Method, system and equipment for measuring impact grounding resistance of base tower and medium

Abstract

The invention discloses a method, a system, equipment and a medium for measuring the impact grounding resistance of a base tower, and particularly relates to the technical field of high voltage, and the technical key points are as follows: establishing a factor directed graph based on a first influence factor set constructed by environment characteristics and soil characteristics of a base tower mounting area and in combination with a pre-constructed factor association rule; processing the node features by using a random forest to obtain an influence factor generation model; obtaining a second influence factor set constructed by the environmental characteristics and soil characteristics of the base tower installation area at the current measurement moment, and inputting the second influence factor set into the influence factor generation model to obtain an influence factor at the current measurement moment; inputting impact current into a grounding loop formed by the first base tower and the second base tower, and measuring to obtain the initial impact grounding resistance of the grounding device of the second base tower; and correcting the initial impact grounding resistance by using the influence factor, and outputting the corrected impact grounding resistance.

Description

Technical Field

[0001] This invention relates to the field of high voltage technology, specifically to a method, system, equipment, and medium for measuring the impulse grounding resistance of a base tower. Background Technology

[0002] The primary function of lightning protection grounding devices on transmission line towers is to effectively dissipate lightning current into the ground when the power system or equipment is struck by lightning, while simultaneously stabilizing the ground potential of the grounding device within a safe range to prevent backflashover accidents caused by lightning overvoltage. The operational status of the lightning protection grounding device directly affects the safe and stable operation of the transmission line, and impulse resistance is a crucial indicator for evaluating the lightning protection characteristics of transmission line towers. The grounding device must not only withstand power frequency short-circuit fault currents but, more importantly, meet the requirements of lightning current impulses. However, during the measurement of impulse grounding resistance on transmission line towers, the results are often affected by environmental factors and soil characteristics, leading to inaccurate measurements and potentially serious consequences. Therefore, accurate measurement of the impulse grounding resistance of transmission line towers is extremely important.

[0003] A Chinese patent with publication number CN108008197A discloses a method for measuring the impulse grounding resistance of a tower considering the spatial distribution of soil moisture, which takes into account the influence of the spatial distribution of soil moisture on the overall impulse grounding resistance; a Chinese patent with publication number CN108008198A discloses a method for measuring the impulse grounding resistance taking into account the temperature of the grounding grid, which takes into account the influence of the temperature distribution around the grounding grid on the overall impulse grounding resistance; a Chinese patent with publication number CN108020725A discloses a method for measuring the impulse grounding resistance of a grounding grid under the influence of different soil pH levels, which takes into account the influence of soil pH on the overall impulse grounding resistance. However, the above-mentioned existing technical solutions only consider a single influencing factor for the measurement of impulse grounding resistance, while the measurement of impulse grounding resistance is affected by multiple factors at the same time. Furthermore, the above-mentioned existing technologies do not consider the interaction between different influencing factors. This measurement method, which only considers a single factor and is relatively independent, is difficult to cope with complex geographical environments and is difficult to obtain accurate measurement data in complex environments.

[0004] Therefore, the present invention aims to provide a method, system, device and medium for measuring the impulse grounding resistance of a base tower, in order to solve the aforementioned related problems. Summary of the Invention

[0005] The technical problem this invention aims to solve is that existing methods for measuring impulse grounding resistance only consider a single influencing factor, making it difficult to cope with complex geographical environments and resulting in a lack of accuracy in measurement data. The goal is to provide a method, system, equipment, and medium for measuring the impulse grounding resistance of base towers. By integrating environmental and soil characteristics, a directed graph representing the relationships between multiple influencing factors is constructed, making the interactions between multiple factors explicit, capturing the synergistic effects between multiple influencing factors, and achieving comprehensive modeling of the multi-dimensional characteristics of the measurement area. Features are extracted from the directed graph using a graph neural network and processed using a random forest algorithm to obtain an influence factor generation model that integrates the synergistic relationships of multiple factors. Simultaneously, the influence factor generation model output by this invention integrates the comprehensive effects of multiple factors, rather than correcting a single physical quantity, and performs multi-dimensional physical constraint correction on the initial impulse grounding resistance value. This solves the accuracy loss of traditional methods in dynamic environments and overcomes the limitations of traditional static correction scenarios, enabling high-precision measurement in extreme environments.

