Measurement point offline judgment method based on monitoring factors

Abstract

The invention provides a measuring point offline judgment method based on monitoring factors, and belongs to the technical field of structure monitoring, and the method comprises the following steps: S1, configuring each measuring point to monitor a structure, obtaining monitoring data after each measuring point monitors the structure, designing an interruption duration parameter of each measuring point through a front-end service, and obtaining an interruption duration parameter of each measuring point; the method comprises the following steps: S1, carrying out measurement point off-line parameter configuration and default measurement point off-line parameter configuration as a forbidden state, S2, during interruption, detecting the last piece of data of each measurement point and the current time interval through a back-end service, and regularly obtaining the latest measurement point data of es through a cronstr timing task, S3, comparing the last piece of data obtained before interruption with the latest measurement point data, according to the technical scheme, the measuring point is judged to be offline according to the current parameter configuration through the goonlie process, the actual state of the measuring point can be comprehensively and accurately reflected according to the monitoring factors of the structure, and the phenomenon of missed judgment or wrong judgment is avoided.

Description

Technical Field

[0001] This invention relates to the field of structural monitoring technology, and specifically to an offline judgment method for measuring points based on monitoring factors. Background Technology

[0002] In the structural monitoring industry, a project may connect to different devices, such as wind speed and direction, displacement, temperature and humidity. Each type of device has a different data acquisition granularity, so the conditions for judging data interruption should also be different. As the basic unit of data acquisition and status monitoring, the normal operation of the measuring point directly affects the reliability of the entire system and the accuracy of the data. When the measuring point goes offline, it may lead to problems such as data loss and control failure, which in turn affects production safety, equipment maintenance and decision analysis. Therefore, accurately judging whether the measuring point is offline is a key link to ensure the stable operation of the system.

[0003] Currently, traditional methods for determining whether a measurement point is offline typically rely on a single monitoring factor. For example, they might determine offline status solely by detecting communication connection interruptions or by checking if the measurement point returns data on time. However, in real-world applications, measurement point offline status can be caused by various factors, such as communication module failure, power supply anomalies, sensor damage, or software malfunctions. A single monitoring factor often fails to comprehensively and accurately reflect the actual state of the measurement point, easily leading to misjudgments or omissions. Therefore, we need to propose a measurement point offline determination method based on monitoring factors. Summary of the Invention

[0004] The purpose of this invention is to provide an offline monitoring point judgment method based on monitoring factors. By configuring multiple monitoring items to comprehensively monitor the structure and combining the interruption duration parameters of each monitoring item, it overcomes the limitations of traditional single monitoring factors. By integrating multiple dimensions such as data interruption duration and data comparison, it can more comprehensively and accurately reflect the actual state of the monitoring points, avoiding misjudgments or omissions, and significantly improving judgment accuracy. By disabling offline monitoring point parameters by default and allowing flexible adjustments, it can adapt to different devices, scenarios, and monitoring needs. Users can customize parameters and monitoring items according to the characteristics of the structure, enhancing the method's versatility and adaptability, and meeting the needs of complex environments. Through the above solution, the monitoring factors of the structure can comprehensively and accurately reflect the actual state of the monitoring points, avoiding omissions or misjudgments, thus solving the problems mentioned in the background art.

[0005] To achieve the above objectives, an offline determination method for monitoring points based on monitoring factors includes the following steps: Step S1: Monitor the structure by configuring each measuring point. After each measuring point monitors the structure, it obtains monitoring data. The interruption duration parameter of each measuring point is designed through the front-end service, and the offline parameter of the measuring point is configured. The offline parameter of the measuring point is disabled by default. Step S2: When interrupted, the backend service detects the time interval between the last data of each measurement point and the current data, and uses a cronstr scheduled task to periodically obtain the latest measurement point data from Elasticsearch. Step S3: Compare the last data obtained before the interruption with the latest measurement point data, and use the goonlie process to determine whether the measurement point is offline based on the current parameter configuration; Step S4: When the duration of the measurement point data interruption exceeds the duration parameter configuration, it is judged as an alarm, and a device data entry is automatically generated and an interruption alarm message is sent to the operation and maintenance personnel.

