An alarm analysis method, device, equipment and medium

Abstract

The application discloses an alarm analysis method and device, equipment and medium, and relates to the technical field of computers. The method comprises the following steps: obtaining a target alarm, and determining a target model corresponding to the target alarm from a preset model library; wherein the preset model library stores a plurality of models representing various correlation relationships between different alarms; obtaining a target performance standard corresponding to performance data out-of-limit, which is determined from a preset performance standard, and determining out-of-limit information corresponding to the target performance standard; and determining a cause and a solution corresponding to the target alarm from a preset alarm knowledge base based on the target model, the target performance standard and the out-of-limit information. Therefore, the target model having the correlation relationship between the target alarm and other alarms, the target performance standard and the out-of-limit information can be used to quickly, conveniently and accurately determine the cause and the solution corresponding to the target alarm.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to an alarm analysis method, apparatus, device, and medium. Background Technology

[0002] Currently, in distributed storage cluster environments, alarms are the most frequently dealt with information by operations and maintenance engineers. When cluster anomalies occur, a large number of alarms will appear. Operations and maintenance personnel often cannot identify or have difficulty identifying important alarm information. At the same time, due to the lack of sufficient information, it is difficult for operations and maintenance personnel to locate the cause of alarms and find solutions, which has a significant impact on the maintenance work of operations and maintenance personnel and the healthy operation of the cluster.

[0003] In conclusion, how to quickly, conveniently, and accurately determine the cause and solution of alarms is an urgent problem to be solved. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide an alarm analysis method, apparatus, device, and medium that can quickly and conveniently determine the cause and solution of an alarm. The specific solution is as follows:

[0005] Firstly, this application discloses an alarm analysis method, including:

[0006] The target alarm is acquired, and the target model corresponding to the target alarm is determined from the preset model library; wherein, the preset model library stores several models that represent various relationships between different alarms;

[0007] Obtain the target performance standard from the preset performance standards where the corresponding performance data exceeds the limit, and determine the limit exceeding information corresponding to the target performance standard;

[0008] Based on the target model, the target performance standard, and the limit violation information, the causes and solutions corresponding to the target alarms are determined from the preset alarm knowledge base.

[0009] Optionally, before acquiring the target alarm and determining the target model corresponding to the target alarm from the preset model library, the method further includes:

[0010] Acquire several original alarms and determine the original numbers corresponding to the original alarms according to the first preset numbering rule;

[0011] All the original alarms of the same alarm type are compressed to obtain the target alarm, and the target number corresponding to the target alarm is obtained based on the second preset numbering rule and the original number;

[0012] Accordingly, the step of acquiring the target alarm and determining the target model corresponding to the target alarm from a preset model library includes:

[0013] Based on the target number, the target model corresponding to the target alarm is determined from the preset model library.

[0014] Optionally, before acquiring the target alarm and determining the target model corresponding to the target alarm from the preset model library, the method further includes:

[0015] Calculate the original frequency of all original alarms in the target alarm;

[0016] Accordingly, determining the cause and solution corresponding to the target alarm from a preset alarm knowledge base based on the target model, the target performance standard, and the limit violation information includes:

[0017] Based on the target model, the original frequency, the target performance standard, and the limit violation information, the causes and solutions corresponding to the target alarms are determined from the preset alarm knowledge base.

[0018] Optionally, before acquiring the target alarm and determining the target model corresponding to the target alarm from the preset model library, the method further includes:

[0019] The frequency of target alarms within a preset time period is counted.

[0020] Accordingly, determining the cause and solution corresponding to the target alarm from a preset alarm knowledge base based on the target model, the target performance standard, and the limit violation information includes:

[0021] Based on the target model, the target frequency, the target performance standard, and the limit violation information, the causes and solutions corresponding to the target alarms are determined from the preset alarm knowledge base.

[0022] Optionally, determining the target model corresponding to the target alarm from a preset model library includes:

[0023] Several original models corresponding to the target alarm are determined from the preset model library, and the target model corresponding to the target alarm is obtained by the client from the original models.

