A method and system for evaluating concrete moisture permeability

Abstract

The present application provides a method and system for evaluating the moisture permeability of concrete. The method first obtains moisture gradient change data within the concrete and performs moving average processing on the data to generate smoothed moisture diffusion data. Secondly, based on the diffusion data, the frequency offset data caused by humidity influence is measured in the microwave frequency band, and diffusion features are extracted from the offset data using phase locking technology. The diffusion features are then subjected to spatiotemporal correlation processing to identify hotspot patterns. Finally, pattern clustering processing is performed on the hotspot patterns, grouping them into multiple levels and outputting the concrete permeability rating. The technical solution provided by the present application not only ensures real-time and lossless capture of the microscopic dynamics of moisture migration, but also achieves zero-delay analysis of the true permeability dynamics, fundamentally resolving problems such as manual experience misjudgment and offline simulation inaccuracy.

Description

Technical Field

[0001] The present application relates to the field of concrete testing technology, and in particular to a method and system for evaluating concrete moisture permeability. Background Art

[0002] In concrete structural projects (such as water conservancy dams and underground pipeline corridors), moisture permeability is a core indicator for evaluating material durability; especially in alternating dry and wet environments, the migration rate of moisture along the pore network directly affects the risk of chloride ion erosion, freeze-thaw damage, and steel corrosion.

[0003] The current technology uses a moisture migration monitoring method based on a fiber grating sensor network. A distributed fiber optic sensor array is embedded in the concrete. The local humidity gradient is inverted by measuring the light wavelength offset caused by humidity changes, and the three-dimensional humidity field distribution is reconstructed by coupling the finite element model. Finally, the permeability is graded based on the moisture diffusion rate threshold recommended by the standard.

[0004] However, this technology has the following significant shortcomings. First, the spatial resolution of the fiber optic sensing network is limited by the density of pre-buried nodes, making it difficult to accurately capture the microscopic paths of moisture migration and non-uniform infiltration hotspots. Second, humidity field reconstruction relies on numerical simulation of preset boundary conditions. When complex structural anomalies such as cracks appear inside the concrete, model deviations will lead to serious distortion of the infiltration evaluation results. In addition, optical signal demodulation requires offline processing on large workstations and cannot support real-time on-site evaluation needs. These shortcomings limit its application value in engineering rapid diagnosis scenarios. Summary of the Invention

[0005] The present application provides a concrete moisture permeability evaluation method and system to solve the problems in the prior art such as insufficient spatial resolution of the optical fiber sensing network leading to failure in identifying microscopic permeability paths, distortion of the simulation model leading to rating deviations, and the inability of the offline processing mechanism to support on-site real-time evaluation.

[0006] In a first aspect, the present application provides a method for evaluating moisture permeability of concrete, comprising:

[0007] Obtain the moisture gradient change data inside the concrete;

[0008] Performing moving average processing on the moisture gradient change data inside the concrete to generate smoothed moisture diffusion data;

[0009] Based on the smoothed humidity diffusion data, measuring frequency shift data caused by humidity influence in the microwave frequency band, and extracting diffusion characteristics from the frequency shift data using phase locking technology;

[0010] performing spatiotemporal correlation processing on the diffusion characteristics to identify moisture migration hotspot patterns within the concrete;

[0011] A pattern clustering process is performed on the moisture migration hotspot patterns to group them into multiple levels, and a permeability score grade of the concrete is output according to the grouped multiple levels.

[0012] Optionally, obtaining the moisture gradient change data inside the concrete includes:

[0013] The original humidity signal of the multi-layer sensing points pre-deployed in the surface area, transition area and core area of ​​the concrete specimen is synchronously collected at preset time intervals by the impedance sensing element, wherein the original humidity signal includes the impedance amplitude and phase angle;

[0014] The impedance amplitude and phase angle are converted into initial physical quantities associated with humidity to form humidity gradient change data inside the concrete.

[0015] Optionally, performing moving average processing on the moisture gradient change data inside the concrete to generate smoothed moisture diffusion data includes:

[0016] Determining a time window size associated with an attenuation law of the moisture gradient change data inside the concrete;

[0017] intercepting a continuous subsequence of the concrete internal moisture gradient change data based on the time window size;

[0018] Calculating the data average of each of the continuous subsequences as a discrete point;

[0019] Connect all discrete points to generate time-series continuous smooth humidity diffusion data.

