A lightning protection grounding resistance data processing method and system for a smart park

Abstract

The present application relates to the field of data processing, more particularly, the present application relates to a kind of wisdom park lightning protection grounding resistance data processing method and system, method includes: according to the resistance of each measuring point of the preset sampling frequency acquisition each day, and construct the resistance time sequence of each day;For any two measuring points, calculate the correction correlation coefficient between the resistance time sequence of the same day of the two measuring points;With each measuring point as node, with correction correlation coefficient as edge weight, construct measuring point relationship diagram;The clustering analysis based on graph structure is carried out to measuring point relationship diagram, all measuring points are divided into multiple categories, the health evaluation value of measuring point in each category is calculated;According to the long-term health state of the corresponding measuring point of the health evaluation value evaluation.This application forms complete analysis chain from data to relationship to insight, provides an automatic, intelligent solution for the long-term health state monitoring of wisdom park grounding grid and other concealed engineering.

Description

Technical Field

[0001] This invention relates to the field of data processing. More specifically, this invention relates to a method and system for processing lightning protection grounding resistance data in a smart park. Background Technology

[0002] The lightning protection safety of smart parks relies heavily on the reliability of the grounding system. The grounding resistance value is a key indicator for evaluating grounding performance, and its magnitude directly affects the lightning current discharge capacity. In actual operation, the grounding resistance value is affected by various factors such as soil moisture, temperature, and material aging, exhibiting a continuous and dynamic change.

[0003] Currently, smart industrial parks commonly deploy distributed grounding resistance monitoring points to achieve online monitoring of grounding resistance. However, existing methods have significant shortcomings when analyzing this massive amount of time-series data to assess the health status of resistance. Traditional periodic testing or simple threshold alarm methods cannot effectively extract the deeper information contained in the data from multiple monitoring points. In particular, it is difficult to accurately identify groups of resistance monitoring points within the park that share common trends due to being in the same soil, moisture, and other environmental conditions. This leads to inaccurate assessments of the health status of the park's lightning protection grounding network. Summary of the Invention

[0004] To address the technical problem that existing technologies cannot effectively extract the deep information contained in data from multiple measurement points, leading to inaccurate assessments of the health status of the park's lightning protection grounding network, this invention provides solutions in the following aspects.

[0005] In the first aspect, a method for processing lightning protection grounding resistance data in a smart park includes:

[0006] The resistance of each measurement point is obtained according to the preset sampling frequency for each day, and the resistance time sequence for each day is constructed.

[0007] For any two measuring points, calculate the corrected correlation coefficient between the resistance time series of the two measuring points on the same day; the corrected correlation coefficient is calculated by introducing a weight related to the change in the difference between the resistance values ​​of the two measuring points at adjacent time points;

[0008] A graph showing the relationship between measurement points is constructed, with each measurement point as a node and the corrected correlation coefficient as the edge weight.

[0009] A graph-based clustering analysis was performed on the measurement point relationship diagram to divide all measurement points into multiple categories, and the health evaluation value of the measurement points in each category was calculated.

[0010] The long-term health status of the corresponding measurement points is assessed based on the health evaluation values.

[0011] Preferably, obtaining the weights includes:

[0012] Calculate the absolute value of the resistance difference between two measuring points at the same time on the same day, and use it as the first element; for each time moment, calculate the difference between the first element of the current time and the previous time moment, and use it as the second element of the current time moment; calculate the mean of all the second elements for the day.

[0013] The exponential function value of the absolute value of the difference between the second element at the current moment and the mean of all second elements on that day is used as a stability factor.

[0014] Calculate the exponential function value of the absolute value of the second element at the current moment, and use it as the synchronization factor;

[0015] The product of the stability factor and the synchronization factor is used as the weight between the two measurement points at the current time.

[0016] Preferably, obtaining the corrected correlation coefficient includes:

[0017] Calculate the weighted mean of the two measurement points on the same day; calculate the weighted covariance and weighted standard deviation of the two measurement points based on the weights and the weighted mean; calculate the weighted Pearson correlation coefficient between the two measurement points based on the weighted covariance and weighted standard deviation; and use the weighted Pearson correlation coefficient as the corrected correlation coefficient.

[0018] Preferably, the cluster analysis employs a spectral clustering algorithm.

[0019] Preferably, the acquisition of the health evaluation value includes:

[0020] Based on the results of the cluster analysis for the current day, select any one of the categories as the target category;

[0021] By tracing back and obtaining the cluster analysis results of each previous day that contain the most elements of the target category, a set of evolution processes of the target category is constructed in chronological order;

[0022] The benchmark measurement points of the target category are selected based on the probability of each measurement point in the evolution process set.

