Looped network box with intelligent power supply data monitoring function

Abstract

The invention relates to the technical field of ring main unit monitoring, in particular to a ring main unit with a power supply data intelligent monitoring function. The data acquisition module is used for acquiring current power supply data of each power supply data type in a power supply data type set at a current monitoring moment and nearest neighbor power supply data and a nearest neighbor power supply data sequence corresponding to the current power supply data; the second acquisition module is used for acquiring an abnormal characterization value corresponding to each power supply data type at the current monitoring moment; the third acquisition module is used for acquiring the influenced weight value corresponding to each power supply data type at the current monitoring moment and the associated change characterization value at the current monitoring moment; and the state monitoring module is used for monitoring the state of the ring main unit at the current monitoring moment according to the abnormal characterization value, the change correlation characterization value and the influenced weight value. According to the invention, the reliability and accuracy of monitoring the state of the ring main unit can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ring main unit monitoring, and particularly relates to a ring main unit with an intelligent power supply data monitoring function. Background Art

[0002] In the power system, as a key distribution equipment, the stability and reliability of the ring main unit are crucial for ensuring the continuity and safety of power supply. Therefore, it is very important to monitor the status of the ring main unit.

[0003] In the prior art, generally, only the power supply data of the ring main unit collected in real time is used to monitor the status of the ring main unit. If the power supply data of the ring main unit collected by the acquisition device in real time exceeds the threshold, then it is determined that the ring main unit is in an abnormal state. However, the credibility or accuracy of the existing method for monitoring the status of the ring main unit is relatively low. If the acquisition device is affected by external interferences such as electromagnetic interference, then the power supply data collected by the corresponding acquisition device will also exceed the threshold. However, the situation where the power supply data exceeds the threshold caused by such external interference does not mean that the ring main unit is in an abnormal state. But based on the existing ring main unit status monitoring method, it will be misjudged that the ring main unit is in an abnormal state. If there are faults such as poor contact inside the ring main unit, the poor contact faults inside the ring main unit usually cause the collected power supply data to change frequently, but may not necessarily cause the collected power supply data to exceed the threshold. Therefore, when there are faults such as poor contact inside the ring main unit, based on the existing ring main unit status monitoring method, it will be misjudged that the ring main unit is in a normal state. Therefore, how to improve the credibility or accuracy of monitoring the status of the ring main unit has become an urgent problem to be solved. Summary of the Invention

[0004] In order to solve the above problems, the present invention provides a ring main unit with an intelligent power supply data monitoring function, and the specific technical solution adopted is as follows: An embodiment of the present invention provides a ring main unit with an intelligent power supply data monitoring function, and the ring main unit with an intelligent power supply data monitoring function includes: A first acquisition module, configured to acquire the current power supply data of each power supply data type in the power supply data type set at the current monitoring moment, as well as the nearest neighbor power supply data and the nearest neighbor power supply data sequence corresponding to the current power supply data, where the power supply data is the power supply data of the ring main unit; A second acquisition module, configured to obtain the abnormal characterization value corresponding to each power supply data type at the current monitoring moment according to the difference and slope between the current power supply data and the nearest neighbor power supply data corresponding to the current power supply data, the difference between adjacent power supply data in the nearest neighbor power supply data sequence, and the variance of the nearest neighbor power supply data sequence; A third acquisition module, configured to acquire the turning sequences corresponding to the respective power supply data types at the current monitoring moment, and obtain the affected weight values corresponding to the respective power supply data types at the current monitoring moment and the associated change characterization value at the current monitoring moment according to the turning sequences; A status monitoring module, configured to monitor the status of the ring main unit at the current monitoring moment according to the abnormal characterization value, the change association characterization value, and the affected weight value.

[0005] Advantageous effects: The present invention includes a first acquisition module, configured to acquire the current power supply data of each power supply data type in the power supply data type set at the current monitoring moment, the nearest neighbor power supply data corresponding to the current power supply data, and the near neighbor power supply data sequence; a second acquisition module, configured to obtain the abnormal characterization values corresponding to the respective power supply data types at the current monitoring moment according to the difference and slope between the current power supply data and the nearest neighbor power supply data corresponding to the current power supply data, the differences between adjacent power supply data in the near neighbor power supply data sequence, and the variance of the near neighbor power supply data sequence; a third acquisition module, configured to acquire the turning sequences corresponding to the respective power supply data types at the current monitoring moment, and obtain the affected weight values corresponding to the respective power supply data types at the current monitoring moment and the associated change characterization value at the current monitoring moment according to the turning sequences; a status monitoring module, configured to monitor the status of the ring main unit at the current monitoring moment according to the abnormal characterization value, the change association characterization value, and the affected weight value. And the present invention can improve the reliability and accuracy of monitoring the status of the ring main unit based on the abnormal characterization value, the change association characterization value, and the affected weight value. Description of the Drawings

[0006] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0007] Figure 1 It is a structural block diagram of a ring main unit with an intelligent monitoring function for power supply data according to the present invention. Detailed Embodiments

[0008] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the embodiments of the present invention.

[0009] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs.

[0010] This embodiment provides a ring main unit with an intelligent power supply data monitoring function, which is described in detail as follows: As Figure 1 shown, a ring main unit with an intelligent power supply data monitoring function provided in this embodiment includes: A first acquisition module 01, configured to acquire the current power supply data of each power supply data type in the power supply data type set at the current monitoring moment, as well as the nearest neighbor power supply data and the nearest neighbor power supply data sequence corresponding to the current power supply data.

[0011] The purpose of this embodiment is to improve the reliability or accuracy of monitoring the state of the ring main unit. The ring main unit in this embodiment is mainly applied to ring main units in daily application places such as residential communities and high-rise buildings. For the sake of easy understanding, the subsequent analysis in this embodiment will take the state monitoring process of any ring main unit during operation as an example.

[0012] First, all power supply data types of the ring main unit are obtained, and the set constructed by all power supply data types of the ring main unit is denoted as the power supply data type set; and the power supply data types include but are not limited to voltage, current, grid frequency, ambient temperature inside the cabinet, etc. The voltage refers to the potential difference between conductors in the main circuit of the ring main unit, and is a physical quantity that measures the ability of electric field energy transmission. The current refers to the directional flow of charges in the conductors of the ring main unit, and represents the amount of charge passing through the cross-section of the conductor per unit time. The grid frequency refers to the rate of periodic change of alternating current, with the unit of hertz (Hz), and reflects the supply-demand balance state of the power system. In the power system, if the ring main unit appears in an abnormal state, the power supply data of the ring main unit will change. Therefore, currently, the state of the ring main unit is usually monitored by analyzing the power supply data of the ring main unit. Then, all subsequent power supply data belongs to the power supply data of this ring main unit. In addition, the abnormal state of the ring main unit mainly refers to the occurrence of an operation failure of the ring main unit, such as poor contact inside the ring main unit.

