A smart ring main unit fault real-time monitoring method and system

Abstract

The application discloses a kind of intelligent loop network cabinet fault real-time monitoring method and system, and the present application relates to intelligent loop network cabinet technical field, solve the problem that there will be fluctuation or other influence between the value, it can cause error in its fault determination process, the present application is accurately analyzed and determined to the related abnormal item of abnormal node, based on its voltage value, determine the change data of its voltage value from the relevant parameters in the past, by confirming the standard deviation, the corresponding line segment is sequentially selected and analyzed before and after, in order to find the most close standard deviation, a plurality of standard deviations are confirmed by using sequential division to determine the most standard current interval and temperature interval, the current interval and temperature interval determined by it have the highest correlation degree with the changed voltage data, can be used as the highest standard in its data evaluation process, that is, the numerical accuracy can reach the highest.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart ring main unit, in particular to a smart ring main unit fault real-time monitoring method and system. BACKGROUND

[0002] The ring main unit is a group of high-voltage switchgear installed in a metal or non-metal insulated cabinet or made into a split interval type ring network power supply unit electrical equipment; the ring main unit is composed of current display, observation window, cabinet, etc.

[0003] The application with publication number CN117436006B discloses a smart ring main unit fault real-time monitoring method and system, which analyzes the time sequence fluctuation change and time correlation of each smart ring main unit current data, and obtains the final division time interval of each smart ring main unit current data; according to the time correlation and numerical fluctuation distribution of the smart ring main unit current data in each final division time interval, the fluctuation credibility is obtained; according to the fluctuation credibility distribution of the interval divided by each extreme point combination, the isolated tree node splitting value selection interval is selected; so that the smart ring main unit current abnormal data is obtained according to the isolated tree node splitting value selection interval combined with the isolated forest algorithm, and the effect of real-time monitoring of smart ring main unit fault according to the smart ring main unit current abnormal data is better.

[0004] In the process of real-time monitoring of the ring main unit, generally based on the numerical change related to it, whether it belongs to the related interval is identified, and whether the related node exists related fault is confirmed through the specific identification result, but this kind of analysis and processing method is not accurate, because there will be fluctuations or other influence conditions between the numbers, which will cause errors in the fault determination process, thereby forming a misjudgment. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a smart ring main unit fault real-time monitoring method and system, which solves the problem of errors in the fault determination process caused by fluctuations or other influence conditions between the numbers.

[0006] To achieve the above purpose, the present application is realized by the following technical scheme: a smart ring main unit fault real-time monitoring method, comprising the following steps:

[0007] S1, the node power of the monitored node of the present smart ring main unit is monitored, and the node power is compared with the preset interval, and based on the comparison result, the abnormal node is locked, including:

[0008] The node power of different nodes monitored in real time is calibrated as Gi, wherein i represents different nodes, Gi is compared with a preset interval corresponding to the node, wherein the endpoint values of the preset interval are both preset values, when Gi ∈ the preset interval, no calibration is performed and monitoring is continued, if Gi the preset interval, the node is calibrated as an abnormal node;

[0009] S2, based on the calibrated abnormal node, a voltage value of the abnormal node at the current time is confirmed, based on the voltage value, a group of voltage change data closest to the current time is extracted, the voltage change data includes the change data of the voltage value, the corresponding continuous time period is confirmed from the voltage change data, and the current data and the temperature data are locked through the continuous time period, the current interval and the temperature interval corresponding to the voltage data changed in the continuous time period are determined through numerical analysis, and the abnormal signal of the abnormal node is determined through numerical evaluation, and the specific mode is:

[0010] S21, the continuous time period is confirmed from the voltage change data, and the current data and the temperature data generated in the continuous time period are confirmed;

[0011] S22, a plurality of groups of voltage change data corresponding to the continuous time period are subjected to standard deviation processing, and a group of standard values F1 is confirmed;

[0012] S23, according to the current data and the temperature data generated in the continuous time period, the current change curve and the temperature change curve are generated according to different data corresponding to different time points, wherein the horizontal coordinate axis of the related curve is the time line, and the vertical coordinate axis is the current data or the temperature data:

