A data analysis method for monitoring hidden dangers of distribution network cables

Abstract

The present invention relates to the field of data processing technology, and in particular to a data analysis method for monitoring hidden dangers of distribution network cables. The method constructs a scatter plot according to cable sheath circulation values ​​in a historical period; constructs a sliding window in the scatter plot, obtains the complexity according to the density of data points in the sliding window, adjusts the length of the sliding window according to the complexity and the difference between every two data points in the sliding window, and obtains a final fitting window; repeats the step of obtaining the final fitting window according to the sliding step length until the final fitting window contains data at the last sampling moment in the scatter plot; fits the data points in each final fitting window to obtain a final fitting value, obtains the abnormality of the real-time cable sheath circulation value according to the real-time cable sheath circulation value and the final fitting value, and gives an early warning to the cable according to the abnormality, thereby improving the accuracy of hidden danger monitoring of the cable.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a data analysis method for monitoring hidden dangers of distribution network cables. Background Art

[0002] Distribution cables are usually made of several or several groups of wires twisted together. Each wire is insulated and covered with a high degree of insulation. In the entire power operation system, it mainly plays the role of transmitting and distributing power. Cable lines have the advantages of high power supply reliability, good safety, and no ground space. They have become the main direction of urban distribution network construction and are gradually widely used in power grids of industrial and mining enterprises such as steel, petrochemical, and coal mines. When the cable is normally energized, a circulating current will be induced on the metal sheath of the cable. Under normal circumstances, the sheath circulating current is very small. When the insulation of the cable outer sheath decreases or the grounding is poor, the sheath circulating current will increase abnormally, posing a threat to the safety of cable operation. The sheath circulating current sensor is installed on the metal shielding layer grounding wire of the cable terminal to collect the sheath circulating current signal, which is then sent to the monitoring and positioning device. By monitoring the long-term change trend of the sheath circulating current, a trend alarm is issued to ensure the safe operation of the cable.

[0003] When warning the sheath circulation data under the traditional method, the sheath circulation data of the past few days is fitted through a fixed-size window to obtain the abnormal data of the sheath circulation. However, if the window is too large or too small, it will cause under-fitting of the data points, resulting in inaccurate acquisition of abnormal data, untimely warning response of the monitoring system, and low monitoring accuracy.

[0004] Therefore, how to obtain the adaptive window and fit the sheath circulation data in the adaptive window to obtain abnormal data to improve the accuracy of the monitoring system in monitoring the safety of the cable has become an urgent problem to be solved. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a data analysis method for monitoring hidden dangers of distribution network cables to solve the problem of how to obtain an adaptive window, fit the sheath circulation data in the adaptive window to obtain abnormal data, so as to improve the accuracy of the monitoring system in monitoring the safety of the cable.

[0006] An embodiment of the present invention provides a data analysis method for monitoring hidden dangers of distribution network cables, the method comprising the following steps:

[0007] Obtain the cable sheath circulating current value of the distribution network cable at each sampling moment within a preset number of cycles in a historical period, obtain the sheath circulating current data sequence corresponding to each cycle, and map the sheath circulating current data sequence corresponding to each cycle to the same scatter plot, where the abscissa of the scatter plot is the sampling moment and the ordinate is the cable sheath circulating current value;

[0008] Constructing a sliding window of a preset size in the scatter plot, obtaining the complexity of the sliding window according to the density of data points in the sliding window, and adjusting the length of the sliding window according to the complexity and the difference between every two data points in the sliding window to obtain a final fitting window;

[0009] The length of the final fitting window is used as a sliding step length, and the step of obtaining the final fitting window is repeated until the final fitting window contains the data point at the last sampling time in the scatter plot, thereby obtaining all the final fitting windows;

[0010] Fit the data points contained in each of the final fitting windows to obtain the final fitting value at each sampling moment contained in each of the final fitting windows, obtain the real-time cable sheath circulating current value, and obtain the abnormality degree of the real-time cable sheath circulating current value according to the real-time cable sheath circulating current value and the final fitting value at the sampling moment of the real-time cable sheath circulating current value, and issue a hidden danger warning for the distribution network cable according to the abnormality degree.

[0011] Further, obtaining the complexity of the sliding window according to the density of data points in the sliding window includes:

[0012] In the sliding window, construct a minimum convex polygon including all data points in the sliding window, and calculate the ratio between the area of ​​the minimum convex polygon and the area of ​​the sliding window to obtain a first ratio;

[0013] Obtaining the number of all data points contained in the sliding window, calculating the ratio between the number of all data points and the area of ​​the minimum convex polygon, and obtaining a second ratio;

[0014] The first ratio and the second ratio are weightedly summed to obtain the complexity of the sliding window.

