Intelligent power system remote monitoring device and monitoring method thereof

Abstract

This invention discloses a remote monitoring device and method for intelligent power systems. It relates to the field of power monitoring anti-interference technology, solving the problem of difficulty in comprehensively and deeply extracting features from complex waveforms. Through detailed analysis of signal waveform characteristics within the monitoring line, this invention establishes a scientific feature value sequence identification system. For each monitoring item's standard waveform, its feature value sequence is precisely locked, and by using change feature analysis and feature sequence matching, it can keenly identify whether the signal waveform is interfered with. The accurate calibration of abnormal waveform points and interference waveforms, as well as the application of proportion determination methods, effectively avoid misjudgments caused by parameter value fluctuations, greatly improving the accuracy of interference identification. For waveforms not belonging to monitoring items, frequency filtering is directly performed, improving filtering efficiency. For waveforms belonging to monitoring items, the timing of different waveforms is cleverly adjusted by determining the time difference feature sequence.

Description

Technical Field

[0001] This invention relates to the field of power monitoring and anti-interference technology, specifically to a remote monitoring device and method for a smart power system. Background Technology

[0002] In the field of remote monitoring of smart power systems, ensuring the accurate collection and transmission of power data is crucial for maintaining the stable operation of the power system. However, due to the extremely complex operating environment of the power system and the widespread presence of various interference factors, existing remote monitoring technologies have revealed significant shortcomings in interference analysis and processing.

[0003] On the one hand, existing technologies have relatively simple methods for analyzing interference signals. When monitoring lines include multiple electrical parameters such as current and voltage, the waveforms of multiple monitoring signals are intertwined. Existing technologies struggle to extract comprehensive and in-depth features from these complex waveforms and cannot effectively lock the feature value sequences of each monitoring waveform. As a result, when identifying interference waveforms, only simple and single standards can be relied upon, which easily leads to misjudging normal fluctuations of parameters as interference signals, resulting in a persistently high misjudgment rate.

[0004] On the other hand, once suspected interference waveforms are identified, subsequent processing methods lack scientific rigor and specificity. Existing technologies do not fully consider the diversity and complexity of interference signals, and fail to systematically trace the source of interference based on key information such as waveform crossover characteristics. When determining interference waveforms and implementing filtering, differentiated strategies are not adopted based on whether the interference waveform originates from a monitored item. This results in either the inability to effectively filter out genuine interference signals or the mistaken deletion of useful monitoring data as interference, severely impacting the accuracy and completeness of remote monitoring data.

[0005] Furthermore, existing technologies lack flexible adaptive adjustment mechanisms when faced with changes in interference signals. The operating conditions of power systems are constantly changing dynamically, and the characteristics of interference signals also change accordingly. However, existing remote monitoring systems cannot adjust their analysis and processing methods in real time according to the actual changes in interference, further weakening the system's ability to cope with interference. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a remote monitoring device and method for intelligent power systems, which solves the problem of difficulty in comprehensively and deeply extracting features from these complex waveforms.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a remote monitoring method for a smart power system, comprising the following steps:

[0008] Step 1: Characteristic confirmation of the signal waveforms associated with different monitoring items within the monitoring line. The specific method is as follows:

[0009] Confirm the preset standard waveform for the corresponding single monitoring item, and calibrate the amplitude associated with different waveform points within the standard waveform as F. i , where i represents different waveform points;

[0010] Starting from the beginning of the standard waveform, the variation characteristics between adjacent waveform points are confirmed sequentially. The amplitude associated with the previous group of waveform points is labeled as F1, and the amplitude associated with the next group of waveform points is labeled as F2. The variation characteristics are locked by using the formula: variation characteristics = (F2-F1) ÷ time interval. Then, according to the sorting method between corresponding waveform points in the standard waveform, the confirmed variation characteristics are sorted to confirm a set of numerical sequences. The optimal set of values ​​in this numerical sequence is copied to the first set, and the original first set of values ​​in this numerical sequence is copied to the last set to generate a feature value sequence belonging to this standard waveform.