[0006] This invention is achieved through the following technical solution: A method for measuring the impulse grounding resistance of a base tower, the method comprising: Based on the first set of influencing factors constructed from the environmental and soil characteristics of the base tower installation area, and combined with pre-constructed factor association rules, a factor directed graph is established; wherein, the factor association rules are used to characterize the association relationship between different influencing factors; By using graph neural networks to extract node features from the directed factor graph, and then using random forests to process the node features, an influence factor generation model is obtained. Obtain a second set of influencing factors constructed from the environmental and soil characteristics of the base tower installation area at the current measurement time, and input the second set of influencing factors into the influencing factor generation model to obtain the influencing factors at the current measurement time; An impulse current is input into the grounding loop formed by the first base tower and the second base tower, and the initial impulse grounding resistance of the grounding device of the second base tower is measured. The initial impulse grounding resistance is corrected using an influence factor, and the corrected impulse grounding resistance is output.

[0007] Furthermore, based on the first set of influencing factors constructed from the environmental and soil characteristics of the base tower installation area, and combined with pre-constructed factor association rules, a directed factor graph is established, specifically as follows: The environmental and soil characteristics at different historical moments and in different base tower installation areas were obtained, and a set of first influencing factors was constructed based on the environmental and soil characteristics. The environmental characteristics included regional temperature and regional altitude; the soil characteristics included soil moisture, soil resistivity and soil salinity. Based on pre-constructed factor association rules, the association relationships between influencing factors in environmental and soil properties are obtained; Each influencing factor is treated as a graph node, and the relationships between influencing factors are treated as edges, to construct a directed graph of factors.

[0008] Furthermore, a graph neural network is used to extract node features from the directed factor graph, and random forest is used to process the node features to obtain the influence factor generation model, specifically: Graph neural networks are used to randomly extract feature information from a directed graph of factors and generate multiple node feature matrices. A classification and regression tree with multiple node feature matrices is generated based on the random forest algorithm, and the Gini coefficient of the multiple node feature matrices is calculated. For the feature matrices of multiple nodes where the current node is located in the classification and regression tree, if the Gini coefficients of the feature matrices of multiple nodes are not less than the Gini coefficient threshold, the Gini coefficient of each feature information in the feature matrix of the node where the current node is located is calculated; if the Gini coefficients of the feature matrices of multiple nodes are less than the Gini coefficient threshold, the random forest algorithm returns the sub-decision tree and stops recursion. The feature information corresponding to the smallest Gini coefficient among the Gini coefficients of each feature information is taken as the optimal feature information. Based on the optimal feature information, the corresponding node feature matrix is ​​divided into the first feature matrix and the second feature matrix, and the first feature matrix and the second feature matrix are respectively taken as the left child node and the right child node of the current node. Calculate the Gini coefficients of the first feature matrix and the second feature matrix. When the Gini coefficients of the first feature matrix and the second feature matrix are less than the Gini coefficient threshold, the random forest algorithm returns a sub-decision tree and stops recursion, generating multiple decision trees. Under pre-constructed relational constraints, multiple classifiers learn from multiple decision trees to obtain an influence factor generation model.

[0009] Further, the Gini coefficients of the first and second characteristic matrices are calculated, specifically as follows: ,in, Denotes the first characteristic matrix. This represents the second characteristic matrix. Gini(D,A) represents the optimal feature information, and Gini(D,A) represents the uncertainty of the node feature matrix. This represents the node feature matrix.

