Alarm information encryption storage and positioning method and system of alarm platform

Abstract

The invention relates to the technical field of computers, in particular to an alarm information encryption storage and positioning method and system for an alarm platform, and the method comprises the following steps: a system architecture design step, a data collection step, a data processing and storage step, a real-time data analysis step, and a platform alarm and fault positioning step. The method has the beneficial effects that the data is stored in a partitioned manner according to the initial and queried based on the encrypted ID, so that the system can quickly locate the table containing the specific ID to query, the time for scanning the whole table is shortened, the query speed is greatly increased, the local index is created, and the overhead caused by the global index is avoided.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and particularly to a method and system for encrypting and storing and positioning alarm information of an alarm platform. Background Art

[0002] With the rapid development of information technology, the scale of various software systems, network services, and Internet of Things devices has become increasingly large. The stability and reliability of these systems have become key factors in enterprise operations and user experience. In this context, as an important tool for monitoring and ensuring the stable operation of the system, the performance and efficiency of the real-time alarm system are directly related to the fault response speed and system recovery ability.

[0003] In existing real-time alarm systems, a common practice is to directly store all alarm information in a database and search for historical records through keyword matching or other simple retrieval methods. However, the alarm information collection and storage method of traditional real-time alarm systems has limitations, especially when dealing with complex systems and large-scale data, which is particularly prominent. Specifically, collecting the same warning into the database using different identifiers not only increases the complexity and redundancy of alarm information but also brings the following specific inconveniences in actual operation and maintenance:

[0004] High storage redundancy: Since the same alarm event may occur frequently, especially in large distributed systems, the same type of error or warning may appear multiple times, resulting in a large number of almost identical alarm information being repeatedly recorded. This not only occupies valuable storage resources but also increases the maintenance cost. As time goes by, software and hardware are constantly updated, and there may be compatibility issues between old and new versions, resulting in inconsistent formats of the same alarm information under different versions, further exacerbating the storage redundancy problem.

[0005] Insufficient security: The original alarm information may contain sensitive data such as user accounts and IP addresses. Storing or transmitting this information directly without encryption poses a great risk of leakage, which may lead to serious privacy violations and security vulnerabilities. Unencrypted alarm data is more likely to become the target of hacker attacks during network transmission, such as Man-in-the-Middle Attacks, thus threatening the security of the entire system.

[0006] Difficult to quickly locate problems: Faced with a large amount of alarm information, operation and maintenance personnel often feel at a loss and find it difficult to screen out truly important alarms for priority processing. This not only reduces work efficiency but also may miss the best repair opportunity. The current alarm management system usually does not have a perfect classification system and identifier generation mechanism, making it impossible to effectively associate similar or related alarms, restricting the in-depth analysis and quick positioning of the root cause of problems.

[0007] The operation and maintenance cost increases: Due to the many inconveniences of the traditional method of collecting and storing alarm information, operation and maintenance personnel need to invest more time and energy in processing alarm information. This not only increases the labor cost, but may also lead to serious consequences such as fault escalation or service interruption due to untimely processing. Summary of the Invention

[0008] The purpose of the present invention is to provide a method and system for encrypting and storing and positioning alarm information of an alarm platform to solve the problems raised in the above background technology.

[0009] To achieve the above purpose, the present invention provides the following technical solutions: A method for encrypting and storing and positioning alarm information of an alarm platform, including the following steps:

[0010] System architecture design step, dividing the system into a data collection layer, a data processing layer, a storage layer, an analysis layer and an alarm layer;

[0011] Data collection step, collecting monitoring data from at least one monitored object, and the monitored objects include cloud servers and hardware devices;

[0012] Data processing and storage step, integrating, cleaning, and format-converting the collected data, encrypting the integrated alarm information using the MD5 algorithm to generate a unique identifier, generating an alarm trigger time according to the collection time, and storing the alarm information into a real-time alarm table and a historical alarm table, and the historical alarm table is stored into 16 different tables according to the first letter of the ID;

[0013] Real-time analysis of data step, querying and analyzing the data in the database, retrieving similar alarm records in the historical database according to the encrypted ID, retrieving according to the first letter using Elasticsearch, extracting the solution if there is a matching item, starting the real-time analysis process if there is no matching item, and using Apache Kafka to quickly analyze and process the real-time data;

[0014] Platform alarm and fault location step, after the real-time analysis of data is completed, the platform triggers an alarm. When there is a matching item, the alarm is attached with historical alarm information and a solution. When there is no matching item, the solution is maintained in the historical alarm table after the alarm is processed.

