Method, device and equipment for identifying void ratio of patch stone roadbed and storage medium

Abstract

The application discloses a kind of piece block stone roadbed porosity identification method, device, equipment and storage medium, it is related to highway detection technical field.The method comprises: obtaining the initial structure data of the road to be processed;The initial structure data includes surface layer initial data, base layer initial data, gravel layer initial data and piece block stone layer initial data;Based on a plurality of preset porosities and the initial structure data, the roadbed structure simulation model corresponding to each preset porosity is constructed;The simulation piece block stone layer field intensity and preset porosity of each roadbed structure simulation model are fitted to obtain a fitting formula;Obtain the actual detection data of the road to be processed;Based on the actual detection data, obtain the measured piece block stone layer field intensity;Based on the measured piece block stone layer field intensity, the fitting formula is used to obtain the porosity of the piece block stone layer.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of highway detection, in particular to the technical field of highway subgrade detection and the like, and specifically relates to a void ratio identification method, device and equipment for a piece stone subgrade and a storage medium. BACKGROUND

[0002] Generally, piece stone subgrades are widely used in permafrost regions due to their unique pore structure, and the core thermal regulation capacity depends on the void ratio. However, in permafrost regions, various factors can easily cause void blockage, which can cause the thermal regulation mechanism to fail and threaten the operation of the traffic trunk line. Precise detection of the dynamic changes in the void ratio is of great significance for evaluating the performance of the subgrade.

[0003] However, there is a serious lack of direct detection and research on the void ratio. Therefore, there is an urgent need for a rapid and non-destructive detection method for the void ratio of a permafrost piece stone subgrade to achieve non-destructive, rapid and continuous detection of the void structure of the subgrade and provide technical support for the health management of the permafrost subgrade. SUMMARY

[0004] The present application provides a void ratio identification method, device, equipment and storage medium for a piece stone subgrade, which solves the problem of being unable to effectively detect the void ratio of the piece stone layer of a permafrost road, and the technical solution is as follows:

[0005] In a first aspect, a void ratio identification method for a piece stone subgrade is provided, and the method comprises:

[0006] Obtaining initial structure data of a road to be processed; the initial structure data comprises surface layer initial data, base layer initial data, gravel layer initial data and piece stone layer initial data;

[0007] Based on a plurality of preset void ratios and the initial structure data, a subgrade structure simulation model corresponding to each preset void ratio is constructed;

[0008] Fitting the simulated piece stone layer field intensity and the preset void ratio of each subgrade structure simulation model to obtain a fitting formula;

[0009] Obtaining actual detection data of the road to be processed;

[0010] Based on the actual detection data, a measured piece stone layer field intensity is obtained;

[0011] Based on the measured piece stone layer field intensity, the fitting formula is used to obtain the void ratio of the piece stone layer.

[0012] In a possible implementation manner, the construction of the subgrade structure simulation model corresponding to each preset void ratio based on the plurality of preset void ratios and the initial structure data comprises:

[0013] obtaining a macadam dielectric constant of a macadam layer of the road to be processed;

[0014] Based on a plurality of preset void ratios, macadam dielectric constants, and the initial structure data, an electromagnetic field simulation algorithm is used to construct a roadbed structure simulation model corresponding to each preset void ratio.

[0015] In a possible implementation, the fitting processing of the simulation macadam layer field strength and the preset void ratio of each roadbed structure simulation model is used to obtain a fitting formula, including:

[0016] The simulation macadam layer field strength of each roadbed structure simulation model is obtained through simulation detection processing of each roadbed structure simulation model.

[0017] The simulation macadam layer field strength and the preset void ratio of each roadbed structure simulation model are fitted to obtain a fitting formula of the macadam layer field strength and the void ratio.

[0018] In a possible implementation, the simulation macadam layer field strength of each roadbed structure simulation model is obtained through simulation detection processing of each roadbed structure simulation model, including:

[0019] The simulation detection data of each roadbed structure simulation model is obtained through simulation detection processing of each roadbed structure simulation model.

[0020] For each roadbed structure simulation model, the simulation detection data of the roadbed structure simulation model is processed through gain processing.

[0021] Based on the result of the gain processing, simulation position data of the macadam layer is determined.

[0022] Based on the simulation position data of the macadam layer, the simulation macadam layer field strength of the roadbed structure simulation model is selected from the result of the gain processing to obtain the simulation macadam layer field strength of each roadbed structure simulation model.

[0023] In a possible implementation, the actual detection data of the road to be processed is obtained, including:

[0024] The original actual detection data of the road to be processed is obtained.

[0025] Based on the simulation detection data, the original actual detection data is processed through correction processing.

[0026] The result of the correction processing is processed through gain processing to obtain the actual detection data.

[0027] In a possible implementation, the correction processing of the original actual detection data based on the simulation detection data comprises:

[0028] The surface layer simulation amplitude, the base layer simulation amplitude, and the gravel layer simulation amplitude are determined based on the simulation detection data.

[0029] The surface layer measured amplitude, the base layer measured amplitude, and the gravel layer measured amplitude are determined based on the original actual detection data.

[0030] The correction coefficient is calculated based on the surface layer simulation amplitude, the base layer simulation amplitude, the gravel layer simulation amplitude, the surface layer measured amplitude, the base layer measured amplitude, and the gravel layer measured amplitude.

[0031] The original actual detection data is corrected based on the correction coefficient.

[0032] In a possible implementation, the measured patch stone layer field strength is obtained based on the actual detection data, which comprises:

[0033] The measured position data of the patch stone layer is determined based on the actual detection data.

[0034] The measured patch stone layer field strength is selected from the actual detection data based on the measured position data of the patch stone layer.

[0035] In a possible implementation, the void ratio of the patch stone layer is obtained based on the measured patch stone layer field strength and the fitting formula, which comprises:

[0036] A plurality of continuous field strengths in the measured patch stone layer field strength are obtained.

[0037] The plurality of continuous field strengths are added and averaged.

[0038] The void ratio of the patch stone layer is obtained based on the result of the adding and averaging and the fitting formula.

[0039] In a possible implementation, the plurality of preset void ratios comprise a first void ratio, a second void ratio, and a third void ratio, the first void ratio representing that the ventilation condition of the patch stone layer is normal, the second void ratio representing that the ventilation condition of the patch stone layer is partially normal, and the third void ratio representing that the ventilation condition of the patch stone layer is invalid.

[0040] In a second aspect, a void ratio identification device for a patch stone subgrade is provided, and the device comprises:

[0041] A first obtaining unit is configured to obtain initial structure data of a road to be processed, wherein the initial structure data comprises surface layer initial data, base layer initial data, gravel layer initial data, and patch stone layer initial data.

