Full life cycle data tracking method and system of feeder automation terminal equipment

Abstract

The invention relates to the technical field of feeder line data tracking, and particularly discloses a full life cycle data tracking method and system for feeder line automation terminal equipment, and the method comprises the steps: carrying out the self-recognition of the line data of each monitoring node, and determining a risk value; the risk values of all the monitoring nodes are counted, and the correlation degree is combined to jointly determine the data acquisition frequency of any monitoring node; wherein the data acquisition frequency of any monitoring node is in direct proportion to the risk value of the related monitoring node. According to the method, the relevancy between the feeder automation terminal devices is firstly calculated, during actual use, risk identification is carried out on all the feeder automation terminal devices, and the data acquisition frequency of a certain feeder automation terminal is jointly determined according to the risk identification results of all enough related feeder automation terminal devices; a dynamic frequency is provided, on the premise that data comprehensiveness is guaranteed, resource requirements are reduced, and the cost reducing function is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of feeder data tracking, and in particular to a method and system for tracking data of a feeder automation terminal device throughout its life cycle. Background Art

[0002] Feeder automation terminal equipment usually refers to the feeder terminal unit (FTU) in the power system. It is a key device used in the distribution automation system and is installed on each branch node of the power feeder. It is mainly used to monitor, control and protect the switchgear in the distribution network to improve the stability and reliability of the power system.

[0003] The data acquisition frequency of existing feeder automation terminal equipment is fixed, and is generally global. Although this can ensure the comprehensiveness of the data, the acquired data is very redundant and requires a large amount of data processing and storage resources. In actual situations, the feeder system is in a stable state most of the time, and a lot of data does not need to be acquired. If the data acquisition process can be optimized, the resource requirements can be greatly reduced. Summary of the invention

[0004] The object of the present invention is to provide a method and system for tracking data of a feeder automation terminal device throughout its life cycle, so as to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for tracking data of a feeder automation terminal device throughout its life cycle, the method comprising:

[0007] Obtain a feeder model, query a power consumption interface of the feeder model, obtain power consumption information at the power consumption interface, and select a monitoring node in the feeder model based on the power consumption information;

[0008] Acquire line data based on the monitoring node, compare and identify the line data, and determine the relevance of the monitoring node;

[0009] Self-identify the line data of each monitoring node and determine the risk value;

[0010] Count the risk values ​​of all monitoring nodes and determine the data acquisition frequency of any monitoring node based on the correlation;

[0011] The data acquisition frequency of any monitoring node is proportional to the risk value of the related monitoring node, and the related monitoring node is a monitoring node whose correlation with the current monitoring node is greater than a preset threshold.

[0012] As a further solution of the present invention: the steps of obtaining a feeder model, querying a power interface of the feeder model, obtaining power usage information at the power interface, and selecting a monitoring node in the feeder model based on the power usage information include:

[0013] Obtain the feeder model, and query the output port in the feeder model as the power consumption interface;

[0014] Obtain the voltage and current at the power interface in real time and calculate the output power;

[0015] Statistic the output power according to the time sequence and fit the output power variation curve;

[0016] The integral characteristics and derivative characteristics of the output power variation curve are obtained, and the monitoring nodes are selected in the feeder model based on the integral characteristics and derivative characteristics of each power interface.

[0017] As a further solution of the present invention: the step of obtaining the integral characteristics and derivative characteristics of the output power variation curve and selecting a monitoring node in the feeder model based on the integral characteristics and derivative characteristics of each power interface includes:

[0018] The time frame for receiving designer input;

[0019] Taking the current moment as the end point, intercepting the power variation curve within the time range;

[0020] Calculate the integral value of the intercepted power variation curve as the integral feature;

[0021] Calculate the derivative of the intercepted power change curve, select the derivative based on a preset step size, and calculate the standard deviation of the derivative as a derivative feature;

[0022] Monitoring nodes are selected in the feeder model based on integral characteristics and derivative characteristics.

[0023] As a further solution of the present invention: the step of selecting monitoring nodes in the feeder model based on the integral characteristics and the derivative characteristics includes:

[0024] For any point in the feeder model, obtain the line distance between it and any power interface;

[0025] Query the integral characteristics and derivative characteristics corresponding to the power interface, and calculate the impact value of the power interface on the point in combination with the line distance;

[0026] Select points according to the impact value as monitoring nodes;

[0027] The selection rules are:

[0028] Calculate the sum of the influence values ​​of any point, and select the point with an influence value greater than the preset threshold as the monitoring node;

[0029] The impact value is calculated as follows:

[0030] Where Y i represents the influence value of the i-th power interface at the point, α represents the correction coefficient, Q represents the integral value, σ represents the standard deviation, and D i Indicates the distance between the i-th power interface and the point.