[0013] Next, in this embodiment, the power supply data of the ring network cabinet collected by the acquisition device at each monitoring moment is utilized. For example, the ambient temperature inside the cabinet can be collected by a temperature sensor, and at each monitoring moment, the power supply data of each power supply data type belonging to the power supply data type set can be obtained. That is, the acquisition method of all power supply data types of the ring network cabinet is synchronous acquisition, and the collected data will be stored in the database. Then, the power supply data of each power supply data type belonging to the power supply data type set collected at the current monitoring moment is denoted as the current power supply data of each power supply data type in the power supply data type set at the current monitoring moment. That is, for any power supply data type, the power supply data of the ring network cabinet belonging to this power supply data type collected at the current monitoring moment is the current power supply data of this power supply data type at the current monitoring moment. For example, for the ambient temperature data type inside the cabinet, the current power supply data of the ambient temperature data type inside the cabinet at the current monitoring moment is the ambient temperature data collected at the current monitoring moment.

[0014] In addition, it should be noted that the voltage, current, ambient temperature inside the cabinet, etc. in this embodiment are collected by sensors, and the power grid frequency can be obtained through a zero-crossing detection method. That is, by detecting the zero-crossing points of the AC voltage or current waveform, the time interval between two adjacent zero-crossing points can be calculated, thereby obtaining the power grid frequency. And in this embodiment, the implementer also needs to set the data acquisition frequency according to the actual situation, that is, the time interval between adjacent monitoring moments. For example, in this embodiment, the time interval between adjacent monitoring moments can be set to 1 second.

[0015] After obtaining the current power supply data, the nearest neighbor power supply data and the near neighbor power supply data sequence corresponding to the current power supply data are continuously obtained. And the nearest neighbor power supply data and the near neighbor power supply data sequence are key data for accurately monitoring the state of the ring network cabinet in the future. Then, the specific acquisition process of the nearest neighbor power supply data and the near neighbor power supply data sequence corresponding to the current power supply data of each power supply data type in the power supply data type set at the current monitoring moment is as follows: For any power supply data type in the set of power supply data types: First, obtain the power supply data belonging to this power supply data type collected at the previous historical monitoring moment of the current monitoring moment, and record it as the nearest neighbor power supply data corresponding to the current power supply data of this power supply data type at the current monitoring moment. The previous historical monitoring moment of the current monitoring moment is the historical monitoring moment closest to the current monitoring moment. Then, obtain the set constructed by a continuous preset number of historical monitoring moments adjacent to the current monitoring moment, and record it as the historical monitoring moment set. After that, obtain the power supply data belonging to this power supply data type collected at each historical monitoring moment in the historical monitoring moment set, and record the power supply data belonging to this power supply data type collected at each historical monitoring moment in the historical monitoring moment set as the historical power supply data of this power supply data type at the corresponding historical monitoring moment. Immediately afterwards, record the time series sequence constructed by the current power supply data of this power supply data type at the current monitoring moment and the historical power supply data of this power supply data type at all historical monitoring moments in the historical monitoring moment set as the nearest neighbor power supply data sequence corresponding to the current power supply data of this power supply data type at the current monitoring moment. In this embodiment, the implementer needs to set the value of the preset number according to the actual situation and experimental statistics. For example, the value of the preset number can be set to 5.

[0016] Therefore, through the above process, this embodiment can obtain the current power supply data of each power supply data type in the set of power supply data types at the current monitoring moment, as well as the nearest neighbor power supply data and the nearest neighbor power supply data sequence corresponding to the current power supply data.

[0017] The second acquisition module 02 is used to obtain the abnormal characterization value corresponding to each power supply data type at the current monitoring moment according to the difference and slope between the current power supply data and the nearest neighbor power supply data corresponding to the current power supply data, the difference between adjacent power supply data in the nearest neighbor power supply data sequence, and the variance of the nearest neighbor power supply data sequence.

[0018] Currently, the status of the ring main unit is generally monitored by whether the power supply data collected in real time exceeds the threshold. However, the interference of external factors on the collection device can also cause the collected power supply data to exceed the threshold. For example, when a certain collection device is interfered by external factors such as electromagnetic interference, the power supply data collected by the corresponding collection device may exceed the threshold. However, the situation where the collected power supply data exceeds the threshold caused by such external interference does not mean that the ring main unit has an abnormal status. That is, although it is monitored that the collected power supply data exceeds the threshold, the ring main unit may still be in a normal operation state at this time. Thus, it can be seen that the interference of external factors on the collection device will cause mismonitoring when using the existing monitoring method to monitor the status of the ring main unit, resulting in a lower monitoring credibility or accuracy. That is, the interference of external factors on the collection device will cause the ring main unit in a normal operation state to be judged as an abnormal state. Also, since not all faults of the ring main unit will cause the power supply data of the ring main unit to exceed the specified range. For example, the poor contact fault that occurs inside the ring main unit usually causes the collected power supply data to change frequently, but does not necessarily cause the collected power supply data to exceed the threshold. Thus, it can be seen that when a poor contact or other faults occur inside the ring main unit, based on the existing monitoring method, the poor contact or other faults that occur inside the ring main unit will be misjudged as the ring main unit being in a normal state, which will also result in a lower monitoring credibility or accuracy. And in this embodiment, in order to avoid the problem of lower monitoring credibility or accuracy caused by the above situation, this embodiment will next combine the change characteristics of the power supply data when affected by external factors, the data change characteristics when the ring main unit has a fault but the power supply data does not exceed the threshold, the change association between different power supply data types, and the influence of data of different power supply data types by other types of data to achieve accurate monitoring of the status of the ring main unit.

