A computer fault alarm system and method

Abstract

The application discloses a computer fault alarm system and method, which is applied to the technical field of fault alarm and solves the technical problem of computer fault alarm. The computer fault alarm method comprises the following steps: (S1) collecting and storing computer fault information in a listener database through a fault management module; (S2) periodically deleting the computer fault information in the listener database on the basis of partitioning by a listener service process module; (S3) updating the computer fault information in an alarm database and an event database by a listener service program module, and transmitting the computer fault information to the listener database; and (S4) receiving fault alarm information output by the listener database by the fault management module, and controlling an alarm to send an alarm sound and light signal. The computer fault alarm system greatly improves the efficiency of processing computer fault information, and ensures that the error of the processing result of the computer fault information is less than 3%.

Description

Technical Field

[0001] This invention relates to the field of computers, and more specifically to a computer fault alarm system and method. Background Technology

[0002] Home desktop computers are now ubiquitous, boasting high operating speeds and storing vast amounts of data and running programs. However, once a computer malfunctions, data may be lost or stolen, causing inconvenience and losses for users. Furthermore, computer malfunctions are unpredictable, and taking action after a failure often proves too late to undo the damage. Existing technologies employ intelligent management techniques to establish computer fault alarm systems, displaying fault results in real-time on the screen and analyzing the results to develop fault diagnosis methods. However, the data display in the established visual model is relatively slow and unsuitable for rapid computer fault diagnosis. Another technical solution uses a branch-and-bound method to optimize the computer fault handling process, ensuring efficient output of computer fault information. However, false alarms occur frequently, resulting in large errors in the processed fault information. Summary of the Invention

[0003] To address the aforementioned problems, this invention discloses a computer fault alarm system and method, which can perform computer fault alarms and realize the analysis and processing of computer fault data.

[0004] To achieve the above-mentioned technical effects, the present invention adopts the following technical solution:

[0005] A computer fault alarm system, comprising:

[0006] An alarm device is used to receive alarm commands from the fault management module and emit alarm sound signals. The alarm device uses a red (Light-Emitting Diode, LED) alarm indicator light and emits alarm sound signals through digital sound waves.

[0007] A fault management module is used to store computer fault information collected from the computer. The fault management module uses a fuzzy clustering analysis algorithm to parse and store the collected computer fault information, and stores the collected computer fault information in the listener database by periodically executing batch submission.

[0008] A listener database is used to store computer fault information sent periodically from the fault management module. The listener database is partitioned based on the time segmentation of computer fault information using the Structured Query Language (SQL Server) program.

[0009] The listener service program module is used to delete computer fault information from the listener database partition by partition. This module employs a multilevel optimization algorithm (MOA) model to classify and process computer fault information in the alarm database and event database from both macro and micro perspectives. The relationship between the MOA macro model and the time period is as follows:

[0010] (1)

[0011] In equation (1), J(X) represents the MOA macro model, t represents the time period for the MOA macro model to process computer fault information, γ represents the degree of influence on the modeling of the MOA macro model, and α k (t) represents the hierarchical expansion vector of the MOA macro model, δ(X) represents the computer fault information sample function under the MOA macro model, x represents the hierarchical coordinate variable of the MOA macro model, and φ k The parameterized dynamic differential coefficients of the MOA macroscopic model are represented by N, and the total number of MOA model levels is represented by N. Formula (5) represents the relationship between the MOA macroscopic model and the time period. The computer fault information parameters are input into the MOA macroscopic model, and the final output result can cause changes in a single MOA microscopic model and serve as the input of the MOA microscopic model. Finally, the relationship between the MOA microscopic model and the time period is obtained as follows:

[0012] (2)

[0013] In equation (2), J(Y) represents the MOA micro-model, w represents the micro-computer fault information bias, the superscript T indicates transpose, j represents the micro-function hierarchy variable, and H k (y) represents the parameterized dynamic differential coefficients of the MOA micro-model, y represents the micro-ordinate variable, δ(Y) represents the computer fault information sample function under the MOA micro-model, and β k The vector represents the micro-level extension vector, and k(Y) represents the number of micro-levels in the MOA micro-model. By using formulas (1) and (2), the relationship between the macro-level model and the micro-level model and the calculation processing time periodic function is calculated to perform hierarchical processing of computer fault information, and finally complete the information processing.

[0014] Alarm database; used to store and manage all alarm data generated in the computer;

[0015] Event database; used to store and manage all events generated on the computer, except for alarms;

[0016] A listener database is used to temporarily store all network viruses encountered by the computer, so that computer users can query network viruses and set up antivirus software. The listener database uses Zigbee wireless communication to forward network viruses to the computer in real time. For this purpose, the listener database temporarily stores all generated network viruses, and each computer receives network virus information by periodically querying the listener database, and obtains the cause of computer fault alarms through network virus information. Computer users can set up antivirus software to protect the computer.

[0017] The system consists of computers A, B, and C. When a computer malfunctions, it outputs new computer information to the computer database. The monitoring database actively queries for new alarm data. The monitoring database is connected to the fault management module, which controls the alarm to emit audible and visual signals. The monitoring database is connected to the monitoring service program module, which periodically deletes computer information from the monitoring database according to partitions. The computer database is also connected to the monitoring service program module, which controls the deletion of computer information from the computer database. Finally, the monitoring service program module submits historically stored computer information in batches to the alarm database and the practice database.

