Method for calculating wireless monitoring signal loss of underground infrastructure

Abstract

The invention discloses a method for calculating wireless monitoring signal loss of an underground infrastructure, and belongs to the technical field of wireless monitoring of the underground infrastructure. According to the method, key parameters such as complex dielectric constants and electric conductivity of underground multilayer media are measured through technologies such as a time domain reflection method and a four-electrode method, and a parameter matrix is constructed through preprocessing such as dispersion correction and temperature and humidity compensation; a total loss model containing multiple components such as free space radiation and medium absorption is established according to a segmented propagation path of a medium layer, and quantitative loss is solved in combination with interface-by-interface reflection / transmission; and subsequently, robust loss calculation is realized through a parameter disturbance set, and a standardized result is output after field actual measurement closed-loop calibration. According to the method, the problem that the signal loss in the underground complex medium environment is difficult to quantify accurately is solved, the calculation consistency and reproducibility are improved, and a reliable quantification basis is provided for engineering decisions such as monitoring network link budget and node deployment.

Description

Technical Field

[0001] This invention relates to the field of wireless monitoring technology for underground infrastructure, specifically a method for calculating signal loss in wireless monitoring of underground infrastructure. Background Technology

[0002] With the acceleration of urbanization, the scale and complexity of underground infrastructure such as water supply, gas, drainage, heating, and integrated utility tunnels continue to increase, making operational status awareness and risk early warning crucial for urban resilience. Compared with wired monitoring, wireless monitoring has advantages such as less disturbance, better scalability, and lower maintenance costs. However, its engineering availability is highly dependent on the accurate calculation of wireless signal propagation loss and link budget in the underground environment. Underground media are usually multi-layered composite structures (such as soil-backfill-concrete-asphalt-air), and the dielectric properties between layers differ significantly and vary with water content and temperature, resulting in a propagation mechanism that is far more complex than in free space.

[0003] Existing methods suffer from common shortcomings in engineering applications. First, they often approximate a single layer or equivalent homogeneous medium, making it difficult to characterize the differentiated contributions of thin-layer high-loss and thick-layer low-loss to attenuation. Second, they fail to adequately handle interface effects, neglecting to perform interface-by-interface accumulation or equivalent overall solution for multi-interface reflection / transmission, easily underestimating the additional losses caused by interfaces. Third, the phase / wavelength terms are significantly simplified, making it difficult to characterize the phase delay and interference caused by dielectric dispersion of different layers. Fourth, the parameters are mostly based on one-time measurements, lacking dispersion correction and band mapping from the measurement frequency band to the system operating frequency band, and also lacking uncertainty assessment and robust verification for time-varying factors such as moisture content and temperature. Fifth, there is a lack of calibration and closed-loop calibration mechanisms between the model and field observations, making it difficult to form reproducible engineering standards.

[0004] The aforementioned shortcomings directly constrain engineering decisions regarding wireless node spacing, relay density, transmit power, and antenna configuration, leading to both redundancy and coverage blind spots, increased maintenance costs, and difficulty in guaranteeing reliability. Therefore, there is an urgent need for a propagation loss calculation method for multi-layered composite media that can perform segmented absorption accumulation and interface-by-interface reflection / transmission solutions, while also considering dispersion correction, field calibration, and robust uncertainty assessment. This method would provide a quantifiable, verifiable, and iterative theoretical tool for link budgeting and deployment design of underground wireless monitoring networks. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for calculating signal loss in wireless monitoring of underground infrastructure. By acquiring hierarchical parameters and performing hierarchical modeling, performing multi-level segmentation and multi-component loss calculation, outputting results, performing robust loss calculation and verification, verifying the model and performing simplified closed-loop calibration, the method achieves accurate quantification of signal loss in complex underground environments, improves the consistency, reproducibility and engineering applicability of the calculation results, and supports the optimized deployment of underground wireless monitoring networks.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for calculating signal loss in wireless monitoring of underground infrastructure includes the following steps:

[0008] Step S1, medium parameter acquisition and sequence modeling: conduct physical and electromagnetic property tests on multiple underground media in the target area, determine the medium sequence, acquire and preprocess the key parameters of each medium layer. The key parameters include at least the relative equivalent complex permittivity, conductivity, water content and layer thickness that characterize the electromagnetic response of the medium. The preprocessing includes correction of each key parameter, data processing and construction of segmented parameter matrices.