[0010] Furthermore, the method also includes: constructing relational constraints based on pre-built factor association rules; wherein the relational constraints include regional temperature-soil moisture constraints, soil moisture-soil resistivity constraints, and regional temperature-soil salinity constraints. The specific regional temperature-soil moisture constraint is as follows: ,in, Indicates soil moisture. Indicates the temperature of the region. All of these represent constraint parameters. This represents the saturation temperature threshold; the soil moisture-soil resistivity constraint specifically refers to: ,in, Indicates soil resistivity, , Indicates the resistivity of dry soil. This represents the coefficient that controls the rate of increase in resistivity of the dry region as humidity decreases. Indicates the soil moisture threshold; Indicates the resistivity of water; Sensitivity to changes in soil resistivity in humidity-saturated areas; This refers to the Archie index. , This indicates the soil saturation moisture in the saturated area. Represents the saturation factor. Indicates the weight of the transition region. The specific regional temperature-soil salinity constraint is as follows: ,in, Indicates soil salinity. , Indicates the baseline salt content. Indicates the attenuation coefficient of the frozen zone. Indicates the reference temperature. Indicates the freezing temperature threshold. Indicates the linear coefficients in the transition region. This represents the Gaussian attenuation coefficient in the transition region. Indicates the threshold temperature. Indicates the saturated salinity of the soil. Represents the hyperbolic tangent function. Saturation coefficient.

[0011] Furthermore, the initial impulse grounding resistance is corrected using an influence factor, and the corrected impulse grounding resistance is output as follows: An influence factor is generated using an influence factor generation model, and this influence factor is used to correct the initial impulse grounding resistance; the correction formula is as follows: ,in, Indicates the initial impulse grounding resistance. Indicates the impact factor. This indicates the corrected impulse grounding resistance.

[0012] The present invention also provides a base tower impulse grounding resistance measurement system, which is used in the base tower impulse grounding resistance measurement method described in any one of the above claims, the system comprising: The first module is used to establish a directed factor graph based on a first set of influencing factors constructed from the environmental and soil characteristics of the base tower installation area, combined with pre-constructed factor association rules; wherein, the factor association rules are used to characterize the association relationships between different influencing factors; The second module is used to extract node features from the directed factor graph using graph neural networks, and to process the node features using random forests to obtain the influence factor generation model. The third module is used to obtain a second set of influencing factors constructed from the environmental and soil characteristics of the base tower installation area at the current measurement time, and input the second set of influencing factors into the influencing factor generation model to obtain the influencing factors at the current measurement time; The fourth module is used to input impulse current into the grounding loop formed by the first base tower and the second base tower, and to measure the initial impulse grounding resistance of the grounding device of the second base tower. The fifth module is used to correct the initial impulse grounding resistance using the influence factor and output the corrected impulse grounding resistance.

[0013] The present invention also provides a computer device, including a system memory and a processor, wherein the system memory stores a computer program, and the processor executes the computer program to implement the steps of any of the methods described above.

[0014] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the methods described above.

[0015] The present invention also provides a computer program product containing instructions that, when executed by a cluster of computer devices, cause the cluster of computer devices to perform the method described in any of the preceding claims.

[0016] Compared with the prior art, the present invention has the following advantages and beneficial effects: In this invention, by integrating environmental and soil characteristics, a directed graph representing the relationships between multiple influencing factors is constructed. This makes the interactions between multiple factors explicit, captures the synergistic effects among multiple influencing factors, and achieves comprehensive modeling of the multi-dimensional characteristics of the measurement area. Features of the directed graph are extracted using a graph neural network and processed using a random forest algorithm to obtain an influence factor generation model that integrates the synergistic relationships between multiple factors. Furthermore, the influence factor generation model output by this invention integrates the comprehensive effects of multiple factors, rather than correcting a single physical quantity. It performs multi-dimensional physical constraint correction on the initial impulse grounding resistance value, solving the accuracy loss of traditional methods in dynamic environments and overcoming the limitations of traditional static correction scenarios. This enables high-precision measurement in extreme environments. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 This is a schematic diagram of the method flow for measuring the impulse grounding resistance of a base tower in this embodiment; Figure 2 This is a schematic diagram of the structure of a base tower impulse grounding resistance measurement system in this embodiment; Figure 3 This is a schematic diagram of the structure of a computer device in this embodiment. Detailed Implementation

[0018] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0019] In this disclosure, unless otherwise stated, the use of terms such as "first," "second," etc., to describe various elements is not intended to limit the positional, temporal, or importance relationships of these elements; such terms are merely used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of that element, while in other cases, based on the context, they may refer to different instances.

[0020] The terminology used in the description of the various examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context explicitly indicates otherwise, an element may be one or more unless the number of elements is specifically limited. Furthermore, the term "and / or" as used in this disclosure covers any one of the listed items and all possible combinations thereof.

