Engineering quantity measurement method based on power construction supervision engineering

Abstract

The application provides an engineering quantity measurement method based on power construction supervision engineering, comprising: acquiring geographic coordinate data of an extra-high voltage tower base position, extracting terrain and landform diversity features and soil type difference information from the geographic coordinate data through satellite remote sensing image processing, and obtaining an extra-high voltage tower base resistance geographic regularity distribution map through geographic information system superposition analysis; collecting high-voltage electric field interference intensity data generated by an extra-high voltage transmission line around the tower base, combining the extra-high voltage tower base resistance geographic regularity distribution map, analyzing the influence degree of high-voltage electric field interference on tower base grounding resistance fluctuation, and obtaining an adjusted tower base resistance change rate and fluctuation frequency; obtaining tower base position analysis results from a differentiated steel reinforcement cage binding quantity measurement strategy, combining a resistance geographic regularity subset to optimize the distribution proportion of the steel reinforcement cage binding quantity, and obtaining a resource configuration optimized binding quantity measurement table.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, and in particular to a method for measuring engineering quantity based on power construction supervision engineering. BACKGROUND

[0002] As the backbone of modern power systems, ultra-high voltage transmission projects bear the heavy responsibility of long-distance and large-capacity power transmission, and their construction quality directly affects the safety and economic benefits of the power grid. In the construction of tower foundation, the measurement of steel reinforcement cage binding quantity and the stability of grounding resistance are key links to ensure the quality of the project and the safety of operation.

[0003] However, the existing methods have obvious shortcomings in dealing with complex environmental factors, and it is difficult to meet the needs of fine management. Therefore, technical breakthroughs are needed to optimize resource allocation and improve engineering efficiency. Currently, the measurement of steel reinforcement cage binding quantity in tower foundation construction relies mainly on manual experience or simple models, which cannot adapt to the diversity of terrain and soil types along the ultra-high voltage transmission line. This method often fails to accurately reflect the fluctuation characteristics of tower foundation grounding resistance in complex geological conditions, resulting in large deviations in measurement results. For example, in special terrains such as mountainous areas or wetlands, the soil resistivity varies significantly, and the existing methods cannot dynamically adjust the measurement strategy, thereby affecting the rational allocation of engineering resources. The high-voltage electric field generated during the operation of ultra-high voltage transmission lines further exacerbates the technical problems. Electric field interference can significantly affect the measurement accuracy of tower foundation grounding resistance, and the fluctuation of grounding resistance is closely related to the terrain and soil type of the tower foundation location. The geographical environment of different tower foundations varies, resulting in spatial heterogeneity in resistance fluctuation. In some areas, the resistance fluctuates dramatically, while in other areas, it is relatively stable. This spatial heterogeneity makes it difficult for a unified measurement method to be applicable, increasing the complexity of engineering quantity estimation. For example, in areas close to the center of the line with high electric field intensity, the grounding resistance may fluctuate frequently due to electric field interference, thereby affecting the accurate calculation of the steel reinforcement cage binding quantity.

[0004] Therefore, how to accurately analyze the geographical distribution of grounding resistance according to the terrain, soil characteristics and electric field interference level of different tower foundation locations, and develop differentiated measurement strategies for steel reinforcement cage binding quantity, has become a key problem for fine management of engineering quantity and optimization of resource allocation. SUMMARY

[0005] The present application provides a method for measuring engineering quantity based on power construction supervision engineering, which mainly includes:

[0006] Obtain the geographical coordinate data of the ultra-high voltage tower foundation location, locate the corresponding image area through satellite remote sensing image processing combined with the geographical coordinate data of the ultra-high voltage tower foundation location, extract the terrain and soil type information in the geographical coordinate data from the satellite remote sensing image data, and generate an ultra-high voltage tower foundation resistance geographical regularity distribution map through geographic information system overlay analysis;

[0007] Collecting high-voltage electric field interference intensity data generated by the ultra-high voltage transmission line around the tower base, combining the ultra-high voltage tower base resistance geographical regularity distribution map, analyzing the influence of the high-voltage electric field interference on the fluctuation of the tower base grounding resistance, and generating an adjusted tower base resistance change rate and fluctuation frequency;

[0008] According to the adjusted tower base resistance change rate and fluctuation frequency, analyze the resistance change deviation between the tower bases, filter the tower base positions with resistance change higher than the average level, divide the tower base area groups with similar resistance fluctuation, and generate a resistance geographical regularity subset of the tower base area group;

[0009] Through the resistance geographical regularity subset, analyze the grounding resistance fluctuation of the tower base position in the tower base area group, generate an average fluctuation level, and develop a resistance fluctuation characteristic compensation scheme according to the average fluctuation level, and generate an optimized grounding resistance fluctuation adjustment strategy;

[0010] According to the optimized grounding resistance fluctuation adjustment strategy, combining the terrain and soil type information, evaluating the steel cage binding amount demand of the tower base position, preferentially adjusting the tower base with resistance change higher than the average level, and generating a differentiated steel cage binding amount measurement strategy;

[0011] Extract the tower base position analysis result from the differentiated steel cage binding amount measurement strategy, combine the resistance geographical regularity subset to optimize the steel cage binding amount distribution proportion, and generate a resource allocation optimized binding amount measurement table.

[0012] Further, the geographical coordinate data of the ultra-high voltage tower base position is obtained, the corresponding image area of the geographical coordinate data of the ultra-high voltage tower base position is located through satellite remote sensing image processing, the terrain and soil type information in the geographical coordinate data is extracted from the satellite remote sensing image data, and the ultra-high voltage tower base resistance geographical regularity distribution map is generated through geographic information system overlay analysis, including:

[0013] Obtain the latitude and longitude coordinate data of the tower base along the ultra-high voltage transmission line, collect the three-dimensional spatial information of the tower base through the satellite positioning system, and establish a coordinate index database;

[0014] For the tower base points in the coordinate index database, obtain the surrounding high-resolution satellite remote sensing image data, process the satellite remote sensing image data by image fusion, and generate clear ground image data;

[0015] Based on the clear ground image data, calculate the terrain slope and fluctuation degree, extract the image texture parameters as the ground roughness coefficient, and generate a tower base topographic feature parameter library;

[0016] The soil type distribution is identified through multispectral remote sensing images, the soil water content and particle structure information are extracted by combining the thermal infrared band and radar remote sensing data, and a tower foundation soil attribute data set is generated;

[0017] The tower foundation soil attribute data set and the tower foundation topographic feature parameter library are input into a geographic information system, spatial overlay analysis is performed, and a super-high voltage tower foundation resistance geographic regularity distribution map is generated.