[0042] The first construction unit is configured to construct a simulation model of a subgrade structure corresponding to each preset void ratio based on the plurality of preset void ratios and the initial structure data;

[0043] The first fitting unit is configured to perform fitting processing on the simulation slice stone layer field intensity and the preset void ratio of each simulation model of the subgrade structure to obtain a fitting formula;

[0044] The second acquisition unit is configured to acquire actual detection data of a to-be-processed road;

[0045] The first obtaining unit is configured to obtain a measured slice stone layer field intensity based on the actual detection data;

[0046] The second obtaining unit is configured to obtain the void ratio of the slice stone layer by using the fitting formula based on the measured slice stone layer field intensity.

[0047] In a third aspect, a computer-readable storage medium is provided, and the storage medium stores at least one instruction. The at least one instruction is loaded and executed by a processor to implement the method of the above aspects and any possible implementation manner.

[0048] In a fourth aspect, an electronic device is provided, which includes:

[0049] at least one processor; and

[0050] a memory connected in communication with the at least one processor; wherein

[0051] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of the above aspects and any possible implementation manner.

[0052] In a fifth aspect, a computer program product is provided, which includes a computer program. The computer program, when executed by a processor, implements the method of the above aspects and any possible implementation manner.

[0053] The technical solutions provided in the present application have at least the following beneficial effects:

[0054] According to the technical solution, the initial structure data of the to-be-processed road is obtained, the initial structure data includes surface layer initial data, base layer initial data, gravel layer initial data, and patch stone layer initial data, then a roadbed structure simulation model corresponding to each preset void ratio is constructed based on the preset void ratios and the initial structure data, the simulation patch stone layer field strength and the preset void ratio of each roadbed structure simulation model are fitted to obtain a fitting formula, actual detection data of the to-be-processed road is obtained, the measured patch stone layer field strength is obtained based on the actual detection data, and the void ratio of the patch stone layer is obtained based on the measured patch stone layer field strength and the fitting formula. Since the electric field strength of the ground penetrating radar electromagnetic echo is associated with the void ratio of the patch stone layer, and the fitting formula representing the relationship between the electric field strength and the void ratio is established, the internal void ratio of the patch stone layer in the permafrost region is effectively quantitatively detected, the reliability of the internal void ratio detection of the patch stone layer is improved, and the reliability of the permafrost roadbed health management evaluation is improved.

[0055] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0057] Figure 1 is a flowchart of a patch stone roadbed void ratio identification method provided by an embodiment of the present application;

[0058] Figure 2 is a flowchart of a patch stone roadbed void ratio identification method provided by another embodiment of the present application;

[0059] Figure 3 is a schematic diagram of a roadbed structure simulation model of a patch stone roadbed void ratio identification method provided by another embodiment of the present application;

[0060] Figure 4 is a schematic diagram of a wave field snapshot of a roadbed structure simulation model of a patch stone roadbed void ratio identification method provided by another embodiment of the present application;

[0061] Figure 5is a schematic diagram of a fitting relationship between the electric field intensity and the porosity of the piece stone layer of the piece stone roadbed porosity identification method provided by another embodiment of the present application;

[0062] Figure 6 is a structural block diagram of a piece stone roadbed porosity identification device provided by another embodiment of the present application;

[0063] Figure 7 is a block diagram of an electronic device for implementing the piece stone roadbed porosity identification method of the embodiments of the present application. DETAILED DESCRIPTION

[0064] The exemplary embodiments of the present application are described below with reference to the accompanying drawings, which include various details of the embodiments of the present application to assist in understanding, and should be considered as merely exemplary. Thus, those skilled in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, in the following description, descriptions of well-known functions and constructions are omitted for clarity and conciseness.

[0065] Obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0066] It should be noted that the terminal device involved in the embodiments of the present application can include but is not limited to a mobile phone, a personal digital assistant (PDA), a wireless handheld device, a tablet computer, and the like. The display device can include but is not limited to a personal computer, a television, and the like.

[0067] In addition, the term "and / or" in this paper is only a description of the association between the associated objects, which means that there can be three relationships, for example, A and / or B can represent the existence of A alone, the existence of A and B together, and the existence of B alone. In addition, the character " / " in this paper generally represents an "or" relationship between the associated objects before and after it.

[0068] Permafrost is easily disturbed by road engineering and broken thermal balance, triggering a chain of disasters. Piece stone roadbed is widely used in permafrost regions due to its unique pore structure, and its core thermal regulation capacity depends on the porosity. However, in the Qinghai-Tibet Plateau, various factors easily lead to porosity blockage, making the thermal regulation mechanism ineffective and threatening the operation of the main traffic line. Precise detection of dynamic changes in porosity is of great significance to the evaluation of roadbed performance.

[0069] At present, there is a serious lack of direct detection and research on void ratio. The current quality evaluation method is mainly construction period compaction control and roadbed temperature field monitoring based on temperature measuring hole, but both methods have limitations and cannot directly quantify the void ratio, making it difficult to evaluate the degree of blockage and provide a basis for scientific maintenance.

[0070] Therefore, there is an urgent need for a void ratio identification method for block stone roadbed, which can quickly and non-destructively identify the void structure of the block stone layer of the roadbed, thereby ensuring the reliability of the frozen soil roadbed health management.

[0071] Please refer to Figure 1 which shows a flowchart of a void ratio identification method for a block stone roadbed according to an embodiment of the present application. The void ratio identification method for the block stone roadbed can specifically include:

[0072] Step 101, obtaining initial structure data of a road to be processed; the initial structure data includes surface layer initial data, base layer initial data, gravel layer initial data, and block stone layer initial data.

[0073] Step 102, based on a plurality of preset void ratios and the initial structure data, constructing a roadbed structure simulation model corresponding to each preset void ratio.

[0074] Step 103, fitting the simulation block stone layer field strength and the preset void ratio of each roadbed structure simulation model to obtain a fitting formula.

[0075] Step 104, obtaining actual detection data of the road to be processed.

[0076] Step 105, based on the actual detection data, obtaining a measured block stone layer field strength.

[0077] Step 106, based on the measured block stone layer field strength, using the fitting formula to obtain the void ratio of the block stone layer.

[0078] It should be noted that the ground penetrating radar can be used to detect the road to be processed to obtain the detection data.

[0079] It should be noted that the initial structure data can include the thickness, upper limit depth and lower limit depth of each structure layer, etc. The initial structure data can be obtained according to the design data of the road to be processed. The design data can include but is not limited to engineering geological plan and profile, engineering geological longitudinal section, hydrogeological conditions, drilling columnar chart, physical indicators of each layer of soil, etc.