[0018] As a further technical solution of the present invention, a computer fault alarm method includes the following steps:

[0019] (S1) Collect and store computer fault information in the listener database through the fault management module;

[0020] (S2) The listener service process module periodically deletes computer fault information from the listener database based on the partition;

[0021] (S3) The computer fault information in the alarm database and event database is updated by the listener service program module and transmitted to the listener database;

[0022] (S4) The fault management module receives the fault alarm information output by the listener database and controls the alarm to emit alarm sound and light signals.

[0023] As a further technical solution of the present invention, the feature is that: the listener service program module uses the MOA model to classify and process computer fault information in the alarm database and event database from both macroscopic and microscopic perspectives. The MOA steps include:

[0024] Step 1: Parameterize the computer fault information and construct the MOA macroscopic model by differential calculation of the computer fault information parameters:

[0025] (3)

[0026] In equation (3), Let X represent the objective function for computer fault information, Y represent the x-coordinate of the constraint coordinate system under macro-constraints, u represent the computer fault information deviation, the superscript T indicates transpose, k(X) represent the hierarchical ordinal number of the MOA macro-model, and i represent the computer fault information parameters in the MOA model. The derivative of the MOA macro-model is then calculated.

[0027] (4)

[0028] In equation (4), J(X) represents the MOA macroscopic model, t represents the time period for the MOA macroscopic model to process computer fault information, γ represents the degree of influence on the modeling of the MOA macroscopic model, δ(X) represents the computer fault information sample function, ▽X represents the gradient operator of the MOA macroscopic model, and v represents the speed at which the MOA macroscopic model processes computer fault information; further partial derivatives of equation (4) for the MOA macroscopic model are obtained:

[0029] (5)

[0030] In equation (5), Represents the hierarchical boundary of the MOA macroscopic model; α k (t) represents the hierarchical expansion vector of the MOA macro model, and N represents the total number of levels in the MOA model;

[0031] Step 2: Based on formulas (4) and (5), the relationship between the MOA macro model and the macro-level extension vector is derived as follows:

[0032] (6)

[0033] In equation (6), x represents the hierarchical coordinate variable of the MOA macroscopic model, and φ k Represents the parameterized dynamic differential coefficients of the MOA macroscopic model;

[0034] Based on formulas (3) to (6), the relationship between the MOA macro model and the time period is derived as follows:

[0035] (7)

[0036] Formula (7) represents the relationship between the MOA macro model and the time period. The computer fault information parameters are input into the MOA macro model, and the final output result can cause changes in the individual MOA micro model and serve as the input of the MOA micro model.

[0037] Step 3: The MOA model is microscopically processed to obtain:

[0038] (8)

[0039] In equation (8), w represents the microscale computer fault information bias, j represents the microscale function hierarchy variable, and k(Y) represents the MOA microscale model hierarchy number. Taking the derivative of the MOA microscale model function, the relationship between the MOA microscale model and the microscale hierarchy extension vector is obtained as follows:

[0040] (9)

[0041] In equation (9), J(Y) represents the MOA micro-model, δ(Y) represents the computer fault information sample function under the MOA micro-model, and Y represents the spatial ordinate β under micro-constraints. k H represents the micro-level extension vector. k (y) represents the parameterized dynamic differential coefficients of the MOA micro-model, and y represents the micro-ordinate variable;

[0042] Step 4: Similarly, the derivation process of formula (10) yields the relationship between the MOA micro-model and the time period as follows:

[0043] (10)

[0044] By using formulas (7) and (10) to calculate the relationship between the macroscopic and microscopic hierarchical models and the periodic function of the processing time, the computer fault information is processed hierarchically, and the information processing is finally completed.

[0045] As a further technical solution of the present invention, the listener service program module includes a listener, and the listener service program module stores the network virus information obtained from the listener database in the alarm database, and records the network virus information in the alarm database when generating fault information.

[0046] As a further technical solution of the present invention, when the network virus information obtained from the listener database is stored in the alarm database and the event database, the listener service program module performs a batch submission. In this batch submission, the data is packaged and processed together with the highest fault level category displayed in the data packet.

[0047] As a further technical solution of the present invention, the listener service program module deletes the database of abnormally terminated computers from the computer database every 24 hours. When the alarm manager has terminated normally, each computer no longer executes queries and deletes its information from the computer column database.

[0048] As a further technical solution of the present invention, the fault management module includes a fault manager, which writes the runtime information of the computer registered on the computer column database during initial operation, and receives the assigned computer identifier.

[0049] As a further technical solution of the present invention, the fault manager includes an alarm data query module. After registering an identifier on the computer database, the computer queries whether there is new alarm data. The computer executes a query to confirm whether the newly arrived alarm information exists in the listener database, and checks whether there is a number greater than the last sequence number to confirm whether the new alarm data has arrived.

