A highway surveying radar measurement system based on electromagnetic waves

Abstract

The application discloses a kind of highway survey radar measurement systems based on electromagnetic wave, through quantum magnetic force gradient tensor analysis and space coordinate calibration, synthetic aperture radar echo feature extraction module cooperation, using quantum magnetic force navigation model and synthetic aperture radar algorithm, realize multi-source data space-time registration and weighted fusion, construct highway geology layered model and inversion geological parameter, after grid processing of fusion data, generate three-dimensional visual model.The system overcomes the defects of traditional technology multi-source data fusion deficiency and low precision of geological parameter inversion, improves the precision, efficiency and reliability of highway survey, provides accurate geological information and visual survey results for highway engineering design and construction, has important application value in the field of highway survey.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of highway surveying, and in particular to a highway surveying radar measurement system based on electromagnetic waves. BACKGROUND

[0002] With the continuous expansion of highway construction scale, higher requirements are put forward for the precision, efficiency and comprehensiveness of highway surveying. Traditional surveying methods rely on manual measurement and simple instruments, which are difficult to meet the surveying needs under complex geological conditions. Highway surveying radar measurement technology based on electromagnetic waves emerges as the times require, which obtains underground information by transmitting and receiving electromagnetic waves, and gradually gets application in the field of highway surveying. However, the existing highway surveying radar measurement technology based on electromagnetic waves still has many deficiencies in data processing and model construction.

[0003] On the one hand, the existing technology has defects in data fusion processing. Most systems cannot effectively integrate multi-source information such as quantum magnetic force data and radar echo data, each data is processed independently, and there is a lack of precise alignment and weighted fusion mechanism in time and space dimensions, resulting in scattered information and difficulty in forming comprehensive and accurate surveying results. On the other hand, in the process of highway geological layer parameter inversion and model construction, the traditional method fails to fully utilize the synergistic advantages of quantum magnetic force navigation model and synthetic aperture radar algorithm model. The model construction process often relies on single data or simple algorithms, and cannot accurately adjust the geological layering model through iterative optimization, making it difficult to accurately invert key parameters such as geological layer thickness and dielectric constant, greatly affecting the accuracy and reliability of highway surveying. SUMMARY

[0004] In order to overcome the shortcomings and deficiencies of the existing technology, the present application provides a highway surveying radar measurement system based on electromagnetic waves.

[0005] The technical scheme adopted by the present application is a highway surveying radar measurement system based on electromagnetic waves, comprising:

[0006] A quantum magnetic force gradient tensor analysis and spatial coordinate calibration module, which is used to collect quantum magnetic force gradient tensor data, analyze magnetic field characteristic information through tensor matrix operation, and perform spatial coordinate calibration through fusion with satellite positioning data;

[0007] A synthetic aperture radar echo signal multi-dimensional feature extraction module, which performs time-frequency domain transformation on the received radar echo signal, and extracts multi-dimensional feature information including amplitude, phase and frequency modulation;

[0008] A highway geological layering parameter inversion and model construction module, which constructs a highway geological layering model through an iterative optimization algorithm based on quantum magnetic force navigation data and synthetic aperture radar feature information, and inverts geological layer thickness and dielectric constant parameters;

[0009] The multi-source data space-time registration and fusion processing module performs space-time dimension alignment on quantum magnetic force data, radar echo data and auxiliary data, and generates fusion data by using a weighted fusion strategy;

[0010] The survey target three-dimensional visualization modeling and analysis module performs grid processing on the fusion data, constructs a highway survey target three-dimensional model by using volume rendering technology, and performs spatial analysis;

[0011] The system control and data management module is responsible for instruction scheduling, data transmission control and data storage management of the above modules,

[0012] The output end of the quantum magnetic gradient tensor analysis and spatial coordinate calibration module is connected with the input end of the synthetic aperture radar echo signal multi-dimensional feature extraction module, the output end of the synthetic aperture radar echo signal multi-dimensional feature extraction module is connected with the input end of the highway geological layering parameter inversion and model construction module, the output ends of the highway geological layering parameter inversion and model construction module and the quantum magnetic gradient tensor analysis and spatial coordinate calibration module are connected with the input end of the multi-source data space-time registration and fusion processing module, the output end of the multi-source data space-time registration and fusion processing module is connected with the input end of the survey target three-dimensional visualization modeling and analysis module, and the system control and data management module is bidirectionally connected with the above modules.

[0013] Further, in the quantum magnetic gradient tensor analysis and spatial coordinate calibration module, a quantum magnetic navigation model is used to construct the correlation between the quantum magnetic gradient tensor and the spatial coordinate, and the construction formula is: wherein, represents the calibrated spatial coordinate vector, G is a quantum magnetic gradient tensor-coordinate conversion matrix, and the elements of G are determined by the geomagnetic background field and the gravity field coupling coefficient of the highway survey area; is a quantum magnetic gradient tensor data vector, which contains six independent gradient components; is a calibration error vector, in the synthetic aperture radar echo signal multi-dimensional feature extraction module, a synthetic aperture radar algorithm model is used to construct an echo signal feature extraction formula: wherein, F is a multi-dimensional feature matrix extracted, is a time-frequency domain transformation operator, S(t, f) is a time-frequency distribution matrix of the radar echo signal, t represents time, and f represents frequency; W is a feature weighting matrix, and the weight coefficient is dynamically adjusted according to the highway pavement material and humidity survey parameters, represents Hadamard product operation.

[0014] Further, in the highway geological layering parameter inversion and model construction module, a geological layering parameter inversion formula is constructed based on the quantum magnetic navigation model and the synthetic aperture radar algorithm model: wherein, is the geological layering parameter vector obtained by inversion, containing geological layer thickness, dielectric constant parameters; P is the parameter vector to be inverted; D m , D r are respectively the forward operator of quantum magnetic force data and synthetic aperture radar data, determined by the prior model of highway geological structure; M o , R o are respectively the quantum magnetic force measurement data vector and the synthetic aperture radar measurement data vector; λ1, λ2 are weight coefficients, set according to the highway survey depth and geological complexity parameters.

[0015] Further, in the multi-source data space-time registration and fusion processing module, a space-time registration formula is constructed based on the quantum magnetic navigation model: wherein, is the space-time registration formula, T q is the space-time conversion matrix of quantum magnetic navigation, wherein the elements are determined by the space-time reference parameters of the highway survey area; is the original multi-source data space coordinate vector; is the registration offset vector, and a data fusion formula is constructed based on the synthetic aperture radar algorithm model: wherein, Y is the fused data matrix, w i is the fusion weight of the i-th type of data, determined according to the highway survey data type and data quality parameters; Y i is the i-th type of original data matrix, and n is the total number of data types.

