Intelligent ring main unit fault real-time monitoring method and system

Abstract

The invention discloses an intelligent ring main unit fault real-time monitoring method and system, relates to the technical field of intelligent ring main units, and solves the problem that errors exist in the fault judgment process due to fluctuation or other influence conditions between factor values. According to the method, the related abnormal items of the abnormal nodes are accurately analyzed and determined, the change data of the voltage values of the abnormal nodes are determined from the previous related parameters based on the voltage values of the abnormal nodes, the corresponding line segments are sequentially selected and analyzed front and back in a standard deviation confirmation mode, and in order to find the closest standard deviation, the abnormal nodes can be found out. A plurality of standard deviations are determined by adopting a sequential division mode to determine the most standard current interval and temperature interval, and the correlation degree between the determined current interval and temperature interval and the changed voltage data is highest, so that the correlation degree can be used as the highest standard in the data evaluation process, namely, the numerical value accuracy can reach the highest.

Description

Technical Field

[0001] The present invention relates to the technical field of smart ring main unit (RMU), and in particular to a real-time fault monitoring method and system for a smart ring main unit. Background Art

[0002] A ring main unit is an electrical device in which a group of high-voltage switchgear is installed in a metal or non-metallic insulating cabinet or made into an assembled interval ring main unit; the ring main unit consists of a current display, an observation window, a cabinet, etc.

[0003] The application with publication number CN117436006B discloses a real-time monitoring method and system for smart ring network cabinet faults. The method analyzes the time series fluctuation changes and time correlation of the current data of each smart ring network cabinet to obtain the final divided time interval of the current data of each smart ring network cabinet; the fluctuation credibility is obtained according to the time correlation and numerical fluctuation distribution between the current data of the smart ring network cabinet in each final divided time interval; the isolated tree node splitting value selection interval is screened out according to the fluctuation credibility distribution of the interval divided by each extreme point combination; the smart ring network cabinet current abnormal data is obtained according to the isolated tree node splitting value selection interval combined with the isolation forest algorithm, so that the real-time monitoring of smart ring network cabinet faults based on the smart ring network cabinet current abnormal data is more effective.

[0004] When the ring main unit is conducting real-time fault monitoring, it generally identifies whether it belongs to the relevant interval based on the relevant numerical changes, and then confirms whether there are relevant faults in the relevant nodes through specific identification results. However, this analysis and processing method is not accurate, because there may be fluctuations or other influencing conditions between the numerical values, which will lead to errors in the fault judgment process and thus misjudgment. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a real-time monitoring method and system for faults in a smart ring main unit, which solves the problem that fluctuations or other influencing conditions between factor values ​​may cause errors in the fault judgment process.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A real-time monitoring method for faults of a smart ring main unit, comprising the following steps:

[0007] S1. Monitor the node power of the nodes monitored by the smart ring network cabinet, and compare the node power with the preset range. Based on the comparison results, lock the abnormal nodes, including:

[0008] The node power of different nodes monitored in real time is calibrated as Gi, where i represents different nodes. Gi is compared with the preset interval corresponding to the corresponding node, where the endpoint values ​​of the preset interval are all preset values. When Gi∈preset interval, no calibration is performed and monitoring continues. If Gi When the preset interval is reached, the node is marked as an abnormal node;

[0009] S2. Based on the calibrated abnormal node, confirm the voltage value of the abnormal node at the current moment. Based on this voltage value, extract the closest set of voltage change data from the current moment forward. The voltage change data includes the change data of this voltage value. Confirm the corresponding continuous time period from the voltage change data. Then lock the current data and temperature data through the continuous time period. Through numerical analysis, determine the current interval and temperature interval corresponding to the voltage data of the change in this continuous time period. Then perform numerical evaluation to determine the abnormal signal of this abnormal node. The specific method is as follows:

[0010] S21, confirming a continuous period from the voltage change data, and then confirming the current data and temperature data generated in the continuous period;