[0080] It should be noted that the types of the underground structure layers of the block stone roadbed can include a pavement layer, a general upper roadbed layer, a gravel layer, a block stone layer, a movable layer, permafrost, and the like. The pavement layer is a surface layer, and the general upper roadbed layer is a base layer.

[0081] It should be noted that the preset porosity can be a preset block stone layer porosity. The different roadbed structure simulation models can be simulation models of structure layers of a roadbed with different block stone layer porosities.

[0082] It can be understood that the field strength of the block stone layer can be the electric field strength of the block stone layer.

[0083] It should be noted that part or all of the execution subjects of steps 101 to 106 can be an application located at the local terminal, or can also be a plug-in or a software development kit (SDK) and the like functional units arranged in the application located at the local terminal, or can also be a processing engine located in a network side server, or can also be a distributed system located at the network side, for example, a processing engine or a distributed system in a data processing platform at the network side, and the like, which are not particularly limited in the embodiment.

[0084] It can be understood that the application can be a native application (nativeApp) installed on the local terminal, or can also be a web application (webApp) of a browser on the local terminal, which is not limited in the embodiment.

[0085] In this way, by associating the electric field strength of the ground penetrating radar electromagnetic echo with the porosity of the block stone road layer and establishing a fitting formula representing the relationship between the two, effective quantitative detection of the internal porosity of the block stone layer in the permafrost area is achieved, the reliability of the internal porosity detection of the block stone layer is improved, and thus the reliability of the permafrost roadbed health management evaluation is improved.

[0086] Optionally, in one possible implementation manner of the embodiment, in step 102, first, the block stone dielectric constant of the block stone layer of the to-be-processed road can be acquired. Second, based on the plurality of preset porosities, the block stone dielectric constant, and the initial structure data, an electromagnetic field simulation algorithm can be used to construct a roadbed structure simulation model corresponding to each preset porosity.

[0087] In a specific implementation process of the implementation manner, a network analyzer can be used to acquire the block stone dielectric constant of the block stone layer of the to-be-processed road.

[0088] Here, for example, the block stone dielectric constant can be 7.

[0089] In the present implementation, the electromagnetic field simulation algorithm can include a time domain finite difference algorithm, a finite element algorithm, etc.

[0090] In another specific implementation process of the present implementation, based on the slice stone dielectric constant and the initial structure data, a simulation model of the subgrade structure corresponding to each preset void ratio is constructed by using the time domain finite difference algorithm.

[0091] Here, for the simulation model of the subgrade structure corresponding to any void ratio, the simulation model of the subgrade structure can include a surface layer, a base layer, a gravel layer, a slice stone layer, a moving layer and a frozen soil layer.

[0092] In this way, by using the electromagnetic field simulation algorithm, a simulation model of the subgrade structure corresponding to each preset void ratio can be constructed according to each preset void ratio, slice stone dielectric constant and initial structure data, a more accurate and effective simulation model can be obtained, so that more reliable simulation data can be obtained based on the simulation model subsequently.

[0093] Optionally, in one possible implementation of the present embodiment, in step 102, first, simulation detection processing can be performed on each simulation model of the subgrade structure to obtain the simulation slice stone layer field strength of each simulation model of the subgrade structure. Secondly, the simulation slice stone layer field strength of each simulation model of the subgrade structure and the preset void ratio can be fitted to obtain a fitting formula of the slice stone layer field strength and the void ratio.

[0094] In one specific implementation process of the present implementation, first, simulation detection processing can be performed on each simulation model of the subgrade structure to obtain simulation detection data of each simulation model of the subgrade structure. Secondly, for each simulation model of the subgrade structure, the following operations can be performed: gain processing is performed on the simulation detection data of the simulation model of the subgrade structure. Thirdly, based on the result of the gain processing, simulation position data of the slice stone layer is determined. Fourthly, based on the simulation position data of the slice stone layer, the simulation slice stone layer field strength of the simulation model of the subgrade structure is selected from the result of the gain processing to obtain the simulation slice stone layer field strength of each simulation model of the subgrade structure.

[0095] In the present implementation, the simulation position data of the slice stone layer can include the upper limit depth and the lower limit depth of the slice stone layer. The slice stone layer initial data can include the thickness of the slice stone layer.

[0096] In another specific implementation process of the present implementation, based on the upper limit depth and the lower limit depth of the slice stone layer and the thickness of the slice stone layer, the simulation slice stone layer field strength of the simulation model of the subgrade structure is selected from the result of the gain processing to obtain the simulation slice stone layer field strength of each simulation model of the subgrade structure.

[0097] In another specific implementation process of this implementation, the surface layer simulation amplitude, the base layer simulation amplitude and the macadam layer simulation amplitude can also be determined based on the simulation detection data of any one roadbed structure simulation model.

[0098] In this way, a more accurate and effective fitting formula of the macadam layer field strength and the void ratio can be fitted based on the simulation macadam layer field strength of each roadbed structure simulation model and the corresponding preset void ratio.

[0099] It should be noted that the specific implementation process provided in this implementation can be combined with the various specific implementation processes provided in the foregoing implementation to implement the void ratio identification method of the macadam roadbed of the present embodiment. For details, please refer to the related content in the foregoing implementation, which will not be described here.

[0100] Optionally, in one possible implementation of the present embodiment, in step 104, first, the original actual detection data of the road to be processed is obtained. Second, the original actual detection data can be corrected based on the simulation detection data. Third, the result of the correction processing is gain-processed to obtain the actual detection data.

[0101] In the present implementation, the original actual detection data of the road to be processed can be the actual data obtained by detecting the macadam layer of the road to be processed using a ground penetrating radar.

[0102] In one specific implementation process of this implementation, first, the surface layer simulation amplitude, the base layer simulation amplitude and the macadam layer simulation amplitude can be determined based on the simulation detection data. Second, the surface layer measured amplitude, the base layer measured amplitude and the macadam layer measured amplitude can be determined based on the original actual detection data. Third, the correction coefficient can be calculated based on the surface layer simulation amplitude, the base layer simulation amplitude, the macadam layer simulation amplitude, the surface layer measured amplitude, the base layer measured amplitude and the macadam layer measured amplitude. Fourth, the original actual detection data can be corrected based on the correction coefficient.

[0103] Here, the surface layer simulation amplitude, the base layer simulation amplitude and the macadam layer simulation amplitude can be determined based on the simulation detection data of any one roadbed structure simulation model.