[0019] Therefore, this embodiment will next combine the change characteristics of the power supply data when affected by external factors and the data change characteristics when the ring main unit has a fault but the power supply data does not exceed the threshold to obtain the abnormal characterization values corresponding to each power supply data type at the previous monitoring moment. That is, this embodiment will next obtain the abnormal characterization values corresponding to each power supply data type at the current monitoring moment according to the difference and slope between the current power supply data of each power supply data type at the current monitoring moment and the nearest neighbor power supply data corresponding to the current power supply data, the difference between adjacent power supply data in the sequence of nearest neighbor power supply data corresponding to the current power supply data of each power supply data type at the current monitoring moment, and the variance of the sequence of nearest neighbor power supply data corresponding to the current power supply data of each power supply data type at the current monitoring moment. And the abnormal characterization value is a key parameter reflecting whether the ring main unit has an abnormal status. Then the specific process of obtaining the abnormal characterization values corresponding to each power supply data type at the current monitoring moment is as follows: For any power supply data type in the set of power supply data types: First, obtain the preset threshold range corresponding to the power supply data type. Usually, the preset threshold ranges corresponding to different power supply data types are determined by grid requirements, the parameters of the ring main unit, and the requirements specified to ensure the operation stability and safety of the ring main unit. For example, if the power supply data type is the voltage data type and the rated voltage of the main circuit of the ring main unit in use is 12 kV, then to ensure the operation stability and safety of the ring main unit, it is usually required that the actual voltage of the ring main unit be maintained within the range of ±10% of the rated voltage. Based on this, it can be known that the preset threshold range corresponding to the voltage data type is [12 kV × 90%, 12 kV × 110%].

[0020] After obtaining the preset threshold interval corresponding to the power supply data type, the nearest neighbor power supply data corresponding to the current power supply data of the power supply data type is recorded as the first data, and the sequence of neighboring power supply data corresponding to the current power supply data of the power supply data type is recorded as the first sequence; then it is determined whether the current power supply data of the power supply data type exceeds the preset threshold interval corresponding to the power supply data type. If it is determined that the current power supply data of the power supply data type is not within the preset threshold interval corresponding to the power supply data type, then according to the difference and slope between the current power supply data of the power supply data type and the first data and the differences between adjacent power supply data in the first sequence, an abnormal characterization value corresponding to the power supply data type at the current monitoring moment is obtained. If it is determined that the current power supply data of the power supply data type is within the preset threshold interval corresponding to the power supply data type, then according to the slope between the current power supply data of the power supply data type and the first data and the variance of the first sequence, an abnormal characterization value corresponding to the power supply data type at the current monitoring moment is obtained; in addition, it should be noted that when the current power supply data of the power supply data type is not within the preset threshold interval corresponding to the power supply data type, it indicates that the current power supply data of the power supply data type is suspected of being abnormal data. However, the suspected abnormal data may also be caused by external interference to the data acquisition device, that is, the suspected abnormal data may also be normal data. The suspected abnormal data caused by external interference has the characteristics of contingency and suddenness. In this embodiment, analyzing in combination with the difference and slope between the current power supply data of the power supply data type and the first data and the differences between adjacent power supply data in the first sequence can reflect the obviousness of the contingency and suddenness characteristics of the current power supply data of the power supply data type, and the magnitude of the obviousness of the contingency and suddenness characteristics of the current power supply data of the power supply data type can characterize the likelihood of the current power supply data of the power supply data type being abnormal data. The abnormal data is caused by the abnormal operation state of the ring main unit; when the current power supply data of the power supply data type is within the preset threshold interval corresponding to the power supply data type, it indicates that the current power supply data of the power supply data type is suspected of being normal data. However, the suspected normal data may also be caused by abnormal states such as poor contact inside the ring main unit, that is, the suspected normal data may also be abnormal data. Abnormal states such as poor contact inside the ring main unit will cause the collected data to have characteristics of mutation and frequent change, and the slope between the current power supply data of the power supply data type and the first data and the variance of the first sequence can reflect the obviousness of the mutation and frequent change characteristics of the current power supply data of the power supply data type. The magnitude of the obviousness of the mutation and frequent change characteristics can characterize the likelihood of the current power supply data of the power supply data type being abnormal data.

[0021] In this embodiment, the specific process of obtaining the abnormal characterization value corresponding to the power supply data type at the current monitoring moment according to the difference and slope between the current power supply data of the power supply data type and the first data, and the difference between adjacent power supply data in the first sequence is as follows: First, a coordinate system corresponding to the power supply data type is constructed. The vertical axis of the coordinate system corresponding to the power supply data type represents the value of the power supply data belonging to the power supply data type, and the abscissa value represents time; then, the current power supply data of the power supply data type, the acquisition time of the current power supply data of the power supply data type, the first data, and the acquisition time of the first data are all mapped into the coordinate system corresponding to the power supply data type, obtaining the data point corresponding to the current power supply data of the power supply data type and the data point corresponding to the first data, and respectively denoted as the first data point and the second data point. The abscissa of the first data point is the acquisition time of the current power supply data of the power supply data type, and the ordinate is the current power supply data of the power supply data type. The abscissa of the second data point is the acquisition time of the first data, and the ordinate is the first data; then, the absolute value of the slope between the first data point and the second data point is obtained and denoted as the slope characterization value of the power supply data type at the current monitoring moment. The slope between two data points is the slope of the straight line formed by the two data points. The slope characterization value of the power supply data type at the current monitoring moment is normalized, and the normalized value obtained by the normalization process is denoted as the first characterization value. And the result of subtracting the first value to be processed from the preset constant is the normalized value obtained by normalizing the slope characterization value of the power supply data type at the current monitoring moment. The first value to be processed is the reciprocal of the sum of the slope characterization value of the power supply data type at the current monitoring moment and the preset constant. The first characterization value is , where is the preset constant, K is the slope between the first data point and the second data point, The larger the value of, the more obvious the mutation degree of the current power supply data of the power supply data type; and in specific applications, the implementer needs to set the preset constant according to the actual situation. For example, in this embodiment, the preset constant is set to 1. The functions of the preset constant in this embodiment include realizing the normalization of parameters and preventing the denominator from being 0. Immediately afterwards, the absolute value of the difference between the current power supply data of the power supply data type and the first data is obtained and denoted as the first difference of the power supply data type at the current monitoring moment. The first difference is normalized, and the normalized value obtained by the normalization process is denoted as the second characterization value. And the result of subtracting the second value to be processed from the preset constant is the normalized value obtained by normalizing the first difference. The second value to be processed is the reciprocal of the result of adding the first difference and the preset constant. The second characterization value is , d is the difference between the current power supply data of the power supply data type and the first data, The larger it is, it indicates that the current power supply data of this power supply data type has changed significantly, and also indicates that the mutation degree of the current power supply data of this power supply data type is more obvious. Then obtain the difference sequence of the first sequence, and the a-th difference in the difference sequence is the absolute value of the difference between the a-th power supply data and the (a + 1)-th power supply data in the first sequence; after that, obtain the absolute value of the difference between the first difference and the mean value of the difference sequence, and perform normalization processing on the absolute value of the difference between the first difference and the mean value of the difference sequence, and record the result obtained from the normalization processing as the third characterization value, and the result of subtracting the third value to be processed from the preset constant is the normalized value obtained by normalizing the first difference, the second value to be processed is the reciprocal of the result obtained by adding the absolute value of the difference between the first difference and the mean value of the difference sequence to the preset constant, and the third characterization value is , is the difference between the first difference and the mean value of the difference sequence. When is larger, it indicates that the first difference is not universal, then the probability that the current power supply data of this power supply data type is extreme data or accidentally occurring data is relatively large, that is, the accidental characteristic of the current power supply data of this power supply data type is more obvious. Finally, obtain the weighted sum value of the first characterization value, the second characterization value, and the third characterization value, and record it as the first weighted fusion characterization value. Record the result of subtracting the weighted fusion characterization value from the preset constant as the abnormal characterization value corresponding to this power supply data type at the current monitoring moment. The larger the abnormal characterization value, the greater the probability that the ring network cabinet is in an abnormal operating state at the current monitoring moment. The first weighted fusion characterization value is , F1 is the first characterization value, F2 is the second characterization value, F3 is the third characterization value, w1 is the first weight, and w2 is the second weight; moreover, when the first characterization value, the second characterization value, and the third characterization value are larger, it indicates that the possibility that the device collecting this power supply data type is interfered by the outside world at the current monitoring moment is greater, and also indicates that the possibility that the current power supply data of this power supply data type is abnormal data is smaller. That is, when the first weighted fusion characterization value is larger, it indicates that the possibility that the current power supply data of this power supply data type is abnormal data is smaller, then the abnormal characterization value corresponding to this power supply data type at the current monitoring moment is smaller; on the contrary, when the first weighted fusion characterization value is smaller, that is, when the abnormal characterization value corresponding to this power supply data type at the current monitoring moment is larger, it indicates that the probability of being interfered by the outside world when collecting the current power supply data of this power supply data type is smaller, indicating that the probability that the current power supply data of this power supply data type is abnormal data is greater, and also indicates that the probability that the ring network cabinet is in an abnormal operating state at the current monitoring moment is greater.

