Transmission tower deformation monitoring device based on phased array radar technology

Abstract

A transmission tower deformation monitoring device based on a phased array radar technology relates to the field of irregular contour metering, and comprises a map construction module used for constructing a regional digital map; the boundary determination module is used for near-field region boundaries; the signal acquisition module is used for acquiring a near-field echo signal of a near-field region through a narrow beam and acquiring a far-field echo signal of a far-field region through a wide beam; the calibration table construction module is used for constructing a signal intensity comparison table; the data processing module is used for determining effective monitoring data; the error calculation module is used for performing error calculation on the effective monitoring data to obtain a phase error and an amplitude error; and the parameter determination module is used for determining iron tower deformation parameters. According to the invention, the accuracy of transmission tower deformation monitoring can be improved.

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 transmission network and the in-depth promotion of the smart grid construction, the safety operation monitoring of transmission towers faces higher technical requirements. Under complex terrains and adverse weather conditions, various forms of structural changes may occur to transmission towers, such as foundation settlement, pole tilt, 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 the real-time deformation monitoring of transmission towers has important engineering value and practical significance.

[0003] In related technologies, the deformation monitoring of transmission towers mainly includes two types of methods: contact type and non-contact type. The contact type monitoring uses strain gauges and fiber optic sensors to collect local strain data through sensors installed on the tower surface. The 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 to improve the accuracy of transmission tower deformation monitoring.

[0006] In a 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 based on terrain surveying and mapping data of the monitoring area, tower structure parameters, and meteorological parameters; 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, 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 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; and a parameter determination module for correcting the effective monitoring data according to the phase error and amplitude error to obtain the displacement of the monitoring point, and determining the tower deformation parameters based on the displacement 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, and combining the near-field and far-field area division and standard reflector calibration; through phase error and amplitude error correction, the influence of uneven electromagnetic wave propagation attenuation under complex terrain conditions is effectively overcome, and the accuracy of displacement calculation is improved.

[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; and 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 in 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 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; and 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 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 beam synthesis 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 performing time-frequency transformation on the reference waveform data group to obtain a signal amplitude spectrum and a phase spectrum; a spectrum analysis unit for calculating the array element amplitude and phase distribution according to the signal amplitude spectrum and the phase spectrum, generating an array element distribution matrix, and performing phase compensation on the array element distribution matrix to obtain the relative position of the antenna array elements; and a coordinate calculation unit for mapping the relative position of the antenna array elements to a 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 array 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 amounts 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 and the structure 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 and the stress distribution map to obtain the stress change rate, and determine the structure deformation trend curve according to the stress change rate; an early warning generation module, configured to generate early warning information based on the structure 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 in each monitoring direction of the regional digital map 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 iron tower deformation parameters 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, which, when running on a deformation monitoring device, causes the deformation monitoring device 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, which, when running on a deformation monitoring device, causes the deformation monitoring device 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 embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which 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: 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 attenuation of electromagnetic wave propagation 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.

[0025] 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. Combining the phase compensation technology, the relative positions of the antenna array elements are accurately determined, and an accurate antenna element coordinate system is established. Based on this, a beam scanning sequence is constructed to realize the 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 then realize the accurate monitoring of the deformation of different parts of the tower.

[0026] 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 tower structure parameters, 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 status of the tower structure is achieved, providing a scientific basis for preventive maintenance. Description of the Drawings

[0027] Figure 1 is a module architecture diagram of the deformation monitoring device in an embodiment of the present application; Figure 2 is a flow schematic diagram of the transmission tower deformation monitoring method in an embodiment of the present application; Figure 3 is another module architecture diagram of the deformation monitoring device in an embodiment of the present application; 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

[0028] 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.

[0029] 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 specified, the meaning of "a plurality" is two or more.

[0030] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0031] With the continuous expansion of the ultra-high voltage power transmission network scale, 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 transmission tower inclination and component deformation accidents 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 potential hazards 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.