[0015] Furthermore, the length of the sliding window is adjusted according to the complexity and the difference between every two data points in the sliding window to obtain a final fitting window, including:

[0016] If the complexity is less than or equal to a preset complexity threshold, the length of the sliding window is increased by a preset length value to obtain a final fitting window;

[0017] If the complexity is greater than the preset complexity threshold, all data points in the sliding window are fitted to obtain fitting points corresponding to each data point contained in the sliding window. According to the difference between every two fitting points in the sliding window, the length of the sliding window is adjusted to obtain a final fitting window.

[0018] Further, adjusting the length of the sliding window according to the difference between every two fitting points in the sliding window to obtain a final fitting window includes:

[0019] For any data point in the sliding window, obtain the left adjacent fitting point and the right adjacent fitting point of the fitting point corresponding to the data point, calculate the absolute value of the slope between the fitting point corresponding to the data point and the left adjacent fitting point to obtain a first absolute value slope, calculate the absolute value of the slope between the fitting point corresponding to the data point and the right adjacent fitting point to obtain a second absolute value slope, and calculate the absolute value of the slope between the left adjacent fitting point and the right adjacent fitting point to obtain a third absolute value slope;

[0020] Among the first absolute value slope and the second absolute value slope, select the maximum value as the maximum slope value, calculate the absolute value of the difference between the maximum slope value and the third absolute value slope, normalize the absolute value of the difference, and obtain the fitting difference degree of the data point;

[0021] The fitting difference degree of each data point in the sliding window is obtained, and the noise points contained in the sliding window are obtained according to the fitting difference degree of each data point and the fitting point corresponding to each data point. The length of the sliding window is adjusted according to the number of the noise points to obtain a final fitting window.

[0022] Furthermore, obtaining the noise points contained in the sliding window according to the fitting difference degree of each of the data points and the fitting point corresponding to each of the data points includes:

[0023] Calculating the absolute value of the difference between each data point in the sliding window and the corresponding fitting point to obtain a fitting error, and normalizing the fitting error corresponding to each data point in the sliding window to obtain a corresponding normalized fitting error value;

[0024] For any data point in the sliding window, a weighted sum is performed on the normalized value of the fitting error of the data point and the degree of fitting difference of the data point to obtain a noise possibility index of the data point. If the noise possibility index of the data point is greater than a preset noise possibility threshold, the data point is regarded as a noise point.

[0025] Furthermore, the adjusting the length of the sliding window according to the number of the noise points to obtain a final fitting window includes:

[0026] Obtaining the number of all data points contained in the sliding window, calculating the ratio between the number of the noise points and the number of all data points, and obtaining the noise ratio of the sliding window;

[0027] If the noise ratio is within a preset noise ratio range, taking the sliding window as a final fitting window;

[0028] If the noise ratio is less than the minimum value in the preset noise ratio range, reducing the length of the sliding window by the preset length value to obtain a final fitting window;

[0029] If the noise ratio is greater than the maximum value in the preset noise ratio range, the length of the sliding window is increased by the preset length value to obtain a final fitting window.

[0030] Furthermore, fitting the data points contained in each of the final fitting windows to obtain the final fitting value at each sampling moment contained in each of the final fitting windows includes:

[0031] The noise points in each of the final fitting windows are removed, and the data points in each of the final fitting windows after the noise points are removed are fitted to obtain the final fitting value at each sampling moment contained in each of the final fitting windows.

[0032] Further, the abnormality degree of the real-time cable sheath circulating current value is obtained according to the real-time cable sheath circulating current value and the final fitting value at the sampling time of the real-time cable sheath circulating current value, including:

[0033] Calculate the absolute value of the difference between the final fitting value at the sampling time of the real-time cable sheath circulating current value and the real-time cable sheath circulating current value to obtain the real-time difference degree;

[0034] In the cycle where the real-time cable sheath circulating current value is located, for any sampling moment before the sampling moment where the real-time cable sheath circulating current value is located, calculate the absolute value of the difference between the cable sheath circulating current value at the sampling moment and the final fitting value at the sampling moment to obtain the historical difference degree, obtain the historical difference degree corresponding to each sampling moment before the sampling moment where the real-time cable sheath circulating current value is located, and obtain the average historical difference degree;

[0035] The absolute value of the difference between the real-time difference degree and the average historical difference degree is calculated to obtain the abnormality degree of the real-time cable sheath circulating current value.

[0036] Furthermore, the step of providing a hidden danger warning to the distribution network cable according to the abnormality degree includes:

[0037] If the abnormality level is greater than a preset abnormality level warning parameter, an abnormality warning is issued for the distribution network cable.