[0011] Confirm the signal waveform generated by the corresponding monitoring item: Using the same processing method as described above for the change characteristics, confirm the change characteristics associated with the real-time generated waveform points and the previous set of waveform points, and confirm whether this change characteristic exists within the feature value sequence:

[0012] If it exists: then this change feature is taken as the primary feature and monitoring continues. The change features generated by the waveform points after the primary feature are confirmed as secondary features. The change features generated by the waveform points before the primary feature are also confirmed as secondary features. A feature sequence is generated: secondary feature - primary feature - secondary feature. It is confirmed whether this feature sequence exists in the feature value sequence. If it does, no calibration is performed. If not, this waveform point is calibrated as an abnormal waveform point.

[0013] If it does not exist: directly mark this waveform point as an abnormal waveform point;

[0014] Identify the zero-value points associated with the abnormal waveform points from the confirmed signal waveform. Record the band associated with the two zero-value points as the characteristic band and determine the proportion of the abnormal waveform points in the characteristic band. The proportion is calculated as: total number of abnormal waveform points ÷ total number of waveform points in the characteristic band. If the proportion is greater than 5%, the signal waveform is labeled as the interfered waveform; if the proportion is less than or equal to 5%, no labeling is performed.

[0015] Step 2: Identify the abnormal bands of the interfering signal waveform. Then, based on other waveforms present in this monitoring line, verify and analyze these other waveforms against the current signal waveform. By analyzing the crossover characteristics between these other waveforms and the current signal waveform, pinpoint the interfering waveform. The specific sub-steps are as follows:

[0016] The confirmed interfered waveform is recorded as the main waveform, and the abnormal waveform points marked in the main waveform are confirmed. The band where the abnormal waveform points are located is recorded as the abnormal band. Other waveforms existing in this monitoring line are recorded as secondary waveforms.

[0017] Identify the frequency period within the main waveform, which is the time range associated with three consecutive zero points. Align the zero points within the secondary waveform with the zero points within the main waveform and execute the corresponding movement process. Control the secondary waveform to move backward, ensuring that the time period of the movement does not exceed the frequency period. Determine the intersection points between the secondary and main waveforms during the movement process. Confirm whether there are multiple intersection points associated with a certain movement process that are all located within the abnormal band of the main waveform. If so, record this waveform as an interference waveform of this signal waveform; otherwise, do not perform any calibration.

[0018] The sub-waveforms are processed sequentially, and the interference waveforms belonging to this signal waveform are confirmed and calibrated sequentially.

[0019] Step 3: Based on the multiple sets of interference waveforms confirmed by this signal waveform, identify whether the corresponding interference waveform belongs to a monitoring item. If it does not exist, filter it directly through the filter. If it exists, determine the time difference characteristic sequence of multiple monitoring items and execute it.

[0020] Preferably, in step three, it is determined whether the interference waveform confirmed by this signal waveform is generated by other monitoring items of this monitoring line. If so, such interference waveform is recorded as a waveform to be determined; if not, such interference waveform is recorded as a waveform to be eliminated.

[0021] Preferably, in step three, the frequency of the waveform to be removed is confirmed, and the frequency is directly filtered out using a filter. The specific method for confirming the frequency is as follows:

[0022] Abnormal waveform points within the waveform to be removed are identified and calibrated. After calibration, the bands associated with three consecutive zero points are recorded as undetermined bands. From the identified undetermined bands, the undetermined bands without abnormal waveform points are recorded as calibration bands. The time length associated with the calibration band is calibrated as CD. The frequency value f associated with this waveform to be removed is confirmed using f=1÷CD. The confirmed frequency value f is then displayed. External personnel confirm this frequency value f to determine whether it can be directly filtered and removed and executed.

[0023] Preferably, in step three, the time difference feature sequence of the waveform to be determined is determined by correlation, multiple waveforms to be determined are processed with the current signal waveform by band processing, the time difference feature sequence is locked and displayed, and the specific processing method for locking is as follows:

[0024] The frequency values ​​f of different unknown waveforms or the interference waveform are confirmed, and based on the different frequency values ​​f associated with different waveforms, the waveform associated with the minimum value f is selected and recorded as the selected waveform.

[0025] Randomly determine the time interval between adjacent zero points from the selected waveform, and based on the number G of unselected waveforms, divide this time interval into (G+1) micro time intervals, and record the time point between each micro time interval as the characteristic time point.

[0026] The zero points associated with the unselected waveforms are moved so that each zero point of the unselected waveform corresponds to a set of characteristic time points. During the movement, it is ensured that the peak points of the unselected waveforms and the selected waveforms do not intersect with other waveforms. After selection, the initial time associated with the time period is taken as the 0 characteristic time. Then, the time difference between the subsequent different characteristic time points within the time period and the 0 characteristic time is confirmed. Starting from the 0 characteristic time, the time difference values ​​of the different characteristic time points are sorted to confirm the time difference characteristic sequence.