[0020] Optionally, based on the smoothed humidity diffusion data, measuring frequency offset data caused by humidity influence in a microwave frequency band includes:

[0021] Determine the local area and scanning path to be scanned inside the concrete based on the smoothed moisture diffusion data;

[0022] Directing the continuous wave signal emitted by the microwave scanning unit to the local area along the scanning path, and receiving the microwave echo signal reflected by the interior of the concrete;

[0023] Mixing the microwave echo signal with a preset reference oscillation signal to generate a mixed signal containing a frequency difference;

[0024] Recording the real-time oscillation frequency of the mixing signal in the local area by a frequency counting unit;

[0025] The real-time oscillation frequency is compared with a preset reference frequency of the concrete drying state, and frequency offset data is generated according to the comparison result.

[0026] Optionally, extracting diffusion features from the frequency offset data using a phase locking technique includes:

[0027] Inputting the frequency offset data into a phase-locked loop, and continuously comparing the phase difference between the frequency offset data and a preset reference signal through an error signal generating unit within the phase-locked loop;

[0028] Dynamically adjusting the output frequency of an oscillating unit within a phase-locked loop according to the phase difference, and monitoring the amplitude of the change in the phase difference;

[0029] When the change amplitude of the phase difference is continuously lower than a first set threshold, determining that the phase-locked loop enters a locked state;

[0030] In a locked state, the output frequency of the internal oscillation unit of the phase-locked loop is recorded, and the instantaneous frequency fluctuation component of the output frequency is extracted as a diffusion feature.

[0031] Optionally, performing spatiotemporal correlation processing on the diffusion features to identify a moisture migration hotspot pattern inside the concrete includes:

[0032] The diffusion characteristics are mapped to the spatial coordinate positions and corresponding time nodes inside the concrete to generate a spatiotemporal index framework;

[0033] Calculating the migration intensity similarity between each spatial coordinate position and adjacent spatial coordinate positions in the spatiotemporal index framework;

[0034] When the migration intensity similarity of adjacent spatial coordinate positions exceeds a second set threshold, the adjacent spatial coordinate positions are associated as the same migration unit;

[0035] Traverse all migration units and count the cumulative value of the diffusion characteristic intensity of each migration unit;

[0036] Based on the diffusion feature intensity accumulation value, a migration unit whose diffusion feature intensity accumulation value exceeds twice the average intensity value of the surrounding migration units is taken as a target migration unit, and all spatially adjacent target migration units are merged into a continuous spatial region;

[0037] Marking continuous spatial regions as moisture migration hotspot patterns.

[0038] Optionally, performing pattern clustering processing on the moisture migration hotspot patterns to group them into multiple levels, and outputting concrete permeability score grades according to the grouped multiple levels, including:

[0039] The spatial distribution characteristics and intensity distribution characteristics of each humidity migration hotspot pattern are extracted respectively;

[0040] Based on the similarity of the spatial distribution characteristics and the intensity distribution characteristics, the humidity migration hotspot patterns with migration synergy effects are associated into migration groups;

[0041] According to the spatial coverage and intensity stability of the migration group, the permeability score level is output according to preset rules.

[0042] In a second aspect, the present application provides a concrete moisture permeability evaluation system, comprising:

[0043] An acquisition module is used to obtain the moisture gradient change data inside the concrete;

[0044] a processing module, configured to perform moving average processing on the moisture gradient change data inside the concrete to generate smoothed moisture diffusion data;

[0045] an extraction module, configured to measure frequency offset data caused by humidity influence in a microwave frequency band based on the smoothed humidity diffusion data, and extract diffusion features from the frequency offset data using a phase locking technique;

[0046] an identification module for performing spatiotemporal correlation processing on the diffusion characteristics to identify a moisture migration hotspot pattern inside the concrete;

[0047] The output module is used to perform pattern clustering processing on the moisture migration hotspot pattern to group it into multiple levels, and output the permeability score grade of the concrete according to the multiple levels after grouping.

[0048] In a third aspect, an embodiment of the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for evaluating concrete moisture permeability as described in the first aspect above.

[0049] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, the method for evaluating moisture permeability of concrete as described in the first aspect is implemented.

