A method and system for optimizing subgrade filling parameters of high-speed railways

Abstract

The present invention relates to the field of railway subgrade engineering technology, and in particular to a method and system for optimizing parameters of high-speed railway subgrade fillers. The method comprises the following steps: acquiring high-speed railway data to be detected; performing multi-frequency electromagnetic wave scanning on the subgrade surface of the high-speed railway data to be detected to obtain electromagnetic response signal data of the original filler; performing time-space registration of the response signal of the original filler electromagnetic response signal data, and extracting the physical characteristics of the subgrade filler to generate physical characteristic data of the subgrade filler; performing spatial characteristic statistics of abnormal areas based on the physical characteristic data of the subgrade filler, and setting a partitioned filler construction plan to generate intelligent partitioned filler construction plan data; adjusting the subgrade filler parameter ratio based on the intelligent partitioned filler construction plan data to achieve high-speed railway subgrade filler parameter optimization operation. The present invention realizes accurate optimization of filler parameters through the zoning defect assessment of railway subgrade fillers, which significantly improves the efficiency and quality of subgrade construction.

Description

Technical Field

[0001] The present invention relates to the technical field of railway roadbed engineering, and in particular to a method and system for optimizing parameters of high-speed railway roadbed filler. Background Art

[0002] The construction of high-speed railways involves a large amount of earthwork, among which roadbed filling is one of the key links. The stability and durability of the roadbed directly affect the safety and comfort of high-speed railway operations, as well as the maintenance costs throughout the entire life cycle. Therefore, how to effectively control the quality of the roadbed filler and ensure the long-term stability of the roadbed. Existing high-speed railway roadbed construction and filler parameter adjustment are adjusted through manual experience adjustment, manual compaction and ring knife sampling. For example, manual compaction depends on the experience and operation level of the construction personnel, and it is difficult to ensure that the compaction degree of the filler meets the design requirements, and it is difficult to detect the compaction condition inside the filler. Although ring knife sampling can obtain compaction data of some fillers, the number of samples is limited, and the selection of sampling locations is somewhat arbitrary, which cannot fully and accurately reflect the overall compaction quality of the filler. This local sampling method easily ignores areas of local unevenness or insufficient compaction, leading to problems such as roadbed settlement, deformation and even cracking in the later stage, causing huge economic losses and safety hazards. However, traditional high-speed railway subgrade construction mainly relies on manual experience and simple detection methods to adjust the filler parameter ratio and compaction process, which makes it difficult to achieve precise control of the compaction quality of the subgrade filler, resulting in the filler compaction effect being difficult to achieve the optimal state, increasing the risk of subgrade settlement, deformation and even cracking in the later stage. Summary of the Invention

[0003] Based on this, the present invention provides a method and system for optimizing parameters of high-speed railway roadbed fillers to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for optimizing parameters of high-speed railway roadbed filler materials comprises the following steps:

[0005] Step S1: Acquire the high-speed railway data to be inspected; perform electromagnetic wave scanning frequency processing on the high-speed railway data to generate an electromagnetic wave emission control strategy; perform multi-frequency electromagnetic wave scanning on the roadbed surface based on the electromagnetic wave emission control strategy to obtain original filler electromagnetic response signal data and roadbed sampling position data;

[0006] Step S2: performing time-space registration of the original filler electromagnetic response signal data to generate filler electromagnetic response signal time series data; performing electromagnetic effective signal screening based on the filler electromagnetic response signal time series data to generate effective electromagnetic response signal data;

[0007] Step S3: performing multi-frequency electromagnetic response signal grouping on the effective electromagnetic response signal data based on the roadbed sampling position data to generate multi-frequency electromagnetic response signal group data; extracting the physical characteristics of the roadbed filler based on the multi-frequency electromagnetic response signal group data to generate roadbed filler physical characteristic data; performing spatial characteristic statistics of abnormal areas based on the roadbed filler physical characteristic data to generate abnormal area spatial characteristic data; setting a partitioned filler construction plan based on the abnormal area spatial characteristic data to generate intelligent partitioned filler construction plan data;

[0008] Step S4: Perform roadbed filler construction processing according to the intelligent zoning filler construction plan data, and adjust the roadbed filler parameter ratio to achieve high-speed railway roadbed filler parameter optimization operation.

[0009] This invention leverages the sensitivity of electromagnetic waves to the internal structure of fillers. Through multi-frequency electromagnetic wave scanning and sophisticated signal analysis, it can deeply detect the compaction level, uniformity, and potential defects of roadbed fillers, completely eliminating the subjectivity and uncertainty of traditional manual judgment. By temporally and spatially aligning the electromagnetic response signals and filtering effective signals, it can accurately identify differences in the physical characteristics of the filler, and thus accurately determine whether the compaction quality meets the standards. This effectively avoids problems such as localized insufficient or uneven compaction, laying a solid foundation for the long-term stability and safety of the roadbed. The automated electromagnetic wave scanning technology used can quickly and extensively acquire filler data. Through intelligent data processing and analysis, it rapidly generates filler physical characteristic data and spatial characteristic data of abnormal areas, greatly shortening detection time and improving construction efficiency. Furthermore, the intelligent partitioned filler construction plan generated based on the analysis results can guide construction personnel in targeted filler parameter ratio adjustment and compaction operations, ensuring that each layer of filler achieves optimal compaction, avoiding blind construction and repeated rework, and significantly improving the overall roadbed construction quality. By precisely controlling the compaction quality of the roadbed filler, it is possible to fundamentally prevent the occurrence of problems such as roadbed settlement, deformation and cracking. The long-term stability of the roadbed is guaranteed, reducing the frequency and workload of subsequent maintenance and repairs, thereby reducing the maintenance cost of the entire life cycle. At the same time, since the safety hazards caused by roadbed quality problems are avoided, the safety and reliability of high-speed railway operations are also improved, ensuring the travel safety of passengers and the smooth operation of railway transportation. Therefore, a high-speed railway roadbed filler parameter optimization method of the present invention analyzes the filler characteristics of different regions through multi-frequency electromagnetic response signal grouping and physical feature extraction, and based on the spatial feature statistics of abnormal regions, intelligently identifies and targetedly processes insufficiently compacted or other abnormal areas, thereby achieving fine adjustment of roadbed filler parameters, ultimately improving the roadbed construction quality and stability, and effectively reducing the risk of later settlement and deformation.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Acquire the high-speed railway data to be detected; extract the roadbed filling material and path width from the high-speed railway data to obtain railway roadbed width data and roadbed filling material parameters respectively;

[0012] Step S12: using a pre-set mobile inspection vehicle to carry a multi-band electromagnetic wave transmitting and receiving device to construct an electromagnetic integrated scanning device;

[0013] Step S13: activating the detection sensor array of the electromagnetic integrated scanning device based on the railway roadbed width data to generate sensor array distribution data;

[0014] Step S14: determining the initial position of the roadbed for the preset mobile inspection vehicle, setting the driving speed according to the high-speed railway data to be inspected, and generating the driving speed data of the inspection vehicle;

[0015] Step S15: performing electromagnetic wave scanning frequency processing according to the roadbed filling material parameters and the detection vehicle driving speed data, and performing electromagnetic wave emission control processing on the electromagnetic integrated scanning device through the sensor array distribution data to generate an electromagnetic wave emission control strategy;

[0016] Step S16: Based on the electromagnetic wave emission control strategy, the electromagnetic integrated scanning device is used to perform multi-frequency electromagnetic wave scanning on the roadbed surface, and the electromagnetic wave reflection signal is synchronously received to obtain the original filler electromagnetic response signal data and the roadbed sampling position data.

[0017] By extracting the roadbed width and filling material parameters, the present invention provides a key basis for the formulation of subsequent electromagnetic wave scanning strategies, ensuring the pertinence and accuracy of the detection. Secondly, by using a preset mobile inspection vehicle equipped with an electromagnetic integrated scanning device, automated, large-scale roadbed detection is achieved, which greatly improves the detection efficiency and reduces the errors caused by manual intervention. At the same time, the sensor array activated based on the roadbed width data ensures the comprehensiveness of electromagnetic wave signal coverage and the integrity of data acquisition. By precisely controlling the driving speed of the inspection vehicle and combining the roadbed filling material parameters, the electromagnetic wave scanning frequency is intelligently adjusted, the penetration and resolution of the electromagnetic wave signal are optimized, and the detection results are more precise and reliable. The generated electromagnetic wave emission control strategy further ensures the orderliness and efficiency of the electromagnetic wave scanning process. Finally, by synchronously receiving the electromagnetic wave reflection signal, the original electromagnetic response signal data containing rich filler physical characteristic information and accurate roadbed sampling position data can be obtained, laying a solid data foundation for subsequent signal processing and compaction quality assessment.

[0018] Preferably, step S15 includes the following steps:

[0019] Step S151: Analyze the material magnetic permeability characteristics according to the roadbed filling material parameters to generate roadbed material characteristic data;

[0020] Step S152: Processing the transmission frequency of the transmitter according to the speed data of the detection vehicle to generate electromagnetic transmission frequency data;

[0021] Step S153: performing electromagnetic wave scanning frequency range matching on the electromagnetic emission frequency data to generate electromagnetic wave scanning frequency range data;

[0022] Step S154: dividing the electromagnetic wave scanning frequency range data into high and low frequency scanning frequency segments according to the roadbed material characteristic data to generate electromagnetic wave scanning frequency sequence data;

[0023] Step S155: Control the phased array beam direction of the electromagnetic integrated scanning device through the sensor array distribution data, and input the electromagnetic wave scanning frequency according to the electromagnetic wave scanning frequency sequence data, thereby obtaining the electromagnetic wave emission control strategy.

[0024] The present invention analyzes the magnetic permeability characteristics of the roadbed filling material, and the obtained roadbed material characteristic data provides a scientific basis for the precise setting of the electromagnetic wave scanning frequency, ensuring the optimal coupling of the electromagnetic wave and the material, thereby improving the signal penetration and data validity. Secondly, the transmission frequency of the transmitting device is adjusted according to the driving speed of the detection vehicle, and combined with the electromagnetic wave scanning frequency range matching, the continuity and integrity of the data acquisition are ensured, and signal distortion or omission caused by speed changes is avoided. The scanning frequency is segmented into high and low frequencies according to the roadbed material characteristics, and electromagnetic wave scanning frequency sequence data is generated, so that the electromagnetic wave can be targeted for different material characteristics. The detection accuracy of the roadbed composed of complex materials is improved. Finally, the sensor array distribution data is used to control the phased array beam direction, and combined with the precise electromagnetic wave scanning frequency sequence input, the directional scanning of the electromagnetic beam and the fine adjustment of the frequency are realized, which enhances the detection capability of specific depths and areas.