[0006] For example, in S1, the duration parameter configuration sets the data interruption duration for the current measuring point based on the monitoring factors, with the default interruption duration set to 60 minutes.

[0007] For example, the monitoring factors can be obtained through structures at each measuring point, and the list of monitoring factors can be displayed by switching the structures.

[0008] For example, in S2, the last piece of data before the interruption is obtained, the data obtained from the offline parameter configuration of the measurement points configured on the front end is stored in the database, and the monitoring point data of each measurement point in the database is obtained through the backend service.

[0009] For example, in S2, the acquisition of current detection point data is scheduled using a cronstr timer. When the scheduled time arrives, the backend service collects the monitoring point data of each current detection point through Elasticsearch.

[0010] For example, in S3, the determination of whether a measurement point is offline is made by calculating the difference between the latest measurement point data obtained from ES and the current system time. If the difference is greater than the data interruption time set by the front end, the measurement point is determined to be offline. If the difference is less than the data interruption time set by the front end, the measurement point is determined to be online.

[0011] For example, in S4, if a measurement point is determined to be offline, a data message is sent through the backend service to trigger an interruption alarm. The backend service then sends an alarm email or SMS to the operations and maintenance personnel.

[0012] For example, when the maintenance personnel receive an alarm message, they can determine the offline problem of the monitoring point based on the time when the alarm data was sent and the duration of the interruption.

[0013] For example, in S4, the goonlie process configures the cronStr timer. The cronStr timer duration is designed to be one minute. When the preset time node of cronStr is reached, the goonlie process checks the acquired data.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This invention provides an offline measurement point judgment method based on monitoring factors. By setting multiple monitoring items, it can comprehensively monitor the equipment. The design of the interruption duration parameter for each monitoring item changes the limitation of the traditional single monitoring factor. By integrating multiple dimensions such as data interruption duration and data comparison, it can more comprehensively and accurately reflect the actual status of the measurement point, avoid misjudgment or omission, and significantly improve the accuracy of judgment. By disabling the offline parameter configuration of the measurement point by default and allowing flexible adjustment, it can adapt to different equipment, scenarios and monitoring needs. Users can customize parameters and monitoring items according to the characteristics of the structure, enhancing the versatility and adaptability of the method and meeting the needs of complex environments. Through the above scheme, the monitoring factors of the structure can comprehensively and accurately reflect the actual status of the measurement point, avoiding the phenomenon of omission or misjudgment.

[0015] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description and the drawings. Attached Figure Description

[0016] Figure 1 This is a flowchart of the offline determination method for monitoring factors according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] like Figure 1 As shown, this invention provides an offline measurement point determination method based on monitoring factors, comprising the following steps: Step S1: Monitor the structure by configuring each measuring point. After each measuring point monitors the structure, it obtains monitoring data. The interruption duration parameter of each measuring point is designed through the front-end service, and the offline parameter of the measuring point is configured. The offline parameter of the measuring point is disabled by default. During monitoring, the data obtained by each measuring point is compared. The interruption duration of each measuring point is set according to the obtained data, thereby realizing comprehensive monitoring of the equipment. This changes the limitation of traditional single monitoring factors. By combining multiple dimensions such as data interruption duration and data comparison, it can more comprehensively and accurately reflect the actual status of the measuring points and avoid misjudgment or omission. Step S2: When interrupted, the backend service detects the time interval between the last data of each measurement point and the current data, and uses a cronstr scheduled task to periodically obtain the latest measurement point data from Elasticsearch. Step S3: Compare the last data obtained before the interruption with the latest measurement point data. The goonlie process determines whether the measurement point is offline based on the current parameter configuration. The goonlie process compares the current interruption duration parameter configuration to determine whether the measurement point is offline. The backend service retrieves the frontend configuration details from the database, and then compares the latest measurement point data with the last data before the interruption stored in the database. At the same time, it calculates the difference between the latest measurement point data and the current system time. Combined with the interruption duration parameter set in S1, the goonlie process determines whether the measurement point is offline if the difference is greater than the interruption duration, otherwise it is online. This forms a judgment chain of "data acquisition - difference calculation - standard comparison". Step S4: When the interruption duration of the measurement point data of each monitoring item exceeds the set interruption duration parameter, it is judged as an alarm and an equipment data interruption alarm is automatically generated. When it is judged to be offline, an alarm is automatically generated and alarm data is sent, so that designated personnel can observe the alarm data to provide time and duration basis for subsequent problem location, realizing a closed loop from judgment to response.