[0024] Optionally, the over-limit information includes the over-limit frequency, the over-limit range value, and the over-limit time.

[0025] Optionally, the relationships include dependency relationships, derivation relationships, homology relationships, speculative relationships, and complex alarm timing relationships.

[0026] Secondly, this application discloses an alarm analysis device, comprising:

[0027] The target model determination module is used to acquire target alarms and determine the target model corresponding to the target alarm from a preset model library; wherein, the preset model library stores several models representing various correlations between different alarms;

[0028] The standard and information determination module is used to obtain the target performance standard from the preset performance standards where the corresponding performance data exceeds the limit, and to determine the limit exceeding information corresponding to the target performance standard;

[0029] The cause and solution determination module is used to determine the cause and solution corresponding to the target alarm from a preset alarm knowledge base based on the target model, the target performance standard and the limit violation information.

[0030] Thirdly, this application discloses an electronic device, including a processor and a memory; wherein, when the processor executes a computer program stored in the memory, it implements the aforementioned alarm analysis method.

[0031] Fourthly, this application discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned alarm analysis method.

[0032] As can be seen, this application acquires a target alarm and determines the target model corresponding to the target alarm from a preset model library; wherein, the preset model library stores several models representing various correlations between different alarms; it acquires the target performance standard for the corresponding performance data exceeding the limit determined from preset performance standards, and determines the limit exceeding information corresponding to the target performance standard; based on the target model, the target performance standard, and the limit exceeding information, it determines the cause and solution corresponding to the target alarm from a preset alarm knowledge base. Therefore, this application can quickly, conveniently, and accurately determine the cause and solution corresponding to the target alarm by utilizing the target model, target performance standard, and limit exceeding information that have correlations between the target alarm and other alarms. Attached Figure Description

[0033] 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 embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0034] Figure 1 A flowchart of an alarm analysis method provided in this application;

[0035] Figure 2A flowchart of a specific alarm analysis method provided in this application;

[0036] Figure 3 A flowchart of a specific alarm analysis method provided in this application;

[0037] Figure 4 A flowchart illustrating an alarm analysis method provided in this application;

[0038] Figure 5 A structural diagram of an alarm analysis device provided in this application;

[0039] Figure 6 This application provides a structural diagram of an electronic device. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] In the current distributed storage cluster environment, alarms are the information that operations and maintenance engineers deal with most frequently. When a cluster anomaly occurs, a large number of alarms will appear. Operations and maintenance personnel often cannot identify or have difficulty identifying important alarm information. At the same time, due to the lack of sufficient information, it is difficult for operations and maintenance personnel to locate the cause of the alarms and find solutions, which has a significant impact on the maintenance work of operations and maintenance personnel and the healthy operation of the cluster.

[0042] To overcome the above problems, this application provides an alarm analysis scheme that can quickly and easily determine the cause and solution of alarms.

[0043] See Figure 1 As shown in the embodiment of this application, an alarm analysis method is disclosed, which includes:

[0044] Step S11: Obtain the target alarm and determine the target model corresponding to the target alarm from the preset model library; wherein, the preset model library stores several models that represent various relationships between different alarms.

[0045] In this embodiment of the application, the target model corresponding to the target alarm is determined from a preset model library. Specifically, this involves determining several original models corresponding to the target alarm from the preset model library, and obtaining the target model corresponding to the target alarm determined by the client from the original models. It should be noted that for a single target alarm, there may be one or more corresponding original models in the preset model library.

[0046] In this embodiment, relationships between different alarms are established according to relevant rules, and several models representing various relationships between different alarms are stored in a preset model library. Simultaneously, the alarm correlation analysis model library is managed. It should be noted that the relationships include dependency, derivation, homology, speculation, and complex alarm timing relationships. Specifically, the dependency relationship indicates that alarm A occurs only if alarm B occurs; the derivation relationship indicates that alarm A triggers alarm B; the homology relationship indicates that alarms A and B are both caused by fault C; the speculation relationship indicates that other rules are used to manually add related alarms; and the complex alarm timing relationship indicates the sequence and interval between alarms.