[0023] For any measurement point in the target category, the health evaluation value of the measurement point is calculated based on the probability of its occurrence in the evolution process set and the corrected correlation coefficient between the measurement point and the benchmark measurement point.

[0024] Preferably, obtaining the benchmark measurement points for the target category includes:

[0025] The measurement point corresponding to the maximum occurrence probability of all measurement points in the target category in the evolution process set is taken as the benchmark measurement point of the target category.

[0026] Preferably, the health evaluation value of the measuring point is the product of the probability of the measuring point appearing in the set of evolution processes and the corrected correlation coefficient between the measuring point and the benchmark measuring point.

[0027] Preferably, the step of assessing the long-term health status of the corresponding measurement point based on the health evaluation value includes:

[0028] When the health evaluation value is less than the preset safety threshold, the grounding resistance health status of the corresponding measuring point is determined to be abnormal, and an alarm message is automatically generated.

[0029] Secondly, a data processing system for lightning protection grounding resistance in a smart park includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the data processing method for lightning protection grounding resistance in a smart park described in any one of the claims is implemented.

[0030] The beneficial effects of this invention are:

[0031] This invention introduces a weighted calculation based on the dynamic characteristics of resistance over time to correct the correlation coefficient, constructs a measurement point relationship diagram, and uses spectral clustering for intelligent segmentation. Combined with probabilistic analysis of historical cluster evolution and baseline measurement point correlation assessment, it achieves accurate discovery of deep collaborative relationships and stability among multiple measurement points in a park's lightning protection grounding network. This method significantly improves the early identification capability of latent anomalies, effectively overcoming the misjudgment and omission problems caused by traditional single-measurement-point assessment or simple correlation coefficient analysis. This greatly improves the accuracy and reliability of grounding resistance health status assessment and provides intelligent decision support for preventative maintenance and risk early warning of the park's lightning protection system. Attached Figure Description

[0032] Figure 1 This is a flowchart of steps S1-S4 in a method for processing lightning protection grounding resistance data in a smart park according to an embodiment of the present invention.

[0033] Figure 2 This is a structural block diagram of a lightning protection grounding resistance data processing system for a smart park according to an embodiment of the present invention. Detailed Implementation

[0034] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0035] The monitoring object of this invention is: the lightning protection grounding resistance of each measuring point within the smart park. For ease of description, the following description uses the measuring points and their resistance values.

[0036] Reference Figure 1A method for processing lightning protection grounding resistance data in a smart park includes steps S1-S4, as detailed below:

[0037] S1: Obtain the resistance of each measurement point for each day according to the preset sampling frequency, and construct the resistance time sequence for each day.

[0038] The resistance values ​​at the measurement points are significantly affected by environmental factors, and the resistance values ​​collected under different environments will vary. To ensure strict alignment of the timestamps of the data from each measurement point, a high-precision clock synchronization protocol can be used, and a consistency verification mechanism can be added when the acquisition command is issued. The acquisition terminal at each measurement point starts sampling after receiving unified time synchronization, thereby eliminating time deviations caused by start-up delays or clock drift and ensuring the comparability of data at the same moment.

[0039] In one embodiment, a fixed data collection period of one day is set, with resistance values ​​collected in real time at multiple measuring points within the smart park, in one-hour increments. Over the course of a day, each measuring point will generate a resistance time series ordered chronologically.

[0040] In addition, to ensure the quality of the collected data, all the collected data were subjected to routine preprocessing operations such as filling missing values, removing outliers, and smoothing the data.

[0041] S2: For any two measuring points, calculate the corrected correlation coefficient between the resistance timing of these two measuring points on the same day.

[0042] When multiple measuring points are in the same or similar environment, their resistance values ​​will show similar trends, such as rising at the same time, falling at the same time, or remaining stable at the same time.

[0043] The traditional Pearson correlation coefficient measures a strictly linear relationship, requiring that the changes in two sequences be perfectly proportional (i.e., satisfying a linear relationship). However, in real-world environments: two measuring points in the same area may have different resistivity values ​​due to uneven soil moisture distribution, and environmental factors may have a non-linear effect on resistivity. These factors cause the traditional Pearson correlation coefficient to underestimate the correlation between measuring points with consistent trends.

[0044] Therefore, by introducing weights based on local trend stability, the traditional Pearson correlation coefficient is improved, making it better reflect the consistency in the changing trends of two resistance time series data (even if there is no strict linear relationship between them). The specific calculation steps are as follows:

[0045] For any two measuring points, firstly, calculate the absolute value of the resistance difference between the two measuring points at the same time on the same day, and use it as the first element. In this way, there is a first element corresponding to a certain time. Calculate the difference between the first element at the current time and the first element at the previous time, and use it as the second element at the current time.

