Equipment operation and maintenance method and system with alarm notification function

Abstract

The invention discloses an equipment operation and maintenance method and system with alarm notification, and relates to the technical field of equipment operation and maintenance management, and the method comprises the steps: carrying out the correlation analysis of alarm information through a first algorithm, and generating a correlation rule; abnormal detection is carried out on the equipment operation data, and equipment faults are alarmed in real time; and automatically backing up and managing the equipment operation log data through the first strategy. According to the equipment operation and maintenance method with the alarm notification function, from a large amount of alarm data generated in the equipment operation process, the data processing efficiency is improved, through generation of the association rules, redundant alarm information is reduced, the accuracy and timeliness of the alarm notification are optimized, the association analysis precision of the alarm information is improved, and the equipment operation and maintenance efficiency is improved. According to the method, effective archiving and classified management are performed on the equipment data, it is ensured that important operation data can be reserved for a long time and can be retrieved at any time, the storage and retrieval efficiency of the equipment log data is improved, and better effects are achieved in the aspects of processing efficiency, accuracy and reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment operation and maintenance management, and in particular to an equipment operation and maintenance method with alarm notification. Background Art

[0002] With the popularization of industrial Internet and smart devices, the demand for intelligent and automated equipment operation and maintenance is growing. Traditional equipment operation and maintenance mainly relies on manual inspection and manual maintenance. With the increase in the number and complexity of equipment, this method can no longer meet the requirements of modern enterprises for efficient and accurate operation and maintenance. In recent years, with the maturity of Internet of Things (IoT), cloud computing, and big data analysis technology, equipment operation and maintenance has gradually transformed into intelligence, especially in the fields of electricity, communications, and manufacturing. With the help of advanced monitoring systems and fault detection algorithms, automated operation and maintenance methods have begun to be widely used. Equipment operation and maintenance technology has gradually shifted from passive fault repair to active fault prevention. Through real-time data monitoring, alarm notification systems and intelligent analysis, early warning and autonomous repair of equipment failures have been achieved. This transformation not only improves the operating efficiency of equipment, but also reduces the economic losses caused by equipment downtime due to failures.

[0003] However, the existing operation and maintenance methods of intelligent equipment still face many challenges. First, although the existing system can realize real-time monitoring of equipment status, its processing capacity for multi-source and multi-dimensional data is limited. Especially when analyzing massive logs and complex alarm information, the existing technology is prone to false alarms or missed alarms, which will lead to inefficiency of operation and maintenance personnel in processing alarm information, and even lead to the failure to discover critical faults in time. Secondly, the existing anomaly detection algorithms are insufficient in dealing with dynamically changing equipment environments. Many algorithms rely on fixed thresholds or preset models, which are difficult to adapt to the complex and changeable operating status of equipment, resulting in low accuracy of fault detection. In addition, although some systems have basic log management and data backup functions, they often lack flexible phased data management strategies, resulting in the inability to efficiently archive and retrieve key equipment operating data. Finally, most of the existing alarm notification systems are based on a single channel and cannot provide differentiated alarm strategies based on the importance of the equipment and the severity of the fault, which further limits the efficiency of the operation and maintenance response. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problems solved by the present invention are: the existing equipment operation and maintenance methods have high false alarms and missed alarms, insufficient fault detection accuracy, inflexible log management, and how to optimize real-time alarm notifications through intelligent means.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a device operation and maintenance method with alarm notification, comprising performing correlation analysis on alarm information through a first algorithm to generate correlation rules; performing anomaly detection on device operation data to alarm device failure in real time; and automatically backing up and managing device operation log data through a first strategy.

[0007] As a preferred solution of the equipment operation and maintenance method with alarm notification described in the present invention, wherein: the correlation analysis of the alarm information through the first algorithm includes aggregating the alarm information in the equipment operation log through the first algorithm, and analyzing the correlation relationship between the equipment and the system.

[0008] As a preferred solution of the equipment operation and maintenance method with alarm notification described in the present invention, wherein: the generation of association rules includes preprocessing and feature extraction of multi-source data, and analyzing the fault correlation between different devices.

[0009] As a preferred solution of the equipment operation and maintenance method with alarm notification described in the present invention, wherein: the abnormality detection of equipment operation data includes determining whether the equipment operation status is abnormal by real-time monitoring of the equipment operation data.

[0010] As a preferred solution of the equipment operation and maintenance method with alarm notification described in the present invention, the real-time alarm of equipment failure includes automatically generating an alarm notification and sending the equipment failure information to the operation and maintenance personnel.