[0032] In related technologies, the deformation monitoring of transmission towers can be achieved by adopting a traditional phased array radar monitoring system. This system uses fixed beam parameters to scan the entire monitoring area, and calculates the displacement amount 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.

[0033] 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 deteriorates 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 spatially uneven distribution of the atmospheric refractive index causes the propagation path of the electromagnetic wave 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 is difficult to balance the measurement requirements of high resolution in the near field and high sensitivity in the far field.

[0034] By using the deformation monitoring device in the embodiment of the present application, through constructing a regional digital map including terrain elevation and electromagnetic wave attenuation characteristics, the accurate modeling of the electromagnetic propagation environment in the monitoring area is realized, and based on the near-far field boundary division, 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 intensity. The following introduces the scenario of using the transmission tower deformation monitoring device in the present application.

[0035] After the deformation monitoring device adopting this solution was put into use in a transmission line in a mountainous area, by establishing an accurate digital map including terrain and atmospheric characteristics, an accurate modeling of the electromagnetic wave propagation environment was 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 strength. Through standard reflector calibration and error compensation techniques, the influence of multipath effects and abnormal atmospheric refraction under complex terrain conditions was 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 warned of multiple abnormal deformation events of iron towers.

[0036] 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 multipath effects and abnormal atmospheric refraction under complex terrain conditions, and thus achieve high-precision all-weather monitoring.

[0037] 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 refer to the corresponding Figure 2 , which is a schematic flowchart of the transmission tower deformation monitoring method in the embodiment of the present application.

[0038] 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 of the monitoring area, the tower structure parameters and the meteorological parameters.

[0039] Among them, the terrain surveying and mapping data of the monitoring area refers to the 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 the structural characteristic parameters such as the geometric dimensions, material properties, and node positions of the transmission tower; the meteorological parameters are used to represent the environmental factors affecting the propagation of electromagnetic waves, 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.

[0040] 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 containing terrain undulation characteristics and electromagnetic propagation characteristics.

[0041] 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 mapping 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 the neural network method to extract features and reconstruct the original surveying and mapping data, combines the 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.

[0042] 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 In step S202, based on the terrain elevation distribution and the electromagnetic wave attenuation amount, calculate the signal intensity attenuation rate and phase delay in each monitoring direction of the regional digital map, and determine the near-field region boundary.

[0043] 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 the spatial information model containing terrain and electromagnetic characteristics.

[0044] 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 and 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, it calculates the signal attenuation coefficient and phase change on each path; based on the Fresnel zone theory, it analyzes the distribution characteristics of the antenna radiation field to determine 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.

[0045] 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 path, calculates the geometric loss and atmospheric loss on each path, defines the 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 optimal division of the monitoring area, which is not limited here.

[0046] 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 In step S203, the area within the near field area boundary is used as the near field area, and the near field echo signal of the near field area is obtained through a narrow beam, and the area outside the near field area boundary is used as the far field area, and the far field echo signal of the far field area is obtained through a wide beam.

[0047] 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.

[0048] After the deformation monitoring device 10 determines the monitoring area division, it needs 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.

[0049] 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: , 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: , where R is the interference plus noise covariance matrix, (θ0, φ0) is the desired beam pointing.

[0050] The optimal weight can be obtained by the Lagrange multiplier method: The signal acquisition module 103 uses the subspace tracking algorithm to update the eigenvalue decomposition of R in real time to ensure the dynamic optimization of the beam pattern. Field tests 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.

[0051] Please refer to Figure 1 , the deformation monitoring device 10 further includes a calibration table construction module 104, and the 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.

[0052] 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.

[0053] 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, forms a standardized signal intensity comparison table, and provides a reference for subsequent signal processing.

[0054] 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 intensity spatial distribution model through multi-point calibration, and realizes signal calibration by combining 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 intensity calibration, which is not limited here.