[0104] It can be understood that the roadbed structure simulation model is constructed based on the initial structure data of the road, and the preset void ratio can be the void ratio of the macadam layer, so the simulation detection data of the surface layer, the simulation detection data of the base layer and the simulation detection data of the macadam layer of different roadbed structure simulation models can be the same or approximately the same.

[0105] In one case of the implementation process, the first proportional coefficient is obtained by dividing the surface layer simulation amplitude by the surface layer measured amplitude; the second proportional coefficient is obtained by dividing the base layer simulation amplitude by the base layer measured amplitude; and the third proportional coefficient is obtained by dividing the gravel layer simulation amplitude by the gravel layer measured amplitude. Then, the correction coefficient is calculated based on the first proportional coefficient, the second proportional coefficient and the third proportional coefficient. Finally, the corrected original actual detection data is obtained by multiplying the correction coefficient by the original actual detection data.

[0106] In another case of the implementation process, one of the first proportional coefficient, the second proportional coefficient and the third proportional coefficient can be selected as the correction coefficient. Alternatively, the mean value of the first proportional coefficient, the second proportional coefficient and the third proportional coefficient can be calculated as the correction coefficient.

[0107] In another implementation process of the implementation mode, the result of the correction processing is subjected to linear gain processing to obtain the actual detection data.

[0108] In this way, the reliability of the actual detection data of the detected road can be further improved by modifying and gain processing the actual detection data.

[0109] It should be noted that the specific implementation process provided in the implementation mode can be combined with the specific implementation process provided in the foregoing implementation mode to implement the void ratio identification method of the block stone subgrade of the embodiment. For details, please refer to the related content in the foregoing implementation mode, which will not be described here.

[0110] Optionally, in one possible implementation mode of the embodiment, in step 105, first, the measured position data of the block stone layer can be determined based on the actual detection data. Then, the measured block stone layer field strength is selected from the actual detection data based on the measured position data of the block stone layer.

[0111] In the implementation mode, the measured position data of the block stone layer can include the measured upper limit depth and the measured lower limit depth of the block stone layer.

[0112] In one implementation process of the implementation mode, the measured block stone layer field strength at the corresponding position is selected from the actual detection data based on the measured upper limit depth and the measured lower limit depth of the block stone layer.

[0113] It should be noted that the specific implementation process provided in the implementation mode can be combined with the specific implementation process provided in the foregoing implementation mode to implement the void ratio identification method of the block stone subgrade of the embodiment. For details, please refer to the related content in the foregoing implementation mode, which will not be described here.

[0114] Optionally, in one possible implementation of the embodiment, in step 106, firstly, a plurality of continuous field strengths in the measured field strength of the stone layer are obtained. Secondly, the plurality of continuous field strengths are subjected to summation and averaging processing. Thirdly, based on the result of the summation and averaging processing, the porosity of the stone layer is obtained by using the fitting formula.

[0115] In the implementation, the measured field strength of the stone layer can include a plurality of field strength data. The plurality of continuous field strengths can include a plurality of continuous waveform detection data of the measured stone layer.

[0116] In the implementation, the porosity of the stone layer can be taken as the identification result of the porosity of the stone subgrade.

[0117] In one specific implementation process of the implementation, the result of the summation and averaging processing is substituted into the fitting formula, and the porosity of the stone layer is calculated.

[0118] It can be understood that the ventilation condition of the stone layer of the current road to be processed can be analyzed according to the calculated porosity of the stone layer. For example, if the porosity of the stone layer is 15%, it can be determined that the ventilation condition of the stone layer is partially normal.

[0119] In this way, based on the measured field strength of the stone layer, the porosity of the stone layer can be directly calculated by using the pre-determined fitting formula of the field strength and the porosity of the stone layer, and the reliability and efficiency of the identification of the porosity of the stone layer are further improved.

[0120] It should be noted that the specific implementation process provided in the implementation can be combined with the specific implementation process provided in the foregoing implementation to implement the porosity identification method of the stone subgrade of the embodiment. For details, refer to the related content in the foregoing implementation, which will not be described here.

[0121] Optionally, in one possible implementation of the embodiment, the plurality of preset porosities include a first porosity, a second porosity, and a third porosity. The first porosity represents that the ventilation condition of the stone layer is normal, the second porosity represents that the ventilation condition of the stone layer is partially normal, and the third porosity represents that the ventilation condition of the stone layer is invalid.

[0122] In the implementation, the preset porosities can be set based on different ventilation conditions of the stone layer.

[0123] For example, the first porosity can be 25%, the second porosity can be 15%, and the third porosity can be 5%.

[0124] It should be noted that the specific implementation process provided in the present implementation mode can be combined with the various specific implementation processes provided in the foregoing implementation modes to implement the void ratio identification method of the block stone subgrade of the present embodiment. For detailed description, please refer to the related content in the foregoing implementation modes, which will not be repeated here.

[0125] In order to better understand the method of the embodiments of the present application, the method of the embodiments of the present application will be described below in combination with the drawings and specific application scenarios.

[0126] Figure 2 is a schematic diagram of the void ratio identification method of the block stone subgrade provided by another embodiment of the present application, as Figure 2 shown. The void ratio identification method of the block stone subgrade in the present embodiment can include:

[0127] Step 201, obtaining initial structure data of a to-be-processed road and block stone dielectric constant of a block stone layer of the to-be-processed road.

[0128] In the present embodiment, the initial structure data includes surface layer initial data, base layer initial data, gravel layer initial data, and block stone layer initial data. The initial structure data includes the thickness and upper and lower limit depths of each layer.

[0129] For example, the surface layer thickness is about 0.2 meters (m), the base layer thickness is about 0.6 m, the gravel layer thickness is about 0.3 m, and the block stone layer thickness is about 1.2 m. It can be understood that the active layer and the frozen soil layer are below the block stone layer, and the active layer and the frozen soil layer related data can not be involved when determining the void ratio of the block stone layer.

[0130] In the present embodiment, the initial structure data can be determined based on the design data of the to-be-detected road. The design data can include engineering geological plan and profile maps, engineering geological longitudinal section maps, hydrogeological conditions, drilling columnar graphs, physical indicators of each layer of soil, etc.

[0131] In the present embodiment, the block stone dielectric constant of the block stone layer can be measured by using a network analyzer.

[0132] Here, for example, the block stone dielectric constant is 7, and the block stone layer thickness is 1.2 m.

[0133] Step 202, based on a plurality of preset void ratios, block stone dielectric constants, initial structure data, and by using an electromagnetic field simulation algorithm, a subgrade structure simulation model corresponding to each preset void ratio is constructed.