[0011] As a preferred solution of the device operation and maintenance method with alarm notification described in the present invention, wherein: the automatic backup and management of device operation log data through the first strategy includes dividing the device operation log according to access frequency and importance.

[0012] As a preferred solution of the device operation and maintenance method with alarm notification described in the present invention, wherein: the automatic backup and management of device operation log data through the first strategy also includes regularly backing up device operation log data in different storage stages according to preset backup rules.

[0013] Another object of the present invention is to provide an equipment operation and maintenance system with alarm notification, which can automatically perform correlation analysis on alarm information through a correlation analysis module and generate correlation rules, thereby solving the current problem of inaccurate fault location caused by the inability to effectively identify the relationship between complex multi-source alarm data.

[0014] As a preferred solution of the equipment operation and maintenance system with alarm notification described in the present invention, it includes: a correlation analysis module, an anomaly detection module, and a backup management module; the correlation analysis module is used to perform correlation analysis on the alarm information through a first algorithm to generate association rules; the anomaly detection module is used to perform anomaly detection on the equipment operation data and alarm equipment failure in real time; the backup management module is used to automatically back up and manage the equipment operation log data through a first strategy.

[0015] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that the processor executes the computer program to implement a step of a device operation and maintenance method with alarm notification.

[0016] A computer-readable storage medium stores a computer program thereon, wherein when the computer program is executed by a processor, the steps of a device operation and maintenance method with alarm notification are implemented.

[0017] Beneficial effects of the present invention: The equipment operation and maintenance method with alarm notification provided by the present invention improves data processing efficiency from a large amount of alarm data generated during the operation of the equipment, reduces redundant information of the alarm by generating association rules, optimizes the accuracy and timeliness of the alarm notification, improves the accuracy of the correlation analysis of the alarm information, improves the accuracy and processing efficiency of fault location, reduces equipment downtime and the impact of faults through real-time alarms, improves the reliability of the overall equipment operation, effectively archives and classifies equipment data, ensures that important operation data can be retained for a long time and can be retrieved at any time, and improves the storage and retrieval efficiency of equipment log data. The present invention achieves better results in processing efficiency, accuracy and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0019] Figure 1 An overall flow chart of a device operation and maintenance method with alarm notification provided for the first and second embodiments of the present invention.

[0020] Figure 2 An overall module diagram of an equipment operation and maintenance system with alarm notification provided in the fourth embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0022] Example 1

[0023] Reference Figure 1 , as an embodiment of the present invention, provides a device operation and maintenance method with alarm notification, comprising:

[0024] S1: Perform association analysis on the alarm information through a first algorithm to generate association rules.

[0025] Furthermore, performing correlation analysis on the alarm information by using the first algorithm includes aggregating the alarm information in the device operation log by using the first algorithm, and analyzing the correlation relationship between the device and the system.

[0026] In the embodiment of the present application, the first algorithm uses machine learning and clustering technology to automatically aggregate alarm information from different devices and systems by analyzing the device operation log;

[0027] This algorithm can handle alarms from a single device, as well as log data from multiple devices and systems. It extracts useful information from a large amount of log data through SVM, and combines the K-Means clustering algorithm to classify the data features in device operation.

[0028] This algorithm can discover hidden patterns in log data and establish associations between different devices through these patterns. It can effectively identify which alarms are related to each other and which alarms may be separate events.

[0029] This algorithm also has self-learning capabilities, and can continuously optimize and update association rules in the process of continuously collecting and analyzing new data. Its adaptability enables the system to cope with the ever-changing equipment environment and ensure the accuracy and timeliness of alarm analysis.

[0030] In an optional embodiment, the first algorithm may also be implemented in other ways, such as using a hierarchical clustering algorithm instead of a K-Means algorithm. Hierarchical clustering gradually clusters the alarm data in a bottom-up or top-down manner to form a clustering tree diagram, which identifies the hierarchical relationship between alarm information in a more detailed manner. It is particularly suitable for devices with multi-level association relationships in complex systems. When a subsystem failure triggers alarms of multiple lower-level devices, hierarchical clustering classifies the alarms of these devices into the same level.

[0031] After clustering is completed, the random forest classification algorithm is used to classify the alarm information. Random forest performs classification by constructing multiple decision trees. Each tree makes classification decisions independently, and the final classification of the alarm information is determined by the voting results of all trees.

[0032] Random forests can process high-dimensional data and are particularly suitable for the various alarm features in device logs (such as alarm time, alarm type, frequency, etc.). They can improve the accuracy of alarm correlation analysis and reduce overfitting.

