Circuit breakers and circuit breaker self-test systems

Abstract

The present invention relates to the field of electrical variable measurement technology, and in particular to a circuit breaker and a circuit breaker self-test system. The circuit breaker uses the extreme points on the current rate curve as the basic analysis object, obtains the surge characteristics of each discrimination time period through the distribution of the extreme points and the current rate, and then determines the local frequency of current surge according to the changes in the surge characteristics within the historical neighborhood of each discrimination time period. The influence of temperature on the change of current rate is characterized by the correlation coefficient, and the time period to be analyzed is continuously searched within the historical neighborhood of the discrimination time period with the idea of ​​iteration and its growth trend is determined, and then the reference weight can be obtained by combining the correlation coefficient, and an accurate prediction result is obtained and a tripping warning is performed. The embodiment of the present invention obtains an accurate prediction result by correcting the prediction weight of the current rate at each time point, and then timely warns the tripping working condition of the circuit breaker, thereby improving the response rate.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical variable measurement, and in particular to a circuit breaker and a circuit breaker self-test system. Background Art

[0002] Circuit breakers usually include high-voltage circuit breakers, low-voltage circuit breakers, etc. They are designed and used according to different application scenarios and voltage levels. The working principle of the circuit breaker is mainly to use a mechanical or electromagnetic drive mechanism to quickly separate the contacts in the circuit breaker when a fault occurs, thereby cutting off the circuit. This process can also be called a tripping process. During the operation of the circuit breaker, it is necessary to have data self-checking capabilities to judge the tripping operation by monitoring the duration of the increase in the abnormal current multiple.

[0003] During the monitoring process, if the current circuit does produce an abnormality, the current rate of each sampling time period in the abnormal surge period has a relatively obvious difference, but the actual monitored current rate itself lasts less than the target tripping time. At this time, the probability of the circuit breaker triggering the tripping is not high, resulting in a slow tripping operation. Therefore, when performing abnormal monitoring in practice, it is necessary not only to monitor the current information in real time, but also to adopt a prediction method based on the current rate change trend to predict the current rate change at future moments, and then timely feedback the early warning information to improve the response rate of the self-test process. However, in the actual working process, due to the complex use behavior of electrical appliances in the circuit system, the change characteristics of the current rate are complex. The current surge is a nonlinear growth with different numerical values ​​in a time period, so each time point should have different growth characteristics; at the same time, the circuit breaker will also be affected by temperature in the actual working process, resulting in different degrees of temperature influence in different time periods. These factors will affect the accuracy of the power rate prediction process. These characteristics are not taken into account in the existing technology, resulting in inaccurate prediction data and unable to effectively improve the tripping response rate. Summary of the invention

[0004] In order to solve the technical problem that the prior art does not consider the temperature influence and the special change trend at each time point in the process of predicting the current multiple change trend of the circuit breaker, the purpose of the present invention is to provide a circuit breaker and a circuit breaker self-test system, and the technical solution adopted is as follows:

[0005] The present invention provides a circuit breaker, comprising a circuit breaker body, wherein a tripping mechanism of the circuit breaker body is warned by a tripping warning system, wherein the tripping warning system comprises:

[0006] A current rate statistics module is used to count the current rate curve in a preset historical time period before the real time; count the extreme value points on the current rate curve, and the extreme value points divide a plurality of discrimination time periods on the current rate curve;

[0007] The current rate surge analysis module is used to obtain the surge characteristics of each discrimination time period according to the time length of each discrimination time period and the current rate change characteristics; obtain the historical neighborhood range of each discrimination time period, and obtain the local frequency of current surge in each discrimination time period according to the time interval of the maximum value points between the discrimination time periods within the historical neighborhood range and the surge characteristic difference;

[0008] A prediction weight setting module is used to obtain the correlation coefficient between the current multiplier and the circuit breaker temperature within the historical neighborhood range; for each discrimination time period, starting from the last time point in the reverse direction of the time series, iteratively divide the time period to be analyzed; according to the size of the time period to be analyzed and the changing trend of the local frequency of current surge, the growth trend of the time period to be analyzed is obtained; according to the growth trend, the iteration process is controlled to obtain the obvious growth time period; according to the growth trend of the obvious growth time period and the correlation coefficient within the historical neighborhood range of each discrimination time period, the reference weight of each discrimination time period is obtained; according to the reference weight and the local frequency of current surge, the weight of each time point in the discrimination time period in the weighted moving average method is adjusted to obtain the prediction weight;

[0009] The trip warning module is used to predict the current multiple at the future moment of the real time by using the weighted moving average method according to the prediction weight, and feedback the warning signal according to the change trend of the current multiple predicted value at the future moment.