[0011] S22, performing standard deviation processing on several groups of voltage change data corresponding to the continuous time periods to determine a group of standard values ​​F1;

[0012] S23. Based on the current data and temperature data generated in the continuous time period, and based on the different data corresponding to different time points, generate a current change curve and a temperature change curve, wherein the horizontal coordinate axis of the relevant curve is the timeline, and the vertical coordinate axis is the current data or the temperature data:

[0013] For the current change curve: perform the standard deviation processing of the first stage: determine the highest and lowest values ​​of the curve, and first perform standard deviation processing from the lowest value to the highest value to determine the first group of standard values ​​B1, then perform standard deviation processing from (lowest value + 1) to the highest value to determine the second group of standard values ​​B2, then perform standard deviation processing from (lowest value + 2) to the highest value to determine the third group of standard values ​​B3, ..., and so on, until (highest value - 1) to the highest value to perform standard deviation processing to determine the last group of standard values ​​Bm of this stage;

[0014] Execute the second stage of standard deviation processing: the same as the first stage of standard deviation processing, use (highest value - 1) to replace the highest value of the first stage, and confirm several groups of standard values;

[0015] Then, the standard deviation processing of the subsequent different stages is performed in sequence, and the highest value of each subsequent different stage changes. The highest value of the first stage is replaced by [highest value - (k-1)] to confirm several groups of standard values, where k represents different stages.

[0016] From the several groups of standard values ​​confirmed in the above different stages, a group of standard values ​​closest to F1 is determined and calibrated as the determined value, and then the processing process corresponding to the determined value is directly locked to determine the minimum value and the maximum value corresponding to the processing process to lock a group of standard current intervals;

[0017] For the temperature change curve: the same processing method as the current change curve is used to lock a set of standard temperature ranges;

[0018] S24, compare the current value D1 generated at the current moment with the current interval. When D1∈the current interval, no processing is performed. When D1 When the current interval is reached, a current abnormality signal is generated and displayed, and the temperature value W1 generated at the current moment is compared with the temperature interval. When W1∈the temperature interval, no processing is performed. When W1 When the temperature is within the range, an abnormal temperature signal is generated and displayed;

[0019] S3. Define a set of monitoring cycles, monitor the current values ​​of two groups of related nodes before and after the abnormal node, and confirm the monitoring waveform based on the real-time monitoring results. Then, perform the same-frequency analysis on the confirmed different monitoring waveforms to determine the same frequency band, and then determine whether there is voltage fluctuation based on the relevant proportion of the same frequency band. The specific method is as follows:

[0020] S31, defining a set of monitoring periods T, where T is a preset value, monitoring and confirming the current values ​​of the previous group of related nodes of the abnormal node within the monitoring period T, and generating two groups of current value change curves of different nodes according to the change of the time line, calibrating the current value change curve of the abnormal node as the standard change curve, and calibrating the current value change curve corresponding to the previous group of related nodes as the associated change curve;

[0021] S32, combining the associated change curve with the standard change curve for analysis, if both have the same frequency change in the same period, marking the part of the line segment with the same frequency change in the standard change curve as the same frequency segment, determining the line length proportion ZB of the whole line segment of the same frequency segment in the standard change curve, wherein: ZB=total length of the line segment of the same frequency segment÷total length of the standard change curve, comparing the line length proportion ZB with the preset value Y1, when ZB≥Y1, generating a same frequency voltage fluctuation signal, and displaying the generated same frequency voltage fluctuation signal, when ZB<Y1, not generating any related signal, and performing the next step of analysis and processing;

[0022] S4. Confirm the current sinusoidal waveform of the abnormal node in the monitoring period, determine the cycle value based on the change cycle of each waveform, and then generate a value change sequence based on the changes of the cycle values ​​at different times. Identify whether the current frequency is abnormal through the display of the value change sequence. The specific method is as follows:

[0023] S41, based on the determined current sinusoidal waveform, confirm the initial point of the standard variation curve, determine the current value related to the initial point, and then determine other subsequent points in the waveform with the same value as the current value, and mark the initial point as 0 according to the time sequence, and mark other subsequent points in succession with natural numbers in sequence;