[0009] Step S2, Multi-layer Segmentation and Multi-component Loss Calculation: Based on the aforementioned medium layer sequence and segmentation parameter matrix, the wireless signal propagation path is divided according to the medium layer. The propagation distance is the sum of the distances of the segments. Based on the principle of segment accumulation and interface interaction in multilayer composite media, a total propagation loss estimation model is constructed. The total propagation loss includes at least free space radiation loss, wavelength / phase loss, medium absorption loss and interface reflection loss. The corresponding layer parameters in the segment parameter matrix are called to quantify each loss component and accumulate to obtain the nominal loss estimate.

[0010] Step S3, Output the result: Output the nominal loss estimate as the calculation result of wireless signal propagation loss in the target area under different propagation path lengths and interlayer combinations, providing a quantitative basis for link budget and monitoring equipment deployment design for wireless monitoring of underground infrastructure.

[0011] Preferably, in step S1, the relative equivalent complex permittivity is , ,in, For the real part of the relative equivalent complex permittivity, This is the imaginary part of the relative equivalent complex permittivity.

[0012] Preferably, in step S1, the steps of conducting physical and electromagnetic property tests on the multi-layer underground media within the target area, determining the media sequence, and obtaining and preprocessing the key parameters of each media layer specifically include:

[0013] (a) By combining borehole sampling with shallow ground-penetrating radar, the stratigraphic sequence and thickness of each layer are determined, forming a layer thickness set. , For media layer sequence numbering;

[0014] (b) The time-domain reflectometry and / or impedance spectroscopy were used to determine the dielectric properties of each layer of the medium. and Dispersion correction and frequency band mapping are performed on the measurement results to adapt the parameters to the operating frequency band of the wireless monitoring signal. ;

[0015] (c) Determine the moisture content of each medium layer by gravimetric method or frequency domain moisture sensor. ;Establish and , The calibration relationship, correcting the equivalent complex permittivity. To reflect the time-varying effect of moisture;

[0016] (d) The conductivity of each dielectric layer was determined using the four-electrode method. Combined with the on-site temperature With moisture content Establish a temperature and humidity compensation relationship to obtain The on-site equivalent value;

[0017] (e) The measurements and corrections made in steps (a) to (d) above , , 、 , Outlier removal, spatial interpolation, and hierarchical weighting are performed, and a segmented parameter matrix is ​​constructed by integrating according to the media layer order. The segmented parameter matrix is ​​used in step S2 to perform loss calculation by calling parameters by layer.

[0018] Preferably, the expression for the total propagation loss estimation model in step S2 is:

[0019]

[0020]

[0021]

[0022] in: For the total propagation distance, For the first Propagation distance in layered media For the number of media segments, The permeability of the medium, It is the dielectric constant in a vacuum. For the first Phase constant of the layered medium, For the first The absorption attenuation coefficient of the layer medium, For adjacent With the The power reflection coefficient of the layer satisfies

[0023]

[0024] in, This represents the relative permittivity of the k-th dielectric layer. , , , , These are the model coefficients determined through experimental calibration or regression fitting.

[0025] Preferably, when the signal propagation path traverses two layers of composite medium, The total propagation loss estimation model is specialized as follows:

[0026]

[0027] in: The distance the signal travels in the first layer of medium. The distance the signal travels in the second medium; satisfying , The Fresnel reflection coefficient is the interface between the two media layers; the specific model coefficient calibration values ​​are as follows: , , , , ;

[0028] When the second layer medium is air, take , ,but , ; It is the speed of light in a vacuum; when any layer is an equivalent high-conductivity region, by increasing Or to An effective penetration depth correction is introduced to reflect strong reflection and weak transmission effects.