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

[0014] perform second-stage standard deviation processing: the same as the first-stage standard deviation processing, (the maximum value - 1) is used to replace the maximum value of the first stage, and a plurality of groups of standard values are confirmed;

[0015] then, the subsequent different stages of standard deviation processing are performed in turn, the maximum value of each subsequent different stage changes, [the maximum value - (k - 1)] is used to replace the maximum value of the first stage, and a plurality of groups of standard values are confirmed, wherein k represents different stages;

[0016] From the several groups of standard values identified in the different stages, a group of standard values closest to F1 is determined and marked as a determination value, and the processing process corresponding to the determination value is directly locked to determine the minimum value and the maximum value corresponding to the processing process, and a group of standard current intervals are locked;

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

[0018] S24, compare the current current value D1 generated at the current time with the current interval, when D1∈current interval, do not perform any processing, when D1 current interval, generate a current abnormal signal and display, compare the current temperature value W1 generated at the current time with the temperature interval, when W1∈temperature interval, do not perform any processing, when W1 temperature interval, generate a temperature abnormal signal and display;

[0019] S3, limit a group of monitoring periods, monitor the current values of the two groups of related nodes before and after the abnormal node, and based on the real-time monitoring results, confirm the monitoring waveform, and then determine the same frequency band by analyzing the same frequency of the different monitoring waveforms confirmed, and then determine whether there is voltage fluctuation based on the relevant proportion of the same frequency band, and the specific method is:

[0020] S31, limit a monitoring period T, where T is a preset value, monitor and confirm the current values of the previous group of related nodes of the abnormal node in the monitoring period T, and generate current value change curves of the two groups of different nodes according to the change of the time line, mark the current value change curve of the abnormal node as a standard change curve, and mark the current value change curve corresponding to the previous group of related nodes as an associated change curve;

[0021] S32, combine and analyze the associated change curve and the standard change curve, if they change at the same frequency at the same time, mark the part of the standard change curve that changes at the same frequency as a same frequency band, determine the line length proportion ZB of the same frequency band in the overall line segment of the standard change curve, where: ZB=same frequency band line segment total length÷standard change curve total line length, compare the line length proportion ZB with a preset value Y1, when ZB≥Y1, generate a same frequency voltage fluctuation signal, and display the generated same frequency voltage fluctuation signal, when ZB

[0022] S4, confirm the current sine waveform of the abnormal node in the monitoring period, determine the period value based on the change period of each waveform, and generate a value change sequence based on the change of the period value at different times. Through the display of the value change sequence, it is identified whether the current frequency is abnormal; the specific method is:

[0023] S41, based on the determined current sine waveform, confirming the initial point of the standard change curve, determining the related current value based on the initial point, and determining other point positions with the same current value in the waveform. According to the time sequence, the initial point is marked as 0, and the other point positions that appear continuously are marked with natural numbers in turn;

[0024] S42, mark the time period between 0 and 2 as the first group of time parameters, and mark the time period between 2 and 4 corresponding to the point position as the second group of time parameters. In this way, the time parameters that appear in turn are sorted to generate the numerical value change sequence;

[0025] S43, identify whether the change trend between the numbers before and after the numerical value change sequence is consistent:

[0026] If the related parameters in the numerical value change sequence are sequentially smaller from front to back or sequentially larger from front to back, it means that the frequency changes normally and no abnormal situation occurs, and no signal is displayed;

[0027] If the numerical value of the related parameters in the numerical value change sequence is chaotic change, a frequency abnormal signal is generated and displayed through the display end.

[0028] Preferably, a smart ring network cabinet 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 interval, locks the abnormal node based on the comparison result;

[0030] The standard interval determination end determines the related voltage change data based on the voltage value corresponding to the current time of the abnormal node, confirms the corresponding continuous time period from the voltage change data, locks the current data and temperature data through the continuous time period, determines the current interval and temperature interval corresponding to the voltage change data of the continuous time period through numerical analysis, and determines the abnormal signal of the abnormal node through numerical evaluation;

[0031] The voltage fluctuation determination end identifies the related change of the current value of the previous group of related nodes by limiting the monitoring period, confirms the monitoring waveform based on the real-time monitoring result, determines the same frequency band by analyzing the different monitoring waveforms, and determines whether there is voltage fluctuation based on the related proportion of the same frequency band.