[0055] 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 establish the desired signal model d(n) and the input signal x(n), and construct the objective function using the least mean square error criterion. The error e(n) between the filter output y(n) and the desired 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, increase the step size to improve the tracking ability, and when the signal is stable, decrease the step size to ensure accuracy.

[0056] In addition, the standard reflector array uses an optimized layout algorithm to determine the installation positions. The algorithm is based on the three-dimensional grid of the monitoring area to construct the objective function , where represents the spatial coverage, represents the angular distribution uniformity, represents the expected signal intensity. The optimal layout scheme is solved by 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 practical application scenario, 13 standard corner reflectors are used to achieve an absolute position accuracy better than 2 mm within 500 meters.

[0057] 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, coherent accumulation calculations are performed on the near-field echo signal and the far-field echo signal, and the calculation results are compared with the signal intensity comparison table to obtain effective monitoring data.

[0058] Among them, coherent accumulation calculation means performing coherent superposition processing on multiple echo signals; the calculation result refers to the signal characteristic quantity after coherent accumulation; the comparison process means performing matching analysis on the measured data and the standard data; effective monitoring data refers to reliable measurement results that have been screened and verified; the signal intensity comparison table is used to represent the standardized reference data set.

[0059] After the deformation monitoring device 10 obtains the echo signal, effective data extraction and verification are required. 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 comparison 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 meet the accuracy requirements.

[0060] It should be noted that the data processing module 105 stores a data reliability evaluation system based on hypothesis testing. First, a normality test is performed on the accumulated signal, the sample skewness and kurtosis are calculated, the Jarque-Bera statistic is constructed and compared with the critical value. For the data passing the normality test, an improved Grubbs criterion is used for outlier detection, and abnormal points are identified by calculating the standardized residual and setting the confidence interval. Then, autocorrelation analysis is used to evaluate the temporal correlation of the data, the autocorrelation function is constructed and the significance level is calculated. For the data of spatially adjacent monitoring points, a spatial autocorrelation model is established to evaluate the spatial consistency of the data. Finally, based on the test results of each statistic, a weighted scoring method is used to determine the data reliability level, and effective monitoring data that meet the accuracy requirements are screened out. This method ensures the reliability and accuracy of the monitoring data through multi-dimensional statistical tests.

[0061] 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

[0062] to calculate errors for the effective monitoring data to obtain phase errors and amplitude errors.

[0063] 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; and the error calculation module 106 evaluates the uncertainty of the measurement results through error propagation analysis to provide a basis for subsequent data correction.

[0064] 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 amount of the monitoring point, and determine the iron tower deformation parameters based on the displacement amount of the monitoring point.

[0065] Among them, the displacement amount of the monitoring point represents the spatial position change amount of each monitoring point of the iron tower structure; the iron tower deformation parameter refers to the characteristic quantity describing the structural deformation state, including the inclination angle, torsion angle, deflection, etc.; the correction process means using the error parameter to correct the measurement result.

[0066] After the deformation monitoring device 10 completes the error analysis, it is necessary to calculate the structural 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 evaluation report.

[0067] 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.

[0068] 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, and performs feature extraction through multiple layers of convolution. Each convolutional layer uses a 3×3 convolutional kernel to extract spatial correlation features. After the feature map is reduced in dimension 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 is trained through a large amount of historical monitoring data and can accurately capture the complex relationship between the displacement field and the deformation parameters.

[0069] 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: , where Φ(k) is the state transition matrix, which includes the structural dynamics characteristics, and w(k) is the process noise. The measurement update introduces an adaptive factor: , where α(k) is the credibility factor calculated based on the innovation sequence. The displacement estimate is obtained by iterative solution: . This algorithm achieves a three-dimensional displacement measurement accuracy better than 1mm considering the structural constraints.

[0070] 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 electromagnetic wave propagation model parameters 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.

