Power transmission tower deformation monitoring device based on phased array radar technology

Abstract

A transmission tower deformation monitoring device based on phased array radar technology, which relates to the field of irregular contour measurement. The deformation monitoring device includes: a map construction module for constructing a regional digital map; a boundary determination module for determining the near-field boundary; a signal acquisition module for acquiring near-field echo signals in the near field through a narrow beam and far-field echo signals in the far field through a wide beam; a calibration table construction module for constructing a signal intensity comparison table; a data processing module for determining effective monitoring data; an error calculation module for calculating errors of the effective monitoring data to obtain phase errors and amplitude errors; and a parameter determination module for determining transmission tower deformation parameters. Implementing this application can improve the accuracy of transmission tower deformation monitoring.

Description

Technical Field

[0001] This application relates to the field of irregular contour measurement, and particularly to a transmission tower deformation monitoring device based on phased array radar technology. Background Art

[0002] With the rapid development of the UHV power transmission network and the in-depth promotion of the construction of smart grids, the safe operation monitoring of transmission towers faces higher technical requirements. Under complex terrain and adverse weather conditions, various forms of structural changes may occur in transmission towers, such as foundation settlement, tower inclination, and component deformation. If these deformations are not detected and accurately evaluated in a timely manner, it will seriously threaten the safe operation of the power grid. Therefore, realizing real-time deformation monitoring of transmission towers has important engineering value and practical significance.

[0003] In related technologies, transmission tower deformation monitoring mainly includes two types of methods: contact type and non-contact type. Contact type monitoring uses strain gauges and fiber optic sensors to collect local strain data through sensors installed on the tower surface. Non-contact type monitoring uses phased array radar technology to achieve remote monitoring by transmitting electromagnetic waves and receiving target echo signals. The phased array radar system obtains the reflection signals on the tower surface through beam scanning and calculates the displacement according to the phase change of the signals.

[0004] However, due to the influence of terrain undulation and atmospheric environment during the propagation of electromagnetic waves, the signal intensity attenuation is uneven. Especially under complex terrain conditions, the phase and amplitude of the echo signals will be distorted, reducing the accuracy of displacement calculation. Summary of the Invention

[0005] This application provides a transmission tower deformation monitoring device based on phased array radar technology, which is used to improve the accuracy of transmission tower deformation monitoring.

[0006] First aspect, the present application provides a deformation monitoring device, which includes: a map construction module for constructing a regional digital map containing terrain elevation distribution and electromagnetic wave attenuation amount according to the terrain surveying and mapping data, tower structure parameters and meteorological parameters of the monitoring area; a boundary determination module for calculating the signal strength attenuation rate and phase delay of the regional digital map in each monitoring direction based on the terrain elevation distribution and electromagnetic wave attenuation amount, and determining the near-field region boundary; a signal acquisition module for taking the region within the near-field region boundary as the near-field region, obtaining the near-field echo signal of the near-field region through a narrow beam, and taking the region outside the near-field region boundary as the far-field region, obtaining the far-field echo signal of the far-field region through a wide beam; a calibration table construction module for obtaining the calibration signal of the standard reflector array, determining the direct wave and reflected wave paths in combination with the regional digital map, and constructing a signal strength comparison table; a data processing module for performing coherent accumulation calculation on the near-field echo signal and the far-field echo signal, and comparing the calculation result with the signal strength comparison table to obtain effective monitoring data; an error calculation module for calculating the error of the effective monitoring data to obtain the phase error and amplitude error; a parameter determination module for correcting the effective monitoring data according to the phase error and amplitude error to obtain the displacement amount of the monitoring point, and determining the tower deformation parameters based on the displacement amount of the monitoring point.

[0007] In the above embodiment, the deformation monitoring device realizes high-precision monitoring of the deformation of the transmission tower by constructing a regional digital map containing terrain elevation distribution and electromagnetic wave attenuation amount, combining the near and far field region division and standard reflector calibration; through phase error and amplitude error correction, it effectively overcomes the influence of uneven electromagnetic wave propagation attenuation under complex terrain conditions and improves the accuracy of displacement calculation.

[0008] Combined with some embodiments of the first aspect, in some embodiments, the map construction module specifically includes: a point cloud processing unit for determining point cloud data according to the scanning data of the three-dimensional laser and generating a terrain elevation scatter array; a grid division unit for performing grid division on the terrain elevation scatter array based on a preset grid resolution to obtain a regional terrain elevation matrix; a parameter mapping unit for mapping the tower structure parameters to the regional terrain elevation matrix to obtain the tower structure grid coordinates; an attenuation calculation unit for calculating the refractive index distribution according to the temperature, humidity and air pressure data collected by the meteorological sensor to obtain the electromagnetic wave propagation attenuation coefficient; a map generation unit for superimposing the tower structure grid coordinates and the electromagnetic wave propagation attenuation coefficient to generate a monitoring area digital map.

[0009] In the above embodiment, the deformation monitoring device systematically constructs a high-precision regional terrain elevation matrix based on the three-dimensional laser scanning data and the preset grid resolution, and calculates the electromagnetic wave propagation attenuation coefficient in combination with the meteorological parameters, realizing the precise modeling of the electromagnetic environment of the monitoring area and providing a reliable digital map basis for deformation monitoring.

[0010] In some embodiments in combination with some embodiments of the first aspect, the data processing module specifically includes: a beamforming unit for beamforming the near-field echo signal to obtain near-field phase correction data; an amplitude compensation unit for amplitude-compensating the far-field echo signal to obtain far-field amplitude correction data; a signal accumulation unit for coherently accumulating the near-field phase correction data and the far-field amplitude correction data to obtain a signal accumulation matrix; and a data screening unit for comparing the signal accumulation matrix with the standard data in the signal intensity look-up table to generate signal deviation data, and screening out valid monitoring data according to the signal deviation data.

[0011] In the above embodiments, the deformation monitoring device performs differential processing on the near-field and far-field echo signals through beamforming and amplitude compensation technologies, and combines coherent accumulation calculation and signal intensity comparison to effectively extract valid monitoring data, improving the signal-to-noise ratio and measurement accuracy of deformation monitoring.

[0012] In some embodiments in combination with some embodiments of the first aspect, the deformation monitoring device further includes: an array calibration module for receiving the beam scanning data of the antenna array to obtain the antenna element coordinates; a parameter calculation module for calculating the phase center coordinates according to the antenna element position data to determine the array calibration data; a sequence generation module for importing the array calibration parameters into the beam control module to generate a beam scanning sequence; and a range determination module for determining the monitoring scanning range according to the beam scanning sequence.

[0013] In the above embodiments, the deformation monitoring device adopts the antenna array beam scanning technology, and through the calibration and phase compensation processing of the corner reflector echo signal, accurately obtains the antenna element coordinates, ensuring the accuracy of beam scanning and the reasonable division of the monitoring range.

[0014] In some embodiments in combination with some embodiments of the first aspect, the array calibration module specifically includes: a signal acquisition unit for receiving the corner reflector echo signal, generating a reference waveform data group, and processing the reference waveform data group using time-frequency transformation to obtain the signal amplitude spectrum and phase spectrum; a spectrum analysis unit for calculating the element amplitude and phase distribution according to the signal amplitude spectrum and phase spectrum, generating an element distribution matrix, and processing the element distribution matrix using phase compensation to obtain the relative position of the antenna elements; and a coordinate calculation unit for mapping the relative position of the antenna elements to the coordinate system to obtain the antenna element coordinates.

[0015] In the above embodiments, the deformation monitoring device processes the reference waveform data based on time-frequency transformation and phase compensation technologies, realizing the precise positioning of the antenna element positions, and providing an accurate spatial reference for beam control.

[0016] In some embodiments in combination with some embodiments of the first aspect, the deformation monitoring device further includes: a vector field generation module, configured to calculate the three-dimensional coordinate change amount of each monitoring point and generate a displacement vector field; a stress analysis module, configured to calculate the deformation parameters of the iron tower structure according to the displacement vector field to obtain a stress distribution map; a region identification module, configured to superimpose the stress distribution map on the structural parameters to determine the stress concentration region; a recording and storage module, configured to collect the monitoring data of the stress concentration region at a preset period, construct a time series record, and store the time series record in the deformation feature database.

[0017] In the above embodiments, the deformation monitoring device systematically evaluates the deformation state of the iron tower structure through displacement vector field and stress distribution analysis, and establishes a deformation feature database, realizing long-term monitoring and early warning of the deformation trend.