[0024] S42, calibrating the time period between 0 and 2 as the first group of time parameters, calibrating the time period of the corresponding points between 2 and 4 as the second group of time parameters, and so on, sorting the subsequent time parameters to generate their value change sequence;

[0025] S43. Identify whether the changing trends of the numbers before and after the numerical change sequence are consistent:

[0026] If the relevant parameters in the numerical change sequence decrease from the beginning to the end or increase from the beginning to the end, it means that the frequency changes normally, there is no abnormality, and no signal display is performed;

[0027] If the values ​​of the relevant parameters in the value change sequence change chaotically, a frequency abnormality signal is generated and displayed through the display terminal.

[0028] Preferably, a smart ring main unit fault real-time monitoring system comprises:

[0029] The abnormal node determination end compares the node power monitored by the smart ring network cabinet with the preset range, and locks the abnormal node based on the comparison result;

[0030] The standard interval determination end determines the relevant voltage change data based on the voltage value corresponding to the abnormal node at the current moment, confirms the corresponding continuous period from the voltage change data, and then locks the current data and temperature data through the continuous period. Through numerical analysis, the current interval and temperature interval corresponding to the voltage data of the continuous period are determined, and then numerical evaluation is performed to determine the abnormal signal of the abnormal node;

[0031] The voltage fluctuation determination end identifies the relevant changes in the current values ​​of the previous group of related nodes by limiting the monitoring period, and confirms the monitoring waveform based on the results of real-time monitoring. Then, the confirmed different monitoring waveforms are subjected to the same frequency analysis to determine the same frequency band, and then based on the relevant proportion of the same frequency band, it is determined whether there is voltage fluctuation;

[0032] The frequency anomaly determination end confirms the current sinusoidal waveform of the corresponding abnormal node within the monitoring period, determines the period value based on the change period of each waveform, and then generates a value change sequence based on the changes in the period value at different times. Through the display of the value change sequence, it is identified whether the current frequency is abnormal.

[0033] The present invention provides a method and system for real-time monitoring of faults in a smart ring main unit. Compared with the prior art, it has the following beneficial effects:

[0034] The present invention accurately analyzes and determines the abnormal items related to the abnormal node, locks its voltage value based on the determination time of the abnormal node, and then determines the change data of its voltage value from the past related parameters. From the current data and temperature data associated with the change data, the corresponding line segments are selected and analyzed in sequence by confirming the standard deviation. Since the standard deviations generated between different values ​​are different, in order to find the closest standard deviation, the above-mentioned method is used to confirm several standard deviations, thereby determining the most standard current interval and temperature interval. The determined current interval and temperature interval have the highest correlation with the changed voltage data, and can be used as the highest standard in the data evaluation process, that is, the numerical accuracy can reach the highest. Subsequently, the most accurate related current interval and temperature interval are used for numerical comparison and analysis to determine and display related signals;

[0035] In the case of abnormal current, voltage fluctuation analysis is performed first to identify whether there is any related abnormality in the voltage fluctuation in the line segment. If there is no related abnormality, the current frequency is analyzed again. By analyzing the relevant change parameters between the current sinusoidal waveforms, based on the specific changes in the parameters, it is identified whether the frequency is abnormal. This application can not only quickly monitor related fault conditions, but also quickly lock in the specific cause of the abnormality and display it for confirmation by relevant maintenance personnel, thereby reducing the related maintenance burden of relevant maintenance personnel and improving maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of the process of the present invention;

[0037] Figure 2 It is a schematic diagram of the principle framework of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Example 1

[0040] See also Figure 1 The present application provides a method for real-time monitoring of faults in a smart ring network cabinet, comprising the following steps:

[0041] S1. Monitor the node power of the nodes monitored by the smart ring network cabinet, and compare the node power with the preset range. Based on the comparison results, lock the abnormal nodes:

