Modular intelligent monitoring box fault diagnosis method and system

Abstract

The invention discloses a modular intelligent monitoring box fault diagnosis method and system, relates to the technical field of intelligent monitoring boxes, and solves the problem that the capability of distinguishing transient parameter fluctuation from persistent faults is weak. The parameter relevance of the power supply module and other modules is concrete into a following relation in a geometric space, and the limitation of traditional single parameter independent judgment is broken through; by means of the characteristics of direction consistency, distance proportion and the like of a main point and a secondary point, faults caused by cooperation abnormity between modules are rapidly identified, the problem that associated faults are misjudged as single modules is avoided, the accuracy of fault positioning is greatly improved, and by monitoring the following characteristics of parameters in a calibration reference polygon in real time, the accuracy of fault positioning is greatly improved for the situation of direction inconsistency. And secondary verification is carried out by extracting associated position points, so that the robustness of anomaly judgment is further improved, and the real continuous anomaly is ensured to be responded.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring box technology, specifically to a modular intelligent monitoring box fault diagnosis method and system. Background Technology

[0002] As a core edge device in scenarios such as power distribution networks, smart cities, and industrial IoT, modular intelligent monitoring boxes integrate multiple functional modules such as power supply, communication, data acquisition, and control. Their stable operation directly determines the reliability of data transmission, status awareness, and command execution of the entire monitoring system.

[0003] With the widespread application of modular design, the functional integration of monitoring boxes is constantly improving. Each module is closely related through power supply links, communication protocols, and data interaction, which makes faults exhibit the characteristics of "strong coupling and ambiguous positioning"—a single module failure may cause abnormal parameters in multiple modules, and the instantaneous fluctuations of parameters are easily confused with real faults, posing challenges to fault diagnosis.

[0004] Existing fault diagnosis methods for intelligent monitoring boxes mostly adopt single-parameter threshold judgment or manual experience-based troubleshooting. The former only sets judgment criteria for the independent operating parameters of a single module (such as power supply voltage and communication signal strength), ignoring the correlation between parameters between modules. This can easily lead to misjudging communication signal attenuation caused by power supply fluctuations as a fault in the communication module itself, resulting in insufficient diagnostic accuracy. The latter relies on the on-site operation and experience accumulation of maintenance personnel, which is not only inefficient and has a long troubleshooting cycle, but also has the problem of limited ability to identify complex related faults and a high misjudgment rate.

[0005] In addition, existing methods are weak in distinguishing between instantaneous parameter fluctuations and persistent faults, often resulting in false alarms or missed diagnoses. Furthermore, they lack a dynamic verification mechanism for abnormal modules, making it difficult to reliably identify the root cause of the fault.

[0006] To address the aforementioned issues, there is an urgent need for a fault diagnosis method that can deeply explore the correlation between module parameters, accurately distinguish between parameter fluctuations and actual faults, and reduce reliance on manual intervention. This would improve the accuracy, reliability, and efficiency of fault diagnosis for modular intelligent monitoring boxes and ensure the stable operation of the monitoring system. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a modular intelligent monitoring box fault diagnosis method and system, which solves the problem of weak ability to distinguish between instantaneous parameter fluctuations and persistent faults.

