Multi-dimensional alarm analysis and customized report generation method

Abstract

The invention discloses a multi-dimensional alarm analysis and customized report generation method, which belongs to the technical field of industrial alarm analysis, and remarkably improves the operation and maintenance efficiency and decision support capability of an industrial system through real-time acquisition and accurate filtering of alarm events in combination with automatic data synchronization and normalization processing. The system subscribes the alarm flow in real time by relying on the OPC client, and accurately recognizes primary and secondary alarms through a classification ID and priority mechanism, so as to ensure that key events are processed preferentially. And meanwhile, the ACM main database is associated to supplement treatment measures and potential influence information, so that the alarm data has a complete service context, and a reliable basis is provided for subsequent analysis. Honeywell original data are periodically synchronized and classified and stored according to report requirements, so that short-time high load of a server is avoided, and data continuity and integrity are guaranteed. Based on a unique identification mechanism of AlarmId, accurate correlation of cross-event type data is realized, and a technical foundation is laid for multi-dimensional index calculation.

Description

Technical Field

[0001] The present invention belongs to the technical field of industrial alarm analysis, and specifically relates to a multi-dimensional alarm analysis and customized report generation method. Background Art

[0002] Industrial automation alarms are a key component of modern industrial control systems. They are responsible for promptly issuing warning signals to operators or management systems when they detect abnormal equipment, processes, or environmental conditions, or when pre-set safety or operational limits are exceeded. These alarms are typically based on real-time data collected by field devices such as sensors and transmitters, and are triggered by logic within control systems such as PLCs, DCSs, or SCADA. Alarm information typically includes descriptive text, severity level, occurrence time, and the associated equipment ID. Alarms are presented through various channels, including audible and visual alarms, control room displays, text messages, emails, or dedicated alarm management systems. Their core purpose is to enable operators to respond quickly and take necessary corrective measures to prevent escalating faults, production interruptions, equipment damage, and even accidents. They are a crucial line of defense for ensuring the continuity, safety, and efficiency of industrial production.

[0003] However, traditional methods rely on manual or semi-automatic methods to collect alarm data, making it impossible to accurately distinguish between primary and secondary alarms in real time, resulting in delayed responses to critical incidents. Furthermore, the lack of a unified data synchronization mechanism and normalized processing flow makes cross-system data correlation difficult. Report generation relies on manual integration, which can easily miss key information and make it difficult to support multi-dimensional real-time analysis, hindering the timeliness and accuracy of operations and maintenance decisions. Summary of the Invention

[0004] The purpose of the present invention is to provide a multi-dimensional alarm analysis and customized report generation method in order to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention is as follows: a multi-dimensional alarm analysis and customized report generation method, the method comprising the following steps:

[0006] S1: Based on the OPC client subscription alarm event, the connection parameters and alarm point information are parsed through the configuration file, and the alarm event stream of the OPC Server is collected in real time;

[0007] S2: According to the preset list of first and second level alarm points and priority identification, the alarm event level is determined, and only the first or second level alarm events are retained;

[0008] S3: Perform normalization processing on the filtered alarm events, extract key fields and associate them with the ACM master database, supplement potential impact and disposal measures information, and store them in the AEMessages table of the intermediate database;

[0009] S4: Call Honeywell's unopened interface GetKPITrends to periodically collect KPI indicator data, parse it according to the structure defined in the AlarmMetrics table, and store it in the KPIData table of the intermediate database;

[0010] S5: Synchronize the original alarm data in the Honeywell database at ten-minute intervals and synchronize it to the event table of the DB4Reporting database according to the reporting requirements, including alarm entry, recovery, and confirmation events;

[0011] S6: Based on the unique identification of the alarm point by AlarmId, the synchronized event data is associated, the alarm duration is calculated by the time difference, and the core indicator of the response time is confirmed;

[0012] S7: Based on daily report requirements, extract indicators from event data from 0:00 on the previous day to 0:00 on the next day to generate reports on alarm top N, timely confirmation rate, timely disposal rate, instrument integrity rate, and AOF duration;

[0013] S8: Perform backtracking on events where the alarm status is not closed or the confirmation timeout has occurred, generate a final report file after correcting the data anomaly, compress the data into binary data and store it in the ReportFiles table;

[0014] S9: Call the ReportFiles table of the intermediate database through the upper-level system, decompress and render the report content on demand, and realize multi-dimensional alarm analysis and customized display.