[0022] In this embodiment, the specific process of obtaining the abnormal characterization value corresponding to the power supply data type at the current monitoring moment according to the slope between the current power supply data of the power supply data type and the first data and the variance of the first sequence is as follows: First, obtain the reciprocal of the sum of the slope characterization value of the power supply data type at the current monitoring moment and the preset constant, and denote it as the slope eigenvalue; then obtain the variance of the first sequence, and denote the reciprocal of the sum of the preset constant and the variance of the first sequence as the variance eigenvalue, and denote the weighted sum result of the variance eigenvalue and the slope eigenvalue as the second weighted fusion characterization value. The second weighted fusion characterization value is , is the slope eigenvalue, The larger the value of, the more obvious the mutation degree of the current power supply data of the power supply data type. is the variance of the first sequence, When it is larger, it indicates that the data belonging to the power supply data type at this time has the characteristics of frequent change or more unstable change. w3 is the third weight value, and w4 is the fourth weight value; finally, denote the result of subtracting the second weighted fusion characterization value from the preset constant as the abnormal characterization value corresponding to the power supply data type at the current monitoring moment; and when is larger and is larger, it indicates that the probability that the current power supply data of the power supply data type is abnormal data is greater. On the contrary, when is smaller and is smaller, it indicates that the probability that the current power supply data of the power supply data type is normal data is greater. And when is larger and is larger, the second weighted fusion characterization value is smaller, and the abnormal characterization value corresponding to the power supply data type at the current monitoring moment is larger. Therefore, when the second weighted fusion characterization value is smaller or the abnormal characterization value corresponding to the power supply data type at the current monitoring moment is larger, it indicates that the probability that the current power supply data of the power supply data type is abnormal data is greater, and it also indicates that the probability that the ring main unit has an operating fault is greater or the probability that the ring main unit is in an abnormal operating state at the current monitoring moment is greater.

[0023] In specific applications, the implementer needs to set the values of w1, w2, w3, and w4 according to the actual situation or the difference in the importance of the abnormal characterization value. For example, when excluding externally interfered data from suspected abnormal data, the mutation feature is more important, then the value of w1 can be set to 0.6 and the value of w2 can be set to 0.4. When screening abnormal data from suspected normal data, the feature of frequent data change is more important, then the value of w3 can be set to 0.4 and the value of w4 can be set to 0.6.

[0024] Therefore, through the above process, this embodiment can obtain the abnormal characterization value corresponding to each power supply data type at the current monitoring moment.

[0025] A third acquisition module 03, configured to acquire a turning sequence corresponding to each of the power supply data types at the current monitoring moment, and obtain an affected weight value corresponding to each of the power supply data types at the current monitoring moment and an associated change characterization value at the current monitoring moment according to the turning sequence.

[0026] In order to further improve the reliability and accuracy of the status monitoring of the ring main unit in this embodiment, after obtaining the abnormal characterization values corresponding to each of the power supply data types at the current monitoring moment, the affected weight values corresponding to each of the power supply data types at the current monitoring moment and the associated change characterization values at the current monitoring moment are continuously obtained; and the reason for obtaining the affected weight values corresponding to each of the power supply data types at the current monitoring moment is that the affected weight values can reflect the influence of the data of different power supply data types on the data of other power supply data types or the influence of the data of different power supply data types on the data of other power supply data types. When the influence of the data of other power supply data types is greater, that is, when the affected weight value corresponding to the corresponding power supply data type is greater, it indicates that the corresponding power supply data type can better reflect the operating status of the ring main unit, and it also indicates that the corresponding power supply data type is more sensitive to the status change of the ring main unit. If the affected weight value corresponding to a certain power supply data type is greater, and at this time there is an operating fault that causes the data belonging to this power supply data type to be abnormal, then the probability that the data abnormality belonging to this power supply data type causes the data of other power supply data types to be abnormal together is relatively large. Then, in order to improve the monitoring accuracy, when determining the status of the ring main unit subsequently, the participation degree of the abnormal characterization value corresponding to the corresponding power supply data type at the current monitoring moment should be higher; and the reason for obtaining the associated change characterization value at the current monitoring moment is that the associated change characterization value can reflect that there are changes in multiple power supply data of the ring main unit at the current monitoring moment compared with the historical power supply data, but the changes at this time are not caused by the influence of different power supply data on each other. And when there are changes in multiple power supply data of the ring main unit compared with the historical power supply data, and these changes are not caused by the influence of different power supply data on each other, it indicates that the probability of abnormal operation of the ring main unit at the current monitoring moment is relatively large. Therefore, it is necessary to increase the attention to the ring main unit at this time.