[0047] Step S12: Obtain the target performance standard from the preset performance standards where the corresponding performance data exceeds the limit, and determine the limit exceeding information corresponding to the target performance standard.

[0048] In this embodiment, performance data analysis is performed on the distributed storage cluster. Specifically, target thresholds for preset performance standards related to the cluster, nodes, storage pool, and OSD (Object-based Storage Device) are determined. These thresholds include bandwidth, IOPS (Input / Output Operations Per Second), memory, and CPU (Central Processing Unit) utilization. Current performance data for these preset performance standards are extracted, and the relationship between the target thresholds and the performance data is compared. Preset performance standards where performance data exceeds the limits are used as target performance standards. Anomaly (limit exceeding) information is collected to form a record table containing the target threshold, limit exceeding time, limit exceeding frequency, and limit exceeding range value. The limit exceeding range value is the difference between the target threshold and the performance data. It should be noted that the limit exceeding includes both exceeding the maximum threshold and falling below the minimum threshold.

[0049] Step S13: Based on the target model, the target performance standard, and the limit violation information, determine the cause and solution corresponding to the target alarm from the preset alarm knowledge base.

[0050] In this embodiment, a pre-defined alarm knowledge base is first created. This knowledge base needs to be maintained by developers. It stores daily generated alarms, alarm generation conditions, alarm models (consistent with the alarm correlation analysis model library), alarm frequencies, associated pre-defined performance standards and target thresholds, and alarm solutions or suggested strategies. This allows the cause and solution for the target alarm to be determined from the pre-defined alarm knowledge base based on the target model, the target performance standard, and the limit-crossing information. Furthermore, utilizing the target performance standard and limit-crossing information improves the accuracy of determining the cause and solution.

[0051] It should be noted that by using the preset alarm knowledge base, the cause and solution of the alarm can be quickly located by looking up the dictionary. The use of the target model representing the relationship improves the degree of alarm analysis and can effectively eliminate interference. The use of target performance standards and limit information improves the accuracy of determining the cause and solution of the alarm.

[0052] As can be seen, this application acquires a target alarm and determines the target model corresponding to the target alarm from a preset model library; wherein, the preset model library stores several models representing various correlations between different alarms; it acquires the target performance standard for the corresponding performance data exceeding the limit determined from preset performance standards, and determines the limit exceeding information corresponding to the target performance standard; based on the target model, the target performance standard, and the limit exceeding information, it determines the cause and solution corresponding to the target alarm from a preset alarm knowledge base. Therefore, this application can quickly and conveniently determine the cause and solution corresponding to the target alarm by utilizing a target model with correlations between the target alarm and other alarms, and can more accurately determine the cause and solution corresponding to the target alarm by utilizing the target performance standard and limit exceeding information.

[0053] See Figure 2 As shown in the figure, this application discloses a specific alarm analysis method, which includes:

[0054] Step S21: Obtain several original alarms and determine the original numbers corresponding to the original alarms according to the first preset numbering rule; compress all the original alarms of the same alarm type to obtain the target alarm, and obtain the target number corresponding to the target alarm based on the second preset numbering rule and the original numbers; count the original frequency of all the original alarms in the target alarm.

[0055] In this embodiment, the acquired raw alarms are preprocessed by an alarm preprocessing module. The alarms are encoded and compressed according to specified rules, and alarms are counted simultaneously. Specifically, several raw alarms are acquired, and their corresponding raw numbers are determined according to a first preset numbering rule. All raw alarms of the same type are compressed to obtain a target alarm, and a target number corresponding to the target alarm is obtained based on a second preset numbering rule and the raw numbers. Finally, the raw frequencies of all raw alarms in the target alarm are counted. It should be noted that alarm compression reduces redundant information in the alarms and improves the efficiency of alarm retrieval.

[0056] It should be noted that alarms are numbered according to specified rules, such as alarm time, alarm type, alarm level, alarm module, etc.; the alarm compression refers to compressing multiple similar alarms into one alarm and counting the alarms to obtain the original frequency, specifically by statistically analyzing the frequency of a type of alarm.