[0015] Preferably, in the data collection step: for the cloud server, design a Python script to send HTTP GET / POST requests to the specified data source using the requests library and process the response data, including parsing data in JSON or XML format, deploy the script to the monitoring object client or server, and configure environment variables for alarm information data collection; for the hardware device, design a Python script to obtain the current device's process status and system utilization using the psutil library, deploy the script to the monitoring object client or server, configure environment variables for data collection, and set data thresholds to generate alarm information when the data exceeds the set thresholds.

[0016] Preferably, in the data processing and storage step: design a Python script to continuously monitor the data transmission channel in the data collection layer to ensure timely and complete data reception; perform preliminary integration on the received data, and uniformly collect data from different data sources and in different formats into the processing queue; deeply clean the data to identify and remove useless information, duplicate data, and outliers; convert the cleaned data into a unified format and encoding standard to ensure data consistency and interoperability.

[0017] Preferably, in the real-time data analysis step: when a new alarm is detected, first retrieve it in the historical database according to its encrypted ID; use Elasticsearch to retrieve and check if there are similar alarm records based on the first letter; when there is a matching item, extract the corresponding solution from the case library for current use; when there is no matching item, start the real-time analysis process to further evaluate the situation, and use the streaming processing technology Apache Kafka to quickly analyze and process the real-time data.

[0018] Preferably, in the platform alarm and fault location step: after the real-time data analysis is completed, the platform triggers an alarm; when there is a matching item, the alarm is accompanied by historical alarm information and historical solutions to quickly locate the problem and resolve the alarm; when there is no matching item, after the alarm is processed, maintain the current processing solution in the historical alarm table, and add a "Solution" field to the historical alarm table. The solution to the resolved alarm can be written into this field.

[0019] A system for the alarm information encryption storage and location method of an alarm platform, including:

[0020] The system architecture module divides the system into a data collection layer, a data processing layer, a storage layer, an analysis layer, and an alarm layer;

[0021] The data collection module is used to collect monitoring data from at least one monitored object, and the monitored objects include cloud servers and hardware devices;

[0022] The data processing and storage module is used to integrate, clean, and convert the format of the collected data, encrypt the integrated alarm information using the MD5 algorithm to generate a unique identifier (ID), generate an alarm trigger time based on the collection time, store the alarm information in the real-time alarm table and the historical alarm table, and store the historical alarm table in 16 different tables according to the first letter of the ID;

[0023] The real-time analysis data module is used to query and analyze the data in the database, retrieve similar alarm records in the historical database according to the encrypted ID, use Elasticsearch to retrieve according to the first letter, extract the solution if there is a match, start the real-time analysis process if there is no match, and use Apache Kafka to quickly analyze and process the real-time data;

[0024] The platform alarm and fault location module is used to trigger an alarm on the platform after the real-time analysis of the data is completed. When there is a match, the alarm is attached with the historical alarm information and the solution. When there is no match, the solution is maintained in the historical alarm table after the alarm is processed.

[0025] Preferably, the data collection module includes: a cloud server data collection unit, which is used to design a Python script to send HTTP GET / POST requests to the specified data source using the requests library and process the response data, including parsing data in JSON or XML format, deploying the script to the monitoring object client or server, and configuring environment variables for alarm information data collection; a hardware device data collection unit, which is used to design a Python script, use the psutil library to obtain the process status and system utilization rate of the current device, deploy the script to the monitoring object client or server, configure environment variables for data collection, and set data thresholds to generate alarm information when the data exceeds the set thresholds.