[0032] The frequency anomaly determining end confirms the current sinusoidal waveforms of the corresponding abnormal nodes in the monitoring period, determines the period values based on the change period of each waveform, generates the value change sequence based on the change of the period values before and after different time, and identifies whether the current frequency is abnormal through the display of the value change sequence.

[0033] The application provides a smart ring main unit fault real-time monitoring method and system.

[0034] The application determines the related abnormal items of the abnormal nodes through accurate analysis, locks the voltage value of the abnormal nodes based on the determination time, determines the voltage value change data from the related parameters in the past, and selects and analyzes the corresponding line segments in sequence through the standard deviation confirmation method from the current data and temperature data associated with the change data.

[0035] For the current abnormal situation, the voltage fluctuation analysis is performed first, the related abnormality of the voltage fluctuation in the line segment is identified, if there is no related abnormality, the current frequency is analyzed, the related change parameters between the current sinusoidal waveforms are analyzed, the frequency is identified based on the specific change of the parameters, the application can not only quickly monitor the related fault situation, but also quickly lock the specific abnormal reason and display it for the related maintenance personnel to confirm, reduce the related maintenance burden of the related maintenance personnel, and improve the maintenance efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 The application provides a smart ring main unit fault real-time monitoring method and system.

[0037] Figure 2 The application provides a smart ring main unit fault real-time monitoring method and system. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the application will be described clearly and completely in combination with the accompanying drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0039] Embodiment 1

[0040] Please refer to Figure 1 The application provides a real-time monitoring method for smart ring main unit faults, comprising the following steps:

[0041] S1, monitoring the node power of the monitored nodes of the smart ring main unit, and comparing the node power with the preset interval, locking the abnormal node based on the comparison result:

[0042] S11, the node power of different nodes monitored in real time is calibrated as Gi, where i represents different nodes, and Gi is compared with the preset interval corresponding to the corresponding node, wherein the endpoint values of the preset interval are both preset values, and the specific values are determined by the operator according to experience, when Gi∈ preset interval, no calibration is performed and the monitoring continues, if Gi When the preset interval, the node is calibrated as an abnormal node;

[0043] S2, based on the calibrated abnormal node, confirming the voltage value of the abnormal node at the current time, based on the voltage value, extracting the closest set of voltage change data from the current time, the voltage change data contains the change data of the voltage value, confirming the corresponding continuous period from the voltage change data, and then locking the current data and temperature data through the continuous period, and through numerical analysis, determining the current interval and temperature interval corresponding to the changed voltage data of the continuous period, and then determining the abnormal signal of the abnormal node through numerical evaluation. Specifically, when the voltage data changes, the current data and the corresponding temperature data change with it. Among them, the current data changing with the voltage data is not particularly accurate. In order to ensure more accurate evaluation interval and evaluation result in the line environment, the closest current interval needs to be determined based on the changed voltage data to ensure the related accuracy of subsequent numerical values. In complex line environment, the current value in the line cannot be confirmed only by the confirmed resistance value and the provided voltage value. The current value often has a related deviation from the value of voltage divided by resistance. Therefore, the relationship between the data needs to be analyzed to ensure the related accuracy of the data.

[0044] Among them, the specific way of determining includes:

[0045] S21, confirm the continuous period of the voltage change data (understand in combination with the example, when the voltage value determined at the current time is 10V, there is a group of change data of 9V-14V in the tracing process from the current time, so the voltage value belongs to the voltage change data, and the continuous period corresponding to the voltage change data is 8 o'clock-8 o'clock and 5 minutes, and the current data and temperature data generated in 8 o'clock-8 o'clock and 5 minutes are extracted from the past data subsequently);

[0046] S22, standard deviation processing is performed on the voltage change data corresponding to the continuous period to confirm a group of standard values F1, wherein the variance processing method is that the voltage change data is marked as Bi, wherein i=1, 2, …, n, and the mean value Jz of the voltage change data is confirmed, and the standard deviation of the voltage change data is calculated as The standard value F1 is confirmed, and because the data between the voltage and the current and the temperature are related, the related standard deviation of the voltage is used as the analysis standard of the subsequent values to determine the relatively accurate evaluation interval;