[0018] In some embodiments in combination with some embodiments of the first aspect, the deformation monitoring device further includes: a projection map construction module, configured to construct a deformation vector projection map based on the spatial coordinate sequence in the deformation feature database; an acceleration calculation module, configured to calculate the displacement acceleration of the monitoring point according to the deformation vector projection map; a trend analysis module, configured to superimpose the displacement acceleration on the stress distribution map to obtain the stress change rate, and determine the structural deformation trend curve according to the stress change rate; an early warning generation module, configured to generate an early warning message based on the structural deformation trend curve.

[0019] In the above embodiments, the deformation monitoring device uses deformation vector projection and stress change rate analysis to achieve accurate prediction of the deformation trend of the iron tower structure, providing a scientific basis for timely discovery of potential risks.

[0020] In a second aspect, an embodiment of the present application provides a method for monitoring the deformation of a transmission iron tower based on phased array radar technology. The method includes: constructing a regional digital map including terrain elevation distribution and electromagnetic wave attenuation amount according to the terrain mapping data, iron tower structure parameters, and meteorological parameters of the monitoring area; calculating the signal intensity attenuation rate and phase delay of the regional digital map in each monitoring direction based on the terrain elevation distribution and electromagnetic wave attenuation amount to determine the near-field region boundary; taking the region within the near-field region boundary as the near-field region, obtaining the near-field echo signal of the near-field region through a narrow beam, and taking the region outside the near-field region boundary as the far-field region, obtaining the far-field echo signal of the far-field region through a wide beam; obtaining the calibration signal of the standard reflector array, determining the direct wave and reflected wave paths in combination with the regional digital map, and constructing a signal intensity comparison table; performing coherent accumulation calculation on the near-field echo signal and the far-field echo signal, and comparing the calculation result with the signal intensity comparison table to obtain effective monitoring data; calculating the error of the effective monitoring data to obtain the phase error and amplitude error; correcting the effective monitoring data according to the phase error and amplitude error to obtain the displacement amount of the monitoring point, and determining the deformation parameters of the iron tower based on the displacement amount of the monitoring point.

[0021] In a third aspect, an embodiment of the present application provides a computer program code including instructions. When the computer program code runs on a deformation monitoring device, the deformation monitoring device is caused to execute the method described in the second aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium including instructions. When the instructions run on a deformation monitoring device, the deformation monitoring device is caused to execute the method described in the second aspect.

[0023] It can be understood that the deformation monitoring method provided in the second aspect, the computer program code provided in the third aspect, and the computer storage medium provided in the fourth aspect are all applicable to the deformation monitoring device provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method and will not be elaborated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0025] 1. By constructing a regional digital map including terrain elevation and electromagnetic wave attenuation characteristics, an accurate model of the electromagnetic propagation environment in the monitoring area is realized. Based on the division of the near-field and far-field boundaries, a differential monitoring strategy is adopted. A narrow beam is used in the near field to obtain displacement information with high spatial resolution, and a wide beam is used in the far field to ensure signal strength. A signal intensity comparison table is established by combining the calibration signals of the standard reflector array. Through phase error and amplitude error correction, the influence of uneven electromagnetic wave propagation attenuation under complex terrain conditions is effectively overcome, the accuracy of monitoring data is improved, the problem of reduced accuracy of traditional monitoring methods under complex terrain conditions is solved, and high-precision real-time monitoring of the deformation of transmission towers is realized.

[0026] 2. By using the echo signal of the corner reflector for antenna array calibration, the signal amplitude spectrum and phase spectrum are obtained through time-frequency transformation processing, and the relative positions of the antenna array elements are accurately determined by combining the phase compensation technology, and an accurate antenna element coordinate system is established. Based on this, a beam scanning sequence is constructed to realize optimized coverage of the monitoring area, effectively solve the problem of beam pointing deviation caused by the positioning error of the traditional antenna array, ensure the consistency of the spatial resolution ability of the monitoring system, and further realize accurate monitoring of the deformation of different parts of the iron tower.

[0027] 3. By calculating the change in the three-dimensional coordinates of the monitoring points to generate a displacement vector field, analyzing the stress distribution characteristics in combination with the structural parameters of the iron tower, a time-series record database for the deformation area is established. Based on the spatial coordinate sequence, a deformation vector projection map is constructed. Through the analysis of displacement acceleration and stress change rate, the accurate prediction of the structural deformation trend is realized, effectively solving the problem that it is difficult for traditional monitoring methods to detect potential risks in a timely manner. Furthermore, the intelligent early warning of the safety state of the iron tower structure is realized, providing a scientific basis for preventive maintenance. Description of the Drawings

[0028] Figure 1 is a module architecture diagram of the deformation monitoring device in an embodiment of the present application;

[0029] Figure 2 is a process schematic diagram of the transmission tower deformation monitoring method in an embodiment of the present application;

[0030] Figure 3 is another module architecture diagram of the deformation monitoring device in an embodiment of the present application;

[0031] Figure 4 is a schematic diagram of the physical device structure of the deformation monitoring device in an embodiment of the present application. Detailed Embodiments

[0032] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application, the singular forms "a", "an", "the above", "the", and "this" are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations including one or more of the listed items.

[0033] Hereinafter, the terms "preset" and "second" are only for descriptive purposes and cannot be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "preset" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0034] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0035] As the scale of the UHV power transmission network continues to expand, transmission towers are increasingly built in complex terrain areas such as mountains and hills. These areas are affected by harsh weather such as strong winds and heavy rains all year round, and coupled with complex geological conditions, it is easy to cause uneven settlement of the tower foundation and structural deformation. A provincial power grid company discovered multiple incidents of transmission tower inclination and component deformation within one year, two of which led to wire breakage and tripping, causing large-scale power outages. The traditional manual inspection method is inefficient and difficult to detect hidden dangers in a timely manner. The contact monitoring method requires a large number of sensors to be installed on the tower surface, which not only has a large construction difficulty and high maintenance cost, but is also easily affected by lightning strikes and electromagnetic interference.

[0036] In related technologies, the deformation monitoring of transmission towers can be achieved by using a traditional phased array radar monitoring system. This system scans the entire monitoring area with fixed beam parameters and calculates the displacement by analyzing the phase change of the echo signal. The following introduces the scenario of using the transmission tower deformation monitoring device in related technologies.

[0037] A power company once used a commercial phased array radar system to monitor the deformation of transmission towers in mountainous areas. This system can achieve millimeter-level measurement accuracy under flat terrain conditions, but its performance degrades in complex terrain environments. The reason is that the electromagnetic wave is affected by multiple reflections and diffractions of the terrain undulation during propagation, resulting in a serious multipath effect. At the same time, the meteorological conditions in mountainous areas are complex and changeable, and the spatial non-uniform distribution of the atmospheric refractive index causes the electromagnetic wave propagation path to bend. These factors cause the phase and amplitude of the echo signal to be distorted, increasing the displacement measurement error to the centimeter level and unable to meet the early warning requirements. Moreover, this system uses unified beam parameters and it is difficult to balance the measurement requirements of high resolution in the near field and high sensitivity in the far field.

[0038] By using the deformation monitoring device in the embodiment of the present application, an accurate model of the electromagnetic propagation environment in the monitoring area is realized by constructing a regional digital map including terrain elevation and electromagnetic wave attenuation characteristics, and a differential monitoring strategy is adopted based on the near-far field boundary division. A narrow beam is used in the near field to obtain displacement information with high spatial resolution, and a wide beam is used in the far field to ensure signal strength. The following introduces the scenario of using the transmission tower deformation monitoring device in the present application.

[0039] After the deformation monitoring device adopting this solution is put into use in a certain mountainous area transmission line, by establishing an accurate digital map including terrain and atmospheric characteristics, an accurate modeling of the electromagnetic wave propagation environment is achieved. The system adaptively adjusts the beam parameters according to the near-field and far-field boundaries, uses a narrow beam for the near-field area to obtain high-spatial-resolution data, and uses a wide beam for the far-field area to ensure signal intensity. Through standard reflector calibration and error compensation techniques, the multi-path effect and the influence of abnormal atmospheric refraction under complex terrain conditions are effectively overcome. The monitoring results show that the system can achieve a displacement measurement accuracy better than 3 mm within a range of 10 km, and successfully warns of multiple abnormal deformation events of iron towers.

[0040] It can be seen that by using the deformation monitoring device in the embodiment of the present application, while achieving accurate deformation monitoring, it can also effectively solve the influence of multi-path effect and abnormal atmospheric refraction under complex terrain conditions, and thus achieve high-precision all-weather monitoring.

[0041] For ease of understanding, the deformation monitoring device and the corresponding method provided in this embodiment will be described below in combination with the above scenario. Please refer to Figure 1 , which is a module architecture diagram of the deformation monitoring device in the embodiment of the present application; please correspondingly refer to Figure 2 , which is a process schematic diagram of the transmission tower deformation monitoring method in the embodiment of the present application.

