Real-time monitoring and evaluation early warning method and system based on multiple state parameters of direct current grounding electrode

Abstract

The invention belongs to the technical field of safety monitoring of a high-voltage direct-current power transmission system, and discloses a real-time monitoring and evaluation early warning method and system based on multiple state parameters of a direct-current grounding electrode. According to the invention, by introducing specific technical means such as multi-parameter cooperative monitoring, data cleaning, multivariate comprehensive evaluation and intelligent early warning feedback, the monitoring comprehensiveness, evaluation scientificity and response real-time performance of the operation state of the grounding electrode are remarkably improved, and the problems of single monitoring means, low data quality, incapability of intelligent early warning and the like in the prior art are effectively solved; the method has important engineering application value and popularization prospect.

Description

Technical Field

[0001] The present invention belongs to the technical field of safety monitoring of high-voltage direct current (HVDC) transmission systems, and in particular relates to a real-time monitoring and evaluation early warning method and system based on multiple state parameters of a DC grounding pole. Background Art

[0002] DC grounding electrodes are key facilities in UHVDC transmission systems. Their primary function is to provide a current path for single-pole loop operation, ensuring system reliability and stability. Existing detection methods primarily assess their performance through measurements of ground potential rise and grounding electrode current detection. These methods primarily focus on the electrical parameters of the grounding electrodes, ignoring the dynamic changes in environmental and material state parameters, such as soil temperature, humidity, and corrosion rate. This single monitoring method is unable to fully reflect the operating status of grounding electrodes, and suffers from shortcomings such as limited monitoring dimensions, poor data timeliness, and a lack of comprehensive evaluation. Therefore, there is an urgent need for a system that can comprehensively consider multiple parameters, monitor and evaluate the safety status of grounding electrodes in real time, and provide engineering early warnings to improve the safety and reliability of DC transmission systems. Summary of the Invention

[0003] The purpose of the present invention is to overcome the shortcomings of the above-mentioned grounding electrode operating status monitoring with a single dimension and incomplete evaluation, and to provide a real-time monitoring and evaluation early warning method and system based on multiple state parameters of a DC grounding electrode.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a real-time monitoring and evaluation early warning method based on multiple state parameters of a DC grounding electrode, comprising the following steps: Obtain the pole address parameters of the grounding electrode and clean the pole address parameters to obtain the required data; Analyze and evaluate the required data to obtain comprehensive evaluation results under different indicators; Based on the comprehensive evaluation results, an operating status report of the grounding level is generated and the operating status report is fed back.

[0005] A further improvement of the present invention is that when obtaining the pole address parameters of the grounding electrode, the pole address parameters of the grounding electrode include step voltage data, contact voltage data, soil temperature data, soil moisture data, guide cable current signal data, monitoring well water level data and grounding electrode corrosion degree data.

[0006] A further improvement of the present invention is that the specific method for cleaning the polar address parameters and obtaining the required data is as follows: Divide the polar address parameters according to parameter types to obtain several different types of parameters; Interpolation is used to fill missing data for different types of parameters; Statistical analysis was used to identify the parameters after filling in the missing data and obtain data after outlier processing; The format of the data after outlier processing is standardized to obtain the required data.

[0007] A further improvement of the present invention is that a grey correlation-entropy weight analysis method is used to analyze and evaluate the required data and obtain comprehensive evaluation results under different indicators.

[0008] A further improvement of the present invention is that the specific method for analyzing and evaluating the required data to obtain comprehensive evaluation results under different indicators is as follows: Construct the original data matrix based on the required data to eliminate the dimension difference; According to the original data matrix, the data types are classified as benefit parameters if the values ​​are larger, and cost parameters if the values ​​are smaller. The entropy weight method is used to calculate the dynamic weight of the classified data; Perform grey correlation analysis on the dynamic weight of the classified data to obtain the correlation between the dynamic weight of the classified data and the target state; The correlation is mapped to a percentage scoring system to obtain comprehensive evaluation results under different indicators.

[0009] A further improvement of the present invention is that the specific method for classifying data types according to the original data matrix is ​​as follows: Determine whether the data type is a benefit parameter or a cost parameter; If it is a cost parameter, the classified data can be obtained by the following formula: :

[0010] If it is a benefit parameter, the classified data can be obtained by the following formula: :

[0011] in, i is the sample serial number, j is the parameter number, x ij For the i The first sample j Item parameter value.