[0008] To achieve the above objectives, the present invention provides a fault diagnosis method for a modular intelligent monitoring box, comprising the following steps: Step 1: From the different operating parameters associated with different modules within the intelligent monitoring box, confirm the parameter standards associated with the power supply module, and then simultaneously confirm the operating parameters associated with other modules. Based on the confirmation results, generate the verification benchmark shape associated with the current intelligent monitoring box. The specific method is as follows: Confirm the multiple operating parameters associated with the power module in the intelligent monitoring box, record these multiple operating parameters as primary parameters, and simultaneously record the operating parameters associated with the primary parameters on other modules as secondary parameters. Using the primary parameters as the measurement standard, generate a set of measurement standard lines, and based on the set parameter standards, set the parameter representation of the primary parameters at different locations on the measurement standard lines. Simultaneously, using this measurement standard line as a reference, determine the parameter representation of the secondary parameters at different locations on the measurement standard lines according to the parameter standards of the secondary parameters associated with the corresponding primary parameters. Based on the total number of main parameters, determine the corresponding total number of measurement standard lines, correct the length of multiple measurement standard lines to a consistent state, make the initial values ​​of multiple measurement standard lines coincide, and keep the included angle between each measurement standard line equal. Then connect the points associated with the end values ​​of several measurement standard lines to confirm a set of verification benchmark polygons. Step 2: Monitor the operating parameters associated with different modules within the intelligent monitoring box in real time. Based on the generated verification reference polygon, confirm the following status of the operating parameters of different modules on the verification reference polygon, and determine whether the intelligent monitoring box is currently in normal operating condition. The specific method is as follows: The operating parameters of different modules in the intelligent monitoring box are monitored in real time, and the main parameters associated with the power supply module are determined. The position points of the main parameters on the corresponding measurement standard line of the verification reference polygon are marked and recorded as main points. At the same time, based on the secondary parameters monitored in real time, the position points on the corresponding measurement standard line are confirmed and recorded as secondary points. Based on the real-time monitored parameters, the system confirms the following characteristics of the secondary point to the primary point, identifies the movement direction of the primary point as the primary direction, confirms the movement direction of the secondary point as the secondary direction, and identifies whether the primary and secondary directions are consistent. If they match, record the line segment lengths of the primary and secondary points. Based on the line segment lengths and the total length of the corresponding measurement standard lines, confirm the length ratio of the corresponding line segment lengths. If the length ratio is ≥0.2, generate an abnormal diagnostic signal, synchronously mark the module associated with the secondary point and record it as an abnormal module, and execute subsequent steps. If the length ratio is <0.2, continue monitoring. If they are not consistent, record the movement progress of the main point at the current moment, identify the part of the movement process that moves in the same direction as the secondary point, and record the endpoint of the part of the process as the associated position point. Then, confirm the length of the line segment between the secondary point and the associated position point. Use the method of determining that the length ratio is the same when the main direction and the secondary direction are consistent to determine whether the subsequent steps need to be executed. Step 3: Based on the intelligent monitoring box's judgment process, identify the marked abnormal modules and perform fault diagnosis on them. By controlling the voltage of the power supply module, confirm the tracking characteristics of the abnormal modules. Then, based on several sets of tracking characteristics confirmed within the monitoring period, assess whether the abnormal module is faulty. The specific method is as follows: The main parameters associated with the power module are randomly changed, and the rate of change of each group of main parameters is different, and the direction of change of the main parameters is also different. A set of monitoring cycles is defined, and the monitoring cycle is a preset cycle. Within the monitoring cycle, the power module changes multiple main parameters in a random manner, and the position points associated with the corresponding main parameters on different measurement standard lines within the verification reference polygon are recorded in real time. Adjacent position points are connected in real time to confirm the power characteristic polygon associated at the corresponding time. Then, the secondary parameters associated with the abnormal module are monitored in real time, and the corresponding position points of the secondary parameters on the measurement standard line are confirmed synchronously. The abnormal feature polygon is determined using the same determination method as the power supply feature polygon. The area of ​​the abnormal feature polygon associated with the corresponding time is marked as M1, and the area of ​​the power supply feature polygon is marked as M2. The following feature ZB associated with the corresponding time is confirmed by M1÷M2=ZB. Then, the different following features associated with different times within the monitoring period are determined. The mean value of the determined groups of following features is then processed to confirm the mean feature. Identify whether the confirmed mean characteristic meets the following criteria: mean characteristic ≥ 0.2. If it meets the criteria, then mark this abnormal module as a faulty module; if it does not meet the criteria, then do not mark it.