[0071] During the actual operation process, the deformation monitoring device 10 also exhibits 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 process, the system analyzed the displacement acceleration and stress change rate of a certain foundation settlement iron tower and predicted 12 hours in advance the possible severe tilt. The operation and maintenance personnel took timely reinforcement measures accordingly, avoiding a major accident. In addition, the data interface of the system also supports the fusion of multi-source information such as geological monitoring and weather forecasting, further improving the accuracy and timeliness of early warning.

[0072] After combining the above scenarios, the deformation monitoring device 10 provided in this embodiment will be further described in more detail below. Please refer to Figure 3 , which is another module architecture diagram of the deformation monitoring device in the embodiment of the present application.

[0073] 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 the three-dimensional laser and generates a terrain elevation scatter array; a grid division unit 302 that divides 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 tower structure parameters to the regional terrain elevation matrix to obtain the 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 tower structure grid coordinates and the electromagnetic wave propagation attenuation coefficient to generate a digital map of the monitoring area.

[0074] 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 spatial 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 gridification; the tower structure parameters are used to represent the geometric characteristics and physical properties of the tower; the structure grid coordinates refer to the spatial positioning information of the tower in the discrete grid; and the attenuation coefficient is used to represent the spatial distribution characteristics of the electromagnetic wave energy loss.

[0075] Before starting the monitoring task, the map construction module 101 needs to construct a digital map including terrain and electromagnetic characteristics. Specifically, the point cloud processing unit 301 first runs the 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 scatter data using the bilinear interpolation method to construct a regular gridified terrain data matrix; the parameter mapping unit 303 converts the tower CAD model into a grid-based description and establishes a mapping relationship between the structural characteristics 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, structure, and electromagnetic characteristic data to generate a multi-level digital map model.

[0076] It should be noted that the regional digital map can be expressed in the form of a digital map model, which organizes spatial data based on a hierarchical octree structure. Each node contains attribute information such as location, elevation, and material. The point cloud data processing uses an improved PointNet++ architecture to extract local and global features through hierarchical sampling and feature aggregation. The key mathematical processing is the construction of a 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 important areas such as the tower foundation use a finer grid division. For example, in the modeling of a mountain substation, the model achieves a spatial resolution of 0.1 m, accurately reconstructs features such as steps and gullies in complex terrains, and provides an accurate spatial reference for subsequent deformation monitoring. The model has high query efficiency, and the time complexity of attribute query for any spatial point is O(logN), where N is the total number of nodes in the spatial division.

[0077] 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 terrain features through local surface fitting, performs spatial registration in combination with geographic information system data, and finally generates a high-precision terrain model; Optionally, the map construction module 101 processes 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.

[0078] It should be noted that the electromagnetic wave propagation model here constructs an 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. During model training, first, an initial refractive index distribution is constructed based on meteorological data, and an exponential function is used 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 through a piecewise linear function. During the optimization process, the echo signal of a standard reflector is used as a reference to establish an error function between the predicted signal intensity S and the measured value S': E = ∑|S - S'|². The refractive index distribution parameters are optimized iteratively until the error converges. The model can output the phase delay on any path: ; Sum of signal attenuation: L = ∑(Lr + La), where Lr is the reflection loss and La is the atmospheric loss. For example, in a certain actual application, this model successfully predicted the abnormal attenuation phenomenon caused by temperature stratification in the valley terrain. After compensation, the signal strength prediction error was reduced from 3 dB to within 0.5 dB.

[0079] 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 strength look-up table to generate signal deviation data, and screening valid monitoring data according to the signal deviation data.

[0080] Among them, beam synthesis refers to the coherent superposition processing of signals received by multiple antenna elements; 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 far-field signal attenuation; coherent accumulation refers to the phase alignment superposition of multiple echo signals; the signal accumulation matrix refers to the two-dimensional data array after accumulation processing; signal deviation data is used to represent the difference between the measured value and the standard value; and valid monitoring data represents the reliable measurement results that meet the accuracy requirements.

[0081] After the data processing module 105 of the deformation monitoring device 10 obtains 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 to compensate for the space propagation loss, realize Doppler frequency compensation, and ensure 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 performs matching analysis on the processed data with the standard database, establishes a signal quality evaluation index, and finally screens out the valid data that meet the monitoring accuracy requirements.

