Full Lifecycle Data Tracking Method and System for Feeder Automation Terminal Equipment

Abstract

The present invention relates to the technical field of feeder data tracking, and specifically discloses a full-life cycle data tracking method and system for a feeder automation terminal device. The method includes self-identifying the line data of each monitoring node to determine a risk value; statistically analyzing the risk values of all monitoring nodes, and jointly determining the data acquisition frequency of any monitoring node in combination with the relevance; wherein, the data acquisition frequency of any monitoring node is directly proportional to the risk value of the relevant monitoring node. The present invention first calculates the relevance between each feeder automation terminal device. During actual use, risk identification is performed on all feeder automation device terminals, and the data acquisition frequency of a certain feeder automation device terminal is jointly determined according to the risk identification results of all sufficiently relevant feeder automation device terminals, providing a dynamic frequency. On the premise of ensuring data comprehensiveness, the resource requirements are reduced, and the cost reduction function is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of feeder data tracking, and specifically to a full-life cycle data tracking method and system for feeder automation terminal equipment. Background Art

[0002] Feeder automation terminal equipment generally refers to the Feeder Terminal Unit (FTU) in the power system. It is a key device used in the distribution automation system, installed at each branch node of the power feeder, mainly used for monitoring, controlling, and protecting the switching equipment 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 generally global acquisition. Although this can ensure the comprehensiveness of 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 mostly in a stable state, and a lot of data does not need to be acquired. If the data acquisition process can be optimized, then the resource requirements can be greatly reduced. Summary of the Invention

[0004] The purpose of the present invention is to provide a full-life cycle data tracking method and system for feeder automation terminal equipment to solve the problems raised in the above background art.

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

[0006] A full-life cycle data tracking method for feeder automation terminal equipment, the method includes:

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

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

[0009] Perform self-identification on the line data of each monitoring node to determine the risk value;

[0010] Statistically calculate the risk values of all monitoring nodes, and jointly determine the data acquisition frequency of any monitoring node in combination with the relevance;

[0011] Among them, the data acquisition frequency of any monitoring node is directly proportional to the risk value of the relevant monitoring node, and the relevant monitoring node is a monitoring node whose relevance to the current monitoring node is greater than a preset threshold.

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

[0013] Obtain the feeder model, query the output ports in the feeder model, and use them as power consumption interfaces;

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

[0015] Statistically analyze the output power according to the time sequence, and fit the output power change curve;

[0016] Obtain the integral features and derivative features of the output power change curve, and select monitoring nodes in the feeder model based on the integral features and derivative features of each power consumption interface.

[0017] As a further solution of the present invention, the steps of obtaining the integral features and derivative features of the output power change curve, and selecting monitoring nodes in the feeder model based on the integral features and derivative features of each power consumption interface include:

[0018] Receive the time range input by the designer;

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

[0020] Calculate the integral value of the intercepted power change 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 the derivative feature;

[0022] Select monitoring nodes in the feeder model based on the integral features and derivative features.

[0023] As a further solution of the present invention, the steps of selecting monitoring nodes in the feeder model based on the integral features and derivative features include:

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

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

[0026] Select points based on the influence value as monitoring nodes;

[0027] The selection rule is:

[0028] Calculate the sum of the influence values of any point, and select the points with influence values greater than the preset threshold as monitoring nodes;

[0029] The calculation method of the influence value is as follows:

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

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

[0032] Obtain line data based on the monitoring node and create a data sequence; the data in the data sequence contains time tags;

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

[0034] Calculate the relevance of the two subsequences as the relevance of the two monitoring nodes.

[0035] As a further solution of the present invention: The step of statistically calculating the risk values of all monitoring nodes and jointly determining the data acquisition frequency of any monitoring node based on the relevance includes:

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

[0037] Compare the relevance with a preset threshold. When the relevance is greater than the threshold, mark the other monitoring node as a relevant monitoring node;

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

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

[0040] In the formula, f represents the data acquisition frequency of the current monitoring node, and F j represents the risk value of the j-th monitoring node among other monitoring nodes and the current monitoring node, β j represents the weight corresponding to the j-th monitoring node among other monitoring nodes, S j represents the relevance between the j-th 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 a feeder automation terminal device, and the system includes:

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

[0043] A correlation judgment module, configured to obtain line data based on the monitoring nodes, perform comparison and identification on the line data, and determine the relevance of the monitoring nodes;