[0057] Step S22: Determine the target model corresponding to the target alarm from the preset model library based on the target number; wherein, the preset model library stores several models representing various relationships between different alarms.

[0058] In this embodiment, the target model corresponding to the target alarm is determined from the preset model library using the target code, without having to directly use the target alarm, making the process of determining the target model more convenient and faster.

[0059] Step S23: Obtain the target performance standard from the preset performance standards where the corresponding performance data exceeds the limit, and determine the limit exceeding information corresponding to the target performance standard.

[0060] In this embodiment, performance data analysis is performed on the distributed storage cluster. Specifically, target thresholds for preset performance standards such as bandwidth, IOPS, memory, and CPU utilization related to the cluster, nodes, storage pool, and OSD are determined. Current performance data for these preset performance standards are extracted, and the relationship between the target thresholds and the performance data is compared. Preset performance standards where performance data exceeds the limits are used as target performance standards. Anomaly (limit exceeding) information is collected to form a record table containing the target threshold, limit exceeding time, limit exceeding frequency, and limit exceeding range value. The limit exceeding range value is the difference between the target threshold and the performance data. It should be noted that the limit exceeding includes both exceeding the maximum threshold and falling below the minimum threshold.

[0061] Step S24: Based on the target model, the original frequency, the target performance standard, and the limit violation information, determine the cause and solution corresponding to the target alarm from the preset alarm knowledge base.

[0062] In this embodiment of the application, the cause and solution corresponding to the target alarm are determined from a preset alarm knowledge base based on the target model, the original frequency, the target performance standard, and the limit violation information. The use of the original frequency is increased to make the determination of the cause and solution more accurate.

[0063] As can be seen, this application acquires several original alarms and determines the original numbers corresponding to the original alarms according to a first preset numbering rule; compresses all the original alarms of the same alarm type to obtain a target alarm, and obtains the target number corresponding to the target alarm based on a second preset numbering rule and the original numbers; counts the original frequencies of all the original alarms in the target alarm; determines the target model corresponding to the target alarm from a preset model library based on the target number; wherein, the preset model library stores several models representing various correlations between different alarms; acquires the target performance standard for the corresponding performance data exceeding the limit determined from preset performance standards, and determines the limit exceeding information corresponding to the target performance standard; and determines the cause and solution corresponding to the target alarm from a preset alarm knowledge base based on the target model, the original frequency, the target performance standard, and the limit exceeding information. As can be seen from the above, this application uses target coding to determine the target model, making the determination process more convenient and faster; this application uses the target model with the correlation between the target alarm and other alarms, the original frequency, the target performance standard and the limit violation information to quickly, conveniently and accurately determine the cause and solution corresponding to the target alarm; this application uses the original frequency to more accurately determine the cause and solution.

[0064] See Figure 3 As shown in the figure, this application discloses a specific alarm analysis method, which includes:

[0065] Step S31: Obtain several original alarms and determine the original numbers corresponding to the original alarms according to the first preset numbering rule; compress all the original alarms of the same alarm type to obtain the target alarm, and obtain the target number corresponding to the target alarm based on the second preset numbering rule and the original number; count the target frequency of the target alarm within a preset time period.

[0066] In this embodiment, the acquired raw alarms are preprocessed by an alarm preprocessing module. The alarms are encoded and compressed according to specified rules, and alarms are counted simultaneously. Specifically, several raw alarms are acquired, and their corresponding raw numbers are determined according to a first preset numbering rule. All raw alarms of the same type are compressed to obtain a target alarm, and the target number corresponding to the target alarm is obtained based on a second preset numbering rule and the raw numbers. Finally, the target frequency of the target alarm within a preset time period is counted. It should be noted that alarm compression reduces redundant information in the alarms and improves the efficiency of alarm retrieval.

[0067] It should be noted that alarms are numbered according to specified rules, such as alarm time, alarm type, alarm level, alarm module, etc.; the alarm compression refers to compressing multiple similar alarms into one alarm and counting the alarms to obtain the target frequency, specifically counting the same alarm according to certain rules (time range).