[0012] A further improvement of the present invention is that the specific method of using the entropy weight method to calculate the dynamic weight of the classified data is as follows: Calculate the j The weight of the item parameters:

[0013] Calculate thej Information entropy value of item parameters:

[0014] Calculate the j The coefficient of variation of the term parameters:

[0015] Determine the j Entropy weight of item parameter:

[0016] in, For the j Under the parameters, i The proportion of sample data.

[0017] A further improvement of the present invention is to perform grey correlation analysis on the dynamic weights of the classified data. The specific method for obtaining the correlation between the dynamic weights of the classified data and the target state is as follows: According to the preset standards, the theoretically best quality of the classified data is selected and the reference sequence is preset. , the ideal state vector:

[0018] in, is the theoretical optimal value of the step voltage data, is the theoretical optimal value of the contact voltage data, is the theoretical optimal value of soil temperature data, is the theoretical optimal value of soil moisture data, is the theoretical optimal value of the current signal data of the guide cable, is the theoretical optimal value of the monitoring well water level data; Calculate the correlation coefficient between the classified data and the reference sequence :

[0019] in, is the difference value, is the resolution coefficient, the difference value The calculation method is as follows:

[0020] Calculate the grey relational degree of each data :

[0021] in, For the j The entropy weight of the parameter.

[0022] A further improvement of the present invention is to map the relevance to a percentage scoring system, and obtain the comprehensive evaluation results under different indicators in the following specific method:

[0023] in, To comprehensively evaluate the results, is the grey relational degree of each data, .

[0024] In a second aspect, the present invention provides a real-time monitoring and evaluation early warning system based on multiple state parameters of a DC grounding electrode, comprising: The pole address on-site detection unit is used to obtain the pole address parameters of the grounding electrode and clean the pole address parameters to obtain the required data; Data collection and transmission unit, used to analyze and evaluate the required data and obtain comprehensive evaluation results under different indicators; The status monitoring and early warning background is used to generate the grounding level operation status report based on the comprehensive evaluation results and provide feedback on the operation status report.

[0025] Compared with the prior art, the present invention has the following beneficial effects: The present invention compensates for the shortcomings of single parameter evaluation through collaborative monitoring of multiple state parameters, and can capture more comprehensive and detailed equipment operation information. The present invention adopts interactive correlation analysis between multiple parameters, which helps to identify potential hidden dangers and anomalies caused by multi-factor coupling. The present invention adopts data cleaning steps to remove outliers, fill in missing data, and standardize data formats, thereby improving the reliability of analysis data and laying a solid foundation for subsequent evaluation. The data quality guarantee of the present invention effectively reduces the misjudgment rate and missed judgment rate, ensuring the objectivity of analysis and evaluation. The comprehensive evaluation method of the present invention can reflect the operating status in real time. Through dynamic weighting of scientific algorithms such as entropy weight, the evaluation results are more in line with reality. The early warning mechanism automatically pushes abnormal information, discovers problems in advance, avoids the expansion of accidents, and provides a scientific basis for operation and maintenance decisions. In summary, the present invention significantly improves the comprehensiveness of monitoring, scientificity of evaluation and real-time response of the grounding electrode operating status by introducing specific technical means such as multi-parameter collaborative monitoring, data cleaning, multi-dimensional comprehensive evaluation and intelligent early warning feedback, effectively solving the problems of single monitoring means, low data quality and inability to provide intelligent early warning in the existing technology, and has important engineering application value and promotion prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a flow chart of the present invention; Figure 2 It is a system diagram of the present invention. DETAILED DESCRIPTION

[0027] In order to further understand the content of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.

[0028] See also Figure 1 The real-time monitoring, evaluation and early warning method based on multiple state parameters of DC grounding pole includes the following steps: S1, obtain the pole address parameters of the grounding electrode, and clean the pole address parameters to obtain the required data.

[0029] S2, analyze and evaluate the required data to obtain comprehensive evaluation results under different indicators.

[0030] S3: Generate an operating status report of the grounding level based on the comprehensive evaluation results, and provide feedback on the operating status report.