[0029] Preferably, the method further includes step S4: robust loss calculation and verification, specifically including:

[0030] (a) Establish the perturbation set of key parameters for each layer of medium perturbation set The range of values ​​is given by on-site statistical data or historical sample data, and meets the physical feasibility constraints;

[0031] (b) Within the loss model framework of step S2, replace the parameters of each layer with... The feasible values ​​in the range are used to calculate the robust upper bound loss:

[0032]

[0033] (c) Output Paired results; setting the allowable deviation for loss calibration. ， When the deviation between the two is greater than At that time, the model parameter recalibration process is triggered and narrowed. The range of values ​​for;

[0034] (d) Impose physical feasibility constraints on all key parameters, and on the interface reflection coefficient. Perform interval projection.

[0035] Preferably, physical feasibility constraints are imposed on all key parameters, and the interface reflectance coefficient is also considered. The specific steps for performing interval projection are as follows:

[0036] Perform parameter trimming and physical feasibility checks on all layers to ensure they meet the constraints. ,

[0037] , , , and the interface reflectance coefficient Perform interval projection to make it fall into ,in It is a tiny quantity greater than 0.

[0038] Preferably, the method further includes step S5: model validation and simplified closed-loop calibration; specifically including:

[0039] (a) Conduct field tests, collect link observations, and convert them into measured path loss. ;

[0040] (b) Set the allowable deviation for loss calibration ， when At that time, for model coefficients and highly sensitive parameters , , Perform joint correction; when the conditions are met Furthermore, if the change in model coefficients between two consecutive calibrations does not exceed a preset threshold, the closed-loop is considered to have converged, and the converged model is output. .

[0041] Preferably, the preset threshold is 5%, and the link observations include at least transmit power, transmit antenna gain, receive antenna gain, cable loss, and received signal strength indication.

[0042] Preferably, the method further includes: step S6, standardized output and interface of results; specifically including: uniformly encoding the loss results after model verification and simplified closed-loop calibration, outputting standardized data in raster, vector and tabular formats, providing callable interfaces, and marking quality control labels to facilitate direct call by downstream link budget and monitoring equipment deployment related systems.

[0043] Compared with the prior art, the present invention has at least one of the following advantages or beneficial effects:

[0044] 1) Accurate Layered Modeling and Reliable Parameters: This invention obtains key parameters of each underground medium layer through various testing methods, and constructs a segmented parameter matrix after preprocessing. The core revolves around "determining the sequence model—accumulating absorption loss layer by layer—calculating reflection / overall transmission loss interface by interface," while combining dispersion correction and frequency band mapping techniques to avoid errors caused by approximating complex underground environments with single-layer media. By standardizing calculations through on-site calibration, path loss estimation becomes more accurate, results are consistent and repeatable, providing a reliable reference for link budgeting.

[0045] 2) Comprehensive loss characterization and robust, stable results: The constructed loss model encompasses multiple aspects of loss, including free-space radiation, wavelength / phase, medium absorption, and interface reflection. To address potential fluctuations in medium parameters, a parameter perturbation set is introduced to establish a robust model, outputting nominal loss, upper bound loss, and minimum link margin. Using the allowable deviation of loss calibration as a threshold, model recalibration and perturbation set range adjustment are triggered. Simultaneously, interval projection and physical constraints are applied to the interface reflection coefficient to prevent numerical anomalies during calculation. Even with changes in the water content and temperature of the underground medium, conservative and stable engineering results are provided.

[0046] 3) High practicality and optimized end-to-end: The model is applicable to both two-layer and multi-layer media scenarios, and the output data is standardized, allowing direct integration with GIS systems and simulation platforms. These results provide quantitative support for engineering decisions such as link budgeting and node deployment in underground wireless monitoring networks, helping to reduce unnecessary node deployments, avoid coverage blind spots, and thus reduce construction and subsequent operation and maintenance costs. Attached Figure Description

[0047] The invention, its features, shape, and advantages will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings. Like reference numerals denote like parts throughout the drawings. The drawings are not drawn to scale; their focus is on illustrating the gist of the invention.

