storage capacity and the operation rate of the remote server, and simultaneously, the method also used for acquiring the total number of diskless workstations managed by the remote server comprises the following steps: the remote probe device is connected to a remote server network to which the monitored diskless workstation is bound, via a wireless communication link based on a frequency division duplex communication mode.
In addition, in the computer diskless workstation application data authentication system, the real-time operation total amount consumed by each process executed by the monitored diskless workstation at the current moment, the maximum communication bandwidth and the maximum capacity of the internal memory of the monitored diskless workstation and the ASCIL code value of the software name of the newly added application software at the current moment are input into the artificial intelligent model in parallel, and the artificial intelligent model is operated to obtain the output newly added occupation proportion of the operation kernel of the monitored diskless workstation after the newly added application software of the monitored diskless workstation at the current moment and serve as the prediction increment proportion corresponding to the newly added application software at the current moment, and the method comprises the following steps: and the obtained new occupation proportion of the operation kernel of the monitored diskless workstation is normalized representation data after the application software is newly added at the current moment of the monitored diskless workstation.
By adopting the application data identification system of the computer diskless workstation, the technical problem that the unmanned management level of the diskless workstation in the prior art is difficult to meet the current requirement is solved, and the unmanned management level of the monitored diskless workstation is maintained by acquiring the newly added operand duty ratio of each application software to the local operation kernel after the monitored diskless workstation in the current state, and identifying each safety application software which does not cause overload fault of the operation kernel.
While the invention has been described with considerable specificity, it should be appreciated that those skilled in the art may change the elements thereof without departing from the spirit and scope of the invention. It is believed that the system of the present invention and the attendant advantages thereof will be understood by the foregoing description and it will be apparent that various changes may be made in the form, construction and arrangement of the components thereof without departing from the scope and spirit of the invention or without sacrificing all of its material advantages, the form herein before described being merely an explanatory embodiment thereof, and further without providing additional material change. The claims are intended to cover and include such modifications.
Claims (10)
1. A computer diskless workstation application data qualification system, the system comprising:
the remote detection device is connected with a remote server network bound with the monitored diskless workstation and is used for acquiring the maximum communication bandwidth, the storage capacity and the operation rate of the remote server, and simultaneously, is also used for acquiring the total number of diskless workstations managed by the remote server, and is also used for acquiring ASCIL code values of software names of each application software stored on the remote server and assigned to the monitored diskless workstation;
the station body measuring device is connected with the monitored diskless workstation and is used for acquiring the maximum communication bandwidth of the monitored diskless workstation, the maximum capacity of the internal memory and the real-time calculation total consumed by each currently executed process;
the model learning device is used for executing fixed number of learning processes on the radial basis function neural network to obtain the radial basis function neural network after the learning process is completed and outputting the radial basis function neural network as an artificial intelligent model, wherein in each learning process, the real-time operation total amount consumed by each process executed by a monitored diskless workstation at a certain moment, the maximum communication bandwidth of the monitored diskless workstation, the maximum capacity of an internal memory and the ASCIL code value of the software name of newly added application software at a certain moment are taken as each input information of the artificial intelligent model, and the newly added occupation proportion of the operation core of the monitored diskless workstation after the application software is newly added at a certain moment of the monitored diskless workstation is taken as single output information of the artificial intelligent model to complete the learning process;
the proportion judging device is respectively connected with the model learning device, the station body measuring device and the remote detecting device and is used for parallelly inputting the real-time operation total amount consumed by each process executed by the monitored diskless workstation at the current moment, the maximum communication bandwidth of the monitored diskless workstation, the maximum capacity of the internal memory and the ASCIL code value of the software name of the newly-added application software at the current moment into the artificial intelligent model, and operating the artificial intelligent model to obtain the newly-added occupation proportion of the operation core of the monitored diskless workstation after the newly-added application software of the monitored diskless workstation is output by the artificial intelligent model and serve as the prediction increment proportion corresponding to the newly-added application software at the current moment;
the traversal processing equipment is connected with the proportion judging equipment and is used for inputting all ASCIL code values which are stored on a remote server and respectively correspond to all application software assigned to the monitored diskless workstation into the artificial intelligent model one by one so as to obtain all predicted increasing proportions respectively corresponding to all application software;
the marking processing device is connected with the traversing processing device and is used for marking the application software of which the corresponding predicted increment proportion in each application software does not exceed the residual unoccupied proportion of the operation kernel at the current moment of the monitored diskless workstation as safe application software.
2. The computer diskless workstation application data qualification system of claim 1, wherein the system further comprises:
the instant display device is connected with the mark processing device and arranged in the monitored diskless workstation and is used for carrying out blue low-brightness marks on each piece of safety application software in the application software;
wherein the instant display device is further configured to highlight red for each of the respective applications.
3. The computer diskless workstation application data qualification system of claim 1, wherein the system further comprises:
the proportion storage device is connected with the marking processing device and is used for temporarily storing the residual unoccupied proportion of the operation kernel at the current moment of the monitored diskless workstation;
the quartz oscillation device is respectively connected with the proportion judging equipment, the model learning device and the remote detection device;
the quartz oscillation device is used for providing different reference clock pulses required by the proportion judging device, the model learning device and the remote detection device respectively.