[0027] Preferably, a remote monitoring device for a smart power system includes:

[0028] The waveform interference analysis end identifies the characteristics of the signal waveforms associated with different monitoring items within the monitoring line. Based on the standard waveforms associated with the corresponding monitoring items, it locks out the feature value sequence and then identifies whether the signal waveforms associated with the corresponding monitoring items are interfered with based on the feature value sequence.

[0029] At the interference waveform determination end, the abnormal band of the signal waveform with interference is calibrated. Then, based on other waveforms existing in this monitoring line, the other waveforms are verified and analyzed with this signal waveform. By analyzing the cross characteristics between other waveforms and this signal waveform, the interference waveform of this signal waveform is locked.

[0030] At the interference feature processing end, based on the multiple sets of interference waveforms confirmed by this signal waveform, it identifies whether the corresponding interference waveform belongs to a monitoring item. If it does not exist, it directly filters out the frequency through the filter. If it exists, it determines the time difference feature sequence of multiple sets of monitoring items and executes it.

[0031] This invention provides a remote monitoring device and method for a smart power system. Compared with the prior art, it has the following advantages:

[0032] This invention establishes a scientific feature value sequence identification system through detailed analysis of signal waveform characteristics within the monitoring line. For the standard waveform of each monitoring item, its feature value sequence is accurately locked, and by using change feature analysis and feature sequence matching, it can keenly identify whether the signal waveform is interfered with. The accurate calibration of abnormal waveform points and interference waveforms, as well as the application of the proportion value determination method, effectively avoid misjudgment caused by parameter value fluctuations and greatly improve the accuracy of interference identification.

[0033] Based on the overlap and movement of the zero points of the main waveform and the secondary waveform, the interference waveform can be quickly located. This method fully considers the uncertainty of the interference situation. By moving the secondary waveform laterally within the frequency period range of the main waveform, the intersection point of the secondary waveform and the main waveform is accurately captured, thereby determining the interference waveform and providing a clear target for subsequent filtering processing.

[0034] For waveforms that are not part of the monitoring items, frequency filtering is performed directly, which improves filtering efficiency. For waveforms that are part of the monitoring items, the timing of different waveforms is cleverly adjusted by determining the time difference characteristic sequence, which avoids the intersection of peak points, reduces the degree of interference between monitoring items, and realizes intelligent anti-interference processing. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0036] Figure 2 This is a schematic diagram illustrating the determination of the time difference feature sequence in this invention. Detailed Implementation

[0037] 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.

[0038] First Embodiment

[0039] Please see Figure 1 This application provides a method for remote monitoring of a smart power system, comprising the following steps:

[0040] Step 1: Confirm the characteristics of the signal waveforms associated with different monitoring items within the monitoring line. Based on the standard waveforms associated with the corresponding monitoring items, identify the characteristic value sequence. Then, based on the characteristic value sequence, identify whether the signal waveforms associated with the corresponding monitoring items are interfered with. Specifically, there are multiple associated monitoring items within a single monitoring line, such as current, voltage, and other related power parameters. By identifying the signal waveforms associated with the corresponding monitoring items and confirming the abnormal characteristics within the signal waveforms, identify whether the signal waveforms of the corresponding monitoring items are interfered with. Confirm the existing interference waveforms and then perform filtering to ensure the accuracy of remote monitoring.

[0041] The specific method for locking the feature value sequence is as follows:

[0042] Confirm the preset standard waveform for the corresponding single monitoring item, and calibrate the amplitude associated with different waveform points within the standard waveform as F. i , where i represents different waveform points, and the standard waveform is a waveform with one time period;