[0015] In a preferred embodiment, in step S1, based on the OPC client subscribing to the alarm event, key parameters are obtained by parsing the configuration file in JSON format;

[0016] The configuration file contains the OPC Server connection URL, a 60-second reconnection time, a category ID filter value of 3, and a list of first- and second-level alarm points. The client collects the OPC Server's alarm event stream in real time and subscribes only to events that match the category ID, ensuring the real-time and accuracy of the data source. If the connection is abnormal, the client automatically attempts to reconnect every 60 seconds to ensure service stability.

[0017] In a preferred embodiment, in step S2, the alarm level is determined based on the preset first-level alarm priority of 900 and the second-level alarm priority of 500, combined with the specific point information in Class1Tags and Class2Tags; if the UseTagList parameter in the configuration is true, the event is completely filtered based on the point list; otherwise, it is judged based on the priority value; non-first-level and second-level alarm events are directly discarded, and only qualified events are retained to enter the subsequent processing flow to ensure the accuracy of data processing.

[0018] In a preferred embodiment, in step S3, the filtered alarm events are normalized to extract the core fields of event time, area, point name, and priority, and are associated with the alarm master database through the ACM server; extended information of potential impact, internal operation action, and external operation action is supplemented and mapped to the AEMessages table fields of the intermediate database, including Source, Severity, EventTime, Impact, and InsideAction; all data are finally written to the intermediate database through SQL insert statements to ensure a unified and complete storage format.

[0019] In a preferred embodiment, in step S4, the GetKPITrends interface that is not open to Honeywell is called through the WCF client, and the serialized DashboardFilter parameters are passed in to periodically obtain KPI indicator data; after parsing the returned results, the values ​​are mapped to the KPIId and Value fields of the KPIData table according to the predefined KPIId and KPIName in the AlarmMetrics table; the data is pushed every ten minutes, and a composite index is established through the timestamp and the workshop source field to optimize query efficiency.

[0020] In a preferred embodiment, in step S5, a data synchronization task is performed every ten minutes, with the synchronization time staggered by ten minutes, and events are filtered by type from the Honeywell database; for example, the alarm TopN report only synchronizes entry and recovery events, and the confirmation timeliness report synchronizes entry and confirmation events; the synchronization range is based on the last end timestamp to avoid data omissions; the classified data is stored in the Events table of the DB4Reporting database, providing a structured basis for subsequent analysis.

[0021] In a preferred embodiment, in step S6, the alarm point is uniquely identified based on AlarmId, and the ID is generated by three parts: point name, alarm identifier, and factory level; the TagId field of the Honeywell database is associated through the TagDataView view to ensure data consistency; when calculating the alarm duration, if the recovery time is not recorded, the default is 24 hours; events with a confirmed response time exceeding 30 seconds or a recovery time exceeding 30 minutes are marked as abnormal and used to generate core indicators.

[0022] In a preferred embodiment, in step S7, daily report generation is started at dawn every day, and indicators are extracted from event data from 0:00 of the previous day to 0:00 of the next day; the alarm TopN report is sorted in descending order by alarm duration, and the top 5, 10 or 20 records are taken; the timely confirmation rate statistics the proportion of events confirmed within 30 seconds; the timely handling rate statistics the proportion of events recovered within 30 minutes; the instrument integrity rate is calculated by dividing the number of IOP and ANS type alarms by the total number of predefined instruments; the AOF duration report calculates the time difference between ON and OFF events, and the abnormal state defaults to 24 hours.

[0023] In a preferred embodiment, in step S8, data correction is performed on unclosed alarms or timed-out confirmation events, and the missing end time or confirmation time is forcibly set to a 24-hour upper limit; after the final report is generated, the file is converted into binary data using the ZIP compression algorithm, and stored in the ReportFiles table according to the report ID predefined in the ReportDefine table, and the generation time and report content are recorded to ensure data traceability.