[0042] Please refer to Figure 1 , the deformation monitoring device 10 includes a map construction module 101, and the map construction module 101 is used to execute Figure 2 in step S201, and construct a regional digital map including terrain elevation distribution and electromagnetic wave attenuation amount according to the terrain surveying and mapping data, tower structure parameters and meteorological parameters of the monitoring area.

[0043] Among them, the terrain surveying and mapping data of the monitoring area represents spatial information data such as terrain surface elevation, slope, vegetation coverage, etc. obtained by means of three-dimensional laser scanning, aerial photogrammetry, etc.; the tower structure parameters refer to structural characteristic parameters such as the geometric dimensions, material properties, and node positions of the transmission tower; the meteorological parameters are used to represent environmental factors affecting electromagnetic wave propagation including temperature, humidity, air pressure, etc.; the terrain elevation distribution represents the spatial variation law of the surface height in the monitoring area; the electromagnetic wave attenuation amount is a quantitative description of the energy loss of electromagnetic waves during propagation.

[0044] When the deformation monitoring device 10 starts a monitoring task, it first needs to establish a digital model of the monitoring area. Specifically, the map construction module 101 receives the point cloud data collected by the 3D laser scanner, converts it into a terrain elevation matrix of a regular grid; at the same time, reads the structural parameters in the CAD drawing of the iron tower to determine the spatial coordinates of the key monitoring points; combines the environmental parameters collected by the real-time weather station, calculates the atmospheric refractive index distribution, and establishes an electromagnetic wave propagation attenuation model; finally, the map construction module 101 performs spatial registration and data fusion on this information to generate a high-precision digital map including terrain undulation characteristics and electromagnetic propagation characteristics.

[0045] In some embodiments, the construction process of the digital map can be implemented in various ways: Optionally, the map construction module 101 uses the Kriging interpolation algorithm to perform spatial interpolation on the discrete terrain survey point cloud data to generate a continuous terrain elevation field, then calculates the spatial distribution of the electromagnetic wave refractive index based on the atmospheric tomography inversion method, and finally integrates the terrain and atmospheric parameters into a unified coordinate system through a multi-source data fusion algorithm; Optionally, the map construction module 101 uses a neural network method to extract and reconstruct the features of the original survey data, combines a physical model to calculate the electromagnetic wave propagation loss, and realizes terrain-atmosphere coupling modeling through deep learning. It can be understood that other data processing and modeling methods can also be used to implement the construction of the digital map, which is not limited here.

[0046] Please refer to Figure 1 , the deformation monitoring device 10 further includes a boundary determination module 102, and the boundary determination module 102 is used to execute Figure 2 Step S202 in, calculate the signal intensity attenuation rate and phase delay in each monitoring direction of the regional digital map based on the terrain elevation distribution and the electromagnetic wave attenuation amount, and determine the near-field region boundary.

[0047] Among them, the signal intensity attenuation rate represents the change rate of the electromagnetic wave signal intensity with the propagation distance attenuation; the phase delay refers to the phase lag amount generated during the propagation of the electromagnetic wave; the near-field region boundary represents the spatial demarcation line between the near field and the far field, which is usually related to the antenna aperture and the operating wavelength; the monitoring direction is used to represent the spatial pointing of the antenna beam scanning; the regional digital map refers to a spatial information model including terrain and electromagnetic characteristics.

[0048] After the deformation monitoring device 10 completes the digital map construction, it is necessary to determine the division of the near and far field monitoring areas. Specifically, the boundary determination module 102 first establishes a polar coordinate scanning grid on the digital map, calculates the electromagnetic wave propagation paths along each azimuth angle; considering the influence of terrain undulation on signal propagation and combining the atmospheric refractive index distribution, calculates the signal attenuation coefficient and phase change on each path; based on the Fresnel zone theory, analyzes the distribution characteristics of the antenna radiation field, determines the spatial range that meets the near field measurement conditions; finally, the boundary determination module 102 generates a three-dimensional monitoring area division scheme containing the near field area boundary information.

[0049] In some embodiments, the division of the monitoring area can be achieved in various ways: Optionally, the boundary determination module 102 uses the ray tracing method to simulate the electromagnetic wave propagation paths, calculates the geometric loss and atmospheric loss on each path, defines a signal intensity threshold in combination with the antenna pattern characteristics, and determines the near field area boundary through iterative calculation; Optionally, the boundary determination module 102 is based on the full-wave electromagnetic field numerical solution method, solves the electromagnetic wave propagation equation in inhomogeneous media, analyzes the field strength distribution law, and determines the monitoring area division in combination with the phase characteristics. It can be understood that other electromagnetic field analysis methods can also be used to achieve the optimized division of the monitoring area, which is not limited here.

[0050] Please refer to Figure 1 , the deformation monitoring device 10 further includes a signal acquisition module 103, and the signal acquisition module 103 is used to execute Figure 2 the steps in S203, takes the area within the near field area boundary as the near field area, obtains the near field echo signal of the near field area through a narrow beam, and takes the area outside the near field area boundary as the far field area, and obtains the far field echo signal of the far field area through a wide beam.

[0051] Among them, the narrow beam refers to a radiation beam with strong spatial directivity formed by the antenna; the wide beam refers to a radiation beam with a large spatial coverage range; the near field echo signal refers to the electromagnetic wave signal reflected by the target in the near field area; the far field echo signal is the electromagnetic wave signal reflected by the target in the far field area; the near field area and the far field area respectively represent different characteristic areas of the antenna radiation field.

[0052] After the deformation monitoring device 10 determines the monitoring area division, it is necessary to adopt a differential beam scanning strategy. Specifically, the signal acquisition module 103 adjusts the excitation amplitude and phase distribution of the antenna array through beam weighting technology, forms a narrow beam for the near field area to improve the spatial resolution, forms a wide beam for the far field area to ensure the signal intensity; adopts an adaptive scanning period for different areas, uses a higher sampling frequency for the near field area to capture small deformations, and uses a lower sampling frequency for the far field area to balance system resources; at the same time, realizes beam real-time tracking to ensure the continuity and reliability of the monitoring signal.

[0053] It should be noted that in the signal acquisition module 103, beamforming adopts the adaptive weight synthesis technology. The signal acquisition module 103 establishes the antenna array manifold vector:

[0054] , where di is the position vector of the i-th array element, r is the unit vector in the target direction, and λ is the operating wavelength. The weight vector w is obtained by solving the constrained optimization problem:

[0055] , where R is the interference plus noise covariance matrix,

[0056] (θ0, φ0) is the desired beam pointing.

[0057] The optimal weight can be obtained by the Lagrange multiplier method:

[0058]

[0059] The signal acquisition module 103 uses the subspace tracking algorithm to update the eigen-decomposition of R in real time to ensure the dynamic optimization of the beam pattern. Field measurements show that this method can achieve a sidelobe level of -35 dB and a beam width of 5° in the near field, effectively improving the spatial resolution ability.

[0060] Please refer to Figure 1 , the deformation monitoring device 10 further includes a calibration table construction module 104, and this calibration table construction module 104 is used to execute Figure 2 the steps S204 in, obtain the calibration signals of the standard reflector array, combine the regional digital map to determine the direct wave and reflected wave paths, and construct a signal intensity comparison table.

[0061] Among them, the standard reflector array refers to a calibration device composed of multiple standard reflectors with known scattering characteristics; the calibration signal refers to the reference signal returned by the standard reflector; the direct wave refers to the electromagnetic wave directly propagated from the transmitting antenna to the target; the reflected wave is the electromagnetic wave that reaches the target after being reflected by the ground or other objects; the signal intensity comparison table is used to represent the corresponding relationship between the signal intensity and the spatial position under different propagation paths.

[0062] Before the deformation monitoring device 10 performs beam scanning, system calibration and signal path analysis are required. Specifically, the calibration table construction module 104 first arranges a standard reflector array with a known radar cross-section in the monitoring area and collects calibration signals at different distances and angles; combines the terrain information of the regional digital map, analyzes the possible multipath effects using the electromagnetic wave propagation theory, and calculates the propagation paths of the direct wave and each level of reflected wave; finally, the calibration table construction module 104 establishes the mapping relationship between the signal intensity and the spatial position change to form a standardized signal intensity comparison table, providing a reference for subsequent signal processing.

[0063] In some embodiments, system calibration and path analysis can be achieved in various ways: Optionally, the calibration table construction module 104 uses an adaptive filtering algorithm to separate the direct wave and reflected wave components, establishes a signal strength spatial distribution model through multi-point calibration, and realizes signal calibration in combination with path loss compensation; Optionally, the calibration table construction module 104 calculates the theoretical echo characteristics of the standard reflector based on the electromagnetic scattering theory, completes system calibration by comparing the measured signal with the theoretical value, and determines the multipath propagation path using ray tracing. It can be understood that other calibration methods and path analysis techniques can also be used to achieve signal strength calibration, which is not limited here.