[0027] Based on the above analysis, it can be known that in the following of this embodiment, it is also necessary to obtain the affected weight values corresponding to each power supply data type at the current monitoring moment and the associated change characterization value at the current monitoring moment. However, in this embodiment, before obtaining the affected weight values and the associated change characterization value, it is necessary to first obtain the turning sequence corresponding to each power supply data type at the current monitoring moment. That is, the turning sequence is the key to subsequent analysis for obtaining the affected weight values and the associated change characterization value. Then the specific process of obtaining the turning sequence corresponding to each power supply data type at the current monitoring moment is as follows: For any power supply data type: First, obtain the slope characterization value of this power supply data type at the historical monitoring moment, and the method for obtaining the slope characterization value of the power supply data type at the historical monitoring moment is the same as the method for obtaining the slope characterization value of the power supply data type at the current monitoring moment, so it will not be described in detail here; then normalize the slope characterization value of this power supply data type at the historical monitoring moment, and record the normalization result as the turning characterization value of this power supply data type at the corresponding historical monitoring moment. And the method for normalizing the slope characterization value of the power supply data type at the historical monitoring moment here is the same as the method for normalizing the slope characterization value of the power supply data type at the current monitoring moment described above; then obtain the historical monitoring moment that is the closest to the current monitoring moment and whose turning characterization value is greater than the preset turning threshold, and record it as the nearest turning monitoring moment corresponding to this power supply data type at the current monitoring moment. Finally, obtain the time series sequence composed of all power supply data belonging to this power supply data type collected from the nearest turning monitoring moment corresponding to this power supply data type at the current monitoring moment to the current monitoring moment, and record it as the turning sequence corresponding to this power supply data type at the current monitoring moment; if the turning characterization values of this power supply data type at all historical monitoring moments from the next historical monitoring moment of the historical monitoring moment t to the current monitoring moment are not greater than the preset turning threshold, while the turning characterization values of this power supply data type at the historical monitoring moment t are greater than the preset turning threshold, then the historical monitoring moment t will be recorded as the nearest turning monitoring moment corresponding to this power supply data type at the current monitoring moment, and the time series sequence composed of all power supply data belonging to this power supply data type collected from the historical monitoring moment t to the current monitoring moment is the turning sequence corresponding to this power supply data type at the current monitoring moment; in addition, in specific applications, the implementer needs to determine the preset turning threshold according to the actual situation and experimental statistics. For example, through experimental statistics, the preset turning threshold in this embodiment is 0.3.

[0028] After obtaining the turning sequence corresponding to the power supply data type at the current monitoring moment, the affected weight values corresponding to each power supply data type at the current monitoring moment and the associated change characterization value at the current monitoring moment are obtained according to the turning sequences corresponding to each power supply data type at the current monitoring moment.

[0029] In this embodiment, the specific process of obtaining the affected weight values corresponding to each power supply data type at the current monitoring moment is as follows: First, all power supply data types in the power supply data type set are arranged and combined pairwise without repetition to obtain all power supply data type combinations. The process of arrangement and combination is a well-known technology, so it will not be described in detail in this embodiment. Then, according to the nearest neighbor turning monitoring moment and turning sequence corresponding to each power supply data type in each power supply data type combination at the current monitoring moment, the influence index value corresponding to each power supply data type combination at the current monitoring moment is obtained. Then, the influence index value corresponding to each power supply data type combination at the historical monitoring moment is obtained. The method for obtaining the influence index value corresponding to each power supply data type combination at the historical monitoring moment is the same as that for obtaining the influence index value corresponding to each power supply data type combination at the current monitoring moment, so it will not be described in detail. Immediately afterwards, according to the influence index value corresponding to each power supply data type combination at the historical monitoring moment and the influence index value corresponding to each power supply data type combination at the current monitoring moment, the affected weight value corresponding to each power supply data type at the current monitoring moment is obtained. And if the affected weight value corresponding to each power supply data type at the current monitoring moment is larger, it indicates that when determining the state of the ring main unit, the participation degree of the abnormal characterization value corresponding to the power supply data type at the current monitoring moment should be higher.

[0030] In this embodiment, the specific process of obtaining the influence index value corresponding to each power supply data type combination at the current monitoring moment according to the nearest neighbor turning monitoring moment and the turning sequence corresponding to each power supply data type in each power supply data type combination at the current monitoring moment is as follows: First, obtain the time interval between the nearest neighbor turning monitoring moment of each power supply data type at the current monitoring moment and the current monitoring moment, and record it as the time distance of the corresponding power supply data type at the current monitoring moment; Then, obtain the number of extreme values in the turning sequence corresponding to each power supply data type at the current monitoring moment, and use it as the trend change characterization value corresponding to the corresponding power supply data type at the current monitoring moment. Extreme values include minimum values and maximum values; Then, take the process of obtaining the influence index value corresponding to any power supply data type combination A at the current monitoring moment as an example for description. That is, the process of obtaining the influence index value corresponding to the power supply data type combination A at the current monitoring moment is as follows: Denote the two power supply data types in the power supply data type combination A as power supply data type A1 and power supply data type A2 respectively, and denote the absolute value of the difference between the time distance of power supply data type A1 at the current monitoring moment and the time distance of power supply data type A2 at the current monitoring moment as the first change index value corresponding to the power supply data type combination A. And when the first change index value corresponding to the power supply data type combination A is smaller, it indicates that the time when the data of these two types of power supply data type A1 and power supply data type A2 change is closer, and it also indicates that the data belonging to power supply data type A1 at the current monitoring moment may have a greater impact on the data belonging to power supply data type A2 or the data belonging to power supply data type A2 at the current monitoring moment may have a greater impact on the data belonging to power supply data type A1. Then, the influence index value corresponding to the power supply data type combination A at the current monitoring moment is also larger; Denote the absolute value of the difference between the trend change characterization value corresponding to power supply data type A1 at the current monitoring moment and the trend change characterization value corresponding to power supply data type A2 at the current monitoring moment as the second change index value corresponding to the power supply data type combination A. And when the second change index value corresponding to the power supply data type combination A is smaller, it indicates that the probability that the trends of these two types of power supply data type A1 and power supply data type A2 are synchronously changing is greater, and it also indicates that the data belonging to power supply data type A1 at the current monitoring moment may have a greater impact on the data belonging to power supply data type A2 or the data belonging to power supply data type A2 at the current monitoring moment may have a greater impact on the data belonging to power supply data type A1. Then, the influence index value corresponding to the power supply data type combination A at the current monitoring moment is larger;Finally, obtain the reciprocals of the sum of the first change index value and the preset constant, and the sum of the second change index value and the preset constant, and denote them as the first reciprocal value and the second reciprocal value respectively. Take the average of the first reciprocal value and the second reciprocal value as the influence index value corresponding to the power supply data type combination A at the current monitoring moment. The specific expression of the influence index value corresponding to the power supply data type combination A at the current monitoring moment is; , where t1 is the time distance of the power supply data type A1 at the current monitoring moment, t2 is the time distance of the power supply data type A2 at the current monitoring moment, g1 is the trend change characterization value corresponding to the power supply data type A1 at the current monitoring moment, and g2 is the trend change characterization value corresponding to the power supply data type A2 at the current monitoring moment.