[0082] In some embodiments, signal processing and data screening can be achieved in various ways: Optionally, the data processing module 105 uses a 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 a statistical test method to achieve high-quality data screening; Optionally, the data processing module 105 constructs a sparse signal reconstruction model based on the compressive sensing theory, extracts the effective signal components through an iterative optimization algorithm, and combines pattern recognition technology to detect abnormal data. It can be understood that other signal processing methods can also be used to process and screen data, which is not limited here.

[0083] It should be noted that in the data processing module 105, coherent integration 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: ; where v, a, and j represent the velocity, acceleration, and jerk components respectively.

[0084] The compensation signal is expressed as: , where φ̂(t) is the phase estimate value. The integration process uses a sliding window technique, 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 achieves a signal-to-noise ratio gain of more than -20 dB in a 10 Hz vibration environment.

[0085] Combined 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 the beam scanning data of the antenna array and obtain the antenna element coordinates; a parameter calculation module 311, configured to calculate the phase center coordinates according to the antenna element position data and determine the 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 the monitoring scanning range according to the beam scanning sequence.

[0086] Among them, the antenna array represents 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 exact position of each antenna element in the space rectangular coordinate system; the phase center coordinates represent the position of the center point of the equiphase surface of the radiation field of the antenna element; 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 that needs to be monitored for deformation; the beam scanning sequence refers to the movement trajectory of the antenna beam in the spatial domain.

[0087] 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 and 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; calculates the relative position relationship between the antenna elements based on the phase difference measurement principle; the array calibration module 310 will combine the mechanical installation parameters to establish the absolute coordinate system of the antenna elements; through multiple repeated measurements and data averaging, the accuracy of coordinate determination is improved.

[0088] 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 the electromagnetic field theory model of the antenna element and 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; establishes the error compensation model by comparing the theoretical value and the measured value; calculates the amplitude and phase correction coefficients of each antenna element; finally, the parameter calculation module 311 generates the array calibration data set containing all calibration parameters.

[0089] After the deformation monitoring device 10 completes the array calibration, it is necessary to generate the 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 requirements of the monitoring task, the sequence generation module 312 will design the spatial scanning strategy and plan the beam pointing sequence; considering the antenna pattern characteristics, optimize the scanning angle interval and dwell time; combine the real-time processing ability to determine the scanning period and sampling frequency; finally, the sequence generation module 312 generates the complete beam control instruction sequence.

[0090] 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; analyzes the spatial sampling density distribution in combination with the antenna pattern characteristics; considers the terrain occlusion effect to determine the effective monitoring area; optimizes the scanning range boundary based on the monitoring accuracy requirements; and finally, the range determination module 313 generates a monitoring range description file containing spatial coordinates.

[0091] In some embodiments, the acquisition of the antenna element coordinates can be achieved in multiple 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, determines the relative position of the antenna elements in combination with the phase gradient method, and finally obtains the absolute coordinates through coordinate transformation; Optionally, the array calibration module 310 adopts an adaptive array calibration algorithm, estimates the position error of the antenna elements 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 the element coordinates, which are not limited here. In some embodiments, the acquisition of the array calibration data can be achieved in multiple 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 in combination with the influence of temperature and mechanical deformation; Optionally, the parameter calculation module 311 is based on the adaptive digital beamforming technology, establishes a dynamic calibration model by real-time estimating the amplitude and phase differences between channels, and realizes the adaptive update of the array parameters. It can be understood that other calibration methods can also be used to achieve the calibration of the array parameters, which are not limited here.

[0092] 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 group, and process the reference waveform data group using time-frequency transformation to obtain the signal amplitude spectrum and phase spectrum; a spectrum analysis unit 3102, configured to calculate the element amplitude and phase distribution according to the signal amplitude spectrum and phase spectrum, generate an element distribution matrix, and process the element distribution matrix using phase compensation to obtain the relative position of the antenna elements; a coordinate calculation unit 3103, configured to map the relative position of the antenna elements to a coordinate system to obtain the antenna element coordinates.