[0047] S23, according to the current data and temperature data generated in the continuous period, different data corresponding to different time points are generated to generate the current change curve and the temperature change curve, wherein the horizontal coordinate axis of the related curve is the time line, and the vertical coordinate axis is the current data or the temperature data:

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

[0049] Perform the second stage of standard deviation processing: the standard deviation processing method is the same as that of the first stage, and (the maximum value-1) is used to replace the maximum value of the first stage to confirm a group of standard values;

[0050] Then, the subsequent different stages of standard deviation processing are performed in turn, the maximum value of each subsequent different stage changes, [the maximum value-(k-1)] is used to replace the maximum value of the first stage to confirm a group of standard values, wherein k represents different stages;

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

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

[0053] Specifically, the specific processing process here is a correlation analysis of the generated values, that is, a standard deviation analysis. In order to identify the closest and continuous standard deviation, the corresponding line segment needs to be selected and analyzed in sequence. Since the standard deviations between different values are different, in order to find the closest standard deviation, the above method is used to determine several standard deviations, thereby determining the most standard current interval and temperature interval. The current interval and temperature interval determined 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 be the highest;

[0054] S24, compare the current current value D1 generated at the current time with the current interval. When D1∈current interval, do not perform any processing. When D1 current interval, generate a current abnormal signal and display. Compare the temperature value W1 generated at the current time with the temperature interval. When W1∈temperature interval, do not perform any processing. When W1 temperature interval, generate a temperature abnormal signal and display.

[0055] When the temperature is abnormal, numerical maintenance can be performed directly. For the current abnormal signal, further analysis can be performed to determine the specific situation of the current abnormality. When the current is abnormal, it is generally caused by voltage fluctuation, and another situation is caused by frequency change. The two situations are different, so the current abnormality is analyzed first to determine whether it is caused by voltage fluctuation. If it is not caused by related voltage fluctuation, frequency analysis is performed.

[0056] S3, limit a group of monitoring periods, monitor the current values of the related nodes of the abnormal node, and based on the real-time monitoring results, confirm the monitoring waveform, then determine the same frequency band by analyzing the different monitoring waveforms, and based on the related proportion of the same frequency band, determine whether there is voltage fluctuation. The specific method for determining is:

[0057] S31, define a set of monitoring periods T, where T is a preset value, the specific value is determined by the operator according to experience, the current values of the previous set of related nodes of the abnormal node in the monitoring period T are monitored and confirmed, and the current value change curves of the two different nodes are generated according to the change of the time line, the current value change curve of the abnormal node is calibrated as the standard change curve, and the current value change curve corresponding to the previous set of related nodes is calibrated as the associated change curve;

[0058] S32, combine and analyze the associated change curve and the standard change curve, if they change at the same frequency at the same time (that is, they change upward or downward synchronously, for example, between 30-55 seconds, the associated change curve climbs upward in the value trend in this period, and the standard change curve also climbs upward in the value trend in this period, so it is the same frequency change in the corresponding period, that is, the same frequency band), the part of the standard change curve that exists in the same frequency change is calibrated as the same frequency band, and the length ratio ZB of the same frequency band in the whole line segment of the standard change curve is determined, wherein: ZB=same frequency band line segment total length÷standard change curve total line length, the line length ratio ZB is compared with the preset value Y1, wherein the specific value of Y1 is determined by the operator according to experience, when ZB≥Y1, the same frequency voltage fluctuation signal is generated, and the generated same frequency voltage fluctuation signal is displayed, when ZB

[0059] Specifically, when there is voltage fluctuation, the current will also fluctuate with the voltage fluctuation, and the current values between the nodes before and after the voltage fluctuation will also fluctuate at the same frequency. When the relevant segment of the same frequency fluctuation occupies a large proportion, the relevant line length ratio will also be large. When the corresponding line length ratio 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 to achieve better monitoring and analysis effect, which can not only analyze the relevant abnormality, but also analyze and display the relevant abnormal reason.