[0018] Further, the high-voltage electric field interference intensity data generated by the ultra-high voltage transmission line around the tower foundation is collected, combined with the super-high voltage tower foundation resistance geographic regularity distribution map, and the influence of the high-voltage electric field interference on the tower foundation grounding resistance fluctuation is analyzed, to generate an adjusted tower foundation resistance change rate and fluctuation frequency, including:

[0019] A network of electric field strength measurement sensors is laid out to record the horizontal and vertical electric field components around the tower foundation, and an electric field strength value is generated;

[0020] The fundamental frequency and harmonic components of the electric field strength value are extracted by Fourier transform to generate a spatio-temporal distribution data set;

[0021] Based on the spatio-temporal distribution data set and the super-high voltage tower foundation resistance geographic regularity distribution map, the correlation coefficient of electric field strength and grounding resistance measurement value is calculated to generate an electric field interference influence coefficient matrix;

[0022] The grounding resistance measurement value sequence of the tower foundation location is extracted through the electric field interference influence coefficient matrix, the resistance change slope is calculated, the resistance fluctuation frequency component is identified, and the adjusted tower foundation resistance change rate and fluctuation frequency are generated.

[0023] Further, according to the adjusted tower foundation resistance change rate and fluctuation frequency, the resistance change deviation between towers is analyzed, the tower foundation locations with resistance change higher than the average level are screened, the tower foundation region groups with similar resistance fluctuation are divided, and the resistance geographic regularity subset of the tower foundation region group is generated, including:

[0024] According to the adjusted tower foundation resistance change rate and fluctuation frequency, the mean and standard deviation of the resistance change rate are calculated to generate a standardized deviation value, and the high-change tower foundation locations are screened;

[0025] Based on the high-change tower foundation locations, a clustering algorithm is used to divide the tower foundation region groups with similar resistance fluctuation;

[0026] The geographic coordinates and resistance characteristic data of the tower foundation region groups are extracted, a resistance distribution continuous surface is constructed, and a resistance geographic regularity subset containing the geographic coordinates, grounding resistance change rate, fluctuation frequency, topographic feature parameters, and soil attribute data of the tower foundation region groups is generated, and a regional resistance distribution continuous surface is constructed by Kriging interpolation.

[0027] Further, the ground resistance fluctuation of the tower foundation position in the tower foundation area group is analyzed through the resistance geographical regularity subset, an average fluctuation level is generated, a resistance fluctuation characteristic compensation scheme is formulated according to the average fluctuation level, and an optimized ground resistance fluctuation adjustment strategy is generated, including:

[0028] The tower foundation ground resistance time series data is extracted through the resistance geographical regularity subset, the moving average and fluctuation amplitude are calculated, and the average fluctuation level of the area group is generated;

[0029] Based on the average fluctuation level, the resistance adjustment coefficient and compensation frequency are generated, the ground material type is determined, and the resistance fluctuation characteristic compensation scheme is formulated;

[0030] According to the resistance fluctuation characteristic compensation scheme, the reinforcement cage binding density and spacing are adjusted, the connection mode is determined, and the adjusted reinforcement cage binding amount configuration parameter is generated;

[0031] In the high fluctuation area, an array of grounding electrodes is added, the application amount of chemical resistance reduction material is adjusted, and an optimized ground resistance fluctuation adjustment strategy is generated.

[0032] Further, according to the optimized ground resistance fluctuation adjustment strategy, the topographic and geomorphic features and the soil type information are combined to evaluate the reinforcement cage binding amount demand of the tower foundation position, the tower foundation with resistance change higher than the average level is preferentially adjusted, and a differentiated reinforcement cage binding amount measurement strategy is generated, including:

[0033] According to the optimized ground resistance fluctuation adjustment strategy, the reinforcement configuration parameters are extracted, the proportion of transverse reinforcing bars is adjusted according to the terrain slope, and the preliminary demand amount of the reinforcement cage is generated;

[0034] The ground resistance contribution coefficient is calculated through the soil type information, the terrain relief degree is combined to generate the stability influence weight of the tower foundation, and the high change tower foundation is screened to form a preferential adjustment sequence;

[0035] According to the preferential adjustment sequence, the reinforcement cage binding density and spacing are adjusted, the number of binding point distributions is determined, and a binding parameter configuration set is generated;

[0036] Based on the binding parameter configuration set, the resistance fluctuation characteristics of the tower foundation area are integrated, and a differentiated reinforcement cage binding amount measurement strategy is generated.

[0037] Further, the tower foundation position analysis result is extracted from the differentiated reinforcement cage binding amount measurement strategy, the reinforcement cage binding amount distribution proportion is optimized in combination with the resistance geographical regularity subset, and a resource configuration optimized binding amount measurement table is generated, including:

[0038] Extract the binding parameters from the differential reinforcement cage binding measurement strategy, combine the resistance geographical law subset to calculate the resource demand weight, and generate a regional weighted reinforcement configuration scheme;

[0039] According to the reinforcement configuration scheme, the total material demand is calculated, the configuration standard is adjusted, and if the total reinforcement demand of a certain regional group exceeds the total reinforcement constraint of the project, the reinforcement configuration standard of the corresponding tower foundation is sequentially reduced according to the priority of the tower foundation resistance change rate from low to high, and the distribution result of the total amount balance is generated.

[0040] Based on the distribution result, the reinforcement consumption is optimized, the data record containing the tower foundation number and the binding parameter is established, and the resource configuration optimized binding amount measurement table is generated.

[0041] The technical scheme provided by the embodiment of the present application can include the following beneficial effects:

[0042] The present application discloses an engineering quantity measurement method based on power construction supervision engineering, which solves the problem of tower foundation grounding resistance fluctuation caused by topography, soil type difference and high voltage electric field interference in extra-high voltage transmission system. The geographic coordinates, topographic features and soil parameters of the tower foundation position are extracted through satellite remote sensing image processing, and the resistance geographical law distribution map is generated combined with geographic information system, the influence of high voltage electric field interference on resistance fluctuation is analyzed, and the change rate and fluctuation frequency are quantified. Based on this, the present application screens the tower foundation with abnormal resistance change, divides regional groups and formulates a differential grounding resistance fluctuation compensation scheme, optimizes the reinforcement cage binding amount, grounding material selection and auxiliary grounding device configuration. The present application integrates the comprehensive influence of topography and soil type on tower foundation stability and resistance, dynamically adjusts the reinforcement cage binding density and distribution ratio, and generates a resource optimized measurement table. The present application significantly improves the stability and safety of the extra-high voltage tower foundation grounding system, reduces the influence of resistance fluctuation on transmission efficiency, and provides efficient technical support for extra-high voltage engineering in complex environment. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 The flowchart of the engineering quantity measurement method based on power construction supervision engineering of the present application. DETAILED DESCRIPTION

[0044] In order to further understand the content of the present application, the present application is described in detail in combination with the drawings and examples. It can be understood that the specific examples described herein are only used to explain the related application, and are not limited to the application. In addition, it should be pointed out that only the parts related to the application are shown in the drawings for convenience of description.