[0064] It should be noted that in the calibration table construction module 104, the adaptive filtering algorithm dynamically adapts to the changes in signal characteristics by iteratively adjusting the filter coefficients. When processing the echo signal of the standard reflector, first, an expected signal model d(n) and an input signal x(n) are established, and an objective function is constructed using the least mean square error criterion. The error e(n) between the filter output y(n) and the expected signal d(n) is used to update the filter weight vector w(n). The weight update equation is w(n + 1) = w(n) + 2μe(n)x(n), where μ is the step size factor, which controls the convergence speed and stability. Through repeated iteration, the filter coefficients gradually converge to the optimal solution, realizing the effective separation of the direct wave and reflected wave components. In practical applications, the step size factor can be dynamically adjusted according to the time-varying characteristics of the signal. When the signal changes violently, the step size is increased to improve the tracking ability, and when the signal is stable, the step size is decreased to ensure accuracy.

[0065] In addition, the standard reflector array uses an optimized layout algorithm to determine the installation position. The algorithm is based on the three-dimensional grid of the monitoring area to construct an objective function , where represents the spatial coverage, represents the angular distribution uniformity, represents the expected signal strength. The optimal layout scheme is solved through the particle swarm optimization method, so that the reflector array forms a uniformly distributed calibration lattice in space. During the calibration signal acquisition process, the system uses pulse compression technology to improve the range resolution, and the transmitted signal uses a linear frequency modulation waveform , where k is the frequency modulation rate and T is the pulse width. After the received signal passes through the matched filter, high-precision range information with a time domain resolution of c / 2B can be obtained, where c is the speed of light and B is the signal bandwidth. In a certain actual application scenario, 13 standard corner reflectors are used to achieve an absolute position accuracy better than 2 mm within 500 meters.

[0066] Please refer to Figure 1 , the deformation monitoring device 10 further includes a data processing module 105, and the data processing module 105 is used to execute Figure 2In step S205, perform coherent integration calculations on the near-field echo signal and the far-field echo signal, and compare the calculation results with the signal intensity look-up table to obtain effective monitoring data.

[0067] Among them, coherent integration calculation refers to the coherent superposition processing of multiple echo signals; the calculation result refers to the signal characteristic quantity after coherent integration; the comparison process refers to the matching analysis of measured data and standard data; effective monitoring data refers to reliable measurement results that have been screened and verified; the signal intensity look-up table is used to represent the standardized reference data set.

[0068] After the deformation monitoring device 10 obtains the echo signal, it is necessary to extract and verify effective data. Specifically, the data processing module 105 performs phase alignment on the near-field and far-field echo signals respectively to achieve Doppler frequency compensation; adopts coherent accumulation technology to improve the signal-to-noise ratio while maintaining the phase information of the signal; compares the accumulated signal characteristics with the pre-established signal intensity look-up table to eliminate abnormal data points; evaluates the data reliability through statistical test methods, and the data processing module 105 finally screens out effective monitoring data that meets the accuracy requirements.

[0069] It should be noted that the data processing module 105 stores a data reliability evaluation system based on hypothesis testing. First, perform a normality test on the accumulated signal, calculate the sample skewness and kurtosis, construct the Jarque-Bera statistic and compare it with the critical value. For the data that passes the normality test, use the improved Grubbs criterion for outlier detection, identify the outliers by calculating the standardized residuals and setting the confidence interval. Then use autocorrelation analysis to evaluate the time correlation of the data, construct the autocorrelation function and calculate the significance level. For the data of spatially adjacent monitoring points, establish a spatial autocorrelation model to evaluate the spatial consistency of the data. Finally, comprehensively consider the test results of each statistic, and use a weighted scoring method to determine the data reliability level, and screen out effective monitoring data that meets the accuracy requirements. This method ensures the reliability and accuracy of the monitoring data through multi-dimensional statistical tests.

[0070] Please refer to Figure 1 , the deformation monitoring device 10 further includes an error calculation module 106, and the error calculation module 106 is used to perform Figure 2 step S206 in, perform error calculation on the effective monitoring data to obtain phase error and amplitude error.

[0071] Among them, error calculation refers to the deviation analysis process of the measurement result; phase error refers to the deviation between the measured value of the signal phase and the theoretical value; amplitude error represents the difference between the measured value of the signal intensity and the standard value; effective monitoring data refers to the measurement results that have been preliminarily processed.

[0072] After the deformation monitoring device 10 obtains effective monitoring data, systematic error analysis needs to be carried out. Specifically, the error calculation module 106 calculates the ideal phase and amplitude responses of the standard reflector based on the theoretical model; compares the measured data with the theoretical values to separate systematic errors and random errors; establishes an error compensation model to determine the phase and amplitude correction coefficients; the error calculation module 106 evaluates the uncertainty of the measurement results through error propagation analysis to provide a basis for subsequent data correction.

[0073] Please refer to Figure 1 , the deformation monitoring device 10 further includes a parameter determination module 107, and the parameter determination module 107 is used to execute Figure 2 the steps in S207, correct the effective monitoring data according to the phase error and amplitude error to obtain the displacement of the monitoring point, and determine the deformation parameters of the iron tower based on the displacement of the monitoring point.

[0074] Among them, the displacement of the monitoring point represents the change in the spatial position of each monitoring point of the iron tower structure; the deformation parameters of the iron tower refer to the characteristic quantities describing the deformation state of the structure, including tilt angle, torsion angle, deflection, etc.; the correction process means using error parameters to correct the measurement results.

[0075] After the deformation monitoring device 10 completes the error analysis, it needs to calculate the structure deformation parameters. Specifically, the parameter determination module 107 corrects the effective monitoring data using the phase error and amplitude error to eliminate the influence of systematic errors; converts the corrected phase information into a spatial displacement amount to establish a three-dimensional coordinate change model of the monitoring point; analyzes the distribution characteristics of the displacement field based on the structural mechanics theory to extract key deformation parameters; finally, the parameter determination module 107 combines historical monitoring data to evaluate the deformation development trend and generates a structural health status assessment report.

[0076] In some embodiments, the calculation of deformation parameters can be achieved in various ways: Optionally, the parameter determination module 107 uses the least squares method to fit the displacement field distribution, extracts the main deformation modes through principal component analysis, and combines the finite element model to evaluate the structural stress state; Optionally, the parameter determination module 107 establishes a displacement-deformation mapping relationship based on the deep learning method and realizes the automatic extraction and prediction of deformation parameters through a neural network. It can be understood that other data analysis methods can also be used to calculate the structural deformation parameters, which are not limited here.

[0077] It should be noted that the parameter determination module 107 constructs an end-to-end displacement-deformation parameter mapping network. The input layer of the network receives the corrected displacement field data, extracts features through multiple layers of convolution, and each convolution layer uses a 3×3 convolution kernel to extract spatial correlation features. After the feature map is dimension-reduced by the pooling layer, a non-linear mapping relationship between the displacement field and the deformation parameters is established through the fully connected layer. The network uses the mean square error as the loss function and optimizes the network parameters using the stochastic gradient descent algorithm with momentum. To improve the generalization ability of the model, the dropout mechanism is introduced during training, randomly discarding some neurons, and at the same time, the batch normalization technique is used to accelerate the training convergence. The final output layer of the network gives the predicted values of the deformation parameters through the activation function, including structural deformation feature quantities such as tilt angle and twist angle. This method can accurately capture the complex relationship between the displacement field and the deformation parameters through training with a large amount of historical monitoring data.

[0078] It should be noted that in the parameter determination module 107, the displacement of the monitoring point is calculated by the multi-source data fusion algorithm. The system constructs an observation equation , where z is the measurement vector, x is the displacement state vector, and v is the measurement noise. The state transition adopts an improved Kalman filter model:

[0079] , where Φ(k) is the state transition matrix, which includes the structural dynamic characteristics, and w(k) is the process noise. The measurement update introduces an adaptive factor:

[0080] , where α(k) is the credibility factor calculated based on the innovation sequence. The displacement estimate is obtained by iterative solution:

[0081] . This algorithm achieves a three-dimensional displacement measurement accuracy better than 1mm considering the structural constraints.

[0082] In the above embodiments, accurate signal strength references can be obtained through antenna array calibration and comparison with the standard reflector. In practical applications, the parameters of the electromagnetic wave propagation model can also be updated in real time in combination with meteorological data to further improve the measurement accuracy. The scenarios of this embodiment are supplemented below.