[0060] S4, confirm the current sinusoidal waveform of the corresponding abnormal node in the monitoring period, determine the period value based on the change period of each waveform, and generate a numerical value change sequence based on the change of the period values at different times. The current frequency is identified through the display of the numerical value change sequence, wherein the specific identification method is:

[0061] S41, based on the determined current sine waveform (specifically, its current sine waveform can be directly determined from the smart ring main unit), confirm the initial point of the standard change curve, determine the related current value based on the initial point, and determine other point positions with the same current value in the waveform. According to the time sequence, the initial point is marked as 0, and the other point positions that appear continuously thereafter are sequentially marked with natural numbers. For example: the initial point is 0, the first other point position is 1, the second other point position is 2, and so on. The last other point position is n;

[0062] S42, mark the time period between 0 and 2 as the first group of time parameters, and mark the time period between 2 and 4 as the second group of time parameters. Continue to sort the time parameters that appear sequentially thereafter to generate a numerical value change sequence;

[0063] S43, identify whether the change trend between the numbers before and after the numerical value change sequence is consistent:

[0064] If the related parameters in the numerical value change sequence decrease sequentially from the front to the back or increase sequentially from the front to the back, it means that the frequency changes normally and no abnormal situation occurs, and no signal is displayed;

[0065] If the numerical value of the related parameters in the numerical value change sequence changes chaotically, a frequency abnormality signal is generated and displayed through the display end;

[0066] Specifically, when the frequency changes, it will affect the related changes of the current period. When the current period changes, it will easily produce abnormal current. Whether the abnormal current is caused by the corresponding frequency abnormality, by analyzing the current sine waveform, the period value change between different point positions in the current sine waveform is identified. When the period value changes uniformly according to the related trend, it means that the frequency does not appear chaotic, and the corresponding frequency abnormality signal is generated for display;

[0067] By sequentially analyzing from front to back, not only can the abnormal related node be quickly identified, but also the related parameters of the node are analyzed, the corresponding abnormal parameters are determined, and the determined abnormal parameters are displayed, which facilitates subsequent maintenance personnel to perform related maintenance to ensure the real-time monitoring effect of the related fault node.

[0068] Embodiment 2

[0069] In combination Figure 2 A smart ring main unit fault real-time monitoring system, comprising:

[0070] An abnormal node determination end compares the node power monitored by the smart ring main unit with the preset interval, and locks the abnormal node based on the comparison result.

[0071] The standard interval determining end determines the abnormal signal of the abnormal node based on the voltage value corresponding to the current time of the abnormal node, extracts a group of voltage change data closest to the current time based on the voltage value, and determines the abnormal signal of the abnormal node according to the voltage change data.

[0072] The voltage fluctuation determining end identifies the relevant change of the current value of the previous group of relevant nodes by limiting the monitoring period, confirms the monitoring waveform based on the real-time monitoring result, determines the same frequency band by performing same frequency analysis on the confirmed different monitoring waveforms, and determines whether there is voltage fluctuation based on the relevant proportion of the same frequency band.

[0073] The frequency abnormality determining end confirms the current sine waveform of the abnormal node in the monitoring period, determines the period value based on the change period of each waveform, generates a value change sequence based on the change of the period value at different times, and identifies whether the current frequency is abnormal by displaying the value change sequence.

[0074] In the present application, other ends (modules) in a smart ring network cabinet fault real-time monitoring system correspond one-to-one to other steps in a smart ring network cabinet fault real-time monitoring method, and therefore will not be described in detail.

[0075] Some data in the above formula are dimensionless for numerical calculation, and the contents not described in detail in the specification all 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 application and are not limiting, and although the present application 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 application can be modified or replaced equivalently without departing from the spirit and scope of the technical method of the present application.