[0025] Preferably, step S2 includes the following steps:

[0026] Step S21: performing time-space registration of the original filler electromagnetic response signal data to generate filler electromagnetic response signal time series data;

[0027] Step S22: performing adaptive wavelet denoising on the filler electromagnetic response signal time series data, and performing response signal error correction to obtain corrected electromagnetic response signal time series data;

[0028] Step S23: performing short-time Fourier transform on the time series data of the corrected electromagnetic response signal to generate frequency domain data of the filler electromagnetic response;

[0029] Step S24: performing signal amplitude and phase value processing on the filler electromagnetic response frequency domain data, and performing signal energy response threshold calculation to generate signal energy response threshold data;

[0030] Step S25: screening the filler electromagnetic response signal time series data for effective electromagnetic signals using the preset electromagnetic frequency determination rule and signal energy response threshold data to generate effective electromagnetic response signal data.

[0031] The present invention generates time series data of the electromagnetic response signal of the filler through time-space registration of the response signal, ensuring the uniformity of the signal data in time and space, and laying the foundation for subsequent analysis. Secondly, adaptive wavelet denoising and error correction are used to effectively remove noise and interference in the signal, improve the purity and authenticity of the signal, and make the subsequent frequency domain analysis more accurate. The time series data is converted into frequency domain data by short-time Fourier transform, which can reveal the distribution characteristics of the signal at different frequencies and provide key information for in-depth analysis of the physical properties of the filler. Subsequently, the intensity and energy distribution of the signal are quantified through signal amplitude and phase value processing and energy response threshold calculation, providing a clear standard for the screening of effective electromagnetic signals. Finally, based on the preset electromagnetic frequency judgment rules and signal energy response threshold data, the electromagnetic response signal of the filler is effectively screened to ensure that the extracted effective electromagnetic response signal can truly reflect the compaction state and physical properties of the filler and eliminate the interference of irrelevant signals.

[0032] Preferably, step S3 includes the following steps:

[0033] Step S31: performing multi-frequency electromagnetic response signal grouping on the effective electromagnetic response signal data according to the roadbed sampling position data to generate multi-frequency electromagnetic response signal group data;

[0034] Step S32: performing frequency band penetration depth analysis on the multi-frequency electromagnetic response signal group data to generate frequency band penetration depth data;

[0035] Step S33: extracting the signal propagation time of the multi-frequency electromagnetic response signal group data, and calculating the electromagnetic wave propagation velocity based on the frequency band penetration depth data to generate multi-frequency electromagnetic wave propagation velocity data;

[0036] Step S34: extracting the physical characteristics of the roadbed filler through the multi-frequency electromagnetic wave propagation velocity data to generate the physical characteristic data of the roadbed filler;

[0037] Step S35: Perform spatial feature statistics of abnormal areas based on the physical feature data of the roadbed filler to generate abnormal area spatial feature data; set a partitioned filler construction plan based on the abnormal area spatial feature data to generate intelligent partitioned filler construction plan data.

[0038] The present invention groups multi-frequency electromagnetic response signals, and the scheme can analyze the signal characteristics of different frequencies, and more accurately capture the physical state of the filler at different depths and regions. Secondly, through frequency band penetration depth analysis and electromagnetic wave propagation velocity calculation, the scheme can accurately obtain the propagation characteristics of electromagnetic waves in the filler, providing key data support for the subsequent extraction of physical characteristics. By using multi-frequency electromagnetic wave propagation velocity data to extract the physical characteristics of roadbed fillers, the scheme can quantify key parameters such as the compaction degree and uniformity of the filler, providing a scientific basis for evaluating the quality of the filler. Subsequently, by performing spatial feature statistics of abnormal areas on these physical feature data, the scheme can accurately identify areas where compaction does not meet standards or where potential defects exist, avoiding the limitations of traditional methods that rely on empirical judgment. Finally, the intelligent partitioned filler construction plan generated based on the spatial feature data of abnormal areas provides clear guidance for construction personnel, realizing targeted construction and refined management.

[0039] Preferably, step S34 includes the following steps:

[0040] Step S341: performing dielectric constant inversion based on the multi-frequency electromagnetic wave propagation velocity data to generate dielectric constant distribution data;

[0041] Step S342: performing electromagnetic frequency weighting on the dielectric constant distribution data, and performing weighted fusion of multi-frequency data to generate weighted fusion dielectric constant data;

[0042] Step S343: Calculating the electromagnetic wave attenuation coefficient based on the multi-frequency electromagnetic wave propagation velocity data and the weighted fusion dielectric constant data, and performing water content contribution processing to generate roadbed moisture content distribution data;

[0043] Step S344: using a preset filler density-dielectric constant linear model to estimate the filler density value of the weighted fusion dielectric constant data to generate filler density distribution characteristic data;

[0044] Step S345: performing filler particle distribution index processing on the roadbed moisture content distribution data using the filler density distribution characteristic data to generate roadbed particle distribution index parameters;

[0045] Step S346: Integrate the filler density distribution characteristic data, the roadbed moisture content distribution data, and the roadbed particle distribution index parameters into the roadbed filler physical characteristic data to generate the roadbed filler physical characteristic data.

[0046] Through dielectric constant inversion and weighted fusion of multi-frequency data, this solution effectively improves the accuracy and reliability of dielectric constant data, laying the foundation for subsequent physical characteristic analysis. As a key electromagnetic parameter of materials, the accurate measurement of dielectric constant is crucial for assessing the compaction and uniformity of fillers. By combining electromagnetic wave propagation velocity with weighted fused dielectric constant data to calculate the electromagnetic wave attenuation coefficient and perform moisture content contribution processing, this solution enables precise measurement of the moisture distribution of the roadbed. Moisture content is a key factor affecting the mechanical properties of fillers, and its accurate measurement helps prevent roadbed settlement and deformation. Furthermore, using a linear filler density-dielectric constant model to estimate filler density further enriches the dimensionality of filler physical characteristics. By processing filler particle distribution indicators from filler density data, the solution successfully extracts subgrade particle distribution index parameters, which, together with density and moisture content data, form a complete set of physical characteristic data for subgrade fillers. This series of steps not only achieves comprehensive quantification of key physical characteristics such as filler density, moisture content, and particle distribution, but also improves the accuracy and reliability of these characteristics through data fusion and comprehensive analysis.

[0047] Preferably, step S35 includes the following steps:

[0048] Step S351: Acquire historical railway roadbed filling engineering data; construct a mapping relationship between filler physical characteristics and compaction degree based on the historical railway roadbed filling engineering data to obtain filler physical characteristics-compactness mapping relationship data;

[0049] Step S352: Using a preset convolutional neural network model to perform model transfer learning on the filler physical characteristics-compactness mapping relationship data, and using historical railway subgrade filler engineering data to perform model training to generate a subgrade filler compaction assessment model;

[0050] Step S353: transmitting the roadbed filler physical characteristic data to the roadbed filler compaction evaluation model for compaction evaluation to generate filler compaction data;

[0051] Step S354: matching roadbed sampling points according to the roadbed filler physical characteristic data, and performing spatial distribution difference of compaction according to the filler compaction data to generate roadbed compaction distribution data;

[0052] Step S355: Process the roadbed defect construction plan based on the roadbed compaction distribution data and the roadbed filler physical characteristic data to generate intelligent roadbed filler construction plan data.

[0053] By leveraging historical engineering data to construct a mapping relationship between filler physical characteristics and compaction, and applying this relationship to transfer learning and training of a convolutional neural network model, the present invention successfully constructed a predictive model specifically for assessing the compaction of roadbed fillers. This process fully leverages existing engineering experience and data resources, improving the model's predictive accuracy and generalization capabilities. By inputting the extracted physical characteristic data of the roadbed filler into the trained compaction assessment model, filler compaction data can be quickly and accurately generated, enabling a quantitative assessment of compaction. This machine learning-based assessment method avoids the human interference inherent in traditional methods, improving the objectivity and reliability of the assessment. Furthermore, by matching roadbed sampling points and performing differential processing of the spatial distribution of compaction, intuitive roadbed compaction distribution data is generated, enabling construction personnel to clearly understand the spatial distribution characteristics of filler compaction. Based on the compaction distribution data and filler physical characteristic data, construction plans for roadbed defects are processed. The resulting intelligent roadbed filler construction plan can specifically address areas with insufficient compaction or defects, enabling intelligent and refined management of construction plans. This data-driven construction plan optimization method not only improves construction efficiency, but also reduces construction costs and ultimately ensures the long-term stability and safety of the high-speed railway subgrade.

[0054] Preferably, step S355 includes the following steps:

[0055] Step S3551: using the preset roadbed filler early warning index library to perform cluster analysis on the measured roadbed condition on the roadbed filler physical characteristic data and roadbed compaction distribution data, and marking abnormal areas to generate preliminary filler abnormal area data;

[0056] Step S3552: performing spatial connectivity analysis on the preliminary filler abnormality area data and merging adjacent abnormal areas to obtain roadbed filler abnormality area data;

[0057] Step S3553: performing regional spatial feature statistics on the abnormal area data of the roadbed filler to generate abnormal area spatial feature data;

[0058] Step S3554: Setting the roadbed filling construction plan according to the spatial characteristic data of the abnormal area, and generating intelligent roadbed filling construction plan data.

[0059] The present invention achieves accurate identification of abnormal areas of subgrade fillers and formulation of targeted construction plans through in-depth analysis and intelligent processing of the physical characteristics and compaction data of subgrade fillers, thereby greatly improving the quality and efficiency of high-speed railway subgrade construction. First, by using a preset subgrade filler early warning index library to perform cluster analysis on the measured subgrade conditions, the scheme can automatically identify abnormal areas that do not meet the early warning indicators, mark them, and generate preliminary filler abnormal area data. This process avoids the limitations of traditional methods that rely on manual experience and judgment, and improves the accuracy and efficiency of abnormal area identification. By performing spatial connectivity analysis and merging adjacent areas on the preliminary filler abnormal areas, the scheme further optimizes the division of abnormal areas, making the final determined subgrade filler abnormal areas more consistent with the actual situation and avoiding misjudgments caused by isolated abnormal points. Subsequently, spatial feature statistics are performed on the abnormal areas, and the generated abnormal area spatial feature data describes in detail the key information such as the location, size, and shape of the abnormal areas. Based on the spatial characteristic data of abnormal areas, the intelligent roadbed filling construction plan is set. The generated plan can provide customized construction strategies based on the characteristics of different abnormal areas, such as adjusting the filler ratio, increasing the number of compaction times, or adopting special construction techniques. This intelligent construction plan not only effectively solves the problem of abnormal roadbed filling, but also improves construction efficiency, reduces construction costs, and ultimately ensures the long-term stability and safety of the high-speed railway subgrade.