[0044] A data self - identification module, configured to perform self - identification on the line data of each monitoring node to determine a risk value;

[0045] An acquisition frequency adjustment module, configured to count the risk values of all monitoring nodes, and jointly determine the data acquisition frequency of any monitoring node in combination with the relevance;

[0046] Wherein, the data acquisition frequency of any monitoring node is directly proportional to the risk value of the relevant monitoring nodes, and the relevant monitoring nodes are the monitoring nodes whose relevance to 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, configured to obtain a feeder model and query the output ports in the feeder model as the power consumption interfaces;

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

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

[0051] A selection execution unit, configured to obtain the integral feature and derivative feature of the output power change curve, and select monitoring nodes in the feeder model based on the integral feature and derivative feature of each power consumption interface.

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

[0053] A data sequence creation unit, configured to obtain line data based on the monitoring nodes and create a data sequence; the data in the data sequence contains time tags;

[0054] A data pairing unit, configured to pair the monitoring nodes pairwise, register the data in the data sequence based on the time tags, and extract subsequences with the same two dimensions;

[0055] A computing execution unit for calculating the correlation degree of two subsequences as the correlation degree of two monitoring nodes.

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

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

[0058] A marking unit for comparing the correlation degree with a preset threshold, and when the correlation degree is greater than the threshold, marking the other monitoring node as a relevant monitoring node;

[0059] A determination unit for querying the risk value of the relevant monitoring node and jointly determining 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 data acquisition frequency is as follows:

[0061] In the formula, f represents the data acquisition frequency of the current monitoring node, F j represents the risk value of the j-th monitoring node among other monitoring nodes and the current monitoring node, β j represents the weight corresponding to the j-th monitoring node among other monitoring nodes, S j represents the correlation degree between the j-th 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 degree between each feeder automation terminal device. During actual use, risk identification is performed on all feeder automation device terminals, and the data acquisition frequency of a certain feeder automation device terminal is jointly determined according to the risk identification results of all sufficiently relevant feeder automation device terminals, providing a dynamic frequency. On the premise of ensuring data comprehensiveness, resource requirements are reduced, and the cost reduction function is achieved. Description of the Drawings

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

[0064] Figure 1 It is a flowchart of the full life cycle data tracking method for feeder automation terminal devices.

[0065] Figure 2 It is a block diagram of the composition structure of the full life cycle data tracking system for feeder automation terminal devices. Detailed Implementation Manner

[0066] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clear and understandable, the present invention will be further described in detail below with reference to 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 used to limit the present invention.

[0067] Figure 1 It is a flowchart of a full - life - cycle data tracking method for a feeder automation terminal device. In an embodiment of the present invention, a full - life - cycle data tracking method for a feeder automation terminal device, the method includes:

[0068] Step S100: Obtain a feeder model, query the power - using interface of the feeder model, obtain the power - using information at the power - using interface, and select monitoring nodes in the feeder model based on the power - using information.

[0069] The feeder automation terminal device generally refers to the Feeder Terminal Unit (FTU) in the power system. It is a key device used in the distribution automation system, installed at each branch node of the power feeder, mainly used to monitor, control and protect the switching equipment 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 ports for outputting power, which are called power - using interfaces. By obtaining the power - using information at the power - using interfaces and identifying the power - using information at the power - using interfaces, points can be selected on the line, which are called monitoring nodes. Line data can be obtained at the monitoring nodes. The purpose of this application is to obtain line data through the monitoring nodes as the data of the entire life cycle of the feeder system.

[0070] Step S200: Obtain line data based on the monitoring nodes, compare and identify the line data, and determine the relevance of the monitoring nodes.

[0071] Detection devices, including ammeters, etc., are installed at the monitoring nodes to obtain line data. There is a frequency in the process of obtaining line data. 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 degree of relevance of each monitoring node can be judged; in the same feeder system, some lines are independent, and some lines are connected. The corresponding line data will also have a certain correlation.

[0072] Step S300: Self - identify the line data of each monitoring node to determine the risk value.

[0073] Self-identify the line data of each monitoring node. The self-identification process is actually to evaluate the line data of each monitoring node with 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 the occurrence of the risk.

[0074] Step S400: Statistically analyze the risk values of all monitoring nodes, and jointly determine the data acquisition frequency of any monitoring node in combination with the relevance.