[0031] See also Figure 2 , a real-time monitoring and evaluation early warning system based on multiple state parameters of DC grounding poles, including: The pole address on-site detection unit is used to obtain the pole address parameters of the grounding electrode and clean the pole address parameters to obtain the required data.

[0032] The data collection and transmission unit is used to analyze and evaluate the required data and obtain comprehensive evaluation results under different indicators.

[0033] The status monitoring and early warning background is used to generate the grounding level operation status report based on the comprehensive evaluation results and provide feedback on the operation status report.

[0034] Example 1: This embodiment further limits the acquisition of the grounding electrode address parameters, as follows: The pole parameters of the grounding electrode include step voltage data, contact voltage data, soil temperature data, soil moisture data, current signal data of the guide cable, water level data of the monitoring well and corrosion degree data of the grounding electrode.

[0035] This embodiment, through real-time collection and analysis of step voltage and contact voltage data, accurately reflects the potential distribution of the grounding electrode and its surrounding area, effectively preventing personal safety accidents caused by abnormal potentials and significantly improving system operation safety. The collection of soil temperature and soil moisture data enables the system to dynamically monitor changes in the physical environment of the soil surrounding the grounding electrode, promptly identifying fluctuations in grounding performance caused by temperature and humidity changes, and providing a scientific basis for grounding electrode performance evaluation and environmental adaptability analysis. By acquiring current signal data from the guide cable, the operating status of the guide cable can be effectively monitored, and faults such as current anomalies, open circuits, or short circuits can be promptly detected. This improves the response speed and diagnostic accuracy of guide channel faults, ensuring the stable operation of the grounding system. The collection of monitoring well water level data helps the system monitor the dynamic changes in the groundwater level in real time and promptly identify the adverse effects of water level fluctuations on grounding electrode performance and corrosion. Furthermore, the collection of grounding electrode corrosion data enables real-time assessment of the grounding electrode metal health, enabling early detection of corrosion risks, guiding the implementation of maintenance and protective measures, extending the grounding electrode's service life, and reducing operation and maintenance costs.

[0036] Example 2: This embodiment further defines the method for cleaning the address parameters and obtaining the required data in step S1 of the above method, as follows: S1, divide the address parameters according to parameter types to obtain several different types of parameters.

[0037] S2, interpolation method is used to fill missing data for different types of parameters.

[0038] S3, using statistical analysis to identify the parameters after filling the missing data and obtain the data after outlier processing.

[0039] S4, standardize the format of the data after outlier processing to obtain the required data.

[0040] This embodiment divides the address parameters by type, and can adopt a more adaptive cleaning strategy for different types of data, avoiding misprocessing due to differences in parameter characteristics, thereby ensuring that the processing of various parameters is more scientific and reasonable, and improving the pertinence and effectiveness of data preprocessing. This embodiment uses interpolation to reasonably fill in the missing data, and can restore the continuity and integrity of the data without introducing additional noise or anomalies, ensuring the representativeness of the data sample, and providing a high-quality data foundation for subsequent analysis and modeling. This embodiment identifies and processes outliers through statistical analysis methods, and can effectively eliminate extreme data caused by acquisition errors, sudden anomalies or equipment failures, reduce the interference of abnormal data on the comprehensive evaluation results, and improve the reliability and analysis accuracy of the overall data set. This embodiment standardizes the format of the cleaned data so that each type of parameter meets the unified data interface and analysis requirements, improves the circulation and compatibility of data between different analysis modules and management systems, and creates conditions for realizing automated, intelligent data analysis and multi-system collaboration. The data in this embodiment, which has undergone categorized cleaning, interpolation and completion, outlier processing, and format standardization, provides high-quality, standardized data input for subsequent multi-parameter comprehensive evaluation and intelligent early warning algorithms. This can significantly improve the model's judgment accuracy and intelligence level, and enhance the scientific nature and reliability of operational status monitoring and early warning. In summary, through the definition of the above-mentioned cleaning method, the integrity, accuracy, and standardization of the pole address parameter data can be effectively improved, providing a solid data foundation for subsequent operational status evaluation and intelligent early warning of DC grounding electrodes, further enhancing the practicality and advancement of the solution of the present invention.

[0041] Example 3: This embodiment further defines step S2 of the above method. The specific method is as follows: The grey correlation-entropy weight analysis method is used to analyze and evaluate the required data and obtain comprehensive evaluation results under different indicators, among which: S21, construct the original data matrix based on the required data to eliminate dimensional differences.