[0050] As a further technical solution of the present invention, the alarm data query method further includes periodically obtaining all network viruses after the last sequence by periodically querying the listener database to obtain newly arrived alarms, wherein the last sequence is used to distinguish newly arrived alarms, and the last sequence is the sequence number of the last alarm read by the computer when periodically performing alarm query.

[0051] The beneficial effects of this invention are as follows:

[0052] Unlike conventional technologies, this invention provides a predictive, reactive, and proactive information system arrangement. Through MOA technology, it enables computer fault alarm systems to process computer fault information efficiently while ensuring that the error of computer fault information is below 3%. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein:

[0054] Figure 1 A diagram of a computer fault alarm system is shown;

[0055] Figure 2 A flowchart of a computer fault alarm method is shown;

[0056] Figure 3 A graph showing the comparison of data processing efficiency of different computer fault alarm methods is presented.

[0057] Figure 4 The graph shows a comparison of data processing errors for different computer fault alarm methods. Detailed Implementation

[0058] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0059] like Figure 1 As shown, a computer fault alarm system includes an alarm, a fault management module, a listener database, a listener service program module, an alarm database, an event database, and a listener database.

[0060] In a specific embodiment, the alarm uses a red LED alarm indicator to receive alarm commands from the fault management module. It alerts or warns the user of a computer malfunction through digital sound waves in the form of sound and light. The alarm housing is made entirely of stainless steel, with strong impact resistance. It is equipped with a powerful buzzer and features stable operation, long service life, low power consumption, and is unaffected by pollutants and water. The alarm internally includes an audible and visual alarm circuit, which consists of a voice alarm circuit and an LED array. When the trigger circuit is activated, the alarm is started, the relay closes, the red LED array illuminates, and a voice alarm sounds. The voice chip in the audible and visual alarm circuit uses an aP89042. This circuit features a wide voltage supply, using 5V power, and requires a reset capacitor to ensure reliable operation. In general applications, this chip can directly drive a low-power speaker, but the loudness is insufficient. Therefore, an LM384 is connected after the voice chip to drive an 8Ω / 5W speaker, achieving a loudness of 85dB. The LM384 is a dedicated 5W audio power amplifier chip. A toggle switch is used to set the address, and a potentiometer is used to adjust the loudness. The LED array has two colors: green and red. In this invention, the voltage drop of the LEDs when they are on is 1.96V, so six LEDs can be connected in series first, then in parallel, with a current-limiting resistor connected in series in each branch. The LED array only illuminates when the green light is on, i.e., when no alarm is triggered, the intrinsically safe power supply outputs 200mA. When the alarm is triggered, the red light illuminates and an audio alarm is triggered; at this time, the intrinsically safe power supply outputs 1A.

[0061] In a specific embodiment, the fault management module is used to store computer fault information collected from the computer. The fault management module uses a fuzzy clustering analysis algorithm to parse and store the collected computer fault information, and stores it in a listener database by periodically performing batch submissions. The listener database is used to store computer fault information periodically sent from the fault management module. The computer stores network viruses received from external sources in the listener database, which can be understood as temporary storage space, and then updates the alarm database and event database with the received network viruses. When a computer receives a network virus causing a computer fault event, the fault management module updates data in the alarm database and event database and generates historical data; this update is performed along with the process of storing the received network virus in the listener database. For this purpose, the listener database has a listener database, which is a fault information identification space for each computer. The computer can read fault information from the listener database allocated to it and identify fault generation; this is implemented by the fault manager, which is an application driven within the computer. That is, if the computer runs the fault manager to handle real-time events, a database is allocated to the fault manager, which is a listener in a database created by the server. The listener database will be created by the number of fault managers driving it. This is intended to forward the results of each independent task performed by the fault manager.

[0062] In a specific embodiment, the listener database is partitioned based on SQL Server partitioning, with computer fault information segmented by time. SQL Server is a relational database management program developed and promoted by Microsoft. The purpose of partitioning is to facilitate data retrieval and management, similar to dividing a hard drive into C, D, E, and F drives, using C drive for system files, D drive for software installation, and E drive for study materials. A common partitioning method is by time, with each minute or second as a separate partition. To find computer fault information for a given minute or second, the query is performed directly at that minute or second.

[0063] In a specific embodiment, the listener service program module is used to delete computer fault information from the listener database partition by partition. The listener service program module uses the MOA model to classify and process computer fault information in the alarm database and event database from both macro and micro perspectives. The relationship between the MOA macro model and the time period is as follows:

[0064] (1)

[0065] In equation (1), J(X) represents the MOA macro model, t represents the time period for the MOA macro model to process computer fault information, γ represents the degree of influence on the modeling of the MOA macro model, and αk (t) represents the hierarchical expansion vector of the MOA macro model, δ(X) represents the computer fault information sample function under the MOA macro model, x represents the hierarchical coordinate variable of the MOA macro model, and φ k The parameterized dynamic differential coefficients of the MOA macroscopic model are represented by N, and the total number of MOA model levels is represented by N. Formula (5) represents the relationship between the MOA macroscopic model and the time period. The computer fault information parameters are input into the MOA macroscopic model, and the final output result can cause changes in a single MOA microscopic model and serve as the input of the MOA microscopic model. Finally, the relationship between the MOA microscopic model and the time period is obtained as follows:

[0066] (2)

[0067] In equation (2), J(Y) represents the MOA micro-model, w represents the micro-computer fault information bias, the superscript T indicates transpose, j represents the micro-function hierarchy variable, and H k (y) represents the parameterized dynamic differential coefficients of the MOA micro-model, y represents the micro-ordinate variable, δ(Y) represents the computer fault information sample function under the MOA micro-model, and β k The vector represents the micro-level extension vector, and k(Y) represents the number of micro-levels in the MOA micro-model. By using formulas (1) and (2), the relationship between the macro-level model and the micro-level model and the calculation processing time periodic function is calculated to perform hierarchical processing of computer fault information, and finally complete the information processing.