[0048] Figure 1 This is a schematic diagram of the method for calculating the signal loss of wireless monitoring of underground infrastructure in an embodiment of the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] like Figure 1 As shown in the figure, this embodiment discloses a method for calculating the signal loss of wireless monitoring of underground infrastructure. Specifically, the calculation method includes the following steps:

[0051] Step S1: Obtaining medium parameters and sequence modeling

[0052] This embodiment is designed for urban shallow underground environments (typically characterized by shallow overburden, multiple layers, and time-varying water content), and the operating frequency band for the wireless monitoring signal is [frequency band missing]. The following process will be executed around representative propagation paths:

[0053] 1) Hierarchical Sequence Recognition and Geometric Modeling

[0054] Typical profiles were obtained using shallow ground-penetrating radar (GPR) / electromagnetic methods, with small-scale sampling for verification when necessary, to determine the stratigraphic sequence and thickness of each layer, thus forming a thickness set. , The medium is sequenced and geometrically discretized into a set of segmented distances based on the path to be analyzed. This forms a multi-layered media sequence model.

[0055] 2) Dielectric parameter measurement and frequency band mapping

[0056] The time-domain reflectometry (TDR) and / or impedance spectroscopy were used to determine the properties of each medium layer. and Dispersion correction and frequency band mapping are performed on the measurement results to adapt the parameters to the operating frequency band of the wireless monitoring signal. Specifically, TDR and / or impedance spectroscopy tests are performed on each layer in situ / by sampling to obtain the relative equivalent complex permittivity. The frequency response; fitting based on a dispersion model (such as Debye / Cole–Cole), and... , Extrapolate / interpolate to 433MHz.

[0057] 3) Moisture content determination and dielectric correction

[0058] Moisture content of each layer was determined using a frequency domain moisture sensor (FDR) / gravimetric method. Establish moisture content and The calibration relationship for the equivalent complex permittivity Humidity correction is performed to correspond to the real-time water content under 433MHz operating conditions, reflecting the time-varying effect of moisture.

[0059] 4) Conductivity and Temperature / Humidity Compensation

[0060] Conductivity was determined using the four-electrode method. Regarding the on-site temperature With moisture content Provide compensation and establish The on-site equivalent relationship (i.e., temperature and humidity compensation relationship) is obtained. The field equivalent value is used to reflect the conductivity of shallow buried environments as time changes.

[0061] 5) Data preprocessing and parameter matrix construction

[0062] Outlier removal, spatial interpolation, and hierarchical weighting were performed on all observations, and physical feasibility pruning (constraints) was also applied. , , , , A standardized segmented parameter matrix is ​​constructed by integrating media layers.

[0063] .

[0064] 6) Initial coefficient calibration and convergence criterion

[0065] A short-range comparison link (433MHz, same frequency) was deployed under shallow burial conditions to record the transmission power. Transmit antenna gain Receiver antenna gain Cable loss Received signal strength indication Parameters, converted to actual path loss

[0066]

[0067] The coefficients of the model are estimated using least squares / robust regression. and with The convergence criterion is used; if it is not met, priority is given to backtracking and updating high-sensitivity parameters. With interface properties.

[0068] 7) Step Output

[0069] Output the calibrated sequence model and piecewise parameter matrix. This is provided for direct use in the subsequent step S2, "Multi-layer Segmentation and Interface-by-Interface Loss Calculation".

[0070] Step S2, Multi-layer segmentation and multi-component loss calculation

[0071] The dielectric sequence model and segmentation parameter matrix obtained in step S1 Based on the system operating frequency (This example uses 433MHz) Calculate the nominal path loss. All values ​​are in dB, and all logarithms are in decimal.

[0072] 1) Calculation of propagation parameters within the layer

[0073] For each layer Based on dispersion / moisture content correction Calculate the phase constant With absorption coefficient :

[0074]

[0075]

[0076] in, The permeability of the medium is usually taken as... , It is the dielectric constant in a vacuum. For the first Phase constant of the layered medium, For the first The absorption attenuation coefficient of the layer medium.