[0016] Further, in the survey target three-dimensional visualization modeling and analysis module, a three-dimensional model geometric constraint formula is constructed based on the quantum magnetic navigation model: wherein, V g is the three-dimensional model volume data containing geometric constraints, is a geometric constraint construction operator, which constructs the model geometric topology according to the quantum magnetic data and the calibrated space coordinates; a three-dimensional model texture mapping formula is constructed based on the synthetic aperture radar algorithm model: wherein, V t is the final three-dimensional visualization model volume data, is a texture mapping operator, which maps the extracted radar echo multi-dimensional features to the surface of the three-dimensional model to form a visual texture.

[0017] Further, in the system control and data management module, a module scheduling strategy formula is constructed based on the quantum magnetic navigation model: wherein, S is the module scheduling strategy matrix, The scheduling policy generation operator generates a scheduling policy according to quantum magnetic force data and a module task queue Q; a data storage optimization formula is constructed based on a synthetic aperture radar algorithm model: Wherein, D s is an optimized data storage structure, is a storage optimization operator, which performs data storage structure optimization according to synthetic aperture radar measurement data and a storage strategy S s .

[0018] Further, the highway geological layering parameter inversion and model construction module comprises: a geological layer interface identification unit, which determines the interface position of the highway geological layer by analyzing the gradient mutation of the quantum magnetic force data and the phase jump of the synthetic aperture radar echo signal; a geological parameter initial value estimation unit, which preliminarily estimates the geological layer thickness and dielectric constant parameters by using the statistical characteristics of the quantum magnetic force data and the backscattering coefficient of the synthetic aperture radar; a model iterative optimization unit, which iteratively optimizes the geological layering model by using a synthetic aperture radar inversion algorithm based on quantum magnetic force navigation constraints to reduce the error between the model and the measured data; and a model verification unit, which verifies the accuracy and reliability of the model by comparing the known geological drilling data with the constructed geological layering model.

[0019] Further, the multi-source data space-time registration and fusion processing module comprises: a time synchronization unit, which aligns the time stamps of the radar echo data and auxiliary measurement data by using the high-precision time reference of quantum magnetic force navigation; a spatial coordinate conversion unit, which converts the multi-source data in different coordinate systems to a unified spatial coordinate system by using the quantum magnetic force gradient tensor-coordinate conversion relationship; a data quality evaluation unit, which evaluates the quality of the quantum magnetic force data and the synthetic aperture radar data according to the signal-to-noise ratio and integrity index of the data; and a weighted fusion execution unit, which performs weighted fusion processing on the quality-evaluated multi-source data according to the set fusion weight.

[0020] Further, the survey target three-dimensional visualization modeling and analysis module comprises: a data gridding unit, which performs spatial gridding processing on the fused multi-source data to generate regular three-dimensional data grids; a volume rendering preprocessing unit, which performs filtering and interpolation preprocessing operations on the gridded data to improve the volume rendering effect; a three-dimensional model rendering unit, which renders the three-dimensional data grids according to the model information constructed by the quantum magnetic force data and the synthetic aperture radar features by using a ray casting algorithm; and a spatial analysis calculation unit, which performs distance measurement, volume calculation, and profile analysis spatial analysis operations on the three-dimensional visualization model.

[0021] Beneficial Effects: This invention proposes a radar measurement system for highway surveying based on electromagnetic waves. This system achieves precise alignment of multi-source information, such as quantum magnetic data and radar echo data, in the spatiotemporal dimension. By assessing the quality of different types of data and setting fusion weights according to the actual needs of highway surveying, a weighted fusion strategy is adopted to integrate scattered multi-source data into a comprehensive and accurate dataset, avoiding the deviation in survey results caused by isolated information processing. In terms of highway geological stratification parameter inversion and model construction, the limitations of traditional single data or simple algorithms are abandoned. The system comprehensively utilizes quantum magnetic navigation data and synthetic aperture radar feature information. First, the location of geological layer interfaces is determined by analyzing data characteristics. Then, the initial values ​​of geological parameters are estimated by combining multiple data. Subsequently, iterative optimization algorithms are used to repeatedly adjust the geological stratification model. This approach can deeply mine the information contained in the data and accurately invert key parameters such as geological layer thickness and dielectric constant, greatly improving the accuracy of highway geological surveying. In addition, the system can also construct an intuitive three-dimensional visualization model from the fused data and perform spatial analysis, providing a more comprehensive and intuitive decision-making basis for highway surveying, significantly improving the efficiency and reliability of highway surveying. Attached Figure Description

[0022] Figure 1 This is a diagram showing the system module composition of the present invention;

[0023] Figure 2 This is a flowchart illustrating the system operation of the present invention. Detailed Implementation

[0024] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] like Figure 1 As shown, a highway survey radar measurement system based on electromagnetic waves includes:

[0026] The quantum magnetic gradient tensor analysis and spatial coordinate calibration module is used to collect quantum magnetic gradient tensor data, analyze magnetic field characteristic information through tensor matrix operations, and fuse it with satellite positioning data to perform spatial coordinate calibration.

[0027] Specifically, the quantum magnetic gradient tensor analysis and spatial coordinate calibration module plays a fundamental role in electromagnetic wave-based highway survey radar measurement systems, serving as a crucial link to ensure the accuracy of subsequent data processing. The core function of this module focuses on the acquisition and analysis of quantum magnetic gradient tensor data. The acquired data encompasses six independent gradient components, enabling precise capture of subtle changes in the magnetic field of the measurement area. Technically, the quantum magnetic sensor used possesses extremely high resolution, typically reaching the picotesla level, allowing it to detect extremely weak magnetic field fluctuations. In terms of significance, through in-depth analysis of these magnetic field characteristics and fusion with satellite positioning data, high-precision spatial coordinate calibration can be achieved. In highway surveying, accurate spatial coordinates are fundamental for constructing geological models and analyzing underground structures. If coordinates are inaccurate, all subsequent data-based analysis and modeling will accumulate errors, severely impacting the reliability of the survey results. This module, by calibrating spatial coordinates, provides a unified and accurate spatial benchmark for multi-source data fusion, ensuring the accuracy of the entire measurement system in determining the location of the survey target.