[0009] Preferably, a modular intelligent monitoring box fault diagnosis system includes: The calibration benchmark generation end confirms the parameter standard associated with the power module from the different operating parameters associated with different modules in the intelligent monitoring box, and then simultaneously confirms the operating parameters associated with other modules. Based on the confirmation results, it generates the calibration benchmark shape associated with the current intelligent monitoring box. The operation status assessment terminal monitors the operation parameters associated with different modules in the intelligent monitoring box in real time. Based on the generated verification benchmark polygon, it confirms the following status of the operation parameters of different modules on the verification benchmark polygon and determines whether the intelligent monitoring box is in normal operation. The module fault assessment end identifies the marked abnormal modules based on the judgment process of the intelligent monitoring box, performs fault diagnosis on the abnormal modules, confirms the following characteristics of the abnormal modules by controlling the voltage of the power supply module, and then assesses whether the abnormal module has a fault based on several sets of following characteristics confirmed within the monitoring period.

[0010] This invention provides a modular intelligent monitoring box fault diagnosis method and system. Compared with the prior art, it has the following advantages: This invention constructs a verification benchmark polygon that links "primary parameters" to "secondary parameters," visualizing the parameter correlation between the power supply module and other modules as a following relationship in geometric space, thus overcoming the limitations of traditional independent judgment of single parameters. By leveraging features such as the consistency of direction and distance ratio between primary and secondary points, faults caused by abnormal inter-module coordination (such as communication signal attenuation due to power supply fluctuations) can be quickly identified, avoiding misjudging associated faults as single-module problems and significantly improving the accuracy of fault location. By monitoring the following characteristics of parameters in the verification benchmark polygon in real time, and combining judgment logic such as "direction consistency verification" and "length proportion threshold", false alarms caused by instantaneous fluctuations in parameters (such as brief jumps in communication signals) are effectively filtered out. At the same time, for cases of inconsistent directions, secondary verification is performed by extracting related position points, which further improves the robustness of anomaly judgment and ensures that only genuine and persistent anomalies are responded to. During the abnormal module diagnosis phase, the main parameters of the power module are randomly varied and feature polygons are constructed. The following capability of secondary parameters is quantified by the area ratio of the abnormal feature polygon to the power feature polygon, forming a periodic dynamic verification of the abnormal module. This active perturbation + mean analysis method can effectively distinguish between "parameter fluctuations" and "module faults", avoid miscalibration caused by occasional interference, and significantly improve the reliability of fault diagnosis results. Transforming multidimensional parameter relationships into intuitively perceptible polygonal geometric models clarifies complex parameter collaboration logic, making it easier for operations and maintenance personnel to quickly understand the influence relationships between modules. Simultaneously, by pre-setting parameter standards, threshold rules, and diagnostic processes, a standardized diagnostic framework is formed, reducing reliance on human experience and improving the efficiency and consistency of fault diagnosis. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a polygonal schematic diagram associated with the fault assessment of this invention. Detailed Implementation

[0012] 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.