[0060] Preferably, step S3554 includes the following steps:

[0061] The abnormal area data of the roadbed filler is located by the abnormal area spatial feature data, and the roadbed filler risk area is divided, and the roadbed filler high hazard area data and roadbed filler low hazard area data are obtained respectively;

[0062] Perform fine grid division on the high-hazard area data of the roadbed filler to generate fine grid data of the high-hazard area;

[0063] Use the physical characteristic data of roadbed filler to calculate filler physical anomaly values on the refined grid data of high-risk areas, and generate filler physical anomaly data of high-risk areas;

[0064] The compaction loss of high-hazard area is calculated based on the refined grid data of high-hazard area through the roadbed compaction distribution data and the physical anomaly data of filler in high-hazard area, and the compaction loss data of high-hazard area is generated;

[0065] Set up multi-dimensional reinforcement plans based on the compaction loss data of high-risk areas and generate reinforcement plan data for high-risk areas;

[0066] Use the physical characteristic data of roadbed fill to conduct routine quality assessment of low-hazard area data of roadbed fill to generate assessment benchmark values for low-hazard area;

[0067] Conduct construction process adaptability analysis based on the assessment benchmark values for low-hazard areas, optimize standard construction parameters, and generate construction plan data for low-hazard areas;

[0068] The construction plan data of low-hazard areas and the reinforcement plan data of high-hazard areas are integrated into partitioned construction plans to obtain intelligent partitioned filling material construction plan data.

[0069] The present invention locates and divides the risks of abnormal areas through the spatial characteristic data of abnormal areas, clarifies the high-hazard and low-hazard areas, and lays the foundation for subsequent differentiated treatment. The high-hazard areas are finely gridded and the physical abnormal values of the fillers and the compaction loss are calculated, making the identification of the problem more specific and quantitative, providing an accurate basis for the formulation of targeted reinforcement plans. The multi-dimensional reinforcement plan based on compaction loss data ensures that the high-hazard areas are effectively treated and reduces potential safety risks. For low-hazard areas, the plan implements standardized management of low-risk areas by using the physical characteristic data of the fillers for routine quality assessment and generating assessment benchmark values. On this basis, the construction process adaptability analysis and standard construction parameter optimization are carried out, and the generated construction plan for low-hazard areas not only ensures construction quality but also avoids waste of resources. This differentiated treatment strategy based on risk level enables the rational allocation of construction resources and improves construction efficiency. The intelligent partitioned filler construction plan generated by integrating the reinforcement plan for high-hazard areas and the construction plan for low-hazard areas achieves comprehensive optimization of the entire roadbed filler construction. This solution not only addresses hidden dangers in high-risk areas but also improves construction quality in low-risk areas, ensuring the overall stability and durability of the roadbed. Through this refined and differentiated construction management approach, the solution effectively reduces subsequent maintenance costs and improves the operational safety and reliability of the high-speed railway.

[0070] Preferably, the present invention further provides a high-speed railway roadbed filler parameter optimization system, which executes the high-speed railway roadbed filler parameter optimization method described above, and the high-speed railway roadbed filler parameter optimization system comprises:

[0071] The roadbed electromagnetic scanning module is used to obtain the data of the high-speed railway to be inspected; the electromagnetic wave scanning frequency processing is performed on the high-speed railway data to generate an electromagnetic wave emission control strategy; based on the electromagnetic wave emission control strategy, a multi-frequency electromagnetic wave scan is performed on the roadbed surface to obtain the electromagnetic response signal data of the original filler and the roadbed sampling position data;

[0072] The electromagnetic response signal analysis module is used to perform time-space registration of the original filler electromagnetic response signal data to generate filler electromagnetic response signal time series data; perform electromagnetic effective signal screening based on the filler electromagnetic response signal time series data to generate effective electromagnetic response signal data;

[0073] The roadbed construction processing module is used to group the effective electromagnetic response signal data into multi-frequency electromagnetic response signals based on the roadbed sampling position data to generate multi-frequency electromagnetic response signal group data; extract the physical characteristics of the roadbed filler based on the multi-frequency electromagnetic response signal group data to generate roadbed filler physical characteristic data; perform spatial feature statistics of abnormal areas based on the roadbed filler physical characteristic data to generate abnormal area spatial characteristic data; and set a partitioned filler construction plan based on the abnormal area spatial characteristic data to generate intelligent partitioned filler construction plan data;

[0074] The subgrade filling parameter optimization module is used to carry out subgrade filling construction processing according to the intelligent partition filling construction plan data, and adjust the subgrade filling parameter ratio to achieve high-speed railway subgrade filling parameter optimization operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 This is a schematic flow chart of the steps of a method for optimizing parameters of high-speed railway roadbed fillers according to the present invention;

[0076] Figure 2 for Figure 1 Detailed implementation steps of step S1 in FIG.

[0077] Figure 3 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0078] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0079] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0080] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0081] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0082] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for optimizing parameters of high-speed railway roadbed filler, comprising the following steps:

[0083] Step S1: Acquire the high-speed railway data to be inspected; perform electromagnetic wave scanning frequency processing on the high-speed railway data to generate an electromagnetic wave emission control strategy; perform multi-frequency electromagnetic wave scanning on the roadbed surface based on the electromagnetic wave emission control strategy to obtain original filler electromagnetic response signal data and roadbed sampling position data;

[0084] Step S2: performing time-space registration of the original filler electromagnetic response signal data to generate filler electromagnetic response signal time series data; performing electromagnetic effective signal screening based on the filler electromagnetic response signal time series data to generate effective electromagnetic response signal data;

[0085] Step S3: performing multi-frequency electromagnetic response signal grouping on the effective electromagnetic response signal data based on the roadbed sampling position data to generate multi-frequency electromagnetic response signal group data; extracting the physical characteristics of the roadbed filler based on the multi-frequency electromagnetic response signal group data to generate roadbed filler physical characteristic data; performing spatial characteristic statistics of abnormal areas based on the roadbed filler physical characteristic data to generate abnormal area spatial characteristic data; setting a partitioned filler construction plan based on the abnormal area spatial characteristic data to generate intelligent partitioned filler construction plan data;

[0086] Step S4: Perform roadbed filler construction processing according to the intelligent zoning filler construction plan data, and adjust the roadbed filler parameter ratio to achieve high-speed railway roadbed filler parameter optimization operation.

[0087] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a flow chart showing the steps of a method for optimizing parameters of a high-speed railway roadbed filler according to the present invention. In this embodiment, the method for optimizing parameters of a high-speed railway roadbed filler comprises the following steps:

[0088] Step S1: Acquire the high-speed railway data to be inspected; perform electromagnetic wave scanning frequency processing on the high-speed railway data to generate an electromagnetic wave emission control strategy; perform multi-frequency electromagnetic wave scanning on the roadbed surface based on the electromagnetic wave emission control strategy to obtain original filler electromagnetic response signal data and roadbed sampling position data;

[0089] In an embodiment of the present invention, high-speed railway subgrade data to be inspected is obtained. This data includes subgrade design parameters, construction records, historical inspection data, and the like. For example, design parameters such as the length, width, height, filler type, and compaction degree of a certain section of high-speed railway subgrade can be obtained, as well as past settlement monitoring data and deformation monitoring data. The acquired subgrade data is then subjected to electromagnetic wave scanning frequency processing. Based on factors such as the type, thickness, and moisture content of the subgrade filler, an appropriate electromagnetic wave frequency range is selected, and the number of scans and intervals for each frequency are determined. For example, for clay fillers, a lower frequency range (e.g., 1MHz-10MHz) can be selected, while for gravel fillers, a higher frequency range (e.g., 10MHz-100MHz) can be selected. By analyzing the propagation characteristics of electromagnetic waves of different frequencies in the subgrade filler, an electromagnetic wave emission control strategy is generated. The strategy includes parameters such as the emission power, emission time, and scanning path of the electromagnetic waves of different frequencies. For example, the emission angle and scanning path of the electromagnetic wave can be set based on the geometry of the subgrade and the distribution of the filler to ensure that the electromagnetic wave can effectively penetrate the subgrade filler and obtain sufficient reflected signals. Finally, based on the generated electromagnetic wave emission control strategy, a multi-frequency electromagnetic wave transmitter is used to scan the roadbed surface. Mounted on a mobile platform, the transmitter scans the roadbed surface along a predetermined path, recording the three-dimensional coordinates of each sampling point as roadbed sampling location data. Simultaneously, a receiver receives the electromagnetic wave signals reflected from the roadbed filler and records information such as the amplitude and phase of the electromagnetic wave at each frequency as the raw filler electromagnetic response signal data.

[0090] Step S2: performing time-space registration of the original filler electromagnetic response signal data to generate filler electromagnetic response signal time series data; performing electromagnetic effective signal screening based on the filler electromagnetic response signal time series data to generate effective electromagnetic response signal data;

[0091] In an embodiment of the present invention, the acquired raw filler electromagnetic response signal data undergoes spatiotemporal registration. Due to time delays and positional deviations during electromagnetic wave transmission and reception, the raw data requires correction to ensure that each data point corresponds to the correct sampling time and location. For example, GPS technology can be used to accurately locate the sampling location, and timestamp technology can be used to synchronize the sampling time. Through spatiotemporal registration, electromagnetic response signal data collected at different frequencies and times can be integrated to generate filler electromagnetic response signal time series data. This time series data contains information about the electromagnetic response signal at each sampling point at different times and frequencies. The filler electromagnetic response signal time series data is then screened for valid signals. Due to factors such as environmental noise and instrument errors, the raw data may contain some invalid or interfering signals. These invalid signals need to be removed based on pre-set thresholds or filtering algorithms to retain valid electromagnetic response signals. For example, signal validity can be determined based on characteristics such as the signal-to-noise ratio, amplitude, and phase. Signal processing methods such as wavelet transform and Fourier transform can also be used to denoise and filter the signal to improve signal quality. After valid signal screening, valid electromagnetic response signal data is generated.