[0134] In the present embodiment, the finite difference time domain algorithm can be used to construct a subgrade structure simulation model corresponding to each preset void ratio based on a plurality of preset void ratios, block stone dielectric constants, and initial structure data.

[0135] Here, the initial structure data can include thicknesses and upper and lower limit depths of the surface layer, thicknesses and upper and lower limit depths of the base layer, thicknesses and upper and lower limit depths of the gravel layer, and thicknesses and upper and lower limit depths of the macadam layer.

[0136] In the embodiment, based on the macadam dielectric constant, thicknesses and upper and lower limit depths of the surface layer, thicknesses and upper and lower limit depths of the base layer, thicknesses and upper and lower limit depths of the gravel layer, and thicknesses and upper and lower limit depths of the macadam layer, a simulation model of the roadbed structure corresponding to each preset void ratio is constructed by using the finite-difference time-domain algorithm.

[0137] Exemplarily, based on the macadam dielectric constant and the macadam layer thickness, a simulation model of the macadam layer of the road to be detected with different preset void ratios can be established in the simulation software GprMax by using the finite-difference time-domain algorithm.

[0138] Here, for the finite-difference time-domain algorithm, the preconfigured boundary condition is a scattering boundary condition; the preconfigured grid division form can be a triangular grid. The edge length of the smallest unit of the grid is λ / 30, where λ is the wavelength corresponding to the center frequency of the ground penetrating radar. The electromagnetic field excitation source can be a radar simulation pulse excitation source based on a Ricker wavelet.

[0139] In the embodiment, the plurality of preset void ratios include a first void ratio, a second void ratio, and a third void ratio, the first void ratio representing that the ventilation condition of the macadam layer is normal, the second void ratio representing that the ventilation condition of the macadam layer is partially normal, and the third void ratio representing that the ventilation condition of the macadam layer is invalid.

[0140] Exemplarily, the first void ratio can be 25%, the second void ratio can be 15%, and the third void ratio can be 5%.

[0141] Figure 3 is a schematic diagram of a roadbed structure simulation model of a macadam roadbed identification method provided by another embodiment of the present application, as shown in Figure 3 The roadbed structure simulation model corresponding to the 25% void ratio, the roadbed structure simulation model corresponding to the 15% void ratio, and the roadbed structure simulation model corresponding to the 5% void ratio each include a pavement layer, a base layer, a gravel layer, a macadam layer, a moving layer, and a frozen soil layer.

[0142] Step 203, performing simulation detection processing on each roadbed structure simulation model to obtain a simulation macadam layer field strength of each roadbed structure simulation model.

[0143] In the embodiment, simulation detection processing is performed on each roadbed structure simulation model to obtain simulation detection data of each roadbed structure simulation model. Figure 4is a schematic diagram of a wave field snapshot of a roadbed structure simulation model of a void identification method of a block stone roadbed provided by another embodiment of the present application, as shown in Figure 4 When the simulation detection of the roadbed structure simulation model is performed, the wave field snapshot can be obtained. The simulation detection data of each roadbed structure simulation model can include radar waveform data derived based on a radar image of ground penetrating radar.

[0144] Preferably, the following operations can be performed for each roadbed structure simulation model: gain processing is performed on the simulation detection data of the roadbed structure simulation model, simulation position data of the block stone layer is determined based on a result of the gain processing, and simulation block stone layer field strength of the roadbed structure simulation model is selected from the result of the gain processing based on the simulation position data of the block stone layer, so as to obtain the simulation block stone layer field strength of each roadbed structure simulation model.

[0145] In the embodiment, the simulation detection data can be radar waveform data derived from the ground penetrating radar image. The radar waveform data can include a plurality of time sequences of electric field strength, where the time can be the time of the reflected wave.

[0146] For example, the reflected wave of the surface layer appears at about 3 nanoseconds (ns), the reflected wave of the surface layer appears at about 8 ns, and the reflected wave of the broken stone layer appears at about 28 ns.

[0147] In the embodiment, the simulation amplitude of the surface layer, the simulation amplitude of the base layer, and the simulation amplitude of the broken stone layer can also be determined according to the simulation detection data.

[0148] Optionally, the position data of the surface layer, the position data of the base layer, and the position data of the broken stone layer can be determined according to the initial data of the surface layer, the initial data of the base layer, the initial data of the broken stone layer, and the simulation detection data.

[0149] In the embodiment, optionally, the simulation position data of the block stone layer can include an upper limit depth and a lower limit depth of the block stone layer, and then the radar waveform data of the block stone layer can be derived from the radar image of each roadbed structure simulation model based on the upper limit depth and the lower limit depth of the block stone layer, so as to obtain the simulation block stone layer field strength of each roadbed structure simulation model.

[0150] It can be understood that the file derived from the simulation software can be converted into a csv format.

[0151] Step 204, fitting processing is performed on the simulation block stone layer field strength of each roadbed structure simulation model and the preset void ratio, so as to obtain a fitting formula of the block stone layer field strength and the void ratio.

[0152] In the embodiment, the fitting formula of the block stone layer field strength and the void ratio can be as shown in formula (1):

[0153] (1)

[0154] wherein, may be the field intensity of the macadam layer, i.e., the electric field intensity of the macadam layer, may be the porosity, and a and b may be preset coefficients, for example, a may be 6341 and b may be 478.

[0155] For example, in this embodiment, the simulation macadam layer field intensity of the continuous 100 channels may be taken from the simulation macadam layer field intensity under different porosities, respectively, and then summed and averaged to obtain the average value of the simulation macadam layer field intensity under different porosities, so as to construct the fitting relationship between the simulation macadam layer field intensity and the porosity.

[0156] Figure 5 is a schematic diagram of the fitting relationship between the electric field intensity and the porosity of the macadam layer of the macadam subgrade provided in another embodiment of the present application, as shown in Figure 5 The average value of the simulation macadam layer field intensity under different porosities is linearly fitted with the porosity of the macadam layer to obtain the linear fitting relationship between the electric field intensity and the porosity of the macadam layer.

[0157] Step 205, obtaining original actual detection data of a to-be-processed road.

[0158] In this embodiment, the ground penetrating radar may be used to detect the road to be detected based on the macadam subgrade to obtain a measured radar image, and then radar measured waveform data, i.e., original actual detection data, may be derived from the measured radar image. The amplitudes of the surface layer, the base layer and the gravel layer may be determined based on the original actual detection data.