[0042] S22, classify the data types according to the original data matrix. The larger the value, the better it is, and the smaller the value, the better it is, and the cost parameter. Specifically: Determine whether the data type is a benefit parameter or a cost parameter; If it is a cost parameter, the classified data can be obtained by the following formula: :

[0043] If it is a benefit parameter, the classified data can be obtained by the following formula: :

[0044] in, i is the sample serial number, j is the parameter number, x ij For the i The first sample j Item parameter value.

[0045] S23, using the entropy weight method to calculate the dynamic weight of the classified data. Specifically: Calculate the j The weight of the item parameters:

[0046] Calculate the j Information entropy value of item parameters:

[0047] Calculate the j The coefficient of variation of the term parameters:

[0048] Determine the j Entropy weight of item parameter:

[0049] S24, performing grey correlation analysis on the dynamic weights of the classified data to obtain the correlation between the dynamic weights of the classified data and the target state. Specifically: According to the preset standards, the theoretically best quality of the classified data is selected and the reference sequence is preset. , the ideal state vector:

[0050] in, is the theoretical optimal value of the step voltage data, is the theoretical optimal value of the contact voltage data, is the theoretical optimal value of soil temperature data, is the theoretical optimal value of soil moisture data, is the theoretical optimal value of the current signal data of the guide cable, is the theoretical optimal value of the monitoring well water level data; Calculate the correlation coefficient between the classified data and the reference sequence :

[0051] in, is the difference value, is the resolution coefficient, the difference value The calculation method is as follows:

[0052] Calculate the grey relational degree of each data :

[0053] in, For the j The entropy weight of the parameter.

[0054] By constructing a raw data matrix and performing normalization, this embodiment effectively eliminates the impact of differences in dimension or value range between parameters, ensuring comparability and consistency across all parameters during the comprehensive evaluation, and enhancing the scientific and impartial nature of the evaluation. This embodiment employs a classification method for benefit-oriented and cost-oriented parameters, employing differentiated data processing strategies for parameters of different natures. This allows each parameter to be rationally transformed according to its practical significance during the comprehensive analysis, enhancing the evaluation model's adaptability to actual operating conditions. This embodiment automatically calculates the information entropy and variance coefficient of each parameter using an entropy weighting method. This method dynamically assigns weights to each parameter in the comprehensive evaluation based on the actual data distribution, overcoming the limitations of traditional subjective weighting, improving the objectivity and adaptability of the evaluation model, and making the evaluation results more relevant to field conditions. This entropy weighting method in this embodiment can sensitively capture the magnitude and uncertainty of parameter data changes, enabling the comprehensive evaluation model to promptly reflect the impact of changes in the face of parameter anomalies and sudden changes, thereby enhancing the ability to identify grounding electrode operational anomalies and the overall system's early warning sensitivity. This embodiment adopts the grey correlation analysis method, which can comprehensively reflect the degree of correlation between each monitoring parameter and the target state by calculating the correlation coefficient and grey correlation degree between each data and the ideal state, thereby more accurately evaluating the comprehensive operating status of the grounding electrode and improving the accuracy and reliability of health assessment and abnormal warning. This embodiment combines the dynamic weight and grey correlation degree of each parameter to comprehensively reflect the impact of each parameter on the health state of the grounding electrode, realize the scientific judgment of the optimal operating state under multiple parameters, and effectively support the grounding electrode operation and maintenance management and optimization decision-making. In summary, the present invention not only improves the scientificity and accuracy of the comprehensive analysis and health assessment of multi-source and multi-type pole address parameters by introducing the grey correlation-entropy weight analysis method, but also greatly enhances the system's sensitivity and warning capabilities to operational anomalies, promotes the intelligent and refined development of DC grounding electrode operation monitoring and evaluation methods, and has outstanding technological advancement and broad practical application value.