[0075] Statistically analyze the risk values of all monitoring nodes, and then determine the data acquisition frequency of each monitoring node. 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 extended. It is not only related to the monitoring node itself, but also related to other relevant monitoring nodes. According to the risk values of multiple monitoring nodes, jointly evaluate the risk situation of a certain monitoring node, and then determine the data acquisition frequency, which is more in line with the global nature of the feeder system. Each module in the feeder system is not independent itself. Conducting a separate analysis of the monitoring node has a certain degree of contingency. The solution provided by this application reduces the probability of accidental situations.

[0076] Specifically, the data acquisition frequency of any monitoring node is directly proportional to the risk value of the relevant monitoring nodes, and the relevant monitoring nodes are the monitoring nodes whose relevance to the current monitoring node is greater than the preset threshold.

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

[0078] Step S101: Obtain the feeder model, and query the output port in the feeder model as the power consumption interface.

[0079] Step S102: Real-time obtain the voltage and current at the power consumption interface, and calculate the output power.

[0080] Step S103: Statistically analyze the output power according to the time sequence, and fit the output power change curve.

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

[0082] Obtain a feeder model, query the output port in the feeder model. The output port is the point where the output power is located, which is called the power consumption interface, and the power consumption end is connected to the power consumption interface; obtain the voltage and current at the power consumption interface in real time, 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 and install feeder automation terminal equipment.

[0083] Further, the steps of obtaining the integral feature and derivative feature of the output power change curve and selecting monitoring nodes in the feeder model based on the integral feature and derivative feature of each power consumption interface include:

[0084] Receive the time range input by the designer;

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

[0086] Calculate the integral value of the intercepted power change 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 the derivative feature;

[0088] Select monitoring nodes in the feeder model based on the integral feature and derivative feature.

[0089] The above content specifically describes the recognition process of the output power change curve. Integrating the output power change curve gives the output power consumption, and differentiating the output power change curve gives the change rate of the output power. The standard deviation of the change rate reflects the fluctuation of the change rate. The larger the standard deviation, the greater the fluctuation; combining the output power consumption (integral feature) and the standard deviation (derivative feature) can jointly calculate the utility value of each point on the line being selected as a monitoring node, and the monitoring nodes 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, it is necessary to discretize the power change curve, that is, select values on the power change curve through a preset step size; since the power change curve is obtained by fitting, a relatively simple solution is actually to directly read the data before fitting corresponding to the power change curve. If the designer wants to control the number of selected derivatives, the preset step size can be input.

[0091] In addition, considering the timeliness of the data, the designer can pre-enter a time range, such as one week or one month, and the original data uses the data within this time range.

[0092] Specifically, the steps of selecting monitoring nodes in the feeder model based on integral features and derivative features include:

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

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

[0095] Select points based on the influence value as monitoring nodes;

[0096] The selection rule is:

[0097] Calculate the sum of the influence values of any point, and select the points with influence values greater than the preset threshold as monitoring nodes;

[0098] The calculation method of the influence value is:

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

[0100] The above content provides a specific selection scheme for monitoring nodes. For any point in the feeder model, the technical solution of the present invention believes that it is affected by all power consumption interfaces. By querying the integral features and derivative features corresponding to each power consumption interface, the monitoring value of the power consumption interface can be judged. Combining the monitoring values of all power consumption interfaces, the comprehensive monitoring value of each point on the line can be determined; among them, 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 features and derivative features, the parameter of line distance also needs to be introduced. The longer the line distance, the smaller the influence degree of the power consumption interface on the point.

[0101] Regarding the calculation process of the influence value, it is described 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, so a composite function based on logarithm is introduced; of course, the designer can also nest other functions to adjust the importance of the three parameters.

[0103] The steps of obtaining line data based on the monitoring nodes, comparing and identifying the line data, and determining the relevance of the monitoring nodes include:

[0104] Step S201: Obtain line data based on the monitoring nodes and create a data sequence; the data in the data sequence contains time tags;

[0105] Step S202: Pair the monitoring nodes in pairs, register the data in the data sequence based on the time tags, and extract two subsequences with the same number of dimensions.

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

[0107] The purpose of the above content is to evaluate the correlation degree of two monitoring nodes, 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. The correlation degree needs to be calculated for any two monitoring nodes. Therefore, first pair the monitoring nodes in pairs. For two monitoring nodes, read their corresponding data sequences. Since the data acquisition frequencies of each monitoring node are different, the data in the data sequence are actually data at different moments, and it is meaningless to compare them together. For this, the present application first registers the data according to the time tags, extracts the same number of data from the two data sequences respectively to obtain two subsequences, and calculates the correlation degree of the two subsequences as the correlation degree of the two monitoring nodes.