[0019] In S1, the duration parameter configuration allows setting the data interruption duration for the current measuring point based on monitoring factors. The default interruption duration is set to 60 minutes, providing a reasonable benchmark threshold for judging whether the measuring point is offline. In terms of scenario adaptability, 60 minutes as the default value provides a universal reference for different scenarios. At the same time, this parameter can be adjusted according to monitoring factors and measuring point characteristics, forming a "default benchmark + flexible customization" mode. For devices with low data update frequency (such as some environmental monitoring sensors that may report data once an hour), the default duration of 60 minutes can adapt to their data transmission patterns. For devices with high-frequency data updates, users can shorten the duration according to actual needs. This design not only lowers the threshold for initial configuration but also meets the monitoring needs of diverse scenarios, enhancing the universality of the method.

[0020] Monitoring factors can be obtained by acquiring all structures under the project. By switching between structures, a list of all monitoring factors under the current structure can be displayed. The list of monitoring factors is displayed intuitively by switching structures, and maintenance personnel can quickly locate and view the monitoring data under a specific structure, avoiding data interference caused by the mixing of monitoring factors from different structures.

[0021] In S2, the acquisition of the last data before the interruption involves storing the data obtained from the offline parameter configuration of the measurement points configured by the front end into the database. The backend service then obtains the configuration details of the front end data from the database. This design, in which the front end stores the offline parameter configuration data of the measurement points configured by the front end into the database and the backend service obtains the configuration details from the database during the acquisition of the last data before the interruption, ensures the accuracy and consistency of data interaction, avoids loss, tampering, or deviation in data transmission, ensures that the parameters used by the backend are completely consistent with the front end configuration, provides an accurate data foundation for subsequent operations, reduces judgment errors, and improves the efficiency and stability of system collaboration.

[0022] In S2, the acquisition of current test point data is scheduled using a cronstr task. Upon reaching the scheduled time, the backend service retrieves the latest test point data from Elasticsearch. This cronstr-based scheduling ensures data timeliness and freshness. The timing mechanism allows for continuous acquisition of the latest data at a preset frequency, ensuring that the backend relies on the latest test point status information when performing offline test point assessments. This avoids inaccurate judgments due to data lag and provides a precise time reference for subsequent comparisons and difference calculations with the last data point before the interruption, improving the timeliness and accuracy of the assessment. The stable operation of the cronstr task guarantees the regularity and reliability of the data acquisition process, providing stable data input support for the smooth operation of the entire offline test point assessment process. By adjusting the cronstr task's time interval, the data update frequency requirements of different test points can be met.