[0046] If the second element is close to 0, it means that the difference in resistance between the two measuring points remains stable at adjacent moments, that is, they rise / fall synchronously or remain unchanged synchronously; if the second element is large (positive or negative), it means that the direction or magnitude of the change in resistance between the two measuring points is inconsistent.

[0047] Then, calculate the mean of all second elements for the day, calculate the exponential function value of the absolute value of the difference between the current second element and the mean of all second elements for the day, and use it as a stability factor; calculate the exponential function value of the absolute value of the current second element, and use it as a synchronization factor.

[0048] The product of the stability factor and the synchronization factor calculated above is used as the weight between the two measurement points at the current time.

[0049] For example, the weights between the two measurement points at the current moment can be expressed by the following formula:

[0050]

[0051] In the formula, For the current moment The weight between two measurement points As the second element, The mean of all the second elements on that day. It is an exponential function with the natural number e as its base.

[0052] in, As a stability factor, it penalizes moments that deviate from the average change. near An increase in weight indicates stable change. keep away A decrease in weight indicates abnormal fluctuations; and even at a certain moment The magnitude is relatively large, but as long as this pattern of change remains consistent throughout the day... near It can still obtain a high weight.

[0053] This represents the synchronicity factor, penalizing moments with large changes in resistance. It applies when the resistance difference between two measuring points changes very little at adjacent moments. Small values ​​indicate a high degree of synchronization between the two, and regardless of their absolute values, they are given high weight; if... A larger value indicates a decrease in weight, suggesting an inconsistent trend.

[0054] The aforementioned weights are greatest when the change is small and stable, and smallest when the change is large or unstable. A high weight indicates that the two resistance values ​​show a highly consistent trend during that period.

[0055] Finally, the corrected correlation coefficient between the resistance time series of the two measuring points is calculated using the weights obtained above. That is, the weighted mean of the two measuring points on the same day is calculated respectively. Based on the weights and the weighted mean, the weighted covariance and weighted standard deviation of the two measuring points are calculated. Based on the weighted covariance and weighted variance, the weighted Pearson correlation coefficient between the two measuring points is calculated. This weighted Pearson correlation coefficient is used as the corrected correlation coefficient.

[0056] The above operation is actually the weighted Pearson correlation coefficient. This method assigns higher weight to periods of consistent trend. Through this weighting, the focus of correlation coefficient calculation is directed to periods of "consistent and stable trend." For example, two measuring points may experience a simultaneous decrease in resistance during the day due to a synchronous rise in temperature (consistent trend period), but a brief divergence at night due to localized interference (inconsistent trend period). Traditional methods significantly lower the correlation coefficient due to nighttime divergence, while this method, by reducing the weight of nighttime data, makes the final result more reflective of the essential relationship between the two points under the influence of the dominant environmental factor (temperature), thus more accurately distinguishing between measuring point pairs "affected by a common environment" and "changing independently."

[0057] For example, the modified correlation coefficient mentioned above can be expressed as follows:

[0058]

[0059] In the formula, The corrected correlation coefficient between the resistance time series of two measurement points on the same day. This represents the total number of moments on that day. For the current moment The weight between two measurement points For the current moment measuring point The resistance value, For the current moment measuring point The resistance value, For the current moment measuring point The weighted average of the resistance values, For the current moment measuring point The weighted mean of the resistance values. The numerator is the weighted covariance, and the denominator is the product of the weighted standard deviations of the two measurement points.

[0060] Since the weighted Pearson correlation coefficient mentioned above is an extension of the standard Pearson correlation coefficient, it will not be elaborated on further here.

[0061] Therefore, based on the above operations, the corrected correlation coefficient between the resistance timing of any two measuring points on any day can be obtained.

[0062] S3: Construct a graph of the relationship between measurement points, with each measurement point as a node and the corrected correlation coefficient as the edge weight.

[0063] After obtaining the corrected correlation coefficient between the resistance time series of any two measurement points on any given day, the measurement points are treated as nodes, and the corrected correlation coefficient is used as the edge weight. This allows the discrete resistance values ​​of the measurement points to be transformed into a connected graph. This structure can intuitively display the network relationships between the measurement points, for example:

[0064] Strongly correlated edges: indicate that two measuring points are affected by common factors (such as soil stratification or lightning strikes), and may form parallel or series paths; weakly correlated edges: may reflect independent faults or local environmental differences.

[0065] This gives us the relationship diagram of the measurement points for each day.