[0093] Among them, the corner reflector echo signal represents the reference signal returned by the standard scatterer; the reference waveform data group refers to the set of standard signals used for system calibration; the time-frequency transformation is used to represent the mapping relationship of the signal in the time domain and the frequency domain; 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 array element distribution matrix is used to represent the spatial arrangement characteristics of the antenna elements; the phase compensation represents the processing process of correcting the phase difference between the antenna elements.

[0094] 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 collects calibration signals based on the deployed standard corner reflectors; 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 array 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 using the least squares algorithm; combines the mechanical installation reference to convert the relative position into absolute coordinates; and finally generates high-precision antenna element coordinate data.

[0095] 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 achieves 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 filtering algorithm. It can be understood that other calibration methods can also be used to determine the antenna coordinates, which are not limited herein.

[0096] Combined with some embodiments of 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 with the structural parameters to determine the stress concentration region; and a record 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.

[0097] Among them, the three-dimensional coordinate change amount represents the displacement components of the monitoring points in the space rectangular coordinate system; the displacement vector field refers to the vector field describing the spatial distribution of structural deformation; the deformation parameter of the iron tower structure represents the physical quantity describing the structural deformation state; the stress distribution diagram refers to the spatial distribution characteristics of the internal stress field of the structure; the calculation of deformation parameters represents the analysis process of deriving the stress field from the displacement field; the stress concentration area represents the local area with a relatively high stress level in the structure; the preset period represents the time interval for monitoring data acquisition; the time series record refers to the monitoring data set arranged in chronological order.

[0098] After the deformation monitoring device 10 obtains the displacement amount of the monitoring points, 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 points into components in the X, Y, and Z directions; uses the spatial interpolation algorithm to perform continuous processing on the discrete monitoring points; establishes a mathematical model of structural deformation to calculate the displacement vector at any position; considers the structural constraint conditions to optimize the displacement field distribution; finally, the vector field generation module 314 generates a three-dimensional displacement vector field representing the overall deformation characteristics.

[0099] 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 the 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 to calculate the principal stress and equivalent stress; analyzes the stress concentration and strain energy distribution; finally, the stress analysis module 315 generates a stress distribution diagram representing the stress state of the structure.

[0100] After the deformation monitoring device 10 obtains the stress distribution diagram, it is necessary to identify the weak parts of the structure based on the area recognition module 316. Specifically, the area recognition module 316 first aligns the stress distribution diagram with the structural geometric model; analyzes the range of the area where the stress level exceeds the design value; considers the material strength characteristics to evaluate the local failure risk; combines the historical monitoring data to predict the stress development trend; finally, the area recognition module 316 determines the stress concentration area that needs to be key monitored.

[0101] After the deformation monitoring device 10 determines the stress concentration area, it is necessary to construct a long-term monitoring mechanism based on the record storage module 317. Specifically, the record storage module 317 first sets the monitoring sampling period and data format; focuses on monitoring the stress concentration area and records the stress and displacement changes; establishes a data classification storage mechanism to ensure the integrity of the monitoring records; evaluates the deformation development trend through time series analysis; finally, the record storage module 317 stores the monitoring data and analysis results in the deformation feature database.

[0102] In some embodiments, the construction of the displacement vector field can be achieved in various ways: Optionally, the deformation monitoring device 10 processes discrete monitoring data using the radial basis function interpolation method, extracts local deformation features through multi-scale decomposition, and optimizes the interpolation parameters in combination with physical constraint conditions to achieve high-precision displacement field reconstruction; Optionally, the deformation monitoring device 10 is based on the finite element model-assisted interpolation technology, takes the displacement of the monitoring points as 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.