[0013] First Embodiment Please see Figure 1 This application provides a fault diagnosis method for a modular intelligent monitoring box, including the following steps: Step 1: From the different operating parameters associated with different modules within the intelligent monitoring box, confirm the parameter standard associated with the power supply module, and then simultaneously confirm the operating parameters associated with other modules. Based on the confirmation results, generate the verification benchmark polygon associated with the current intelligent monitoring box. Specifically, the intelligent monitoring box generally includes a power supply module, a communication module, a data acquisition module, and a control module. The output voltage of the power supply module is stable at DC12V±0.5V, the signal strength of the communication module is between -60 and -85dBm, the sampling accuracy of the data acquisition module is controlled at (±2%), and the response delay of the control module is (<50ms). The specific method for generating the verification reference polygon associated with the intelligent monitoring box is as follows: Confirm the multiple operating parameters associated with the power module in the intelligent monitoring box, record these multiple operating parameters as primary parameters, and simultaneously record the operating parameters associated with the primary parameters on other modules, treating them as secondary parameters (i.e., parameters with correlation, such as the voltage of the power module, which is related to the signal strength of the communication module; when the corresponding voltage parameter is larger, the corresponding signal strength will also be stronger; other modules operate based on the power supply parameters of the power module). Using the primary parameters as the measurement standard, generate a set of measurement standard lines, and based on the set parameter standards, set the parameter representation of the primary parameters at different locations on the measurement standard lines (i.e., at what location, what voltage is represented). Simultaneously, using this measurement standard line as a reference, determine the parameter representation of the secondary parameters at different locations on the measurement standard lines according to the parameter standards of the secondary parameters associated with the corresponding primary parameters. Based on the total number of main parameters, determine the corresponding total number of measurement standard lines, correct the length of multiple measurement standard lines to a consistent state (that is, the length is equal), make the initial values ​​of multiple measurement standard lines coincide, and keep the included angle between each measurement standard line equal. Then connect the points associated with the end values ​​of several measurement standard lines to confirm a set of verification benchmark polygons. Specifically, the measurement polygon determined here is based on the correlation between each parameter and the set parameter standards. According to the correlation between the corresponding parameters, the corresponding reference is set on the corresponding measurement standard line. When the corresponding main parameter is voltage, when the voltage is 12V, the associated signal strength is -70dBm. The two value points are at the same point on the corresponding measurement standard line. Subsequently, based on the following characteristics between the values, it is comprehensively confirmed whether the intelligent monitoring box is in normal operation. Multiple operating parameters include voltage, current, and ripple, etc. The selection of the main parameters is based on the existence of related secondary parameters, so as to comprehensively evaluate the correlation characteristics between multiple parameters. Step 2: Monitor the operating parameters associated with different modules in the intelligent monitoring box in real time. Based on the generated verification reference polygon, confirm the following status of the operating parameters of different modules on the verification reference polygon, and determine whether the intelligent monitoring box is in normal operation. The specific method for making the judgment is as follows: The operating parameters of different modules in the intelligent monitoring box are monitored in real time, and the main parameters associated with the power supply module are determined. The position points of the main parameters on the corresponding measurement standard line of the verification reference polygon are marked and recorded as main points. At the same time, based on the secondary parameters monitored in real time, the position points on the corresponding measurement standard line are confirmed and recorded as secondary points. Based on the real-time monitored parameters, the system confirms the following characteristics of the secondary point to the primary point, identifies the movement direction of the primary point as the primary direction, confirms the movement direction of the secondary point as the secondary direction, and identifies whether the primary and secondary directions are consistent. If they match, record the line segment lengths of the main point and the secondary point (located on the measurement standard line). Based on the line segment lengths and the total length of the corresponding measurement standard line, confirm the length ratio of the corresponding line segment lengths. If the length ratio is ≥0.2, generate an abnormal diagnostic signal, synchronously mark the module associated with the secondary point and record it as an abnormal module, and execute subsequent steps. If the length ratio is <0.2, continue monitoring. If they are not consistent, record the movement progress of the main point at the current moment, identify the part of the movement process that moves in the same direction as the secondary point, and record the endpoint of the part of the process as the associated position point. Then, confirm the length of the line segment between the secondary point and the associated position point. The method of determining whether the length of the line segment corresponding to the length is the same when the main direction and the secondary direction are consistent is adopted to determine whether the subsequent steps need to be executed (if the proportion does not meet the standard, it means that anomaly diagnosis is required; if it meets the standard, then monitoring can continue). Specifically, based on the operation process of the corresponding parameters between different modules, the change status of the corresponding parameters within the corresponding polygon is confirmed, thereby confirming the following process between different points within the corresponding line segment. This allows for a comprehensive evaluation of the specific differences between the corresponding line segment points, thereby comprehensively assessing whether the corresponding intelligent monitoring box is in a normal operating state and conducting subsequent fault diagnosis.