[0021] Example 1 In this embodiment, see Figure 1 , Figure 1 A schematic flowchart of a method for measuring the impulse grounding resistance of a base tower is shown, wherein the method includes: S1: Based on the first set of influencing factors constructed from the environmental and soil characteristics of the base tower installation area, and combined with pre-constructed factor association rules, a factor directed graph is established; wherein, the factor association rules are used to characterize the association relationship between different influencing factors; Specifically, in this embodiment, a first set of influencing factors is constructed based on the environmental and soil characteristics of the base tower installation area at historical moments. The environmental characteristics include regional temperature and regional altitude; the soil characteristics include soil moisture, soil resistivity, and soil salinity. Based on pre-constructed factor association rules, the association relationships between the influencing factors in the environmental and soil characteristics are obtained. Each influencing factor is used as a graph node, and the association relationship between the influencing factors is used as an edge to construct a directed graph of factors.

[0022] It should be noted that in this embodiment, the environmental and soil characteristics of different base tower installation areas and different historical times are obtained. The acquisition method adopts conventional technical means in the field, which will not be elaborated here. At the same time, factor association rules are constructed through the experience and knowledge of domain experts, such as the relationship between regional temperature and soil moisture (high temperature will accelerate soil moisture evaporation, and low temperature will cause soil freezing), and the relationship between soil moisture and soil resistivity (increased humidity usually reduces soil resistivity, but there is a saturation effect), etc. The construction method of the factor directed graph adopts conventional technical means in the field, which will not be elaborated here.

[0023] In this embodiment, a network of influence relationships among multiple factors is constructed using a directed graph (DAG), which can characterize the mutual influence between different factors. For example, taking regional temperature change as an example, it not only directly affects the soil moisture evaporation rate, but also indirectly affects the soil condensation process by changing soil moisture. By using nodes to represent variables such as temperature, soil moisture, ion migration, and soil resistivity, and labeling causal relationship weights with directed edges, the cascading effect of the foundation pile's geographical location (altitude, latitude and longitude) and environmental factors can be intuitively presented. For example, in high-altitude areas, the decrease in atmospheric pressure leads to a decrease in the boiling point of water, which accelerates soil moisture vaporization, thereby affecting the ion conduction channels between soil particles and ultimately changing the soil resistivity. In coastal areas, the fluctuation of groundwater level caused by tides and soil salinization form a two-way feedback mechanism, requiring the addition of cyclic edges in the model to characterize the dynamic coupling relationship. The above examples are only to provide an implementation method that can be achieved in this embodiment and should not be construed as a specific limitation on this technical solution.

[0024] S2: Extract node features from the directed graph of factors using a graph neural network, and process the node features using a random forest to obtain the influence factor generation model; Specifically, in this embodiment, a graph neural network is used to randomly extract feature information from a directed graph of factors and generate multiple node feature matrices; a classification and regression tree with multiple node feature matrices is generated based on a random forest algorithm, and the Gini coefficients of the multiple node feature matrices are calculated; for the multiple node feature matrices of the current node in the classification and regression tree, if the Gini coefficients of the multiple node feature matrices are not less than a Gini coefficient threshold, the Gini coefficient of each feature information in the node feature matrix of the current node is calculated; if the Gini coefficients of the multiple node feature matrices are less than the Gini coefficient threshold, the random forest algorithm returns to the sub-decision tree and stops recursion; and the Gini coefficients of each feature information are... The feature information corresponding to the smallest Gini coefficient in the Gini coefficient is taken as the optimal feature information. Based on the optimal feature information, the corresponding node feature matrix is ​​divided into a first feature matrix and a second feature matrix, and the first feature matrix and the second feature matrix are taken as the left child node and the right child node of the current node, respectively. The Gini coefficient of the first feature matrix and the second feature matrix is ​​calculated. When the Gini coefficient of the first feature matrix and the second feature matrix is ​​less than the Gini coefficient threshold, the random forest algorithm returns a sub-decision tree and stops recursion, generating multiple decision trees. Under the pre-constructed relation constraints, multiple classifiers learn from multiple decision trees to obtain the influence factor generation model.