[0031] As a further solution of the present invention: the step of acquiring line data based on the monitoring node, comparing and identifying the line data, and determining the relevance of the monitoring node includes:

[0032] Acquire line data based on monitoring nodes and create a data sequence; the data in the data sequence contains a time tag;

[0033] Pair the monitoring nodes in pairs, align the data in the data sequence based on the time tags, and extract subsequences with the same two dimensions;

[0034] Calculate the correlation between the two subsequences as the correlation between the two monitoring nodes.

[0035] As a further solution of the present invention: the step of counting the risk values ​​of all monitoring nodes and determining the data acquisition frequency of any monitoring node in combination with the correlation comprises:

[0036] For any monitoring node, query the correlation between the monitoring node and all other monitoring nodes;

[0037] Comparing the correlation with a preset threshold, and when the correlation is greater than the threshold, marking another monitoring node as a related monitoring node;

[0038] Query the risk value of the relevant monitoring node, and determine the data acquisition frequency of the current monitoring node in combination with the risk value of the current monitoring node;

[0039] The process of determining the frequency of data acquisition is as follows:

[0040] Where f represents the data acquisition frequency of the current monitoring node, F j represents the risk value of the jth monitoring node among other monitoring nodes and the current monitoring node, β j represents the weight corresponding to the jth monitoring node among other monitoring nodes, S j It represents the correlation between the jth monitoring node among other monitoring nodes and the current monitoring node, A is a preset constant, E is a preset risk threshold, and M is the total number of monitoring nodes.

[0041] The technical solution of the present invention also provides a full life cycle data tracking system for feeder automation terminal equipment, the system comprising:

[0042] A monitoring node selection module is used to obtain a feeder model, query a power consumption interface of the feeder model, obtain power consumption information at the power consumption interface, and select a monitoring node in the feeder model based on the power consumption information;

[0043] A correlation determination module, used to obtain line data based on the monitoring node, compare and identify the line data, and determine the correlation of the monitoring node;

[0044] Data self-identification module, used to self-identify the line data of each monitoring node and determine the risk value;

[0045] The acquisition frequency adjustment module is used to count the risk values ​​of all monitoring nodes and determine the data acquisition frequency of any monitoring node based on the correlation;

[0046] The data acquisition frequency of any monitoring node is proportional to the risk value of the related monitoring node, and the related monitoring node is a monitoring node whose correlation with the current monitoring node is greater than a preset threshold.

[0047] As a further solution of the present invention: the monitoring node selection module includes:

[0048] An interface query unit, used to obtain a feeder model and query an output port in the feeder model as a power interface;

[0049] Output power calculation unit, used to obtain the voltage and current at the power interface in real time and calculate the output power;

[0050] A curve fitting unit, used to count the output power according to the time sequence and fit the output power variation curve;

[0051] An execution unit is selected to obtain the integral characteristics and derivative characteristics of the output power change curve, and a monitoring node is selected in the feeder model based on the integral characteristics and derivative characteristics of each power interface.

[0052] As a further solution of the present invention: the correlation determination module includes:

[0053] A data sequence creation unit, used to acquire line data based on monitoring nodes and create a data sequence; the data in the data sequence contains a time tag;

[0054] A data pairing unit is used to pair monitoring nodes in pairs, align the data in the data sequence based on the time tag, and extract subsequences with the same two dimensions;

[0055] The calculation execution unit is used to calculate the correlation between two subsequences as the correlation between two monitoring nodes.

[0056] As a further solution of the present invention: the acquisition frequency adjustment module includes:

[0057] A query unit, used for querying the correlation between any monitoring node and all other monitoring nodes;

[0058] A marking unit, used to compare the correlation with a preset threshold, and when the correlation is greater than the threshold, mark another monitoring node as a related monitoring node;

[0059] A determination unit, used to query the risk value of the relevant monitoring node, and determine the data acquisition frequency of the current monitoring node in combination with the risk value of the current monitoring node;

[0060] The process of determining the frequency of data acquisition is as follows:

[0061] Where f represents the data acquisition frequency of the current monitoring node, F j represents the risk value of the jth monitoring node among other monitoring nodes and the current monitoring node, β j represents the weight corresponding to the jth monitoring node among other monitoring nodes, S j It represents the correlation between the jth monitoring node among other monitoring nodes and the current monitoring node, A is a preset constant, E is a preset risk threshold, and M is the total number of monitoring nodes.