[0083] During the actual operation process, the deformation monitoring device 10 also demonstrates excellent intelligent and expandable performance. By establishing a deformation feature database, the system can not only monitor the displacement state of the iron tower in real time, but also analyze the deformation development trend. During an extreme weather event, the system analyzed the displacement acceleration and stress change rate of a certain foundation-settling iron tower and predicted 12 hours in advance the possible severe tilt that might occur. Based on this, the operation and maintenance personnel promptly took reinforcement measures, thus avoiding a major accident. In addition, the data interface of the system also supports the integration of multi-source information such as geological monitoring and weather forecasting, further improving the accuracy and timeliness of early warning.

[0084] After combining the above scenarios, the following provides a further and more specific description of the deformation monitoring device 10 provided in this embodiment. Please refer to Figure 3 , which is another module architecture diagram of the deformation monitoring device in the embodiment of the present application.

[0085] Please refer to Figure 3 , in some embodiments, the map construction module 101 of the deformation monitoring device 10 specifically includes: a point cloud processing unit 301 that determines point cloud data according to the scanning data of three-dimensional laser and generates a terrain elevation scatter array; a grid division unit 302 that performs grid division on the terrain elevation scatter array based on a preset grid resolution to obtain a regional terrain elevation matrix; a parameter mapping unit 303 that maps the iron tower structure parameters to the regional terrain elevation matrix to obtain the iron tower structure grid coordinates; an attenuation calculation unit 304 that calculates the refractive index distribution according to the temperature, humidity, and air pressure data collected by the meteorological sensor to obtain the electromagnetic wave propagation attenuation coefficient; and a map generation unit 305 that superimposes the iron tower structure grid coordinates and the electromagnetic wave propagation attenuation coefficient to generate a digital map of the monitoring area.

[0086] Among them, the point cloud data represents a set of spatially discrete sampling points obtained by three-dimensional laser scanning; the terrain elevation scatter array refers to a three-dimensional space point set representing the terrain undulation; the preset grid resolution is used to represent the sampling interval of spatial discretization; the regional terrain elevation matrix represents a set of terrain data after regular grid division; the iron tower structure parameters are used to represent the geometric features and physical properties of the iron tower; the structure grid coordinates refer to the spatial positioning information of the iron tower in the discrete grid; and the attenuation coefficient is used to represent the spatial distribution characteristics of the electromagnetic wave energy loss.

[0087] Before starting the monitoring task, the map construction module 101 needs to construct a digital map containing terrain and electromagnetic characteristics. Specifically, the point cloud processing unit 301 first runs a three-dimensional laser scanning device to obtain spatial sampling points in the monitoring area, and generates an initial terrain model through point cloud registration and filtering processing; the grid division unit 302 sets the grid resolution according to the monitoring accuracy requirements, and resamples the scattered point data using the bilinear interpolation method to construct a regularly gridded terrain data matrix; the parameter mapping unit 303 converts the iron tower CAD model into a gridded description and establishes the mapping relationship between the structural features and the spatial grid; the attenuation calculation unit 304 collects meteorological data in real time and calculates the variation law of the electromagnetic wave refractive index with space and time based on the atmospheric physical model; finally, the map generation unit 305 fuses the terrain, structural, and electromagnetic characteristic data to generate a multi-level digital map model.

[0088] It should be noted that the regional digital map can be expressed in the form of a digital map model. This model organizes spatial data based on a hierarchical octree structure, and each node contains attribute information such as position, elevation, and material. The point cloud data processing adopts an improved PointNet++ architecture, and local and global features are extracted through hierarchical sampling and feature aggregation. The key mathematical processing is the construction of the spatial feature function: F(x, y, z) = ∑wiφi(x, y, z), where φi is the basis function and wi is the weight coefficient. The optimization objective includes three items: reconstruction error, smoothing constraint, and feature preservation: E = Er + λsEs + λfEf. The terrain features are extracted through principal curvature analysis, and the curvature tensor K = [kij] is established to calculate the feature lines and feature points. The model supports adaptive resolution, and finer grid division is adopted for important areas such as the iron tower foundation. For example, in the modeling of a mountainous area substation, the model achieves a spatial resolution of 0.1 m, accurately reconstructs features such as steps and gullies in complex terrain, and provides an accurate spatial reference for subsequent deformation monitoring. The model has high query efficiency, and the time complexity of querying the attributes of any spatial point is O(logN), where N is the total number of nodes in the spatial division.

[0089] In some embodiments, the construction of the digital map can be achieved in various ways: Optionally, the map construction module 101 uses an adaptive octree structure to hierarchically divide the point cloud data, extracts the terrain features through local surface fitting, and performs spatial registration in combination with geographic information system data to finally generate a high-precision terrain model; Optionally, the map construction module 101 processes the meteorological data based on a neural network method, predicts the distribution of the atmospheric refractive index field through a deep learning model, and establishes an electromagnetic wave propagation model considering the influence of various meteorological factors. It can be understood that other modeling methods can also be used to achieve the construction of the digital map, which is not limited here.

[0090] It should be noted that the electromagnetic wave propagation model here constructs the electromagnetic wave propagation equation based on ray tracing. The core is to solve the refractive index distribution function n(h, x, y), where h is the height and x, y are the horizontal coordinates. When training the model, first construct the initial refractive index distribution based on meteorological data, and use an exponential function to describe the vertical variation characteristics: , where is the surface refractive index and H is the elevation factor. In the horizontal direction, the refractive index change caused by temperature and humidity gradients is considered and fitted by a piecewise linear function. During the optimization process, use the echo signal of the standard reflector as a reference to establish the error function between the predicted signal intensity S and the measured value S': E = ∑|S - S'|². By iteratively optimizing the refractive index distribution parameters until the error converges. The model can output the phase delay on any path:

[0091] ;

[0092] and the signal attenuation: L = ∑(Lr + La), where Lr is the reflection loss and La is the atmospheric loss. For example, in a certain actual application, the model successfully predicted the abnormal attenuation phenomenon caused by the temperature stratification in the valley terrain, and after compensation, the signal intensity prediction error was reduced from 3 dB to within 0.5 dB.

[0093] In some embodiments, the data processing module 105 of the deformation monitoring device 10 specifically includes: a beam synthesis unit 306 for performing beam synthesis on the near-field echo signal to obtain near-field phase correction data; an amplitude compensation unit 307 for performing amplitude compensation on the far-field echo signal to obtain far-field amplitude correction data; a signal accumulation unit 308 for coherently accumulating the near-field phase correction data and the far-field amplitude correction data to obtain a signal accumulation matrix; and a data screening unit 309 for comparing the signal accumulation matrix with the standard data in the signal intensity look-up table to generate signal deviation data, and screening valid monitoring data according to the signal deviation data.

[0094] Among them, beam synthesis refers to the coherent superposition processing of the signals received by multiple antenna elements; the near-field phase correction data refers to the phase correction amount for compensating the near-field effect; amplitude compensation is used to represent the correction process of the far-field signal attenuation; coherent accumulation refers to the phase alignment and superposition of multiple echo signals; the signal accumulation matrix refers to the two-dimensional data array after the accumulation process; the signal deviation data is used to represent the difference between the measured value and the standard value; and the valid monitoring data represents the reliable measurement results that meet the accuracy requirements.

[0095] After the data processing module 105 of the deformation monitoring device 10 acquires the echo signal, signal processing and data screening are required. Specifically, the beam synthesis unit 306 first performs spherical wave compensation on the echo signal in the near-field region to eliminate the phase distortion caused by the near-field effect, and adopts the adaptive beamforming technology to optimize the antenna pattern characteristics; the amplitude compensation unit 307 performs distance normalization processing on the far-field echo signal, compensates for the space propagation loss, realizes Doppler frequency compensation, and ensures signal coherence; the signal accumulation unit 308 improves the signal-to-noise ratio through weighted accumulation while maintaining the phase information of the signal; the data screening unit 309 matches and analyzes the processed data with the standard database, establishes a signal quality evaluation index, and finally screens out the effective data that meets the monitoring accuracy requirements.

[0096] In some embodiments, signal processing and data screening can be achieved in various ways: Optionally, the data processing module 105 uses the subspace adaptive processing algorithm to perform spatio-temporal two-dimensional filtering on the echo signal, extracts the target scattering characteristics through eigenvalue decomposition, and establishes a data reliability evaluation criterion based on the statistical test method to achieve high-quality data screening; Optionally, the data processing module 105 constructs a sparse signal reconstruction model based on the compressed sensing theory, extracts the effective signal components through the iterative optimization algorithm, and combines the pattern recognition technology to realize the detection of abnormal data. It can be understood that other signal processing methods can also be used to process and screen the data, which is not limited here.