[0028] In the implementation process, an array of multiple high-precision quantum magnetic sensors was first deployed. The sensor layout was rationally planned based on the scope and terrain characteristics of the highway survey area to ensure comprehensive coverage of the target area. The sensor array continuously collected quantum magnetic gradient tensor data. During the acquisition process, parameters such as the sampling frequency and acquisition duration were precisely set to ensure the acquisition of complete and effective magnetic field information. Simultaneously, a satellite positioning system was connected to receive centimeter-level precision positioning data. Subsequently, the collected quantum magnetic gradient tensor data was input into a powerful processing unit. Using specific tensor matrix operation rules, the data underwent deep analysis to extract key information such as magnetic field strength and gradient direction. Based on this, a pre-constructed magnetic field-coordinate transformation model was used to fuse the analyzed magnetic field information with the satellite positioning data. Through a complex coordinate transformation algorithm, the spatial coordinates of the quantum magnetic data were calibrated to a coordinate system consistent with the satellite positioning, completing the spatial coordinate calibration. For example, in a mountainous highway survey project, this module successfully reduced the spatial coordinate error from the initial level of several meters to the level of centimeters by processing quantum magnetic data and satellite positioning data, laying a solid foundation for subsequent survey work.

[0029] A multi-dimensional feature extraction module for synthetic aperture radar echo signals is used to perform time-frequency domain transformation on the received radar echo signals and extract multi-dimensional feature information including amplitude, phase, and frequency modulation.

[0030] Specifically, the synthetic aperture radar echo signal multidimensional feature extraction module is the core component of the system for in-depth mining and analysis of radar echo signals. Its functionality directly affects the accuracy of underground target identification and analysis during highway surveys. The core task of this module is to perform time-frequency domain transformation on the received radar echo signal, extracting multidimensional feature information including amplitude, phase, and frequency modulation. In terms of technical parameters, the radar signal receiving device used has a high sampling frequency, typically reaching several GHz, ensuring complete acquisition of high-frequency information from the radar echo signal and avoiding information loss. This multidimensional feature information contains rich characteristics of the target object. Amplitude features reflect the intensity of electromagnetic wave reflection by the underground medium, allowing for a preliminary judgment of the medium's properties, such as rock, soil, or water. Phase features carry crucial information about the distance and structure of the medium, helping to determine the spatial location and geometry of the target object. Frequency modulation features can be used to analyze the dynamic changes of the medium, such as the presence of fluid movement. By extracting these multidimensional features, a detailed and crucial data foundation can be provided for subsequent inversion of highway geological stratification parameters and model construction. This plays an irreplaceable role in identifying adverse geological structures such as underground cavities and faults during highway surveying.

[0031] In terms of implementation, a high-performance radar signal receiving device is first configured. Based on the actual needs of highway surveying, parameters such as the radar's transmission frequency and pulse width are adjusted to ensure effective detection of underground information within the target depth range. The receiving device receives radar echo signals in real time and transmits them to the signal processing unit. In the signal processing unit, mature time-frequency analysis algorithms, such as short-time Fourier transform or wavelet transform, are used to transform the radar echo signal in the time domain, generating a two-dimensional time-frequency distribution matrix containing time and frequency information. This transformation process clearly presents signal features that were originally difficult to distinguish in the time domain in the form of a time-frequency distribution. Next, specific feature extraction algorithms are used on the time-frequency distribution matrix to extract features such as amplitude, phase, and frequency modulation. During the extraction process, reasonable thresholds and window sizes are set to ensure accurate extraction of effective features. These features are then quantified and encoded, transforming them into multi-dimensional feature information data that can be recognized and processed by a computer. Finally, the processed multi-dimensional feature information data is output to subsequent modules to support highway geological analysis. For example, in a highway survey project in a plain area, this module successfully extracted multi-dimensional feature information from radar echo signals, clearly identified the interface locations of different underground geological layers and potential underground cavity areas, providing an important basis for subsequent engineering design.

[0032] The highway geological stratification parameter inversion and model building module, based on quantum magnetic navigation data and synthetic aperture radar feature information, constructs a highway geological stratification model through iterative optimization algorithms, and inverts geological layer thickness and dielectric constant parameters.

[0033] Specifically, the highway geological stratification parameter inversion and model building module is one of the core functional modules of the entire highway survey radar measurement system. Its main function is to construct an accurate highway geological stratification model based on the quantum magnetic navigation data and synthetic aperture radar characteristic information acquired by the preceding modules, and to invert key parameters such as geological layer thickness and dielectric constant. In highway engineering construction, accurately understanding the geological stratification and related parameters is a prerequisite for rational design and construction. The thickness of the geological layer determines the construction depth and method of the foundation engineering, while parameters such as the dielectric constant affect the selection of materials and the determination of construction techniques. Failure to accurately obtain these parameters may lead to serious problems such as unstable highway foundations and reduced durability. This module, by constructing a reliable geological stratification model, provides detailed underground geological structure information for highway engineering, helping engineers to predict construction risks in advance, optimize design schemes, and ensure the quality and safety of highway engineering.

[0034] In the specific implementation process, the statistical characteristics of quantum magnetic data and the backscattering coefficient of synthetic aperture radar (SAR) are first used to make preliminary estimates of parameters such as geological layer thickness and dielectric constant. During the estimation process, based on a pre-established empirical relationship model between geological parameters, quantum magnetic data, and SAR backscattering coefficient, combined with actual measurement data, statistical analysis methods are used to derive initial values ​​for the geological parameters. Then, an iterative optimization algorithm is used to construct and adjust the geological stratification model. Gradient mutations in quantum magnetic data and phase jumps in SAR echo signals are used as constraints and incorporated into the model construction process. In each iteration, the quantum magnetic data and SAR echo data predicted by the current model are compared with the actual measurement data, and the error between the two is calculated. Based on the magnitude of the error, a specific optimization algorithm is used to correct the model parameters, continuously adjusting the structure and parameter values ​​of the geological stratification model. After multiple iterations, the error between the model output data and the measured data is minimized, thus constructing a highway geological stratification model that conforms to the actual geological conditions and completing the geological parameter inversion work. For example, in a highway survey project in a hilly area, this module successfully constructed an accurate geological stratification model through multiple iterations and optimizations. It accurately reflected parameters such as the thickness and dielectric constant of each geological layer, providing scientific and reliable data support for subsequent roadbed design and construction, and effectively avoiding engineering risks caused by unclear geological conditions.