[0026] Preferably, the data processing and storage module includes: a data receiving unit, which is used to monitor the data transmission channel of the data collection layer in real time to ensure timely and complete data reception;

[0027] A data integration unit, which is used to perform preliminary integration on the received data, and uniformly collect data from different data sources and different formats into the processing queue;

[0028] A data cleaning unit, which is used to deeply clean the data, identify and remove useless information, duplicate data, and outliers;

[0029] A data format conversion unit, which is used to convert the cleaned data into a unified format and coding standard to ensure data consistency and interoperability;

[0030] A data encryption unit, which is used to encrypt the integrated alarm information by using the MD5 algorithm implemented in Python, generate a unique identifier, and generate an alarm trigger time according to the time when the alarm information is collected, and write it into the integrated alarm information;

[0031] A data storage unit, which is used to store the alarm information into a real-time alarm table and a historical alarm table, retrieve the two tables. If the ID already exists in the table, only the time needs to be updated. If the ID does not exist in the table, the alarm information is directly imported into the real-time alarm table and the historical alarm table. The historical alarm table adds a "Solution" field, and the solution to the solved alarm can be written into this field.

[0032] Preferably, the real-time analysis data module includes: a data retrieval unit, which is used to retrieve in the historical database according to its encrypted ID when a new alarm is detected, and use Elasticsearch to retrieve according to the first letter to check whether there are similar alarm records;

[0033] A solution extraction unit, which is used to extract the corresponding solution from the case library for current use when there is a match;

[0034] A real-time analysis unit, which is used to start a real-time analysis process to further evaluate the situation when there is no match, and use the streaming processing technology Apache Kafka to quickly analyze and process the real-time data.

[0035] Preferably, after the real-time analysis of the data is completed, the platform triggers an alarm. In the case of a match, the alarm will be accompanied by historical alarm information and historical solutions, quickly locating the problem and solving the alarm; in the case of no match, the current processing solution will be maintained in the historical alarm table after the alarm is processed. The historical alarm table adds a "Solution" field, and the solution to the solved alarm can be written into this field.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] The method and system for encrypting and storing and locating alarm information of the alarm platform proposed by the present invention store the data partitioned according to the first letter and query based on the encrypted ID. The system can quickly locate the table containing the specific ID for query, reducing the time for full table scanning, greatly improving the query speed, and creating a local index to avoid the overhead brought by the global index.

[0038] By generating a unique identifier (ID) through MD5 encryption for each alarm message, it can be ensured that even the same alarm message can be recognized as a duplicate. In this way, when storing new alarms, the system can check whether there are records with the same ID, avoiding duplicate storage and significantly reducing data redundancy. Introducing a deduplication logic in the data processing layer ensures that only new or different alarm messages will be added to the database, further reducing unnecessary storage overhead.

[0039] Since all alarm messages and their corresponding encrypted IDs are saved in the historical database, this provides a solid foundation for long-term data analysis. By analyzing historical data, common failure modes or security threat trends can be discovered, helping to formulate preventive measures. After each alarm problem is successfully solved, its solution can be recorded and form a case library. When encountering similar alarms in the future, a reference solution can be quickly obtained from the case library to accelerate the problem-solving process.

[0040] New alarm messages are collected every five minutes, and their encrypted IDs are stored in the real-time monitoring database, ensuring that the latest alarms can be quickly recognized and processed. When a new alarm appears, the system can quickly determine whether there are the same alarm records based on the encrypted ID, so as to locate the problem faster and take corresponding measures.

[0041] By quickly responding to and resolving system failures, the alarm platform can reduce service interruption time and ensure the continuity and stability of user services. This not only enhances users' trust and satisfaction with the system but also strengthens the enterprise's brand image and market competitiveness. Brief Description of the Drawings

[0042] Figure 1 It is the system architecture diagram of the present invention;

[0043] Figure 2 It is the data processing and storage flow chart of the present invention;

[0044] Figure 3 It is the method flow chart of the present invention. Detailed Embodiments

[0045] In order to clearly and completely describe the purpose, technical solution of the present invention and make the advantages more clear, the following further details the embodiments of the present invention with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are part of the embodiments of the present invention, rather than all of the embodiments, and are only used to explain the embodiments of the present invention, not to limit the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0046] Embodiment 1, please refer to Figures 1 to 3, the present invention provides a technical solution: a method for encrypting and storing alarm information and positioning in an alarm platform, including the following steps:

[0047] Step 1: System architecture design

[0048] The system is divided into five parts, namely the data collection layer, the data processing layer, the storage layer, the analysis layer, and the alarm layer. See Figure 1 .