[0010] Furthermore, the method for dividing the time period includes:

[0011] The extreme point in the current rate curve is used as a segmentation point, the minimum point is used as a starting point, and the next maximum point in the time sequence is used as an end point to obtain the determination time period.

[0012] Furthermore, the method for obtaining the surge feature includes:

[0013] The range of the current multiple in the discrimination time period is obtained; the product of the range and the current multiple of the maximum value in the discrimination time period is used as the numerator, and the time length of the discrimination time period is used as the denominator to obtain the surge feature.

[0014] Furthermore, the method for obtaining the local frequency of current surge includes:

[0015] Any one of the judgment time periods is taken as the target time period, and the judgment time period in the historical neighborhood range of the target time period is taken as the neighborhood time period of the target time period; the inverse of the time interval between the maximum point of the neighborhood time period and the maximum point of the next judgment time period is taken as the time confidence weight of the neighborhood time period, and the surge feature difference between the target time period and each of the neighborhood time periods is obtained; the surge feature difference is weightedly summed according to the time confidence weight to obtain the current surge local frequency.

[0016] Furthermore, the correlation coefficient acquisition method includes:

[0017] The current magnifications within the historical neighborhood range constitute a current magnification sequence, the temperatures within the historical neighborhood range constitute a temperature sequence, and the Pearson correlation coefficient between the current magnification sequence and the temperature sequence is used as the correlation coefficient.

[0018] Furthermore, the method for obtaining the growth trend includes:

[0019] In the time period to be analyzed, the local frequencies of current surges in the included discrimination time period are arranged in the reverse direction of the timing to obtain a sequence to be analyzed; the previous element in the sequence to be analyzed is subtracted from the next element to obtain a differential sequence of the sequence to be analyzed; the previous element in the differential sequence is used as the denominator and the next element as the numerator to obtain an element ratio, and all element ratios of the differential sequence are accumulated to obtain a decreasing trend feature; the decreasing trend feature is subtracted from the time length of the time period to be analyzed to obtain the increasing trend.

[0020] Furthermore, the obvious growth time period is the time period to be analyzed with the largest growth trend during the iteration process.

[0021] Furthermore, the method for obtaining the reference weight includes:

[0022] The growth trend within the obvious growth time period is normalized to obtain abnormal growth characteristics, and an average value of the abnormal growth characteristics and the correlation coefficient is used as the reference weight.

[0023] Furthermore, the method for obtaining the prediction weight includes:

[0024] The inverse of the time interval between each time point in the judgment time period and the real time is used as the initial time weight of each time point; the initial time weight of each time point in the judgment time period, the reference weight and the local frequency of current surge are multiplied and normalized to obtain the predicted weight.

[0025] The present invention also proposes a circuit breaker self-test system, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any step involved in the trip warning system in the circuit breaker.

[0026] The present invention has the following beneficial effects:

[0027] The embodiment of the present invention uses the extreme point on the current rate curve as the basic analysis object, obtains the surge feature of each discrimination time period through the distribution of the extreme point and the current rate, and then determines the local frequency of current surge according to the change of the surge feature within the historical neighborhood of each discrimination time period. That is, the local frequency of current surge and the surge feature can quantify the current rate change characteristics within the local time period, and then the weight can be further analyzed and determined in the subsequent process. In the prediction weight setting module, the influence of temperature on the current rate change is first characterized by the correlation coefficient, and then the time period to be analyzed is continuously searched within the historical neighborhood of the discrimination time period with the idea of ​​iteration and its growth trend is determined. The growth trend is used to find the obvious growth time period in the entire historical neighborhood range. The obvious growth time period represents the time period with obvious surge of current rate in the current discrimination time period. The growth trend under this time period can be used to characterize the current rate change trend at each time point in the discrimination time period, and then the reference weight can be obtained by combining the correlation coefficient. The weight in the weighted moving average method is adjusted by referring to the weight and the local frequency of current surge, so that accurate prediction results can be obtained and trip warning can be performed. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are 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 work.