[0035] The multi-source data spatiotemporal registration and fusion processing module aligns quantum magnetic data, radar echo data, and auxiliary data in spatiotemporal dimensions and generates fused data using a weighted fusion strategy.

[0036] Specifically, the multi-source data spatiotemporal registration and fusion processing module plays a crucial role in data integration and optimization in electromagnetic wave-based highway survey radar measurement systems. Since the system collects data from quantum magnetics measurements, synthetic aperture radar (SAR) detection, and other auxiliary measurement equipment, these data differ in spatiotemporal dimensions. Without processing, they cannot be effectively fused and utilized. The core task of this module is to resolve the inconsistencies between multi-source data in time and space. Through spatiotemporal registration, data collected from different sources and at different times are unified under the same spatiotemporal reference. Then, using a weighted fusion strategy, appropriate weights are assigned according to the importance and reliability of each data point, fusing the multi-source data into a complete and accurate dataset. In highway surveying, accurate fusion of multi-source data can eliminate contradictions and redundancies between data, providing more comprehensive and accurate underground information. This provides high-quality data support for subsequent 3D visualization modeling and analysis of survey targets, improving the efficiency and accuracy of the entire survey work.

[0037] In the specific implementation process, the high-precision time base of quantum magnetic navigation is first used as a unified time reference. Quantum magnetic data, radar echo data, and other auxiliary data are timestamped, and time synchronization algorithms are used to calibrate all data to ensure consistency across the time dimension. For spatial coordinate registration, a unified spatial coordinate system transformation model is established based on the transformation relationship between the quantum magnetic gradient tensor and coordinates. This model is used to transform the coordinates of multi-source data from different coordinate systems to the unified spatial coordinate system. In the data fusion stage, a specialized evaluation algorithm is designed based on factors such as the type of highway survey data and data quality. This algorithm evaluates each type of data from multiple dimensions, including signal-to-noise ratio, completeness, and accuracy, and assigns corresponding fusion weights to each type of data based on the evaluation results. Finally, the registered data are weighted and summed according to the set weights to fuse the multi-source data into a new dataset. For example, in a city road survey project, this module successfully performed spatiotemporal registration and fusion processing on quantum magnetic data, radar echo data, and geological drilling auxiliary data, eliminating contradictions and redundancies between data. The generated fused dataset provided an accurate data foundation for subsequent 3D visualization modeling, enabling engineers to have a more intuitive and comprehensive understanding of underground geological conditions and providing strong support for the formulation of road design schemes.

[0038] The 3D visualization modeling and analysis module for survey targets will process the fused data into a grid, use volume rendering technology to construct a 3D model of the highway survey target, and perform spatial analysis.

[0039] Specifically, the 3D visualization modeling and analysis module for survey targets is a crucial step in transforming abstract survey data into intuitive visual results, possessing significant practical value in highway surveying work. Its main function is to convert multi-source data fusion processing into an intuitive 3D visualization model and perform spatial analysis operations on the model. In the field of highway engineering, traditional two-dimensional data display methods are insufficient to fully present the spatial distribution and complex morphology of underground geological structures. 3D visualization models, however, can display the geological structure and spatial distribution of target objects in a three-dimensional and intuitive manner, enabling engineers to understand the underground conditions more clearly and accurately, facilitating engineering design and decision-making. Simultaneously, the spatial analysis function allows for distance measurement, volume calculation, and profile analysis of the model, further uncovering the information behind the data and providing more comprehensive technical support for highway engineering. This helps identify potential geological problems, optimize engineering plans, and improve the scientific rigor and rationality of engineering construction.

[0040] In the specific implementation process, the fused data is first processed into a grid. Based on the data distribution range, accuracy requirements, and computer processing capabilities, the space is divided into a regular three-dimensional grid. Parameters such as grid size and spacing are determined to ensure that each grid cell accurately corresponds to a data value, thus discretizing the continuous exploration data for easier computer processing. Next, the gridded data undergoes preprocessing operations such as filtering and interpolation. Appropriate filtering algorithms are used to remove noise interference and improve data smoothness; interpolation algorithms are used to supplement missing or sparse areas of data, enhancing accuracy and providing high-quality data for volume rendering. Then, volume rendering techniques, such as ray casting algorithms, are used to calculate the propagation path and color of light in three-dimensional space based on the data's attribute values. During the calculation process, corresponding color and transparency parameters are set according to the attribute characteristics of different geological layers. By simulating the interaction between light and the data volume, a realistic three-dimensional visualization model is constructed. Finally, professional spatial analysis software is used to perform various spatial analysis operations on the three-dimensional model. For example, the distance measurement function can be used to measure the distance between different geological interfaces, the volume calculation function can be used to calculate the volume of underground cavities or specific geological bodies, and the profile analysis function can be used to view the distribution of geological structures on specific profiles. In a highway survey project, this module successfully constructed a high-precision 3D visualization model and discovered a potential underground karst cave through spatial analysis, providing important basis for adjusting the engineering design scheme and avoiding safety hazards and economic losses in later construction.

[0041] The system control and data management module is responsible for command scheduling, data transmission control, and data storage management for the aforementioned modules.

[0042] Specifically, the system control and data management module, as the core hub of the entire electromagnetic wave-based highway survey radar measurement system, undertakes crucial management and coordination responsibilities. Its main function is to schedule commands, control data transmission, and manage data storage for other modules within the system, ensuring that all modules operate collaboratively according to predetermined workflows and sequences, avoiding data transmission conflicts and module malfunctions. Simultaneously, since the system generates a large amount of data during operation, this module needs to effectively store and manage this data, ensuring its security and traceability, facilitating subsequent data retrieval, analysis, and reuse. In highway surveying work, efficient system control and data management can improve the efficiency and accuracy of surveying work, ensure the integrity and reliability of survey data, and provide strong data support for highway engineering planning, design, and construction.