[0045] As Figure 1 , the engineering quantity measurement method based on power construction supervision engineering of the present application can specifically include:

[0046] S101, acquire geographic coordinate data of the tower base position of the ultra-high voltage, locate the corresponding image area through satellite remote sensing image processing combined with the geographic coordinate data of the tower base position of the ultra-high voltage, extract the topographic and geomorphic features and soil type information in the geographic coordinate data from the satellite remote sensing image data, and generate an ultra-high voltage tower base resistance geographic regularity distribution map through geographic information system overlay analysis.

[0047] Acquire the latitude and longitude coordinate data of all tower bases along the ultra-high voltage transmission line, collect the three-dimensional spatial information of each tower base point through the Beidou satellite positioning system, establish a coordinate index database according to the tower base number, acquire high-resolution satellite remote sensing image data within a radius of 500 meters for each tower base point, and eliminate the cloud and fog blocking effect using a multi-temporal remote sensing image fusion method to obtain clear ground surface image data for each tower base position. Based on the clear ground surface image data for each tower base position, calculate the terrain slope value of each tower base position through a digital elevation model, determine the terrain undulation degree according to the adjacent elevation point difference value, use the contrast and correlation parameters of the image texture extracted by the gray level co-occurrence matrix as the ground roughness coefficient, and if the slope exceeds the preset threshold value, mark it as a complex terrain area to form a tower base terrain feature parameter library containing slope, undulation degree and roughness coefficient. Use the coordinate position information in the tower base terrain feature parameter library to identify the soil type distribution through the band combination of multispectral remote sensing images, invert the soil water content distribution according to the thermal infrared band brightness temperature value, and judge the particle structure composition using the correlation between the radar remote sensing backscattering coefficient and the soil particle size, combined with geological survey data to determine the resistivity value range of each type of soil to generate a tower base soil attribute data set. Input the tower base soil attribute data set and the tower base terrain feature parameter library into a geographic information system, perform rasterization processing on the resistivity value, terrain slope, undulation degree and soil water content distribution through spatial overlay analysis, mark each tower base position according to the resistivity value range into three levels of high, medium and low, establish a spatial correlation matrix between the parameters, and output an ultra-high voltage tower base resistance geographic regularity distribution map.

[0048] Specifically, in one embodiment, when acquiring the accurate position information of the tower base along the ultra-high voltage transmission line, the carrier phase difference technology of the Beidou satellite positioning system is used to achieve centimeter-level positioning accuracy.

[0049] Deploy a Beidou receiver at each tower base construction point to receive navigation signals from at least 4 visible satellites, and obtain the three-dimensional coordinates of the tower base center point in the WGS-84 coordinate system by solving the pseudo-range observation value and the carrier phase observation value. The coordinate index database establishes a two-level index structure according to the line direction and tower base number, facilitating fast retrieval and spatial query.

[0050] For the acquisition of remote sensing images for each tower base point, high-resolution series satellite data with a spatial resolution better than 2 meters are selected.

[0051] The multi-temporal remote sensing image fusion method fuses multiple images of the same area at different time points using a weighted average method or a principal component analysis method. When a certain temporal image is obscured by clouds or fog, the corresponding pixel values are extracted from the clear areas of other temporal images to replace the obscured areas. The color consistency of the replaced areas is adjusted through histogram matching, and finally a complete and clear ground surface image data is generated.

[0052] In one implementation, the construction of the digital elevation model is based on stereo image pairs or laser radar point cloud data, and a continuous elevation surface is generated by irregular triangle mesh interpolation. The terrain slope value is obtained by calculating the elevation difference between the target point and the surrounding 8 neighborhood points, and the relief degree is determined according to the combination of the extreme value difference and the standard deviation of the elevation in the analysis window.

[0053] As the core technology of texture analysis, the gray level co-occurrence matrix quantifies the texture features by statistically analyzing the joint distribution probability of the gray levels of two pixels at a specific distance and direction. In the specific processing process, the gray levels of the remote sensing image are first compressed to 16 levels, and then the co-occurrence matrices in four directions of 0 degrees, 45 degrees, 90 degrees and 135 degrees are calculated. The contrast parameters are extracted from the co-occurrence matrix in each direction. The contrast reflects the clarity and depth of the texture, and the calculation formula involves the product sum of the matrix elements and the square of the difference between their row and column indices. The correlation parameter measures the similarity of the texture in a specific direction, which is obtained by normalized covariance calculation. The contrast and correlation parameters in the four directions are averaged to obtain the isotropic ground roughness coefficient. The larger the roughness coefficient, the more intense the ground surface fluctuation, and the more significant the impact on the tower foundation construction and grounding resistance.

[0054] For example, in the soil type identification process, the reflectance difference between the red light band and the near-infrared band of the multi-spectral remote sensing image can effectively distinguish different soil types. Clay has lower reflectance in the red light band and sand has higher reflectance. Threshold range is set for classification and identification.

[0055] In one embodiment, the principle of soil moisture content inversion by thermal infrared remote sensing is based on the positive correlation between soil thermal inertia and moisture content.

[0056] The diurnal temperature difference value is calculated by acquiring the land surface temperature data at two time phases of day and night, and the smaller the temperature difference is, the greater the soil heat inertia is, and the higher the water content is. The backscattering coefficient of radar remote sensing is mainly affected by the soil dielectric constant and surface roughness, and the dielectric constant is closely related to the soil particle size. The backscattering coefficient of coarse-grained soil is usually greater than that of fine-grained soil, and by establishing an empirical relationship model between the scattering coefficient and the particle composition, the relative content of sand, silt and clay can be inverted. Combined with the measured data of soil resistivity obtained by geological exploration, a corresponding relationship table of different soil types and resistivity values is established, and the resistivity of sandy soil is generally in the range of 1000 to 5000 ohm·m, and the resistivity of clay is in the range of 10 to 100 ohm·m.

[0057] The spatial overlay analysis function of geographic information system realizes information synthesis by spatial operation of multiple thematic layers. First, the resistivity value, terrain slope, relief degree and soil water content distribution parameters are converted into a unified raster data format, and each raster cell corresponds to an actual range of 10 meters x 10 meters on the ground.

[0058] It should be noted that the three-level labeling of resistivity value adopts the natural breakpoint classification method, and the classification threshold is determined according to the inherent characteristics of data distribution. High resistivity area usually corresponds to dry sandy soil or bedrock outcrop area, medium resistivity area is the conventional soil covered area, and low resistivity area is mostly clay or marsh area with high water content.

[0059] The establishment of spatial correlation matrix is realized by calculating the Moran index of each parameter in the spatial neighborhood, which reflects the spatial aggregation degree of parameter value. When the resistivity and terrain slope show a positive correlation, it indicates that the soil resistivity in steep slope area is generally high, and this correlation rule has important guiding significance for the differentiated configuration of subsequent reinforcement cage binding amount. By comprehensively analyzing the spatial distribution characteristics and mutual relationship of each parameter, the generated ultra-high voltage tower foundation resistance geographic law distribution map can intuitively show the advantages and disadvantages of the grounding conditions of each tower foundation position.