[0029] Figure 1 A structural block diagram of a trip warning system provided by an embodiment of the present invention;

[0030] Figure 2 A circuit breaker tripping characteristic curve is provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation, structure, features and effects of a circuit breaker and a circuit breaker self-test system proposed according to the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0032] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0033] The circuit breaker of the embodiment of the present invention includes a circuit breaker body and a trip warning system, and the trip warning system is used to warn the trip mechanism of the circuit breaker body. It should be noted that the specific structure of the trip mechanism and the circuit breaker body is a technical means well known to those skilled in the art, and the specific structure and function are not described in detail here. Figure 1 , which shows a structural block diagram of a trip warning system provided by an embodiment of the present invention, the trip warning system includes a current rate statistics module 101, a current rate surge analysis module 102, a prediction weight setting module 103 and a trip warning module 104.

[0034] See also Figure 2 , which shows a circuit breaker tripping characteristic curve provided by an embodiment of the present invention, Figure 2 This is the tripping characteristic curve of the C-type instantaneous release. The electromagnetic tripping action current is 5-10 times the approved working current and is used for ordinary power distribution. Figure 2 In the figure, the horizontal axis is the current rate, and the vertical axis is the duration of the current. Figure 2 The two curves divide the circuit breaker into three areas: no tripping area, possible area, and tripping area. The possible area indicates that the circuit breaker may or may not trip, and the specific setting needs to be made according to the circuit breaker model. Thermal tripping and electromagnetic tripping represent two tripping modes. Figure 2It can be known that when the circuit breaker is working, the execution condition for judging the tripping is mainly based on the current ratio and its duration. Therefore, in order to accurately analyze the working data of the circuit breaker, the current ratio statistics module 101 of the embodiment of the present invention can be combined with various sensors in the circuit breaker to collect the current data of the circuit breaker, and transmit the data to the terminal through the multi-functional Internet of Things interface for subsequent analysis and processing. Among them, the current ratio statistics module 101 is mainly used to count the current ratio curve in the preset historical time period before the real time moment, that is, the horizontal axis of the current ratio curve is time, and the vertical axis is the current ratio. Further count the extreme points on the current ratio curve, because the extreme points can represent the limit of the current ratio, so using the extreme points as the basic analysis data can effectively analyze the change trend of the current ratio, and the extreme points can also be used as segmentation points to divide multiple judgment time periods on the current ratio curve, that is, each judgment time period can represent the duration of a change process.

[0035] In the embodiment of the present invention, the preset historical time period can be set to one hour, and the sampling frequency of the current multiple can be set to collect once every 0.001 seconds. It should be noted that the method for obtaining the extreme point is a technical means well known to those skilled in the art and will not be described in detail here.

[0036] Preferably, in one embodiment of the present invention, the method for determining the division of time periods includes:

[0037] The extreme point in the current rate curve is used as the segmentation point, the minimum point is used as the starting point, and the next maximum point in the time sequence is used as the end point to obtain the discrimination time period. That is, each discrimination time period represents the duration of a growth trend.

[0038] The present invention aims to analyze the current rate growth characteristics of the local position on the current rate curve, and then analyze the local surge characteristics and change trends to weight the data at each time point. Therefore, the current rate surge analysis module 102 continues to perform statistical analysis on the current rate in the judgment time period. For a judgment time period, the shorter the time length, the faster the current rate changes; the more changes in the current rate growth, the more drastic the current rate changes. Therefore, the surge characteristics of each judgment time period can be obtained according to the time length of each judgment time period and the current rate change characteristics. That is, the larger the surge characteristics, the more obvious the surge of the current rate in the judgment time period.