[0049] Data collection layer: Collect alarm information data from various data sources and platforms;

[0050] Data processing layer: Receive data from the data collection layer and preprocess the collected original alarm information, including standardization, integration, and encryption operations;

[0051] Storage layer: Store the processed alarm information data in an appropriate storage medium for subsequent analysis and query;

[0052] Analysis layer: Perform real-time analysis on the data in the storage layer, identify and discover anomalies, and support decision-making;

[0053] Alarm layer: Trigger corresponding alarms according to the analysis results and transmit relevant information to relevant responsible persons or systems.

[0054] Step 2: Data collection

[0055] Collect monitoring data from at least one monitored object. It can be that the monitored object actively reports alarm information data, or it can be that data is collected from the monitored object and then analyzed to generate alarm information after discovering anomalies.

[0056] In an optional implementation method, the monitored object can be the alarm information of the cloud server reported to Prometheus or Openstack. A python script can be designed to send HTTP GET / POST requests to the specified data source (possibly the OpenStack API or other HTTP services) using the requests library and process the response data, which may include parsing JSON or XML format data. Deploy the script to the monitored object client or server, configure the environment variables, and collect alarm information data.

[0057] In an optional implementation method, the monitored object can be a hardware device, such as CPU, GPU, CPU usage rate, disk usage rate, running process status, etc. A python script can be designed to use the psutil library to obtain the process status and system utilization rate of the current device, etc. Deploy the script to the monitored object client or server, configure the environment variables, collect data, and set data thresholds to generate alarm information when the data exceeds the set thresholds.

[0058] Step 3: Data Processing and Storage

[0059] Design a Python script to process and store data in a database, and continuously monitor the data transmission channel in the data acquisition layer to ensure timely and complete data reception. Conduct preliminary integration on the received data, and uniformly collect data from different data sources and in different formats into the processing queue. Deep clean the data, identify and remove useless information, duplicate data (by comparing key fields such as ID, timestamp, etc.), and outliers (such as values outside the reasonable range, illogical data, etc.). Convert the cleaned data into a unified format and encoding standard to ensure data consistency and interoperability.

[0060] Use the MD5 algorithm implemented in Python to encrypt the integrated alarm information, generate a unique identifier (ID), and generate the alarm trigger time based on the time when the alarm information is collected, and write it into the integrated alarm information.

[0061] The storage part is divided into two tables, namely the real-time alarm table and the historical alarm table. Retrieve the two tables. If the ID already exists in the table, only update the time. If the ID does not exist in the table, directly import the alarm information into the real-time alarm table and the historical alarm table. At this time, the real-time alarm table only contains the alarm information that is currently in the alarm state, and the historical alarm table stores all historical alarm information. Add a "Solution" field to the historical alarm table, and the solution to the resolved alarm can be written into this field. At the same time, due to the large amount of historical data, the IDs can be stored in 16 different historical alarm tables according to the first letter.

[0062] For the complete steps, see Figure 2 .

[0063] Step 4: Real-time Data Analysis

[0064] Query and analyze the data in the database. When a new alarm is detected, first retrieve it in the historical database according to its encrypted ID, and use Elasticsearch to retrieve and check whether there are similar alarm records according to the first letter. If there are matching items, extract the corresponding solutions from the case library for current use; if no matching items are found, start the real-time analysis process to further evaluate the situation. And use the streaming processing technology Apache Kafka to quickly analyze and process the real-time data.

[0065] Step 5: Platform Alarm and Fault Location

[0066] After the real-time analysis of data is completed, the platform triggers an alarm. When there are matching items in step four, the alarm will be accompanied by historical alarm information and historical solutions, quickly locating the problem and resolving the alarm; if there are no matching items, the current solution will be maintained in the historical alarm table after the alarm is processed.