[0038] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0039] The present invention obtains the cable sheath circulation value of the distribution network cable at each sampling moment within a preset number of cycles in a historical period, obtains the sheath circulation data sequence corresponding to each cycle, and maps the sheath circulation data sequence corresponding to each cycle to the same scatter plot, wherein the abscissa of the scatter plot is the sampling moment, and the ordinate is the cable sheath circulation value; constructs a sliding window of a preset size in the scatter plot, obtains the complexity of the sliding window according to the density of data points in the sliding window, and adjusts the length of the sliding window according to the complexity and the difference between every two data points in the sliding window to obtain a final fitting window; .... The length of the final fitting window is used as the sliding step length, and the acquisition step of the final fitting window is repeated until the final fitting window contains the data point at the last sampling time in the scatter plot, and all the final fitting windows are obtained; the data points contained in each of the final fitting windows are fitted to obtain the final fitting value at each sampling time contained in each of the final fitting windows, and the real-time cable sheath circulation value is obtained. According to the real-time cable sheath circulation value and the final fitting value at the sampling time where the real-time cable sheath circulation value is located, the abnormality of the real-time cable sheath circulation value is obtained, and the hidden danger warning of the distribution network cable is carried out according to the abnormality. Among them, a sliding window of a preset size is constructed for the historical sheath circulation data, and the length of the sliding window is adjusted according to the complexity of the data points in the sliding window to obtain the final fitting window, so as to increase the number of data points in the sliding window with a small complexity and reduce the fitting error caused by the noise points in the sliding window with a large complexity, so as to obtain a better fitting effect, fit the data points in the final fitting window, and calculate the abnormality of the real-time interlayer circulation value according to the fitting result, so as to carry out abnormal warning for the distribution network cable and improve the accuracy of safety monitoring of the cable. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0041] Figure 1It is a method flow chart of a data analysis method for distribution network cable hidden danger monitoring provided by Example 1 of the present invention. DETAILED DESCRIPTION

[0042] Embodiments of the present disclosure are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, but should not be understood as limiting the present disclosure.

[0043] It should be noted that the terms "first", "second", etc. in the specification of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0044] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0045] See also Figure 1 , is a method flow chart of a data analysis method for monitoring hidden dangers of distribution network cables provided in the first embodiment of the present invention, such as Figure 1 As shown, the method may include:

[0046] Step S101, obtain the cable sheath circulating current value of the distribution network cable at each sampling moment within a preset number of cycles within a historical period, obtain the sheath circulating current data sequence corresponding to each cycle, and map the sheath circulating current data sequence corresponding to each cycle to the same scatter plot, where the horizontal axis of the scatter plot is the sampling moment, and the vertical axis is the cable sheath circulating current value.

[0047] Since the size of the cable sheath circulation of the distribution network cable is affected by many factors, including environmental conditions such as temperature and humidity, which will change the size of the sheath circulation. Therefore, when issuing an early warning for the circulation data of the day, the circulation data of a certain period of time does not have a reference value. Therefore, it is necessary to comprehensively consider the historical data of the sheath circulation for many days in the past. In order to facilitate the subsequent fitting of the historical data of the sheath circulation, the historical data of the sheath circulation for many days collected by the sensor are mapped to the same scatter plot.

[0048] Set the preset number to 3, and each cycle is 1 day. There is no restriction here. The implementer can obtain the cable sheath circulation value of each day in the past 3 days according to the implementation scenario settings, and obtain the corresponding sheath circulation data sequence for each day. The sheath circulation data sequence corresponding to the 3 days is mapped to the same scatter plot, where the ordinate of the scatter plot is the cable sheath circulation value, and the abscissa is the sampling time of each day.

[0049] Step S102, constructing a sliding window of a preset size in the scatter plot, obtaining the complexity of the sliding window according to the density of data points in the sliding window, and adjusting the length of the sliding window according to the complexity and the difference between every two data points in the sliding window to obtain a final fitting window.

[0050] Because when fitting a fixed-size window of data points in the traditional way, the data in the window is often under-fitted due to the small window size or the small number of data points in the window, or the data in the window is over-fitted due to the high complexity of the data points in the window, therefore, in the example of the present invention, the size of the window is adaptively changed according to the distribution of data points in the scatter plot, so that the abnormal warning of real-time sheath circulation data is more accurate.

[0051] First, a sliding window of preset size is constructed in the scatter plot, where the length of the sliding window is set to 5. There is no restriction here. The implementer can adjust the sliding window according to the implementation scenario. The height of the sliding window is the absolute value of the difference between the maximum and minimum values ​​in the collected sheath circulation data. The sliding window contains the data points at the first sampling time in the scatter plot. Then, the size of the sliding window is adjusted according to the data points in the sliding window to obtain the final fitting window.

[0052] The specific process of adjusting the size of the sliding window according to the data points in the sliding window to obtain the final fitting window is as follows:

[0053] (1) According to the density of data points in the sliding window, the complexity of the sliding window is obtained.