[0043] In the specific implementation process, the system control and data management module first formulates a detailed module scheduling strategy based on the needs of the highway survey task and the working status of each module. By monitoring the operating status of each module in real time, including data processing progress and resource usage, control commands are sent to each module according to pre-set priorities and workflows to coordinate the start, stop, and data transmission operations of each module. Regarding data transmission control, high-speed and reliable data transmission protocols, such as an optimized version of the TCP / IP protocol, are used to package, verify, and transmit data. During data packaging, data is classified and encapsulated according to its type and purpose, and necessary metadata information is added to facilitate parsing by the receiver. During data transmission, a verification mechanism ensures the integrity and accuracy of the data; if a data transmission error is detected, a retransmission operation is immediately performed. For data storage management, a dedicated database system is established, and data is classified and stored according to data type and purpose. For example, quantum magnetic data, radar echo data, and geological stratification model data are stored in different database tables, with corresponding indexes and relationships established to improve data query efficiency. Simultaneously, data backup and recovery technologies are employed, and the database is backed up regularly to ensure data security. In addition, it provides a data query interface, allowing users to retrieve and call stored data based on various criteria, facilitating the analysis and reuse of historical data by engineers. For example, in a large-scale highway survey project, this module, through effective command scheduling and data management, ensured the efficient collaborative work of all modules in the system, while also guaranteeing the secure storage and convenient retrieval of a large amount of survey data, providing a solid guarantee for the smooth progress of the project.

[0044] The output of the quantum magnetic gradient tensor analysis and spatial coordinate calibration module is connected to the input of the synthetic aperture radar echo signal multidimensional feature extraction module. The output of the synthetic aperture radar echo signal multidimensional feature extraction module is connected to the input of the highway geological stratification parameter inversion and model building module. The outputs of the highway geological stratification parameter inversion and model building module and the quantum magnetic gradient tensor analysis and spatial coordinate calibration module are both connected to the input of the multi-source data spatiotemporal registration and fusion processing module. The output of the multi-source data spatiotemporal registration and fusion processing module is connected to the input of the exploration target three-dimensional visualization modeling and analysis module. The system control and data management module is bidirectionally connected to each of the above modules.

[0045] Preferably, in the quantum magnetic gradient tensor analysis and spatial coordinate calibration module, a quantum magnetic navigation model is used to construct the correlation between the quantum magnetic gradient tensor and spatial coordinates, and the construction formula is as follows: in, denoted as the calibrated spatial coordinate vector, G is the quantum magnetic gradient tensor-coordinate transformation matrix, where the elements are determined by the coupling coefficients of the geomagnetic background field and the gravitational field in the highway survey area; This is a quantum magnetic gradient tensor data vector containing six independent gradient components; To calibrate the error vector, the synthetic aperture radar echo signal multi-dimensional feature extraction module constructs an echo signal feature extraction formula based on the synthetic aperture radar algorithm model: Where F is the extracted multidimensional feature matrix. Here, S(t, f) is the time-frequency domain transform operator, where t represents time and f represents frequency; W is the characteristic weighting matrix, and the weighting coefficients are dynamically adjusted according to the road surface material and humidity survey parameters. This represents the Hadamard product operation.

[0046] Specifically, in the quantum magnetic gradient tensor analysis and spatial coordinate calibration module, the correlation between the quantum magnetic gradient tensor and spatial coordinates is constructed. This is significant because it provides a precise mathematical basis for spatial coordinate calibration, enabling the calibrated spatial coordinates to more accurately reflect the actual location of the survey target. By introducing a quantum magnetic gradient tensor-coordinate transformation matrix, whose elements are determined by the coupling coefficients of the geomagnetic background field and the gravitational field of the highway survey area, high-precision coordinate calibration is ensured under different geological environments. In implementation, gradient tensor data is first collected using a high-precision quantum magnetic sensor, and satellite positioning data is simultaneously acquired. Both are input into the computing unit, and the transformation matrix is ​​calculated based on the correlation model to complete the coordinate calibration. In the synthetic aperture radar echo signal multidimensional feature extraction module, the echo signal feature extraction formula, constructed based on the synthetic aperture radar algorithm model, extracts multidimensional features of the radar echo signal through a time-frequency domain transformation operator and a feature weighting matrix. The weight coefficients of the feature weighting matrix are dynamically adjusted according to survey parameters such as highway pavement material and humidity, effectively enhancing the targeting and accuracy of feature extraction. The process involves receiving radar echo signals, performing time-frequency domain transformation, and then combining the weighted matrix to extract multi-dimensional feature information such as amplitude and phase, providing a rich data foundation for subsequent geological analysis.

[0047] Preferably, in the highway geological stratification parameter inversion and model building module, the geological stratification parameter inversion formula is constructed based on the quantum magnetic navigation model and the synthetic aperture radar algorithm model: in, P is the geological layering parameter vector obtained from the inversion, containing geological layer thickness and dielectric constant parameters; D is the parameter vector to be inverted; m D r The forward modeling operators for quantum magnetic data and synthetic aperture radar data, respectively, are determined by a priori models of highway geological structures; M o R o These are the quantum magnetic field measurement data vector and the synthetic aperture radar measurement data vector, respectively; λ1 and λ2 are weighting coefficients, set according to parameters such as highway survey depth and geological complexity.

[0048] Specifically, the geological stratification parameter inversion formula, built based on the quantum magnetic navigation model and synthetic aperture radar (SAR) algorithm model, plays a crucial role in highway surveying. This formula achieves accurate inversion of geological stratification parameters by minimizing the error between the forward modeling results of quantum magnetic data and SAR data and the actual measurement data. The forward modeling operator is determined by a priori model of the highway geological structure, and the weighting coefficients are set according to parameters such as highway survey depth and geological complexity. This design fully considers the characteristics of different survey scenarios, improving the reliability of the inversion results. In terms of implementation, initial geological parameter values ​​are first estimated based on the statistical characteristics of quantum magnetic data and the backscattering coefficient of SAR. These values ​​are then substituted into the formula for forward modeling to obtain theoretical quantum magnetic data and radar echo data. Next, the theoretical data is compared with the actual measurement data, the error value is calculated, and the geological parameters are adjusted through optimization algorithms. This process is iterated until the error is minimized, ultimately constructing an accurate highway geological stratification model and providing reliable geological parameter basis for highway engineering design.

[0049] Preferably, in the multi-source data spatiotemporal registration and fusion processing module, a spatiotemporal registration formula is constructed based on the quantum magnetic navigation model: in, T represents the spatial coordinate vector of the registered multi-source data. q This is the spatiotemporal transformation matrix for quantum magnetic navigation, where the elements are determined by the spatiotemporal reference parameters of the highway survey area; The original multi-source data spatial coordinate vector; To register the offset vector, a data fusion formula is constructed based on the synthetic aperture radar algorithm model: Where Y is the fused data matrix, w i The fusion weight for the i-th type of data is determined based on the type of highway survey data and data quality parameters; Y i Let n be the original data matrix of the i-th type, and n be the total number of data types.