Claims

1. A real-time fault monitoring method for smart ring main unit, characterized in that, The method comprises the following steps: S1, monitoring the node power of the monitored nodes of the intelligent ring main unit, and comparing the node power with the preset interval, and locking the abnormal node based on the comparison result; S2, based on the calibrated abnormal node, confirming the voltage value of the abnormal node at the current time, based on the voltage value, extracting the closest group of voltage change data from the current time, and determining the abnormal signal of the abnormal node according to the voltage change data; S3, limiting a group of monitoring periods, monitoring the current values of the related nodes before and after the abnormal node, and based on the real-time monitoring result, confirming the monitoring waveform, and then determining the same frequency band by analyzing the same frequency of the confirmed different monitoring waveforms, and based on the related proportion of the same frequency band, determining whether there is voltage fluctuation; S4, confirming the current sine waveform of the abnormal node in the monitoring period, determining the period value based on the change period of each waveform, and generating a numerical change sequence based on the change of the period value at different times, and identifying whether the current frequency is abnormal through the display of the numerical change sequence; Wherein, according to the voltage change data, the abnormal signal of the abnormal node is determined, which comprises: The voltage change data includes the change data of the voltage value, the corresponding continuous period is confirmed from the voltage change data, and the current data and temperature data are locked through the continuous period, the current interval and the temperature interval corresponding to the changed voltage data in the continuous period are determined through numerical analysis, and the abnormal signal of the abnormal node is determined through numerical evaluation; Wherein, in step S3, the specific way of determining whether there is voltage fluctuation is: S31, limiting a group of monitoring periods T, wherein T is a preset value, monitoring and confirming the current value of the related nodes before the abnormal node in the monitoring period T, and generating the current value change curve of the two groups of different nodes according to the change of the time line, and marking the current value change curve of the abnormal node as the standard change curve, and marking the current value change curve corresponding to the related nodes before the abnormal node as the associated change curve; S32, combining and analyzing the associated change curve and the standard change curve, if the same frequency changes at the same time, marking the part of the standard change curve with the same frequency change as the same frequency band, determining the line length ratio ZB of the same frequency band in the whole line segment of the standard change curve, wherein: ZB=same frequency band line segment total length ÷ standard change curve total line length, comparing the line length ratio ZB with the preset value Y1, when ZB≥Y1, the same frequency voltage fluctuation signal is generated, and the generated same frequency voltage fluctuation signal is displayed, when ZB 2.The real-time fault monitoring method of the smart ring main unit according to claim 1, characterized in that, In step S1, the specific way of locking the abnormal node comprises: The node power of different nodes monitored in real time is calibrated as G i wherein i represents different nodes, G i is compared with the preset interval corresponding to the corresponding node, wherein the endpoint values of the preset interval are both preset values, when the preset interval, no calibration is performed and monitoring is continued, and when the preset interval, the node is calibrated as an abnormal node. 3.The real-time fault monitoring method of the smart ring main unit according to claim 1, characterized in that, In step S2, the specific way of numerical analysis is: S21, confirming the continuous period from the voltage change data, and then confirming the current data and temperature data generated in the continuous period; S22, standard deviation processing of the several groups of voltage change data corresponding to the continuous period, confirming a group of standard values F1; S23, generating the current change curve and the temperature change curve according to different data corresponding to different time points based on the current data and the temperature data generated in the continuous time period, wherein the horizontal coordinate axis of the related curve is the time line, and the vertical coordinate axis is the current data or the temperature data, locking a group of standard current intervals according to the current change curve, and locking a group of standard temperature intervals according to the temperature change curve; S24, the current value D1 generated at the current time is compared with the current range, when the current range, no processing is performed, when the current range, a current abnormality signal is generated and displayed, the temperature value W1 generated at the current time is compared with the temperature range, when the temperature range, no processing is performed, when the temperature range, a temperature abnormality signal is generated and displayed.

4. The real-time fault monitoring method for the smart ring main unit according to claim 3, characterized in that, locking a group of standard current intervals according to the current change curve, comprising: For the current change curve: performing first-stage standard deviation processing: determining the maximum value and the minimum value of the curve, preferentially performing standard deviation processing from the minimum value to the maximum value to determine a first group of standard values B1, then performing standard deviation processing from (the minimum value + 1) to the maximum value to determine a second group of standard values B2, and performing standard deviation processing from (the minimum value + 2) to the maximum value to determine a third group of standard values B3, and so on, until (the maximum value - 1) to the maximum value to determine the last group of standard values Bm of the current stage; performing second-stage standard deviation processing: the same as the first-stage standard deviation processing, using (the maximum value - 1) to replace the maximum value of the first stage, and confirming a plurality of groups of standard values; then performing subsequent different-stage standard deviation processing, the maximum value of each subsequent different stage being changed, using [the maximum value - (k - 1)] to replace the maximum value of the first stage, and confirming a plurality of groups of standard values, wherein k represents different stages; from the plurality of groups of standard values confirmed in the above different stages, determining a group of standard values closest to F1 and marking the group of standard values as the determined value, and directly locking the processing process corresponding to the determined value to determine the minimum value and the maximum value corresponding to the processing process, and locking a group of standard current intervals.