[0043] Starting from the beginning of the standard waveform, sequentially confirm the variation characteristics between adjacent waveform points. Label the amplitude associated with the preceding set of waveform points as F1, and the amplitude associated with the following set as F2. Use the formula: Variation characteristic = (F2 - F1) ÷ Time interval (this time interval is the time interval between adjacent waveform points, directly confirmed from the standard waveform; the time interval associated with each different standard waveform may be the same or different, depending on the corresponding standard waveform). Lock the variation characteristics of adjacent waveform points. Then, according to the sorting method of corresponding waveform points within the standard waveform, sort the sequentially confirmed variation characteristics to confirm a set of numerical sequences. Copy the optimal set of values ​​from this numerical sequence to the first set, and then copy the original first set of values ​​from this numerical sequence. The values ​​are copied to the last group, generating a sequence of feature values ​​belonging to this standard waveform. For example, suppose the original standard waveform's confirmed value sequence is {1, 2, 3, 4, 5, 4, 3, 2, 1, -1, -2, -3, -4, -5, -4, -3, -2, -1}. Because this sequence is generated regularly during subsequent waveform confirmation, to better confirm features and improve accuracy, and to avoid missing data, the values ​​before and after the first group are confirmed and adjusted to {-1, 1, 2, 3, 4, 5, 4, 3, 2, 1, -1, -2, -3, -4, -5, -4, -3, -2, -1, 1}. The original value sequence is a periodic wave, but since periodic waves are generated regularly, there may be cases where the values ​​before and after the first group are not covered. Therefore, copying and confirming is performed.

[0044] Confirm the signal waveform generated by the corresponding monitoring item: Using the same processing method as described above for the change characteristics, confirm the change characteristics associated with the real-time generated waveform points and the previous set of waveform points, and confirm whether this change characteristic exists within the feature value sequence:

[0045] If it exists: then this change feature is taken as the primary feature and monitoring continues. The change features generated by the waveform points after the primary feature are confirmed as secondary features. The change features generated by the waveform points before the primary feature are also confirmed as secondary features. A feature sequence is generated: secondary feature - primary feature - secondary feature. It is confirmed whether this feature sequence exists in the feature value sequence. If it does, no calibration is performed. If not, this waveform point is calibrated as an abnormal waveform point.

[0046] If it does not exist: directly mark this waveform point as an abnormal waveform point;

[0047] Identify the zero-value points (i.e., points with an amplitude of 0) associated with the abnormal waveform points from the confirmed signal waveform. Record the band associated between the two zero-value points as the characteristic band and determine the proportion of the abnormal waveform points in the characteristic band. The proportion is calculated as: total number of abnormal waveform points ÷ total number of waveform points in the characteristic band. If the proportion is greater than 5%, the signal waveform is labeled as the interfered waveform; otherwise, no labeling is performed.

[0048] Specifically, when the relevant parameters monitored by the corresponding monitoring item fluctuate, abnormal waveforms will also occur. However, the fluctuations are usually only momentary and will not last long. Therefore, the resulting abnormal waveforms are not long. Thus, this application uses 5% to determine the fluctuation. The actual parameters can also be changed and operated by relevant personnel. By confirming the total percentage of abnormal waveform points, the corresponding interfered waveform is locked and relevant analysis is performed to confirm the interference wave.

[0049] Step 2: Identify the abnormal bands of the interfering signal waveforms. Then, based on other waveforms existing in this monitoring line, verify and analyze the other waveforms with this signal waveform. By analyzing the crossover characteristics between other waveforms and this signal waveform, the interfering waveform of this signal waveform can be identified. Specifically, when the signal waveform of the corresponding monitoring item is interfered with, it is basically because other waveforms intersect with this waveform, which will cause the abnormal band of this waveform to appear. Therefore, based on this crossover characteristic, the corresponding interfering waveform of this signal waveform can be determined and subsequently filtered out to ensure the overall monitoring stability of this monitoring line.

[0050] The specific sub-steps for locking the interference waveform of this signal waveform are as follows:

[0051] The confirmed interfered waveform is recorded as the main waveform, and the abnormal waveform points marked in the main waveform are confirmed. The band where the abnormal waveform points are located is recorded as the abnormal band. Other waveforms existing in this monitoring line are recorded as secondary waveforms.

[0052] Confirm the frequency period within the main waveform, which is the time range associated with three consecutive zero points (i.e., confirm three consecutive zero points within the main waveform, and the overall time length of the three zero points is the confirmed frequency period). Align the zero points within the secondary waveform with the zero points within the main waveform (zero points can be randomly selected, as long as they are zero points), and execute the corresponding movement process to control the secondary waveform to move forward. The movement time period must not exceed the frequency period. Determine the intersection points between the secondary and main waveforms during the movement process: confirm whether there are multiple sets of intersection points associated with a certain set of movement processes. If an abnormal band exists within the main waveform, this waveform is recorded as an interference waveform of the main signal waveform; otherwise, no calibration is performed. Specifically, due to the uncertainty of the interference situation, the existing secondary waveform needs to be moved laterally. The time range of the movement is a regular period range corresponding to the main waveform, which is also a time period corresponding to the main waveform. During the movement, the intersection points between the secondary waveform and the main waveform change in real time. When all the resulting intersection points are located within the corresponding abnormal band, the corresponding secondary waveform should constitute the interference behavior of the main waveform. Therefore, the interference waveform can be directly calibrated.