[0068] In a specific embodiment, the listener service program is a continuously running program whose purpose is to handle periodic service requests that the computer system is expected to receive. The background program, running in the background, performs tasks related to system operation and correctly forwards collected requests to other programs or processes. Therefore, the network virus receiving service program is a listener service program module that remains in the background and then starts running automatically, performing necessary tasks when conditions for generating pending tasks arise. For example, when a release alarm is received, the fault management module, acting as the network virus receiving daemon, uses alarm generation information such as location and time to search for the corresponding alarm in the existing generated and stored alarms, and writes the alarm release or executes an alarm summary task to indicate the database-level alarm on the upper-layer network graph.

[0069] In a specific embodiment, an alarm database is used to store and manage all alarm data generated in the computer, and an event database is used to store and manage all events generated in the computer other than alarms. This additional function is performed whenever a network virus is generated. That is, each computer receives the network virus processed as described above using a query method and displays the information on the screen. For example, if a computer queries ten newly arrived alarms 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 in the listener database, the last sequence (the last sequence) is 10. In traditional synchronous alarm processing methods, certain related tasks need to be performed before finally storing each generated alarm information in order to forward alarm information in real time. For example, before performing tasks, each computer cannot query alarms, such as releasing alarms, processing proxy database alarms, or increasing the alarm count for alarms generated in an overlapping manner. To this end, the network virus receiving daemon forms a single commit to store alarms in all databases. Each computer cannot query alarms before executing this single commit. A commit refers to a database update performed when a transaction is successfully completed.

[0070] In a specific embodiment, the listener database is used to temporarily store a database of all network viruses encountered by the computer, so that computer users can query network viruses and set up antivirus software. The listener database uses Zigbee wireless communication to forward real-time network viruses to the computer. For this purpose, the listener database temporarily stores all generated network viruses, and each computer receives network virus information by periodically querying the listener database, and obtains the reason for computer fault alarms through the network virus information. Computer users can set up antivirus software to protect their computers.

[0071] In a specific embodiment, when network virus information obtained from the listener database is stored in the alarm database and the event database, the listener service module performs batch submission, where data is packaged and centrally processed, and in this process, the class displaying the highest fault severity in the data packet is selected. Therefore, a collective database-based alarm selection is formed based on the selected category. The most important function of the listener service module includes periodic data partition deletion. Alarm information stored in the listener database is intended for querying by the computer. Queryed information should be deleted periodically. Therefore, due to the periodic deletion of stored information, the storage in the listener database can be understood as temporary storage. The invention is characterized by deleting stored data (i.e., data that has already been read) from the alarm information stored in the listener database partition by partition, rather than searching and deleting old data one by one. At this time, a partition is created every ten minutes, and alarms contained within those ten minutes are stored in the same partition. If the time has elapsed, the partition is deleted, i.e., the old partition in 10-minute units, so that the data contained in that partition is deleted all at once. This aims to improve the processing speed latency caused by searching and deleting old data one by one as described above, and the collective deletion based on partitions can significantly improve processing speed.

[0072] The system consists of computers A, B, and C. When a computer malfunctions, it outputs new computer information to the computer database. The monitoring database actively queries for new alarm data. The monitoring database is connected to the fault management module, which controls the alarm to emit audible and visual signals. The monitoring database is connected to the monitoring service program module, which periodically deletes computer information from the monitoring database according to partitions. The computer database is also connected to the monitoring service program module, which controls the deletion of computer information from the computer database. Finally, the monitoring service program module submits historically stored computer information in batches to the alarm database and the practice database.

[0073] In a specific embodiment, a computer fault alarm method includes the following steps:

[0074] (S1) Collect and store computer fault information in the listener database through the fault management module;

[0075] In a specific embodiment, the fault management module parses the arriving network virus data into a storable format. Then, when a network virus generated in the network arrives at the fault management module, it is temporarily stored in the listener database. When a network virus arrives, the timer of the fault management module is driven to periodically execute batch submission.

[0076] (S2) The listener service module periodically deletes computer fault information from the listener database based on the partition;

[0077] In a specific embodiment, the listener service program module periodically extracts all network virus information after the last sequence from the listener database. The listener service program module stores the network virus information extracted from the listener database in the alarm database and the event database. The listener service program module performs collective database alarm selection according to the selected class. The listener service program module periodically deletes old data partitions and, based on the partitions, periodically deletes computer fault information. The alarm information stored in the listener database is used for computer querying, and the queried information is periodically deleted.