[0014] Step 3: Based on the judgment process of the intelligent monitoring box, identify the marked abnormal modules and perform fault diagnosis on the abnormal modules. By controlling the voltage of the power module, identify the following characteristics of the abnormal modules. Based on several sets of following characteristics identified within the monitoring period, assess whether the abnormal module is faulty. The specific methods for fault assessment are as follows: Combination Figure 2 This causes multiple main parameters associated with the power module to change randomly, and the rate of change of each set of main parameters is different, and the direction of change of multiple main parameters is also different. A set of monitoring cycles is defined, which is a preset cycle, generally 2 minutes, and is determined in advance by the operator based on experience. Within the monitoring cycle, the power module changes multiple main parameters in a random manner, and the position points associated with the corresponding main parameters on different measurement standard lines within the verification reference polygon are recorded in real time. Adjacent position points are connected in real time to confirm the power characteristic polygon associated at the corresponding time. Then, the secondary parameters associated with the abnormal module are monitored in real time, and the corresponding position points of the secondary parameters on the measurement standard line are confirmed synchronously. The abnormal feature polygon is determined using the same determination method as the power supply feature polygon. The area of ​​the abnormal feature polygon associated with the corresponding time is marked as M1, and the area of ​​the power supply feature polygon is marked as M2. The following feature ZB associated with the corresponding time is confirmed by M1÷M2=ZB. Then, the different following features associated with different times within the monitoring period are determined. The mean value of the determined groups of following features is then processed to confirm the mean feature. Identify whether the confirmed mean characteristic meets the following condition: if the mean characteristic is ≥ 0.2, then mark this abnormal module as a faulty module; otherwise, do not mark it. Specifically, during the evaluation of abnormal modules, fluctuations in the secondary parameters associated with a certain module may cause normally operating modules to be marked as abnormal. Therefore, when diagnosing the fault of the abnormal module, a periodic evaluation method is adopted. During the monitoring period, the primary and secondary parameters are monitored in real time, and the change process of the polygon associated with the primary and secondary parameters is confirmed based on the actual operation process. Based on the change process, the calibration process of the faulty module is completed to avoid misjudgment due to parameter fluctuations.

[0015] Second Embodiment A modular intelligent monitoring box fault diagnosis system includes: The calibration benchmark generation end confirms the parameter standard associated with the power module from the different operating parameters associated with different modules in the intelligent monitoring box, and then simultaneously confirms the operating parameters associated with other modules. Based on the confirmation results, it generates the calibration benchmark shape associated with the current intelligent monitoring box. The operation status assessment terminal monitors the operation parameters associated with different modules in the intelligent monitoring box in real time. Based on the generated verification benchmark polygon, it confirms the following status of the operation parameters of different modules on the verification benchmark polygon and determines whether the intelligent monitoring box is in normal operation. The module fault assessment end identifies the marked abnormal modules based on the judgment process of the intelligent monitoring box, performs fault diagnosis on the abnormal modules, confirms the following characteristics of the abnormal modules by controlling the voltage of the power supply module, and then assesses whether the abnormal module has a fault based on several sets of following characteristics confirmed within the monitoring period.

[0016] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0017] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A modular intelligent monitoring box fault diagnosis method, characterized in that, The method comprises the following steps: Step one, confirming the parameter standard associated with the power module from different operating parameters associated with different modules in the intelligent monitoring box, and then synchronously confirming the operating parameters associated with other modules, and generating a verification reference polygon associated with the current intelligent monitoring box according to the confirmation result; Step two, monitoring the operating parameters associated with different modules in the intelligent monitoring box in real time, confirming the following state of the operating parameters of different modules on the verification reference polygon according to the generated verification reference polygon, and judging whether the current intelligent monitoring box is in a normal operating state; Step three, confirming the marked abnormal module according to the judgment process of the intelligent monitoring box, and performing fault diagnosis on the abnormal module, changing the voltage of the power module to confirm the following characteristics of the abnormal module, and then evaluating whether the abnormal module has a fault based on the several groups of following characteristics confirmed in the monitoring period.

2. The method according to claim 1, wherein, In step one, the specific way of generating the verification reference polygon is as follows: Confirming multiple operating parameters associated with the power module in the intelligent monitoring box, recording the multiple operating parameters as main parameters, and synchronously recording the operating parameters associated with the main parameters on other modules as secondary parameters, taking the main parameters as the measurement standard, generating a group of measurement standard lines, and setting parameter representations of the secondary parameters at different positions on the measurement standard lines according to the parameter standard of the secondary parameters corresponding to the main parameters based on the set parameter standard, and taking the measurement standard line as the reference to determine the parameter representations of the secondary parameters at different positions on the measurement standard line according to the parameter standard of the secondary parameters corresponding to the main parameters; According to the total number of main parameters, a measurement standard line corresponding to the total number is determined, the lengths of the multiple measurement standard lines are corrected to be consistent, the initial values of the multiple measurement standard lines are overlapped, the included angles between each measurement standard line are kept equal, and then the points associated with the end values of the several measurement standard lines are connected to confirm a group of verification reference polygons.