[0031] In this embodiment, according to the influence index values corresponding to each power supply data type combination at the historical monitoring moment and the influence index values corresponding to each power supply data type combination at the current monitoring moment, the specific process of obtaining the affected weight values corresponding to each power supply data type at the current monitoring moment is as follows: For any power supply data type: First, among all the power supply data type combinations, all the power supply data type combinations containing this power supply data type are denoted as characteristic combinations, and obtain the set of influence index values corresponding to each characteristic combination at the current monitoring moment. The set of influence index values corresponding to any characteristic combination at the current monitoring moment consists of the influence index value corresponding to this characteristic combination at the current monitoring moment and the influence index values corresponding to this characteristic combination at the nearest continuous preset number of historical monitoring moments to the current monitoring moment. If the preset number in this embodiment is 5 and the current monitoring moment is the jth monitoring moment, then the combination of the influence index values corresponding to this characteristic combination obtained from the (j - 5)th monitoring moment to the jth monitoring moment is the set of influence index values corresponding to this characteristic combination at the current monitoring moment. The set of influence index values includes the influence index values corresponding to this characteristic combination at the (j - 5)th monitoring moment and the jth monitoring moment; Then, obtain the average of the set of influence index values corresponding to each characteristic combination at the current monitoring moment, and denote it as the comprehensive index value corresponding to the corresponding characteristic combination at the current monitoring moment; Then, obtain the average of the comprehensive index values corresponding to all characteristic combinations at the current monitoring moment, and perform normalization processing on the average of the comprehensive index values corresponding to all characteristic combinations at the current monitoring moment. Take the result of the normalization processing as the affected weight value corresponding to this power supply data type at the current monitoring moment. Here, the normalization function Norm() is used to perform normalization processing on the average of the comprehensive index values corresponding to all characteristic combinations at the current monitoring moment.

[0032] In this embodiment, the specific process of obtaining the associated change characterization value at the current monitoring moment is as follows: First, obtain the change impact characterization values corresponding to all power supply data type combinations at the current monitoring moment, and record the average value of the change impact characterization values corresponding to all power supply data type combinations at the current monitoring moment as the associated change characterization value at the current monitoring moment. Moreover, when the change impact characterization values corresponding to all power supply data type combinations at the current monitoring moment are larger or the associated change characterization value at the current monitoring moment is larger, it indicates that the probability of data changes occurring in multiple power supply data types at the current monitoring moment is higher, and the probability that the data changes occurring at this time are caused by the influence between different power supply data types is lower. Then, the probability of abnormal operation of the ring main unit at the current monitoring moment will also be relatively high.

[0033] And the specific process of obtaining the change impact characterization value corresponding to the power supply data type combination at the current monitoring moment is as follows: For the power supply data type combination A: First, record the absolute value of the difference between the reciprocal of the first difference of the power supply data type A1 and the reciprocal of the first difference of the power supply data type A2 at the current monitoring moment as the first characteristic difference. The first characteristic difference can reflect the change similarity between the power supply data type A1 and the power supply data type A2 at the current moment, and the smaller it is, the more similar. That is, the first characteristic difference is the absolute value of the reciprocal difference between the first difference of the power supply data type A1 and the first difference of the power supply data type A2 at the current monitoring moment. Then, obtain the reciprocal of the result obtained by adding the preset constant and the first characteristic difference, and record it as the change similarity characteristic value corresponding to the power supply data type combination A. Obtain the result of subtracting the influence index value corresponding to the power supply data type combination A from the preset constant, and record it as the second characteristic difference. After that, obtain the result of multiplying the second characteristic difference and the change similarity characteristic value corresponding to the power supply data type combination A, and record it as the change impact characterization value corresponding to the power supply data type combination A at the current monitoring moment.

[0034] Therefore, through the above process, this embodiment obtains the affected weight values corresponding to each power supply data type at the current monitoring moment and the associated change characterization value at the current monitoring moment.

[0035] The status monitoring module 04 is used to monitor the status of the ring main unit at the current monitoring moment according to the abnormal characterization value, the change association characterization value, and the affected weight value.

[0036] After obtaining the abnormal characterization values corresponding to each power supply data type, the affected weight values corresponding to each power supply data type, and the associated change characterization value at the current monitoring moment, the ring main unit status determination index value at the current monitoring moment is obtained according to the abnormal characterization values corresponding to each power supply data type, the affected weight values corresponding to each power supply data type, and the associated change characterization value at the current monitoring moment. The specific process of obtaining the ring main unit status determination index value at the current monitoring moment is as follows: Obtain the weighted abnormal characterization values corresponding to each power supply data type at the current monitoring moment. The weighted abnormal characterization value corresponding to any power supply data type at the current monitoring moment is the product of the abnormal characterization value corresponding to the power supply data type and the affected weight value corresponding to the power supply data type at the current monitoring moment. Then, obtain the sum of the weighted abnormal characterization values corresponding to all power supply data types at the current monitoring moment, and denote the mean of the result obtained by adding the sum of the weighted abnormal characterization values corresponding to all power supply data types at the current monitoring moment and the associated change characterization value at the current monitoring moment as the ring main unit status determination index value at the current monitoring moment. That is, the expression of the ring main unit status determination index value at the current monitoring moment is , where H is the sum of the weighted abnormal characterization values corresponding to all power supply data types at the current monitoring moment, and Z is the ring main unit status determination index value at the current monitoring moment. Then, determine whether the ring main unit status determination index value at the current monitoring moment is greater than the preset determination threshold. If so, it is determined that the ring main unit has an abnormal operating state at the current monitoring moment. Otherwise, it is determined that the ring main unit is in a normal operating state at the current monitoring moment. In specific applications, the implementer needs to set the preset determination threshold according to the actual situation, experimental statistics, and the value range of the ring main unit status determination index value. For example, in this embodiment, the preset determination threshold is set to 0.4.