[0025] It should be noted that in this embodiment, the Gini coefficient is used to measure the uncertainty of the random forest algorithm; the lower the uncertainty, the better the random forest algorithm. The feature information is obtained by randomly sampling from the directed graph of factors and generating multiple node feature matrices. Therefore, assuming the probability of generating an influencing factor in the node feature matrix is ​​p, the Gini coefficient of the probability distribution is as follows: The calculation is as follows: Then, in the node feature matrix, the number of nodes is defined as |D|, the influence factor is defined as |C|, and the Gini coefficient expression is: ; Furthermore, if the node feature matrix is ​​divided into the optimal feature information A... and Then the expression for the Gini coefficient of the node feature matrix is ​​defined as: ,in, Denotes the first characteristic matrix. Let represent the second feature matrix. Here, Gini(D,A) represents the uncertainty of the node feature matrix. D is grouped by the optimal feature information A. The smaller the Gini coefficient, the lower the uncertainty of the node feature matrix, thus increasing the accuracy of the final generated result.

[0026] In addition, this embodiment also includes: constructing relationship constraints based on pre-built factor association rules; the relationship constraints include regional temperature-soil moisture constraints, soil moisture-soil resistivity constraints, and regional temperature-soil salinity constraints. The specific regional temperature-soil moisture constraints are as follows: ,in, Indicates soil moisture. Indicates the temperature of the region. Both represent constraint parameters, for example This indicates that for every 1°C increase in regional temperature, soil moisture decreases by 0.05%. Indicates the saturation temperature threshold. This indicates that when the regional temperature exceeds 30℃, the decreasing trend of soil moisture slows down. The soil moisture-soil resistivity constraint is specifically as follows: ,in, Indicates soil resistivity, , This represents the resistivity of dry soil, which is the resistivity when the soil moisture content is 0, and is determined by the electrical conductivity of solid particles. This represents a coefficient that controls the rate of increase in resistivity of the dry region as humidity decreases. ; This represents the soil moisture threshold, which marks the boundary between the dry zone and the transition zone. Below this value, soil moisture becomes discontinuous, and resistivity increases sharply. This represents the resistivity of water; the soil resistivity in saturated humidity areas approaches this value. This value represents the sensitivity of soil resistivity changes in a moisture-saturated area and is related to soil texture. This refers to the Archie index, a value used to describe the effect of porosity tortuosity on soil resistivity. ; This indicates the soil saturation moisture in the moisture-saturated area. This value represents the moisture content (water content) when the soil is fully saturated, and is determined by porosity. This represents the saturation factor, a value used to adjust for the nonlinear contribution of humidity to resistivity. ; This indicates the weight of the transition region, and this value is used to balance the resistivity contributions of the dry and saturated regions. ; It should be noted that, in this embodiment, a dry area refers to an area where the soil moisture is at a certain level. The area within the range, the transition zone refers to the area where the soil moisture is at a certain level. The saturated region refers to the area within the range where soil moisture is at a certain level. The region of the interval.

[0027] For example, in the clay scene, , , , , , , , .

[0028] The specific constraints of regional temperature-soil salinity are as follows: ,in, Indicates soil salinity. , Indicates the baseline salt content. This represents the freezing zone attenuation coefficient, which is used to control the rate at which salinity decreases with decreasing temperature at low temperatures; This represents the baseline temperature, which serves as a reference point for regional temperature variations. It corresponds to the soil salinity under standard conditions and is used to normalize the effects of temperature. This indicates the freezing temperature threshold, which means that below this temperature, soil moisture freezes, salt crystallizes and precipitates, resulting in a decrease in salt content. This represents the linear coefficient of the transition region, which is used to describe the promoting effect of temperature on salt dissolution. This represents the Gaussian decay coefficient in the transition region, used to reflect the equilibrium effect of temperature on dissolution and evaporation, for example... This indicates that the peak value is relatively wide near the threshold temperature (the effect of reaction temperature on the balance between dissolution and evaporation). This represents the threshold temperature, which indicates the boundary between the high-temperature zone and the transition zone. Below this temperature, salt dissolution is dominant, while above this temperature, evaporation and concentration are dominant. This indicates the saturated salinity of the soil, representing the limit of soil salinity under high-temperature evaporation, at which point the salt cannot be further concentrated. Represents the hyperbolic tangent function. The saturation coefficient is a value used to control the rate at which the salinity approaches the soil's saturation level. For example... This indicates that for every 1°C increase, the salt content approaches the saturation value by 10% (the rate at which salt concentrates to saturation during accelerated evaporation at high temperatures). It should be noted that, in this embodiment, the frozen area refers to the area within... The area within the interval, the transition area refers to the region within the interval. The area within the range, the high-temperature area refers to the region where... The region within the interval.