[0097] It should be noted that in the data processing module 105, coherent accumulation adopts an adaptive Doppler compensation mechanism. The system first establishes a signal phase model , where r(t) is the target distance function and φ0 is the initial phase. The distance function is estimated by piecewise polynomial fitting:

[0098] ;

[0099] where v, a, and j represent the velocity, acceleration, and jerk components respectively.

[0100] The compensation signal is expressed as: ,

[0101] where φ̂(t) is the phase estimation value. The accumulation process adopts the sliding window technology, and the window length L is dynamically adjusted according to the coherence time: L = min(Tc, Td), where Tc is the signal coherence time and Td is the Doppler resolution time. Through this method, the system realizes a signal-to-noise ratio gain of more than -20dB in a 10Hz vibration environment.

[0102] In combination with some embodiments of the first aspect, in some embodiments, the deformation monitoring device 10 further includes: an array calibration module 310, configured to receive beam scanning data of the antenna array and obtain antenna element coordinates; a parameter calculation module 311, configured to calculate phase center coordinates based on the antenna element position data and determine array calibration data; a sequence generation module 312, configured to import the array calibration parameters into the beam control module to generate a beam scanning sequence; a range determination module 313, configured to determine a monitoring scanning range according to the beam scanning sequence.

[0103] Wherein, the antenna array refers to a spatial sampling device composed of multiple antenna elements arranged in a specific geometry; the beam scanning data refers to the measurement results of the radiation characteristics of the antenna array in different directions; the antenna element coordinates are used to represent the precise position of each antenna element in the spatial rectangular coordinate system; the phase center coordinates represent the position of the center point of the equiphase surface of the antenna element radiation field; the array calibration data refers to the compensation parameters used to correct the actual working state of the antenna array; the beam scanning sequence is used to represent the scanning path and timing arrangement of the beam in the spatial domain; the monitoring scanning range represents the spatial area where deformation monitoring needs to be performed; the beam scanning sequence refers to the movement trajectory of the antenna beam in the spatial domain.

[0104] After the deformation monitoring device 10 constructs the digital map, it is necessary to determine the spatial layout characteristics of the antenna array based on the array calibration module 310. Specifically, the array calibration module 310 first activates each unit of the antenna array, collects the response data of the standard test source; measures the phase center of each antenna element, and records the amplitude and phase response curves; based on the phase difference measurement principle, calculates the relative position relationship between the antenna elements; the array calibration module 310 combines the mechanical installation parameters to establish an absolute coordinate system for the antenna elements; through multiple repeated measurements and data averaging, the accuracy of coordinate determination is improved.

[0105] After the deformation monitoring device 10 obtains the antenna element coordinates, it is necessary to calculate the array calibration parameters based on the parameter calculation module 311. Specifically, the parameter calculation module 311 first establishes an electromagnetic field theory model of the antenna elements, calculates the phase center distribution in the ideal state; uses the near-field measurement method to obtain the actual radiation field distribution and extracts the phase characteristics; by comparing the theoretical values and the measured values, establishes an error compensation model; calculates the amplitude and phase correction coefficients of each antenna element; finally, the parameter calculation module 311 generates an array calibration data set containing all calibration parameters.

[0106] After the deformation monitoring device 10 completes array calibration, it is necessary to generate a beam scanning control sequence based on the sequence generation module 312. Specifically, the sequence generation module 312 first loads the calibration parameters into the data buffer of the beam control module; according to the monitoring task requirements, the sequence generation module 312 designs a spatial scanning strategy and plans the beam pointing sequence; considering the antenna pattern characteristics, it optimizes the scanning angle interval and dwell time; combining the real-time processing ability, it determines the scanning period and sampling frequency; finally, the sequence generation module 312 generates a complete beam control instruction sequence.

[0107] After the deformation monitoring device 10 obtains the beam scanning sequence, it is necessary to determine the actual monitoring range based on the range determination module 313. Specifically, the range determination module 313 calculates the beam coverage range according to the beam scanning sequence; combining the antenna pattern characteristics, it analyzes the spatial sampling density distribution; considering the terrain occlusion effect, it determines the effective monitoring area; based on the monitoring accuracy requirements, it optimizes the scanning range boundary; finally, the range determination module 313 generates a monitoring range description file containing spatial coordinates.

[0108] In some embodiments, the acquisition of antenna element coordinates can be achieved in various ways: Optionally, the array calibration module 310 obtains the antenna aperture surface field distribution through near-field plane scanning technology, reconstructs the far-field pattern using Fourier transform, combines the phase gradient method to determine the relative position of the antenna elements, and finally obtains the absolute coordinates through coordinate transformation; Optionally, the array calibration module 310 adopts an adaptive array calibration algorithm, estimates the antenna element position error based on subspace eigenvalue decomposition, corrects the installation deviation through an iterative optimization method, and realizes high-precision coordinate determination. It can be understood that other antenna measurement methods can also be used to achieve the acquisition of element coordinates, which are not limited here. In some embodiments, the acquisition of array calibration data can be achieved in various ways: Optionally, the parameter calculation module 311 analyzes the antenna near-field measurement data using the spherical wave expansion method, extracts the phase center characteristics through modal decomposition, establishes a mutual coupling compensation model, and performs comprehensive calibration considering the influence of temperature and mechanical deformation; Optionally, the parameter calculation module 311 is based on the adaptive digital beamforming technology, estimates the amplitude and phase differences between channels in real time, establishes a dynamic calibration model, and realizes the adaptive update of array parameters. It can be understood that other calibration methods can also be used to achieve the calibration of array parameters, which are not limited here.

[0109] In some embodiments, the array calibration module 310 specifically includes: a signal acquisition unit 3101, configured to receive the corner reflector echo signal, generate a reference waveform data set, and perform time-frequency transformation on the reference waveform data set to obtain a signal amplitude spectrum and a phase spectrum; a spectrum analysis unit 3102, configured to calculate the element amplitude and phase distribution according to the signal amplitude spectrum and the phase spectrum, generate an element distribution matrix, and perform phase compensation on the element distribution matrix to obtain the relative positions of the antenna elements; and a coordinate calculation unit 3103, configured to map the relative positions of the antenna elements to a coordinate system to obtain the antenna element coordinates.

[0110] Among them, the corner reflector echo signal represents a reference signal returned by a standard scatterer; the reference waveform data set refers to a set of standard signals for system calibration; time-frequency transformation is used to represent the mapping relationship between the time domain and the frequency domain of a signal; the signal amplitude spectrum represents the distribution characteristics of the signal intensity with respect to frequency; the phase spectrum refers to the variation law of the signal phase with respect to frequency; the element distribution matrix is used to represent the spatial arrangement characteristics of the antenna elements; and phase compensation represents the processing process of correcting the phase difference between antenna elements.

[0111] When the array calibration module 310 performs system calibration, it is necessary to determine the exact positions of the antenna elements. Specifically, the signal acquisition unit 3101 first acquires calibration signals based on the deployed standard corner reflector; the spectrum analysis unit 3102 performs time-domain sampling and spectrum analysis on the echo signals to extract amplitude and phase characteristics; the coordinate calculation unit 3103 calculates the element excitation distribution based on the antenna array theory, establishes a spatial phase difference model; estimates the relative position relationship of the antenna elements by the phase gradient method; optimizes the position parameters by the least squares algorithm; combines the mechanical installation reference to convert the relative position into an absolute coordinate; and finally generates high-precision antenna element coordinate data.

[0112] In some embodiments, the calibration of the antenna coordinates can be achieved in various ways: Optionally, the array calibration module 310 uses holographic measurement technology to obtain the near-field distribution of the antenna, reconstructs the radiation source distribution through the backpropagation algorithm, combines the phase center extraction method to determine the positions of the antenna elements, and realizes high-precision coordinate calibration; Optionally, the array calibration module 310 is based on the multi-source data fusion technology, comprehensively uses mechanical measurement data and electromagnetic measurement results, and realizes the dynamic optimization of coordinate estimation through the Kalman filter algorithm. It can be understood that other calibration methods can also be used to determine the antenna coordinates, which are not limited herein.

[0113] In some embodiments in combination with the first aspect, in some embodiments, the deformation monitoring device 10 further includes: a vector field generation module 314, configured to calculate the three-dimensional coordinate change amounts of each monitoring point and generate a displacement vector field; a stress analysis module 315, configured to calculate the deformation parameters of the iron tower structure according to the displacement vector field to obtain a stress distribution map; a region identification module 316, configured to superimpose the stress distribution map on the structural parameters to determine the stress concentration region; and a recording and storage module 317, configured to collect the monitoring data of the stress concentration region at a preset period, construct a time series record, and store the time series record in the deformation feature database.