[0023] In S2, the determination of whether a measurement point is offline is achieved by performing a difference calculation between the latest measurement point data obtained from Elasticsearch and the current system time. If the time difference is greater than the data interruption duration set by the front end, the measurement point is determined to be offline; if the time difference is less than the data interruption duration set by the front end, the measurement point is determined to be online. Through the quantitative judgment logic of the difference calculation, the comparison result of the latest data and the system time is accurately matched with the flexibly configured interruption duration by the front end, making the offline judgment more objective and accurate, reducing the interference of subjective factors. With the cooperation of the cronstr scheduled task to automatically execute data acquisition, no manual intervention is required, reducing the risk of operational errors. The backend service performs difference calculation and judgment in a timely manner based on the acquired data, forming a closed loop process of "scheduled collection - real-time calculation - automatic judgment", which greatly improves the system's operating efficiency and stability, and ensures real-time monitoring of the measurement point status.

[0024] If a measurement point is determined to be offline, a data interruption alarm is sent. Alarm emails or SMS messages can be sent to alarm recipients through alarm policy configuration. By sending a data interruption alarm when an offline point is detected, an automatic alarm mechanism is formed, ensuring that the system can proactively push alarm information as soon as a measurement point goes offline. Compared with the traditional method of discovering faults through manual inspection, this greatly shortens the time difference between fault exposure and discovery, allowing maintenance personnel to quickly intervene and handle the situation, reducing the duration of data loss due to offline status, and minimizing the impact on the continuity of production and monitoring services.

[0025] When alarm receivers receive an alarm message, they can locate and resolve the offline problem of the measurement point based on the time of alarm generation and the duration of the interruption. The alarm generation time is defined as the precise time node of the fault occurrence, and the interruption duration directly reflects the degree of problem persistence. Combining the two can quickly narrow down the scope of investigation and facilitate rapid intervention by maintenance personnel.

[0026] In S3, the cronStr timer configuration in the goonlie process can be set to perform a check every minute. The check task is set to trigger every minute. When the preset minute interval is reached, the cronStr timer configuration triggers the goonlie process to execute the check task. The process checks the data interruption status of each measurement point based on the interruption duration parameter configured in step S1 and the offline status of the measurement points determined in step S2. This minute-per-minute check frequency allows for real-time monitoring of the measurement point status and timely detection of data interruptions. Even if a measurement point is briefly offline, it can be detected in a very short time. Compared to long-interval checks, this significantly improves the timeliness of anomaly detection, saving valuable time for rapid fault handling.

[0027] The monitoring items of the front-end measuring point offline parameter configuration function module include: temperature, wind speed and direction, support displacement, main beam lateral displacement, anchor slippage, bridge tilt, tower top deviation, bridge tower tilt, cracks, and bridge expansion joints.

[0028] In practical use: Step S1 lays the foundation for the parameter system of the entire judgment process: First, the system comprehensively monitors the equipment by configuring multi-dimensional monitoring items. Then, based on the historical and real-time data collected by each monitoring item, it analyzes the data transmission patterns, equipment characteristics, and the impact of interruptions, and designs corresponding interruption duration parameters. The default interruption duration parameter is set to 60 minutes, which serves as a general benchmark threshold. At the same time, it supports flexible adjustment based on monitoring factors and measurement point characteristics (such as data update frequency), forming a "default benchmark + flexible customization" mode. Finally, the system disables the offline parameter configuration of measurement points by default to avoid accidental triggering of judgments or alarms before personalized configuration is completed, ensuring that the parameters are adapted to actual needs. Step S2 relies on the parameter configuration of S1 to achieve dynamic judgment: When a data interruption occurs, the backend service retrieves details of parameters such as the interruption duration configured by the frontend from the database. Simultaneously, it obtains the latest measurement point data from Elasticsearch via a cron job. Then, it compares the latest measurement point data with the last data record before the interruption stored in the database and calculates the difference between the latest data and the current system time. Next, the goonlie process calls the interruption duration parameter set by S1 and compares the difference with this parameter. If the difference is greater than the interruption duration, the measurement point is determined to be offline; otherwise, it is determined to be online, forming a complete judgment chain of "data acquisition - difference calculation - standard comparison". Step S3 constructs a response closed loop based on the judgment result of S2: When S2 determines that a measurement point is offline, meaning the data interruption duration exceeds the set interruption duration parameter, the system automatically generates a device data interruption alarm and pushes an email or SMS to designated personnel through the alarm policy configuration. Simultaneously, the goonlie process, based on the cronstr timer configuration (checking once per minute), continuously verifies the data interruption status of each measurement point. Combining the parameters of S1 and the judgment result of S2, it ensures timely capture of offline status and triggers alarms. The alarm information includes the generation time and interruption duration, providing a basis for maintenance personnel to locate problems. Ultimately, it achieves closed-loop management of the entire process from "parameter configuration" to "status judgment" to "alarm response." Through multi-dimensional collaboration, it improves the accuracy and timeliness of measurement point offline judgment. This solution enables a comprehensive and accurate reflection of the actual status of measurement points for the monitoring factors of structures, avoiding missed or incorrect judgments.