[0062] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention first calculates the correlation between each feeder automation terminal device, and in actual use, performs risk identification on all feeder automation equipment terminals, and jointly determines the data acquisition frequency of a certain feeder automation equipment terminal based on the risk identification results of all sufficiently relevant feeder automation equipment terminals, thereby providing a dynamic frequency, reducing resource requirements and achieving cost reduction while ensuring the comprehensiveness of data. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0064] Figure 1 The flowchart is a method for tracking data throughout the life cycle of feeder automation terminal equipment.

[0065] Figure 2 This is a structural block diagram of the composition of the full life cycle data tracking system for feeder automation terminal equipment. DETAILED DESCRIPTION

[0066] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0067] Figure 1 The flowchart of the method for tracking data of the whole life cycle of feeder automation terminal equipment is as follows. In an embodiment of the present invention, a method for tracking data of the whole life cycle of feeder automation terminal equipment is provided. The method includes:

[0068] Step S100: obtaining a feeder model, querying a power interface of the feeder model, obtaining power usage information at the power interface, and selecting a monitoring node in the feeder model based on the power usage information;

[0069] Feeder automation terminal equipment usually refers to the feeder terminal unit (Feeder Terminal Unit, FTU for short) in the power system. It is a key device used in the distribution automation system, installed on each branch node of the power feeder, and is mainly used to monitor, control and protect the switchgear in the distribution network to improve the stability and reliability of the power system; when building a power feeder, a feeder model will be created synchronously. The feeder model contains a port for outputting power, called a power interface, to obtain power consumption information at the power interface, identify the power consumption information at the power interface, and select points on the line, called monitoring nodes. Line data can be obtained at the monitoring nodes. The purpose of this application is to obtain line data through monitoring nodes as data for the entire life cycle of the entire feeder system.

[0070] Step S200: acquiring line data based on the monitoring node, comparing and identifying the line data, and determining the relevance of the monitoring node;

[0071] Detection equipment, including ammeters, are installed at the monitoring nodes to obtain line data. The process of obtaining line data has a frequency. Therefore, the obtained line data is actually discrete data. Each monitoring node corresponds to a batch of discrete data. By comparing the line data of different monitoring nodes, the correlation degree of each monitoring node can be determined. In the same feeder system, some lines are independent, and some lines are connected, and the corresponding line data will also have a certain correlation.

[0072] Step S300: self-identify the line data of each monitoring node and determine the risk value;

[0073] The line data of each monitoring node is self-identified. The self-identification process is actually to evaluate the line data of each monitoring node with the help of the existing identification model to determine whether there is a risk, which is represented by the risk value parameter. The larger the risk value, the higher the probability of risk.

[0074] Step S400: Count the risk values ​​of all monitoring nodes, and determine the data acquisition frequency of any monitoring node in combination with the correlation;

[0075] The risk values ​​of all monitoring nodes are counted, and then the data acquisition frequency of each monitoring node is determined. The higher the risk value, the higher the data acquisition frequency, and the more data is acquired. In the technical solution of the present invention, the original technology is expanded. It is not only related to the monitoring node itself, but also to other related monitoring nodes. According to the risk values ​​of multiple monitoring nodes, the risk situation of a certain monitoring node is jointly evaluated, and then the data acquisition frequency is determined. This is more in line with the global nature of the feeder system. The various modules in the feeder system are not independent in themselves. There is a certain degree of randomness in the separate analysis of the monitoring nodes. The solution provided by the present application reduces the probability of accidental situations.

[0076] Specifically, the data acquisition frequency of any monitoring node is proportional to the risk value of a related monitoring node, where the related monitoring node is a monitoring node whose correlation with the current monitoring node is greater than a preset threshold.