[0077] 2) Interface-to-interface power reflection coefficient

[0078] Adjacent number With the Power reflection coefficient of the layer (Under the conditions of normal incidence and non-magnetic approximation)

[0079]

[0080] in Indicates the first The relative permittivity of the dielectric layer (used in engineering) (Approximately). For numerical stability, for... Perform interval projection (suggestion ).

[0081] 3) Decomposition and summation of nominal path loss

[0082] The propagation path is divided according to the medium layer. Section, satisfying

[0083]

[0084] Calculate and sum the various loss components:

[0085]

[0086]

[0087] in: For free space radiation loss, For wavelength / phase loss, For medium absorption loss, For the interface reflection loss, a simplified solution is used here, employing a first-order cumulative approximation for the interface reflection loss of multilayer media. For the total propagation distance, For the first Propagation distance in layered media The number of segments, , , , , These are the model coefficients determined through experimental calibration or regression fitting in step S1.

[0088] 4) Special layers and physical constraints

[0089] When a certain layer is air: Take , ,but Only the phase constant and reflection loss at adjacent interfaces are considered; when a thin conductive layer of equal height to the reinforcing steel is present, the effective penetration depth can be used to determine the loss. Add to and merge with interface items. Maintain physical feasibility across all layers: , , , , .

[0090] When the signal propagation path passes through two layers of composite medium The total propagation loss estimation model is specialized as follows:

[0091]

[0092] in: The distance the signal travels in the first layer of medium. The distance the signal travels in the second medium; satisfying , The Fresnel reflection coefficient is the interface between the two media layers; the specific model coefficient calibration values ​​are as follows: ;

[0093] When the second layer is an air layer, take ； It is the speed of light in a vacuum; when any layer is an equivalent high-conductivity region, by increasing Or to An effective penetration depth correction is introduced to reflect strong reflection and weak transmission effects.

[0094] 5) Step Output

[0095] return and each loss component and output array , , The cumulative value is used for subsequent steps S3.

[0096] Step S3, Output the result: Output the above nominal loss estimate. As the calculation result of wireless signal propagation loss in the target area under different propagation path lengths and interlayer combinations, this result directly provides a quantitative basis for the link budget and monitoring equipment deployment design of wireless monitoring of underground infrastructure.

[0097] Step S4: Robust loss calculation and uncertainty verification

[0098] The nominal path loss is obtained in step S2. Based on this, in order to characterize the impact of parameter fluctuations caused by moisture content, temperature, and sampling errors on the results, this embodiment (continuing to use) The robustness assessment is conducted as follows, with all losses expressed in dB and logarithms in decimal:

[0099] 1) Establish the disturbance set of key parameters for each layer of medium ( )

[0100] The calibration value around step S1 is the first... Layer establish parameter perturbation set Its value range is given by on-site statistical data / historical sample data and operating condition window (interval or ellipsoidal constraints), and satisfies physical feasibility: , , , , .

[0101] 2) Numerical stability constraints

[0102] Inter-interface power reflection coefficient for interval projection

[0103]

[0104] Avoid occurrence under extreme dielectric contrast. Divergent.

[0105] 3) Solving for robust upper bound loss

[0106] Within the loss model framework of step S2, Replace the nominal parameter (replace the parameter as) Given the feasible values ​​in the given list, find the worst-case combination (robust upper bound loss, i.e., maximizing total loss):

[0107]

[0108] Implementation options include: interval extreme value scanning / scenario sampling (Monte Carlo or Latin hypercube), ellipsoidal set dual relaxation, or heuristic hybrid search.

[0109] 4) Minimum Link Margin Measurement

[0110] Output nominal-upper bound pairwise results And calculate the minimum link margin.

[0111] This margin serves as a safety margin indicator for subsequent link budgets, used to assess the impact of parameter fluctuations on loss calculations.

[0112] 5) Recalibrate triggering and processing

[0113] like ( (For the allowable deviation of the loss calibration), when the deviation between the two is greater than If the nominal model is deemed insufficiently conservative in addressing current uncertainties, the model parameter recalibration process (parameter sampling and recalibration) is triggered and narrowed. The range of values; specifically, prioritize updating the high-sensitivity set. In conjunction with interface attributes, and based on newly added observation pairs Shrink clusters based on the upper and lower bounds or covariance until... .