[0050] Specifically, the spatiotemporal registration formula based on the quantum magnetic navigation model achieves precise registration of multi-source data in spatial coordinates through a quantum magnetic navigation spatiotemporal transformation matrix. The elements of the transformation matrix are determined by the spatiotemporal reference parameters of the highway survey area, ensuring the consistency of data from different sources in spatial dimensions. The data fusion formula based on the synthetic aperture radar algorithm model weights the multi-source data by setting fusion weights. These weights are determined based on parameters such as the type and quality of the highway survey data, effectively eliminating contradictions and redundancy between data. In implementation, the multi-source data is first synchronized using the quantum magnetic navigation time reference, and then the spatiotemporal registration formula is used to transform the data to a unified spatial coordinate system. Subsequently, a data quality assessment algorithm determines the fusion weights of each data point. Finally, the registered data is weighted and summed according to the fusion formula to generate a high-quality fused dataset, providing reliable data support for subsequent 3D visualization modeling and analysis of the survey target.

[0051] Preferably, in the three-dimensional visualization modeling and analysis module of the exploration target, the geometric constraint formula of the three-dimensional model is constructed based on the quantum magnetic navigation model: Among them, V g For 3D model volume data containing geometric constraints, Operators are constructed to define geometric constraints, and the geometric topological relationships of the model are established based on quantum magnetic data and calibrated spatial coordinates. A texture mapping formula for the 3D model is constructed based on the synthetic aperture radar algorithm. Among them, V t For the final 3D visualization model volume data, This is a texture mapping operator that maps the extracted multidimensional features of radar echoes onto the surface of a 3D model to form a visual texture.

[0052] Specifically, the geometric constraint formula for the 3D model, based on the quantum magnetic navigation model, utilizes quantum magnetic data and calibrated spatial coordinates to construct the geometric topological relationship of the model, ensuring that the geometric structure of the 3D model conforms to the spatial distribution of the actual survey target. The texture mapping formula for the 3D model, based on the synthetic aperture radar algorithm model, maps the extracted multi-dimensional features of the radar echo onto the surface of the 3D model, forming a visual texture that allows the model to intuitively present the characteristic differences of the underground geological structure. In implementation, the fused data is first meshed to generate a 3D data mesh, followed by preprocessing operations such as filtering and interpolation. Then, the geometric framework of the model is constructed according to the geometric constraint formula, and the radar echo features are assigned to the model surface through the texture mapping formula. Finally, volume rendering techniques such as ray casting algorithms are used to render and generate a 3D visual model. Spatial analysis functions are then used to perform distance measurement, volume calculation, and other operations on the model, providing intuitive and comprehensive analysis results for highway surveying.

[0053] Preferably, in the system control and data management module, the module scheduling strategy formula is constructed based on the quantum magnetic navigation model: Where S is the module scheduling strategy matrix, Operators are generated for the scheduling strategy, and the scheduling strategy is generated based on quantum magnetic data and the module task queue Q; a data storage optimization formula is constructed based on the synthetic aperture radar algorithm model. Among them, D s For the optimized data storage structure, To optimize the storage operator, based on the synthetic aperture radar measurement data and the storage strategy S s Optimize the data storage structure.

[0054] Specifically, for the system control and data management module, a module scheduling strategy formula based on the quantum magnetic navigation model is used. This formula is generated by analyzing quantum magnetic data and module task queues to ensure that each module operates in an orderly manner according to priority and task requirements. A data storage optimization formula based on the synthetic aperture radar (SAR) algorithm model is used to optimize the data storage structure according to SAR measurement data and storage strategies, improving data storage efficiency and query speed. During implementation, the system control and data management module monitors quantum magnetic data and the task status of each module in real time, generating control commands based on the scheduling strategy formula to coordinate the start, stop, and data transmission of each module. Regarding data storage management, based on the characteristics and storage strategies of SAR measurement data, the data storage optimization formula is used to classify, index, and compress the data, establishing an efficient database storage structure. Data is also backed up regularly to ensure data security, and a convenient data query interface is provided for engineers to retrieve and analyze the data.

[0055] Preferably, the highway geological stratification parameter inversion and model construction module includes: a geological layer interface identification unit, which determines the interface location of the highway geological layer by analyzing the gradient abrupt changes in quantum magnetic data and the phase jumps in synthetic aperture radar echo signals; a geological parameter initial value estimation unit, which makes preliminary estimates of geological layer thickness and dielectric constant parameters using the statistical characteristics of quantum magnetic data and the backscattering coefficient of synthetic aperture radar; a model iterative optimization unit, which uses a synthetic aperture radar inversion algorithm based on quantum magnetic navigation constraints to iteratively optimize the geological stratification model and reduce the error between the model and the measured data; and a model verification unit, which verifies the accuracy and reliability of the model by comparing known geological borehole data with the constructed geological stratification model.

[0056] Specifically, the highway geological stratification parameter inversion and model building module consists of the following components: The geological layer interface identification unit, by analyzing gradient abrupt changes in quantum magnetic data and phase jumps in synthetic aperture radar (SAR) echo signals, can accurately determine the interface location of highway geological layers, providing crucial boundary information for geological stratification. The geological parameter initial estimation unit utilizes the statistical characteristics of quantum magnetic data and the backscattering coefficient of SAR, combined with an empirical relational model, to make preliminary estimates of parameters such as geological layer thickness and dielectric constant, providing initial parameters for subsequent model building. The model iterative optimization unit employs a SAR inversion algorithm based on quantum magnetic navigation constraints, guided by the error between actual measurement data and model prediction data, to iteratively optimize the geological stratification model, gradually improving its accuracy. The model verification unit compares known geological borehole data with the constructed geological stratification model from multiple dimensions to verify the model's accuracy and reliability, ensuring that the model truly reflects the actual geological conditions and provides a scientific basis for highway engineering design.

[0057] Preferably, the multi-source data spatiotemporal registration and fusion processing module includes: a time synchronization unit, which timestamps radar echo data and auxiliary measurement data using a high-precision time reference for quantum magnetic navigation; a spatial coordinate transformation unit, which uses the quantum magnetic gradient tensor-coordinate transformation relationship to transform multi-source data from different coordinate systems to a unified spatial coordinate system; a data quality assessment unit, which assesses the quality of quantum magnetic data and synthetic aperture radar data based on the data's signal-to-noise ratio and integrity indicators; and a weighted fusion execution unit, which performs weighted fusion processing on the quality-assessed multi-source data according to a set fusion weight.