[0039] Preferably, in one embodiment of the present invention, the method for acquiring the surge feature includes:

[0040] The range of the current rate in the discrimination time period is obtained; the product of the range and the current rate of the maximum value in the discrimination time period is used as the numerator, and the time length of the discrimination time period is used as the denominator to obtain the surge feature. That is, the larger the current rate of the maximum value is, the greater the peak to which the current rate grows in the growth trend of the discrimination time period is, and the greater the surge feature is; the range represents the growth rate, and the larger the range is, the greater the surge feature is; and the time length is used as the denominator, that is, the shorter the time length is, the greater the surge feature is, which means that the growth of the current rate is a surge in a short time.

[0041] Because the change of current rate is carried out in the time series range, and the surge feature only analyzes the current rate surge situation in a local time period, it is necessary to further analyze the change characteristics of the surge feature in the time series range. The current rate surge analysis module 102 further sets the historical neighborhood range of each discrimination time period, further analyzes the change characteristics of the surge feature in the historical neighborhood range, and then determines the local frequency of current surge in this discrimination time period. Among them, the shorter the time interval between the maximum points of the discrimination time periods in the historical neighborhood range, the closer the time series distance between the two discrimination time periods, which means that a high-frequency surge process has occurred in the historical neighborhood range, and the larger the local frequency of current surge; at the same time, the greater the difference in the surge characteristics between the discrimination time periods in the historical neighborhood range, the more drastic the change in the surge characteristics, and the larger the local frequency of current surge. Therefore, the larger the local frequency of current surge, the faster the surge characteristics change in the local time range for the discrimination time period, and the stronger the change characteristics.

[0042] In the embodiment of the present invention, for each determination time period, the first two determination time periods and the current determination time period form a historical neighborhood range of the current determination time period.

[0043] Preferably, in one embodiment of the present invention, the method for obtaining the local frequency of current surge includes:

[0044] Any discriminant time period is taken as the target time period, and the discriminant time period in the historical neighborhood range of the target time period is the neighborhood time period of the target time period. Because the historical neighborhood range is composed of multiple discriminant time periods before the discriminant time period, for a historical neighborhood range, the target time period is the last discriminant time period in the time series, and the other discriminant time periods are all neighborhood time periods.

[0045] The inverse of the time interval between the maximum point of the neighborhood time period and the maximum point of the next discrimination time period is used as the time confidence weight of the neighborhood time period. That is, the shorter the time interval, the greater the confidence weight, indicating that the difference in the surge characteristics represented by the neighborhood time period relative to the target time period has a stronger reference significance. And because the last discrimination time period in the neighborhood range is the target time period, each neighborhood time period will correspond to a confidence weight.

[0046] The surge feature differences between the target time period and each neighboring time period are obtained, and the surge feature differences are weighted and summed according to the time confidence weight to obtain the local frequency of current surge. That is, the local frequency of current surge is expressed by the formula: ;in, is the local frequency of current surge in the target time period x, is the number of neighboring time periods within the neighborhood of the target time period x, is the time interval between the maximum point of the yth neighborhood time period and the maximum point of the next discrimination time period, is the surge feature of the target time period x, is the surge feature of the yth neighborhood time period.

[0047] When predicting data, the circuit breaker can use the weighted moving average method in the prior art to predict data. This algorithm determines different weights based on the time series difference between various historical data and the latest collected data. That is, the closer the historical data is to the real time, the greater the weight is and the stronger the reference is. The data at future moments is predicted based on the time weight. However, due to the characteristics of the circuit breaker, Figure 2 It can be seen that triggering the tripping requires a larger current ratio and a sufficient duration. However, in actual operation, the internal heat dissipation performance of the closed circuit breaker is poor, which leads to the time required for the circuit breaker to recover to a suitable working state. That is, the temperature will also affect the operation of the circuit breaker, resulting in different influences of the current ratio data at different time positions on the future prediction data. Therefore, it is necessary to analyze the impact of temperature on the current ratio data and incorporate it into the weight of the weighted average algorithm.