[0055] Example 4: This embodiment further defines step S3 in the above method. The specific method is as follows: The comprehensive correlation Linear mapping to a 100-point scoring system:

[0056] The scores are divided into levels according to the following table: Table 1 DC grounding electrode safety level classification table

[0057] When the score is above 85, the DC grounding electrode is in a safe operating state. When the score is below 70, a parameter abnormality may have occurred in the DC grounding electrode, requiring staff to be alerted. When the score is below 60, a grounding electrode safety issue may have occurred and the electrode is in an abnormal state, prompting staff to conduct on-site inspections and, if necessary, excavation inspections. The online monitoring and early warning platform generates a safety status assessment report based on these calculation results.

[0058] Example 5: The following seven original data of the pole address parameters were obtained during a DC grounding pole monitoring cycle (units are shown in the table):

[0059] The remaining sample data within the sampling period are as follows (the samples involved in this evaluation have 3 cycles):

[0060] The data were filled with missing values, detected for outliers, and formatted normally.

[0061] Efficiency parameter (the larger the better): step voltage U , contact voltage U t , soil temperature T , monitoring well water level L , corrosion degree W。

[0062] Cost parameter (the smaller the better): soil moisture F , guide cable current signal I。

[0063] The normalization process is as follows (taking sample 1 as an example, the three groups of samples are normalized separately): Step voltage U Take for example (benefit type):

[0064]

[0065] Similarly, other parameters are processed according to the corresponding formulas. The normalized results are as follows:

[0066] Step voltage U Take this as an example (other parameters are the same and the process is the same): The sum of the normalized parameters is: 1+0+0.333=1.333 Proportion of each sample : Sample 1: 1 / 1.333 ≈ 0.75 Sample 2: 0 / 1.333 = 0 Sample 3: 0.333 / 1.333 ≈ 0.25 Information entropy :

[0067] Where n=3, ln3≈1.0986; Sample 1: 0.75 ln(0.75) ≈ 0.75 (-0.2877) ≈ -0.2158 Sample 2: 0 (ignored) Sample 3: 0.25 ln(0.25) ≈ 0.25 (-1.3863) ≈ -0.3466 Total: -0.2158 + 0 + (-0.3466) = -0.5624 = -1 / 1.0986×(-0.5624) ≈ 0.512 Coefficient of variation: =1-0.512=0.488 The same is true for other parameters. , calculate the sum, and finally calculate the weight of each parameter .

[0068] Assume that the final weight distribution is as follows (for example, in practice, they should be calculated one by one):

[0069] Assuming ideal conditions = [1,1,1,1,1,1,1] (the optimal normalized value of all parameters is 1) calculate , such as the step voltage U Under the parameters, sample 1 Δ=0, sample 2 Δ=1, sample 3 Δ=0.667, ρ=0.5, Δ_min=0, Δ_max=1 Correlation coefficient γ_ij:

[0070] Step voltage U Below is sample 1: = (0+0.5×1) / (0+0.5×1)=0.5 / 0.5=1 Sample 2: =(0+0.5×1) / (1+0.5×1)=0.5 / 1.5=0.333 Sample 3: =(0+0.5×1) / (0.667+0.5×1)=0.5 / 1.167≈0.429 Calculate all samples under all parameters in turn .

[0071] Calculate the grey relational degree of each group of data

[0072] Take sample 1 as an example:

[0073] The γ value of sample 1 is 1, sample 2 is 0.333, and sample 3 is 0.5 (for example only, each parameter needs to be calculated separately). = 1.00, = 0.40, = 0.65.

[0074]

[0075] The current cycle's grounding electrode score is 100 points, placing it at the A (safe) level, with all parameters in ideal condition. The previous cycle's score was 65 points, placing it at the C (warning) level. Please recheck the soil moisture and current signals in the diversion cable. The previous cycle's score was 40 points, reaching the D (abnormal) level. Please arrange for an on-site inspection and necessary maintenance checks.

[0076] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A real-time monitoring and evaluation early warning method based on multiple state parameters of DC grounding poles, characterized in that: The following steps are involved: Obtain the pole address parameters of the grounding electrode and clean the pole address parameters to obtain the required data; Analyze and evaluate the required data to obtain comprehensive evaluation results under different indicators; Based on the comprehensive evaluation results, an operating status report of the grounding level is generated and the operating status report is fed back.

2. The real-time monitoring and evaluation early warning method based on multiple state parameters of DC grounding electrodes according to claim 1 is characterized in that: When obtaining the pole address parameters of the grounding electrode, the pole address parameters of the grounding electrode include step voltage data, contact voltage data, soil temperature data, soil moisture data, current signal data of the guide cable, water level data of the monitoring well, and corrosion degree data of the grounding electrode.