[0114] 6) Impose physical feasibility constraints:

[0115] Physical feasibility constraints were imposed on all key parameters, and the interface reflection coefficient was also considered. Perform interval projection. Specifically, this step involves: performing parameter trimming and physical feasibility checks on all layers to ensure they meet the constraints. and the interface reflectance coefficient Perform interval projection to make it fall into ,in It is a tiny quantity greater than 0.

[0116] 7) Step Output

[0117] Output , And the corresponding worst-case scenario parameter set (optional), which can be directly called by the closed-loop calibration in step S5 and the standardized interface in step S6.

[0118] Step S5, Model Validation and Closed-Loop Calibration

[0119] The nominal loss is obtained in step S2. The robust upper bound loss is obtained in step S4. Based on this, the model-measurement consistency verification and parameter recalibration are carried out in conjunction with field observations, forming a closed-loop process of "observation-judgment-update-solidification".

[0120] 1) Link observation and acquisition synchronized with the environment

[0121] Observations for recording co-frequency communication: Transmit power Transmit / receive antenna gain , Cable / connection loss Receive strength (Use robust statistics, such as median or quantiles), and simultaneously collect temperature data. Moisture content Environmental parameters such as timestamps should be included to ensure consistency with the model input.

[0122] 2) Calculation of measured path loss

[0123] For each link

[0124]

[0125] The measured path loss is calculated; if necessary, time / frequency smoothing is performed on small-scale fading to ensure consistency with the statistical caliber of the nominal / robust model.

[0126] 3) Deviation Judgment and Triggering Conditions

[0127] Compare separately If the allowable deviation threshold is exceeded. If the current model is deemed insufficient to represent the actual working conditions, a recalibration process will be initiated to jointly correct the model coefficients and highly sensitive parameters. When satisfied Furthermore, if the change in model coefficients between two consecutive calibrations does not exceed a preset threshold, the closed-loop is considered to have converged, and the converged model is output. The preset threshold can be 5%.

[0128] 4) Coefficient recalibration and sensitivity parameter update

[0129] Update the model coefficients using least squares / robust regression (such as Huber, Theil-Sen) or Bayesian methods. With a set of highly sensitive parameters The nominal values ​​are jointly corrected; synchronous updates are performed. and → The calibration relationship, recalculated , With interface reflection items Recalculate after update. and .

[0130] 5) Physical feasibility and numerical stability check

[0131] Perform cropping on the updated parameters: ;right Perform interval projection To avoid Numerical divergence.

[0132] 7) Convergence determination and version fixation

[0133] satisfy Furthermore, if the change in coefficients between two consecutive calibrations is less than a preset threshold (e.g., ≤5%), the closed-loop is considered to have converged, and the latest coefficient set is output. Parameter matrix Disturbance set Interface Item Aperture (Engineering First Order), Frequency Band Include the model / coefficient version number and effective date. If convergence fails, return to step S4 for iteration.

[0134] 8) Output

[0135] This step outputs the results for each link. and experimental link

[0136] The comparison results, final coefficients and parameter sets, convergence status and metadata are used for calling the standardized output in step S6.

[0137] S6, Standardized Output and Interface

[0138] After completing the calculations and calibrations in steps S2 to S5, the results are uniformly coded and published for easy direct use in design:

[0139] 1) Data Products and Formats

[0140] Nominal output loss Robust upper bound Interface items and minimum link margin Spatialization results:

[0141] Raster: GeoTIFF (dB, log base 10), resolution ;

[0142] Vector: GeoJSON equal loss line and equal margin line (including attributes: level, mode=nominal / robust).

[0143] It also outputs a segment / link-by-link table (CSV): the fields include link_id, start_xy, end_xy, d, L_est, L_est_plus, M, LR, alpha_i, beta_i, R_k, mode, timestamp.