[0068] Step S32: Determine the target model corresponding to the target alarm from the preset model library based on the target number; wherein, the preset model library stores several models representing various relationships between different alarms.

[0069] In this embodiment, the target model corresponding to the target alarm is determined from the preset model library using the target code, without having to directly use the target alarm, making the process of determining the target model more convenient and faster.

[0070] Step S33: Obtain the target performance standard from the preset performance standards where the corresponding performance data exceeds the limit, and determine the limit exceeding information corresponding to the target performance standard.

[0071] In this embodiment, performance data analysis is performed on the distributed storage cluster. Specifically, target thresholds for preset performance standards such as bandwidth, IOPS, memory, and CPU utilization related to the cluster, nodes, storage pool, and OSD are determined. Current performance data for these preset performance standards are extracted, and the relationship between the target thresholds and the performance data is compared. Preset performance standards where performance data exceeds the limits are used as target performance standards. Anomaly (limit exceeding) information is collected to form a record table containing the target threshold, limit exceeding time, limit exceeding frequency, and limit exceeding range value. The limit exceeding range value is the difference between the target threshold and the performance data. It should be noted that the limit exceeding includes both exceeding the maximum threshold and falling below the minimum threshold.

[0072] Step S34: Based on the target model, the target frequency, the target performance standard, and the limit violation information, determine the cause and solution corresponding to the target alarm from the preset alarm knowledge base.

[0073] In this embodiment of the application, the cause and solution corresponding to the target alarm are determined from a preset alarm knowledge base based on the target model, the target frequency, the target performance standard, and the limit violation information. Increasing the utilization of the target frequency can more accurately determine the cause and solution.

[0074] As can be seen, this application acquires several original alarms and determines the original numbers corresponding to the original alarms according to a first preset numbering rule; compresses all the original alarms of the same alarm type to obtain a target alarm, and obtains the target number corresponding to the target alarm based on a second preset numbering rule and the original number; counts the target frequency of the target alarm within a preset time period; determines the target model corresponding to the target alarm from a preset model library based on the target number; wherein, the preset model library stores several models representing various correlations between different alarms; acquires the target performance standard for the corresponding performance data exceeding the limit determined from preset performance standards, and determines the limit exceeding information corresponding to the target performance standard; and determines the cause and solution corresponding to the target alarm from a preset alarm knowledge base based on the target model, the target frequency, the target performance standard, and the limit exceeding information. As can be seen from the above, this application uses target coding to determine the target model, making the determination process more convenient and faster; this application uses the target model with the correlation between the target alarm and other alarms, the target frequency, the target performance standard and the limit violation information to quickly, conveniently and accurately determine the cause and solution corresponding to the target alarm; this application uses the target frequency to more accurately determine the cause and solution.

[0075] See Figure 4The diagram illustrates the overall alarm analysis process, completed within the alarm analysis module. It involves: retrieving raw alarms from the alarm database and preprocessing them, including alarm numbering, alarm compression, and alarm counting; obtaining the target alarm, its target number, and the original frequency of the raw alarms within the same alarm type or the target frequency of the target alarms within a preset time period; selecting a target model representing the correlation between the target alarm and other alarms from the association analysis model library (preset model library) based on the target code; performing performance data analysis on preset performance standards in the distributed storage cluster to obtain a performance data record table containing limit-breaking information of the target performance standards; and determining the cause and solution of the target alarm from a preset knowledge base using the target model, raw frequency, target performance standards, and limit-breaking information, or using the target model, target frequency, target performance standards, and limit-breaking information to determine the cause and solution of the target alarm from the preset knowledge base.

[0076] It should be noted that this system can quickly pinpoint the root cause of problems and provide solutions. Preprocessing alarms reduces the impact of redundant alarms and improves alarm retrieval efficiency. Building an alarm correlation analysis model library to link the mapping relationships between alarms improves the analysis of alarm correlation issues by operations and maintenance personnel and can effectively eliminate interfering factors. A pre-built alarm knowledge base, combined with the alarm correlation analysis model, allows for rapid identification of alarm root causes through dictionary lookups. Exceeding limits information in the target performance standards and performance data record tables serves as supplementary conditions for alarm root cause identification, improving the accuracy of alarm location.