[0042] S11. Calibrate the node power of different nodes monitored in real time as Gi, where i represents different nodes, and compare Gi with the preset interval corresponding to the corresponding node. The endpoint values ​​of the preset interval are all preset values, and the specific values ​​are determined by the operator based on experience. When Gi∈the preset interval, no calibration is performed and monitoring continues. If Gi When the preset interval is reached, the node is marked as an abnormal node;

[0043] S2. Based on the marked abnormal node, confirm the voltage value of the abnormal node at the current moment. Based on this voltage value, extract the closest set of voltage change data from the current moment forward. The voltage change data includes the change data of this voltage value. Confirm the corresponding continuous time period from the voltage change data. Then lock the current data and temperature data through the continuous time period. Through numerical analysis, determine the current interval and temperature interval corresponding to the voltage data of the continuous time period. Then perform numerical evaluation to determine the abnormal signal of the abnormal node. Specifically, when the voltage data changes, the current data and the corresponding temperature data will change accordingly. The temperature data, in which the voltage data is changing, the current data that changes at all times is not particularly accurate. In order to ensure that a more accurate assessment interval and assessment result can be achieved in this line environment, it is necessary to determine the closest current interval based on the changing voltage data to ensure the accuracy of its subsequent values. In a complex line environment, it is not possible to confirm the relevant current value in this line by relying on the confirmed resistance value and the provided voltage value. The current value often has a relevant deviation from the value of voltage divided by resistance, so it is necessary to analyze the relationship between the data to ensure the accuracy of the data;

[0044] The specific methods for determination include:

[0045] S21. Confirm the continuous period from the voltage change data (here, in conjunction with an example, when the voltage value determined at the current moment is 10V, based on the current moment, tracing back to the source, there is a set of change data of 9V-14V in the tracing process, then this voltage value belongs to the voltage change data, and the continuous period corresponding to the voltage change data is 8:00-8:05, and then the current data and temperature data generated from 8:00-8:05 are extracted from the past data), and then confirm the current data and temperature data generated in this continuous period;

[0046] S22, perform standard deviation processing on several groups of voltage change data corresponding to the continuous time periods, and confirm a group of standard values ​​F1, wherein the variance processing method is: calibrate several groups of voltage change data as Bi, where i = 1, 2, ..., n, and then confirm the mean value Jz of several groups of voltage change data, using Confirm its standard value F1. Since there is a linkage relationship between the voltage, current and temperature data, the relevant standard deviation of voltage is used as the analysis standard for subsequent values ​​to determine its relatively accurate evaluation range;

[0047] S23. Based on the current data and temperature data generated in the continuous time period, and based on the different data corresponding to different time points, generate a current change curve and a temperature change curve, wherein the horizontal coordinate axis of the relevant curve is the timeline, and the vertical coordinate axis is the current data or the temperature data:

[0048] For the current change curve: perform the standard deviation processing of the first stage: determine the highest and lowest values ​​of the curve, and first perform standard deviation processing from the lowest value to the highest value to determine the first group of standard values ​​B1, then perform standard deviation processing from (lowest value + 1) to the highest value to determine the second group of standard values ​​B2, then perform standard deviation processing from (lowest value + 2) to the highest value to determine the third group of standard values ​​B3, ..., and so on, until (highest value - 1) to the highest value to perform standard deviation processing to determine the last group of standard values ​​Bm of this stage;

[0049] Execute the second stage of standard deviation processing: the same as the first stage of standard deviation processing, use (highest value - 1) to replace the highest value of the first stage, and confirm several groups of standard values;

[0050] Then, the standard deviation processing of the subsequent different stages is performed in sequence, and the highest value of each subsequent different stage changes. The highest value of the first stage is replaced by [highest value - (k-1)] to confirm several groups of standard values, where k represents different stages.