[0029] For example, in a temperate grassland scenario, , , , , , , , , .

[0030] S3: Obtain the second set of influencing factors constructed from the environmental and soil characteristics of the base tower installation area at the current measurement time, and input the second set of influencing factors into the influencing factor generation model to obtain the influencing factors at the current measurement time; S4: Input impulse current into the grounding loop formed by the first base tower and the second base tower, and measure the initial impulse grounding resistance of the grounding device of the second base tower; Specifically, in this embodiment, the grounding loop formed by the first base tower and the second base tower is formed by the OPGW ground wire connecting the first base tower, the second base tower, the grounding down conductor of the first base tower, the grounding down conductor of the second base tower, the grounding device of the first base tower, the grounding device of the second base tower, and the second base tower.

[0031] It should be noted that in this embodiment, an impulse current with a steep wavefront and a large amplitude is generated using an impulse current generator. This impulse current flows through the grounding loop formed by the first base tower and the second base tower. Finally, the initial impulse grounding resistance of the grounding device of the second base tower is measured. The measurement method adopts conventional technical means, which will not be described in detail here.

[0032] S5: Correct the initial impulse grounding resistance using the influence factor and output the corrected impulse grounding resistance.

[0033] It should be noted that an impact factor is generated using an impact factor generation model. Using this influencing factor The initial impulse grounding resistance is corrected using the following formula: ,in, Indicates the initial impulse grounding resistance. This indicates the corrected impulse grounding resistance.

[0034] Specifically, in this embodiment, by integrating environmental and soil characteristics, a directed graph representing the relationship between multiple influencing factors is constructed, making the interaction between multiple factors explicit, capturing the synergistic influence between multiple influencing factors, and realizing a comprehensive modeling of the multi-dimensional characteristics of the measurement area. The features of the directed graph are extracted by a graph neural network and processed using a random forest algorithm to obtain an influence factor generation model that integrates the synergistic relationship between multiple factors. At the same time, the influence factor generation model of this invention integrates the comprehensive effect of multiple factors rather than a single physical quantity correction, and performs multi-dimensional physical constraint correction on the initial impulse grounding resistance value, solving the accuracy loss of traditional methods in dynamic environments and overcoming the scenario limitations of traditional static correction, enabling high-precision measurement in extreme environments.

[0035] Example 2 See Figure 2 As shown, the present invention also provides a base tower impulse grounding resistance measurement system, which is used in any of the above-described base tower impulse grounding resistance measurement methods, the system comprising: The first module 100 is used to establish a directed factor graph based on a first set of influencing factors constructed from the environmental and soil characteristics of the base tower installation area, combined with pre-constructed factor association rules; wherein, the factor association rules are used to characterize the association relationship between different influencing factors; The second module 200 is used to extract node features in the directed graph of factors using a graph neural network, and to process the node features using a random forest to obtain an influence factor generation model. The third module 300 is used to obtain a second set of influencing factors constructed from the environmental characteristics and soil characteristics of the base tower installation area at the current measurement time, and input the second set of influencing factors into the influencing factor generation model to obtain the influencing factors at the current measurement time; The fourth module 400 is used to input impulse current into the grounding loop formed by the first base tower and the second base tower, and to measure the initial impulse grounding resistance of the grounding device of the second base tower. The fifth module 500 is used to correct the initial impulse grounding resistance using the influence factor and output the corrected impulse grounding resistance.

[0036] It should be noted that the modules in the system of Embodiment 2 correspond to the steps in the method of Embodiment 1. The steps in the method of Embodiment 1 have been described in detail in Embodiment 1, and the module content in the system will not be described in detail in this Embodiment 2.

[0037] Example 3 See Figure 3 As shown, this embodiment also provides a computer device, including a system memory 1005 and a processor 1001. The system memory 1005 stores a computer program, and the processor 1001 executes the computer program to implement the steps of any of the methods described above.