[0054] Specifically, within the sliding window, a minimum convex polygon including all data points within the sliding window is constructed, and a ratio between an area of ​​the minimum convex polygon and an area of ​​the sliding window is calculated to obtain a first ratio;

[0055] Obtaining the number of all data points contained in the sliding window, calculating the ratio between the number of all data points and the area of ​​the minimum convex polygon, and obtaining a second ratio;

[0056] The first ratio and the second ratio are weightedly summed to obtain the complexity of the sliding window.

[0057] In one implementation, the calculation formula for the complexity of the sliding window is:

[0058]

[0059] Where D represents the complexity of the sliding window. represents the area of ​​the smallest convex polygon, represents the area of ​​the sliding window, n represents the number of all data points contained in the sliding window, represents the first weight, Represents the second weight.

[0060] It should be noted that setting The value of is 0.5, The value is 0.5. There is no limit here. The implementer can set it according to the implementation scenario. The larger the value of , the larger the area of ​​the minimum convex polygon. The larger the value of is, the more discrete the distribution of data points in the sliding window is, and the larger the value of D is, the more complex the sliding window is; the larger the value of n is, the more data points are contained in the sliding window, and the larger the value of D is, the more complex the sliding window is.

[0061] (2) According to the complexity and the difference between every two data points in the sliding window, the length of the sliding window is adjusted to obtain a final fitting window.

[0062] If the complexity is less than or equal to a preset complexity threshold, the length of the sliding window is increased by a preset length value to obtain a final fitting window;

[0063] If the complexity is greater than the preset complexity threshold, all data points in the sliding window are fitted to obtain fitting points corresponding to each data point contained in the sliding window. According to the difference between every two fitting points in the sliding window, the length of the sliding window is adjusted to obtain a final fitting window.

[0064] In one implementation, the preset complexity threshold is set to 0.4, and the preset length value is 2. There is no restriction here, and the implementer can set it according to the implementation scenario. If the complexity of the sliding window is less than or equal to 0.4, it means that the number of data points in the sliding window is small, and the distribution is relatively concentrated, and the stability is strong. In this case, underfitting will occur. In order to obtain a better fitting effect, the number of data points in the sliding window needs to be increased to increase the complexity of the sliding window. Therefore, the length of the sliding window is increased by 2 along the positive direction of the x-axis of the scatter plot. At this time, the length of the sliding window is 7. At this time, the number of data points in the sliding window increases, and the sliding window at this time is used as the final fitting window.

[0065] If the complexity of the sliding window is greater than 0.4, it means that the number of data points in the sliding window is large, the distribution is relatively discrete, and the probability of containing noise points is high. It is necessary to perform polynomial fitting on each data point in the sliding window by the least squares method to obtain the fitting point corresponding to each data point contained in the sliding window and the fitting value of each fitting point. According to the difference between every two fitting points in the sliding window, the length of the sliding window is adjusted to obtain the final fitting window. Among them, polynomial fitting by the least squares method is a prior art and will not be repeated here. The method for obtaining the final fitting window is:

[0066] For any data point in the sliding window, obtain the left adjacent fitting point and the right adjacent fitting point of the fitting point corresponding to the data point, calculate the absolute value of the slope between the fitting point corresponding to the data point and the left adjacent fitting point to obtain a first absolute value slope, calculate the absolute value of the slope between the fitting point corresponding to the data point and the right adjacent fitting point to obtain a second absolute value slope, and calculate the absolute value of the slope between the left adjacent fitting point and the right adjacent fitting point to obtain a third absolute value slope;

[0067] Among the first absolute value slope and the second absolute value slope, select the maximum value as the maximum slope value, calculate the absolute value of the difference between the maximum slope value and the third absolute value slope, normalize the absolute value of the difference, and obtain the fitting difference degree of the data point;

[0068] The fitting difference degree of each data point in the sliding window is obtained, and the noise points contained in the sliding window are obtained according to the fitting difference degree of each data point and the fitting point corresponding to each data point. The length of the sliding window is adjusted according to the number of the noise points to obtain a final fitting window.

[0069] In one embodiment, since the fitting curve may fluctuate or turn sharply due to the interference of noise points, the difference between the slope of the fitting point corresponding to each data point in its neighborhood and the slope of the overall fitting curve is calculated so as to filter out the noise points according to the difference.

[0070] For the i-th data point in the sliding window, the calculation formula for the fitting difference degree of the i-th data point is:

[0071]

[0072] in, represents the degree of fitting difference of the i-th data point, It represents the slope between the fitting point corresponding to the sampling time before the sampling time where the ith data point is located (the fitting point to the left of the fitting point corresponding to the ith data point) and the fitting point corresponding to the ith data point. It represents the slope between the fitting point corresponding to the ith data point and the fitting point corresponding to the next sampling moment after the sampling moment where the ith data point is located (the fitting point to the right of the fitting point corresponding to the ith data point). It represents the slope between the left neighboring fitting point of the fitting point corresponding to the i-th data point and the right neighboring fitting point of the fitting point corresponding to the i-th data point. represents the absolute value symbol, Indicates taking the maximum value, It means to find the difference. represents the linear normalization function.