[0058] Specifically, the multi-source data spatiotemporal registration and fusion processing module consists of several components. The time synchronization unit uses the high-precision time reference of quantum magnetic navigation as a reference to align radar echo data and auxiliary measurement data with timestamps, eliminating differences in the time dimension and ensuring consistent data timeliness. The spatial coordinate transformation unit utilizes the quantum magnetic gradient tensor-coordinate transformation relationship to transform multi-source data from different coordinate systems into a unified spatial coordinate system, resolving the problem of inconsistent spatial positioning. The data quality assessment unit uses professional evaluation algorithms to assess the quality of quantum magnetic data and synthetic aperture radar data based on indicators such as signal-to-noise ratio and completeness, providing a quality basis for data fusion. The weighted fusion execution unit performs weighted fusion processing on the quality-assessed multi-source data according to the set fusion weights, integrating the scattered data into a complete and accurate dataset, improving data usability and reliability, and laying a solid data foundation for subsequent exploration work.

[0059] Preferably, the three-dimensional visualization modeling and analysis module for the survey target includes: a data gridding unit, which performs spatial gridding processing on the fused multi-source data to generate a regular three-dimensional data grid; a volume rendering preprocessing unit, which performs filtering and interpolation preprocessing operations on the gridded data to improve the volume rendering effect; a three-dimensional model rendering unit, which uses a ray casting algorithm to render the three-dimensional data grid based on the model information constructed from quantum magnetic data and synthetic aperture radar features; and a spatial analysis calculation unit, which performs distance measurement, volume calculation, and profile analysis spatial analysis operations on the three-dimensional visualization model.

[0060] Specifically, the 3D visualization modeling and analysis module for the survey target comprises the following components: The data gridding unit performs spatial gridding on the fused multi-source data, discretizing continuous data into regular 3D data grids according to a set grid size and spacing, facilitating computer processing and storage. The volume rendering preprocessing unit performs filtering, interpolation, and other preprocessing operations on the gridded data to remove noise, supplement missing data, and improve data smoothness and accuracy, providing high-quality data input for volume rendering. The 3D model rendering unit uses techniques such as ray casting algorithms to render the 3D data grid based on model information constructed from quantum magnetic data and synthetic aperture radar features, generating a realistic 3D visualization model. The spatial analysis and calculation unit performs spatial analysis operations such as distance measurement, volume calculation, and profile analysis on the 3D visualization model, uncovering the spatial information behind the data and providing more comprehensive and in-depth technical support for highway surveying and design, helping engineers better understand underground geological structures.

[0061] like Figure 2 As shown, a measurement system for a highway survey radar measurement system based on electromagnetic waves is described. The system operation includes the following steps:

[0062] Step S1: Start the quantum magnetic gradient tensor analysis and spatial coordinate calibration module, collect quantum magnetic gradient tensor data and combine it with satellite positioning data, and perform spatial coordinate calibration through tensor matrix operation and coordinate transformation algorithm;

[0063] Step S2: The synthetic aperture radar echo signal multidimensional feature extraction module receives the radar echo signal, performs time-frequency domain transformation, and extracts amplitude, phase, and frequency modulation multidimensional feature information.

[0064] Step S3: The highway geological stratification parameter inversion and model building module uses quantum magnetic navigation data and synthetic aperture radar feature information to construct a highway geological stratification model and invert geological layer parameters using an iterative optimization algorithm.

[0065] Step S4: The multi-source data spatiotemporal registration and fusion processing module performs spatiotemporal registration on quantum magnetic data, radar echo data and auxiliary data, and generates fused data using a weighted fusion strategy;

[0066] Step S5: The 3D visualization modeling and analysis module for the survey target will mesh and preprocess the fused data, and use volume rendering technology to construct a 3D model of the highway survey target and perform spatial analysis.

[0067] Step S6: The system control and data management module performs command scheduling, data transmission control, and data storage management for each module in the entire measurement process, completing the highway survey radar measurement work.

[0068] An electromagnetic wave-based radar measurement system for highway surveying addresses the shortcomings of traditional technologies in effectively integrating multi-source data. This system achieves precise alignment of various information types, including quantum magnetic data and radar echo data, across time and space by constructing a high-precision spatiotemporal registration mechanism. Based on factors such as data type and quality in highway surveying scenarios, fusion weights are scientifically set, and a weighted fusion strategy is employed to deeply fuse disparate data. This process avoids information isolation and bias caused by independent data processing, enabling the system to acquire comprehensive and accurate survey data, providing a solid data foundation for subsequent analysis.

[0069] In terms of geological parameter inversion and model building, traditional methods, relying on single data or simple algorithms, struggle to accurately reflect the true geological conditions. This system fully leverages the synergistic advantages of quantum magnetic navigation and synthetic aperture radar (SAR) technology, comprehensively analyzing the characteristics of quantum magnetic data and radar echo signals. First, it accurately determines geological layer interfaces through key information such as gradient abrupt changes and phase jumps in the data. Then, it estimates initial values ​​of geological parameters by combining multi-source data. Finally, it uses iterative optimization algorithms to repeatedly adjust and optimize the geological stratification model. This approach of in-depth data mining and multi-dimensional collaborative analysis significantly improves the accuracy of inversion of key parameters such as geological layer thickness and dielectric constant, substantially enhancing the reliability of highway survey results. Simultaneously, the system can transform fused data into an intuitive 3D visualization model and perform spatial analysis, providing a more intuitive and efficient decision-making basis for highway engineering design and construction, comprehensively improving the technical level and application value of highway surveying.