3. The real-time monitoring and evaluation early warning method based on multiple state parameters of DC grounding electrodes according to claim 1 is characterized in that: The specific method for cleaning the polar address parameters and obtaining the required data is as follows: Divide the polar address parameters according to parameter types to obtain several different types of parameters; Interpolation is used to fill missing data for different types of parameters; Statistical analysis was used to identify the parameters after filling in the missing data and obtain data after outlier processing; The format of the data after outlier processing is standardized to obtain the required data.

4. The real-time monitoring and evaluation early warning method based on multiple state parameters of DC grounding electrodes according to claim 1 is characterized in that: When analyzing and evaluating the required data and obtaining comprehensive evaluation results under different indicators, the grey correlation-entropy weight analysis method is used.

5. The real-time monitoring and evaluation early warning method based on multiple state parameters of DC grounding electrodes according to claim 1 is characterized in that: The specific methods for analyzing and evaluating the required data and obtaining comprehensive evaluation results under different indicators are as follows: Construct the original data matrix based on the required data to eliminate the dimension difference; According to the original data matrix, the data types are classified as benefit parameters if the values ​​are larger, and cost parameters if the values ​​are smaller. The entropy weight method is used to calculate the dynamic weight of the classified data; Perform grey correlation analysis on the dynamic weight of the classified data to obtain the correlation between the dynamic weight of the classified data and the target state; The correlation is mapped to a percentage scoring system to obtain comprehensive evaluation results under different indicators.

6. The real-time monitoring, evaluation and early warning method based on multiple state parameters of a DC grounding electrode according to claim 5 is characterized in that: The specific method of classifying data types according to the original data matrix is ​​as follows: Determine whether the data type is a benefit parameter or a cost parameter; If it is a cost parameter, the classified data can be obtained by the following formula: : If it is a benefit parameter, the classified data can be obtained by the following formula: : in, i is the sample serial number, j is the parameter number, x ij For the i The first sample j Item parameter value.

7. The real-time monitoring, evaluation and early warning method based on multiple state parameters of a DC grounding electrode according to claim 5 is characterized in that: The specific method of using the entropy weight method to calculate the dynamic weight of classified data is as follows: Calculate the j The weight of the item parameters: Calculate the j Information entropy value of item parameters: Calculate the j The coefficient of variation of the term parameters: Determine the j Entropy weight of item parameter: in, For the j Under the parameters, i The proportion of sample data.

8. The real-time monitoring, evaluation and early warning method based on multiple state parameters of a DC grounding electrode according to claim 5 is characterized in that: The specific method of performing grey correlation analysis on the dynamic weight of the classified data and obtaining the correlation between the dynamic weight of the classified data and the target state is as follows: According to the preset standards, the theoretically best quality of the classified data is selected and the reference sequence is preset. , the ideal state vector: in, is the theoretical optimal value of the step voltage data, is the theoretical optimal value of the contact voltage data, is the theoretical optimal value of soil temperature data, is the theoretical optimal value of soil moisture data, is the theoretical optimal value of the current signal data of the guide cable, is the theoretical optimal value of the monitoring well water level data; Calculate the correlation coefficient between the classified data and the reference sequence : in, is the difference value, is the resolution coefficient, the difference value The calculation method is as follows: Calculate the grey relational degree of each data : in, For the j The entropy weight of the parameter.

9. The real-time monitoring, evaluation and early warning method based on multiple state parameters of a DC grounding electrode according to claim 5 is characterized in that: The specific method of mapping the correlation degree to the percentage scoring system and obtaining the comprehensive evaluation results under different indicators is as follows: in, To comprehensively evaluate the results, is the grey relational degree of each data, .

10. A real-time monitoring and evaluation warning system based on multiple state parameters of DC grounding poles, characterized by: include: The pole address on-site detection unit is used to obtain the pole address parameters of the grounding electrode and clean the pole address parameters to obtain the required data; Data collection and transmission unit, used to analyze and evaluate the required data and obtain comprehensive evaluation results under different indicators; The status monitoring and early warning background is used to generate the grounding level operation status report based on the comprehensive evaluation results and provide feedback on the operation status report.

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