[0078] (S3) The computer fault information in the alarm database and event database is updated by the listener service program module and transmitted to the listener database;

[0079] In a specific embodiment, the listener service program module periodically deletes computer information. The storage in the listener database is temporary. The listener service program module monitors the computer column database and compares the monitoring time with the computer's last query time to determine if an abnormal termination has occurred. When an abnormal termination is determined, the listener service program module deletes the abnormally terminated computer column database from the computer column database. The computer registers its identifier in the computer column database, writes its runtime information, and receives the assigned computer identifier after registering the identifier in the computer column database. The computer queries whether there is new alarm data and performs a query to confirm whether newly arrived alarm information exists in the listener database, and checks whether there is a number greater than the last sequence number to confirm whether new alarm data has arrived.

[0080] (S4) The fault management module receives the fault alarm information output by the listener database and controls the alarm to emit alarm sound and light signals.

[0081] In a specific embodiment, the listener service program module stores network virus information obtained from the listener database in the alarm database, and records network virus information in the alarm database when generating fault information. When the network virus information obtained from the listener database is stored in the alarm database and the event database, the listener service program module performs a batch submission. In this batch submission, the data is packaged and processed together with the highest fault severity category selected in the data packet.

[0082] In a specific embodiment, the fault management module includes a fault manager, which initially registers the identifiers of computers in the computer column database, writes their runtime information, and receives assigned computer identifiers. Furthermore, the listener service module deletes abnormally terminated computers from the computer database every 24 hours. When the alarm manager has terminated normally, each computer no longer performs queries and its information is deleted from the computer column database.

[0083] In a specific embodiment, the fault manager includes an alarm data query module. After registering an identifier in the computer database, the computer queries whether new alarm data exists. The computer executes a query to confirm whether newly arrived alarm information exists in the listener database, and checks whether there is a number greater than the last sequence number to confirm whether new alarm data has arrived. The alarm data query method further includes periodically retrieving all network viruses after the last sequence number by periodically querying the listener database to obtain newly arrived alarms. The last sequence number is used to distinguish newly arrived alarms and is the sequence number of the last alarm read when the computer periodically executes the alarm query.

[0084] In a specific embodiment, the listener service program module uses the MOA model to classify and process computer fault information in the alarm database and event database from both macro and micro perspectives. The MOA steps include:

[0085] Step 1: Parameterize the computer fault information and construct the MOA macroscopic model by differential calculation of the computer fault information parameters:

[0086] (3)

[0087] In equation (3), Let X represent the objective function for computer fault information, Y represent the x-coordinate of the constraint coordinate system under macro-constraint conditions, u represent the deviation of computer fault information, the superscript T indicates transpose, k(X) represents the ordinal number of the MOA macro-model hierarchy, and i represent the computer fault information parameters in the MOA model.

[0088] In the process of computer fault information processing, classifying computer fault information is regarded as a linear programming problem. If we further classify it according to the observation method of computer fault information phenomena, it can be divided into macroscopic models and microscopic models. When examining computer fault information phenomena, we only pay attention to the small parts of space and time. This is the microscopic observation method, and the resulting mathematical model is microscopic. If we take a long-term, macroscopic view of computer fault information and observe it in a general or overall way, we get a macroscopic model. The equations derived by the microscopic observation method are mostly differential equations. The equations derived by the macroscopic observation method are mostly integral equations. Therefore, equation (3) is the MOA macroscopic model, so the calculated The objective function representing computer fault information is an integral equation. Further differentiation of the MOA macroscopic model is then performed:

[0089] (4)

[0090] In equation (4), J(X) represents the MOA macroscopic model, t represents the time period for the MOA macroscopic model to process computer fault information, γ represents the degree of influence on the modeling of the MOA macroscopic model, δ(X) represents the computer fault information sample function, ▽X represents the gradient operator of the MOA macroscopic model, and v represents the speed at which the MOA macroscopic model processes computer fault information. Further partial derivatives of equation (2) for the MOA macroscopic model are obtained:

[0091] (5)

[0092] In equation (5), Represents the hierarchical boundary of the MOA macroscopic model; α k (t) represents the hierarchical expansion vector of the MOA macro model, and N represents the total number of levels in the MOA model;

[0093] In the process of computer fault information processing, to derive the relationship between the MOA macroscopic model J(X) and the time period, the first step is to eliminate the hierarchical boundary of the MOA macroscopic model. Hierarchical extension vector α with MOA macro model k The expression for (t) is obtained by substituting the expression through partial derivatives, thus yielding the relationship between the MOA macroscopic model J(X) and the time period.

[0094] Step 2: Based on formulas (4) and (5), the relationship between the MOA macro model and the macro-level extension vector is derived as follows:

[0095] (6)

[0096] In equation (6), x represents the hierarchical coordinate variable of the MOA macroscopic model, and φ kRepresents the parameterized dynamic differential coefficients of the MOA macroscopic model;

[0097] Based on formulas (3) to (6), the relationship between the MOA macro model and the time period is derived as follows:

[0098] (7)

[0099] Formula (7) represents the relationship between the MOA macro model and the time period. The computer fault information parameters are input into the MOA macro model, and the final output result can cause changes in the individual MOA micro model and serve as the input of the MOA micro model.