[0070] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0071] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A highway survey radar measurement system based on electromagnetic waves, characterized in that, include: The quantum magnetic gradient tensor analysis and spatial coordinate calibration module is used to collect quantum magnetic gradient tensor data, analyze magnetic field characteristic information through tensor matrix operations, and fuse it with satellite positioning data to perform spatial coordinate calibration. A multi-dimensional feature extraction module for synthetic aperture radar echo signals is used to perform time-frequency domain transformation on the received radar echo signals and extract multi-dimensional feature information including amplitude, phase, and frequency modulation. The module for inverting and building highway geological stratification parameters, based on quantum magnetic navigation data and synthetic aperture radar feature information, constructs a highway geological stratification model through iterative optimization algorithms and inverts geological layer thickness and dielectric constant parameters. The multi-source data spatiotemporal registration and fusion processing module aligns quantum magnetic data, radar echo data, and auxiliary data in terms of spatiotemporal dimensions. The module uses a weighted fusion strategy to generate fused data. The 3D visualization modeling and analysis module for survey targets integrates data for mesh processing, uses volume rendering technology to construct a 3D model of highway survey targets, and performs spatial analysis. The system control and data management module is used for instruction scheduling, data transmission control, and data storage management of the above modules. In the highway geological stratification parameter inversion and model building module, the geological stratification parameter inversion formula is constructed based on the quantum magnetic navigation model and the synthetic aperture radar algorithm model: ,in, This is the geological layering parameter vector obtained from the inversion, which includes geological layer thickness and dielectric constant parameters; The parameter vector to be inverted; , The forward modeling operators for quantum magnetic data and synthetic aperture radar data, respectively, are determined by a priori models of highway geological structures. These are the quantum magnetic force measurement data vector and the synthetic aperture radar measurement data vector, respectively. The weighting coefficient is set based on parameters such as highway survey depth and geological complexity. The highway geological stratification parameter inversion and model construction module includes: a geological layer interface identification unit, which determines the interface location of the highway geological layer by analyzing the gradient abrupt changes in quantum magnetic data and the phase jumps in the synthetic aperture radar echo signal; a geological parameter initial value estimation unit, which makes preliminary estimates of geological layer thickness and dielectric constant parameters using the statistical characteristics of quantum magnetic data and the backscattering coefficient of synthetic aperture radar; a model iterative optimization unit, which uses a synthetic aperture radar inversion algorithm based on quantum magnetic navigation constraints to iteratively optimize the geological stratification model and reduce the error between the model and the measured data; and a model verification unit, which verifies the accuracy and reliability of the model by comparing known geological borehole data with the constructed geological stratification model.

2. The highway survey radar measurement system based on electromagnetic waves according to claim 1, characterized in that, In the quantum magnetic gradient tensor analysis and spatial coordinate calibration module, a quantum magnetic navigation model is used to construct the correlation between the quantum magnetic gradient tensor and spatial coordinates. The construction formula is as follows: ,in, This represents the calibrated spatial coordinate vector. is the quantum magnetic gradient tensor-coordinate transformation matrix, where the elements are determined by the coupling coefficients of the geomagnetic background field and the gravitational field in the highway survey area; This is a quantum magnetic gradient tensor data vector containing six independent gradient components; To calibrate the error vector, the synthetic aperture radar echo signal multi-dimensional feature extraction module constructs an echo signal feature extraction formula based on the synthetic aperture radar algorithm model: ,in, For the extracted multidimensional feature matrix, For time-frequency domain transform operators, The time-frequency distribution matrix of the radar echo signal. Indicates time, Indicates frequency; This is a feature weighting matrix, with weight coefficients dynamically adjusted based on highway pavement material and humidity survey parameters. ○ represents the Hadamard product operation.

3. The highway survey radar measurement system based on electromagnetic waves according to claim 1, characterized in that, In the multi-source data spatiotemporal registration and fusion processing module, a spatiotemporal registration formula is constructed based on the quantum magnetic navigation model: ,in, The registered multi-source data spatial coordinate vector, This is the spatiotemporal transformation matrix for quantum magnetic navigation, where the elements are determined by the spatiotemporal reference parameters of the highway survey area; The original multi-source data spatial coordinate vector; To register the offset vector, a data fusion formula is constructed based on the synthetic aperture radar algorithm model: ,in, The merged data matrix For the first The fusion weights for different types of data are determined based on the types of highway survey data and data quality parameters. For the first Similar to the original data matrix, This represents the total number of data types.

4. The highway survey radar measurement system based on electromagnetic waves according to claim 1, characterized in that, In the three-dimensional visualization modeling and analysis module for the exploration target, the geometric constraint formula for the three-dimensional model is constructed based on the quantum magnetic navigation model: ,in, For 3D model volume data containing geometric constraints, Operators are constructed to define geometric constraints, and the geometric topological relationships of the model are established based on quantum magnetic data and calibrated spatial coordinates. A texture mapping formula for the 3D model is constructed based on the synthetic aperture radar algorithm model. ,in, For the final 3D visualization model volume data, This is a texture mapping operator that maps the extracted multidimensional features of radar echoes onto the surface of a 3D model to form a visual texture.

5. The highway survey radar measurement system based on electromagnetic waves according to claim 1, characterized in that, In the system control and data management module, a module scheduling strategy formula is constructed based on the quantum magnetic navigation model: ,in, For module scheduling strategy matrix, Operators are generated for the scheduling strategy based on quantum magnetic data and the module task queue. Generate scheduling strategies; construct data storage optimization formulas based on synthetic aperture radar algorithm models: ,in, For the optimized data storage structure, To optimize the storage operator, based on synthetic aperture radar measurement data and storage strategy... Optimize the data storage structure.

6. The highway survey radar measurement system based on electromagnetic waves according to claim 1, characterized in that, The multi-source data spatiotemporal registration and fusion processing module includes: a time synchronization unit, which timestamps radar echo data and auxiliary measurement data using a high-precision time reference for quantum magnetic navigation; a spatial coordinate transformation unit, which uses the quantum magnetic gradient tensor-coordinate transformation relationship to transform multi-source data from different coordinate systems to a unified spatial coordinate system; a data quality assessment unit, which assesses the quality of quantum magnetic data and synthetic aperture radar data based on the data's signal-to-noise ratio and integrity indicators; and a weighted fusion execution unit, which performs weighted fusion processing on the quality-assessed multi-source data according to the set fusion weights.

7. The highway survey radar measurement system based on electromagnetic waves according to claim 1, characterized in that, The three-dimensional visualization modeling and analysis module for the survey target includes: a data gridding unit, which performs spatial gridding processing on the fused multi-source data to generate a regular three-dimensional data grid; a volume rendering preprocessing unit, which performs filtering and interpolation preprocessing operations on the gridded data to improve the volume rendering effect; a three-dimensional model rendering unit, which uses a ray casting algorithm to render the three-dimensional data grid based on the model information constructed from quantum magnetic data and synthetic aperture radar features; and a spatial analysis and calculation unit, which performs spatial analysis operations such as distance measurement, volume calculation, and profile analysis on the three-dimensional visualization model.

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