[0092] Step S3: performing multi-frequency electromagnetic response signal grouping on the effective electromagnetic response signal data based on the roadbed sampling position data to generate multi-frequency electromagnetic response signal group data; extracting the physical characteristics of the roadbed filler based on the multi-frequency electromagnetic response signal group data to generate roadbed filler physical characteristic data; performing spatial characteristic statistics of abnormal areas based on the roadbed filler physical characteristic data to generate abnormal area spatial characteristic data; setting a partitioned filler construction plan based on the abnormal area spatial characteristic data to generate intelligent partitioned filler construction plan data;

[0093] In an embodiment of the present invention, effective electromagnetic response signal data is grouped using roadbed sampling location data. Based on the spatial location of the sampling points, electromagnetic response signal data from adjacent areas is grouped together to generate multi-frequency electromagnetic response signal group data. Each group represents a specific roadbed area, and its electromagnetic response signal reflects the characteristics of the filler in that area. The physical characteristics of the roadbed filler are then extracted based on the multi-frequency electromagnetic response signal group data. For example, physical parameters such as the dielectric constant, conductivity, and magnetic permeability of the filler can be calculated based on the attenuation and phase changes of electromagnetic waves at different frequencies. These physical parameters reflect filler properties such as density, moisture content, and particle size. The extracted physical characteristic data is compared with a pre-established filler characteristic database to determine filler type, quality, and any anomalies. Next, spatial feature statistics are generated based on the roadbed filler physical characteristic data for abnormal areas. Statistical analysis is performed on the physical characteristic parameters of each group, such as the mean, standard deviation, maximum, and minimum values. The physical characteristic parameters are compared with preset thresholds to identify areas with abnormal parameters. The spatial distribution, area, shape, and other characteristics of these abnormal areas are then statistically analyzed to generate spatial feature data for these abnormal areas. Finally, based on the spatial characteristic data of the abnormal areas, a zoned filling construction plan is developed. Based on the spatial distribution of the abnormal areas and the filler characteristics, a targeted filling construction plan is developed. For example, areas with insufficient density can be compacted; areas with excessively high water content can be drained; and areas where the filler type does not meet the requirements can be replaced or improved. By setting construction plans based on zones, refined control of the roadbed filler can be achieved, improving construction efficiency and quality, and generating intelligent zoned filling construction plan data.

[0094] Step S4: Perform roadbed filler construction processing according to the intelligent zoning filler construction plan data, and adjust the roadbed filler parameter ratio to achieve high-speed railway roadbed filler parameter optimization operation.

[0095] In an embodiment of the present invention, the intelligent zoned filling construction plan data includes specific construction plans for different areas of the roadbed, such as areas requiring filler replacement, compaction, and grouting reinforcement, as well as information such as the required filler type, quantity, and parameter ratio. Based on this data, construction personnel can be guided to perform precise construction operations. For example, if the plan data indicates that area A requires filler replacement and specifies the required filler type as 100 cubic meters of graded crushed stone with a maximum particle size of 50 mm, the construction personnel will then need to remove the existing filler in area A, procure and transport 100 cubic meters of graded crushed stone as required, ensuring that the maximum particle size does not exceed 50 mm, and then fill area A with it. For areas requiring compaction, the plan data specifies parameters such as the compaction target value, number of compaction passes, and type of roller. Construction personnel will operate the roller according to these parameters and monitor the compaction in real time until the target value is achieved. For example, if area B requires compaction, the plan data specifies a compaction target of 95%, eight compaction passes, and the use of a vibratory roller. Construction workers will then need to use a vibratory roller to compact area B eight times, measuring the compaction degree after each pass to ensure a final compaction degree of 95%. For example, for a certain area, the intelligent zoning filler construction plan data indicates that the area has localized looseness and requires reinforcement. It also provides a specific reinforcement material ratio and construction process. During the roadbed filler construction process, it is necessary to continuously monitor the changes in filler parameters and dynamically adjust the filler parameter ratio to ensure that all parameters of the roadbed filler are in the optimal state. Ultimately, the high-speed railway roadbed filler parameter optimization operation is achieved, improving the stability and durability of the roadbed.

[0096] As an example of the present invention, refer to Figure 2 As shown, Figure 1 Detailed implementation steps of step S1 are shown in the flowchart. In this example, step S1 includes:

[0097] Step S11: Acquire the high-speed railway data to be detected; extract the roadbed filling material and path width from the high-speed railway data to obtain railway roadbed width data and roadbed filling material parameters respectively;

[0098] In an embodiment of the present invention, digital design information, construction records and historical inspection data of the high-speed railway section to be inspected are obtained. These data include information such as the geometry of the roadbed, design parameters, type of filling material used, compaction degree, etc. For example, the roadbed cross-section and longitudinal section of a certain section of the road can be obtained, as well as parameters such as the type of filler used in the section (such as graded gravel, clay, etc.), and design compaction degree. Then, the roadbed filling material parameters and path width data are extracted from these data. Roadbed filling material parameters include material type, particle size distribution, density, water content, etc. Path width data refers to the width of the top surface of the roadbed. For example, by analyzing the roadbed cross-section, the width of the roadbed and the type and parameters of the filler used at different depths can be determined.

[0099] Step S12: using a pre-set mobile inspection vehicle to carry a multi-band electromagnetic wave transmitting and receiving device to construct an electromagnetic integrated scanning device;

[0100] In this embodiment of the present invention, a mobile inspection vehicle suitable for high-speed railway roadbed inspection is used as the platform. This mobile inspection vehicle should have stable driving performance and sufficient load capacity to carry electromagnetic wave transmitters and receivers and related auxiliary equipment. A multi-band electromagnetic wave transmitter and receiver are installed on the mobile inspection vehicle. The transmitter is capable of generating electromagnetic wave signals of multiple frequencies, while the receiver is responsible for receiving electromagnetic wave signals reflected from the roadbed filler material. The selection of these devices should be determined based on the characteristics of the roadbed filler material to be inspected and the required inspection accuracy. For example, for detecting deep filler layers, a device capable of emitting low-frequency electromagnetic waves is required, while for high-precision imaging, a device capable of emitting high-frequency electromagnetic waves is required. The transmitter and receiver are integrated to form a complete electromagnetic integrated scanning device. This device should be able to simultaneously transmit and receive signals and accurately record the time information of transmission and reception. Furthermore, to improve inspection efficiency and accuracy, multiple transmitter and receiver units can be combined into an array to form a multi-channel electromagnetic integrated scanning device. For example, a linear array or area array layout can be used to achieve rapid scanning of the roadbed and high-resolution imaging. The resulting electromagnetic integrated scanning device is a key device that can efficiently and accurately collect electromagnetic response data of roadbed fillers.

[0101] Step S13: activating the detection sensor array of the electromagnetic integrated scanning device based on the railway roadbed width data to generate sensor array distribution data;

[0102] In this embodiment of the present invention, the range of the detection sensor array that needs to be activated on the electromagnetic integrated scanning device is determined based on the railway roadbed width data extracted in step S11. Since the roadbed width determines the scanning range, the activation range of the sensor array needs to be adjusted based on the roadbed width to ensure that the electromagnetic wave covers the entire roadbed width. For example, if the roadbed width is 12 meters and the total width of the sensor array is 15 meters, the portion of the sensor array covering the 12-meter width needs to be activated based on the position of the roadbed centerline. After activating the sensor array, the position information of each activated sensor is recorded to generate sensor array distribution data. This data describes the relative position of each sensor in the array and is crucial for subsequent data processing and analysis. For example, the sensor array distribution data may include information such as the sensor number, coordinates, and orientation of each sensor. This information can be used to determine the spatial location corresponding to the data collected by each sensor, thereby enabling spatial distribution analysis of roadbed filler characteristics. Furthermore, the sensor array distribution data can be used to calibrate positional deviations between sensors, improving detection accuracy.

[0103] Step S14: determining the initial position of the roadbed for the preset mobile inspection vehicle, setting the driving speed according to the high-speed railway data to be inspected, and generating the driving speed data of the inspection vehicle;

[0104] In an embodiment of the present invention, before starting the roadbed inspection, it is necessary to determine the initial driving position of the mobile inspection vehicle. Select a suitable starting point to ensure that the inspection vehicle can perform continuous scanning along the roadbed direction and cover the entire area to be inspected. For example, the initial position of the inspection vehicle can be set at the starting point of the roadbed or a known reference point. After determining the initial position, its coordinate information is recorded to facilitate spatial positioning during subsequent data processing. At the same time, the driving speed of the mobile inspection vehicle is set according to the relevant information in the high-speed railway data to be inspected. The setting of the driving speed requires comprehensive consideration of multiple factors, including detection accuracy requirements, data acquisition rate, roadbed length, etc. For example, if high-precision detection data is required, the driving speed should be reduced to increase the data sampling density; if the detection task needs to be completed quickly, the driving speed can be appropriately increased. The set driving speed should ensure that it remains stable throughout the detection process to avoid uneven detection data due to speed changes.

[0105] Step S15: performing electromagnetic wave scanning frequency processing according to the roadbed filling material parameters and the detection vehicle driving speed data, and performing electromagnetic wave emission control processing on the electromagnetic integrated scanning device through the sensor array distribution data to generate an electromagnetic wave emission control strategy;

[0106] In the embodiments of the present invention, since different materials respond differently to electromagnetic waves of different frequencies, it is necessary to select an appropriate scanning frequency based on the type and characteristics of the filling material to achieve optimal detection results. For example, for soils with high water content, a lower frequency of electromagnetic waves should be selected to increase penetration depth; for denser materials, the frequency should be adjusted to enhance the strength of the reflected signal. The speed of the inspection vehicle also influences the frequency selection, as speed determines the data sampling density. Therefore, the frequency needs to be adjusted based on speed to ensure data continuity and integrity. An electromagnetic wave emission control strategy is developed based on the sensor array distribution data. The sensor array distribution data provides information about the position of each sensor in the array. Based on this information, the timing and frequency of each sensor's electromagnetic wave emission can be determined. For example, each sensor can be activated sequentially in a specific order, or multiple sensors can be activated simultaneously for multi-channel scanning. Furthermore, a specific emission frequency is assigned to each sensor based on the requirements of the filling material and the driving speed. The resulting electromagnetic wave emission control strategy should include parameters such as the activation order, emission frequency, and emission power of each sensor to ensure that the electromagnetic wave effectively covers the entire width of the roadbed and obtain high-quality inspection data.