[0060] S102, collect the high-voltage electric field interference intensity data generated by the ultra-high voltage transmission line around the tower foundation, combine the ultra-high voltage tower foundation resistance geographic law distribution map, analyze the influence of the high-voltage electric field interference on the fluctuation of tower foundation grounding resistance, and generate the adjusted tower foundation resistance change rate and fluctuation frequency.

[0061] The electric field strength measurement sensor network is arranged around the tower base, a three-dimensional electric field measuring instrument is used to record the horizontal and vertical electric field components, the measurement points are determined according to the relative position relationship between the tower base and the power transmission line, the electric field strength values of different height layers are obtained, the fundamental frequency and harmonic components of the electric field are extracted through Fourier transform, and the space-time distribution data set of the high-voltage electric field around the tower base is obtained. Based on the electric field strength values in the space-time distribution data set, combined with the electric resistance reference values in the geographic regular distribution map of the ultra-high voltage tower base, the linear correlation degree between the electric field strength and the measured value of the grounding resistance is determined by calculating the Pearson correlation coefficient, and if the electric field strength exceeds the preset threshold, it is marked as a strong interference area, and the electric field interference influence coefficient matrix is constructed according to the correlation coefficient value. Using the electric field interference influence coefficient matrix, the grounding resistance measurement value sequence of each tower base position at different time periods is extracted, the change slope of the resistance value is calculated by setting a fixed length sliding window moving along the time axis, the dominant frequency component of the resistance fluctuation is identified by using fast Fourier transform, and the original change rate is divided by the interference influence coefficient of the corresponding position to obtain the real resistance change rate after eliminating the electric field interference. For the real resistance change rate sequence, the number of times that the resistance value exceeds the preset percentage of the reference value in a unit time is counted as the initial value of the fluctuation frequency, the peak position is identified by calculating the autocorrelation function value under different time delays to identify the fluctuation period, the fluctuation frequency is corrected combined with the daily variation law of the electric field strength, and the adjusted tower base resistance change rate and fluctuation frequency are output.

[0062] Specifically, in an embodiment, the arrangement of the electric field strength measurement sensor network needs to consider the spatial relationship between the tower base and the power transmission line.

[0063] The measurement points are arranged on concentric circles at distances of 5 meters, 10 meters and 15 meters from the center of the tower base, and 8 measurement points are uniformly arranged on each circle. The three-dimensional electric field measuring instrument adopts a spherical electrode structure, and measures the electric field components in X, Y and Z directions through three mutually perpendicular electrode pairs. The measurement frequency is set to 50 samples per second.

[0064] When processing the electric field measurement data by Fourier transform, the collected time domain signal is first processed by windowing, and Hamming window is used to reduce spectrum leakage. The time domain signal is converted to frequency domain by discrete Fourier transform, and the fundamental frequency component of 50Hz and the harmonic components of 100Hz and 150Hz are extracted. These frequency components reflect the electric field characteristics of ultra-high voltage alternating current transmission.

[0065] It should be noted that the calculation of the Pearson correlation coefficient involves the standardization of two key variables. First, the electric field intensity sequence and the grounding resistance measurement sequence for the same time period are extracted, and the mean and standard deviation of each sequence are calculated. Each data point is then standardized by subtracting the mean from the original value and dividing by the standard deviation. The two standardized sequences are then multiplied point-by-point and summed, then divided by the sequence length minus 1 to obtain the correlation coefficient. The closer the absolute value of the correlation coefficient is to 1, the stronger the influence of the electric field on the grounding resistance. Based on the magnitude of the correlation coefficient, an interference influence coefficient between 0.1 and 1.0 is assigned to each tower base location. When the absolute value of the correlation coefficient is greater than 0.8, the interference influence coefficient is close to 1.0; when the absolute value of the correlation coefficient is less than 0.3, the interference influence coefficient is close to 0.1. This quantification method accurately reflects the differences in electric field interference at different locations.

[0066] In one implementation, the sliding window settings need to balance time resolution and computational stability. The window duration is set to 30 minutes, the sliding step is 5 minutes, and each window contains 1500 ground resistance measurement data points.

[0067] The correction process for the resistance change rate employs a step-by-step approach. The slope of the linear regression equation, obtained by fitting the resistance value sequence within a window using the least squares method, is used as the original change rate. The dominant frequency component identified by the Fast Fourier Transform is used to determine the type of interference. If the dominant frequency is 50Hz or its harmonics, the fluctuation is confirmed to be caused by electric field interference. During correction, the original change rate is divided by the interference influence coefficient at the corresponding location to obtain the true change rate after eliminating electric field interference. For example, if the original resistance change rate at a certain tower base location increases by 20 ohms per hour, and the interference influence coefficient at that location is 0.8, then the corrected true resistance change rate increases by 25 ohms per hour. This correction method can restore the true trend of grounding resistance changes caused by natural factors such as soil moisture and temperature, eliminating spurious fluctuations caused by electric field interference.

[0068] For example, when calculating the fluctuation frequency, the number of times the grounding resistance value exceeds the reference value by 10% per hour is counted as the initial estimate of the fluctuation frequency.

[0069] The autocorrelation function was calculated using a normalization method. For the true resistance change rate sequence, the autocorrelation value was calculated at different time delays τ, ranging from 0 to 24 hours, with a step size of 1 hour. When the autocorrelation function shows a significant peak at a certain delay value, that delay value is the fluctuation period.

[0070] In one embodiment, the diurnal variation of the electric field intensity exhibits a distinct bimodal characteristic, peaking at 10:00 AM and 8:00 PM, and troughing at 3:00 AM. Based on this pattern, the fluctuation frequency is adjusted using a time-weighted method, with the weighting coefficient for peak periods set to 1.2 and the weighting coefficient for trough periods set to 0.8.

[0071] Understandably, the final output of the adjusted tower base resistance change rate is in ohms per hour, and the fluctuation frequency is in cycles per hour. These two parameters together constitute a dynamic characteristic description of the tower base grounding resistance, providing a quantitative basis for the subsequent optimization of the reinforcement cage binding quantity.

[0072] S103. Based on the adjusted tower base resistance change rate and fluctuation frequency, analyze the resistance change deviation between tower bases, screen tower base locations with resistance changes higher than the average level, divide tower base area groups with similar resistance fluctuations, and generate a subset of the resistance geographical patterns of the tower base area groups.