[0029] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for offline determination of measuring points based on monitoring factors, characterized in that, Includes the following steps: Step S1: Monitor the structure by configuring each measuring point. After each measuring point monitors the structure, it obtains monitoring data. The interruption duration parameter of each measuring point is designed through the front-end service, and the offline parameter of the measuring point is configured. The offline parameter of the measuring point is disabled by default. Step S2: When interrupted, the backend service detects the time interval between the last data of each measurement point and the current data, and uses a cronstr scheduled task to periodically obtain the latest measurement point data from Elasticsearch. Step S3: Compare the last data obtained before the interruption with the latest measurement point data, and use the goonlie process to determine whether the measurement point is offline based on the current parameter configuration; Step S4: When the duration of the measurement point data interruption exceeds the duration parameter configuration, it is judged as an alarm, and a device data entry is automatically generated and an interruption alarm message is sent to the operation and maintenance personnel.

2. The method for offline determination of measuring points based on monitoring factors according to claim 1, characterized in that: In S1, the duration parameter configuration sets the data interruption duration for the current measuring point based on the monitoring factors, with the default interruption duration set to 60 minutes.

3. The method for offline determination of measuring points based on monitoring factors according to claim 2, characterized in that: The monitoring factors can be obtained through the structures at each measuring point. By switching the structures, the list of monitoring factors can be displayed.

4. The method for offline determination of measuring points based on monitoring factors according to claim 1, characterized in that: In S2, the last piece of data before the interruption is obtained, and the data obtained from the offline parameter configuration of the measurement points configured on the front end is stored in the database. The monitoring point data of each measurement point in the database is obtained through the backend service.

5. The method for offline determination of measuring points based on monitoring factors according to claim 1, characterized in that: In S2, the acquisition of current detection point data is scheduled using a cronstr timer. When the scheduled time arrives, the backend service collects the monitoring point data of each current detection point through Elasticsearch.

6. The method for offline determination of measuring points based on monitoring factors according to claim 1, characterized in that: In S3, the determination of whether a measurement point is offline is made by calculating the difference between the latest measurement point data obtained from Elasticsearch and the current system time. If the difference is greater than the data interruption time set by the front end, the measurement point is determined to be offline. If the difference is less than the data interruption time set by the front end, the measurement point is determined to be online.

7. The method for offline determination of measuring points based on monitoring factors according to claim 1, characterized in that: In S4, if a measurement point is determined to be offline, a data message is sent through the backend service to trigger an interruption alarm. The backend service then sends an alarm email or SMS to the operations and maintenance personnel.

8. The method for offline determination of measuring points based on monitoring factors according to claim 7, characterized in that: When maintenance personnel receive an alarm message, they can determine the offline status of the monitoring point based on the time the alarm data was sent and the duration of the interruption.

9. The method for offline determination of measuring points based on monitoring factors according to claim 1, characterized in that: In S4, the goonlie process is used to configure cronStr for timing. The cronStr timing duration is designed to be one minute. When the preset time node of cronStr is reached, the goonlie process checks the acquired data.

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