[0144] 2) Coordinate system and unit conventions

[0145] Specify the CRS (e.g., EPSG:4490 or EPSG:3857), elevation datum, and raster resolution / interpolation method in the metadata; all loss quantities should be expressed in dB, and logarithms should be uniformly set to decimal, with frequency... Temperature / moisture content values ​​are recorded along with the results.

[0146] 3) Model and version information

[0147] Provide model_version (coefficient) with the results (Version number and effective date) , epsilon_R (for The interval projection parameters), interface aperture (interface_mode=first_order), and the parameter matrix used. Abstract (sequence, (Sampling time window).

[0148] 4) Interface and Invocation Specifications

[0149] Provides a lightweight API / script (example: HTTP GET / local CLI) to return a GeoTIFF / GeoJSON / CSV download address or embedded data; request parameters include bbox / frequency / version / mode (levels=…). The log base and unit are re-declared in the API documentation, and the return value includes a CRC / hash for integrity verification.

[0150] 5) Quality control labeling

[0151] Provide qc_pass and residual metrics for each link and grid block. The statistical data facilitates downstream applications in screening trusted regions.

[0152] The above technical solution is only one feasible technical solution of the present invention. The scope of protection of the present invention is not limited thereto. Those skilled in the art can reasonably adjust the specific design according to actual needs.

[0153] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not affect the substantive content of the present invention. Therefore, any simple modifications and substitutions made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solutions of the present invention shall still fall within the protection scope of the technical solutions of the present invention.

Claims

1. A method for calculating signal loss in wireless monitoring of underground infrastructure, characterized in that, Includes the following steps: Step S1, medium parameter acquisition and sequence modeling: conduct physical and electromagnetic property tests on multiple underground media in the target area, determine the medium sequence, acquire and preprocess the key parameters of each medium layer. The key parameters include at least the relative equivalent complex permittivity, conductivity, water content and layer thickness that characterize the electromagnetic response of the medium. The preprocessing includes correction of each key parameter, data processing and construction of segmented parameter matrices. Step S2, Multi-layer Segmentation and Multi-component Loss Calculation: Based on the aforementioned medium layer sequence and segmentation parameter matrix, the wireless signal propagation path is divided according to the medium layer. The propagation distance is the sum of the distances of the segments. Based on the principle of segment accumulation and interface interaction in multilayer composite media, a total propagation loss estimation model is constructed. The total propagation loss includes at least free space radiation loss, wavelength / phase loss, medium absorption loss and interface reflection loss. The corresponding layer parameters in the segment parameter matrix are called to quantify each loss component and accumulate to obtain the nominal loss estimate. Step S3, Output the result: Output the nominal loss estimate as the calculation result of wireless signal propagation loss in the target area under different propagation path lengths and interlayer combinations, providing a quantitative basis for link budget and monitoring equipment deployment design for wireless monitoring of underground infrastructure.

2. The method for calculating signal loss in wireless monitoring of underground infrastructure as described in claim 1, characterized in that, In step S1, the relative equivalent complex permittivity is , ,in, For the real part of the relative equivalent complex permittivity, This is the imaginary part of the relative equivalent complex permittivity.

3. The method for calculating signal loss in wireless monitoring of underground infrastructure as described in claim 2, characterized in that, In step S1, the steps of conducting physical and electromagnetic property tests on the multi-layer underground media within the target area, determining the media sequence, and obtaining and preprocessing the key parameters of each media layer specifically include: (a) By combining borehole sampling with shallow ground-penetrating radar, the stratigraphic sequence and thickness of each layer are determined, forming a layer thickness set. , For media layer sequence numbering; (b) The time-domain reflectometry and / or impedance spectroscopy were used to determine the properties of each medium layer. and Dispersion correction and frequency band mapping are performed on the measurement results to adapt the parameters to the operating frequency band of the wireless monitoring signal. ; (c) Determine the moisture content of each medium layer by gravimetric method or frequency domain moisture sensor. ;Establish and , The calibration relationship, correcting the equivalent complex permittivity. To reflect the time-varying effect of moisture; (d) The conductivity of each dielectric layer was determined using the four-electrode method. Combined with the on-site temperature With moisture content Establish a temperature and humidity compensation relationship to obtain The on-site equivalent value; (e) The measurements and corrections made in steps (a) to (d) above , , 、 , Outlier removal, spatial interpolation, and hierarchical weighting are performed, and a segmented parameter matrix is ​​constructed by integrating according to the media layer order. The segmented parameter matrix is ​​used in step S2 to perform loss calculation by calling parameters by layer.