[0033] Furthermore, generating association rules includes preprocessing and feature extraction of multi-source data and analyzing the fault correlation between different devices.

[0034] It should be noted that in the process of generating association rules, data from multiple sources need to be automatically preprocessed. Multi-source data includes different types of equipment logs, system monitoring data, user operation logs, etc. In order to enable data in different formats to be analyzed uniformly, preprocessing steps such as data cleaning and format conversion are required to eliminate noise data and redundant information;

[0035] Extract key features from logs through feature extraction techniques (such as principal component analysis (PCA) or independent component analysis (ICA). Features include device operating time, fault frequency, communication delay between different devices, etc. Based on the extracted features, use algorithms to perform multi-dimensional analysis to determine whether there is fault correlation between devices. For example, the failure of some devices may cause a series of secondary failures in other devices. This information can be discovered through feature analysis.

[0036] When generating association rules, the system generates a series of rules for explaining the relationship between faults based on the operating status and historical data of the equipment, helping relevant personnel to quickly identify potential fault chains and take measures. The automation and intelligence of the process reduces the need for human intervention and improves the accuracy and relevance of alarm processing.

[0037] Example 2

[0038] Reference Figure 1 , as an embodiment of the present invention, provides a device operation and maintenance method with alarm notification, comprising:

[0039] S2: Detect abnormalities in equipment operation data and warn of equipment failures in real time.

[0040] Furthermore, the abnormality detection of the equipment operation data includes monitoring the equipment operation data in real time to determine whether the equipment operation status is abnormal.

[0041] It should be noted that during the equipment operation and maintenance process, anomaly detection identifies abnormal conditions in equipment operation by real-time monitoring of equipment operation data, especially time series data. Time series data includes the real-time status of the equipment, such as CPU usage, memory usage, network traffic, equipment temperature, etc. These data change continuously on the time axis, reflecting the real-time operation of the equipment.

[0042] Anomaly detection models are usually implemented based on statistical analysis, machine learning or deep learning algorithms. Statistical analysis methods (such as ARIMA, Holt-Winters, etc.) can model time series and predict future equipment status. If the actual status deviates significantly from the predicted value, an anomaly is considered to have occurred.

[0043] Models such as LSTM (Long Short-Term Memory Network) in deep learning are also used to process time series data. LSTM models are good at capturing dependencies in long time series and can better cope with anomaly detection problems in complex equipment operating environments.

[0044] Through this real-time monitoring and anomaly detection model, problems can be quickly identified and fault alarms can be triggered when equipment fails or operates abnormally, reducing the response time and impact range of equipment failures.

[0045] Furthermore, real-time alarming of equipment failures includes automatically generating alarm notifications and sending equipment failure information to operation and maintenance personnel.

[0046] It should be noted that when an abnormality occurs during the operation of the equipment, the real-time alarm mechanism will promptly generate an alarm notification and transmit the relevant fault information to the operation and maintenance personnel through various means. The triggering conditions of the alarm notification are usually based on the output of the anomaly detection algorithm. When the system detects that the operating status of the equipment deviates from the preset normal value, or detects that a sudden fault event has occurred in the equipment, the system will automatically generate a fault alarm;

[0047] Alarm notifications are sent through multiple channels, including email, SMS, and instant messaging applications. Through these means, operation and maintenance personnel can learn about the fault status of the equipment in the first place and take prompt action. Different levels of alarms are defined based on the severity of the alarm and the scope of the fault. For example, some minor faults only require notification via email, while serious system faults require emergency notification via phone or SMS.

[0048] The content of the alarm notification includes information such as the operating status of the equipment, the specific time when the fault occurred, the scope of impact, etc., to help operation and maintenance personnel quickly locate the cause of the fault. The system integrates the historical operating data of the equipment and provides detailed log information before and after the fault occurs, helping operation and maintenance personnel to have a more comprehensive understanding of the operating status of the equipment.

[0049] S3: Automatically back up and manage device operation log data through the first strategy.

[0050] Furthermore, automatically backing up and managing device operation log data through the first strategy includes dividing the device operation logs according to access frequency and importance.

[0051] In the embodiment of the present application, the first strategy is a phased data management strategy. The equipment operation log includes equipment status, operation records, fault logs, etc. To ensure efficient management and long-term preservation of these data, the log data is divided into three storage stages: real-time storage, historical storage, and archive storage;

[0052] The real-time storage stage is used to save the latest operation data of the device. High-performance storage devices (such as SSDs) are usually used to ensure fast data reading and writing. In this stage, the data is read frequently because operation and maintenance personnel need to view the operation status of the device in real time.