[0037] In addition, as another implementation, the status and handling method of the ring main unit can also be determined according to multiple preset judgment intervals. For example, if the value of the status determination index of the ring main unit at the current monitoring moment belongs to the preset first judgment interval, it is determined that the ring main unit is in a normal operating state at the current monitoring moment, and no warning needs to be issued. If the value of the status determination index of the ring main unit at the current monitoring moment belongs to the preset second judgment interval, a first-level warning is issued to remind the staff to pay attention to the subsequent status of the ring main unit. If the value of the status determination index of the ring main unit at the current monitoring moment belongs to the preset third judgment interval, a second-level warning is issued to inform the staff that there is a high possibility of a fault in the ring main unit and immediate inspection is required. If the value of the status determination index of the ring main unit at the current monitoring moment belongs to the preset fourth judgment interval, a third-level warning is issued to notify the staff that the ring main unit is operating abnormally and immediate shutdown for inspection and repair is required. And in this embodiment, the implementer also needs to set the judgment intervals according to the actual situation, experimental statistics, and the value range of the status determination index value of the ring main unit. For example, in this embodiment, the preset first judgment interval can be set to [0, 0.2], the preset second judgment interval can be set to (0.2, 0.4], the preset third judgment interval can be set to (0.4, 0.6], and the preset fourth judgment interval can be set to (0.6, 1].

[0038] So far, this embodiment has completed the monitoring of the status of the ring main unit.

[0039] In summary, this embodiment includes a first acquisition module for acquiring the current power supply data of each power supply data type in the power supply data type set at the current monitoring moment, the nearest neighbor power supply data corresponding to the current power supply data, and the nearest neighbor power supply data sequence; a second acquisition module for obtaining the abnormal characterization value corresponding to each power supply data type at the current monitoring moment according to the difference and slope between the current power supply data and the nearest neighbor power supply data corresponding to the current power supply data, the difference between adjacent power supply data in the nearest neighbor power supply data sequence, and the variance of the nearest neighbor power supply data sequence; a third acquisition module for acquiring the turning sequence corresponding to each power supply data type at the current monitoring moment, and obtaining the affected weight value corresponding to each power supply data type at the current monitoring moment and the associated change characterization value at the current monitoring moment according to the turning sequence; and a status monitoring module for monitoring the status of the ring main unit at the current monitoring moment according to the abnormal characterization value, the change association characterization value, and the affected weight value. And based on the abnormal characterization value, the change association characterization value, and the affected weight value, this embodiment can improve the credibility and accuracy of monitoring the status of the ring main unit.

[0040] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included within the protection scope of the present application.

Claims

1. A ring network box with intelligent power supply data monitoring function, characterized in that: The ring network box with intelligent power supply data monitoring function includes: A first acquisition module is used to acquire current power supply data of each power supply data type in the power supply data type set at the current monitoring moment and the nearest neighbor power supply data and the neighbor power supply data sequence corresponding to the current power supply data, wherein the power supply data is the power supply data of the ring network box; A second acquisition module is used to obtain the abnormal characterization value corresponding to each power supply data type at the current monitoring time according to the difference and slope between the current power supply data and the nearest neighbor power supply data corresponding to the current power supply data, the difference between adjacent power supply data in the nearest neighbor power supply data sequence, and the variance of the nearest neighbor power supply data sequence; A third acquisition module is used to obtain the transition sequence corresponding to each power supply data type at the current monitoring moment, and obtain the affected weight value corresponding to each power supply data type at the current monitoring moment and the associated change representation value at the current monitoring moment according to the transition sequence; The status monitoring module is used to monitor the status of the ring network box at the current monitoring moment according to the abnormal characterization value, the change association characterization value and the affected weight value.

2. A ring main box with intelligent power supply data monitoring function as claimed in claim 1, characterized in that: The method for acquiring the nearest neighbor power supply data and the neighbor power supply data sequence corresponding to the current power supply data includes: For any power supply data type, the power supply data belonging to the power supply data type collected at the previous historical monitoring moment before the current monitoring moment is recorded as the nearest neighbor power supply data corresponding to the current power supply data of the power supply data type, and the time series sequence composed of all power supply data belonging to the power supply data type collected at the current monitoring moment and a preset number of consecutive historical monitoring moments adjacent to the current monitoring moment is recorded as the neighbor power supply data sequence corresponding to the current power supply data of the power supply data type.

3. The ring main box with intelligent power supply data monitoring function as claimed in claim 1, characterized in that: The method for obtaining the abnormal characterization value corresponding to each power supply data type at the current monitoring moment includes: For any power supply data type: the nearest neighbor power supply data corresponding to the current power supply data of the power supply data type is recorded as the first data, and the neighbor power supply data sequence corresponding to the current power supply data of the power supply data type is recorded as the first sequence, and it is determined whether the current power supply data of the power supply data type exceeds the preset threshold interval corresponding to the power supply data type. If so, the abnormal characterization value corresponding to the power supply data type at the current monitoring moment is obtained according to the difference and slope between the current power supply data of the power supply data type and the first data and the difference between adjacent power supply data in the first sequence; otherwise, the abnormal characterization value corresponding to the power supply data type at the current monitoring moment is obtained according to the slope between the current power supply data of the power supply data type and the first data and the variance of the first sequence.

4. The ring main box with intelligent power supply data monitoring function as claimed in claim 3, characterized in that: The method for obtaining an abnormal characterization value corresponding to the power supply data type at a current monitoring moment according to a difference and a slope between current power supply data of the power supply data type and the first data and a difference between adjacent power supply data in the first sequence includes: Obtain the data points corresponding to the current power supply data of the power supply data type and the data points corresponding to the first data, which are respectively recorded as the first data point and the second data point, the horizontal coordinate of the first data point is the acquisition time of the current power supply data of the power supply data type, and the vertical coordinate is the current power supply data of the power supply data type, the horizontal coordinate of the second data point is the acquisition time of the first data, and the vertical coordinate is the first data; record the absolute value of the slope between the first data point and the second data point as the slope characterization value of the power supply data type at the current monitoring time, and record the normalized value of the slope characterization value of the power supply data type at the current monitoring time as the first characterization value; record the current power supply data of the power supply data type The absolute value of the difference between the ath power supply data and the first data is recorded as the first difference of the power supply data type at the current monitoring moment, and the normalized value of the first difference is recorded as the second characterization value; the difference sequence of the first sequence is obtained, and the ath difference in the difference sequence is the absolute value of the difference between the ath power supply data and the a+1th power supply data in the first sequence; the normalized value of the absolute value of the difference between the first difference and the mean of the difference sequence is recorded as the third characterization value; the weighted sum of the first characterization value, the second characterization value, and the third characterization value is recorded as the first weighted fusion characterization value; the result of subtracting the weighted fusion characterization value from the preset constant is recorded as the abnormal characterization value corresponding to the power supply data type at the current monitoring moment.