[0107] Step S16: Based on the electromagnetic wave emission control strategy, the electromagnetic integrated scanning device is used to perform multi-frequency electromagnetic wave scanning on the roadbed surface, and the electromagnetic wave reflection signal is synchronously received to obtain the original filler electromagnetic response signal data and the roadbed sampling position data.

[0108] In an embodiment of the present invention, an electromagnetic integrated scanning device is activated to perform a multi-frequency electromagnetic wave scan of the roadbed surface. A mobile inspection vehicle travels at a constant speed along the roadbed at a set speed. The electromagnetic integrated scanning device sequentially activates each sensor in the sensor array according to a control strategy, emitting electromagnetic waves of different frequencies. The emitted electromagnetic waves propagate through the roadbed filler and are reflected and refracted when encountering interfaces between different media. A receiving device synchronously receives the electromagnetic wave signals reflected from the roadbed filler. These reflected signals contain information about the filler's internal structure and properties. During the scanning process, parameters such as the electromagnetic wave reflection signal intensity and phase at each sampling point are synchronously recorded to generate raw filler electromagnetic response signal data. Simultaneously, a high-precision positioning system (such as GPS, inertial navigation, etc.) is used to record the spatial position of each sampling point to generate roadbed sampling position data. The sampling position data corresponds one-to-one to the electromagnetic response signal data. For example, the electromagnetic response signal can be plotted as a two-dimensional or three-dimensional image of the roadbed filler based on the sampling position data, intuitively displaying the spatial distribution of the filler properties.

[0109] Preferably, step S15 includes the following steps:

[0110] Step S151: Analyze the material magnetic permeability characteristics according to the roadbed filling material parameters to generate roadbed material characteristic data;

[0111] Step S152: Processing the transmission frequency of the transmitter according to the speed data of the detection vehicle to generate electromagnetic transmission frequency data;

[0112] Step S153: performing electromagnetic wave scanning frequency range matching on the electromagnetic emission frequency data to generate electromagnetic wave scanning frequency range data;

[0113] Step S154: dividing the electromagnetic wave scanning frequency range data into high and low frequency scanning frequency segments according to the roadbed material characteristic data to generate electromagnetic wave scanning frequency sequence data;

[0114] Step S155: Control the phased array beam direction of the electromagnetic integrated scanning device through the sensor array distribution data, and input the electromagnetic wave scanning frequency according to the electromagnetic wave scanning frequency sequence data, thereby obtaining the electromagnetic wave emission control strategy.

[0115] In an embodiment of the present invention, the magnetic permeability characteristics of the roadbed filling material are analyzed. Different types of fillers, such as sand, clay, fly ash, etc., have different magnetic permeabilities. The magnetic permeability values of different fillers can be obtained by consulting a material database or conducting laboratory tests. The filler type and its corresponding magnetic permeability value are stored as roadbed material characteristic data. For example, if the magnetic permeability of a certain graded gravel is 1.2, then "graded gravel, 1.2" is stored in the roadbed material characteristic data. The range of the electromagnetic wave emission frequency is determined based on the driving speed of the inspection vehicle. The faster the driving speed, the higher the sampling frequency required to ensure sufficient spatial resolution. For example, if the inspection vehicle speed is 10m / s, in order to achieve a spatial resolution of 0.1m, the electromagnetic wave emission frequency needs to be at least 100Hz. The calculated appropriate frequency range is stored as electromagnetic emission frequency data. The electromagnetic emission frequency data is matched with a preset electromagnetic wave scanning frequency range. The preset scanning frequency range is usually relatively wide, for example, 1MHz to 100MHz. Based on the electromagnetic transmission frequency data, an appropriate sub-range, such as 10 MHz to 20 MHz, is selected to improve scanning efficiency and accuracy. The scanning frequency range is divided into high-frequency and low-frequency bands based on the response characteristics of different materials to electromagnetic waves of different frequencies. For example, for materials with high magnetic permeability or electrical conductivity, selecting the low-frequency band can improve penetration depth; for situations requiring detection of surface details or resolution of fine structures, selecting the high-frequency band can improve resolution. This division process can be based on empirical values or theoretical calculations. After the division is completed, specific scanning frequency points are determined within each frequency band, and electromagnetic wave scanning frequency sequence data is generated. The phased array of the electromagnetic integrated scanning device is then beam-directed. Phased array technology achieves electronic scanning and directional transmission of the beam by adjusting the phase of each antenna element in the array. Based on the sensor array geometry and roadbed inspection requirements, the required phase delay for each antenna element is calculated to achieve directional scanning of specific roadbed areas. Simultaneously, based on the electromagnetic wave scanning frequency sequence data generated in step S154, each scanning frequency point is sequentially input into the electromagnetic integrated scanning device. This allows the device to transmit at each scanning frequency point in the predetermined beam direction. Finally, the beam direction control parameters and scanning frequency sequence data are integrated to generate a complete electromagnetic wave emission control strategy.

[0116] As an example of the present invention, refer to Figure 3 As shown, Figure 1 Detailed implementation steps of step S3 are shown in the flowchart. In this example, step S3 includes:

[0117] Step S21: performing time-space registration of the original filler electromagnetic response signal data to generate filler electromagnetic response signal time series data;

[0118] In an embodiment of the present invention, the original filler electromagnetic response signal data is obtained. These data are usually collected by an electromagnetic integrated scanning device when it moves and scans the roadbed surface. Due to the influence of factors such as the driving speed of the mobile detection vehicle and the electromagnetic wave emission frequency, these data are not aligned on the time axis and the space axis. In order to perform accurate analysis, it is necessary to perform spatiotemporal registration of the response signal of the original data. The specific operations of spatiotemporal registration include: associating each electromagnetic response signal data with the corresponding spatial position based on the roadbed sampling position data; determining the exact time of collection of each signal data based on the electromagnetic wave emission timestamp and the driving speed of the detection vehicle. Then, the signal data is resampled using an interpolation algorithm (such as linear interpolation, spline interpolation, etc.) to make it evenly distributed in time and space. For example, the data can be resampled according to fixed time intervals or spatial intervals to generate a continuous, equally spaced signal sequence to obtain the filler electromagnetic response signal time series data.

[0119] Step S22: performing adaptive wavelet denoising on the filler electromagnetic response signal time series data, and performing response signal error correction to obtain corrected electromagnetic response signal time series data;

[0120] In an embodiment of the present invention, due to the influence of environmental noise and systematic errors, the filler electromagnetic response signal time series data contains noise and errors. An adaptive wavelet denoising method is used to denoise the signal. Adaptive wavelet denoising can automatically adjust the wavelet basis function and the number of decomposition layers according to the local characteristics of the signal, thereby effectively removing noise while retaining the detailed information of the signal. For example, for areas with large signal fluctuations, a smaller number of decomposition layers can be selected to retain more details; for areas with small signal fluctuations, a larger number of decomposition layers can be selected to better remove noise. In addition, according to a pre-established error model or calibration data, the signal is error corrected, for example, to remove systematic deviations or compensate for temperature effects. The denoised and corrected data is stored as corrected electromagnetic response signal time series data.

[0121] Step S23: performing short-time Fourier transform on the time series data of the corrected electromagnetic response signal to generate frequency domain data of the filler electromagnetic response;

[0122] In an embodiment of the present invention, a short-time Fourier transform (STFT) is performed on the time series data of the corrected electromagnetic response signal. The STFT can convert the time domain signal into a frequency domain signal and retain the time information of the signal. Through the STFT, the energy distribution of different frequency components in different time periods can be analyzed. For example, it can be observed that the energy of certain frequency components in a specific time period is enhanced or attenuated, which is related to the change in the characteristics of the roadbed filler. The transformed data is stored as filler electromagnetic response frequency domain data, which contains the response amplitude and phase information of each position at different frequencies.

[0123] Step S24: performing signal amplitude and phase value processing on the filler electromagnetic response frequency domain data, and performing signal energy response threshold calculation to generate signal energy response threshold data;

[0124] In an embodiment of the present invention, the frequency domain data of the electromagnetic response of the filler is processed for signal amplitude and phase values, for example, the signal amplitude and phase at each frequency are calculated. Then, based on the amplitude and phase values of the signal, the energy response of the signal is calculated. For example, the square of the signal amplitude can be used as the signal energy. The signal energy response threshold is calculated by statistically analyzing the signal energy distribution at different positions and frequencies. The signal energy response threshold is calculated using the amplitude spectrum data. The signal energy response threshold is used to distinguish between valid signals and noise signals. For example, the mean and standard deviation of the amplitude spectrum can be calculated, and the mean plus several times the standard deviation can be used as the threshold; or, a suitable threshold can be selected based on the background noise level of the signal so that the amplitude of the noise signal is lower than the threshold. The calculated signal energy response threshold will be recorded as signal energy response threshold data.

[0125] Step S25: screening the filler electromagnetic response signal time series data for effective electromagnetic signals using the preset electromagnetic frequency determination rule and signal energy response threshold data to generate effective electromagnetic response signal data.

[0126] In an embodiment of the present invention, based on preset electromagnetic frequency judgment rules, it is determined which frequency ranges of signals need to be paid attention to. These rules are formulated based on the physical properties of the roadbed filler and the detection target. For example, if the focus is on the change in moisture content in the deep layer of the roadbed, a lower frequency range will be selected; if the focus is on the defects in the surface layer of the roadbed, a higher frequency range will be selected. Then, the signal energy response threshold data calculated in step S24 is used to filter the time series data. The specific operations include: performing a short-time Fourier transform on the time series data to obtain the spectrum at each time point; extracting the spectrum amplitude values within the frequency range of interest; comparing these amplitude values with the signal energy response threshold value. If the amplitude value is greater than the threshold, the signal is considered to be a valid signal, otherwise it is considered to be a noise signal. Finally, the time points and amplitude values corresponding to all valid signals are retained to generate valid electromagnetic response signal data.