[0067] The following is a process of an embodiment, taking the alarm of the user server as an example:

[0068] Step s101: Use a Python script to collect alarm data from the server once every five minutes;

[0069] Step s201: Process and integrate the data;

[0070] Step s202: Use the hashlib module of Python to encrypt the integrated data with MD5;

[0071] Step s203: Set the identifier (id) of the data as the generated MD5 code;

[0072] Step s301: Retrieve the id column of the real-time alarm table. If the alarm id exists, proceed to step s302; if the alarm id does not exist, proceed to step s303;

[0073] Step s302: Update the time of this alarm in the real-time alarm table;

[0074] Step s303: Import the alarm information into the real-time alarm table;

[0075] Step s304: Retrieve the id column of the historical alarm table. If the alarm id exists, proceed to step s305 and step s401; if the alarm id does not exist, proceed to step s306;

[0076] Step s305: Update the time of this alarm in the historical alarm table;

[0077] Step s306: Import the alarm information into the historical alarm table;

[0078] Step s401: Query the data of this id in the historical alarm table and read the historical solution of the alarm information of this id;

[0079] Step s501: Send the alarm information and the historical solution to the platform to trigger an alarm;

[0080] Step s601: The operation and maintenance personnel handle the alarm and form a solution for this alarm;

[0081] Step s602: The operation and maintenance personnel maintain the solution of this alarm in the historical alarm table.

[0082] For the whole process, please refer to Figure 3。

[0083] Embodiment 2, based on Embodiment 1, proposes a system for the encrypted storage and positioning method of alarm information of an alarm platform, including:

[0084] System architecture module, which divides the system into a data acquisition layer, a data processing layer, a storage layer, an analysis layer, and an alarm layer;

[0085] Data acquisition module, used to collect monitoring data from at least one monitored object, and the monitored objects include cloud servers and hardware devices; the data acquisition module includes: a cloud server data acquisition unit, used to design a Python script to send HTTP GET / POST requests to the specified data source using the requests library and process the response data, including parsing data in JSON or XML format, deploying the script to the monitored object client or server, and configuring environment variables for alarm information data acquisition; a hardware device data acquisition unit, used to design a Python script, use the psutil library to obtain the process status and system utilization rate of the current device, deploy the script to the monitored object client or server, configure environment variables for data acquisition, and set data thresholds, and generate alarm information when the data exceeds the set thresholds.

[0086] Data processing and storage module, used to integrate, clean, and convert the format of the collected data, and encrypt the integrated alarm information using the MD5 algorithm to generate a unique identifier (ID), generate an alarm trigger time according to the collection time, store the alarm information in the real-time alarm table and the historical alarm table, and the historical alarm table is stored in 16 different tables according to the first letter of the ID; the data processing and storage module includes: a data receiving unit, used to monitor the data transmission channel of the data acquisition layer in real time to ensure timely and complete data reception; a data integration unit, used to perform preliminary integration on the received data, and uniformly collect data from different data sources and different formats into the processing queue; a data cleaning unit, used to deeply clean the data, identify and remove useless information, duplicate data, and outliers; a data format conversion unit, used to convert the cleaned data into a unified format and coding standard to ensure data consistency and interoperability; a data encryption unit, used to encrypt the integrated alarm information using the MD5 algorithm implemented in Python to generate a unique identifier, and generate an alarm trigger time according to the time when the alarm information is collected, and write the integrated alarm information; a data storage unit, used to store the alarm information in the real-time alarm table and the historical alarm table, retrieve the two tables, if the ID already exists in the table, only update the time, if the ID does not exist in the table, directly import the alarm information into the real-time alarm table and the historical alarm table, and the historical alarm table adds a "Solution" field, and the solution to the resolved alarm can be written into this field.