[0024] In a preferred embodiment, in step S9, the upper-level system associates the ReportDefine table through the ReportFiles table foreign key of the intermediate database, and queries the binary data of the specified report ID on demand; after decompressing the ZIP format content, combined with the predefined report name and description, the chart or table is rendered through the visualization component to realize the dynamic display and analysis of multi-dimensional alarm data, and support user-customized viewing and export.

[0025] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0026] 1. In the present invention, the operation and maintenance efficiency and decision-making support capabilities of the industrial system are significantly improved through real-time collection and precise filtering of alarm events, combined with automated data synchronization and normalization processing. The system relies on the OPC client to subscribe to the alarm stream in real time, and accurately identifies the first and second level alarms through the classification ID and priority mechanism to ensure that key events are handled first. At the same time, the ACM master database is associated with supplementary disposal measures and potential impact information, so that the alarm data has a complete business context, providing a reliable basis for subsequent analysis. Honeywell raw data is periodically synchronized and stored according to report requirements, which not only avoids short-term high load on the server, but also ensures data continuity and integrity. Based on the unique identification mechanism of AlarmId, accurate association of data across event types is achieved, laying a technical foundation for the calculation of multi-dimensional indicators.

[0027] 2. In the present invention, comprehensive operational insights are provided to enterprises by automatically generating a variety of customized reports. Core indicators such as alarm TopN and confirmation timeliness rate intuitively present system performance bottlenecks, helping to quickly locate high-frequency fault points; disposal timeliness rate and instrument integrity rate reports directly reflect the response efficiency of the operation and maintenance team and the health status of the equipment, providing a basis for optimizing resource allocation. The AOF duration report further reveals the duration of special working conditions and assists in formulating preventive maintenance strategies. After compressed storage, all reports can be flexibly called and visualized through the upper-level system, supporting real-time monitoring and historical backtracking, and ultimately forming a closed-loop management from data collection to decision-making implementation, comprehensively improving the reliability and management level of industrial systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic diagram of the process principle of the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0030] Example:

[0031] Reference Figure 1 A multi-dimensional alarm analysis and customized report generation method includes the following steps:

[0032] S1: Based on the OPC client subscription alarm event, the connection parameters and alarm point information are parsed through the configuration file, and the alarm event stream of the OPC Server is collected in real time;

[0033] S2: According to the preset list of first and second level alarm points and priority identification, the alarm event level is determined, and only the first or second level alarm events are retained;

[0034] S3: Perform normalization processing on the filtered alarm events, extract key fields and associate them with the ACM master database, supplement them with information such as potential impact and disposal measures, and then store them in the AEMessages table of the intermediate database;

[0035] S4: Call Honeywell's unopened interface GetKPITrends to periodically collect KPI indicator data, parse it according to the structure defined in the AlarmMetrics table, and store it in the KPIData table of the intermediate database;

[0036] S5: Synchronize the original alarm data in the Honeywell database at ten-minute intervals and synchronize it to the event table of the DB4Reporting database according to the reporting requirements, including events such as alarm entry, recovery, and confirmation;

[0037] S6: Based on the unique identification of the alarm point by AlarmId, the synchronized event data is associated, and the core indicators such as the alarm duration and confirmation response time are calculated by the time difference;

[0038] S7: Based on daily report requirements, extract indicators from event data from 0:00 on the previous day to 0:00 on the next day, and generate reports such as alarm top N, confirmation timeliness rate, disposal timeliness rate, instrument integrity rate, and AOF duration;

[0039] S8: Perform backtracking on events where the alarm status is not closed or the confirmation timeout has occurred, generate a final report file after correcting the data anomaly, compress the data into binary data and store it in the ReportFiles table;

[0040] S9: Call the ReportFiles table of the intermediate database through the upper-level system, decompress and render the report content on demand, and realize multi-dimensional alarm analysis and customized display.

[0041] In step S1, based on the OPC client subscription alarm event, key parameters are obtained by parsing the configuration file in JSON format.