[0048] The prediction weight setting module 103 obtains the correlation coefficient between the current rate and the circuit breaker temperature in the historical neighborhood range. When there is a short-term normal current rate surge that does not trigger a trip, it usually does not affect the temperature inside the circuit breaker, that is, the correlation coefficient under this process is small; if the correlation coefficient is larger, it means that the current rate may be within the current rate specified by the thermal trip and has lasted for a certain period of time, then the data at this time needs more attention, and the weight of this part of the data should be larger in the prediction process. Further considering that if the current rate in a time period is in an abnormal state, there will be obvious and dense extreme points in this time period, indicating that with the change of time, the local frequency of current surges shows a clear upward trend. Therefore, the prediction weight setting module 103 further divides the time period to be analyzed in the historical neighborhood range, starting from the most recent time period in the time series, that is, the last time point in the historical neighborhood range as the starting point, and iterates forward to continuously divide the time period to be analyzed, that is, the historical neighborhood range can be regarded as the forward order in the time series, and the method of dividing the time period to be analyzed is to divide it in the reverse order in the time series.

[0049] For the time period to be analyzed, since its purpose is to analyze the changing trend of the local frequency of current surge, in order to improve the iteration efficiency in one embodiment of the present invention, the iteration method of the time period to be analyzed is based on a judgment time period as a step size, that is, each iteration selects a nearest judgment time period in the reverse direction of the timing to be added to the previous time period to be analyzed, so that the time period to be analyzed contains multiple judgment time period information, that is, contains multiple current surge local frequency information, thereby facilitating the analysis of the changing trend of the current surge local frequency.

[0050] For the time period to be analyzed, the larger the amount of data selected, the more obvious the change trend of the local frequency of the current surge, which means that the time period to be analyzed is in a time period with an abnormal growth trend. Therefore, the prediction weight setting module 103 further obtains the growth trend of the time period to be analyzed according to the size of the time period to be analyzed and the change trend of the local frequency of the current surge, and controls the iteration process according to the growth trend to obtain the obvious growth time period. That is, the iteration is stopped after the obvious growth time period is obtained. The obvious growth time period is the time period to be analyzed with a larger growth trend. The obvious growth change trend of the current discrimination time period is reflected in the obvious growth time period. The larger the growth trend in the obvious growth time period, the more the current discrimination time period is in a more drastic change and the current rate increases abnormally. The more attention should be paid to the information of the discrimination time period during the prediction. Therefore, the reference weight of each discrimination time period can be obtained by further combining the correlation coefficient. The reference weight combined with the local frequency of the current surge in the current discrimination time period can adjust the weight of each time period in the weighted moving average method to obtain the prediction weight.

[0051] Preferably, in one embodiment of the present invention, the current magnification within the historical neighborhood constitutes a current magnification sequence, the temperature within the historical neighborhood constitutes a temperature sequence, and the Pearson correlation coefficient between the current magnification sequence and the temperature sequence is used as the correlation coefficient. It should be noted that the Pearson correlation coefficient is a technical means well known to those skilled in the art and will not be described in detail here.

[0052] Preferably, in one embodiment of the present invention, the method for obtaining the growth trend includes:

[0053] In the time period to be analyzed, the local frequencies of current surges in the included discrimination time period are arranged in the reverse direction of the time sequence to obtain the sequence to be analyzed. That is, the number of elements in the time period to be analyzed is equal to the number of included discrimination time periods.

[0054] Subtract the previous element from the next element in the sequence to be analyzed to obtain the differential sequence of the sequence to be analyzed. Because the sequence to be analyzed is in the reverse direction of the time series, the previous element minus the next element represents the information of the time point closest to the real time in the time series minus the information of the previous time point, that is, the larger the element in the differential sequence, the more likely the local frequency of the current surge is to follow the growth trend of the time series.

[0055] The previous element in the differential sequence is used as the denominator and the next element as the numerator to obtain the element ratio. The smaller the element ratio, the greater the local frequency of current surge as the time series progresses. Therefore, all the element ratios of the differential sequence are accumulated to obtain the decreasing trend characteristics, that is, the larger the accumulated value of the element ratio, the more likely it is a decreasing trend at this time, and the smaller the accumulated value of the element ratio, the more likely it is an increasing trend at this time.