[0038] It should be noted that the processor 1001 is used to execute the steps in the above method embodiments according to the instructions in the program code. Alternatively, when the processor 1001 executes the computer program, it implements the functions of each module / unit in the above system / device embodiments.

[0039] Specifically, in this embodiment, the computer program can be divided into one or more modules / units. One or more modules / units are stored in the system memory 1005 and executed by the processor 1001 to complete this application. One or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the terminal device.

[0040] The terminal device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor 1001 and a system memory 1005. Those skilled in the art will understand that this does not constitute a limitation on the terminal device, which may include more or fewer components than shown, or a combination of certain components, or different components. For example, the terminal device may also include an input / output device 1003, a network access device 1002, a bus 1006, etc.

[0041] The processor 1001 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0042] System memory 1005 can be an internal storage unit of the terminal device, such as a hard drive or RAM. System memory 1005 can also be a storage device 1004 of the terminal device, such as an external hard drive, SmartMedia Card (SMC), Secure Digital (SD) card, or FlashCard. Furthermore, system memory 1005 can include both internal storage units and storage device 1004. System memory 1005 is used to store computer programs and other programs and data required by the terminal device. System memory 1005 can also be used to temporarily store data that has been output or will be output.

[0043] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0044] Example 4 This embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0045] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), registers, hard disks, optical fibers, compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof, or any other form of computer-readable storage medium in the art.

[0046] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside within an application-specific integrated circuit (ASIC). In embodiments of the invention, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device.

[0047] Example 5 This embodiment also provides a computer program product containing instructions that, when executed by a cluster of computer devices, cause the cluster of computer devices to perform the method described in Embodiment 1.

[0048] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method of measuring the impulse grounding resistance of a foundation tower, characterized by, The method comprises: Based on the first influence factor set constructed by the environmental characteristics and soil characteristics of the base tower installation area, the factor directed graph is established in combination with the pre-constructed factor association rule; wherein, the factor association rule is used to represent the association relationship between different influence factors; Node features are extracted in the factor directed graph by using a graph neural network, and the node features are processed by using a random forest to obtain an influence factor generation model; A second influence factor set constructed by the environmental characteristics and soil characteristics of the base tower installation area at the current measurement time is obtained, and the second influence factor set is input into the influence factor generation model to obtain the influence factor at the current measurement time; An impulse current is input into the grounding loop composed of the first base tower and the second base tower, and the initial impulse grounding resistance of the grounding device of the second base tower is measured; The initial impulse grounding resistance is corrected by using the influence factor, and the corrected impulse grounding resistance is output.

2. The method for measuring the impulse grounding resistance of a foundation tower according to claim 1, characterized in that, Based on the first influence factor set constructed by the environmental characteristics and soil characteristics of the base tower installation area, the factor directed graph is established in combination with the pre-constructed factor association rule, specifically: The environmental characteristics and soil characteristics at different historical times and in different base tower installation areas are obtained, and the first influence factor set is constructed based on the environmental characteristics and soil characteristics, wherein the environmental characteristics include regional temperature and regional altitude; the soil characteristics include soil moisture, soil resistivity, and soil salt content; Based on the pre-constructed factor association rule, the association relationship between the influence factors in the environmental characteristics and soil characteristics is obtained; Each influence factor is taken as a graph node, and the association relationship between the influence factors is taken as an edge to construct the factor directed graph.

3. The method for measuring the impulse grounding resistance of a foundation tower according to claim 1, characterized in that, Node features are extracted in the factor directed graph by using a graph neural network, and the node features are processed by using a random forest to obtain an influence factor generation model, specifically: The feature information of the factor directed graph is randomly extracted by using a graph neural network to generate a plurality of node feature matrices; A plurality of classification regression trees of the node feature matrices are generated based on a random forest algorithm, and the Gini coefficients of the node feature matrices are calculated; For the plurality of node feature matrices in which the current node of the classification regression tree is located, when the Gini coefficients of the plurality of node feature matrices are not less than the Gini coefficient threshold, the Gini coefficients of each feature information in the node feature matrix in which the current node is located are calculated; when the Gini coefficients of the plurality of node feature matrices are less than the Gini coefficient threshold, the random forest algorithm returns a sub-decision tree and stops recursion; The feature information corresponding to the smallest Gini coefficient in the Gini coefficients of each feature information is taken as optimal feature information, the corresponding node feature matrix is divided into a first feature matrix and a second feature matrix based on the optimal feature information, and the first feature matrix and the second feature matrix are taken as the left child node and the right child node of the current node respectively; The Gini coefficients of the first feature matrix and the second feature matrix are calculated, when the Gini coefficients of the first feature matrix and the second feature matrix are less than the Gini coefficient threshold, the random forest algorithm returns a sub-decision tree and stops recursion, and a plurality of decision trees are generated; Under the pre-built relationship constraint, the multiple decision trees are learned by multiple classifiers to obtain an impact factor generation model.