[0077] The steps of obtaining a feeder model, querying a power interface of the feeder model, obtaining power usage information at the power interface, and selecting a monitoring node in the feeder model based on the power usage information include:

[0078] Step S101: Obtain a feeder model, and query an output port in the feeder model as a power interface;

[0079] Step S102: acquiring the voltage and current at the power interface in real time and calculating the output power;

[0080] Step S103: Count the output power according to the time sequence and fit the output power variation curve;

[0081] Step S104: Obtain the integral characteristics and derivative characteristics of the output power variation curve, and select a monitoring node in the feeder model based on the integral characteristics and derivative characteristics of each power interface.

[0082] Obtain the feeder model and query the output port in the feeder model. The output port is the point of output power, called the power interface, and the power end is connected to the power interface. Obtain the voltage and current at the power interface in real time to calculate the output power. Count the output power at each moment and perform fitting to obtain the output power change curve. Identify the output power change curve, and then select some points on the line as monitoring nodes to install feeder automation terminal equipment.

[0083] Furthermore, the step of obtaining the integral characteristics and derivative characteristics of the output power variation curve and selecting a monitoring node in the feeder model based on the integral characteristics and derivative characteristics of each power interface includes:

[0084] The time frame for receiving designer input;

[0085] Taking the current moment as the end point, intercepting the power variation curve within the time range;

[0086] Calculate the integral value of the intercepted power variation curve as the integral feature;

[0087] Calculate the derivative of the intercepted power change curve, select the derivative based on a preset step size, and calculate the standard deviation of the derivative as a derivative feature;

[0088] Monitoring nodes are selected in the feeder model based on integral characteristics and derivative characteristics.

[0089] The above content specifically describes the identification process of the output power change curve. By integrating the output power change curve, we get the output power. By differentiating the output power change curve, we get the rate of change of the output power. The standard deviation of the rate of change reflects the fluctuation of the rate of change. The larger the standard deviation, the greater the fluctuation. Combining the output power (integral feature) and the standard deviation (derivative feature), we can jointly calculate the utility value of each point on the line selected as a monitoring node, and the monitoring node can be selected based on the utility value.

[0090] It is worth mentioning that regarding the standard deviation, the standard deviation is actually the probability in discrete data. Therefore, the power change curve needs to be discretized, that is, the value on the power change curve can be selected by the preset step size; since the power change curve is obtained by fitting, a simpler solution is actually to directly read the pre-fitting data corresponding to the power change curve. If the designer wants to control the number of selected derivatives, he can enter the preset step size.

[0091] In addition, considering the timeliness of the data, the designer can input a time range in advance, such as a week or a month, and the original data will use the data within this time range.

[0092] Specifically, the step of selecting a monitoring node in the feeder model based on the integral feature and the derivative feature includes:

[0093] For any point in the feeder model, obtain the line distance between it and any power interface;

[0094] Query the integral characteristics and derivative characteristics corresponding to the power interface, and calculate the impact value of the power interface on the point in combination with the line distance;

[0095] Select points according to the impact value as monitoring nodes;

[0096] The selection rules are:

[0097] Calculate the sum of the influence values ​​of any point, and select the point with an influence value greater than the preset threshold as the monitoring node;

[0098] The impact value is calculated as follows:

[0099] Where Y i represents the influence value of the i-th power interface at the point, α represents the correction coefficient, Q represents the integral value, σ represents the standard deviation, and D i Indicates the distance between the i-th power interface and the point.

[0100] The above content provides a specific monitoring node selection scheme. For any point in the feeder model, the technical solution of the present invention believes that it is affected by all power interfaces. By querying the integral characteristics and derivative characteristics corresponding to each power interface, the monitoring value of the power interface can be determined. Combined with the monitoring values ​​of all power interfaces, the comprehensive monitoring value of each point on the line can be determined; wherein, the monitoring value is represented by the influence value, and the comprehensive monitoring value is the sum of the influence values; it should be noted that in the process of calculating the influence value, in addition to the integral characteristics and derivative characteristics, it is also necessary to introduce the line distance parameter. The longer the line distance, the smaller the influence of the power interface on the point.

[0101] The calculation process of the impact value is explained as follows:

[0102] The influence value is inversely proportional to the distance, directly proportional to the integral feature, and directly proportional to the derivative feature. In addition, the importance of the derivative feature is relatively low. Therefore, a logarithmic-based composite function is introduced; of course, the designer can also nest other functions to adjust the importance of the three parameters.