[0053] When log data is no longer frequently used, the system transfers it to the historical storage stage. The data reading frequency in the historical storage stage is relatively low. It is suitable for situations where you need to query the operating status of the device in the past period of time. This data is still important, but it no longer needs to be accessed frequently. Therefore, it is stored on a storage medium with a balanced performance and capacity.

[0054] The archival storage stage is used to store rarely accessed data, usually historical logs that are stored for a long time. Archival storage uses large-capacity, low-cost storage devices, such as tape libraries or cold storage. The main purpose of archiving data is to ensure fault tracing and long-term data preservation.

[0055] Through a phased data management strategy, the system efficiently utilizes storage resources to ensure efficient data access and long-term preservation.

[0056] In an optional embodiment, the first strategy may also be implemented in other ways, such as a log management strategy based on cloud distributed storage and intelligent data compression;

[0057] The equipment operation log data is transmitted to the cloud distributed storage system in real time. In the cloud storage system, the log data is automatically distributed to different storage nodes to ensure high availability and fast access to the data. The cloud storage system provides multi-layer storage services, including high-performance storage (for real-time data), standard storage (for historical data) and cold storage (for long-term archived data).

[0058] Through automated management of cloud storage, the system can dynamically adjust the data storage level based on the age and access frequency of log data. Log data within the last 7 days will be stored in high-performance storage nodes for real-time access, while older log data will be automatically migrated to standard storage or cold storage nodes.

[0059] Furthermore, automatically backing up and managing the device operation log data through the first strategy also includes regularly backing up the device operation log data in different storage stages according to preset backup rules.

[0060] In the embodiment of the present application, under the phased data management strategy, the automatic backup mechanism regularly backs up the operation log data of the device according to the preset backup rules. Data in different storage stages have different backup strategies. The system will perform differentiated processing based on the importance, access frequency and storage stage of the data;

[0061] For real-time stored data, the backup strategy is usually high-frequency to ensure that the latest device operation logs can be quickly restored. The backup of these data is performed every day or every hour to prevent data loss due to sudden failures;

[0062] In the historical storage stage, the backup frequency is relatively low, with backups performed once a week or a month. The data changes relatively little, and the reduction in backup frequency will not have a serious impact on data integrity.

[0063] The data in the archive storage stage is usually historical data that is stored for a long time. The backup is mainly used to ensure the permanent traceability of the data. The backup frequency of archive data is further reduced, and a complete backup is performed only when the data enters the archive stage.

[0064] The system can also reduce the storage space and time required for backup through snapshot technology or incremental backup technology. The automatic backup mechanism will verify the data after backup to ensure that the integrity of the data is not damaged during the backup process and can be quickly restored in the event of a failure.

[0065] In an optional embodiment, in order to optimize storage space, the system intelligently compresses the device log data during the backup process. When the log data is transmitted to the cloud, it is processed by a real-time compression algorithm to remove redundant information and duplicate data to reduce storage occupancy. At the same time, the log data is fragmented and stored in different nodes to ensure that even if some nodes fail, the system can restore the complete data from other nodes;

[0066] Intelligent compression algorithms can adaptively select the best compression method based on the characteristics of log data and dynamically adjust the compression rate to ensure that data is stored efficiently without losing key information.

[0067] The system uses a multi-copy backup mechanism to ensure the high reliability of log data in the cloud storage platform. Multiple copies are generated for each log data and distributedly stored in different physical locations to prevent data loss due to single point failures. The backup strategy can also dynamically adjust the backup frequency and number of copies according to the importance of the data.

[0068] When data needs to be restored, the system will select the most recent valid copy from multiple backup nodes and quickly restore the required device log data. Through the intelligent data recovery mechanism, the system can adjust the data recovery priority in real time according to the access needs of operation and maintenance personnel to ensure that critical data is available in the shortest time.

[0069] Example 3

[0070] An embodiment of the present invention provides a device operation and maintenance method with alarm notification. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0071] The experiment set up multiple groups of equipment operating environments and collected and analyzed data under different conditions. The main purpose of the experiment was to evaluate the advantages of the system in fault alarm correlation analysis, anomaly detection and log management, especially in terms of the timeliness of alarm notification, the accuracy of fault detection and the efficiency of log data backup management.

[0072] The experiment used five devices (A, B, C, D, and E) for log collection and alarm management. These devices represent different types of terminal devices, simulating various scenarios in actual operation, including normal status, abnormal status, and potential failures of equipment operation. Each device is connected to a real-time monitoring system to collect data such as CPU load, memory usage, network traffic, and alarm frequency during operation, and transmit these data to the central server in real time through the log recording module. Alarm data and operation logs are regularly collected and analyzed to verify the effectiveness of the alarm correlation analysis and anomaly detection modules in the invention.