[0073] It should be noted that That is, the first absolute value slope, which represents the slope of the fitting point corresponding to the i-th data point in its left neighborhood. That is, the second absolute value slope, which represents the slope of the fitting point corresponding to the i-th data point in its right neighborhood. That is, the third absolute value slope, which represents the slope of the overall fitting curve of the fitting point corresponding to the i-th data point. It represents the maximum value of the first absolute value slope and the second absolute value slope, that is, the maximum slope value. The greater the difference between the maximum slope value and the third absolute value slope, the more it indicates that there is a sharp fluctuation or turning point at the fitting point corresponding to the i-th data point. The larger the value of , the greater the possibility that the i-th data point is a noise point.

[0074] Similarly, the fitting difference degree of each data point in the sliding window is obtained. Further, according to the fitting difference degree of each data point and the fitting value of the fitting point corresponding to each data point, the noise points contained in the sliding window are obtained. The method for obtaining the noise points in the sliding window is:

[0075] Calculating the absolute value of the difference between each data point in the sliding window and the corresponding fitting point to obtain a fitting error, and normalizing the fitting error corresponding to each data point in the sliding window to obtain a corresponding normalized fitting error value;

[0076] For any data point in the sliding window, a weighted sum is performed on the normalized value of the fitting error of the data point and the degree of fitting difference of the data point to obtain a noise possibility index of the data point. If the noise possibility index of the data point is greater than a preset noise possibility threshold, the data point is regarded as a noise point.

[0077] In one embodiment, for the i-th data point in the sliding window, the calculation formula for the normalized value of the fitting error of the i-th data point is:

[0078]

[0079] in, represents the normalized value of the fitting error of the ith data point, Represents the ordinate value of the i-th data point, represents the fitting value of the fitting point corresponding to the i-th data point, K represents the number of all data points in the sliding window, Represents the absolute value symbol.

[0080] It should be noted that is the fitting error, The larger the value of is, the greater the difference between the i-th data point and the fitting point corresponding to the i-th data point is. The larger the value of , the greater the possibility that the i-th data point is a noise point.

[0081] According to the normalized value of the fitting error of the ith data point and the degree of fitting difference of the ith data point, the noise possibility index of the ith data point is obtained. The calculation formula of the noise possibility index of the ith data point is:

[0082]

[0083] in, represents the noise likelihood indicator of the ith data point, represents the normalized value of the fitting error of the ith data point, represents the degree of fitting difference of the i-th data point, represents the first weight coefficient, Represents the second weight coefficient.

[0084] It should be noted that setting The value of is 0.5, The value of is 0.5, which is not limited here and can be set by the implementer according to the implementation scenario; The larger the value of is, the greater the difference between the i-th data point and the fitting point corresponding to the i-th data point is. The larger the value of , the greater the possibility that the i-th data point is a noise point; The larger the value of is, the greater the sharp fluctuation or turning point occurs at the fitting point corresponding to the i-th data point. The larger the value of , the greater the possibility that the i-th data point is a noise point.

[0085] The preset noise point possibility threshold is set to 0.7. There is no restriction here. The implementer can set it according to the implementation scenario. If the noise possibility index of the i-th data point is greater than 0.7, the i-th data point is regarded as a noise point.

[0086] Similarly, the noise possibility index of each data point contained in the sliding window is obtained to obtain all the noise points in the sliding window, and then the length of the sliding window is adjusted according to the number of noise points to obtain the final fitting window. The specific adjustment method is:

[0087] Obtaining the number of all data points contained in the sliding window, calculating the ratio between the number of the noise points and the number of all data points, and obtaining the noise ratio of the sliding window;

[0088] If the noise ratio is within a preset noise ratio range, taking the sliding window as a final fitting window;

[0089] If the noise ratio is less than the minimum value in the preset noise ratio range, reducing the length of the sliding window by the preset length value to obtain a final fitting window;

[0090] If the noise ratio is greater than the maximum value in the preset noise ratio range, the length of the sliding window is increased by the preset length value to obtain a final fitting window.

[0091] In one embodiment, the ratio between the number of noise points contained in the sliding window and the number of all data points contained in the sliding window is calculated to obtain the noise ratio of the sliding window. The preset noise ratio range is set to [0.1, 0.3], which is not limited here, and the implementer can adjust it according to the implementation scenario. If the noise ratio of the sliding window is within [0.1, 0.3], that is, the noise ratio of the sliding window is greater than or equal to 0.1 and less than or equal to 0.3, the length of the sliding window is not adjusted, and the sliding window is used as the final fitting window; if the noise ratio of the sliding window is less than 0.1, it means that there are fewer noise points in the sliding window and the data points in the sliding window are relatively stable. In order to amplify the local features of the data points, it is necessary to reduce the length of the sliding window by 2 to obtain the final fitting window; if the noise ratio of the sliding window is greater than 0.3, it means that there are more noise points in the sliding window and fewer real data points. In order to increase the amount of real data, it is necessary to increase the length of the sliding window by 2 to obtain the final fitting window.