[0103] The step of acquiring line data based on the monitoring node, comparing and identifying the line data, and determining the relevance of the monitoring node comprises:

[0104] Step S201: acquiring line data based on monitoring nodes and creating a data sequence; the data in the data sequence contains a time tag;

[0105] Step S202: pairing the monitoring nodes in pairs, aligning the data in the data sequence based on the time tags, and extracting subsequences with the same two dimensions;

[0106] Step S203: Calculate the correlation between the two subsequences as the correlation between the two monitoring nodes.

[0107] The purpose of the above content is to evaluate the correlation between two monitoring nodes and obtain the line data of the two monitoring nodes. The line data is the data corresponding to each moment, and the obtained data sequence is essentially a time series. Any two monitoring nodes need to calculate the correlation once. Therefore, the monitoring nodes are first paired with each other. For the two monitoring nodes, their corresponding data sequences are read. Since the data acquisition frequency of each monitoring node is different, the data in the data sequence is actually data at different moments, and it is meaningless to compare them together. For this, the present application first aligns the data according to the time tag, extracts the same amount of data from the two data sequences respectively, obtains two subsequences, and calculates the correlation of the two subsequences as the correlation of the two monitoring nodes.

[0108] As for the correlation between two subsequences, since they are essentially time series, the Dynamic Time Warping (DTW) algorithm can be used to calculate the distance between the two subsequences and determine the correlation based on the inverse ratio of the distance; DTW allows nonlinear deformation of the time series on the time axis to find the optimal alignment between the two sequences.

[0109] The step of counting the risk values ​​of all monitoring nodes and determining the data acquisition frequency of any monitoring node in combination with the correlation comprises:

[0110] Step S401: for any monitoring node, query the correlation between the monitoring node and all other monitoring nodes;

[0111] Step S402: comparing the correlation with a preset threshold, and when the correlation is greater than the threshold, marking another monitoring node as a related monitoring node;

[0112] Step S403: query the risk value of the relevant monitoring node, and determine the data acquisition frequency of the current monitoring node in combination with the risk value of the current monitoring node.

[0113] Finally, for any monitoring node, the monitoring nodes related to it can be queried according to the correlation, which are called related monitoring nodes. The risk value of the related monitoring node is queried, and the data acquisition frequency of the current monitoring node is determined together with the risk value of the current monitoring node.

[0114] The process of determining the frequency of data acquisition is as follows:

[0115] Where f represents the data acquisition frequency of the current monitoring node, F j represents the risk value of the jth monitoring node among other monitoring nodes and the current monitoring node, β j represents the weight corresponding to the jth monitoring node among other monitoring nodes, S j It represents the correlation between the jth monitoring node among other monitoring nodes and the current monitoring node, A is a preset constant, E is a preset risk threshold, and M is the total number of monitoring nodes.

[0116] The rule for determining the frequency of data acquisition is very simple. If the correlation is small, the weight of the corresponding risk value is zero and has no effect. If the risk value is sufficient, the data acquisition frequency is adjusted according to the direct proportion of the risk value. Among them, M-1 means the number of monitoring nodes other than the current monitoring node.

[0117] Figure 2 The structure diagram of the whole life cycle data tracking system of feeder automation terminal equipment is as follows. In an embodiment of the present invention, a whole life cycle data tracking system of feeder automation terminal equipment is provided. The system 10 includes:

[0118] A monitoring node selection module 11 is used to obtain a feeder model, query a power interface of the feeder model, obtain power consumption information at the power interface, and select a monitoring node in the feeder model based on the power consumption information;

[0119] A correlation determination module 12 is used to obtain line data based on the monitoring node, compare and identify the line data, and determine the correlation of the monitoring node;

[0120] The data self-identification module 13 is used to self-identify the line data of each monitoring node and determine the risk value;

[0121] The acquisition frequency adjustment module 14 is used to count the risk values ​​of all monitoring nodes and determine the data acquisition frequency of any monitoring node in combination with the correlation;

[0122] The data acquisition frequency of any monitoring node is proportional to the risk value of the related monitoring node, and the related monitoring node is a monitoring node whose correlation with the current monitoring node is greater than a preset threshold.

[0123] Furthermore, the monitoring node selection module 11 includes:

[0124] An interface query unit, used to obtain a feeder model and query an output port in the feeder model as a power interface;

[0125] Output power calculation unit, used to obtain the voltage and current at the power interface in real time and calculate the output power;

[0126] A curve fitting unit, used to count the output power according to the time sequence and fit the output power variation curve;

[0127] An execution unit is selected to obtain the integral characteristics and derivative characteristics of the output power change curve, and a monitoring node is selected in the feeder model based on the integral characteristics and derivative characteristics of each power interface.