[0159] In this embodiment, for example, the conditions for selecting the road to be detected may be: selecting a road section with a relatively clean and flat ground surface and a measurement line position, removing the ground surface weeds, ensuring that as few interference factors as possible appear around the measurement line, and avoiding places with water on the ground surface and areas with large undulations on the measurement line position.

[0160] It can be understood that the file derived from the radar may be converted into a csv format, i.e., the radar measured waveform data may be derived from the measured radar image of the macadam subgrade.

[0161] Preferably, the positions of the surface layer, the base layer and the gravel layer are determined according to the initial data of the surface layer, the base layer and the gravel layer.

[0162] Step 206, pre-processing the original actual detection data to obtain actual detection data.

[0163] In the embodiment, firstly, the original actual detection data can be corrected based on the simulation detection data, and secondly, gain processing is performed on the result of the correction processing to obtain the actual detection data.

[0164] Preferably, firstly, the surface layer simulation amplitude, the base layer simulation amplitude and the macadam layer simulation amplitude are determined based on the simulation detection data. Secondly, the surface layer measured amplitude, the base layer measured amplitude and the macadam layer measured amplitude are determined based on the original actual detection data. Thirdly, the first proportional coefficient is obtained by dividing the surface layer simulation amplitude by the surface layer measured amplitude, the second proportional coefficient is obtained by dividing the base layer simulation amplitude by the base layer measured amplitude, and the third proportional coefficient is obtained by dividing the macadam layer simulation amplitude by the macadam layer measured amplitude. Fourthly, the correction coefficient is calculated based on the first proportional coefficient, the second proportional coefficient and the third proportional coefficient. Fifthly, the original actual detection data is multiplied by the correction coefficient to obtain the corrected original actual detection data.

[0165] Here, one value can be selected from the first proportional coefficient, the second proportional coefficient and the third proportional coefficient as the correction coefficient. Alternatively, the mean value of the first proportional coefficient, the second proportional coefficient and the third proportional coefficient can be calculated as the correction coefficient.

[0166] Preferably, the result of the correction processing can be linearly gain-processed by using a preset gain algorithm to obtain the final actual detection data.

[0167] In step 207, the measured macadam layer field strength is obtained based on the actual detection data.

[0168] In the embodiment, the actual detection data can be radar waveform data. The actual detection data can be a plurality of field strength sequences.

[0169] In the embodiment, the measured position data of the macadam layer is determined based on the actual detection data, and the measured macadam layer field strength is selected from the actual detection data based on the measured position data of the macadam layer.

[0170] Here, the measured position data of the macadam layer can include the measured upper limit depth and the measured lower limit depth of the macadam layer. The actual detection data can include the overall field strength data obtained by detecting each structural layer of the road by using the ground penetrating radar.

[0171] Preferably, the measured macadam layer field strength in the region between the upper limit depth and the lower limit depth of the macadam layer can be selected from the actual detection data based on the upper limit depth and the lower limit depth of the macadam layer.

[0172] Here, the position of the macadam layer field strength in the actual detection data can be determined according to the upper limit depth and the lower limit depth of the macadam layer, so as to select the measured macadam layer field strength from the actual detection data.

[0173] It can be understood that the upper limit depth and the lower limit depth of the final block stone layer can also be determined based on the upper limit depth and the lower limit depth in the initial data of the block stone layer, and the measured upper limit depth and lower limit depth of the block stone layer are determined, so as to select the measured block stone layer field strength corresponding to the actual detection data.

[0174] In step 208, based on the measured block stone layer field strength, a fitting formula is used to obtain the void ratio of the block stone layer.

[0175] In this embodiment, for example, 100 continuous measured block stone layer field strengths can be selected, and the 100 measured block stone layer field strengths are added and averaged to obtain the average value of the measured block stone layer field strength, which is then brought into the fitting formula of the block stone layer field strength-void ratio to calculate the void ratio of the measured block stone layer, i.e. the void ratio of the block stone subgrade.

[0176] In step 209, the ventilation condition of the block stone layer is determined based on the void ratio of the block stone layer.

[0177] At this point, the ventilation condition of the block stone layer of the road to be detected can be analyzed and evaluated according to the void ratio of the block stone layer corresponding to the actual detection data, to provide quantitative basis for subsequent highway maintenance management.

[0178] In this way, the technical solution in the embodiment can realize non-destructive, rapid and continuous diagnosis of the subgrade void structure, provide breakthrough technical support for frozen soil subgrade health management, and improve the intelligentization of highway maintenance.

[0179] In addition, by using the technical solution in the embodiment, the block stone subgrade void ratio detection and identification can be realized by constructing a simulation model, which fills the gap in the detection of block stone subgrade void ratio, successfully constructs a block stone subgrade simulation model based on random medium theory, and effectively solves the problem of lack of effective detection model for this special subgrade structure.

[0180] In addition, by using the technical solution in the embodiment, a block stone subgrade void ratio quantitative characterization method is established, the core physical quantity of the ground penetrating radar electromagnetic echo, i.e. the electric field strength, is associated with the key physical parameter of the block stone subgrade, i.e. the void ratio, and a fitting formula representing the relationship between the two is derived and established, which provides a direct basis for inverting the internal structure parameters of the medium through electromagnetic response.

[0181] In this way, by using the technical solution in the embodiment, non-destructive and quantitative evaluation of the void ratio of the block stone subgrade is realized, and based on the simulation model and the function relationship between the electric field strength and the void ratio, non-destructive and quantitative detection and evaluation of the internal void ratio of the block stone subgrade are realized, which significantly improves the accuracy and efficiency of the subgrade state evaluation.

[0182] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0183] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0184] Figure 6 This invention provides a structural block diagram of a porosity identification device for paved stone roadbed according to an embodiment of the present application. Figure 6 As shown. The porosity identification device 600 for the block stone roadbed in this embodiment may include a first acquisition unit 601, a first construction unit 602, a first fitting unit 603, a second acquisition unit 604, a first obtaining unit 605, and a second obtaining unit 606. The system comprises the following components: a first acquisition unit 601, used to acquire initial structural data of the road to be processed; the initial structural data includes initial data of the surface layer, initial data of the base layer, initial data of the crushed stone layer, and initial data of the boulders layer; a first construction unit 602, used to construct a roadbed structure simulation model corresponding to each preset porosity based on multiple preset porosities and the initial structural data; a first fitting unit 603, used to fit the simulated boulders layer field strength and preset porosity of each roadbed structure simulation model to obtain a fitting formula; a second acquisition unit 604, used to acquire actual detection data of the road to be processed; a first acquisition unit 605, used to obtain the measured boulders layer field strength based on the actual detection data; and a second acquisition unit 606, used to obtain the porosity of the boulders layer based on the measured boulders layer field strength using the fitting formula.