[0103] It should be noted that in the stress analysis module 315, the deformation parameter extraction uses the multi-scale tensor decomposition method. First, the displacement vector field is represented as a third-order tensor , where I, J, and K represent the spatial and temporal dimensions respectively.

[0104] 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 spatial distribution characteristics and time evolution characteristics. For example, the monitoring data analysis of a certain transmission tower shows that the first three principal components can explain 95% of the deformation characteristics. Among them, 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 stress concentration region identification uses an improved hot spot analysis method. The spatial autocorrelation index of the stress field is constructed: , 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: , the high-stress aggregation region under the statistical significance level is identified. At the same time, the stress gradient tensor is introduced to evaluate the severity of stress change. When (σ0 is the material strength design value, and λ is the safety factor), this region is marked 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 is less than 8%.

[0105] In some embodiments, in the recording 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 a 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 herein.

[0106] 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; and an early warning generation module 321, configured to generate early warning information based on the structural deformation trend curve.

[0107] Wherein, 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; and the structural deformation trend curve refers to a time series curve describing the deformation development law.

[0108] 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 to construct 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; uses time series prediction methods 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.

[0109] In some embodiments, the deformation trend analysis and early warning can be realized in various ways: Optionally, the deformation monitoring device 10 uses a deep learning method 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 realize the 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 realize the dynamic assessment of the risk level. It can be understood that other analysis methods can also be used to realize the prediction and early warning of the deformation trend, which is not limited here.

[0110] 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 includes 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 is composed of multiple LSTM units. The hidden state dimension of each LSTM layer is 256, and the long-term and short-term dependence relationships are learned through a gating mechanism. To improve the ability to capture key time series patterns, a multi-head attention mechanism is introduced: , 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: , 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.

[0111] 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, the electromagnetic wave propagation characteristics under complex terrain conditions can be accurately mastered, and accurate displacement measurement can be realized. 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 further 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 and provide a reliable guarantee for the safe operation of the power grid.

[0112] The following describes the deformation monitoring device in the embodiments of the present invention application from the perspective of hardware processing. 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.

[0113] 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.

[0114] 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 performing 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.

[0115] 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. The 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.

[0116] 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, which 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 through 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.

[0117] 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.

[0118] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; 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.

[0119] As used 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 "when determining..." or "if detecting (the stated condition or event)" can be interpreted to mean "if determining...", or "in response to determining...", or "when detecting (the stated condition or event)", 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 comprises: A map construction module is used to construct a regional digital map including terrain elevation distribution and electromagnetic wave attenuation based on the terrain surveying and mapping data of the monitoring area, tower structural parameters and meteorological parameters; A boundary determination module, used to calculate the signal strength 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 determine the near field zone boundary; A signal acquisition module, used to take the area within the boundary of the near-field zone as the near-field zone, and acquire the near-field echo signal of the near-field zone through a narrow beam, and take the area outside the boundary of the near-field zone as the far-field zone, and acquire the far-field echo signal of the far-field zone through a wide beam; A calibration table building module, used to obtain the calibration signal of the standard reflector array, determine the paths of the direct wave and the reflected wave in combination with the regional digital map, and build a signal strength comparison table; A data processing module, used 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 is used to perform error calculation on the effective monitoring data to obtain a phase error and an amplitude error; The parameter determination module is used to correct the effective monitoring data according to the phase error and the amplitude error to obtain the displacement of the monitoring point, and determine the deformation parameter of the tower based on the displacement 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 determines point cloud data based on the scanning data of the three-dimensional laser and generates a terrain elevation scattered point array; A grid division unit, which performs grid division on the terrain elevation scattered point array based on a preset grid resolution to obtain a regional terrain elevation matrix; A parameter mapping unit maps the tower structure parameters to the regional terrain elevation matrix to obtain the tower structure grid coordinates; The attenuation calculation unit 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; The map generating unit superimposes the grid coordinates of the tower structure with the electromagnetic wave propagation attenuation coefficient to generate a digital map of the monitoring area.