[0100] In the MOA macroscopic model, macroscopic and microscopic represent two aspects of understanding matter. The macroscopic world is composed of the microscopic world, but under certain relative conditions, the two are dialectically related. Therefore, computer fault information processing in the MOA macroscopic model can affect the feedback response of a single MOA microscopic model, and the influence of the microscopic model can constrain the development of the macroscopic model. Thus, a comprehensive analysis of the computer fault information processing problem is necessary.

[0101] Step 3: The MOA model is microscopically processed to obtain:

[0102] (8)

[0103] In equation (8), w represents the microscale computer fault information bias, j represents the microscale function hierarchy variable, and k(Y) represents the MOA microscale model hierarchy number. Taking the derivative of the MOA microscale model function, the relationship between the MOA microscale model and the microscale hierarchy extension vector is obtained as follows:

[0104] (9)

[0105] In equation (9), J(Y) represents the MOA micro-model, δ(Y) represents the computer fault information sample function under the MOA micro-model, and Y represents the spatial ordinate β under micro-constraints. k H represents the micro-level extension vector. k (y) represents the parameterized dynamic differential coefficients of the MOA micro-model, and y represents the micro-ordinate variable;

[0106] Step 4: Similarly, the derivation process of formula (10) yields the relationship between the MOA micro-model and the time period as follows:

[0107] (10)

[0108] By using formulas (7) and (10) to calculate the relationship between the macroscopic and microscopic hierarchical models and the periodic function of the processing time, the computer fault information is processed hierarchically, and the information processing is finally completed.

[0109] In the process of computer fault information processing, fault information categories, including both macroscopic and microscopic levels, include:

[0110] (1) Frequent crashes: The virus opened many files or occupied a lot of memory; it was unstable; it ran large-capacity software that occupied a lot of memory and disk space; it used some test software (which had many bugs); it did not have enough hard disk space, etc.; Frequent crashes when running software on the Internet may be due to slow network speed, large program size, or low hardware configuration of your workstation.

[0111] (2) The system cannot start: The virus has modified the hard drive's boot information or deleted some boot files. For example, the boot files of a boot virus are corrupted; the hard drive is damaged or the parameters are set incorrectly; system files are accidentally deleted by human, etc.

[0112] (3) Files cannot be opened: The virus has modified the file format; the virus has modified the file link location; the file is corrupted; the hard drive is damaged; the link location corresponding to the file shortcut has changed; the software that originally edited the file has been deleted; if it is in a local area network, it is often manifested as the file storage location on the server has changed, and the workstation has not updated the server content in time (the file explorer has been open for a long time).

[0113] (4) Frequent reports of insufficient memory: viruses illegally occupy a large amount of memory; a large number of software programs are opened; software that requires memory resources is running; the system configuration is incorrect; the memory is insufficient (the current basic memory requirement is 128M), etc.

[0114] (5) Not enough hard disk space: The virus has copied a large number of virus files; the capacity of each partition on the hard disk is too small; a large number of large-capacity software programs have been installed; all software programs are installed in one partition.

[0115] (6) Read / write signals appear when floppy disks or other devices are not accessed: virus infection; the floppy disk has taken away files that were previously opened on the floppy disk.

[0116] (7) A large number of files of unknown origin appear: virus copy files; temporary files generated during software installation; or software configuration information and running records.

[0117] (8) Black screen on startup: virus infection; monitor failure; graphics card failure; motherboard failure; excessive overclocking; CPU damage, etc.

[0118] (9) Data loss: The virus deleted the file; the hard disk sector was damaged; the original file was overwritten due to file recovery; if it is a file on the network, it may also be due to other users accidentally deleting it.

[0119] (10) The keyboard or mouse locks up for no reason: the virus is the culprit, pay special attention to "Trojan"; the keyboard or mouse is damaged; the keyboard or mouse interface on the motherboard is damaged; a keyboard or mouse locking program is running, the program is too large, the system is busy for a long time, and the keyboard or mouse does not work.

[0120] (11) Slow system speed: The virus occupies memory and CPU resources and runs a lot of illegal operations in the background; low hardware configuration; too many or too large programs are open; incorrect system configuration; if it is running a program on the network, it is mostly due to your machine configuration being too low, or it may be that the network is busy at the time and many users open a program at the same time; another possibility is that your hard drive space is not enough to be used for temporary data exchange when running programs.

[0121] (12) Automatic operation of the system: The virus performs illegal operations in the background; the user has set up automatic operation of related programs in the registry or startup group; some software needs to automatically restart the system after installation or upgrade.

[0122] In a specific embodiment, the computer information processing method MOA of the present invention was simulated and tested. The present invention simultaneously detected the fault conditions of 20 computers, performed computer fault diagnosis through fault information recording, and analyzed the causes of faulty computers. The main simulation computer was an Intel Core eight-core 64+128GB memory computer for algorithm processing, and MATLAB 2019 simulation software was used to verify the results. Some computer status parameters and detection results are shown in Table 1.