3. The method of claim 1, wherein, In step two, the specific way of judging whether the intelligent monitoring box is in a normal operating state is as follows: Real-time monitoring the operating parameters associated with different modules in the intelligent monitoring box, and determining the main parameters associated with the power module, marking the position points of the main parameters on the corresponding measurement standard lines of the verification reference polygon as main points, and confirming the position points on the corresponding measurement standard lines according to the real-time monitored secondary parameters as secondary points; According to the parameter operating process of real-time monitoring, confirming the following characteristics of the secondary points following the main points, confirming the moving direction of the main points as the main direction, and then confirming the moving direction of the secondary points as the secondary direction, and identifying whether the main direction is consistent with the secondary direction: If they are consistent, record the length of the line segment between the main point and the secondary point, confirm the length ratio of the corresponding line segment length based on the length of the line segment and the total length of the corresponding measurement standard line, if the length ratio is greater than or equal to 0.2, generate an abnormal diagnosis signal, mark the module associated with the secondary point as an abnormal module, and execute the subsequent steps, if the length ratio is less than 0.2, continue monitoring.

4. The method of claim 3, wherein, If the main direction is not consistent with the secondary direction, the moving process of the main point at the current time is recorded, the part of the process in the same moving direction as the secondary point is confirmed from the moving process, and the end point of the part is recorded as the associated position point. The length of the line segment between the secondary point and the associated position point is confirmed, and the same determination method as the length ratio in the consistent state of the main direction and the secondary direction is used to determine whether the subsequent step needs to be performed.

5. The method of claim 1, wherein, The specific way of fault evaluation of the abnormal module in step three is: Make the multiple main parameters associated with the power module change randomly, and the change rate of each group of main parameters is different, and the change direction of multiple main parameters is also different; Limit a group of monitoring periods, and the monitoring period is a preset period. In the monitoring period, the power module changes multiple main parameters in a random change manner, and records the position points associated with the corresponding main parameters on different measuring standard lines in the calibration reference polygon in real time, and connects the adjacent position points in real time to confirm the power feature polygon associated with the corresponding time; Real-time monitoring of the secondary parameters associated with the abnormal module is performed, and the position points associated with the corresponding secondary parameters on the measuring standard line are confirmed synchronously. The power feature polygon is determined in the same way to determine the abnormal feature polygon; The area of the abnormal feature polygon associated with the corresponding time is marked as M1, and the area of the power feature polygon is marked as M2. The following is used to confirm the following feature ZB associated with the corresponding time: M1 ÷ M2 = ZB. The different following features associated with different times in the monitoring period are determined, and the determined several groups of following features are processed by averaging to confirm the average feature. Determine whether the confirmed average feature meets the condition: average feature ≥ 0.

2. If it meets the condition, mark this abnormal module as a fault module.

6. The method of claim 5, wherein, If the average feature does not meet the condition: average feature ≥ 0.2, do not perform any marking.

7. A modular intelligent monitoring box fault diagnosis system, which operates according to the modular intelligent monitoring box fault diagnosis method of any one of claims 1-6, characterized in that, It includes: The calibration reference generation end confirms the parameter standard associated with the power module from different operating parameters associated with different modules in the intelligent monitoring box, and synchronously confirms the operating parameters associated with other modules. According to the confirmation result, a calibration reference polygon associated with the current intelligent monitoring box is generated; The running state evaluation end performs real-time monitoring on the operating parameters associated with different modules in the intelligent monitoring box, confirms the following state of the different module operating parameters on the calibration reference polygon according to the generated calibration reference polygon, and determines whether the current intelligent monitoring box is in a normal operating state; The module fault evaluation end confirms the marked abnormal module according to the judgment process of the intelligent monitoring box, performs fault diagnosis on the abnormal module, changes the voltage of the power module to confirm the following feature of the abnormal module, and evaluates whether the abnormal module has a fault based on the several groups of following features confirmed in the monitoring period.

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