[0073] Based on the adjusted resistance change rate and fluctuation frequency of the tower bases, the mean and standard deviation of the resistance change rate for all tower base locations are calculated. The standardized deviation is obtained by dividing the difference between the resistance change rate and the mean for each tower base by the standard deviation. If the standardized deviation is greater than 1.5, the tower base is marked as a high-variance tower base, resulting in a set of high-variance tower base locations. For this set of high-variance tower base locations, the K-means clustering algorithm is used to cluster based on both resistance change rate and fluctuation frequency. The Euclidean distance between each tower base and the cluster center is calculated to determine the affiliation. If adjacent tower bases are assigned to different cluster groups but their resistance characteristics differ by less than a preset threshold, they are adjusted to the same cluster group, resulting in a tower base region group with similar resistance fluctuations. Based on this tower base region group, the geographical coordinates, resistance change rate, fluctuation frequency, and topographic and soil attribute data of all tower bases within each region group are extracted. A continuous surface of resistance distribution within the region is constructed using the Kriging interpolation method. The rate of change of the surface in each direction is calculated to identify spatial trends of increasing or decreasing resistance, forming a subset of geographical patterns of resistance containing spatial distribution characteristics and variation laws.

[0074] Specifically, in one implementation, the standardized deviation value is calculated using statistical methods to quantitatively evaluate the characteristics of tower base resistance variation.

[0075] First, collect the resistance change rate data for all tower base locations, calculate the arithmetic mean as the benchmark reference value, then calculate the sum of squares of the differences between each data point and the average value, divide by the total number of tower bases to obtain the variance, and finally take the square root of the variance to obtain the standard deviation.

[0076] For 100 tower bases of a certain ultra-high voltage transmission line, if the average resistance change rate is 15 ohms per hour and the standard deviation is 5 ohms per hour, when the resistance change rate of a certain tower base reaches 22.5 ohms per hour, its standardized deviation value is calculated to be 1.5, which exceeds the preset threshold and is marked as a high-change tower base.

[0077] It should be noted that the K-means clustering algorithm needs to consider both electrical characteristics and geographical location constraints when grouping tower bases. Initial cluster centers are determined by randomly selecting K highly variable tower base locations. In each iteration, the Euclidean distance from each tower base to the cluster center is calculated, taking into account both the rate of change of resistance and the fluctuation frequency. The adjustment mechanism for geographical adjacency constraints is implemented by checking the cluster affiliation of adjacent tower bases. When two geographically adjacent tower bases are assigned to different cluster groups, but their difference in rate of change of resistance is less than 3 ohms per hour and their difference in fluctuation frequency is less than 2 times per hour, the two tower bases are adjusted to the same cluster group. This adjustment ensures the geographical continuity of the regional groups.

[0078] The Kriging interpolation method is based on geostatistical principles and constructs a variogram model by analyzing the spatial autocorrelation of tower base resistance data.

[0079] In one implementation, the interpolation process first calculates the experimental variability function, calculates the semivariance value using base pairs with different distance intervals, and then determines the three key parameters—range, abutment value, and nugget value—by fitting a spherical model or a Gaussian model.

[0080] The resistivity geographic pattern subset includes the spatial distribution feature matrix, resistivity gradient vector, and trend indicator for each regional group. This information provides a quantitative basis for the subsequent differentiated configuration of rebar cage binding quantity.

[0081] S104. Analyze the grounding resistance fluctuations at the tower base locations within the tower base area group using the subset of resistance geographical patterns, generate an average fluctuation level, formulate a resistance fluctuation characteristic compensation scheme based on the average fluctuation level, and generate an optimized grounding resistance fluctuation adjustment strategy.

[0082] Grounding resistance time-series data for each tower base within each regional group are extracted using a subset of resistance geographic patterns. A moving average is calculated using the arithmetic mean of data points within a fixed time window. The fluctuation amplitude is determined based on the difference between the resistance value at adjacent time points and the moving average. The mean of the fluctuation amplitude for all tower bases within the regional group is calculated to obtain the average grounding resistance fluctuation level for that group. Based on this average fluctuation level, the fluctuation level is multiplied by a preset scaling factor to obtain a resistance adjustment coefficient. The main fluctuation period is determined based on the spectral analysis of the fluctuation data, and the reciprocal of the period duration is used as the compensation frequency. Grounding material types with corresponding conductivity are selected based on the soil resistivity range, and a resistance fluctuation characteristic compensation scheme including the adjustment coefficient and compensation frequency is formulated. Based on the adjustment coefficient in the resistance fluctuation characteristic compensation scheme, the adjustment coefficient is multiplied by the baseline binding amount to obtain the additional binding density requirement. The spacing adjustment of the longitudinal and transverse reinforcement bars in the rebar cage is determined by multiplying the compensation frequency by a preset conversion coefficient. Welding or bolting connection methods are selected based on the resistivity of the grounding material to obtain the adjusted rebar cage binding amount configuration parameters. Based on the configuration parameters of the rebar cage binding amount, a grounding electrode array is added in the high fluctuation area. The grounding effect is enhanced by the burial depth and distribution density of the electrodes. The application amount of chemical resistance reducing material is determined according to the soil moisture content. This forms a grounding resistance fluctuation adjustment strategy that includes adjusting the rebar cage binding amount, optimizing the selection of grounding materials, and adding auxiliary grounding devices.

[0083] Specifically, in one implementation, the extraction of grounding resistance time-series data is based on historical measurements recorded in a subset of resistance geographic patterns.

[0084] Each tower base is equipped with an automatic monitoring device that collects grounding resistance values ​​hourly, forming continuous time-series data. The moving average method uses a fixed 5-hour time window, and the arithmetic mean of the 5 data points within the window is used as the smoothed value for that moment.

[0085] The fluctuation amplitude is determined by the absolute value of the difference between the measured resistance values ​​at adjacent time points and the corresponding moving average values. If the resistance of a certain tower base is 50 ohms at a certain moment, and the moving average value at that moment is 45 ohms, then the fluctuation amplitude is 5 ohms. All fluctuation amplitude values ​​within an observation period are statistically analyzed, and their arithmetic mean is calculated as the fluctuation characteristic value of that tower base.

[0086] It should be noted that the resistance adjustment coefficient is determined using a graded mapping method. Different scaling factors are set according to the numerical range of the average fluctuation level: when the fluctuation level is in the range of 0 to 10 ohms, the scaling factor is 0.1; when the fluctuation level is in the range of 10 to 20 ohms, the scaling factor is 0.15; and when the fluctuation level exceeds 20 ohms, the scaling factor is 0.2.

[0087] The spectrum analysis process identifies the fluctuation period through Fast Fourier Transform (FFT). A Discrete Fourier Transform (DFT) is performed on the grounding resistance time-series data, converting the time-domain signal into a frequency-domain representation. The frequency component with the largest amplitude in the spectrum corresponds to the dominant fluctuation frequency. The dominant fluctuation period is equal to the reciprocal of this frequency; for example, when the dominant frequency is 0.05Hz, the corresponding fluctuation period is 20 hours. The determination of the compensation frequency needs to consider the feasibility of actual construction, and the theoretical compensation frequency is usually rounded down to the nearest integer hour. In the practical application of UHV tower foundations, due to the daily periodic changes in the grid load, the grounding resistance often exhibits a 24-hour dominant fluctuation period; therefore, the compensation frequency is often set to once or twice per day. This spectrum analysis method can accurately identify periodic fluctuation patterns caused by electric field interference, providing a scientific basis for developing targeted compensation schemes.