5. The real-time fault monitoring method for the smart ring main unit according to claim 4, characterized in that, locking a group of standard temperature intervals according to the temperature change curve, comprising: For the temperature change curve: the same as the processing method of the current change curve, to lock a group of standard temperature intervals.

6. The real-time fault monitoring method for the smart ring main unit according to claim 3, characterized in that, In step S22, confirming a group of standard values F1, comprising: The several sets of voltage change data are calibrated as B i where i = 1, 2,..., n, and the mean value Jz of the several sets of voltage change data is reconfirmed, and the following is adopted The standard value F1 is confirmed. 7.The real-time fault monitoring method of the smart ring main unit according to claim 1, characterized in that, In the step S4, the specific method for identifying whether the current frequency is abnormal is: S41, based on the determined current sinusoidal waveform, confirming the initial point of the current change curve, determining the related current value based on the initial point, and then determining other points with the same current value in the waveform, marking the initial point as 0 according to the time sequence, and marking the other points appearing continuously subsequently with natural numbers in sequence; S42, marking the time period between 0 and 2 as the first group of time parameters, marking the time period corresponding to the points between 2 and 4 as the second group of time parameters, and so on, to sort the time parameters appearing subsequently in sequence to generate a numerical change sequence; S43, identifying whether the change trend between the numbers before and after the numerical change sequence is consistent: If the related parameters in the numerical change sequence are sequentially smaller from front to back or sequentially larger from front to back, it means that the frequency changes normally and no abnormal situation occurs, and no signal is displayed; If the numerical value of the related parameters in the numerical change sequence is chaotically changed, an abnormal frequency signal is generated and displayed through the display end.

8. A smart ring main unit fault real-time monitoring system, characterized in that, Comprise: Abnormal node determination end, compare the node power monitored by the smart ring network cabinet with the preset interval, and based on the comparison result, lock the abnormal node; Standard interval determination end, based on the voltage value corresponding to the abnormal node at the current time, based on the voltage value, extract the closest set of voltage change data from the current time, determine the abnormal signal of the abnormal node according to the voltage change data; Voltage fluctuation determination end, by limiting the monitoring period, the related changes of the current value of the related nodes in the previous group are identified, and based on the real-time monitoring result, the monitoring waveform is confirmed, and different monitoring waveforms are confirmed, and the same frequency band is determined, and based on the related proportion of the same frequency band, whether there is voltage fluctuation is determined; The frequency abnormality determination end confirms the current sine waveform of the abnormal node in the monitoring period, determines the period value based on the change period of each waveform, generates a numerical change sequence based on the change of the period value at different times, and identifies whether the current frequency is abnormal through the display of the numerical change sequence. Wherein, according to the voltage change data, the abnormal signal of the abnormal node is determined, including: The voltage change data includes the change data of the voltage value, the corresponding continuous period is confirmed from the voltage change data, and the current data and temperature data are locked through the continuous period, the current interval and temperature interval corresponding to the changed voltage data in the continuous period are determined through numerical analysis, and the abnormal signal of the abnormal node is determined through numerical evaluation; Wherein, in step S3, the specific way of determining whether there is voltage fluctuation is: S31, limit a group of monitoring periods T, wherein T is a preset value, monitor and confirm the current value of the current node in the monitoring period T, and generate two groups of current value change curves of different nodes according to the change of time line, and mark the current value change curve of the current node as standard change curve, and mark the current value change curve corresponding to the previous group of related nodes as related change curve; S32, combine and analyze the related change curve and the standard change curve, if the same frequency changes at the same time, mark the part of the standard change curve with the same frequency change as the same frequency band, determine the line length ratio ZB of the same frequency band in the whole line segment of the standard change curve, wherein: ZB=same frequency band line segment total length ÷ standard change curve total line length, compare the line length ratio ZB with the preset value Y1, when ZB≥Y1, generate the same frequency voltage fluctuation signal, and display the generated same frequency voltage fluctuation signal, when ZB

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