[0042] The configuration file contains the OPC server's connection URL, a 60-second reconnection timer, a Classification ID filter value of 3, and a list of primary and secondary alarm points (such as Area1 and its corresponding point name in the Class1 tag). The client collects the OPC server's alarm event stream in real time and subscribes only to events that match the Classification ID, ensuring the real-time and accuracy of the data source. If the connection fails, the client automatically attempts to reconnect every 60 seconds to ensure service stability.

[0043] In step S2, the alarm level is determined based on the preset level 1 alarm priority of 900 and level 2 alarm priority of 500, combined with the specific location information in Class1Tags and Class2Tags. If the UseTagList parameter is set to true, events are filtered entirely based on the location list; otherwise, the priority value is used for filtering. Events not meeting the level 1 or level 2 alarm criteria are discarded, and only eligible events are retained for subsequent processing, ensuring accurate data processing.

[0044] In step S3, the filtered alarm events are normalized to extract core fields such as event time, region, location name, and priority. These fields are then linked to the master alarm database via the ACM server. Extended information such as potential impact, internal and external actions is added and mapped to the AEMessages table in the intermediate database, including fields such as Source, Severity, EventTime, Impact, and InsideAction. All data is ultimately written to the intermediate database using SQL insert statements to ensure consistent and complete storage format.

[0045] In step S4, the WCF client calls Honeywell's unavailable GetKPITrends interface, passing in the serialized DashboardFilter parameter to periodically retrieve KPI indicator data. After parsing the returned results, the values ​​are mapped to the KPIId and Value fields of the KPIData table based on the predefined KPIId (e.g., 2 for average alarm rate) and KPIName in the AlarmMetrics table. Data is pushed every ten minutes, and a composite index is established using the timestamp and workshop source fields to optimize query efficiency.

[0046] In step S5, data synchronization is performed every ten minutes, with synchronization times staggered by full ten minutes (e.g., starting at the third minute). Events are filtered by type from the Honeywell database. For example, the Alarm TopN report only synchronizes incoming and outgoing events, while the Confirmation Timeliness report synchronizes incoming and confirmed events. The synchronization range is based on the last end timestamp to prevent data omissions. The categorized data is stored in the Events table of the DB4Reporting database, providing a structured foundation for subsequent analysis.

[0047] In step S6, the alarm location is uniquely identified based on the AlarmId, which is generated from the location name (e.g., _TT-1101), the alarm identifier (e.g., HH, LL), and the plant level. The TagId field in the Honeywell database is linked via the TagDataView view to ensure data consistency. When calculating the alarm duration, if the recovery time is not recorded, a default of 24 hours is used. Events with a confirmed response time exceeding 30 seconds or a recovery time exceeding 30 minutes are marked as anomalies and used to generate core metrics.

[0048] In step S7, daily report generation begins at midnight each day, extracting metrics from event data from midnight of the previous day to midnight of the next day. The Alarm TopN report sorts alarms in descending order by duration, selecting the top 5, 10, or 20 records. The Confirmation Timeliness Rate measures the percentage of events confirmed within 30 seconds. The Disposition Timeliness Rate measures the percentage of events resolved within 30 minutes. The Instrument Uptime Rate is calculated by dividing the number of alarms (IOP, ANS, etc.) by the total number of predefined instruments. The AOF Duration Report calculates the time difference between ON and OFF events, with an exception state defaulting to 24 hours.

[0049] In step S8, data correction is performed for unclosed alarms or timed-out confirmation events, forcing the missing end time or confirmation time to the 24-hour limit. After the final report is generated, the file is converted into binary data using the ZIP compression algorithm and stored in the ReportFiles table according to the predefined report ID in the ReportDefine table (for example, ID2 for AlarmTop5). The generation time and report content are recorded to ensure data traceability.

[0050] In step S9, the upper-level system links the ReportDefine table to the ReportFiles table in the intermediate database through a foreign key, allowing it to query binary data for a specific report ID on demand. After decompressing the ZIP file, the system uses visualization components to render charts or tables based on predefined report names and descriptions (such as "Alarm Top 5 Report"), enabling dynamic display and analysis of multi-dimensional alarm data and supporting customized viewing and export.