[0088] In one implementation, the selection of grounding materials is categorized according to the soil resistivity range. When the soil resistivity is below 100 ohm-meters, ordinary steel is used as the grounding material; when the resistivity is in the range of 100 to 500 ohm-meters, galvanized steel is used; and when the resistivity exceeds 500 ohm-meters, copper-clad steel is used.

[0089] The calculation of the reinforcement cage binding quantity configuration parameters involves multiple conversion steps. The product of the adjustment coefficient and the baseline binding quantity determines the additional binding requirement. The baseline binding quantity is preset according to the tower foundation bearing capacity requirements, typically 150 to 200 kg of reinforcement per cubic meter of concrete. When the adjustment coefficient is 0.15, the additional binding quantity is 15% of the baseline value, i.e., an increase of 22.5 to 30 kg of reinforcement per cubic meter. The spacing adjustment of the longitudinal and transverse reinforcement bars is related to the conversion coefficient through the compensation frequency. The conversion coefficient is taken as 10 mm / day / time based on engineering experience. If the compensation frequency is twice a day, the spacing is reduced by 20 mm. This quantitative parameter conversion method ensures an accurate mapping from electrical characteristic analysis to structural design parameters, so that the reinforcement cage configuration meets both grounding performance requirements and structural strength standards.

[0090] For example, welded connections are suitable for grounding materials with resistivity below 200 ohm-meters, while bolted connections are used for combinations of materials with higher resistivity.

[0091] In one embodiment, the grounding electrode array is designed in a ring arrangement, with 8 to 12 vertical grounding electrodes buried around the tower base at equal intervals. The electrode length is determined according to the soil structure, typically 2.5 to 4 meters. The horizontal grounding grid is made of flat steel, and the grid spacing is adjusted according to the resistance fluctuation intensity, with the grid spacing reduced to 0.5 meters in high fluctuation areas.

[0092] Understandably, the application rate of chemical resistance-reducing materials is inversely proportional to soil moisture content. When the soil moisture content is below 15%, 8 to 10 kg of resistance-reducing agent is applied per cubic meter of soil; when the moisture content is between 15% and 25%, the application rate is reduced to 5 to 7 kg; and when the moisture content exceeds 25%, the application rate is further reduced to 3 to 4 kg. Resistance-reducing agents improve the conductivity of the soil, lowering the baseline value of the grounding resistance, thereby reducing the relative amplitude of resistance fluctuations.

[0093] S105. Based on the optimized grounding resistance fluctuation adjustment strategy, combined with the topographic features and soil type information, assess the reinforcement cage binding quantity requirement at the tower base location, prioritize adjusting tower bases with resistance changes higher than the average level, and generate a differentiated reinforcement cage binding quantity measurement strategy.

[0094] Based on the optimized grounding resistance fluctuation adjustment strategy, the rebar configuration parameters for each tower foundation are extracted. The amount of rebar cage binding is calculated based on the tower foundation bearing capacity requirements. The slope value, considering the diverse topographic features, is compared with a preset slope threshold. If the slope exceeds the threshold, the proportion of transverse reinforcement bars is increased by a preset percentage, resulting in a preliminary rebar cage requirement considering topographic factors. Based on this preliminary requirement, the soil resistivity value is divided by the reference resistivity to obtain the grounding resistance contribution coefficient. This coefficient is multiplied by the topographic relief value to determine the tower foundation stability impact weight. The resistance change rate of each tower foundation is compared with the average change rate of all tower foundations, and those tower foundations higher than the average are selected and arranged in descending order of impact weight to form a priority adjustment sequence. For each tower foundation in the priority adjustment sequence, the rebar cage binding density increment is determined based on the ratio of the resistance fluctuation amplitude to the preset reference value. The fluctuation frequency is converted into an adjustment factor and multiplied by the reference spacing to calculate the longitudinal and transverse reinforcement spacing adjustment value. The number of binding points at different depths is determined using a depth stratification method, resulting in a binding parameter configuration set for each tower foundation. Based on the binding parameter configuration set, combined with the spatial distribution characteristics and resistance fluctuation similarity of the tower bases within the regional group, linear interpolation is used to supplement the parameter transition values ​​between adjacent tower bases. The binding density value, longitudinal and transverse reinforcement spacing value, and number of binding points in each layer of each tower base are integrated to form a differentiated steel cage binding quantity measurement strategy that conforms to the regional resistance fluctuation characteristics of each tower base.

[0095] Specifically, in one implementation, the extraction of rebar configuration parameters is based on various adjustment coefficients and compensation frequency data recorded in the optimized grounding resistance fluctuation adjustment strategy.

[0096] The amount of steel reinforcement cage tied for each tower foundation is determined based on the tower foundation's design bearing capacity. The bearing capacity calculation takes into account factors such as tower height, conductor tension, and wind load. Typically, 150 to 200 kilograms of steel reinforcement is used as a benchmark value for each cubic meter of concrete foundation.

[0097] The terrain slope threshold is set at 15 degrees. When the measured slope at a certain tower base location reaches 20 degrees, the configuration ratio of transverse reinforcement bars is increased by 30% on the original basis. This increase ratio is determined linearly according to the degree to which the slope exceeds the threshold, with an increase of 2% for every 1 degree exceeding the threshold.

[0098] It should be noted that the grounding resistance contribution coefficient is determined using the relative ratio method. The reference resistivity is set at 100 ohm-meters, representing the standard value under ordinary soil conditions. When the measured soil resistivity at a certain tower foundation location is 250 ohm-meters, the contribution coefficient is calculated to be 2.5. The topographic relief is determined by analyzing the elevation changes within a 100-meter radius around the tower foundation; the relief value is equal to the maximum elevation difference divided by 10. The stability impact weight is equal to the product of the contribution coefficient and the relief value. This weight value reflects the combined impact of topography and soil factors on the stability of the tower foundation. In actual engineering, the relief of tower foundations in mountainous areas may reach 5 to 8. Combined with higher soil resistivity, the stability impact weight can reach 15 to 20. Such tower foundations require priority adjustment of the reinforcement cage configuration. By ranking all tower foundations according to their impact weight, a clear construction priority sequence is formed, ensuring that resources are prioritized for tower foundation locations with higher stability risks.

[0099] The benchmark for resistance change rate is obtained by calculating the arithmetic mean of the change rates of all tower bases. If the average change rate of 100 tower bases on a transmission line is 12 ohms per hour, then tower bases with a change rate exceeding 15 ohms are marked as high-change tower bases.