[0051] From the several groups of standard values ​​confirmed in the above different stages, a group of standard values ​​closest to F1 is determined and calibrated as the determined value, and then the processing process corresponding to the determined value is directly locked to determine the minimum value and the maximum value corresponding to the processing process to lock a group of standard current intervals;

[0052] For the temperature change curve: the same processing method as the current change curve is used to lock a set of standard temperature ranges;

[0053] Specifically, the specific processing process here is to perform correlation analysis on the generated values, that is, standard deviation analysis. In order to identify the closest and continuous standard deviation, it is necessary to select and analyze the corresponding line segments in sequence. Because the standard deviations generated between different values ​​are different, in order to find the closest standard deviation, the above method is used to confirm several standard deviations, thereby determining the most standard current range and temperature range. The current range and temperature range determined by it have the highest correlation with the changed voltage data, which can be used as the highest standard in the data evaluation process, that is, the numerical accuracy can reach the highest;

[0054] S24, compare the current value D1 generated at the current moment with the current interval. When D1∈the current interval, no processing is performed. When D1 When the current interval is reached, a current abnormality signal is generated and displayed, and the temperature value W1 generated at the current moment is compared with the temperature interval. When W1∈the temperature interval, no processing is performed. When W1 When the temperature is within the specified range, an abnormal temperature signal is generated and displayed.

[0055] When the temperature is abnormal, generally, numerical maintenance can be performed directly. For the abnormal current signal that appears, it can be analyzed again to determine the specific situation of the abnormal current. When the current is abnormal, it is generally caused by voltage fluctuations. Another situation is caused by frequency changes. The two situations are different, so it is priority to perform relevant analysis on the current abnormality to see if it is caused by voltage fluctuations. If it is not caused by relevant voltage fluctuations, then perform frequency analysis.

[0056] S3. Define a set of monitoring cycles, monitor the current values ​​of a group of related nodes before the abnormal node, and confirm the monitoring waveform based on the real-time monitoring results, and then perform the same-frequency analysis on the confirmed different monitoring waveforms to determine the same frequency band, and then determine whether there is voltage fluctuation based on the relevant proportion of the same frequency band, wherein the specific method of determination is:

[0057] S31. Define a set of monitoring periods T, where T is a preset value, and its specific value is determined by the operator based on experience. Monitor and confirm the current values ​​of the previous group of related nodes of the abnormal node within the monitoring period T, and generate two groups of current value change curves of different nodes according to the change of the time line. Calibrate the current value change curve of the abnormal node as the standard change curve, and calibrate the current value change curve corresponding to the previous group of related nodes as the associated change curve;

[0058] S32, combining the associated change curve with the standard change curve for analysis, if both change at the same frequency in the same period (that is, change upward or change downward synchronously, for example, between 30-55 seconds, the value trend of the associated change curve in this period climbs upward, and the value trend of the standard change curve in this period also climbs synchronously, then in the corresponding period, it belongs to the same frequency change, that is, the corresponding same frequency band), mark the part of the line segment with the same frequency change in the standard change curve as the same frequency band, determine the line length proportion ZB of the whole line segment in the standard change curve, where: ZB = the total length of the line segment of the same frequency band ÷ the total length of the standard change curve, compare the line length proportion ZB with the preset value Y1, where the specific value of Y1 is determined by the operator based on experience, when ZB≥Y1, a same frequency voltage fluctuation signal is generated, and the generated same frequency voltage fluctuation signal is displayed, when ZB<Y1, no related signal is generated, and the next step of analysis and processing is performed;

[0059] Specifically, when there is voltage fluctuation, the current will also fluctuate with the voltage fluctuation, and the current value between the front and rear nodes will also fluctuate at the same frequency. When the proportion of the relevant segments with the same frequency fluctuation is large, the proportion of the relevant line length generated will also be large. When the corresponding line length proportion is compared with the preset value Y1, the relevant processing signal can be determined and displayed according to the relevant value comparison result, so as to ensure the comprehensiveness of the fault analysis and positioning process, and achieve better monitoring and analysis effects, which can not only analyze the relevant abnormalities, but also analyze and display the relevant abnormal causes;