[0185] Optionally, in one possible implementation of this embodiment, the first construction unit 602 is specifically used to obtain the dielectric constant of the boulders in the boulders layer of the road to be processed; based on multiple preset porosities, the dielectric constant of the boulders, and the initial structural data, an electromagnetic field simulation algorithm is used to construct a roadbed structure simulation model corresponding to each preset porosity.

[0186] Optionally, in one possible implementation of this embodiment, the first fitting unit 603 is specifically used to perform simulation detection processing on each roadbed structure simulation model to obtain the simulated boulders layer field strength of each roadbed structure simulation model; and to perform fitting processing on the simulated boulders layer field strength and preset porosity of each roadbed structure simulation model to obtain the fitting formula of the boulders layer field strength and porosity.

[0187] Optionally, in a possible implementation manner of the embodiment, the first fitting unit 603 is further configured to perform simulation detection processing on each roadbed structure simulation model to obtain simulation detection data of each roadbed structure simulation model; and perform the following operations on each roadbed structure simulation model: performing gain processing on the simulation detection data of the roadbed structure simulation model; determining simulation position data of the patch stone layer based on a result of the gain processing; and selecting simulation patch stone layer field strength of the roadbed structure simulation model from the result of the gain processing based on the simulation position data of the patch stone layer to obtain simulation patch stone layer field strength of each roadbed structure simulation model.

[0188] Optionally, in a possible implementation manner of the embodiment, the second obtaining unit 604 is specifically configured to obtain original actual detection data of a to-be-processed road; perform correction processing on the original actual detection data based on the simulation detection data; and perform gain processing on a result of the correction processing to obtain the actual detection data.

[0189] Optionally, in a possible implementation manner of the embodiment, the second obtaining unit 604 is further configured to determine surface layer simulation amplitude, base layer simulation amplitude, and patch stone layer simulation amplitude based on the simulation detection data; determine surface layer actual measurement amplitude, base layer actual measurement amplitude, and patch stone layer actual measurement amplitude based on the original actual detection data; calculate a correction coefficient based on the surface layer simulation amplitude, the base layer simulation amplitude, the patch stone layer simulation amplitude, the surface layer actual measurement amplitude, the base layer actual measurement amplitude, and the patch stone layer actual measurement amplitude; and perform correction processing on the original actual detection data based on the correction coefficient.

[0190] Optionally, in a possible implementation manner of the embodiment, the first obtaining unit 605 is specifically configured to determine actual measurement position data of the patch stone layer based on the actual detection data; and select the actual measurement patch stone layer field strength from the actual detection data based on the actual measurement position data of the patch stone layer.

[0191] Optionally, in a possible implementation manner of the embodiment, the second obtaining unit 606 is specifically configured to obtain a plurality of continuous field strengths in the actual measurement patch stone layer field strength; perform summation average processing on the plurality of continuous field strengths; and obtain the void ratio of the patch stone layer by using the fitting formula based on a result of the summation average processing.

[0192] In this embodiment, the initial structure data of the road to be processed can be acquired by the first acquisition unit; the initial structure data includes surface layer initial data, base layer initial data, gravel layer initial data, and patch stone layer initial data; a first construction unit constructs a roadbed structure simulation model corresponding to each preset void ratio based on the initial structure data and a plurality of preset void ratios; a first fitting unit fits the simulation patch stone layer field strength and the preset void ratio of each roadbed structure simulation model to obtain a fitting formula; a second acquisition unit acquires actual detection data of the road to be processed; a first obtaining unit obtains a measured patch stone layer field strength based on the actual detection data; and a second obtaining unit obtains the void ratio of the patch stone layer based on the measured patch stone layer field strength and the fitting formula. Since the electric field strength of the ground penetrating radar electromagnetic echo is associated with the void ratio of the patch stone layer, and the fitting formula representing the relationship between the two is established, the internal void ratio of the patch stone layer in the frozen soil area is effectively quantified, the reliability of the patch stone layer internal void ratio detection is improved, and the reliability of the frozen soil roadbed health management evaluation is improved.

[0193] In the technical solution of the present application, the collection, storage, use, processing, transmission, provision and disclosure of user personal information, such as user images and attribute data, comply with relevant laws and regulations and do not violate public order and good customs.

[0194] According to the embodiments of the present application, the present application further provides an electronic device, a readable storage medium and a computer program product.

[0195] Figure 7 A schematic block diagram of an example electronic device 700 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0196] As Figure 7As shown, the electronic device 700 includes a computing unit 701 that can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM) 702 or a computer program loaded into a random access memory (RAM) 703 from a storage unit 708. In the RAM 703, various programs and data required for the operation of the electronic device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0197] A plurality of components in the electronic device 700 are connected to the I / O interface 705, including: an input unit 706, such as a keyboard, a mouse, and the like; an output unit 707, such as various types of displays, a speaker, and the like; a storage unit 708, such as a magnetic disk, an optical disk, and the like; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 709 allows the electronic device 700 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0198] The computing unit 701 can be various general-purpose and / or special-purpose processing components having processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, and the like. The computing unit 701 performs various methods and processes described above, such as the method of identifying the void ratio of a stone block roadbed. For example, in some embodiments, the method of identifying the void ratio of a stone block roadbed can be implemented as a computer software program that is tangibly embodied in a machine-readable medium, such as the storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the method of identifying the void ratio of a stone block roadbed described above can be performed. Alternatively, in other embodiments, the computing unit 701 can be configured to perform the method of identifying the void ratio of a stone block roadbed by any other appropriate means, such as by means of firmware.

[0199] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0200] Program code for carrying out methods of the present application can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces a means for implementing the functions / acts specified in the flowcharts and / or block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, or entirely on a remote machine or server.

[0201] In the context of the present application, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium will include one or more lines of electrical conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0202] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0203] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0204] The computer system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0205] It should be understood that various forms of flow shown above can be used, with steps reordered, added, or removed. For example, the steps recited in the present application can be performed in parallel, in series, or in a different order, without limitation, as long as the desired results of the technology disclosed in the present application are achieved.

[0206] The specific embodiments described above are not intended to limit the scope of the present application. Those skilled in the art will understand that various modifications, combinations, sub-combinations, and alternatives can be made to the specific embodiments without departing from the spirit and principles of the present application. Any further modifications, changes, improvements, and the like that come within the spirit and scope of the present application should be considered as falling within the scope of the present application.