[0056] Subtract the decreasing trend feature from the length of the time period to be analyzed to obtain the growth trend. That is, the time length is positively correlated with the growth trend, and the decreasing trend feature is negatively correlated with the growth trend. If the length of the time period to be analyzed is large and has a significantly smaller decreasing trend feature, it means that this is a more obvious growth trend.

[0057] Preferably, in one embodiment of the present invention, the obvious growth time period is the time period to be analyzed with the largest growth trend during the iteration process. It should be noted that each determination time period may correspond to an obvious growth time period.

[0058] Preferably, in one embodiment of the present invention, the method for obtaining the reference weight includes:

[0059] The growth trend in the obvious growth time period is normalized to obtain abnormal growth features. Because each discrimination time period can correspond to an obvious growth time period, the normalization can select the maximum and minimum value normalization method, that is, the growth trends of the obvious growth time periods corresponding to all discrimination time periods are counted, and each growth trend is normalized based on these statistical growth trends. The average value of the abnormal growth feature and the correlation coefficient is used as the reference weight.

[0060] Preferably, in one embodiment of the present invention, the method for obtaining the prediction weight includes:

[0061] The reciprocal of the time interval between each time point and the real time in the discrimination time period is used as the initial time weight of each time point. It should be noted that, because the embodiments of the present invention are analyzed in units of the discrimination time period, the obtained reference weights, current surge local frequency and other characteristics correspond to all time points in the discrimination time period, that is, the reference weights, current surge local frequency and other characteristics of each time point are the same.

[0062] The initial time weight, reference weight and local frequency of current surge at each time point in the discrimination time period are multiplied and normalized to obtain the prediction weight.

[0063] It should be noted that the normalization may be a common normalization function such as a sigmoid function, and no specific limitation or elaboration is made.

[0064] The trip warning module 104 can predict the current rate at the future moment of the real time by using the weighted moving average method according to the prediction weight, and then feedback the warning signal according to the change trend of the current rate prediction value at the future moment. That is, it is judged whether the magnitude and duration of the current rate prediction value at the future moment will meet the tripping area standard in the tripping characteristic curve, and then the warning is given in advance, and the tripping operation is performed appropriately, so that the entire power system can be checked faster and the circuit breaker tripping response rate can be improved.

[0065] In summary, the embodiment of the present invention uses the extreme points on the current rate curve as the basic analysis object, obtains the surge characteristics of each discrimination time period through the distribution of extreme points and the current rate, and then determines the local frequency of current surge according to the change of the surge characteristics within the historical neighborhood of each discrimination time period. The correlation coefficient is used to characterize the degree of influence of temperature on the change of current rate, and further, the idea of ​​iteration is used to continuously search for the time period to be analyzed within the historical neighborhood of the discrimination time period and determine its growth trend. The growth trend is used to find the obvious growth time period in the entire historical neighborhood. The growth trend under this time period can be used to characterize the current rate change trend at each time point in the discrimination time period, and then the reference weight can be obtained by combining the correlation coefficient. The weight in the weighted moving average method is adjusted by the reference weight and the local frequency of current surge, so that accurate prediction results can be obtained and tripping warning can be performed. The embodiment of the present invention obtains accurate prediction results by correcting the prediction weight of the current rate at each time point, and then timely warns the tripping working condition of the circuit breaker to improve the response rate.

[0066] Based on the same inventive concept, the present invention also proposes a circuit breaker self-test system, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the processor implements any steps involved in a trip warning system in a circuit breaker.