4. A computer diskless workstation application data qualification system as claimed in any one of claims 1-3, wherein:
the marking processing device is further used for marking the application software with the corresponding predicted increment proportion in each application software exceeding or being equal to the residual unoccupied proportion of the operation kernel at the current moment of the monitored diskless workstation as excessive application software.
5. A computer diskless workstation application data qualification system as claimed in any one of claims 1-3, wherein:
in each learning process, taking the real-time operation total amount consumed by each process executed by the monitored diskless workstation at a certain moment, the maximum communication bandwidth and the maximum capacity of an internal memory of the monitored diskless workstation and the ASCIL code value of the software name of the newly added application software at a certain moment as each input information of the artificial intelligent model, taking the newly added occupation ratio of the operation kernel of the monitored diskless workstation after the newly added application software of the monitored diskless workstation at a certain moment as each output information of the artificial intelligent model, and completing the learning process comprises the following steps: in each learning process, respectively carrying out normalization processing on the total real-time operation amount consumed by each process executed by the monitored diskless workstation at a certain moment, the maximum communication bandwidth of the monitored diskless workstation, the maximum capacity of an internal memory and the ASCIL code value of the software name of the newly added application software at a certain moment, and then inputting the normalized ASCIL code value into the artificial intelligent model in parallel.
6. The computer diskless workstation application data qualification system of claim 5, wherein:
in each learning process, taking the real-time operation total amount consumed by each process executed by the monitored diskless workstation at a certain moment, the maximum communication bandwidth and the maximum capacity of an internal memory of the monitored diskless workstation and the ASCIL code value of the software name of the newly added application software at a certain moment as each input information of the artificial intelligent model, taking the newly added occupation ratio of the operation kernel of the monitored diskless workstation after the newly added application software of the monitored diskless workstation at a certain moment as each output information of the artificial intelligent model, and completing the learning process comprises the following steps: and normalizing the newly increased occupation proportion of the operation kernel of the monitored diskless workstation after newly adding application software at a certain moment, and taking the newly increased occupation proportion as single output information of the artificial intelligent model.
7. A computer diskless workstation application data qualification system as claimed in any one of claims 1-3, wherein:
inputting the total real-time operation amount consumed by each process executed by the monitored diskless workstation at the current moment, the maximum communication bandwidth of the monitored diskless workstation, the maximum capacity of an internal memory and the ASCIL code value of the software name of the newly added application software at the current moment into the artificial intelligent model in parallel, operating the artificial intelligent model to obtain the output newly added occupation proportion of the operation kernel of the monitored diskless workstation after the newly added application software of the monitored diskless workstation at the current moment, and taking the newly added occupation proportion as the prediction increment proportion corresponding to the newly added application software at the current moment, wherein the method comprises the following steps of: and respectively carrying out normalization processing on the total real-time operation amount consumed by each process executed by the monitored diskless workstation at the current moment, the maximum communication bandwidth of the monitored diskless workstation, the maximum capacity of the internal memory and the ASCIL code value of the software name of the newly added application software at the current moment, and then inputting the normalized ASCIL code value into the artificial intelligent model in parallel.
8. A computer diskless workstation application data qualification system as claimed in any one of claims 1-3, wherein:
the remote detection device is connected with a remote server network bound with the monitored diskless workstation, and is used for acquiring the communication bandwidth, the storage capacity and the operation rate of the remote server, and simultaneously, the total number of diskless workstations managed by the remote server is also acquired, and the remote detection device comprises: the remote server is a big data server or a cloud server.
9. The computer diskless workstation application data qualification system of claim 8, wherein:
the remote detection device is connected with a remote server network bound with the monitored diskless workstation, and is used for acquiring the communication bandwidth, the storage capacity and the operation rate of the remote server, and simultaneously, the total number of diskless workstations managed by the remote server is also acquired, and the remote detection device comprises: the remote probe device is connected to a remote server network to which the monitored diskless workstation is bound via a wireless communication link based on a time division duplex communication mode.
10. The computer diskless workstation application data qualification system of claim 8, wherein:
the remote detection device is connected with a remote server network bound with the monitored diskless workstation, and is used for acquiring the communication bandwidth, the storage capacity and the operation rate of the remote server, and simultaneously, the total number of diskless workstations managed by the remote server is also acquired, and the remote detection device comprises: the remote probe device is connected to a remote server network to which the monitored diskless workstation is bound, via a wireless communication link based on a frequency division duplex communication mode.
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| CN202310323144.XA CN116340104B (en) | 2023-03-29 | 2023-03-29 | Application data authentication system for computer diskless workstation |
| PCT/CN2023/106685 WO2024198155A1 (en) | 2023-03-29 | 2023-07-11 | Computer diskless workstation application data identification system |
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| WO2024198155A1 (en) * | 2023-03-29 | 2024-10-03 | 陈春兰 | Computer diskless workstation application data identification system |
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