[0060] S4. Confirm the current sinusoidal waveform of the abnormal node in the monitoring period, determine the cycle value based on the change cycle of each waveform, and then generate a value change sequence based on the changes of the cycle values ​​at different times. Identify whether the current frequency is abnormal through the display of the value change sequence. The specific method of identification is as follows:

[0061] S41. Based on the determined current sinusoidal waveform (specifically, the current sinusoidal waveform can be directly determined from the smart ring network cabinet), confirm the initial point of the standard change curve, determine the relevant current value based on the initial point, and then determine other subsequent points in the waveform with the same numerical value as the current value. According to the time sequence, the initial point is calibrated as 0, and the subsequent consecutive points are calibrated in sequence using natural numbers. For example, the initial point is 0, the first other point is 1, the second other point is 2, and so on, until the last other point is n;

[0062] S42, calibrating the time period between 0 and 2 as the first group of time parameters, calibrating the time period of the corresponding points between 2 and 4 as the second group of time parameters, and so on, sorting the subsequent time parameters to generate their value change sequence;

[0063] S43. Identify whether the changing trends of the numbers before and after the numerical change sequence are consistent:

[0064] If the relevant parameters in the numerical change sequence decrease from the beginning to the end or increase from the beginning to the end, it means that the frequency changes normally, there is no abnormality, and no signal display is performed;

[0065] If the values ​​of the relevant parameters in the value change sequence are chaotically changing, a frequency abnormality signal is generated and displayed through the display terminal;

[0066] Specifically, when the frequency changes, it will affect the related changes of the current cycle. When the current cycle changes, it will easily generate abnormal current. Whether the abnormal current is caused by the corresponding frequency abnormality is determined by analyzing the current sine waveform to identify the periodic value changes between different points in the current sine waveform. When the values ​​of the periodic changes change uniformly according to the relevant trend, it means that there is no related confusion in the frequency, and the corresponding frequency abnormality signal can be generated for display;

[0067] By analyzing from front to back, not only can we quickly identify the related nodes with abnormalities, but we can also analyze the related parameters of the nodes, determine the corresponding abnormal parameters, and display the determined abnormal parameters to facilitate subsequent maintenance personnel to carry out related maintenance, so as to ensure the real-time monitoring effect of related fault nodes.

[0068] Example 2

[0069] Combination Figure 2 , a smart ring main unit fault real-time monitoring system, comprising:

[0070] The abnormal node determination end compares the node power monitored by the smart ring network cabinet with the preset range, and locks the abnormal node based on the comparison result;

[0071] The standard interval determination end extracts the closest set of voltage change data from the current moment based on the voltage value corresponding to the abnormal node at the current moment, and determines the abnormal signal of the abnormal node based on the voltage change data;

[0072] The voltage fluctuation determination end identifies the relevant changes in the current values ​​of the previous group of related nodes by limiting the monitoring period, and confirms the monitoring waveform based on the results of real-time monitoring. Then, the confirmed different monitoring waveforms are subjected to the same frequency analysis to determine the same frequency band, and then based on the relevant proportion of the same frequency band, it is determined whether there is voltage fluctuation;

[0073] The frequency anomaly determination end confirms the current sinusoidal waveform of the corresponding abnormal node within the monitoring period, determines the period value based on the change period of each waveform, and then generates a value change sequence based on the changes in the period value at different times. Through the display of the value change sequence, it is identified whether the current frequency is abnormal.

[0074] In the present invention, the other terminals (modules) in a smart ring main unit fault real-time monitoring system correspond one-to-one to the other steps in a smart ring main unit fault real-time monitoring method of the present invention, and therefore will not be described in detail.