[0067] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0068] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A circuit breaker, comprising a circuit breaker body, characterized in that: The tripping mechanism of the circuit breaker body is warned by a tripping warning system, and the tripping warning system includes: A current rate statistics module is used to count the current rate curve in a preset historical time period before the real time; count the extreme value points on the current rate curve, and the extreme value points divide a plurality of discrimination time periods on the current rate curve; The current rate surge analysis module is used to obtain the surge characteristics of each discrimination time period according to the time length of each discrimination time period and the current rate change characteristics; obtain the historical neighborhood range of each discrimination time period, and obtain the local frequency of current surge in each discrimination time period according to the time interval of the maximum value points between the discrimination time periods within the historical neighborhood range and the surge characteristic difference; A prediction weight setting module is used to obtain the correlation coefficient between the current multiplier and the circuit breaker temperature within the historical neighborhood range; for each discrimination time period, starting from the last time point in the reverse direction of the time series, iteratively divide the time period to be analyzed; according to the size of the time period to be analyzed and the changing trend of the local frequency of current surge, the growth trend of the time period to be analyzed is obtained; according to the growth trend, the iteration process is controlled to obtain the obvious growth time period; according to the growth trend of the obvious growth time period and the correlation coefficient within the historical neighborhood range of each discrimination time period, the reference weight of each discrimination time period is obtained; according to the reference weight and the local frequency of current surge, the weight of each time point in the discrimination time period in the weighted moving average method is adjusted to obtain the prediction weight; The trip warning module is used to predict the current multiple at the future moment of the real time by using the weighted moving average method according to the prediction weight, and feedback the warning signal according to the change trend of the current multiple predicted value at the future moment.

2. A circuit breaker according to claim 1, characterized in that: The method for dividing the time period includes: The extreme point in the current rate curve is used as a segmentation point, the minimum point is used as a starting point, and the next maximum point in the time sequence is used as an end point to obtain the determination time period.

3. A circuit breaker according to claim 1, characterized in that: The method for obtaining the surge feature includes: The range of the current multiple in the discrimination time period is obtained; the product of the range and the current multiple of the maximum value in the discrimination time period is used as the numerator, and the time length of the discrimination time period is used as the denominator to obtain the surge feature.

4. A circuit breaker according to claim 1, characterized in that: The method for obtaining the local frequency of current surge includes: Any one of the judgment time periods is taken as the target time period, and the judgment time period in the historical neighborhood range of the target time period is taken as the neighborhood time period of the target time period; the inverse of the time interval between the maximum point of the neighborhood time period and the maximum point of the next judgment time period is taken as the time confidence weight of the neighborhood time period, and the surge feature difference between the target time period and each of the neighborhood time periods is obtained; the surge feature difference is weightedly summed according to the time confidence weight to obtain the current surge local frequency.

5. A circuit breaker according to claim 1, characterized in that: The correlation coefficient acquisition method comprises: The current magnifications within the historical neighborhood range constitute a current magnification sequence, the temperatures within the historical neighborhood range constitute a temperature sequence, and the Pearson correlation coefficient between the current magnification sequence and the temperature sequence is used as the correlation coefficient.

6. A circuit breaker according to claim 1, characterized in that: The method for obtaining the growth trend includes: In the time period to be analyzed, the local frequencies of current surges in the included discrimination time period are arranged in the reverse direction of the timing to obtain a sequence to be analyzed; the previous element in the sequence to be analyzed is subtracted from the next element to obtain a differential sequence of the sequence to be analyzed; the previous element in the differential sequence is used as the denominator and the next element as the numerator to obtain an element ratio, and all element ratios of the differential sequence are accumulated to obtain a decreasing trend feature; the decreasing trend feature is subtracted from the time length of the time period to be analyzed to obtain the increasing trend.

7. A circuit breaker according to claim 1, characterized in that: The obvious growth time period is the time period to be analyzed with the largest growth trend during the iteration process.

8. A circuit breaker according to claim 1, characterized in that: The method for obtaining the reference weight includes: The growth trend within the obvious growth time period is normalized to obtain abnormal growth characteristics, and an average value of the abnormal growth characteristics and the correlation coefficient is used as the reference weight.

9. A circuit breaker according to claim 1, characterized in that: The method for obtaining the prediction weight includes: The inverse of the time interval between each time point in the judgment time period and the real time is used as the initial time weight of each time point; the initial time weight of each time point in the judgment time period, the reference weight and the local frequency of current surge are multiplied and normalized to obtain the predicted weight.

10. A circuit breaker self-test system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the functions of each module involved in the trip warning system in the circuit breaker according to any one of claims 1 to 9 are realized.

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