[0092] Step S103, taking the length of the final fitting window as the sliding step, repeating the step of obtaining the final fitting window until the final fitting window contains the data point at the last sampling time in the scatter plot, and obtaining all the final fitting windows.

[0093] According to step S102, the first final fitting window in the scatter plot is obtained, and then the length of the first final fitting window is used as the sliding step length, the sliding window is moved using the sliding step length, and the second final fitting window is obtained according to the method for obtaining the first final fitting window. After that, the length of the second final fitting window is used as the new sliding step length, the sliding window is moved using the new sliding step length, and the third final fitting window is obtained according to the method for obtaining the first final fitting window, and so on, until the final fitting window contains the data point at the last sampling moment in the scatter plot. At this time, the traversal of the sliding window is completed, and multiple final fitting windows containing all the data points in the scatter plot are obtained, which are used to fit the data points in each final fitting window to improve the accuracy of fitting.

[0094] In particular, when the sliding window contains the last data point in the scatter plot, the length of the sliding window is not adjusted and the sliding window is used as the final fitting window.

[0095] Step S104, fit the data points contained in each of the final fitting windows to obtain the final fitting value at each sampling moment contained in each of the final fitting windows, obtain the real-time cable sheath circulating current value, and obtain the abnormality degree of the real-time cable sheath circulating current value according to the real-time cable sheath circulating current value and the final fitting value at the sampling moment of the real-time cable sheath circulating current value, and issue a hidden danger warning for the distribution network cable according to the abnormality degree.

[0096] After all the final fitting windows are obtained, the data points in each final fitting window can be fitted with a polynomial by the least squares method to obtain the final fitting value of each sampling moment included in each final fitting window. The specific method for obtaining the final fitting value of each sampling moment is as follows:

[0097] The noise points in each of the final fitting windows are removed, and the data points in each of the final fitting windows after the noise points are removed are fitted to obtain the final fitting value at each sampling moment contained in each of the final fitting windows.

[0098] It should be noted that since the noise points in the final fitting window will increase the fitting error, the data points in each final fitting window after the noise points are removed are fitted to obtain a better fitting effect.

[0099] Furthermore, after obtaining the final fitting value at each sampling moment, the final fitting value at each sampling moment can be used as a standard for abnormality detection to detect real-time abnormalities of distribution network cables, wherein the method for detecting real-time abnormalities of distribution network cables is as follows: obtaining the real-time cable sheath circulating current value, obtaining the abnormality degree of the real-time cable sheath circulating current value according to the final fitting value at the sampling moment of the real-time cable sheath circulating current value, and setting the preset abnormality degree warning parameter to 1.5, which is not limited here and can be set by the implementer according to the implementation scenario. If the abnormality degree of the real-time cable sheath circulating current value is greater than 1.5, an abnormality warning is issued for the distribution network cable.

[0100] Among them, the specific method of obtaining the abnormal degree of the real-time cable sheath circulating current value is:

[0101] Calculate the absolute value of the difference between the final fitting value at the sampling time of the real-time cable sheath circulating current value and the real-time cable sheath circulating current value to obtain the real-time difference degree;

[0102] In the cycle where the real-time cable sheath circulating current value is located, for any sampling moment before the sampling moment where the real-time cable sheath circulating current value is located, calculate the absolute value of the difference between the cable sheath circulating current value at the sampling moment and the final fitting value at the sampling moment to obtain the historical difference degree, obtain the historical difference degree corresponding to each sampling moment before the sampling moment where the real-time cable sheath circulating current value is located, and obtain the average historical difference degree;

[0103] The absolute value of the difference between the real-time difference degree and the average historical difference degree is calculated to obtain the abnormality degree of the real-time cable sheath circulating current value.

[0104] In one embodiment, assuming that the real-time cable sheath circulating current value is the jth sampling moment of the day, the calculation formula for the abnormality degree of the real-time cable sheath circulating current value is:

[0105]

[0106] in, Indicates the abnormality of the real-time cable sheath circulating current value, that is, the abnormality at the jth sampling moment of the day. It represents the cable sheath circulating current value at the jth sampling moment of the day, that is, the real-time cable sheath circulating current value. It represents the final fitting value at the jth sampling moment, that is, the final fitting value at the sampling moment of the real-time cable sheath circulating current value. Indicates the cable sheath circulating current value at the tth sampling time before the real-time cable sheath circulating current value. It represents the final fitting value at the tth sampling time before the sampling time of the real-time cable sheath circulating current value. Represents the absolute value symbol.