[0075] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0076] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A real-time monitoring method for faults of smart ring main units, characterized in that: The following steps are involved: S1. Monitor the node power of the nodes monitored by the smart ring network cabinet, compare the node power with the preset range, and lock the abnormal nodes based on the comparison results; S2. Based on the calibrated abnormal node, confirm the voltage value of the abnormal node at the current moment, based on the voltage value, extract the closest set of voltage change data from the current moment forward, and determine the abnormal signal of the abnormal node according to the voltage change data; S3. Define a set of monitoring cycles, monitor the current values ​​of two groups of related nodes before and after the abnormal node, and confirm the monitoring waveform based on the real-time monitoring results, then perform the same-frequency analysis on the confirmed different monitoring waveforms to determine the same frequency band, and then determine whether there is voltage fluctuation based on the relevant proportion of the same frequency band; S4. Confirm the current sinusoidal waveform of the abnormal node within the monitoring period, determine the cycle value based on the change cycle of each waveform, and then generate a value change sequence based on the changes of the cycle value at different times. Identify whether the current frequency is abnormal through the display of the value change sequence.

2. A method for real-time monitoring of faults of a smart ring main unit according to claim 1, characterized in that: According to the voltage change data, determine the abnormal signal of the abnormal node, including: The voltage change data includes the change data of this voltage value. The corresponding continuous time period is confirmed from the voltage change data, and then the current data and temperature data are locked through the continuous time period. By performing numerical analysis, the current interval and temperature interval corresponding to the changed voltage data in this continuous time period are determined, and then a numerical evaluation is performed to determine the abnormal signal of the abnormal node.

3. A method for real-time monitoring of faults of a smart ring main unit according to claim 1, characterized in that: In step S1, the specific method of locking the abnormal node includes: The node power of different nodes monitored in real time is calibrated as G i , where i represents different nodes, G i Compare with the preset interval corresponding to the corresponding node, where the endpoint values ​​of the preset interval are all preset values. i ∈ preset interval, no calibration is performed and monitoring continues. When the preset interval is reached, this node is marked as an abnormal node.

4. A method for real-time monitoring of faults of a smart ring main unit according to claim 2, characterized in that: In step S2, the specific method of performing numerical analysis is: S21, confirming a continuous period from the voltage change data, and then confirming the current data and temperature data generated in the continuous period; S22, performing standard deviation processing on several groups of voltage change data corresponding to the continuous time periods to determine a group of standard values ​​F1; S23, based on the current data and temperature data generated in the continuous time period, according to the different data corresponding to different time points, generate a current change curve and a temperature change curve, wherein the horizontal coordinate axis of the relevant curve is the time line, and the vertical coordinate axis is the current data or the temperature data, according to the current change curve, lock a set of standard current intervals, and according to the temperature change curve, lock a set of standard temperature intervals; S24, compare the current value D1 generated at the current moment with the current interval. When D1∈current interval, no processing is performed. When the current interval is exceeded, a current abnormality signal is generated and displayed, and the temperature value W1 generated at the current moment is compared with the temperature interval. When W1∈temperature interval, no processing is performed. When the temperature is within the specified range, an abnormal temperature signal is generated and displayed.

5. A method for real-time monitoring of faults of a smart ring main unit according to claim 4, characterized in that: According to the current change curve, a set of standard current ranges are locked, including: For the current change curve: perform the standard deviation processing of the first stage: determine the highest and lowest values ​​of the curve, and first perform standard deviation processing from the lowest value to the highest value to determine the first group of standard values ​​B1, then perform standard deviation processing from (lowest value + 1) to the highest value to determine the second group of standard values ​​B2, then perform standard deviation processing from (lowest value + 2) to the highest value to determine the third group of standard values ​​B3, ..., and so on, until (highest value - 1) to the highest value to perform standard deviation processing to determine the last group of standard values ​​Bm of this stage; Execute the second stage of standard deviation processing: the same as the first stage of standard deviation processing, use (highest value - 1) to replace the highest value of the first stage, and confirm several groups of standard values; Then, the standard deviation processing of the subsequent different stages is performed in sequence, and the highest value of each subsequent different stage changes. The highest value of the first stage is replaced by [highest value - (k-1)] to confirm several groups of standard values, where k represents different stages. From the several groups of standard values ​​confirmed in the above different stages, a group of standard values ​​closest to F1 is determined and calibrated as the determined value, and then the processing process corresponding to the determined value is directly locked to determine the minimum value and the maximum value corresponding to the processing process to lock a group of standard current ranges.