[0100] In one implementation, the increment of the rebar cage binding density is determined based on the ratio between the resistance fluctuation amplitude and a preset benchmark value. The preset benchmark value is typically 10 ohms; when the fluctuation amplitude is 15 ohms, the density increment is 50% of the benchmark density. The process of converting the fluctuation frequency into an adjustment factor uses a logarithmic transformation; the frequency value is taken as its natural logarithm and multiplied by 0.1 to obtain the adjustment factor. The benchmark spacing is typically 200 mm; when the adjustment factor is 0.2, the spacing between the longitudinal and transverse reinforcement bars is adjusted to 240 mm. The depth-layered approach divides the tower foundation into three layers: upper, middle, and lower, each approximately 1 meter thick. The upper layer, being closer to the ground surface and more susceptible to environmental influences, has 16 binding points per square meter; the middle layer, as the main load-bearing area, has 12 binding points per square meter; and the lower layer, penetrating deep into the stable soil layer, can have its binding points reduced to 8 per square meter. This layered configuration ensures structural strength while achieving economical material use.

[0101] For example, if the fluctuation range of a certain high-variable tower base reaches 18 ohms, the calculated density increment is 80%, which means that the amount of steel reinforcement needs to be increased by 80% on the basis of the baseline value.

[0102] Linear interpolation is used to supplement the parameter transition values ​​between adjacent tower bases. When the binding densities of two adjacent tower bases are 120 kg / m³ and 180 kg / m³ respectively, and the distance is 500 m, the interpolation density at the middle position is 150 kg / m³.

[0103] In one embodiment, spatial distribution characteristics are characterized by calculating the Euclidean distance matrix between tower bases, and resistance fluctuation similarity is quantified by a correlation coefficient matrix. A similarity threshold is set to 0.7, and tower bases with correlation coefficients exceeding this value are grouped into the same similarity group.

[0104] Understandably, the final formulation of a differentiated rebar cage binding quantity measurement strategy requires the integration of configuration information across three dimensions: binding density in kilograms per cubic meter, longitudinal and transverse bar spacing in millimeters, and the number of binding points per square meter for each layer. This multi-dimensional quantitative expression provides clear technical parameter guidance for construction, achieving a precise mapping from electrical performance requirements to structural design parameters.

[0105] S106. Extract the tower base location analysis results from the differentiated rebar cage binding quantity measurement strategy, optimize the rebar cage binding quantity allocation ratio by combining the resistivity geographical law subset, and generate a binding quantity measurement table with optimized resource allocation.

[0106] The binding density, spacing, and number of binding points for each tower base are extracted from the differentiated rebar cage binding quantity measurement strategy. Based on the spatial distribution characteristics of the resistivity geographic law subset, the product of the number of tower bases and the resistivity fluctuation intensity within each regional group is calculated as the resource demand weight. This weight is multiplied by the baseline allocation ratio to obtain the adjusted allocation ratio, forming a regionally weighted rebar configuration scheme. Based on this rebar configuration scheme, the total rebar material demand for each tower base is calculated. The tower base configuration priority is determined according to the magnitude of the resistivity change rate. If the total demand for a certain regional group exceeds a preset resource limit, the configuration standard of the tower bases is reduced sequentially from low to high priority until the constraint conditions are met, resulting in a balanced allocation. Based on the final configuration parameters of each tower base in the allocation result, the rebar usage is optimized by combining the material sharing coefficient between adjacent tower bases. By summarizing the binding density, spacing, and number of binding points for each tower base, a data record containing the tower base number, location coordinates, and various binding parameters is established, forming a binding quantity measurement table for resource allocation optimization.

[0107] Specifically, in one implementation, the parameters extracted from the differentiated reinforcement cage binding quantity measurement strategy include the binding density, longitudinal and transverse reinforcement spacing, and the number of binding points in each layer for each tower foundation. These parameters have been analyzed in advance based on resistance fluctuation analysis and topographic and soil assessment, reflecting the actual demand characteristics of each tower foundation.

[0108] The calculation of resource demand weights is based on two key factors: the number of tower bases within the regional group and the intensity of resistance fluctuations.

[0109] A certain regional group contains 10 tower bases with an average resistance fluctuation of 15 ohms. Therefore, the resource demand weight for this region is 150. The baseline allocation ratio is determined based on the average distribution of the total project volume, with the initial ratio being the same for each regional group. The adjusted allocation ratio is equal to the product of the weight value and the baseline ratio, then normalized to ensure that the sum of the allocation ratios for all regions is 1.

[0110] It should be noted that the priority of tower base configuration is determined directly based on the resistance change rate. Tower bases with a higher resistance change rate exhibit greater fluctuations in grounding performance, requiring more steel reinforcement to ensure stability, and thus receive a higher priority. When the total regional demand exceeds the resource limit, starting with the lowest priority tower base, its configuration standard is reduced by 10%, and this process is repeated upwards until the total demand meets the constraints.

[0111] Preferably, the material sharing coefficient between adjacent tower bases is determined based on the distance between the tower bases. For adjacent tower bases less than 500 meters apart, the sharing coefficient is set to 0.9, indicating that some material reserves can be shared; for distances between 500 and 1000 meters, the sharing coefficient is 0.95; and for distances exceeding 1000 meters, material sharing is not considered.

[0112] In one implementation, the binding quantity measurement table uses a structured data format, including fields such as tower base number, latitude and longitude coordinates, binding density, longitudinal reinforcement spacing, transverse reinforcement spacing, number of binding points in the upper, middle and lower layers, and total amount of steel reinforcement. Each record corresponds to a tower base and is arranged in order of route.

[0113] This resource allocation optimization method enables the rational allocation of steel reinforcement materials while meeting the overall project constraints, with a focus on ensuring the configuration needs of high-risk tower foundations.

[0114] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the concept of this application. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for measuring the quantity of work in power construction supervision projects, characterized in that, The method includes: The geographic coordinate data of the location of the UHV tower base is obtained. The corresponding image area is located by combining the geographic coordinate data of the UHV tower base with satellite remote sensing image technology. The topographic features and soil type information in the geographic coordinate data are extracted from the satellite remote sensing image data. The geographic distribution map of the resistance of the UHV tower base is generated by overlay analysis through a geographic information system. Data on the high-voltage electric field interference intensity generated by the ultra-high-voltage transmission lines around the tower base is collected. Combined with the geographical distribution map of the ultra-high-voltage tower base resistance, the impact of the high-voltage electric field interference intensity data on the fluctuation of the tower base grounding resistance is analyzed, and adjusted tower base resistance change rate and fluctuation frequency are generated, including: A network of electric field strength measurement sensors is deployed to record the horizontal and vertical electric field components around the tower base and generate electric field strength values. The fundamental frequency and harmonic components of the electric field intensity values ​​are extracted by Fourier transform to generate a spatiotemporal distribution dataset. Based on the spatiotemporal distribution dataset and the geographical distribution map of the UHV tower base resistance, the correlation coefficient between the electric field strength and the measured grounding resistance is calculated, and an electric field interference influence coefficient matrix is ​​generated. Using the electric field interference influence coefficient matrix, the grounding resistance measurement sequence at the tower base location is extracted, the resistance change slope is calculated, the resistance fluctuation frequency component is identified, and the adjusted tower base resistance change rate and fluctuation frequency are generated. Based on the adjusted tower base resistance change rate and fluctuation frequency, analyze the resistance change deviation between tower bases, screen tower base locations with resistance changes higher than the average level, divide tower base area groups with similar resistance fluctuations, and generate a subset of the resistance geographical patterns of the tower base area groups. By analyzing the grounding resistance fluctuations at the tower base locations within the tower base area group using the subset of resistance geographical patterns, an average fluctuation level is generated. Based on the average fluctuation level, a resistance fluctuation characteristic compensation scheme is formulated, and an optimized grounding resistance fluctuation adjustment strategy is generated. Based on the optimized grounding resistance fluctuation adjustment strategy, combined with the topographic features and soil type information, the required amount of rebar cage binding at the tower base location is assessed, and tower bases with resistance changes higher than the average level are prioritized for adjustment, thereby generating a differentiated rebar cage binding amount measurement strategy. Extract the tower foundation location analysis results from the differentiated rebar cage binding quantity measurement strategy, and combine the optimized rebar cage binding quantity allocation ratio with the resistivity geographical law subset to generate a binding quantity measurement table with optimized resource allocation.