[0077] See Figure 5 As shown in the figure, this application discloses an alarm analysis device, including:

[0078] The target model determination module 11 is used to acquire target alarms and determine the target model corresponding to the target alarm from a preset model library; wherein, the preset model library stores a number of models representing various correlations between different alarms;

[0079] The standard and information determination module 12 is used to obtain the target performance standard from the preset performance standards where the corresponding performance data exceeds the limit, and to determine the limit exceeding information corresponding to the target performance standard.

[0080] The cause and solution determination module 13 is used to determine the cause and solution corresponding to the target alarm from a preset alarm knowledge base based on the target model, the target performance standard and the limit violation information.

[0081] As can be seen, this application acquires a target alarm and determines the target model corresponding to the target alarm from a preset model library; wherein, the preset model library stores several models representing various correlations between different alarms; it acquires the target performance standard for the corresponding performance data exceeding the limit determined from preset performance standards, and determines the limit exceeding information corresponding to the target performance standard; based on the target model, the target performance standard, and the limit exceeding information, it determines the cause and solution corresponding to the target alarm from a preset alarm knowledge base. Therefore, this application can quickly and conveniently determine the cause and solution corresponding to the target alarm by utilizing the target model, target performance standard, and limit exceeding information that have correlations between the target alarm and other alarms.

[0082] In some embodiments, the alarm analysis apparatus further includes:

[0083] The first numbering unit is used to acquire several original alarms and determine the original number corresponding to each original alarm according to the first preset numbering rule.

[0084] An alarm compression unit is used to compress all the original alarms of the same alarm type to obtain the target alarm;

[0085] The second numbering unit is used to obtain the target number corresponding to the target alarm based on the second preset numbering rule and the original number;

[0086] Accordingly, the target model determination module 11 includes:

[0087] The target model determination unit is used to determine the target model corresponding to the target alarm from a preset model library based on the target number;

[0088] In some embodiments, the alarm analysis apparatus further includes:

[0089] The original frequency statistics module is used to count the original frequency of all the original alarms in the target alarm;

[0090] Accordingly, the cause and solution determination module 13 includes:

[0091] Based on the target model, the original frequency, the target performance standard, and the limit violation information, the causes and solutions corresponding to the target alarms are determined from the preset alarm knowledge base.

[0092] In some embodiments, the alarm analysis apparatus further includes:

[0093] The target frequency statistics module is used to count the target frequency of the target alarm within a preset time period;

[0094] Accordingly, the cause and solution determination module 13 includes:

[0095] Based on the target model, the target frequency, the target performance standard, and the limit violation information, the causes and solutions corresponding to the target alarms are determined from the preset alarm knowledge base.

[0096] In some embodiments, the target model determination module 11 includes:

[0097] The target model determination unit is used to determine several original models corresponding to the target alarm from a preset model library, and to obtain the target model corresponding to the target alarm determined by the client from the original models.

[0098] Furthermore, embodiments of this application also provide an electronic device. Figure 6 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0099] Figure 6 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, an input / output interface 24, a communication interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps of the alarm analysis method disclosed in any of the foregoing embodiments.

[0100] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 25 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 24 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0101] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The memory 22 can be a random access memory that can be used as running memory and a non-volatile memory used for external memory storage. The storage resources on it include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0102] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 on the source host and the computer program 222. The operating system 221 can be Windows, Unix, Linux, etc. In addition to the computer program that can be used to perform the alarm analysis method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs that can be used to perform other specific tasks.

[0103] In this embodiment, the input / output interface 24 may include, but is not limited to, a USB interface, a hard disk read interface, a serial interface, a voice input interface, a fingerprint input interface, etc.

[0104] Furthermore, embodiments of this application also disclose a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned alarm analysis method.

[0105] For the specific steps of this method, please refer to the relevant content disclosed in the foregoing embodiments, which will not be repeated here.