[0127] Preferably, step S3 includes the following steps:

[0128] Step S31: performing multi-frequency electromagnetic response signal grouping on the effective electromagnetic response signal data according to the roadbed sampling position data to generate multi-frequency electromagnetic response signal group data;

[0129] Step S32: performing frequency band penetration depth analysis on the multi-frequency electromagnetic response signal group data to generate frequency band penetration depth data;

[0130] Step S33: extracting the signal propagation time of the multi-frequency electromagnetic response signal group data, and calculating the electromagnetic wave propagation velocity based on the frequency band penetration depth data to generate multi-frequency electromagnetic wave propagation velocity data;

[0131] Step S34: extracting the physical characteristics of the roadbed filler through the multi-frequency electromagnetic wave propagation velocity data to generate the physical characteristic data of the roadbed filler;

[0132] Step S35: Perform spatial feature statistics of abnormal areas based on the physical feature data of the roadbed filler to generate abnormal area spatial feature data; set a partitioned filler construction plan based on the abnormal area spatial feature data to generate intelligent partitioned filler construction plan data.

[0133] In an embodiment of the present invention, valid electromagnetic response signal data is grouped based on roadbed sampling location data. Each group corresponds to a specific roadbed area. For example, sampling points with similar distances can be grouped. Each group contains electromagnetic response signal data of multiple frequencies, forming multi-frequency electromagnetic response signal group data. The multi-frequency electromagnetic response signal group data generated in step S31 is subjected to frequency band penetration depth analysis. Electromagnetic waves of different frequencies have different penetration depths in roadbed fillers. High-frequency electromagnetic waves attenuate faster and have shallower penetration depths, while low-frequency electromagnetic waves attenuate slower and have deeper penetration depths. The analysis method can be based on a theoretical model of electromagnetic wave propagation. For example, the penetration depth of electromagnetic waves of different frequencies can be calculated based on their attenuation coefficients. The penetration depth of electromagnetic waves of different frequencies in specific fillers can also be determined through experimental measurements. The analysis results are stored as frequency band penetration depth data, recording the penetration depth value corresponding to each frequency band. The multi-frequency electromagnetic response signal group data is analyzed to extract the propagation time of the signal from transmission to reception. The propagation time can be determined by analyzing the signal's time domain waveform or frequency domain characteristics. For example, the propagation time can be determined by identifying the signal's peak or zero crossing. Then, combined with the frequency band penetration depth data obtained in step S32, the propagation velocity of the electromagnetic wave in different frequency bands is calculated. The propagation velocity is equal to the penetration depth divided by the propagation time. The calculated results are stored as multi-frequency electromagnetic wave propagation velocity data, recording the electromagnetic wave propagation velocity corresponding to each frequency band. Based on this multi-frequency electromagnetic wave propagation velocity data, the physical characteristics of the roadbed filler are extracted. The propagation velocity of electromagnetic waves in different media is related to their physical properties, such as dielectric constant and magnetic permeability. For example, a faster propagation velocity generally indicates a lower dielectric constant. By establishing a relationship model between electromagnetic wave propagation velocity and filler physical properties, the filler's physical characteristics, such as dielectric constant, magnetic permeability, density, and moisture content, can be inverted and calculated based on the multi-frequency electromagnetic wave propagation velocity data to generate roadbed filler physical characteristic data. Based on this filler physical characteristic data, such as density and moisture content, the presence of abnormal areas in the roadbed can be determined. For example, if the density of a certain area is significantly lower than that of other areas, this area is experiencing insufficient compaction. Statistical analysis is performed on the spatial distribution, area, shape, and other characteristics of the abnormal areas to generate spatial characteristic data for these areas. Based on the spatial characteristics of the abnormal areas and the filler properties, a targeted filler construction plan can be developed. For example, for areas with insufficient compaction, the number of compaction passes can be increased; for areas with excessively high water content, drainage measures can be implemented. Construction plans for different areas can be integrated to generate intelligent zoned filler construction plan data.

[0134] Preferably, step S34 includes the following steps:

[0135] Step S341: performing dielectric constant inversion based on the multi-frequency electromagnetic wave propagation velocity data to generate dielectric constant distribution data;

[0136] Step S342: performing electromagnetic frequency weighting on the dielectric constant distribution data, and performing weighted fusion of multi-frequency data to generate weighted fusion dielectric constant data;

[0137] Step S343: Calculating the electromagnetic wave attenuation coefficient based on the multi-frequency electromagnetic wave propagation velocity data and the weighted fusion dielectric constant data, and performing water content contribution processing to generate roadbed moisture content distribution data;

[0138] Step S344: using a preset filler density-dielectric constant linear model to estimate the filler density value of the weighted fusion dielectric constant data to generate filler density distribution characteristic data;

[0139] Step S345: performing filler particle distribution index processing on the roadbed moisture content distribution data using the filler density distribution characteristic data to generate roadbed particle distribution index parameters;

[0140] Step S346: Integrate the filler density distribution characteristic data, the roadbed moisture content distribution data, and the roadbed particle distribution index parameters into the roadbed filler physical characteristic data to generate the roadbed filler physical characteristic data.

[0141] In an embodiment of the present invention, the relationship between electromagnetic wave propagation velocity and dielectric constant is exploited to inversely calculate multi-frequency electromagnetic wave propagation velocity data to obtain dielectric constants at different frequencies. Because electromagnetic waves of different frequencies have varying sensitivities to dielectric constants, dielectric constant values corresponding to multiple frequencies can be obtained. This data is stored as dielectric constant distribution data, indexed by spatial location. Electromagnetic frequency weights are assigned to the dielectric constant distribution data based on the sensitivity of electromagnetic waves of different frequencies to the properties of the roadbed filler. For example, for a specific filler type, certain frequencies of electromagnetic waves are more likely to reflect its dielectric properties, and these frequencies are assigned higher weights. The dielectric constant data at different frequencies are then weighted averaged to obtain a fused dielectric constant value, generating weighted fused dielectric constant data. The multi-frequency electromagnetic wave propagation velocity data and the weighted fused dielectric constant data are combined to calculate the attenuation coefficient of electromagnetic waves in the roadbed filler. When electromagnetic waves propagate through a medium, energy attenuation occurs. The attenuation coefficient reflects the degree of attenuation and is related to properties such as the medium's conductivity and dielectric constant. After calculating the attenuation coefficient, the contribution of moisture content to the attenuation coefficient is further analyzed. Moisture content significantly affects the conductivity and dielectric constant of the filler, and thus the attenuation coefficient of electromagnetic waves. By establishing a relationship model between the attenuation coefficient and moisture content, such as an empirical formula or theoretical model, the moisture content distribution data of the roadbed filler can be calculated based on the inverse calculation of the attenuation coefficient. Using a pre-established linear model of filler density and dielectric constant, the weighted fused dielectric constant data generated in step S342 is used to estimate the filler density value. This linear model describes the linear relationship between filler density and dielectric constant and can be derived through experimental measurement or theoretical derivation. Substituting the weighted fused dielectric constant data into the linear model, the filler density value at the corresponding location is calculated. The calculated density value is combined with the spatial position information to generate filler density distribution characteristic data. Combined with the filler density distribution characteristic data generated in step S344 and the roadbed moisture content distribution data generated in step S343, filler particle distribution index processing is performed. The filler particle distribution affects its density and moisture content. For example, fillers with uniform particle gradation typically have higher density and lower moisture content. By analyzing the spatial distribution characteristics of filler density and moisture content, the filler particle distribution can be inferred. For example, based on the correlation between density and moisture content, the particle distribution inhomogeneity coefficient or other particle distribution indices can be calculated. By integrating filler density distribution characteristic data, subgrade moisture content distribution data, and subgrade particle distribution index parameters, physical characteristic data for subgrade fillers can be generated. This data contains information on various physical properties of subgrade fillers.

[0142] Preferably, step S35 includes the following steps:

[0143] Step S351: Acquire historical railway roadbed filling engineering data; construct a mapping relationship between filler physical characteristics and compaction degree based on the historical railway roadbed filling engineering data to obtain filler physical characteristics-compactness mapping relationship data;

[0144] Step S352: Using a preset convolutional neural network model to perform model transfer learning on the filler physical characteristics-compactness mapping relationship data, and using historical railway subgrade filler engineering data to perform model training to generate a subgrade filler compaction assessment model;

[0145] Step S353: transmitting the roadbed filler physical characteristic data to the roadbed filler compaction evaluation model for compaction evaluation to generate filler compaction data;

[0146] Step S354: matching roadbed sampling points according to the roadbed filler physical characteristic data, and performing spatial distribution difference of compaction according to the filler compaction data to generate roadbed compaction distribution data;

[0147] Step S355: Process the roadbed defect construction plan based on the roadbed compaction distribution data and the roadbed filler physical characteristic data to generate intelligent roadbed filler construction plan data.

[0148] In an embodiment of the present invention, historical railway roadbed filling engineering data are collected. These data should include physical characteristic parameters of the filler (such as density, water content, particle grading, etc.) and corresponding compaction data. The compaction data can be obtained through field testing, for example, by using methods such as light dynamic probing or heavy dynamic probing. Then, the historical data is analyzed to construct a mapping relationship between the physical characteristics of the filler and the compaction. For example, regression analysis, neural network and other methods can be used to establish a model with the physical characteristics of the filler as input and the compaction as output. For example, ResNet, VGG, etc. are used as basic models. The filler physical characteristics-compactness mapping relationship data obtained in step S351 is used for model transfer learning. Transfer learning refers to applying a pre-trained model to a new related task, which can effectively improve the training efficiency and generalization ability of the model. The specific operation includes initializing the weights of the pre-trained model to the new model, and then fine-tuning the new model using historical railway roadbed filling engineering data. During the training process, the physical characteristics of the filler are used as input and the compaction as output, and the model parameters are continuously adjusted so that it can accurately predict the compaction of the roadbed filling. The resulting trained model will serve as a subgrade fill compaction assessment model. The model predicts the compaction of subgrade fill based on input physical characteristic parameters. For example, physical characteristic data such as density and moisture content are input to the model, and the model outputs the compaction value for that sampling point. The predicted compaction values for all sampling points are integrated to generate filler compaction data. Based on the subgrade filler physical characteristic data, the filler compaction data is matched to the corresponding subgrade sampling point, ensuring that each compaction value corresponds to the correct spatial location. Because there are spatial gaps between sampling points, spatial compaction distribution interpolation is required to generate continuous subgrade compaction distribution data. Interpolation methods such as linear interpolation and kriging interpolation can be used. For example, compaction values at adjacent sampling points can be used to calculate the compaction value at any location between them. Based on the subgrade compaction distribution data and subgrade filler physical characteristic data, defects in the subgrade can be identified, such as insufficient compaction, excessive moisture content, and improper particle grading. Appropriate construction plans are then developed for each defect type. For example, for areas with insufficient compaction, the number of compaction passes can be increased or the compaction equipment can be replaced; for areas with too high water content, drainage measures can be taken or dry materials can be added. The construction plans of different areas can be integrated to generate intelligent roadbed filler construction plan data.