[0053] The sub-waveforms are processed sequentially, and the interference waveforms belonging to this signal waveform are confirmed and calibrated sequentially.

[0054] Step 3: Based on the multiple sets of interference waveforms confirmed by this signal waveform, identify whether the corresponding interference waveform belongs to a monitoring item. If it does not exist, filter it directly by frequency filtering. If it exists, determine the time difference characteristic sequence of multiple monitoring items and execute it. Specifically, during the interference confirmation process, there may be related interference between monitoring items. In this case, the waveforms associated with such monitoring items cannot be filtered out. In order to reduce the degree of interference and avoid the intersection of peak points, perform time difference characteristic sequence analysis and execute it so that the intersection between corresponding waveforms will not appear at the peak point. Related waveforms that do not belong to the corresponding monitoring item can be directly filtered out by determining their frequency values.

[0055] The specific method for detailed processing in step three is as follows:

[0056] Identify whether the interference waveform identified by this signal waveform is generated by other monitoring items of this monitoring line. If so, record such interference waveform as a waveform to be determined; otherwise, record such interference waveform as a waveform to be eliminated.

[0057] The frequency of the waveform to be removed is confirmed, and then the frequency is directly filtered out using a filter. The specific method for frequency confirmation is as follows:

[0058] Abnormal waveform points within the waveform to be removed are identified and calibrated. After calibration, the bands associated with three consecutive zero points are recorded as undetermined bands. From the identified undetermined bands, the undetermined bands without abnormal waveform points are recorded as calibration bands (since signal waveforms are generated periodically, the corresponding waveforms without any signal interference can be effectively identified based on previously generated related waveforms). The time length associated with the calibration band is calibrated as CD. The frequency value f associated with this waveform to be removed is confirmed using f=1÷CD. The confirmed frequency value f is then displayed. External personnel confirm this frequency value f to determine whether it can be directly filtered and removed (there is a situation where it cannot be removed, which is that the frequency value f is completely consistent with the waveform frequency generated by other monitoring items. In this case, it is necessary to adjust the waveform frequency of other monitoring items before removal. Therefore, relevant operators need to perform manual operation to filter and remove the waveform).

[0059] The time difference feature sequence of the waveform to be determined is determined by correlation. Multiple waveforms to be determined are compared with the current signal waveform by band processing. The time difference feature sequence is locked and displayed. The specific locking method is as follows:

[0060] The frequency values ​​f of different unknown waveforms or the interference waveform are confirmed, and based on the different frequency values ​​f associated with different waveforms, the waveform associated with the minimum value f is selected and recorded as the selected waveform.

[0061] Randomly determine the time interval between adjacent zero points from the selected waveform, and based on the number G of unselected waveforms, divide this time interval into (G+1) micro time intervals, and record the time point between each micro time interval as the characteristic time point.

[0062] The zero points associated with unselected waveforms are moved so that each zero point of an unselected waveform corresponds to a set of characteristic time points. During the movement, it is ensured that the peak points of the unselected and selected waveforms do not intersect with other waveforms (i.e., peak points cannot be intersection points). After selection, the initial time associated with the time period is taken as the 0 characteristic time. Then, the time difference between the subsequent different characteristic time points within the time period and the 0 characteristic time is confirmed. Starting from the 0 characteristic time, the time difference values ​​of different characteristic time points are sorted to confirm the time difference characteristic sequence. Different characteristic times within the time difference characteristic sequence are associated with different waveforms. This time difference characteristic sequence is displayed. Subsequently, the operators adjust the time of different monitoring items associated with different waveforms according to this sequence. That is, each zero point generated by a signal waveform has a corresponding time difference characteristic. Adjustments are made one by one according to this sequence to ensure that the waveform interference between multiple different monitoring items is minimized, thereby achieving the best anti-interference processing effect and ensuring the overall monitoring effect of different monitoring items.