[0106] The computer-readable storage medium referred to herein includes random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, magnetic disks, optical disks, or any other form of storage medium known in the art. The computer program, when executed by a processor, implements the aforementioned alarm analysis method. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0107] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the alarm analysis method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0108] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0109] The steps of the algorithm described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0110] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0111] The above provides a detailed description of the alarm analysis method, apparatus, device, and medium provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. An alarm analysis method, characterized in that, include: The system acquires a target alarm and determines the target model corresponding to the target alarm from a preset model library. The preset model library stores several models that represent various relationships between different alarms. The relationships include dependency relationships, derivation relationships, homology relationships, inference relationships, and complex alarm timing relationships. The complex alarm timing relationships are represented by the order and interval between alarms. Obtain the target performance standard for the occurrence of performance data exceeding the limit determined from the preset performance standards, and determine the limit exceeding information corresponding to the target performance standard; the limit exceeding information includes the target threshold, the frequency of limit exceeding, the range of limit exceeding, and the time of limit exceeding. Based on the target model, the target performance standard, and the limit violation information, the causes and solutions corresponding to the target alarms are determined from the preset alarm knowledge base.

2. The alarm analysis method according to claim 1, characterized in that, Before acquiring the target alarm and determining the target model corresponding to the target alarm from the preset model library, the method further includes: Acquire several original alarms and determine the original numbers corresponding to the original alarms according to the first preset numbering rule; All the original alarms of the same alarm type are compressed to obtain the target alarm, and the target number corresponding to the target alarm is obtained based on the second preset numbering rule and the original number; Accordingly, the step of acquiring the target alarm and determining the target model corresponding to the target alarm from a preset model library includes: Based on the target number, the target model corresponding to the target alarm is determined from the preset model library.

3. The alarm analysis method according to claim 2, characterized in that, Before acquiring the target alarm and determining the target model corresponding to the target alarm from the preset model library, the method further includes: Calculate the original frequency of all original alarms in the target alarm; Accordingly, determining the cause and solution corresponding to the target alarm from a preset alarm knowledge base based on the target model, the target performance standard, and the limit violation information includes: Based on the target model, the original frequency, the target performance standard, and the limit violation information, the causes and solutions corresponding to the target alarms are determined from the preset alarm knowledge base.

4. The alarm analysis method according to claim 2, characterized in that, Before acquiring the target alarm and determining the target model corresponding to the target alarm from the preset model library, the method further includes: The frequency of target alarms within a preset time period is counted. Accordingly, determining the cause and solution corresponding to the target alarm from a preset alarm knowledge base based on the target model, the target performance standard, and the limit violation information includes: Based on the target model, the target frequency, the target performance standard, and the limit violation information, the causes and solutions corresponding to the target alarms are determined from the preset alarm knowledge base.

5. The alarm analysis method according to claim 1, characterized in that, The step of determining the target model corresponding to the target alarm from the preset model library includes: Several original models corresponding to the target alarm are determined from the preset model library, and the target model corresponding to the target alarm is obtained by the client from the original models.

6. An alarm analysis device, characterized in that, include: The target model determination module is used to acquire target alarms and determine the target model corresponding to the target alarm from a preset model library; wherein, the preset model library stores several models representing various relationships between different alarms; the relationships include dependency relationships, derivation relationships, homology relationships, inference relationships and complex alarm timing relationships, and the complex alarm timing relationships are represented by the order and interval time between alarms; The standard and information determination module is used to acquire the target performance standard for which the corresponding performance data exceeds the limit, which is determined from the preset performance standards, and to determine the limit exceeding information corresponding to the target performance standard; the limit exceeding information includes the target threshold, the limit exceeding frequency, the limit exceeding range value, and the limit exceeding time. The cause and solution determination module is used to determine the cause and solution corresponding to the target alarm from a preset alarm knowledge base based on the target model, the target performance standard and the limit violation information.

7. An electronic device, characterized in that, It includes a processor and a memory; wherein, when the processor executes a computer program stored in the memory, it implements the alarm analysis method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, Used to store computer programs; wherein, when the computer programs are executed by a processor, they implement the alarm analysis method as described in any one of claims 1 to 5.

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