[0107] It should be noted that That is, the real-time difference degree, That is, the degree of historical difference, That is, the average historical difference degree. The greater the difference between the real-time difference degree and the average historical difference degree, the greater the probability of abnormality at the jth sampling moment of the day, that is, the greater the probability of abnormality in the real-time cable sheath circulating current value. The larger the value is, the greater the abnormality of the real-time cable sheath circulating current value is.

[0108] It is worth noting that the focus of the present invention is how to adaptively adjust the size of the preset window according to the collected historical cable sheath circulation data to obtain the final fitting window, and perform fitting according to the data in the final fitting window to obtain the abnormality of the real-time cable sheath circulation data. It is a prior art to carry out early warning of hidden dangers of distribution network cables according to the abnormality of the real-time cable sheath circulation data, which will not be described in detail here.

[0109] In summary, the present invention obtains the cable sheath circulation value of the distribution network cable at each sampling moment within a preset number of cycles within a historical period, obtains the sheath circulation data sequence corresponding to each cycle, and maps the sheath circulation data sequence corresponding to each cycle to the same scatter plot, wherein the horizontal axis of the scatter plot is the sampling moment, and the vertical axis is the cable sheath circulation value; constructs a sliding window of a preset size in the scatter plot, obtains the complexity of the sliding window according to the density of data points in the sliding window, and adjusts the length of the sliding window according to the complexity and the difference between every two data points in the sliding window to obtain a final fitting window; The length of the final fitting window is used as the sliding step length, and the acquisition step of the final fitting window is repeated until the final fitting window contains the data point at the last sampling time in the scatter plot, and all the final fitting windows are obtained; the data points contained in each of the final fitting windows are fitted to obtain the final fitting value at each sampling time contained in each of the final fitting windows, and the real-time cable sheath circulation value is obtained. According to the real-time cable sheath circulation value and the final fitting value at the sampling time where the real-time cable sheath circulation value is located, the abnormality of the real-time cable sheath circulation value is obtained, and the hidden danger warning of the distribution network cable is carried out according to the abnormality. Among them, a sliding window of a preset size is constructed for the historical sheath circulation data, and the length of the sliding window is adjusted according to the complexity of the data points in the sliding window to obtain the final fitting window, so as to increase the number of data points in the sliding window with a small complexity and reduce the fitting error caused by the noise points in the sliding window with a large complexity, so as to obtain a better fitting effect, fit the data points in the final fitting window, and calculate the abnormality of the real-time interlayer circulation value according to the fitting result, so as to carry out abnormal warning for the distribution network cable and improve the accuracy of safety monitoring of the cable.

[0110] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A data analysis method for monitoring hidden dangers of distribution network cables, characterized in that: The data analysis method for monitoring hidden dangers of distribution network cables comprises: Obtain the cable sheath circulating current value of the distribution network cable at each sampling moment within a preset number of cycles in a historical period, obtain the sheath circulating current data sequence corresponding to each cycle, and map the sheath circulating current data sequence corresponding to each cycle to the same scatter plot, where the abscissa of the scatter plot is the sampling moment and the ordinate is the cable sheath circulating current value; Constructing a sliding window of a preset size in the scatter plot, obtaining the complexity of the sliding window according to the density of data points in the sliding window, and adjusting the length of the sliding window according to the complexity and the difference between every two data points in the sliding window to obtain a final fitting window; The length of the final fitting window is used as a sliding step length, and the step of obtaining the final fitting window is repeated until the final fitting window contains the data point at the last sampling time in the scatter plot, thereby obtaining all the final fitting windows; Fit the data points contained in each of the final fitting windows to obtain the final fitting value at each sampling moment contained in each of the final fitting windows, obtain the real-time cable sheath circulating current value, and obtain the abnormality degree of the real-time cable sheath circulating current value according to the real-time cable sheath circulating current value and the final fitting value at the sampling moment of the real-time cable sheath circulating current value, and issue a hidden danger warning for the distribution network cable according to the abnormality degree.

2. A data analysis method for monitoring hidden dangers of distribution network cables according to claim 1, characterized in that: The obtaining the complexity of the sliding window according to the density of the data points in the sliding window includes: In the sliding window, construct a minimum convex polygon including all data points in the sliding window, and calculate the ratio between the area of ​​the minimum convex polygon and the area of ​​the sliding window to obtain a first ratio; Obtaining the number of all data points contained in the sliding window, calculating the ratio between the number of all data points and the area of ​​the minimum convex polygon, and obtaining a second ratio; The first ratio and the second ratio are weightedly summed to obtain the complexity of the sliding window.