[0073] As shown in Table 1, in terms of CPU load and memory usage, device D has the highest load and the highest memory usage, while devices A and E have relatively low load and memory usage. These data indicate that device D is in a high-load operation state, which may lead to more alarms. In fact, the alarm frequency of device D reaches 16 times / day, which is much higher than the average. This is related to the system's real-time monitoring of device load and the combined operation of the alarm notification module. When the system detects an abnormal device load, it can generate an alarm in time and notify the operation and maintenance personnel through multiple channels.

[0074] In terms of alarm triggering time, the alarm time of all devices is between 3.1 and 3.6 seconds, and the overall average is 3.36 seconds, indicating that the alarm notification system in the present invention has a high response speed. Even on devices running under high load (such as device D), the system can still trigger an alarm within 3.1 seconds, which is significantly improved compared with the common 5-10 seconds alarm time in the prior art. Rapid alarm notification helps operation and maintenance personnel take intervention measures in the early stage of equipment failure and reduce the risk of equipment downtime.

[0075] In terms of log backup management, the backup frequency of each device is dynamically adjusted according to the alarm frequency and load conditions of the device. For example, the alarm frequency of device D is higher, and the log backup frequency is correspondingly increased to 8 times / day, while the alarm frequency of device A is lower, so the backup frequency is 5 times / day. The phased backup mechanism based on the invention not only effectively reduces redundant backups, but also optimizes the utilization of storage resources, ensuring timely backup and long-term preservation of log data. Through automated log management and hierarchical storage strategies, the system can achieve effective management of device operation logs, avoiding the log data loss or backup redundancy problems caused by inflexible backup strategies in the prior art.

[0076] Table 1 Experimental data table

[0077]

[0078] Example 4

[0079] Reference Figure 2 , as an embodiment of the present invention, provides a device operation and maintenance system with alarm notification, including: a correlation analysis module, an anomaly detection module, and a backup management module.

[0080] The correlation analysis module is used to perform correlation analysis on the alarm information through the first algorithm to generate correlation rules; the anomaly detection module is used to detect anomalies in the equipment operation data and alarm equipment failures in real time; the backup management module is used to automatically back up and manage the equipment operation log data through the first strategy.

[0081] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0082] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0083] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0084] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0085] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A device operation and maintenance method with alarm notification, characterized in that: include: Performing association analysis on the alarm information through a first algorithm to generate association rules; Detect abnormalities in equipment operation data and warn of equipment failures in real time; Automatically back up and manage device operation log data through the first strategy.

2. The device operation and maintenance method with alarm notification according to claim 1, characterized in that: The performing correlation analysis on the alarm information by using the first algorithm includes aggregating the alarm information in the device operation log by using the first algorithm, and analyzing the correlation relationship between the device and the system.

3. The device operation and maintenance method with alarm notification according to claim 2, characterized in that: The generating of association rules includes preprocessing and feature extraction of multi-source data, and analyzing the fault correlation between different devices.

4. The device operation and maintenance method with alarm notification according to claim 3, characterized in that: The abnormality detection of the equipment operation data includes monitoring the equipment operation data in real time to determine whether the equipment operation status is abnormal.

5. The device operation and maintenance method with alarm notification according to claim 4, characterized in that: The real-time alarming of equipment failures includes automatically generating an alarm notification and sending equipment failure information to operation and maintenance personnel.

6. The device operation and maintenance method with alarm notification according to claim 5, characterized in that: The automatically backing up and managing the device operation log data by the first strategy includes dividing the device operation log according to the access frequency and importance.

7. The device operation and maintenance method with alarm notification according to claim 6, characterized in that: The automatically backing up and managing the device operation log data through the first strategy also includes regularly backing up the device operation log data in different storage stages according to preset backup rules.

8. A system using the equipment operation and maintenance method with alarm notification as claimed in any one of claims 1 to 7, characterized in that: Including correlation analysis module, anomaly detection module, and backup management module; The association analysis module is used to perform association analysis on the alarm information through a first algorithm to generate association rules; The anomaly detection module is used to detect anomalies in equipment operation data and to warn equipment failures in real time; The backup management module is used to automatically back up and manage device operation log data through a first strategy.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the equipment operation and maintenance method with alarm notification described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the equipment operation and maintenance method with alarm notification described in any one of claims 1 to 7 are implemented.

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