[0051] From the above we can know:

[0052] In the present invention, the operation and maintenance efficiency and decision-making support capabilities of the industrial system are significantly improved through real-time collection and precise filtering of alarm events, combined with automated data synchronization and normalization processing. The system relies on the OPC client to subscribe to the alarm stream in real time, and accurately identifies the first and second level alarms through the classification ID and priority mechanism to ensure that key events are handled first. At the same time, the ACM master database is associated with supplementary disposal measures and potential impact information, so that the alarm data has a complete business context, providing a reliable basis for subsequent analysis. The Honeywell raw data is periodically synchronized and stored according to the requirements of the report, which not only avoids short-term high load on the server, but also ensures data continuity and integrity. Based on the unique identification mechanism of AlarmId, accurate association of data across event types is achieved, laying a technical foundation for the calculation of multi-dimensional indicators.

[0053] In the present invention, comprehensive operational insights are provided to enterprises through the automatic generation of a variety of customized reports. Core indicators such as alarm TopN and confirmation timeliness rate intuitively present the system performance bottlenecks, helping to quickly locate high-frequency fault points; disposal timeliness rate and instrument integrity rate reports directly reflect the response efficiency of the operation and maintenance team and the health status of the equipment, providing a basis for optimizing resource allocation. The AOF duration report further reveals the duration of special working conditions and assists in formulating preventive maintenance strategies. After compressed storage, all reports can be flexibly called and visualized through the upper-level system, supporting real-time monitoring and historical backtracking, and ultimately forming a closed-loop management from data collection to decision-making implementation, comprehensively improving the reliability and management level of the industrial system.

[0054] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also includes elements that are inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0055] The above description is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-dimensional alarm analysis and customized report generation method, characterized by: The method comprises the following steps: S1: Based on the OPC client subscription alarm event, the connection parameters and alarm point information are parsed through the configuration file, and the alarm event stream of the OPC Server is collected in real time; S2: According to the preset list of first and second level alarm points and priority identification, the alarm event level is determined, and only the first or second level alarm events are retained; S3: Perform normalization processing on the filtered alarm events, extract key fields and associate them with the ACM master database, supplement potential impact and disposal measures information, and store them in the AEMessages table of the intermediate database; S4: Call Honeywell's unopened interface GetKPITrends to periodically collect KPI indicator data, parse it according to the structure defined in the AlarmMetrics table, and store it in the KPIData table of the intermediate database; S5: Synchronize the original alarm data in the Honeywell database at ten-minute intervals and synchronize it to the event table of the DB4Reporting database according to the reporting requirements, including alarm entry, recovery, and confirmation events; S6: Based on the unique identification of the alarm point by AlarmId, the synchronized event data is associated, the alarm duration is calculated by the time difference, and the core indicator of the response time is confirmed; S7: Based on daily report requirements, extract indicators from event data from 0:00 on the previous day to 0:00 on the next day to generate reports on alarm top N, timely confirmation rate, timely disposal rate, instrument integrity rate, and AOF duration; S8: Perform backtracking on events where the alarm status is not closed or the confirmation timeout has occurred, generate a final report file after correcting the data anomaly, compress the data into binary data and store it in the ReportFiles table; S9: Call the ReportFiles table of the intermediate database through the upper-level system, decompress and render the report content on demand, and realize multi-dimensional alarm analysis and customized display.

2. The multi-dimensional alarm analysis and customized report generation method according to claim 1, characterized in that: In step S1, based on the OPC client subscribing to the alarm event, key parameters are obtained by parsing the configuration file in JSON format; The configuration file contains the OPC Server connection URL, a reconnection time of 60 seconds, a category ID filter value of 3, and a list of first and second level alarm points. The client collects the alarm event stream of the OPC Server in real time and subscribes only to events that match the classification ID to ensure the real-time and accuracy of the data source; if the connection is abnormal, the client automatically attempts to reconnect in a 60-second cycle to ensure service stability.