3. A data analysis method for monitoring hidden dangers of distribution network cables according to claim 1, characterized in that: The step of adjusting the length of the sliding window according to the complexity and the difference between every two data points in the sliding window to obtain a final fitting window comprises: If the complexity is less than or equal to a preset complexity threshold, the length of the sliding window is increased by a preset length value to obtain a final fitting window; If the complexity is greater than the preset complexity threshold, all data points in the sliding window are fitted to obtain fitting points corresponding to each data point contained in the sliding window. According to the difference between every two fitting points in the sliding window, the length of the sliding window is adjusted to obtain a final fitting window.

4. A data analysis method for monitoring hidden dangers of distribution network cables according to claim 3, characterized in that: The step of adjusting the length of the sliding window according to the difference between every two fitting points in the sliding window to obtain a final fitting window comprises: For any data point in the sliding window, obtain the left adjacent fitting point and the right adjacent fitting point of the fitting point corresponding to the data point, calculate the absolute value of the slope between the fitting point corresponding to the data point and the left adjacent fitting point to obtain a first absolute value slope, calculate the absolute value of the slope between the fitting point corresponding to the data point and the right adjacent fitting point to obtain a second absolute value slope, and calculate the absolute value of the slope between the left adjacent fitting point and the right adjacent fitting point to obtain a third absolute value slope; Among the first absolute value slope and the second absolute value slope, select the maximum value as the maximum slope value, calculate the absolute value of the difference between the maximum slope value and the third absolute value slope, normalize the absolute value of the difference, and obtain the fitting difference degree of the data point; The fitting difference degree of each data point in the sliding window is obtained, and the noise points contained in the sliding window are obtained according to the fitting difference degree of each data point and the fitting point corresponding to each data point. The length of the sliding window is adjusted according to the number of the noise points to obtain a final fitting window.

5. A data analysis method for monitoring hidden dangers of distribution network cables according to claim 4, characterized in that: The obtaining of the noise points contained in the sliding window according to the fitting difference degree of each of the data points and the fitting point corresponding to each of the data points comprises: Calculating the absolute value of the difference between each data point in the sliding window and the corresponding fitting point to obtain a fitting error, and normalizing the fitting error corresponding to each data point in the sliding window to obtain a corresponding normalized fitting error value; For any data point in the sliding window, a weighted sum is performed on the normalized value of the fitting error of the data point and the degree of fitting difference of the data point to obtain a noise possibility index of the data point. If the noise possibility index of the data point is greater than a preset noise possibility threshold, the data point is regarded as a noise point.

6. A data analysis method for monitoring hidden dangers of distribution network cables according to claim 4, characterized in that: The step of adjusting the length of the sliding window according to the number of noise points to obtain a final fitting window includes: Obtaining the number of all data points contained in the sliding window, calculating the ratio between the number of the noise points and the number of all data points, and obtaining the noise ratio of the sliding window; If the noise ratio is within a preset noise ratio range, taking the sliding window as a final fitting window; If the noise ratio is less than the minimum value in the preset noise ratio range, reducing the length of the sliding window by the preset length value to obtain a final fitting window; If the noise ratio is greater than the maximum value in the preset noise ratio range, the length of the sliding window is increased by the preset length value to obtain a final fitting window.

7. A data analysis method for monitoring hidden dangers of distribution network cables according to claim 4, characterized in that: The step of fitting the data points contained in each of the final fitting windows to obtain a final fitting value at each sampling moment contained in each of the final fitting windows comprises: The noise points in each of the final fitting windows are removed, and the data points in each of the final fitting windows after the noise points are removed are fitted to obtain the final fitting value at each sampling moment contained in each of the final fitting windows.

8. The data analysis method for monitoring hidden dangers of distribution network cables according to claim 1 is characterized in that: The method of obtaining the abnormality degree of the real-time cable sheath circulating current value according to the real-time cable sheath circulating current value and the final fitting value at the sampling time of the real-time cable sheath circulating current value comprises: Calculate the absolute value of the difference between the final fitting value at the sampling moment of the real-time cable sheath circulating current value and the real-time cable sheath circulating current value to obtain the real-time difference degree; In the cycle where the real-time cable sheath circulating current value is located, for any sampling moment before the sampling moment where the real-time cable sheath circulating current value is located, calculate the absolute value of the difference between the cable sheath circulating current value at the sampling moment and the final fitting value at the sampling moment to obtain the historical difference degree, obtain the historical difference degree corresponding to each sampling moment before the sampling moment where the real-time cable sheath circulating current value is located, and obtain the average historical difference degree; The absolute value of the difference between the real-time difference degree and the average historical difference degree is calculated to obtain the abnormality degree of the real-time cable sheath circulating current value.

9. A data analysis method for monitoring hidden dangers of distribution network cables according to claim 1, characterized in that: The step of providing a hidden danger warning to the distribution network cable according to the abnormality degree includes: If the abnormality level is greater than a preset abnormality level warning parameter, an abnormality warning is issued for the distribution network cable.

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