[0087] A real-time data analysis module is used to query and analyze the data in the database, retrieve similar alarm records in the historical database according to the encrypted ID, retrieve by the first letter using Elasticsearch, extract the solution if there are matching items, and start the real-time analysis process if there are no matching items, and use Apache Kafka to quickly analyze and process the real-time data. The real-time data analysis module includes: a data retrieval unit, which is used to, when a new alarm is detected, first retrieve in the historical database according to its encrypted ID, and use Elasticsearch to retrieve by the first letter to check if there are similar alarm records; a solution extraction unit, which is used to extract the corresponding solution from the case library for current use when there are matching items; a real-time analysis unit, which is used to start the real-time analysis process to further evaluate the situation when there are no matching items, and use the streaming processing technology Apache Kafka to quickly analyze and process the real-time data.

[0088] A platform alarm and fault location module is used to trigger an alarm on the platform after the real-time data analysis is completed. When there are matching items, the alarm is attached with historical alarm information and solutions. When there are no matching items, the processing solution is maintained in the historical alarm table after the alarm is processed. After the real-time data analysis is completed, the platform triggers an alarm. In the case of matching items, the alarm will be attached with historical alarm information and historical solutions to quickly locate the problem and solve the alarm; when there are no matching items, the current processing solution is maintained in the historical alarm table after the alarm is processed. The historical alarm table adds a "Solution" field, and the solution to the solved alarm can be written into this field.

[0089] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for encrypting and storing and locating alarm information of an alarm platform, characterized in that: Including the following steps: System architecture design step, dividing the system into a data acquisition layer, a data processing layer, a storage layer, an analysis layer, and an alarm layer; Data acquisition step, collecting monitoring data from at least one monitored object, where the monitored objects include cloud servers and hardware devices; Data processing and storage step, integrating, cleaning, and converting the format of the collected data, encrypting the integrated alarm information using the MD5 algorithm to generate a unique identifier, generating an alarm trigger time based on the collection time, storing the alarm information in a real-time alarm table and a historical alarm table, and storing the historical alarm table in 16 different tables according to the first letter of the ID; Real-time analysis of data step, querying and analyzing the data in the database, retrieving similar alarm records in the historical database according to the encrypted ID, using Elasticsearch to retrieve according to the first letter, extracting solutions if there are matching items, starting a real-time analysis process if there are no matching items, and using Apache Kafka to quickly analyze and process the real-time data; Platform alarm and fault location step, after the real-time analysis of data is completed, the platform triggers an alarm. When there are matching items, the alarm is attached with historical alarm information and solutions. When there are no matching items, the processing solution is maintained in the historical alarm table after the alarm is processed.

2. The method for encrypting and storing and positioning alarm information of an alarm platform according to claim 1, wherein: In the data acquisition step: For cloud servers, design a Python script to send HTTP GET / POST requests to the specified data source using the requests library and process the response data, including parsing data in JSON or XML format, deploy the script to the monitored object client or server, and configure environment variables for alarm information data acquisition; For hardware devices, design a Python script to obtain the process status and system utilization rate of the current device using the psutil library, deploy the script to the monitored object client or server, configure environment variables for data acquisition, and set data thresholds to generate alarm information when the data exceeds the set thresholds.

3. The warning information encryption storage and positioning method of a warning platform according to claim 2, characterized in that: In the data processing and storage step: Design a Python script to monitor the data transmission channel of the data acquisition layer in real time to ensure timely and complete reception of data; perform preliminary integration of the received data, unify and collect data from different data sources and different formats into a processing queue; deeply clean the data, identify and remove useless information, duplicate data, and outliers; convert the cleaned data into a unified format and encoding standard to ensure data consistency and interoperability.

4. The method for encrypting and storing and positioning alarm information of an alarm platform according to claim 3, characterized in that: In the real-time analysis of data step: When a new alarm is detected, first retrieve it in the historical database according to its encrypted ID; use Elasticsearch to retrieve according to the first letter to check if there are similar alarm records; when there are matching items, extract the corresponding solution from the case library for current use; when there are no matching items, start a real-time analysis process to further evaluate the situation, and use the streaming processing technology Apache Kafka to quickly analyze and process the real-time data.