[0063] Combination Figure 2 In this example, the waveform with the lowest frequency is selected. Based on the time interval between the corresponding zero points, it is divided into micro-time intervals to lock in two characteristic time points. Since there are two waveforms, A and B, it is sufficient to identify two characteristic time points. Then, by moving either A or B, its zero point is made to coincide with the corresponding characteristic time point, thereby offsetting the corresponding intersection point from the peak point of the corresponding waveform. This achieves the effectiveness of anti-interference for multiple different monitoring items and a better anti-interference processing effect.

[0064] Second Embodiment

[0065] A remote monitoring device for a smart power system includes:

[0066] The waveform interference analysis end identifies the characteristics of the signal waveforms associated with different monitoring items within the monitoring line. Based on the standard waveforms associated with the corresponding monitoring items, it locks out the feature value sequence and then identifies whether the signal waveforms associated with the corresponding monitoring items are interfered with based on the feature value sequence.

[0067] At the interference waveform determination end, the abnormal band of the signal waveform with interference is calibrated. Then, based on other waveforms existing in this monitoring line, the other waveforms are verified and analyzed with this signal waveform. By analyzing the cross characteristics between other waveforms and this signal waveform, the interference waveform of this signal waveform is locked.

[0068] At the interference feature processing end, based on the multiple sets of interference waveforms confirmed by this signal waveform, it identifies whether the corresponding interference waveform belongs to a monitoring item. If it does not exist, it directly filters out the frequency through the filter. If it exists, it determines the time difference feature sequence of multiple sets of monitoring items and executes it.

[0069] 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.

[0070] 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 smart power system remote monitoring method, characterized in that, Comprising the following steps: Step one, confirm the characteristics of the signal waveform associated with different monitoring items in the monitoring line, lock the characteristic value sequence from the standard waveform associated with the corresponding monitoring item, and identify whether the signal waveform associated with the corresponding monitoring item is disturbed based on the characteristic value sequence, the specific method is: Confirming the standard waveform preset for the corresponding single group of monitoring items, and marking the amplitude associated with different waveform points in the standard waveform as F i where i represents different waveform points. From the starting point of the standard waveform, confirm the change characteristics between adjacent waveform points in turn, mark the amplitude associated with the previous group of waveform points as F1, and mark the amplitude associated with the next group of waveform points as F2, adopt: change characteristics=(F2-F1)÷time interval, lock the change characteristics of adjacent waveform points, and then sort the change characteristics confirmed in turn according to the sorting method between the corresponding waveform points in the standard waveform, confirm a group of numerical sequences, and then copy the optimal group of numerical sequences of this numerical sequence to the first group, copy the original first group of numerical values of this numerical sequence to the last group, and generate the characteristic value sequence belonging to this standard waveform; Confirm the signal waveform generated by the corresponding monitoring item: use the same processing method of the above change characteristics to confirm the change characteristics of the real-time generated waveform point and the previous group of waveform points, and confirm whether this change characteristic exists in the characteristic value sequence: If it exists: take this change characteristic as the main characteristic, and continue to monitor, take the change characteristic generated by the next group of waveform points after the main characteristic as the secondary characteristic, and take the change characteristic generated by the previous group of waveforms of the main characteristic as the secondary characteristic, and generate the characteristic sequence: secondary characteristic-main characteristic-secondary characteristic, and confirm whether this characteristic sequence exists in the characteristic value sequence, if yes, do not make any marking, if no, mark this waveform point as an abnormal waveform point; If it does not exist: directly mark this waveform point as an abnormal waveform point; Confirm the zero value points associated with the abnormal waveform points before and after the confirmed signal waveform, record the wave band between the two zero value points as the characteristic wave band, and confirm the proportion of the abnormal waveform points in the characteristic wave band, the proportion=abnormal waveform point total number÷waveform point total number in the characteristic wave band, if the proportion>5%, mark this signal waveform as a disturbed waveform; Step two, mark the abnormal wave band of the signal waveform with disturbance, and then based on other waveforms existing in the monitoring line, check and analyze other waveforms and the signal waveform, lock the interference waveform of the signal waveform by analyzing the cross characteristics between other waveforms and the signal waveform; Step three, based on the multiple interference waveforms confirmed by the signal waveform, identify whether the corresponding interference waveform exists in the monitoring item, if not, directly filter out the frequency through the filter, if yes, determine the time difference characteristic sequence of multiple monitoring items and execute.