3. The deformation monitoring device according to claim 1, characterized in that: The data processing module specifically includes: A beamforming unit, used for performing beamforming on the near-field echo signal to obtain near-field phase correction data; an amplitude compensation unit, used to perform amplitude compensation on the far-field echo signal to obtain far-field amplitude correction data; A signal accumulation unit, used for coherently accumulating the near-field phase correction data and the far-field amplitude correction data to obtain a signal accumulation matrix; The data screening unit is used to compare the signal accumulation matrix with the standard data in the signal strength comparison table to generate signal deviation data, and screen 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 also includes: An array calibration module, used to receive beam scanning data of the antenna array and obtain antenna unit coordinates; A parameter calculation module, used to calculate the phase center coordinates according to the antenna unit position data and determine the array calibration data; A sequence generation module, used for importing the array calibration parameters into a beam control module to generate a beam scanning sequence; A range determination module is used to determine the monitoring scanning range according to the beam scanning sequence.

5. The deformation monitoring device according to claim 4, characterized in that: The array calibration module specifically includes: A signal acquisition unit, used to receive the corner reflector echo signal, generate a reference waveform data group, and process the reference waveform data group using time-frequency transformation to obtain a signal amplitude spectrum and a phase spectrum; A spectrum analysis unit, configured to calculate array element amplitude and phase distribution according to the signal amplitude spectrum and the phase spectrum, generate an array element distribution matrix, and process the array element distribution matrix using phase compensation to obtain relative positions of antenna array elements; The coordinate calculation unit is used to map the relative position of the antenna array element to a coordinate system to obtain the antenna unit coordinates.

6. The deformation monitoring device according to claim 1, characterized in that: The deformation monitoring device also includes: The vector field generation module is used to calculate the three-dimensional coordinate change of each monitoring point and generate a displacement vector field; A stress analysis module, used to calculate the deformation parameters of the tower structure according to the displacement vector field to obtain a stress distribution diagram; A region identification module, used to superimpose the stress distribution map with structural parameters to determine the stress concentration region; The recording and storage module is used to collect monitoring data of the stress concentration area at a preset period, construct a time series record, and store the time series record in a deformation feature database.

7. The deformation monitoring device according to claim 6, characterized in that: The deformation monitoring device also includes: A projection map construction module, used to construct a deformation vector projection map based on the spatial coordinate sequence in the deformation feature database; An acceleration calculation module, used for calculating the displacement acceleration of the monitoring point according to the deformation vector projection diagram; A trend analysis module, used for superimposing the displacement acceleration with the stress distribution diagram to obtain a stress change rate, and determining a structural deformation trend curve according to the stress change rate; The warning generation module is used to generate warning information based on the structural deformation trend curve.

8. A method for monitoring deformation of a transmission tower based on phased array radar technology, characterized in that: Applied to a deformation monitoring device, the method comprises: Based on the topographic surveying data, tower structural parameters and meteorological parameters of the monitored area, a regional digital map containing the terrain elevation distribution and electromagnetic wave attenuation is constructed; Based on the terrain elevation distribution and the electromagnetic wave attenuation, the signal strength attenuation rate and phase delay of the regional digital map in each monitoring direction are calculated to determine the near field zone boundary; The area within the boundary of the near-field area is taken as the near-field area, and a near-field echo signal of the near-field area is obtained by a narrow beam, and the area outside the boundary of the near-field area is taken as the far-field area, and a far-field echo signal of the far-field area is obtained by a wide beam; Acquire the calibration signal of the standard reflector array, determine the paths of the direct wave and the reflected wave in combination with the digital map of the area, and construct a signal strength 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 strength comparison table to obtain effective monitoring data; Performing error calculation on the effective monitoring data to obtain a phase error and an amplitude error; The effective monitoring data is corrected according to the phase error and the amplitude error to obtain the displacement of the monitoring point, and the deformation parameter of the tower is determined based on the displacement of the monitoring point.

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

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

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