2. The method for measuring engineering quantities based on power construction supervision projects according to claim 1, characterized in that, The process involves acquiring the geographic coordinate data of the UHV tower base locations, processing satellite remote sensing imagery to locate corresponding image areas using the geographic coordinate data, extracting topographic features and soil type information from the geographic coordinate data, and generating a geographic distribution map of the UHV tower base resistance patterns through overlay analysis using a geographic information system. This includes: Acquire the latitude and longitude coordinates of the tower foundations along the ultra-high voltage transmission line, collect the three-dimensional spatial information of the tower foundations through a satellite positioning system, and establish a coordinate index database; For the tower base locations in the coordinate index database, high-resolution satellite remote sensing image data of the surrounding area is acquired, and the satellite remote sensing image data is processed by image fusion to generate clear surface image data; Based on the clear surface image data, the terrain slope and undulation are calculated, the image texture parameters are extracted as the surface roughness coefficient, and a base base terrain feature parameter library is generated. Soil type distribution is identified by multispectral remote sensing images, and soil moisture content and particle structure information are extracted by combining thermal infrared band and radar remote sensing data to generate a data set of soil properties for the tower base. The database of topographic feature parameters of the tower base and the dataset of soil properties of the tower base are input into the geographic information system for spatial overlay analysis to generate a geographic distribution map of the resistance of the UHV tower base.

3. The method for measuring engineering quantities based on power construction supervision projects according to claim 1, characterized in that, The process involves analyzing the resistance variation deviation between tower bases based on the adjusted tower base resistance change rate and fluctuation frequency, screening tower base locations with resistance changes higher than the average level, dividing tower base regions with similar resistance fluctuations into groups, and generating a subset of the geographical patterns of resistance within these tower base regions, including: Based on the adjusted tower base resistance change rate and fluctuation frequency, calculate the mean and standard deviation of the resistance change rate, generate a standardized deviation value, and screen for tower base locations with high change rates. Based on the highly variable tower base locations, a clustering algorithm is used to divide the tower base regions with similar resistance fluctuations into groups. The geographic coordinates and resistance characteristic data of the tower base area group are extracted, a continuous surface of resistance distribution is constructed, and data including geographic coordinates of the tower base area group, grounding resistance change rate, fluctuation frequency, terrain feature parameters and soil properties are generated. Furthermore, a subset of the geographic resistance law of the continuous surface of resistance distribution within the region is constructed by Kriging interpolation.

4. The method for measuring engineering quantities based on power construction supervision projects according to claim 1, characterized in that, The process involves analyzing the grounding resistance fluctuations at tower base locations within the tower base area group using the subset of geographical resistance patterns, generating an average fluctuation level, and formulating a resistance fluctuation characteristic compensation scheme based on the average fluctuation level to generate an optimized grounding resistance fluctuation adjustment strategy, including: The time series data of tower base grounding resistance is extracted by the subset of the resistance geographical patterns, the moving average and fluctuation amplitude are calculated, and the average fluctuation level of grounding resistance of the regional group is generated. Based on the average fluctuation level, a resistance adjustment coefficient and a compensation frequency are generated, the grounding material type is determined, and a resistance fluctuation characteristic compensation scheme is formulated. Based on the resistance fluctuation characteristic compensation scheme, adjust the reinforcement cage binding density and spacing, determine the connection method, and generate the adjusted reinforcement cage binding quantity configuration parameters. By adding a grounding electrode array in the high-fluctuation region and adjusting the amount of chemical resistance-reducing material applied, an optimized grounding resistance fluctuation adjustment strategy is generated.

5. The method for measuring engineering quantities based on power construction supervision projects according to claim 1, characterized in that, The optimized grounding resistance fluctuation adjustment strategy, combined with the topographic features and soil type information, assesses the required amount of rebar cage binding at the tower foundation location, prioritizing adjustments for tower foundations with resistance changes higher than the average level, and generating a differentiated rebar cage binding quantity measurement strategy, including: Based on the optimized grounding resistance fluctuation adjustment strategy, the steel reinforcement configuration parameters are extracted, and the proportion of transverse reinforcement bars is adjusted in combination with the terrain slope to generate the initial steel cage requirement. The grounding resistance contribution coefficient is calculated using the soil type information, and the influence weight of tower base stability is generated by combining the topographic relief. Tower bases with high variation are screened to form a priority adjustment sequence. Based on the priority adjustment sequence, adjust the reinforcement cage binding density and spacing, determine the number of binding points, and generate a binding parameter configuration set. Based on the binding parameter configuration set, the resistance fluctuation characteristics of the tower base area are integrated to generate a differentiated steel cage binding quantity measurement strategy.

6. The method for measuring engineering quantities based on power construction supervision projects according to claim 1, characterized in that, The step of extracting tower foundation location analysis results from the differentiated rebar cage binding quantity measurement strategy, combining the optimized rebar cage binding quantity allocation ratio with the resistivity geographical pattern subset, and generating a binding quantity measurement table for resource allocation optimization includes: The binding parameters are extracted from the differentiated rebar cage binding quantity measurement strategy, and the resource demand weights are calculated by combining the resistivity geographical law subset to generate a regionally weighted rebar configuration scheme. The total material demand is calculated based on the steel reinforcement configuration scheme. The configuration standard is adjusted. If the total steel reinforcement demand of a certain area group exceeds the project's preset total steel reinforcement limit, the steel reinforcement configuration standard of the corresponding tower base is reduced in order of priority from low to high according to the tower base resistance change rate, and a total balance distribution result is generated. Based on the allocation results, optimize the amount of steel reinforcement used, establish data records including tower base numbers and binding parameters, and generate a binding quantity measurement table for resource allocation optimization.

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