[0149] Preferably, step S355 includes the following steps:

[0150] Step S3551: using the preset roadbed filler early warning index library to perform cluster analysis on the measured roadbed condition on the roadbed filler physical characteristic data and roadbed compaction distribution data, and marking abnormal areas to generate preliminary filler abnormal area data;

[0151] Step S3552: performing spatial connectivity analysis on the preliminary filler abnormality area data and merging adjacent abnormal areas to obtain roadbed filler abnormality area data;

[0152] Step S3553: performing regional spatial feature statistics on the abnormal area data of the roadbed filler to generate abnormal area spatial feature data;

[0153] Step S3554: Setting the roadbed filling construction plan according to the spatial characteristic data of the abnormal area, and generating intelligent roadbed filling construction plan data.

[0154] In an embodiment of the present invention, a pre-established roadbed filler early warning index library contains characteristic indicators of various filler defects, such as the normal density range, moisture content range, and compaction range for different types of fillers. Using this index library, cluster analysis is performed on roadbed filler physical characteristic data and roadbed compaction distribution data. The measured data is compared with the early warning index, and areas that do not meet the early warning index are marked as abnormal areas. Spatial connectivity analysis is performed on the preliminary abnormal filler area data. If two adjacent areas are both marked as abnormal areas and their defect types are the same or similar, they are merged into a larger abnormal area. For example, if two adjacent areas both suffer from insufficient compaction, they are merged into a larger insufficient compaction area. Statistics include geometric characteristics of each abnormal area, such as area, perimeter, shape, depth, and center coordinates, as well as statistical values of the filler physical characteristic parameters (such as density, moisture content, and compaction) within the abnormal area, such as mean, standard deviation, maximum, and minimum values. This statistical information is integrated to generate spatial characteristic data for the abnormal areas. Based on the abnormal area spatial characteristic data and the roadbed filler early warning index library, a targeted construction plan is formulated. For example, large areas with insufficient compaction can be treated with large-scale compaction equipment; areas with complex shapes and difficult to treat can be refined by manual labor or small equipment; and different treatment methods, such as grouting and replacement filling, can be used for different types of defects. Construction plans for different areas can be integrated to generate intelligent roadbed filling construction plan data.

[0155] Preferably, step S3554 includes the following steps:

[0156] The abnormal area data of the roadbed filler is located by the abnormal area spatial feature data, and the roadbed filler risk area is divided, and the roadbed filler high hazard area data and roadbed filler low hazard area data are obtained respectively;

[0157] Perform fine grid division on the high-hazard area data of the roadbed filler to generate fine grid data of the high-hazard area;

[0158] Use the physical characteristic data of roadbed filler to calculate filler physical anomaly values on the refined grid data of high-risk areas, and generate filler physical anomaly data of high-risk areas;

[0159] The compaction loss of high-hazard area is calculated based on the refined grid data of high-hazard area through the roadbed compaction distribution data and the physical anomaly data of filler in high-hazard area, and the compaction loss data of high-hazard area is generated;

[0160] Set up multi-dimensional reinforcement plans based on the compaction loss data of high-risk areas and generate reinforcement plan data for high-risk areas;

[0161] Use the physical characteristic data of roadbed fill to conduct routine quality assessment of low-hazard area data of roadbed fill to generate assessment benchmark values for low-hazard area;

[0162] Conduct construction process adaptability analysis based on the assessment benchmark values for low-hazard areas, optimize standard construction parameters, and generate construction plan data for low-hazard areas;

[0163] The construction plan data of low-hazard areas and the reinforcement plan data of high-hazard areas are integrated into partitioned construction plans to obtain intelligent partitioned filling material construction plan data.

[0164] In an embodiment of the present invention, the abnormal area of the roadbed filler is located based on the spatial characteristic data of the abnormal area (such as area, shape, depth, etc.), and the abnormal area is divided into high-hazard area and low-hazard area according to pre-set thresholds or rules. For example, an area with an area greater than 10 square meters and a compaction degree less than 90% can be classified as a high-hazard area. The high-hazard area is finely gridded, for example, the area is divided into 1m×1m grids. This can more finely analyze and process the differences within the high-hazard area. The physical characteristic data of the roadbed filler (such as density, water content, etc.) are used to calculate the abnormal value of the filler physical characteristics in each grid. For example, the deviation of the density of each grid from the average density can be calculated. Combining the roadbed compaction distribution data and the filler physical anomaly data, the compaction loss value of each grid is calculated. For example, the theoretical maximum compaction of the filler can be inferred based on its density and water content, and then compared with the measured compaction. Based on the compaction loss value of each grid and the filler physical anomaly data, a multi-dimensional reinforcement plan is set. For example, areas with severe compaction loss and low density can be reinforced with dynamic tamping, while areas with high water content can be reinforced with drainage. Reinforcement plan data for high-hazard areas is generated, including specific reinforcement measures for each grid. Using the physical characteristics of the roadbed fill material, a routine quality assessment is conducted for low-hazard areas, such as calculating average density, average water content, and average compaction, to generate a baseline assessment value for the low-hazard areas. Based on the baseline assessment value for the low-hazard areas, the adaptability of different construction techniques is analyzed, and standard construction parameters are optimized. For example, if the fill material quality in a low-hazard area is good, standard compaction techniques and parameters can be used; if the fill material quality is poor, parameters such as the number of compaction passes and rolling speed need to be adjusted. Construction plan data for the low-hazard areas is generated, including specific construction techniques and parameters for each area. The reinforcement plan data for high-hazard areas and the construction plan data for low-hazard areas are integrated to generate the final intelligent zoned fill material construction plan data. This data contains detailed construction plans for all areas, guiding construction personnel in targeted operations to ensure that the roadbed fill material quality meets requirements.

[0165] Preferably, the present invention further provides a high-speed railway roadbed filler parameter optimization system, which executes the high-speed railway roadbed filler parameter optimization method described above, and the high-speed railway roadbed filler parameter optimization system comprises:

[0166] The roadbed electromagnetic scanning module is used to obtain the data of the high-speed railway to be inspected; the electromagnetic wave scanning frequency processing is performed on the high-speed railway data to generate an electromagnetic wave emission control strategy; based on the electromagnetic wave emission control strategy, a multi-frequency electromagnetic wave scan is performed on the roadbed surface to obtain the electromagnetic response signal data of the original filler and the roadbed sampling position data;

[0167] The electromagnetic response signal analysis module is used to perform time-space registration of the original filler electromagnetic response signal data to generate filler electromagnetic response signal time series data; perform electromagnetic effective signal screening based on the filler electromagnetic response signal time series data to generate effective electromagnetic response signal data;

[0168] The roadbed construction processing module is used to group the effective electromagnetic response signal data into multi-frequency electromagnetic response signals based on the roadbed sampling position data to generate multi-frequency electromagnetic response signal group data; extract the physical characteristics of the roadbed filler based on the multi-frequency electromagnetic response signal group data to generate roadbed filler physical characteristic data; perform spatial feature statistics of abnormal areas based on the roadbed filler physical characteristic data to generate abnormal area spatial characteristic data; and set a partitioned filler construction plan based on the abnormal area spatial characteristic data to generate intelligent partitioned filler construction plan data;

[0169] The subgrade filling parameter optimization module is used to carry out subgrade filling construction processing according to the intelligent partition filling construction plan data, and adjust the subgrade filling parameter ratio to achieve high-speed railway subgrade filling parameter optimization operations.

[0170] This application leverages the sensitivity of electromagnetic waves to the internal structure of fillers. Through multi-frequency electromagnetic wave scanning and sophisticated signal analysis, it can deeply detect the compaction level, uniformity, and potential defects of roadbed fillers, completely breaking away from the subjectivity and uncertainty of traditional manual judgment. By temporally and spatially registering the electromagnetic response signals and filtering effective signals, it can accurately identify differences in the physical characteristics of the filler, and thus accurately determine whether the compaction quality meets the standards, effectively avoiding problems such as localized insufficient or uneven compaction. Through intelligent data processing and analysis, it rapidly generates filler physical characteristic data and spatial characteristic data of abnormal areas, greatly shortening detection time and improving construction efficiency. Furthermore, the intelligent partitioned filler construction plan generated based on the analysis results can guide construction personnel in targeted filler parameter ratio adjustment and compaction operations, ensuring that each layer of filler achieves optimal compaction, avoiding blind construction and repeated rework, and significantly improving the overall roadbed construction quality. By precisely controlling the compaction quality of roadbed fillers, problems such as subsidence, deformation, and cracking can be fundamentally prevented. The long-term stability of the roadbed is ensured, reducing the frequency and workload of subsequent maintenance and repairs, thereby lowering maintenance costs throughout the entire lifecycle. Furthermore, by avoiding safety hazards caused by roadbed quality issues, the safety and reliability of high-speed rail operations are improved, ensuring passenger safety and smooth rail transportation.

[0171] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0172] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing parameters of high-speed railway roadbed filling materials, characterized in that: The following steps are involved: Step S1: Acquire high-speed railway data to be detected; Perform electromagnetic wave scanning frequency processing on the high-speed railway data to be inspected and generate an electromagnetic wave emission control strategy; Based on the electromagnetic wave emission control strategy, multi-frequency electromagnetic wave scanning is performed on the roadbed surface to obtain the original filler electromagnetic response signal data and roadbed sampling position data respectively; Step S2: performing time-space registration of the original filler electromagnetic response signal data to generate filler electromagnetic response signal time series data; performing electromagnetic effective signal screening based on the filler electromagnetic response signal time series data to generate effective electromagnetic response signal data; Step S3: performing multi-frequency electromagnetic response signal grouping on the effective electromagnetic response signal data based on the roadbed sampling position data to generate multi-frequency electromagnetic response signal group data; extracting the physical characteristics of the roadbed filler based on the multi-frequency electromagnetic response signal group data to generate roadbed filler physical characteristic data; performing spatial characteristic statistics of abnormal areas based on the roadbed filler physical characteristic data to generate abnormal area spatial characteristic data; setting a partitioned filler construction plan based on the abnormal area spatial characteristic data to generate intelligent partitioned filler construction plan data; Step S4: Perform roadbed filler construction processing according to the intelligent zoning filler construction plan data, and adjust the roadbed filler parameter ratio to achieve high-speed railway roadbed filler parameter optimization operation.