[0114] Wherein, the three-dimensional coordinate change amount represents the displacement components of the monitoring point in the space rectangular coordinate system; the displacement vector field refers to the vector field describing the spatial distribution of the structural deformation; the deformation parameter of the iron tower structure represents the physical quantity describing the deformation state of the structure; the stress distribution map refers to the spatial distribution characteristics of the internal stress field of the structure; the deformation parameter calculation represents the analysis process of deriving the stress field from the displacement field; the stress concentration region represents the local region with a relatively high stress level in the structure; the preset period represents the time interval for collecting the monitoring data; and the time series record refers to the monitoring data set arranged in chronological order.

[0115] After the deformation monitoring device 10 obtains the displacement amount of the monitoring point, it is necessary to construct a complete displacement vector field based on the vector field generation module 314. Specifically, the vector field generation module 314 first decomposes the displacement amount of the monitoring point into components in the X, Y, and Z directions; performs continuous processing on the discrete monitoring points by using a spatial interpolation algorithm; establishes a mathematical model of the structural deformation, calculates the displacement vector at any position; considers the structural constraint conditions, and optimizes the displacement field distribution; finally, the vector field generation module 314 generates a three-dimensional displacement vector field characterizing the overall deformation characteristics.

[0116] After the deformation monitoring device 10 obtains the displacement vector field, it is necessary to calculate the structural stress distribution based on the stress analysis module 315. Specifically, the stress analysis module 315 first establishes a strain-displacement relationship based on the theory of elasticity; uses the constitutive equation to convert the strain into stress components; considers the material properties and geometric nonlinearity, calculates the principal stress and equivalent stress; analyzes the stress concentration and strain energy distribution; finally, the stress analysis module 315 generates a stress distribution map characterizing the stress state of the structure.

[0117] After the deformation monitoring device 10 obtains the stress distribution map, it is necessary to identify the weak parts of the structure based on the region identification module 316. Specifically, the region identification module 316 first aligns the stress distribution map with the structural geometric model; analyzes the range of the region where the stress level exceeds the design value; considers the material strength characteristics, evaluates the local failure risk; combines the historical monitoring data, and predicts the stress development trend; finally, the region identification module 316 determines the stress concentration region that needs to be key monitored.

[0118] After the deformation monitoring device 10 determines the stress concentration area, it is necessary to construct a long-term monitoring mechanism based on the recording and storage module 317. Specifically, the recording and storage module 317 first sets the monitoring sampling period and data format; focuses on monitoring the stress concentration area, records the stress and displacement changes; establishes a data classification and storage mechanism to ensure the integrity of the monitoring records; evaluates the deformation development trend through time series analysis; and finally the recording and storage module 317 stores the monitoring data and analysis results in the deformation feature database.

[0119] In some embodiments, the construction of the displacement vector field can be achieved in various ways: Optionally, the deformation monitoring device 10 uses the radial basis function interpolation method to process the discrete monitoring data, extracts the local deformation features through multi-scale decomposition, combines the physical constraint conditions to optimize the interpolation parameters, and realizes the high-precision reconstruction of the displacement field; Optionally, the deformation monitoring device 10 is based on the finite element model-assisted interpolation technology, uses the displacement of the monitoring points as the boundary conditions, solves the structural mechanics equations, and obtains the full-field displacement distribution. It can be understood that other interpolation methods can also be used to construct the displacement field, which is not limited here.

[0120] It should be noted that in the stress analysis module 315, the multi-scale tensor decomposition method is used to extract the deformation parameters. First, the displacement vector field is expressed as a third-order tensor ,

[0121] where I, J, and K represent the spatial and temporal dimensions respectively.

[0122] The Tucker decomposition model is adopted , where G is the core tensor, and U, V, and W are the modal factor matrices. By setting the energy retention rate η, the number of principal components of each mode is determined, so that the reconstruction error satisfies . The principal component analysis results are used to identify the main deformation modes, and each mode contains the spatial distribution characteristics and the time evolution characteristics. For example, the analysis of the monitoring data of a certain transmission tower shows that the first three principal components can explain 95% of the deformation characteristics, where the first principal component reflects the overall inclination trend, the second principal component represents the torsional deformation, and the third principal component corresponds to the local bending. It should be noted that in the region identification module 316, the improved hot spot analysis method is used to identify the stress concentration area. The spatial autocorrelation index of the stress field is constructed:

[0123] , where wij is the spatial weight matrix, zi is the standardized stress value, and S is the standard deviation. By calculating the local Getis-Ord statistic:

[0124] , the high-stress aggregation area under the statistical significance level is identified. At the same time, the stress gradient tensor is introduced to evaluate the severity of the stress change. When When (σ0 is the design value of material strength and λ is the safety factor), mark this area as the key monitoring object. This method successfully identified the stress concentration phenomenon at the bolt connection part in a certain actual project, and the relative error between the predicted value and the measured data of the strain gauge was less than 8%.

[0125] In some embodiments, in the record storage module 317, the monitoring data management can be implemented in various ways: Optionally, the deformation monitoring device 10 adopts a distributed storage architecture to manage a large amount of monitoring data, improves the access efficiency through data compression and index optimization, analyzes the deformation evolution law by combining data mining techniques, and realizes intelligent early warning; Optionally, the deformation monitoring device 10 constructs a real-time data processing system based on the cloud computing platform, improves the monitoring accuracy through multi-source data fusion, and establishes a prediction model to assist decision-making analysis. It can be understood that other data management methods can also be used to implement the storage and analysis of monitoring records, which are not limited here.

[0126] In some embodiments, the deformation monitoring device 10 further includes: a projection map construction module 318, configured to construct a deformation vector projection map based on the spatial coordinate sequence in the deformation feature database; an acceleration calculation module 319, configured to calculate the displacement acceleration of the monitoring point according to the deformation vector projection map; a trend analysis module 320, configured to superimpose the displacement acceleration and the stress distribution map to obtain the stress change rate, and determine the structural deformation trend curve according to the stress change rate; an early warning generation module 321, configured to generate early warning information based on the structural deformation trend curve.

[0127] Among them, the spatial coordinate sequence represents a data set of the positions of the monitoring points changing with time; the deformation vector projection map refers to the projection representation of the structural deformation on a specific plane; the stress change rate represents the change characteristic of the internal stress of the structure with time; the structural deformation trend curve refers to a time series curve describing the deformation development law.

[0128] During the long-term monitoring process of the deformation monitoring device 10, deformation trend analysis and early warning are required. Specifically, the projection map construction module 318 first extracts historical monitoring data from the database and constructs a spatio-temporal sequence model; the acceleration calculation module 319 analyzes the main direction and amplitude of the deformation through vector decomposition technology, calculates the velocity and acceleration characteristics of the monitoring points, and evaluates the motion state; the trend analysis module 320 performs correlation analysis on the dynamic characteristics and the stress distribution to identify key deformation modes; adopts a time series prediction method to establish a deformation development trend model; the early warning generation module 321 evaluates the structural safety state in real time based on multi-level early warning thresholds; when the prediction result exceeds the safety threshold, early warning information is generated in a timely manner.

[0129] In some embodiments, the deformation trend analysis and early warning can be achieved in various ways: Optionally, the deformation monitoring device 10 uses deep learning methods to establish a deformation prediction model, processes time series data through a recurrent neural network, and combines an attention mechanism to extract key features to achieve intelligent prediction of the deformation trend; Optionally, the deformation monitoring device 10 is based on statistical pattern recognition technology, extracts the dominant deformation pattern through principal component analysis, and establishes a probability early warning model to achieve dynamic assessment of the risk level. It can be understood that other analysis methods can also be used to achieve the prediction and early warning of the deformation trend, which is not limited here.

[0130] It should be noted that the deformation prediction model here uses a hierarchical recurrent neural network structure to process multi-scale time series features. The input layer contains three types of time series data: displacement p(t), stress σ(t), and meteorological parameter w(t). After feature normalization, they are sent into the network. The core layer consists of multiple LSTM units, and the hidden state dimension of each LSTM layer is 256. The long-term and short-term dependencies are learned through the gating mechanism. To improve the ability to capture key time series patterns, a multi-head attention mechanism is introduced:

[0131] , where Q, K, and V are the query, key-value matrices respectively, and d is the feature dimension. The model training adopts a sequence-to-sequence architecture, and the loss function includes two parts: displacement prediction error and trend judgment accuracy:

[0132] , where α and β are weight coefficients. When 24 hours of historical data is input, the model can predict the displacement change trend in the next 12 hours. Verified on the measured data, the trend prediction accuracy reaches 93.5%, and the root mean square error of displacement prediction is less than 2.8 mm.