2. The remote monitoring method of a smart power system according to claim 1, wherein, If the proportion≤5%, do not make any marking.

3. The remote monitoring method of a smart power system according to claim 1, wherein, In the step two, the specific sub-step of locking the interference waveform of the signal waveform is: Mark the confirmed disturbed waveform as the main waveform, and confirm the abnormal waveform points marked in the main waveform, mark the wave band where the abnormal waveform points are as the abnormal wave band, and mark the other waveforms existing in the monitoring line as the secondary waveform; Confirm the frequency period existing in the main waveform, the frequency period is the time range associated with three consecutive zero points, the zero points existing in the secondary waveform are overlapped with the zero points in the main waveform, and the corresponding moving process is performed to move the secondary waveform backward, the moving time period should not exceed the frequency period, and the intersection points generated by the secondary waveform and the main waveform in the moving process are determined: confirm whether the intersection points associated with a group of moving processes are located in the abnormal wave band of the main waveform, if so, mark this waveform as an interference waveform of the signal waveform, otherwise, do not mark it; The secondary waveforms are processed in turn, and the interference waveforms belonging to the signal waveform are marked and confirmed in turn.

4. The remote monitoring method of a smart power system according to claim 1, wherein, In step three, identify whether the interference waveform confirmed by the signal waveform is generated by other monitoring items of the monitoring line, if so, mark such interference waveform as a to-be-determined waveform, if not, mark such interference waveform as a to-be-removed waveform.

5. The remote monitoring method of a smart power system according to claim 4, wherein, In step three, the frequency of the to-be-removed waveform is confirmed, and the frequency is directly filtered through a filter, wherein the specific way of frequency confirmation is: Confirm and mark the abnormal waveform points existing in the to-be-removed waveform, after the marking is completed, mark the wave band associated with the three consecutive zero points as a to-be-determined wave band, from the confirmed to-be-determined wave bands, mark the to-be-determined wave band without abnormal waveform points as a marked wave band, mark the time length associated with the marked wave band as CD, confirm the frequency value f associated with the to-be-removed waveform by f=1÷CD, and then display the confirmed frequency value f, and an external person confirms whether the frequency value f can be directly filtered and removed.

6. The remote monitoring method of a smart power system according to claim 4, wherein, In step three, the correlation of the time difference characteristic sequence of the to-be-determined waveform is determined, the wave bands of the to-be-determined waveforms and the signal waveform are processed, the time difference characteristic sequence is locked and displayed, wherein the specific processing way of locking is: Confirm the frequency value f of different to-be-determined waveforms or the signal waveform, and select the waveform associated with the minimum value f based on the different frequency values f associated with different waveforms, and mark this waveform as a selected waveform; Randomly determine the time period between adjacent zero points in the selected waveform, and divide the time period into (G+1) micro time periods based on the number G of unselected waveforms, and mark the time points between each micro time period as characteristic time points; Move the zero points associated with the unselected waveforms so that the zero points of each unselected waveform correspond to a group of characteristic time points, and ensure that the peak points of the unselected waveforms and the selected waveform do not intersect with other waveforms during the moving process, and after the selection is completed, the initial time associated with the time period is marked as 0 characteristic time, and then the time difference values between the subsequent different characteristic time points and the 0 characteristic time in the time period are confirmed, the time difference values of different characteristic time points are sorted from the 0 characteristic time, and the time difference characteristic sequence is confirmed.

7. A smart power system remote monitoring device, which operates according to the smart power system remote monitoring method of any one of claims 1-6, characterized in that, It includes: The waveform interference analysis end confirms the characteristics of the signal waveforms associated with different monitoring items in the monitoring line, locks the characteristic value sequence based on the standard waveform associated with the corresponding monitoring item, and identifies whether the signal waveform associated with the corresponding monitoring item is interfered based on the characteristic value sequence; The interference waveform determination end labels the abnormal wave band of the signal waveform with interference, and then checks and analyzes the signal waveform with other waveforms existing in the monitoring line. By analyzing the cross characteristics between the other waveforms and the signal waveform, the interference waveform of the signal waveform is locked. The interference characteristic processing end identifies whether the corresponding interference waveform exists in the monitoring item based on the multiple groups of interference waveforms confirmed by the signal waveform. If not, the frequency is directly filtered through the filter. If so, the time difference characteristic sequence of multiple groups of monitoring items is determined and executed.

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