5. The warning information encryption storage and positioning method of a warning platform according to claim 4, characterized in that: In the platform alarm and fault location steps: After real-time data analysis is completed, the platform triggers an alarm; when there is a matching item, the alarm is accompanied by historical alarm information and historical solutions to quickly locate the problem and resolve the alarm; when there is no matching item, after the alarm is processed, the current solution is maintained in the historical alarm table, and the historical alarm table adds a "Solution" field. The solutions for resolved alarms can be written to this field.

6. A system for the encrypted storage and positioning method of the alarm information of the alarm platform according to claim 5, characterized in that: Including: The system architecture module divides the system into a data collection layer, a data processing layer, a storage layer, an analysis layer, and an alarm layer; The data collection module is used to collect monitoring data from at least one monitored object, and the monitored objects include cloud servers and hardware devices; The data processing and storage module is used to integrate, clean, and convert the format of the collected data, encrypt the integrated alarm information using the MD5 algorithm to generate a unique identifier (ID), generate an alarm trigger time based on the collection time, and store the alarm information in the real-time alarm table and the historical alarm table. The historical alarm table is stored in 16 different tables according to the first letter of the ID; The real-time data analysis module is used to query and analyze the data in the database, retrieve similar alarm records in the historical database according to the encrypted ID, retrieve according to the first letter using Elasticsearch, extract the solution if there is a matching item, start the real-time analysis process if there is no matching item, and quickly analyze and process the real-time data using Apache Kafka; The platform alarm and fault location module is used to trigger an alarm on the platform after real-time data analysis is completed. When there is a matching item, the alarm is accompanied by historical alarm information and solutions. When there is no matching item, the solution is maintained in the historical alarm table after the alarm is processed.

7. A system according to claim 6, wherein: The data collection module includes: a cloud server data collection unit, which is used to design a Python script to send HTTP GET / POST requests to the specified data source using the requests library and process the response data, including parsing data in JSON or XML format, deploying the script to the monitored object client or server, and configuring environment variables for alarm information data collection; a hardware device data collection unit, which is used to design a Python script, use the psutil library to obtain the process status and system utilization rate of the current device, deploy the script to the monitored object client or server, configure environment variables for data collection, and set data thresholds to generate alarm information when the data exceeds the set thresholds.

8. A system according to claim 7, characterized in that: The data processing and storage module includes: a data receiving unit, which is used to monitor the data transmission channel of the data collection layer in real time to ensure timely and complete data reception; A data integration unit, which is used to perform preliminary integration on the received data and uniformly collect data from different data sources and in different formats into the processing queue; A data cleaning unit, which is used to deeply clean the data, identify and remove useless information, duplicate data, and outliers; A data format conversion unit, which is used to convert the cleaned data into a unified format and encoding standard to ensure data consistency and interoperability; A data encryption unit, which is used to encrypt the integrated alarm information by using the MD5 algorithm implemented in Python, generate a unique identifier, and generate an alarm trigger time according to the time when the alarm information is collected, and write it into the integrated alarm information; A data storage unit, which is used to store the alarm information into a real-time alarm table and a historical alarm table, retrieve the two tables. If the ID already exists in the table, only the time needs to be updated. If the ID does not exist in the table, the alarm information is directly imported into the real-time alarm table and the historical alarm table. The historical alarm table adds a "Solution" field, and the solution to the resolved alarm can be written into this field.

9. A system according to claim 8, characterized in that: The real-time analysis data module includes: a data retrieval unit, which is used to, when a new alarm is detected, first retrieve it in the historical database according to its encrypted ID, and use Elasticsearch to retrieve and check whether there are similar alarm records according to the first letter; A solution extraction unit, which is used to extract the corresponding solution from the case library for current use when there is a match; A real-time analysis unit, which is used to start a real-time analysis process to further evaluate the situation when there is no match, and use the streaming processing technology Apache Kafka to quickly analyze and process the real-time data.

10. A system according to claim 9, characterized in that: After the real-time analysis of the data is completed, the platform triggers an alarm. In the case of a match, the alarm will be accompanied by historical alarm information and historical solutions, quickly locating the problem and resolving the alarm; in the case of no match, the current processing solution will be maintained in the historical alarm table after the alarm is processed. The historical alarm table adds a "Solution" field, and the solution to the resolved alarm can be written into this field.

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