[0133] In the embodiments of the present application, due to the use of the transmission tower deformation monitoring device 10 based on phased array radar technology, combined with the near-far field differential monitoring strategy and multi-source data fusion technology, it is possible to accurately grasp the electromagnetic wave propagation characteristics under complex terrain conditions and achieve accurate displacement measurement. It effectively solves the problem that the traditional monitoring method is affected by the multipath effect and abnormal atmospheric refraction in the mountainous environment, resulting in a decrease in measurement accuracy, and thus realizes all-weather and high-precision transmission tower deformation monitoring. By constructing a deformation feature database and an intelligent early warning mechanism, the system can also predict the deformation development trend, providing a reliable guarantee for the safe operation of the power grid.

[0134] The deformation monitoring device in the embodiments of the present invention application will be described from the perspective of hardware processing below. Please refer to Figure 4 , which is a schematic structural diagram of an entity device of the deformation monitoring device in the embodiments of the present application.

[0135] It should be noted that Figure 4The structure of the deformation monitoring device shown is only an example and should not impose any limitations on the functions and scope of use of the embodiments of the present invention.

[0136] As Figure 4 shown, the deformation monitoring device includes a CPU 401, which can perform various appropriate actions and processes according to the program stored in the ROM 402 or the program loaded into the RAM 403 from the storage section 408, such as executing the deformation monitoring device described in the above embodiments. In the RAM 403, various programs and data required for system operation are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other via a bus 404. The I / O interface 405 is also connected to the bus 404.

[0137] The following components are connected to the I / O interface 405: an input section 406 including an audio input device, a button switch, etc.; an output section 407 including a liquid crystal display (LCD), an audio output device, an indicator light, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read from it can be installed into the storage section 408 as needed.

[0138] Specifically, according to the embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments of the present invention include a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication section 409 and / or installed from the removable medium 411. When the computer program is executed by the CPU 401, various functions defined in the present invention are executed.

[0139] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above module, program segment, or part of code includes one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings.

[0140] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present application.

[0141] In the above embodiments, depending on the context, the term "when..." can be interpreted to mean "if...", or "after...", or "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "upon determining..." or "if (the stated condition or event) is detected" can be interpreted to mean "if determined...", or "in response to determining...", or "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

Claims

1. A transmission tower deformation monitoring device based on phased array radar technology, characterized in that, The deformation monitoring device includes: A map construction module for constructing a regional digital map containing terrain elevation distribution and electromagnetic wave attenuation based on the terrain surveying data, tower structure parameters, and meteorological parameters of the monitoring area; A boundary determination module for calculating the signal intensity attenuation rate and phase delay of the regional digital map in each monitoring direction based on the terrain elevation distribution and the electromagnetic wave attenuation, and determining the near-field boundary; A signal acquisition module for taking the area within the near-field boundary as the near field, obtaining the near-field echo signal of the near field through a narrow beam, and taking the area outside the near-field boundary as the far field, obtaining the far-field echo signal of the far field through a wide beam; A calibration table construction module for obtaining the calibration signal of the standard reflector array, determining the direct wave and reflected wave paths in combination with the regional digital map, and constructing a signal intensity comparison table; A data processing module for performing coherent accumulation calculations on the near-field echo signal and the far-field echo signal, and comparing the calculation results with the signal intensity comparison table to obtain effective monitoring data; An error calculation module for calculating errors of the effective monitoring data to obtain phase errors and amplitude errors; A parameter determination module for correcting the effective monitoring data according to the phase errors and the amplitude errors to obtain the displacement amount of the monitoring point, and determining the tower deformation parameters based on the displacement amount of the monitoring point.

2. The deformation monitoring device according to claim 1, characterized in that The map construction module specifically includes: A point cloud processing unit for determining point cloud data based on the scanning data of the three-dimensional laser and generating a terrain elevation scatter array; A grid division unit for dividing the terrain elevation scatter array based on a preset grid resolution to obtain a regional terrain elevation matrix; A parameter mapping unit for mapping the tower structure parameters to the regional terrain elevation matrix to obtain the tower structure grid coordinates; An attenuation calculation unit for calculating the refractive index distribution according to the temperature, humidity, and air pressure data collected by the meteorological sensor to obtain the electromagnetic wave propagation attenuation coefficient; A map generation unit for superimposing the tower structure grid coordinates and the electromagnetic wave propagation attenuation coefficient to generate a monitoring area digital map.

3. The deformation monitoring device according to claim 1, characterized in that, The data processing module specifically includes: A beam synthesis unit for performing beam synthesis on the near-field echo signal to obtain near-field phase correction data; An amplitude compensation unit for performing amplitude compensation on the far-field echo signal to obtain far-field amplitude correction data; A signal accumulation unit for coherently accumulating the near-field phase correction data and the far-field amplitude correction data to obtain a signal accumulation matrix; A data screening unit for comparing the signal accumulation matrix with the standard data in the signal intensity comparison table to generate signal deviation data, and screening the effective monitoring data according to the signal deviation data.

4. The deformation monitoring device according to claim 1, characterized in that The deformation monitoring device further includes: An array calibration module for receiving the beam scanning data of the antenna array and obtaining the antenna element coordinates; A parameter calculation module for calculating the phase center coordinates according to the antenna element coordinates and determining the array calibration parameters; A sequence generation module for importing the array calibration parameters into the beam control module to generate a beam scanning sequence. A range determination module for determining a monitoring scan range according to the beam scanning sequence.

5. The deformation monitoring device according to claim 4, wherein, The array calibration module specifically includes: A signal acquisition unit for receiving the corner reflector echo signal, generating a reference waveform data group, and processing the reference waveform data group using time-frequency transformation to obtain a signal amplitude spectrum and a phase spectrum; A spectrum analysis unit for calculating the element amplitude and phase distribution according to the signal amplitude spectrum and the phase spectrum, generating an element distribution matrix, and processing the element distribution matrix using phase compensation to obtain the relative positions of the antenna elements; A coordinate calculation unit for mapping the relative positions of the antenna elements to a coordinate system to obtain the antenna element coordinates.

6. The deformation monitoring device according to claim 1, wherein The deformation monitoring device further includes: A vector field generation module for calculating the three-dimensional coordinate change amount of each monitoring point to generate a displacement vector field; A stress analysis module for calculating the deformation parameters of the iron tower structure according to the displacement vector field to obtain a stress distribution map; A region identification module for superimposing the stress distribution map and the structural parameters to determine the stress concentration region; A record storage module for collecting the monitoring data of the stress concentration region at a preset period, constructing a time series record, and storing the time series record in the deformation feature database.

7. The deformation monitoring device according to claim 6, wherein The deformation monitoring device further includes: A projection map construction module for constructing a deformation vector projection map based on the spatial coordinate sequence in the deformation feature database; An acceleration calculation module for calculating the displacement acceleration of the monitoring point according to the deformation vector projection map; A trend analysis module for superimposing the displacement acceleration and the stress distribution map to obtain a stress change rate, and determining a structural deformation trend curve according to the stress change rate; An early warning generation module for generating early warning information based on the structural deformation trend curve.

8. A method for monitoring the deformation of transmission towers based on phased array radar technology, characterized in that, Applied to a deformation monitoring device, the method includes: Constructing a regional digital map including terrain elevation distribution and electromagnetic wave attenuation based on the terrain mapping data, iron tower structure parameters, and meteorological parameters of the monitoring area; Calculating the signal intensity attenuation rate and phase delay in each monitoring direction of the regional digital map based on the terrain elevation distribution and the electromagnetic wave attenuation, and determining the near-field region boundary; Taking the region within the near-field region boundary as the near-field region, obtaining the near-field echo signal of the near-field region through a narrow beam, and taking the region outside the near-field region boundary as the far-field region, obtaining the far-field echo signal of the far-field region through a wide beam; Obtaining the calibration signal of the standard reflector array, determining the direct wave and reflected wave paths in combination with the regional digital map, and constructing a signal intensity comparison table; Performing coherent accumulation calculation on the near-field echo signal and the far-field echo signal, and comparing the calculation result with the signal intensity comparison table to obtain effective monitoring data; Calculating the error of the effective monitoring data to obtain a phase error and an amplitude error; Correcting the effective monitoring data according to the phase error and the amplitude error to obtain the displacement amount of the monitoring point, and determining the iron tower deformation parameters based on the displacement amount of the monitoring point.

9. A deformation monitoring device, characterized in that, The deformation monitoring device includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, and the computer program code includes computer instructions, so that the deformation monitoring device executes the deformation monitoring method as described in claim 8.

10. A computer-readable storage medium, comprising instructions, characterized in that, When the instructions run on the deformation monitoring device, the deformation monitoring device is caused to execute the deformation monitoring method as described in claim 8.

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