[0066] S4: Perform graph-based cluster analysis on the measurement point relationship diagram to divide all measurement points into multiple categories and calculate the health evaluation value of the measurement points in each category; assess the long-term health status of the corresponding measurement points based on the health evaluation value.

[0067] In the application scenario of this invention, the change of resistance value over time may exhibit a complex pattern, and the resistance change pattern under different environments may not be convex. The spectral clustering algorithm can handle such non-convex clusters.

[0068] Spectral clustering obtains a low-dimensional representation of data through the eigenvectors of the Laplacian matrix of a graph. This process considers the similarity between all nodes in the graph (i.e., global information), not just local information. This helps to discover potential global structures in the data.

[0069] In this diagram of measurement point relationships:

[0070] Community structure: Through cluster analysis, strongly connected clusters of nodes may correspond to areas within the park with similar soil composition, humidity, or underground structure, and the electrical characteristics of the grounding grid in these areas tend to be similar.

[0071] Key node identification: Measurement points with a large number of strong connection edges (height number) may be located in areas sensitive to environmental factors or at the hub of electrical networks. Changes in their status will have a wide impact on surrounding measurement points, making them the focus of monitoring.

[0072] Weak edges and bridging: Weakly correlated edges connecting different strong communities may reflect weak coupling between different geological regions or electrical circuits. A sudden increase in their weight may indicate abnormal diffusion or the emergence of new common interference sources.

[0073] The measurement point relationship diagram generated each day is a "snapshot" of the environmental and electrical conditions of that day. By comparing the graph structure over several consecutive days, we can observe the evolution of edge weights, the formation and dissipation of communities, and thus dynamically track the spatiotemporal propagation path of the impact of environmental factors (such as seasonal changes in dryness and wetness) on the overall grounding network of the park.

[0074] Then, spectral clustering algorithm analysis was performed on the measurement point relationship diagram for each day to obtain the corresponding spectral clustering results for each day.

[0075] However, the spectral clustering results change dynamically every day because environmental conditions and resistance values ​​are constantly changing. It is necessary to find the measurement points that exist stably in each cluster category in the long term, as representative measurement points of that category, i.e., benchmark measurement points.

[0076] In one embodiment, based on the spectral clustering results of the current day, any one of the categories is selected as the target category. The clustering results of each previous day are then traced back to obtain the category containing the most elements of the target category. These categories are then arranged in chronological order to construct an evolutionary set for the target category. For any measurement point within the target category, the probability of that measurement point appearing in the evolutionary set is calculated.

[0077] Tracing back and constructing the evolutionary set of the target category is essentially a time-dimensional filtering process. A high probability of a measurement point appearing in this evolutionary set not only indicates that it belongs to the target category in the long term, but also means that its resistance change pattern has maintained a long-term stable correlation with the core environmental driving factors of the target category, which is a sign of its "reliable identity".

[0078] Furthermore, the measurement point with the highest probability of occurrence is selected as the benchmark measurement point for the target category, thereby representing the measurement point that is stably in the environment corresponding to the target category in the long term.

[0079] Using the benchmark measurement point as a reference, the modified correlation coefficient between the benchmark measurement point and other measurement points in the target category on the same day is calculated using the modified correlation coefficient calculation method of S2 mentioned above.

[0080] Finally, the health evaluation value of the measurement point is the product of the probability of other measurement points in the target category appearing in the evolution process set and the modified correlation coefficient between the measurement point and the benchmark measurement point.

[0081] By coupling time stability and state synergy, a low probability of occurrence indicates that the category to which the measurement point belongs is unstable, possibly in a transition zone or frequently disturbed by independent factors, and its state itself is worth paying attention to. When the correction correlation coefficient with the benchmark measurement point is low, it indicates that even if it is in the same category, its change trend is not synchronized with the benchmark measurement point of that category, which may indicate local failure or performance degradation.

[0082] The smaller the product of the two, the more likely that the measuring point has deviated from its expected long-term stable pattern.

[0083] Finally, based on the preset safety threshold, when the calculated health evaluation value is less than the preset safety threshold, the grounding resistance health status of the corresponding measuring point is determined to be abnormal, and alarm information is automatically generated. In addition to the measuring point ID, abnormal time, and evaluation value, it can also be associated with its category, the current status of the benchmark measuring point, and a comparison of the health trend of the same period in history. This helps maintenance personnel quickly locate whether the problem is localized (single measuring point failure), categorical (abnormality of a group of measuring points in a certain environment), or systemic (failure of the benchmark measuring point itself or drastic environmental changes).

[0084] The aforementioned security threshold can be set as follows:

[0085] The average health status of resistors in the past is statistically analyzed, and a safety threshold is set as the difference between the average health status and 0.1. This 0.1 can retain a 10% safety margin.