[0128] Specifically, the correlation determination module 12 includes:

[0129] A data sequence creation unit, used to acquire line data based on monitoring nodes and create a data sequence; the data in the data sequence contains a time tag;

[0130] A data pairing unit is used to pair monitoring nodes in pairs, align the data in the data sequence based on the time tag, and extract subsequences with the same two dimensions;

[0131] The calculation execution unit is used to calculate the correlation between two subsequences as the correlation between two monitoring nodes.

[0132] In addition, the acquisition frequency adjustment module 14 includes:

[0133] A query unit, used for querying the correlation between any monitoring node and all other monitoring nodes;

[0134] A marking unit, used to compare the correlation with a preset threshold, and when the correlation is greater than the threshold, mark another monitoring node as a related monitoring node;

[0135] A determination unit, used to query the risk value of the relevant monitoring node, and determine the data acquisition frequency of the current monitoring node in combination with the risk value of the current monitoring node;

[0136] The process of determining the frequency of data acquisition is as follows:

[0137] Where f represents the data acquisition frequency of the current monitoring node, F j represents the risk value of the jth monitoring node among other monitoring nodes and the current monitoring node, β j represents the weight corresponding to the jth monitoring node among other monitoring nodes, S j It represents the correlation between the jth monitoring node among other monitoring nodes and the current monitoring node, A is a preset constant, E is a preset risk threshold, and M is the total number of monitoring nodes.

[0138] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for tracking data of a feeder automation terminal device throughout its life cycle, characterized in that: The method comprises: Obtain a feeder model, query a power consumption interface of the feeder model, obtain power consumption information at the power consumption interface, and select a monitoring node in the feeder model based on the power consumption information; Acquire line data based on the monitoring node, compare and identify the line data, and determine the relevance of the monitoring node; Self-identify the line data of each monitoring node and determine the risk value; Count the risk values ​​of all monitoring nodes and determine the data acquisition frequency of any monitoring node based on the correlation; The data acquisition frequency of any monitoring node is proportional to the risk value of the related monitoring node, and the related monitoring node is a monitoring node whose correlation with the current monitoring node is greater than a preset threshold.

2. The method for tracking data of the entire life cycle of feeder automation terminal equipment according to claim 1, characterized in that: The steps of obtaining a feeder model, querying a power interface of the feeder model, obtaining power usage information at the power interface, and selecting a monitoring node in the feeder model based on the power usage information include: Obtain the feeder model, and query the output port in the feeder model as the power consumption interface; Obtain the voltage and current at the power interface in real time and calculate the output power; Statistic the output power according to the time sequence and fit the output power variation curve; The integral characteristics and derivative characteristics of the output power variation curve are obtained, and the monitoring nodes are selected in the feeder model based on the integral characteristics and derivative characteristics of each power interface.

3. The method for tracking data of the entire life cycle of feeder automation terminal equipment according to claim 2, characterized in that: The step of obtaining the integral characteristics and derivative characteristics of the output power variation curve and selecting a monitoring node in the feeder model based on the integral characteristics and derivative characteristics of each power interface includes: The time frame for receiving designer input; Taking the current moment as the end point, intercepting the power variation curve within the time range; Calculate the integral value of the intercepted power variation curve as the integral feature; Calculate the derivative of the intercepted power change curve, select the derivative based on a preset step size, and calculate the standard deviation of the derivative as a derivative feature; Monitoring nodes are selected in the feeder model based on integral characteristics and derivative characteristics.

4. The method for tracking data of the entire life cycle of feeder automation terminal equipment according to claim 3 is characterized in that: The step of selecting monitoring nodes in the feeder model based on the integral feature and the derivative feature includes: For any point in the feeder model, obtain the line distance between it and any power interface; Query the integral characteristics and derivative characteristics corresponding to the power interface, and calculate the impact value of the power interface on the point in combination with the line distance; Select points according to the impact value as monitoring nodes; The selection rules are: Calculate the sum of the influence values ​​of any point, and select the point with an influence value greater than the preset threshold as the monitoring node; The impact value is calculated as follows: Where Y i represents the influence value of the i-th power interface at the point, α represents the correction coefficient, Q represents the integral value, σ represents the standard deviation, and D i Indicates the distance between the i-th power interface and the point.