[0123] Table 1 Computer Status Parameter Data

[0124] Computer serial number Rated power / kW Pulse frequency / kHz CPU / % Test results #1 35 0.2506 99 Malfunction #4 45 0.2953 54 No fault #6 50 1.263 66 No fault #13 60 1.295 98 Malfunction

[0125] Table 1 shows that the judgment of the collected computer information verification results depends on the CPU usage percentage. When the CPU occupies almost all of the computer's resources, it indicates that a large number of network virus programs are running in the background, causing computer failure. Referring to computer status parameter data similar to those in Table 1, the status parameter data of 20 computers were compiled into a computer fault information database. Simulations were conducted comparing Scheme 1 (establishing a computer fault alarm system based on intelligent management technology), Scheme 2 (optimizing the computer fault handling process using the branch and bound method), and this invention. The first comparison indicator was data processing speed. By extracting computer fault information from 0 to 2400 MB and comparing the data processing time of different methods, the simulation results are as follows: Figure 3 As shown. From Figure 3Analysis of the data processing time reveals that Scheme 1 processes data fastest up to 600MB of input data, while processing speed slows down between 600MB and 2400MB, with processing time gradually increasing. Scheme 2 exhibits a relatively stable overall processing speed, reaching its slowest point at 2000MB of input data, taking 70 seconds. The present invention, however, has the shortest overall processing time, remaining relatively stable at around 50 seconds. This demonstrates that the present invention offers the fastest data processing speed and best performance.

[0126] The second comparison metric is data processing speed. By extracting 0-1000MB of computer fault information, the data processing errors of different methods are compared, and the simulation results are as follows: Figure 4 As shown. Through comparison, it was found that the error range of Scheme 1 fluctuates between 0 and 3%, and the error gradually increases with the increase of computer fault information; the error range of Scheme 2 is 0 to 2.7%, with smaller error fluctuations; the error range of the present invention increases between 0 and 1%, which is relatively stable, and solves the problems of cumbersome process, slow data processing speed and large error of existing computer fault alarm systems, thus verifying the feasibility of the present invention.

[0127] While specific embodiments of the present invention have been described above, those skilled in the art should understand that these specific embodiments are merely illustrative. Those skilled in the art can omit, substitute, and modify the details of the above methods and systems in various ways without departing from the principles and essence of the present invention. For example, combining the above method steps to perform substantially the same function in substantially the same way to achieve substantially the same result falls within the scope of the present invention. Therefore, the scope of the present invention is defined only by the appended claims.

Claims

1. A computer fault alarm system, characterized in that: The computer fault alarm system includes: An alarm device is used to receive alarm commands from the fault management module and emit alarm sound signals. The alarm device uses a red LED alarm indicator and emits alarm sound signals through digital sound waves. A fault management module is used to store computer fault information collected from the computer. The fault management module uses a fuzzy clustering analysis algorithm to parse and store the collected computer fault information, and stores the collected computer fault information in the listener database by periodically executing batch submission. A listener database is used to store computer fault information sent periodically from the fault management module; the listener database is partitioned based on SQL Server partition settings, with computer fault information divided into time segments. The listener service program module is used to delete computer fault information from the listener database partition by partition. This module employs the MOA model to classify and process computer fault information in the alarm database and event database from both macro and micro perspectives. The relationship between the MOA macro model and the time period is as follows: (1) In equation (1), J(X) represents the MOA macro model, t represents the time period for the MOA macro model to process computer fault information, γ represents the degree of influence on the modeling of the MOA macro model, and α k (t) represents the hierarchical expansion vector of the MOA macro model, δ(X) represents the computer fault information sample function under the MOA macro model, x represents the hierarchical coordinate variable of the MOA macro model, and φ k The parameterized dynamic differential coefficients of the MOA macroscopic model are represented by N, and the total number of MOA model levels is represented by N. Formula (1) represents the relationship between the MOA macroscopic model and the time period. The computer fault information parameters are input into the MOA macroscopic model, and the final output result can cause changes in a single MOA microscopic model and serve as the input of the MOA microscopic model. The final relationship between the MOA microscopic model and the time period is as follows: (2) In equation (2), J(Y) represents the MOA micro-model, w represents the micro-computer fault information bias, the superscript T indicates transpose, j represents the micro-function hierarchy variable, and H k (y) represents the parameterized dynamic differential coefficients of the MOA micro-model, y represents the micro-ordinate variable, δ(Y) represents the computer fault information sample function under the MOA micro-model, and β k The vector represents the micro-level extension vector, and k(Y) represents the number of micro-levels in the MOA micro-model. By using formulas (1) and (2), the relationship between the macro-level model and the micro-level model and the calculation processing time periodic function is calculated to perform hierarchical processing of computer fault information, and finally complete the information processing. Alarm database; used to store and manage all alarm data generated in the computer; Event database; used to store and manage all events generated on the computer, except for alarms; A listener database is used to temporarily store all network viruses encountered by the computer, so that computer users can query network viruses and set up antivirus software. The listener database uses Zigbee wireless communication to forward network viruses to the computer in real time. For this purpose, the listener database temporarily stores all generated network viruses, and each computer receives network virus information by periodically querying the listener database, and obtains the cause of computer fault alarms through network virus information. Computer users can set up antivirus software to protect the computer. The system consists of computers A, B, and C. When computers A, B, or C malfunction, they output new computer information to the computer database. The monitoring database actively queries for new alarm data. The monitoring database is connected to the fault management module, which controls the alarm to emit audible and visual signals. The monitoring database is connected to the monitoring service program module, which periodically deletes computer information from the monitoring database according to partitions. The computer database is also connected to the monitoring service program module, which controls the deletion of computer information from the computer database. Finally, the monitoring service program module submits historically stored computer information in batches to the alarm database and the practice database.