2. The method for optimizing parameters of high-speed railway roadbed filling materials according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire the high-speed railway data to be detected; extract the roadbed filling material and path width from the high-speed railway data to obtain railway roadbed width data and roadbed filling material parameters respectively; Step S12: using a pre-set mobile inspection vehicle to carry a multi-band electromagnetic wave transmitting and receiving device to construct an electromagnetic integrated scanning device; Step S13: activating the detection sensor array of the electromagnetic integrated scanning device based on the railway roadbed width data to generate sensor array distribution data; Step S14: determining the initial position of the roadbed for the preset mobile inspection vehicle, setting the driving speed according to the high-speed railway data to be inspected, and generating the driving speed data of the inspection vehicle; Step S15: performing electromagnetic wave scanning frequency processing according to the roadbed filling material parameters and the detection vehicle driving speed data, and performing electromagnetic wave emission control processing on the electromagnetic integrated scanning device through the sensor array distribution data to generate an electromagnetic wave emission control strategy; Step S16: Based on the electromagnetic wave emission control strategy, the electromagnetic integrated scanning device is used to perform multi-frequency electromagnetic wave scanning on the roadbed surface, and the electromagnetic wave reflection signal is synchronously received to obtain the original filler electromagnetic response signal data and the roadbed sampling position data.

3. The method for optimizing parameters of high-speed railway roadbed filling materials according to claim 2, characterized in that: Step S15 includes the following steps: Step S151: Analyze the material magnetic permeability characteristics according to the roadbed filling material parameters to generate roadbed material characteristic data; Step S152: Processing the transmission frequency of the transmitter according to the speed data of the detection vehicle to generate electromagnetic transmission frequency data; Step S153: performing electromagnetic wave scanning frequency range matching on the electromagnetic emission frequency data to generate electromagnetic wave scanning frequency range data; Step S154: dividing the electromagnetic wave scanning frequency range data into high and low frequency scanning frequency segments according to the roadbed material characteristic data to generate electromagnetic wave scanning frequency sequence data; Step S155: Control the phased array beam direction of the electromagnetic integrated scanning device through the sensor array distribution data, and input the electromagnetic wave scanning frequency according to the electromagnetic wave scanning frequency sequence data, thereby obtaining the electromagnetic wave emission control strategy.

4. The method for optimizing parameters of high-speed railway roadbed filler according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing time-space registration of the original filler electromagnetic response signal data to generate filler electromagnetic response signal time series data; Step S22: performing adaptive wavelet denoising on the filler electromagnetic response signal time series data, and performing response signal error correction to obtain corrected electromagnetic response signal time series data; Step S23: performing short-time Fourier transform on the time series data of the corrected electromagnetic response signal to generate frequency domain data of the filler electromagnetic response; Step S24: performing signal amplitude and phase value processing on the filler electromagnetic response frequency domain data, and performing signal energy response threshold calculation to generate signal energy response threshold data; Step S25: screening the filler electromagnetic response signal time series data for effective electromagnetic signals using the preset electromagnetic frequency determination rule and signal energy response threshold data to generate effective electromagnetic response signal data.

5. The method for optimizing parameters of high-speed railway roadbed filling materials according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing multi-frequency electromagnetic response signal grouping on the effective electromagnetic response signal data according to the roadbed sampling position data to generate multi-frequency electromagnetic response signal group data; Step S32: performing frequency band penetration depth analysis on the multi-frequency electromagnetic response signal group data to generate frequency band penetration depth data; Step S33: extracting the signal propagation time of the multi-frequency electromagnetic response signal group data, and calculating the electromagnetic wave propagation velocity based on the frequency band penetration depth data to generate multi-frequency electromagnetic wave propagation velocity data; Step S34: extracting the physical characteristics of the roadbed filler through the multi-frequency electromagnetic wave propagation velocity data to generate the physical characteristic data of the roadbed filler; Step S35: Perform spatial feature statistics of abnormal areas based on the physical feature data of the roadbed filler to generate abnormal area spatial feature data; set a partitioned filler construction plan based on the abnormal area spatial feature data to generate intelligent partitioned filler construction plan data.

6. The method for optimizing parameters of high-speed railway roadbed filling materials according to claim 5, characterized in that: Step S34 includes the following steps: Step S341: performing dielectric constant inversion based on the multi-frequency electromagnetic wave propagation velocity data to generate dielectric constant distribution data; Step S342: performing electromagnetic frequency weighting on the dielectric constant distribution data, and performing weighted fusion of multi-frequency data to generate weighted fusion dielectric constant data; Step S343: Calculating the electromagnetic wave attenuation coefficient based on the multi-frequency electromagnetic wave propagation velocity data and the weighted fusion dielectric constant data, and performing water content contribution processing to generate roadbed moisture content distribution data; Step S344: using a preset filler density-dielectric constant linear model to estimate the filler density value of the weighted fusion dielectric constant data to generate filler density distribution characteristic data; Step S345: performing filler particle distribution index processing on the roadbed moisture content distribution data using the filler density distribution characteristic data to generate roadbed particle distribution index parameters; Step S346: Integrate the filler density distribution characteristic data, the roadbed moisture content distribution data, and the roadbed particle distribution index parameters into the roadbed filler physical characteristic data to generate the roadbed filler physical characteristic data.

7. The method for optimizing parameters of high-speed railway roadbed filling materials according to claim 5, characterized in that: Step S35 includes the following steps: Step S351: Acquire historical railway roadbed filling engineering data; construct a mapping relationship between filler physical characteristics and compaction degree based on the historical railway roadbed filling engineering data to obtain filler physical characteristics-compactness mapping relationship data; Step S352: Using a preset convolutional neural network model to perform model transfer learning on the filler physical characteristics-compactness mapping relationship data, and using historical railway subgrade filler engineering data to perform model training to generate a subgrade filler compaction assessment model; Step S353: transmitting the roadbed filler physical characteristic data to the roadbed filler compaction evaluation model for compaction evaluation to generate filler compaction data; Step S354: matching roadbed sampling points according to the roadbed filler physical characteristic data, and performing spatial distribution difference of compaction according to the filler compaction data to generate roadbed compaction distribution data; Step S355: Process the roadbed defect construction plan based on the roadbed compaction distribution data and the roadbed filler physical characteristic data to generate intelligent roadbed filler construction plan data.

8. The method for optimizing parameters of high-speed railway roadbed filling materials according to claim 7, characterized in that: Step S355 includes the following steps: Step S3551: using the preset roadbed filler early warning index library to perform cluster analysis on the measured roadbed condition on the roadbed filler physical characteristic data and roadbed compaction distribution data, and marking abnormal areas to generate preliminary filler abnormal area data; Step S3552: performing spatial connectivity analysis on the preliminary filler abnormality area data and merging adjacent abnormal areas to obtain roadbed filler abnormality area data; Step S3553: performing regional spatial feature statistics on the abnormal area data of the roadbed filler to generate abnormal area spatial feature data; Step S3554: Setting the roadbed filling construction plan according to the spatial characteristic data of the abnormal area, and generating intelligent roadbed filling construction plan data.

9. The method for optimizing parameters of high-speed railway roadbed filling materials according to claim 8, characterized in that: Step S3554 includes the following steps: The abnormal area data of the roadbed filler is located by the abnormal area spatial feature data, and the roadbed filler risk area is divided, and the roadbed filler high hazard area data and roadbed filler low hazard area data are obtained respectively; Perform fine grid division on the high-hazard area data of the roadbed filler to generate fine grid data of the high-hazard area; Use the physical characteristic data of roadbed filler to calculate filler physical anomaly values on the refined grid data of high-risk areas, and generate filler physical anomaly data of high-risk areas; The compaction loss of high-hazard area is calculated based on the refined grid data of high-hazard area through the roadbed compaction distribution data and the physical anomaly data of filler in high-hazard area, and the compaction loss data of high-hazard area is generated; Set up multi-dimensional reinforcement plans based on the compaction loss data of high-risk areas and generate reinforcement plan data for high-risk areas; Use the physical characteristic data of roadbed fill to conduct routine quality assessment of low-hazard area data of roadbed fill to generate assessment benchmark values for low-hazard area; Conduct construction process adaptability analysis based on the assessment benchmark values for low-hazard areas, optimize standard construction parameters, and generate construction plan data for low-hazard areas; The construction plan data of low-hazard areas and the reinforcement plan data of high-hazard areas are integrated into partitioned construction plans to obtain intelligent partitioned filling material construction plan data.

10. A high-speed railway roadbed filling parameter optimization system, characterized in that: For executing the high-speed railway roadbed filling material parameter optimization method according to claim 1, the high-speed railway roadbed filling material parameter optimization system comprises: The roadbed electromagnetic scanning module is used to obtain the data of the high-speed railway to be inspected; the electromagnetic wave scanning frequency processing is performed on the high-speed railway data to generate an electromagnetic wave emission control strategy; based on the electromagnetic wave emission control strategy, a multi-frequency electromagnetic wave scan is performed on the roadbed surface to obtain the electromagnetic response signal data of the original filler and the roadbed sampling position data; The electromagnetic response signal analysis module is used to perform time-space registration of the original filler electromagnetic response signal data to generate filler electromagnetic response signal time series data; perform electromagnetic effective signal screening based on the filler electromagnetic response signal time series data to generate effective electromagnetic response signal data; The roadbed construction processing module is used to group the effective electromagnetic response signal data into multi-frequency electromagnetic response signals based on the roadbed sampling position data to generate multi-frequency electromagnetic response signal group data; extract the physical characteristics of the roadbed filler based on the multi-frequency electromagnetic response signal group data to generate roadbed filler physical characteristic data; perform spatial feature statistics of abnormal areas based on the roadbed filler physical characteristic data to generate abnormal area spatial characteristic data; and set a partitioned filler construction plan based on the abnormal area spatial characteristic data to generate intelligent partitioned filler construction plan data; The subgrade filling parameter optimization module is used to carry out subgrade filling construction processing according to the intelligent partition filling construction plan data, and adjust the subgrade filling parameter ratio to achieve high-speed railway subgrade filling parameter optimization operations.

Images