5. The method for tracking data of the entire life cycle of feeder automation terminal equipment according to claim 1, characterized in that: The step of acquiring line data based on the monitoring node, comparing and identifying the line data, and determining the relevance of the monitoring node comprises: Acquire line data based on monitoring nodes and create a data sequence; the data in the data sequence contains a time tag; Pair the monitoring nodes in pairs, align the data in the data sequence based on the time tags, and extract subsequences with the same two dimensions; Calculate the correlation between the two subsequences as the correlation between the two monitoring nodes.

6. The method for tracking data of the entire life cycle of feeder automation terminal equipment according to claim 1, characterized in that: The step of counting the risk values ​​of all monitoring nodes and determining the data acquisition frequency of any monitoring node in combination with the correlation comprises: For any monitoring node, query the correlation between the monitoring node and all other monitoring nodes; Comparing the correlation with a preset threshold, and when the correlation is greater than the threshold, marking another monitoring node as a related monitoring node; Query the risk value of the relevant monitoring node, and determine the data acquisition frequency of the current monitoring node in combination with the risk value of the current monitoring node; The process of determining the frequency of data acquisition is as follows: Where f represents the data acquisition frequency of the current monitoring node, F j represents the risk value of the jth monitoring node among other monitoring nodes and the current monitoring node, β j represents the weight corresponding to the jth monitoring node among other monitoring nodes, S j It represents the correlation between the jth monitoring node among other monitoring nodes and the current monitoring node, A is a preset constant, E is a preset risk threshold, and M is the total number of monitoring nodes.

7. A full life cycle data tracking system for feeder automation terminal equipment, characterized in that: The system comprises: A monitoring node selection module is used to obtain a feeder model, query a power consumption interface of the feeder model, obtain power consumption information at the power consumption interface, and select a monitoring node in the feeder model based on the power consumption information; A correlation determination module, used to obtain line data based on the monitoring node, compare and identify the line data, and determine the correlation of the monitoring node; Data self-identification module, used to self-identify the line data of each monitoring node and determine the risk value; The acquisition frequency adjustment module is used to count the risk values ​​of all monitoring nodes and determine the data acquisition frequency of any monitoring node based on the correlation; The data acquisition frequency of any monitoring node is proportional to the risk value of the related monitoring node, and the related monitoring node is a monitoring node whose correlation with the current monitoring node is greater than a preset threshold.

8. The method and system for tracking data of the entire life cycle of feeder automation terminal equipment according to claim 7, characterized in that: The monitoring node selection module includes: An interface query unit, used to obtain a feeder model and query an output port in the feeder model as a power interface; Output power calculation unit, used to obtain the voltage and current at the power interface in real time and calculate the output power; A curve fitting unit, used to count the output power according to the time sequence and fit the output power variation curve; An execution unit is selected to obtain the integral characteristics and derivative characteristics of the output power change curve, and a monitoring node is selected in the feeder model based on the integral characteristics and derivative characteristics of each power interface.

9. The method and system for tracking data of the entire life cycle of feeder automation terminal equipment according to claim 7, characterized in that: The correlation determination module comprises: A data sequence creation unit, used to acquire line data based on monitoring nodes and create a data sequence; the data in the data sequence contains a time tag; A data pairing unit is used to pair monitoring nodes in pairs, align the data in the data sequence based on the time tag, and extract subsequences with the same two dimensions; The calculation execution unit is used to calculate the correlation between two subsequences as the correlation between two monitoring nodes.

10. The method and system for tracking data of the entire life cycle of feeder automation terminal equipment according to claim 7, characterized in that: The acquisition frequency adjustment module includes: A query unit, used for querying the correlation between any monitoring node and all other monitoring nodes; A marking unit, used to compare the correlation with a preset threshold, and when the correlation is greater than the threshold, mark another monitoring node as a related monitoring node; A determination unit, used to query the risk value of the relevant monitoring node, and determine the data acquisition frequency of the current monitoring node in combination with the risk value of the current monitoring node; The process of determining the frequency of data acquisition is as follows: Where f represents the data acquisition frequency of the current monitoring node, F j represents the risk value of the jth monitoring node among other monitoring nodes and the current monitoring node, β j represents the weight corresponding to the jth monitoring node among other monitoring nodes, S j It represents the correlation between the jth monitoring node among other monitoring nodes and the current monitoring node, A is a preset constant, E is a preset risk threshold, and M is the total number of monitoring nodes.

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