2. The computer fault alarm system according to claim 1, characterized in that: The listener service program module includes a listener, and the listener service program module stores network virus information obtained from the listener database in the alarm database, and records the network virus information in the alarm database when generating fault information.

3. A computer fault alarm system according to claim 2, characterized in that: The listener service module deletes the database of abnormally terminated computers from the computer database every 24 hours. When the alarm manager has terminated normally, each computer no longer performs queries and deletes its information from the computer column database.

4. A computer fault alarm system according to claim 1, characterized in that: The fault management module includes a fault manager that writes the runtime information of computers registered on the computer column database during initial operation, and receives assigned computer identifiers.

5. A computer fault alarm system according to claim 4, characterized in that: The fault manager includes an alarm data query module. The alarm data query module queries data by registering an identifier in the computer database, then querying whether new alarm data exists. The computer executes a query to confirm whether the newly arrived alarm information exists in the listener database, and checks whether there is a number greater than the last sequence number to confirm whether new alarm data has arrived.

6. A computer fault alarm method, characterized in that... The steps include: (S1) Collect and store computer fault information in the listener database through the fault management module; (S2) The listener service process module periodically deletes computer fault information from the listener database based on partitions; when network virus information obtained from the listener database is stored in the alarm database and event database, the listener service program module performs batch submission. In the batch submission, the data is packaged and processed together with the highest fault severity category displayed in the data packet. (S3) The computer fault information in the alarm database and event database is updated by the listener service program module and transmitted to the listener database; (S4) The fault management module receives the fault alarm information output from the listener database and controls the alarm to emit alarm sound and light signals. In step (S2), the listener service module uses the MOA model to classify and process computer fault information in the alarm database and event database from both macro and micro perspectives. The MOA steps include: Step 1: Parameterize the computer fault information and construct the MOA macroscopic model by differential calculation of the computer fault information parameters: (3) In equation (3), Let X represent the objective function for computer fault information, Y represent the x-coordinate of the constraint coordinate system under macro-constraints, u represent the computer fault information deviation, the superscript T indicates transpose, k(X) represent the hierarchical ordinal number of the MOA macro-model, and i represent the computer fault information parameters in the MOA model. The derivative of the MOA macro-model is then calculated. (4) In equation (4), J(X) represents the MOA macro model, t represents the time period for the MOA macro model to process computer fault information, γ represents the degree of influence on the modeling of the MOA macro model, δ(X) represents the computer fault information sample function, ▽X represents the gradient operator of the MOA macro model, and v represents the speed at which the MOA macro model processes computer fault information; further partial derivatives of equation (2) for the MOA macro model are obtained: (5) In equation (5), Represents the hierarchical boundary of the MOA macroscopic model; α k (t) represents the hierarchical expansion vector of the MOA macro model, and N represents the total number of levels in the MOA model; Step 2: Based on formulas (4) and (5), the relationship between the MOA macro model and the macro-level extension vector is derived as follows: (6) In equation (6), x represents the hierarchical coordinate variable of the MOA macroscopic model, and φ k Represents the parameterized dynamic differential coefficients of the MOA macroscopic model; Based on formulas (3) to (6), the relationship between the MOA macro model and the time period is derived as follows: (7) Formula (7) represents the relationship between the MOA macro model and the time period. The computer fault information parameters are input into the MOA macro model, and the final output result can cause changes in the individual MOA micro model and serve as the input of the MOA micro model. Step 3: The MOA model is microscopically processed to obtain: (8) In equation (8), w represents the microscale computer fault information bias, j represents the microscale function hierarchy variable, and k(Y) represents the MOA microscale model hierarchy number. Taking the derivative of the MOA microscale model function, the relationship between the MOA microscale model and the microscale hierarchy extension vector is obtained as follows: (9) In equation (9), J(Y) represents the MOA micro-model, δ(Y) represents the computer fault information sample function under the MOA micro-model, and Y represents the spatial ordinate β under micro-constraints. k H represents the micro-level extension vector. k (y) represents the parameterized dynamic differential coefficients of the MOA micro-model, and y represents the micro-ordinate variable; Step 4: Similarly, the derivation process of formula (7) yields the relationship between the MOA micro-model and the time period as follows: (10) By using formulas (7) and (10) to calculate the relationship between the macroscopic and microscopic hierarchical models and the periodic function of the processing time, the computer fault information is processed hierarchically, and the information processing is finally completed.

7. A computer fault alarm method according to claim 6, characterized in that: The alarm data query method further includes periodically retrieving all network viruses after the last sequence by periodically querying the listener database to obtain newly arriving alarms, wherein the last sequence is used to distinguish newly arriving alarms, and the last sequence is the sequence number of the last alarm read when the computer periodically performs an alarm query.

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