[0108] For the correlation degree of the 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 the correlation degree is determined according to the inverse of the distance; DTW allows non-linear deformation of the time series on the time axis to find the best alignment between the two sequences.

[0109] The steps of statistically calculating the risk values of all monitoring nodes and jointly determining the data acquisition frequency of any monitoring node in combination with the correlation degree include:

[0110] Step S401: For any monitoring node, query the correlation degree between this monitoring node and all other monitoring nodes.

[0111] Step S402: Compare the correlation degree with a preset threshold. When the correlation degree is greater than the threshold, mark the other monitoring node as a related monitoring node.

[0112] Step S403: Query the risk value of the related monitoring node, and jointly 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 related monitoring nodes can be queried according to the correlation degree. The risk value of the related monitoring node is queried, and the data acquisition frequency of the current monitoring node is jointly determined in combination with the risk value of the current monitoring node.

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

[0115] In the formula, f represents the data acquisition frequency of the current monitoring node, and F j represents the risk value of the j-th monitoring node among other monitoring nodes and the current monitoring node, and β j represents the weight corresponding to the j-th monitoring node among other monitoring nodes, and S j represents the correlation degree between the j-th 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 data acquisition frequency is very simple. If the correlation degree is small, the weight of the corresponding risk value is zero and has no impact. If the risk value is sufficient, the data acquisition frequency is adjusted and determined according to the direct proportion of the risk value; among them, the meaning of M−1 is the number of other monitoring nodes except the current monitoring node.

[0117] Figure 2 FIG. is a block diagram of the composition structure of the full-life cycle data tracking system for the feeder automation terminal device. In an embodiment of the present invention, a full-life cycle data tracking system for a feeder automation terminal device, the system 10 includes:

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

[0119] A correlation judgment module 12, configured to obtain line data based on the monitoring node, compare and identify the line data, and determine the correlation degree of the monitoring node;

[0120] A data self-identification module 13, configured to perform self-identification on the line data of each monitoring node to determine the risk value;

[0121] An acquisition frequency adjustment module 14, configured to count the risk values of all monitoring nodes, and jointly determine the data acquisition frequency of any monitoring node in combination with the correlation degree;

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

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

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

[0125] An output power calculation unit for obtaining the voltage and current at the power consumption interface in real time and calculating the output power;

[0126] A curve fitting unit for statistically calculating the output power according to the time sequence and fitting the output power change curve;

[0127] A selection execution unit for obtaining the integral feature and derivative feature of the output power change curve and selecting monitoring nodes in the feeder model based on the integral feature and derivative feature of each power consumption interface.

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

[0129] A data sequence creation unit for obtaining line data based on the monitoring nodes and creating a data sequence; the data in the data sequence contains time tags;

[0130] A data pairing unit for pairing the monitoring nodes in pairs, registering the data in the data sequence based on the time tags, and extracting subsequences with the same two dimensions;

[0131] A calculation execution unit for calculating the correlation degree of the two subsequences as the correlation degree of the two monitoring nodes.

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

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

[0134] A marking unit for comparing the correlation degree with a preset threshold, and when the correlation degree is greater than the threshold, marking the other monitoring node as a relevant monitoring node;

[0135] A determination unit for querying the risk value of the relevant monitoring node and jointly determining 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 data acquisition frequency is as follows:

[0137] In the formula, f represents the data acquisition frequency of the current monitoring node, F j represents the risk value of the j-th monitoring node among other monitoring nodes and the current monitoring node, β j represents the weight corresponding to the j-th monitoring node among other monitoring nodes, S j represents the correlation degree between the j-th 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 are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within 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 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 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; 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: ; In the formula, Indicates The impact value of the power interface at this point, represents the correction factor, represents the integral value, is the standard deviation, Indicates The distance between each power interface and the point; 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 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.

3. 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: ; ; In the formula, Indicates the data acquisition frequency of the current monitoring node. Indicates the number of other monitoring nodes The risk value of each monitoring node and the current monitoring node, Indicates the number of other monitoring nodes The weight corresponding to each monitoring node is Indicates the number of other monitoring nodes The correlation between the monitoring nodes and the current monitoring node, is a preset constant, is the preset risk threshold, is the total number of monitoring nodes.

Images