[0086] The method described in this invention ensures data quality through synchronous acquisition and preprocessing, characterizes the dynamic relationships between measuring points by introducing a modified correlation coefficient with trend consistency weighting, provides insights into the system structure from a network perspective by constructing and clustering a graph of the relationship between measuring points, and finally achieves accurate anomaly early warning by integrating health evaluation values ​​that combine time stability and state synergy. This entire process forms a complete analytical chain from data to relationships to insights, providing an automated and intelligent solution for long-term health monitoring of concealed projects such as grounding grids in smart parks.

[0087] This invention also provides a data processing system for lightning protection grounding resistance in a smart park. For example... Figure 2 As shown, the system includes a processor and a memory. The memory stores computer program instructions. When the computer program instructions are executed by the processor, the lightning protection grounding resistance data processing method for smart parks according to the first aspect of the present invention is implemented.

[0088] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0089] It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the scope of protection of this invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for processing lightning protection grounding resistance data in a smart park, characterized in that, include: The resistance values ​​of each measurement point are obtained according to the preset sampling frequency for each day, and the resistance time sequence for each day is constructed. For any two measuring points, calculate the corrected correlation coefficient between the resistance timing of these two measuring points on the same day; The corrected correlation coefficient is calculated by introducing a weight that is related to the change in the difference between the resistance values ​​of the two measuring points at adjacent time points. The acquisition of the weight includes: Calculate the absolute value of the resistance difference between two measuring points at the same time on the same day, and use it as the first element; for each time moment, calculate the difference between the first element of the current time and the previous time moment, and use it as the second element of the current time moment; calculate the mean of all the second elements for the day. The exponential function value of the absolute value of the difference between the second element at the current moment and the mean of all second elements on that day is used as a stability factor. Calculate the exponential function value of the absolute value of the second element at the current moment, and use it as the synchronization factor; The product of the stability factor and the synchronization factor is used as the weight between the two measurement points at the current time. A graph showing the relationship between measurement points is constructed, with each measurement point as a node and the corrected correlation coefficient as the edge weight. A graph-based clustering analysis was performed on the measurement point relationship diagram to divide all measurement points into multiple categories. The health evaluation value of the measurement points in each category was calculated. The acquisition of the health evaluation value included: Based on the results of the cluster analysis for the current day, select any one of the categories as the target category; By tracing back and obtaining the cluster analysis results of each previous day that contain the most elements of the target category, a set of evolution processes of the target category is constructed in chronological order; The benchmark measurement points of the target category are selected based on the probability of each measurement point in the evolution process set. For any measurement point in the target category, the health evaluation value of the measurement point is calculated based on the probability of the measurement point appearing in the set of evolution processes and the corrected correlation coefficient between the measurement point and the benchmark measurement point. The long-term health status of the corresponding measurement points is assessed based on the health evaluation values.

2. The method for processing lightning protection grounding resistance data in a smart park according to claim 1, characterized in that, The acquisition of the corrected correlation coefficient includes: Calculate the weighted mean of the two measurement points on the same day; calculate the weighted covariance and weighted standard deviation of the two measurement points based on the weights and the weighted mean; calculate the weighted Pearson correlation coefficient between the two measurement points based on the weighted covariance and weighted standard deviation; and use the weighted Pearson correlation coefficient as the corrected correlation coefficient.

3. The method for processing lightning protection grounding resistance data in a smart park according to claim 1, characterized in that, The clustering analysis employs the spectral clustering algorithm.

4. The method for processing lightning protection grounding resistance data in a smart park according to claim 1, characterized in that, The acquisition of benchmark measurement points for the target category includes: The measurement point corresponding to the maximum occurrence probability of all measurement points in the target category in the evolution process set is taken as the benchmark measurement point of the target category.

5. The method for processing lightning protection grounding resistance data in a smart park according to claim 4, characterized in that, The health evaluation value of a measurement point is the product of its probability of occurrence in the set of evolutionary processes and the corrected correlation coefficient between the measurement point and the benchmark measurement point.

6. The method for processing lightning protection grounding resistance data in a smart park according to claim 1, characterized in that, The assessment of the long-term health status of the corresponding measurement points based on the health evaluation values ​​includes: When the health evaluation value is less than the preset safety threshold, the grounding resistance health status of the corresponding measuring point is determined to be abnormal, and an alarm message is automatically generated.

7. A data processing system for lightning protection grounding resistance in a smart park, characterized in that, include: A processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement the lightning protection grounding resistance data processing method for a smart park according to any one of claims 1-6.

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