6. A method for real-time monitoring of faults of a smart ring main unit according to claim 5, characterized in that: According to the temperature change curve, a set of standard temperature ranges are locked, including: For the temperature change curve: the same processing method as the current change curve is used to lock a set of standard temperature ranges.

7. A method for real-time monitoring of faults of a smart ring main unit according to claim 4, characterized in that: In step S22, a set of standard values ​​F1 is confirmed, including: Mark several groups of voltage change data as B i , where i = 1, 2, ..., n, and then confirm the mean value Jz of several groups of voltage change data, using Confirm its standard value F1.

8. A method for real-time monitoring of faults of a smart ring main unit according to claim 4, characterized in that: In step S3, the specific method of determining whether there is voltage fluctuation is: S31, defining a set of monitoring periods T, where T is a preset value, monitoring and confirming the current values ​​of the previous group of related nodes of the abnormal node within the monitoring period T, and generating two groups of current value change curves of different nodes according to the change of the time line, calibrating the current value change curve of the abnormal node as the standard change curve, and calibrating the current value change curve corresponding to the previous group of related nodes as the associated change curve; S32. Combine and analyze the associated change curve and the standard change curve. If the same frequency changes occur in the same period, mark the line segments with the same frequency changes in the standard change curve as the same frequency segment, and determine the line length proportion ZB of the whole line segment of the same frequency segment in the standard change curve, where: ZB = total length of the line segment of the same frequency segment ÷ total length of the standard change curve. Compare the line length proportion ZB with the preset value Y1. When ZB≥Y1, generate a same frequency voltage fluctuation signal and display the generated same frequency voltage fluctuation signal. When ZB<Y1, do not generate any related signal and perform the next step of analysis and processing.

9. A method for real-time monitoring of faults of a smart ring main unit according to claim 8, characterized in that: In step S4, the specific method of identifying whether the current frequency is abnormal is: S41, based on the determined current sinusoidal waveform, confirm the initial point of the standard variation curve, determine the current value related to the initial point, and then determine other subsequent points in the waveform with the same value as the current value, and mark the initial point as 0 according to the time sequence, and mark other subsequent points in succession with natural numbers in sequence; S42, calibrating the time period between 0 and 2 as the first group of time parameters, calibrating the time period of the corresponding points between 2 and 4 as the second group of time parameters, and so on, sorting the subsequent time parameters to generate their value change sequence; S43. Identify whether the changing trends of the numbers before and after the numerical change sequence are consistent: If the relevant parameters in the numerical change sequence decrease from the beginning to the end or increase from the beginning to the end, it means that the frequency changes normally, there is no abnormality, and no signal display is performed; If the values ​​of the relevant parameters in the value change sequence change chaotically, a frequency abnormality signal is generated and displayed through the display terminal.

10. A smart ring main unit fault real-time monitoring system, characterized in that: include: The abnormal node determination end compares the node power monitored by the smart ring network cabinet with the preset range, and locks the abnormal node based on the comparison result; The standard interval determination end extracts the closest set of voltage change data from the current moment based on the voltage value corresponding to the abnormal node at the current moment, and determines the abnormal signal of the abnormal node based on the voltage change data; The voltage fluctuation determination end identifies the relevant changes in the current values ​​of the previous group of related nodes by limiting the monitoring period, and confirms the monitoring waveform based on the results of real-time monitoring. Then, the confirmed different monitoring waveforms are subjected to the same frequency analysis to determine the same frequency band, and then based on the relevant proportion of the same frequency band, it is determined whether there is voltage fluctuation; The frequency anomaly determination end confirms the current sinusoidal waveform of the corresponding abnormal node within the monitoring period, determines the cycle value based on the change period of each waveform, and then generates a value change sequence based on the changes in the cycle value at different times. Through the display of the value change sequence, it is identified whether the current frequency is abnormal.

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