5. A ring main box with intelligent power supply data monitoring function as claimed in claim 4, characterized in that: The method for obtaining an abnormal characterization value corresponding to the power supply data type at a current monitoring moment according to a slope between current power supply data of the power supply data type and the first data and a variance of the first sequence includes: The reciprocal of the sum of the slope characterization value of the power supply data type at the current monitoring moment and the preset constant is recorded as the slope characteristic value, the reciprocal of the sum of the preset constant and the variance of the first sequence is recorded as the variance characteristic value, the weighted sum of the slope characteristic value and the variance characteristic value is recorded as the second weighted fusion characterization value, and the result of subtracting the second weighted fusion characterization value from the preset constant is recorded as the abnormal characterization value corresponding to the power supply data type at the current monitoring moment.

6. A ring main box with intelligent power supply data monitoring function as claimed in claim 4, characterized in that: The method for obtaining the transition sequence comprises: For any power supply data type, obtain the slope characterization value of the power supply data type at the historical monitoring moment, and use the normalized value of the slope characterization value of the power supply data type at the historical monitoring moment as the turning characterization value of the power supply data type at the corresponding historical monitoring moment; obtain the historical monitoring moment closest to the current monitoring moment and with a turning characterization value greater than a preset turning threshold, and record it as the nearest neighbor turning monitoring moment corresponding to the power supply data type at the current monitoring moment, and record the time series consisting of all power supply data belonging to the power supply data type collected from the nearest neighbor turning monitoring moment to the current monitoring moment as the turning sequence corresponding to the power supply data type at the current monitoring moment.

7. A ring main box with intelligent power supply data monitoring function as claimed in claim 6, characterized in that: The method for obtaining the affected weight value corresponding to each power supply data type at the current monitoring moment includes: All power supply data types in the power supply data type set are arranged and combined in pairs without repetition to obtain all power supply data type combinations; according to the nearest neighbor turning monitoring time and turning sequence corresponding to each power supply data type in the power supply data type combination at the current monitoring time, the impact index value corresponding to the power supply data type combination at the current monitoring time is obtained; For any power supply data type: among all power supply data type combinations, all power supply data type combinations containing the power supply data type are recorded as feature combinations, and the impact indicator value corresponding to each feature combination at the historical monitoring moment and the impact indicator value set corresponding to each feature combination at the current monitoring moment are obtained. The impact indicator value set corresponding to any feature combination at the current monitoring moment is composed of the impact indicator value corresponding to the corresponding feature combination at the current monitoring moment and the impact indicator values ​​corresponding to the corresponding feature combinations at a preset number of historical monitoring moments. The mean of the impact indicator value set corresponding to each feature combination at the current monitoring moment is recorded as the comprehensive indicator value corresponding to the corresponding feature combination at the current monitoring moment; the normalized value of the mean of the comprehensive indicator values ​​corresponding to all feature combinations at the current monitoring moment is used as the affected weight value corresponding to the power supply data type at the current monitoring moment.

8. A ring main box with intelligent power supply data monitoring function as claimed in claim 7, characterized in that: The method for obtaining the impact index value corresponding to the power supply data type combination at the current monitoring moment includes: The time interval between the nearest turning monitoring time of each power supply data type at the current monitoring time and the current monitoring time is recorded as the time distance of the corresponding power supply data type at the current monitoring time, and the number of extreme values ​​in the turning sequence corresponding to each power supply data type at the current monitoring time is taken as the trend change characterization value corresponding to the corresponding power supply data type at the current monitoring time; For any power supply data type combination A: the two power supply data types in the power supply data type combination A are respectively recorded as power supply data type A1 and power supply data type A2, the absolute value of the difference between the time distance of the power supply data type A1 at the current monitoring moment and the time distance of the power supply data type A2 at the current monitoring moment is recorded as the first change index value corresponding to the power supply data type combination A, the absolute value of the difference between the trend change characterization value corresponding to the power supply data type A1 at the current monitoring moment and the trend change characterization value corresponding to the power supply data type A2 at the current monitoring moment is recorded as the second change index value corresponding to the power supply data type combination A, the reciprocal of the first change index value added to the preset constant and the reciprocal of the second change index value added to the preset constant are respectively recorded as the first reciprocal value and the second reciprocal value, and the average of the first reciprocal value and the second reciprocal value is used as the impact index value corresponding to the power supply data type combination A at the current monitoring moment.

9. A ring main box with intelligent power supply data monitoring function as claimed in claim 8, characterized in that: The method for obtaining the correlation change representation value at the current monitoring moment includes: For the power supply data type combination A, the absolute value of the difference between the reciprocal of the first difference of the power supply data type A1 at the current monitoring moment and the reciprocal of the first difference of the power supply data type A2 at the current monitoring moment is recorded as the first characteristic difference, the reciprocal of the result obtained by adding a preset constant to the first characteristic difference is recorded as the change similarity characteristic value corresponding to the power supply data type combination A, and the result obtained by multiplying the change similarity characteristic value corresponding to the power supply data type combination A by the result obtained by subtracting the influence index value corresponding to the power supply data type combination A from the preset constant is recorded as the change influence characterization value corresponding to the power supply data type combination A at the current monitoring moment; The average of the change impact characterization values ​​corresponding to all power supply data type combinations at the current monitoring moment is recorded as the associated change characterization value at the current monitoring moment.

10. The ring main box with intelligent power supply data monitoring function as claimed in claim 1, characterized in that: The method for monitoring the state of the ring network box at the current monitoring moment according to the abnormal characterization value, the change association characterization value and the affected weight value includes: For any power supply data type, the product of the abnormal characterization value corresponding to the power supply data type at the current monitoring time and the affected weight value corresponding to the power supply data type at the current monitoring time is recorded as the weighted abnormal characterization value corresponding to the power supply data type at the current monitoring time; The average value of the result obtained by adding the cumulative sum of the weighted abnormal characterization values ​​corresponding to all power supply data types at the current monitoring moment and the associated change characterization value at the current monitoring moment is recorded as the ring network box status judgment index value at the current monitoring moment; if the ring network box status judgment index value is greater than the preset judgment threshold, the state of the ring network box is judged to be abnormal.

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