3. The multi-dimensional alarm analysis and customized report generation method according to claim 1, characterized in that: In step S2, the alarm level is determined based on the preset first-level alarm priority of 900 and the second-level alarm priority of 500, combined with the specific point information in Class1Tags and Class2Tags; if the UseTagList parameter in the configuration is true, the event is completely filtered based on the point list; otherwise, it is determined based on the priority value; non-first-level and second-level alarm events are directly discarded, and only qualified events are retained to enter the subsequent processing flow to ensure the accuracy of data processing.

4. The multi-dimensional alarm analysis and customized report generation method according to claim 1, characterized in that: In step S3, normalization processing is performed on the filtered alarm events to extract the core fields of event time, area, point name, and priority, and the alarm master database is linked through the ACM server; extended information on potential impact, internal operation action, and external operation action is supplemented and mapped to the AEMessages table fields of the intermediate database, including Source, Severity, EventTime, Impact, and InsideAction; all data is finally written to the intermediate database through SQL insert statements to ensure a unified and complete storage format.

5. The multi-dimensional alarm analysis and customized report generation method according to claim 1, characterized in that: In step S4, Honeywell's unopened GetKPITrends interface is called through the WCF client, and the serialized DashboardFilter parameters are passed in to periodically obtain KPI indicator data; after parsing the returned results, the values ​​are mapped to the KPIId and Value fields of the KPIData table according to the predefined KPIId and KPIName in the AlarmMetrics table; the data is pushed every ten minutes, and a composite index is established through the timestamp and the workshop source field to optimize query efficiency.

6. The multi-dimensional alarm analysis and customized report generation method according to claim 1, characterized in that: In step S5, the data synchronization task is executed every ten minutes, with the synchronization time staggered by ten minutes, and events are filtered by type from the Honeywell database; for example, the Alarm TopN report only synchronizes entry and recovery events, and the Confirmation Timeliness Report synchronizes entry and confirmation events; The synchronization range is based on the last end timestamp to avoid data omissions; the classified data is stored in the Events table of the DB4Reporting database, providing a structured basis for subsequent analysis.

7. The multi-dimensional alarm analysis and customized report generation method according to claim 1, characterized in that: In step S6, the alarm point is uniquely identified based on AlarmId, which is generated by three parts: the point name, the alarm identifier, and the factory level. The TagId field of the Honeywell database is associated through the TagDataView view to ensure data consistency. When calculating the alarm duration, if the recovery time is not recorded, it defaults to 24 hours. It is confirmed that events with a response time exceeding 30 seconds or a recovery time exceeding 30 minutes are marked as abnormal and used to generate core indicators.

8. The multi-dimensional alarm analysis and customized report generation method according to claim 1, characterized in that: In step S7, daily report generation is started at dawn every day, and indicators are extracted from event data from 0:00 of the previous day to 0:00 of the next day; the alarm TopN report is sorted in descending order by alarm duration, and the top 5, 10 or 20 records are taken; the timely confirmation rate statistics the proportion of events confirmed within 30 seconds; the timely disposal rate statistics the proportion of events recovered within 30 minutes; the instrument integrity rate is calculated by dividing the number of IOP and ANS type alarms by the total number of predefined instruments; the AOF duration report calculates the time difference between ON and OFF events, and the abnormal state defaults to 24 hours.

9. The multi-dimensional alarm analysis and customized report generation method according to claim 1, characterized in that: In step S8, data correction is performed on the unclosed alarm or timeout confirmation event, and the missing end time or confirmation time is forcibly set to the upper limit of 24 hours; After the final report is generated, the file is converted into binary data using the ZIP compression algorithm and stored in the ReportFiles table according to the report ID predefined in the ReportDefine table. The generation time and report content are recorded to ensure data traceability.

10. The multi-dimensional alarm analysis and customized report generation method according to claim 1, characterized in that: In step S9, the upper-level system associates the ReportDefine table with the ReportFiles table foreign key of the intermediate database, and queries the binary data of the specified report ID on demand; after decompressing the ZIP format content, combined with the predefined report name and description, the upper-level system renders the chart or table through the visualization component to realize the dynamic display and analysis of multi-dimensional alarm data, and supports user-customized viewing and export.

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