4. The method for calculating signal loss in wireless monitoring of underground infrastructure as described in claim 1, characterized in that, The expression for the total propagation loss estimation model in step S2 is as follows: in: For the total propagation distance, For the first Propagation distance in layered media For the number of media segments, The permeability of the medium, It is the dielectric constant in a vacuum. For the first Phase constant of the layered medium, For the first The absorption attenuation coefficient of the layer medium. For adjacent With the The power reflection coefficient of the layer satisfies in, This represents the relative permittivity of the k-th dielectric layer. , , , , These are the model coefficients determined through experimental calibration or regression fitting.

5. The method for calculating signal loss in wireless monitoring of underground infrastructure as described in claim 4, characterized in that, When the signal propagation path passes through two layers of composite medium The total propagation loss estimation model is specialized as follows: in: The distance the signal travels in the first layer of medium. The distance the signal travels in the second medium; satisfying , The Fresnel reflection coefficient is the interface between the two media layers; the specific model coefficient calibration values ​​are as follows: , , , , ; When the second layer medium is air, take , ,but , ; It is the speed of light in a vacuum; when any layer is an equivalent high-conductivity region, by increasing Or to An effective penetration depth correction is introduced to reflect strong reflection and weak transmission effects.

6. The method for calculating signal loss in wireless monitoring of underground infrastructure as described in claim 1, characterized in that, The method further includes step S4: robust loss calculation and verification, specifically including: (a) Establish the perturbation set of key parameters for each layer of medium perturbation set The range of values ​​is given by on-site statistical data or historical sample data, and meets the physical feasibility constraints; (b) Within the loss model framework of step S2, replace the parameters of each layer with... The feasible values ​​in the range are used to calculate the robust upper bound loss: (c) Output Paired results; setting the allowable deviation for loss calibration. ， When the deviation between the two is greater than At that time, the model parameter recalibration process is triggered and narrowed. The range of values ​​for; (d) Impose physical feasibility constraints on all key parameters, and on the interface reflection coefficient. Perform interval projection.

7. The method for calculating signal loss in wireless monitoring of underground infrastructure according to claim 6, characterized in that, Physical feasibility constraints were imposed on all key parameters, and the interface reflection coefficient was also considered. The specific steps for performing interval projection are as follows: Perform parameter trimming and physical feasibility checks on all layers to ensure they meet the constraints. , , , , and the interface reflectance coefficient Perform interval projection to make it fall into ,in It is a tiny quantity greater than 0.

8. The method for calculating signal loss in wireless monitoring of underground infrastructure according to claim 6, characterized in that, The method further includes step S5: model validation and simplified closed-loop calibration; specifically including: (a) Conduct field tests, collect link observations, and convert them into measured path loss. ; (b) Set the allowable deviation for loss calibration ， when At that time, for model coefficients and highly sensitive parameters , , Joint corrections will be made; when full Furthermore, if the change in model coefficients between two consecutive calibrations does not exceed a preset threshold, the closed-loop is considered to have converged, and the converged model is output. .

9. The method for calculating signal loss in wireless monitoring of underground infrastructure according to claim 8, characterized in that, The preset threshold is 5%, and the link observations include at least transmit power, transmit antenna gain, receive antenna gain, cable loss, and received signal strength indication.

10. The method for calculating signal loss in wireless monitoring of underground infrastructure according to claim 8, characterized in that, The method further includes: step S6, standardized output and interface of results; specifically including: uniformly encoding the loss results after model verification and simplified closed-loop calibration, outputting standardized data in raster, vector and tabular formats, providing callable interfaces, and marking quality control labels to facilitate direct call by downstream link budget and monitoring equipment deployment related systems.

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