4. The method of measuring the impulse grounding resistance of a foundation tower according to claim 3, characterized in that, Gini coefficients of the first feature matrix and the second feature matrix are calculated, and specifically: wherein, represents the first feature matrix, represents the second feature matrix, represents optimal feature information, and Gini(D, A) represents uncertainty of the node feature matrix, represents the node feature matrix.

5. The method of measuring the impulse grounding resistance of a foundation tower according to claim 1, characterized in that, The method further comprises: constructing a relationship constraint based on the pre-built factor association rule; wherein the relationship constraint comprises a regional temperature-soil moisture constraint, a soil moisture-soil resistivity constraint, and a regional temperature-soil salinity constraint; The zone temperature-soil moisture constraint is specifically: wherein, represents soil moisture, represents zone temperature, each represents a constraint parameter, represents a saturation temperature threshold; the soil moisture-soil resistivity constraint is specifically: wherein, represents soil resistivity, , represents dry soil resistivity, represents a growth rate coefficient that controls the decrease of dry zone resistivity with moisture; represents a soil moisture threshold; represents resistivity of water; represents soil resistivity variation sensitivity in the moisture saturation zone; represents an Archie exponent, , represents soil saturation moisture in the moisture saturation zone, represents a saturation factor, represents a transition zone weight, ; the zone temperature-soil salinity constraint is specifically: wherein, represents soil salinity, , represents a reference salinity, represents a freezing zone decay coefficient, represents a reference temperature, represents a freezing temperature threshold, represents a transition zone linear coefficient, represents a transition zone Gaussian decay coefficient, represents a threshold temperature, represents soil saturation salinity, represents a hyperbolic tangent function, a saturation coefficient.

6. The method of measuring the impulse grounding resistance of a foundation tower according to claim 1, characterized in that, The initial impulse grounding resistance is corrected by using the impact factor, and a corrected impulse grounding resistance is output, specifically: An influence factor is generated by using an influence factor generation model, and the initial impulse grounding resistance is corrected by using the influence factor; wherein, the correction formula is: wherein, represents the initial impulse grounding resistance, represents the influence factor, represents the corrected impulse grounding resistance.

7. A system for measuring the impulse ground resistance of a foundation tower, characterized by The system is used in the method for measuring the impulse grounding resistance of a base tower according to any one of claims 1-6, and the system comprises: A first module is configured to establish a factor directed graph based on a first set of impact factors constructed from environmental characteristics and soil characteristics of a base tower installation area, in combination with a pre-built factor association rule; wherein the factor association rule is used to represent the association relationship between different impact factors; A second module is configured to extract node features in the factor directed graph by using a graph neural network, and process the node features by using a random forest to obtain an impact factor generation model; A third module is configured to obtain a second set of impact factors constructed from environmental characteristics and soil characteristics of the base tower installation area at a current measurement time, and input the second set of impact factors into the impact factor generation model to obtain an impact factor at the current measurement time; A fourth module is configured to input an impulse current into a grounding circuit composed of a first base tower and a second base tower, and measure an initial impulse grounding resistance of a grounding device of the second base tower; A fifth module is configured to correct the initial impulse grounding resistance by using the impact factor, and output a corrected impulse grounding resistance.

8. A computer device comprising a system memory storing a computer program and a processor, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method according to any one of claims 1-6.

10. A computer program product comprising instructions, characterized in that, When the instructions are executed by the computer device cluster, the computer device cluster is caused to perform the method according to any one of claims 1-6.

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