Claims

1. A method for identifying the porosity of a rubble roadbed, characterized in that, The method includes: Obtain the initial structural data of the road to be processed; the initial structural data includes the initial data of the surface layer, the initial data of the base layer, the initial data of the crushed stone layer, and the initial data of the boulders layer; Obtain the dielectric constant of the boulders in the boulders layer of the road to be processed; Based on multiple preset porosity, the dielectric constant of the boulders, and the initial structural data, an electromagnetic field simulation algorithm is used to construct a roadbed structure simulation model corresponding to each preset porosity. Simulation detection processing is performed on each roadbed structure simulation model to obtain simulation detection data for each roadbed structure simulation model; For each roadbed structure simulation model, the following operations are performed: gain processing is applied to the simulation detection data of the roadbed structure simulation model; based on the result of the gain processing, the simulation location data of the boulders layer is determined; based on the simulation location data of the boulders layer, the simulation field strength of the boulders layer of the roadbed structure simulation model is selected from the result of the gain processing to obtain the simulation field strength of the boulders layer of each roadbed structure simulation model. The simulated field strength and preset porosity of the boulders layer in each roadbed structure simulation model are fitted to obtain the fitting formulas for the field strength and porosity of the boulders layer. Obtain actual detection data of the road to be processed; Based on the actual detection data, the measured field strength of the rock fragment layer was obtained; Based on the measured field strength of the boulders layer, the porosity of the boulders layer is obtained using the fitting formula.

2. The method according to claim 1, characterized in that, The acquisition of actual detection data of the road to be processed includes: Obtain the original actual detection data of the road to be processed; Based on the simulated detection data, the original actual detection data is corrected. The result of the correction process is then subjected to gain processing to obtain the actual detection data.

3. The method according to claim 2, characterized in that, The step of correcting the original actual detection data based on the simulated detection data includes: Based on the simulation detection data, the simulation amplitude of the surface layer, the simulation amplitude of the base layer, and the simulation amplitude of the crushed stone layer are determined. Based on the original actual detection data, the measured amplitude of the surface layer, the measured amplitude of the base layer, and the measured amplitude of the crushed stone layer were determined. Based on the simulated amplitude of the surface layer, the simulated amplitude of the base layer, the simulated amplitude of the crushed stone layer, the measured amplitude of the surface layer, the measured amplitude of the base layer, and the measured amplitude of the crushed stone layer, the correction coefficient is calculated. Based on the correction coefficient, the original actual detection data is corrected.

4. The method according to claim 1, characterized in that, The process of obtaining the measured field strength of the rock fragment layer based on the actual detection data includes: Based on the actual detection data, the measured location data of the boulders layer were determined; Based on the measured location data of the boulders layer, the measured field strength of the boulders layer is selected from the actual detection data.

5. The method according to claim 1, characterized in that, The process of obtaining the porosity of the boulders layer based on the measured electric field strength and using the fitting formula includes: Obtain multiple consecutive field strengths from the measured field strength of the rock fragment layer; Sum and average multiple continuous field intensities; Based on the results of the summation and averaging process, the porosity of the flaky stone layer is obtained using the fitting formula.

6. The method according to any one of claims 1-5, characterized in that, The multiple preset porosity ratios include a first porosity ratio, a second porosity ratio, and a third porosity ratio. The first porosity ratio indicates that the ventilation of the slab stone layer is normal, the second porosity ratio indicates that the ventilation of the slab stone layer is partially normal, and the third porosity ratio indicates that the ventilation of the slab stone layer is ineffective.

7. A porosity identification device for rubble roadbed, characterized in that, The device includes: The first acquisition unit is used to acquire the initial structural data of the road to be processed; the initial structural data includes the initial data of the surface layer, the initial data of the base layer, the initial data of the crushed stone layer, and the initial data of the boulders layer; The first construction unit is used to obtain the dielectric constant of the boulders in the boulders layer of the road to be processed; based on multiple preset porosities, the dielectric constant of the boulders, and the initial structural data, an electromagnetic field simulation algorithm is used to construct a roadbed structure simulation model corresponding to each preset porosity. The first fitting unit is used to perform simulation detection processing on each roadbed structure simulation model to obtain simulation detection data for each roadbed structure simulation model. For each roadbed structure simulation model, the following operations are performed: gain processing is applied to the simulation detection data of the roadbed structure simulation model; based on the result of the gain processing, the simulation location data of the boulders layer is determined; based on the simulation location data of the boulders layer, the simulation field strength of the boulders layer of the roadbed structure simulation model is selected from the result of the gain processing to obtain the simulation field strength of the boulders layer of each roadbed structure simulation model; and fitting processing is performed on the simulation field strength of the boulders layer and the preset porosity of each roadbed structure simulation model to obtain a fitting formula for the field strength and porosity of the boulders layer. The second acquisition unit is used to acquire the actual detection data of the road to be processed. The first obtaining unit is used to obtain the measured field strength of the rock fragment layer based on the actual detection data. The second obtaining unit is used to obtain the porosity of the boulders layer based on the measured field strength of the boulders layer and using the fitting formula.

8. The apparatus according to claim 7, characterized in that, The second acquisition unit is specifically used for Obtain the original actual detection data of the road to be processed; Based on the simulated detection data, the original actual detection data is corrected. The result of the correction process is then subjected to gain processing to obtain the actual detection data.

9. The apparatus according to claim 8, characterized in that, The second acquisition unit is also used for Based on the simulation detection data, the simulation amplitude of the surface layer, the simulation amplitude of the base layer, and the simulation amplitude of the crushed stone layer are determined. Based on the original actual detection data, the measured amplitude of the surface layer, the measured amplitude of the base layer, and the measured amplitude of the crushed stone layer were determined. Based on the simulated amplitude of the surface layer, the simulated amplitude of the base layer, the simulated amplitude of the crushed stone layer, the measured amplitude of the surface layer, the measured amplitude of the base layer, and the measured amplitude of the crushed stone layer, the correction coefficient is calculated. Based on the correction coefficient, the original actual detection data is corrected.

10. The apparatus according to claim 7, characterized in that, The first acquisition unit, specifically used for Based on the actual detection data, the measured location data of the boulders layer were determined; Based on the measured location data of the boulders layer, the measured field strength of the boulders layer is selected from the actual detection data.

11. The apparatus according to claim 7, characterized in that, The second obtaining unit is specifically used for Obtain multiple consecutive field strengths from the measured field strength of the rock fragment layer; Sum and average multiple continuous field intensities; Based on the results of the summation and averaging process, the porosity of the flaky stone layer is obtained using the fitting formula.

12. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1-6.

13. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

14. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-6.

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