[0050] This embodiment of the application constructs an integrated quantitative assessment system for concrete moisture permeability through continuous and serial dynamic capture of moisture gradients, microwave dielectric response measurement, diffusion feature separation and extraction, and intelligent migration pattern rating. Its core value lies in overcoming the spatiotemporal limitations of traditional testing methods, achieving end-to-end connectivity from raw data acquisition to permeability rating output. Microwave physical field measurement coupled with phase-locked frequency feature extraction technology ensures real-time and non-destructive capture of the microscopic dynamics of moisture migration. A dual processing mechanism of spatiotemporal correlation and pattern clustering precisely adapts to the heterogeneous permeability characteristics of concrete, outputting an engineering usability rating, and fundamentally addressing issues such as human experience misjudgment and offline simulation inaccuracies.

[0051] Furthermore, by constructing a closed-loop phase tracking system between the frequency offset signal and the reference signal, and utilizing dynamic loop adjustment to eliminate ambient background noise interference, the researchers achieved a stable output of microsecond-level physical signals dominated by the humidity response in a locked state. This technology upgrades traditional passive filtering to active synchronous purification, significantly enhancing the signal-to-noise ratio of weak diffusion features. Its millisecond-level locked response synchronizes with the dielectric relaxation process of water migration, enabling zero-delay analysis of true penetration dynamics. This provides high-fidelity input for subsequent hotspot identification and cluster ranking, laying the physical foundation for the reliability of the overall method.

[0052] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0054] Figure 1 A flow chart of a method for evaluating moisture permeability of concrete provided by the present application is shown;

[0055] Figure 2 A structural schematic diagram of a concrete moisture permeability evaluation system provided by the present application is shown;

[0056] Figure 3 A schematic structural diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0057] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0058] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0059] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0060] Figure 1 A flowchart of a method for evaluating moisture permeability of concrete is provided for the embodiment of the present application. Figure 1 As shown, the method includes:

[0061] Step 101: Acquire moisture gradient change data inside concrete.

[0062] In this step, the moisture gradient data within the concrete refers to the dynamic variation in moisture content within different areas of the concrete, captured by embedded sensors. This data is comprised of two key parameters: the impedance amplitude, which represents the degree of resistance to current flow from concrete pore water; and the phase angle, which reflects the temporal shift in the current and voltage signals caused by moisture migration. Monitoring areas are divided into three categories based on depth: the surface zone, the shallow layer near the concrete surface; the transition zone, the interface between the cement paste and aggregate; and the core zone, the central region within the concrete.

[0063] In an embodiment of the present application, first, the original signal is periodically and synchronously collected by a layered impedance sensing array embedded in a concrete specimen, and the array is deployed in the surface area, transition area and core area respectively; secondly, the collected impedance amplitude and phase angle are input into the physical quantity conversion relationship model of the material calibration to convert them into the real-time humidity physical quantity of each sensing point; then, the humidity values ​​of sensing points at different depths at the same time nodes are aligned, and the humidity difference matrix of the spatial dimension is calculated; finally, the humidity difference matrix of the continuous time series is integrated into the concrete internal humidity gradient change data containing spatial position, timestamp, and humidity value.

[0064] Step 102: Perform moving average processing on the moisture gradient change data inside the concrete to generate smoothed moisture diffusion data.

[0065] In this step, moving average processing involves weighted fusion of time-series humidity gradient information using a dynamically sliding data processing window. Key features include the sliding time window's continuous capture function, a mechanism that prioritizes weighting of near-instantaneous data points within the window, and the suppression of abnormal fluctuations achieved through the superposition of multiple time points. The sliding time window, as the basic processing unit, moves forward in real time with the analysis node. A proximal weighting mechanism assigns greater influence to data closer to the current moment, ultimately resulting in glitch suppression that eliminates distorted signals caused by transient sensor interference.

[0066] In this embodiment of the present application, the internal humidity gradient change data of the concrete output from the previous step is first read, and the humidity difference matrix sequence of the current node and its previous consecutive moments is intercepted according to a preset time span. A differentiated weight coefficient is then assigned to each time node within the window, with the current moment data receiving the highest weight, and the weight of historical moment data gradually decreasing as the time distance increases. A weighted fusion calculation is then performed on all humidity difference matrices within the window to form a humidity matrix that aggregates multi-time point features and is marked as the smoothed output of the current node. Finally, the processing window is iteratively slid along the time axis to generate a smoothed humidity diffusion dataset covering the entire monitoring period.

[0067] Step 103 : Based on the smoothed humidity diffusion data, frequency offset data caused by humidity influence is measured in a microwave frequency band, and diffusion features are extracted from the frequency offset data using a phase locking technique.

[0068] In this step, the frequency offset data in the microwave band refers to the electromagnetic field oscillation frequency offset generated by the humidity distribution disturbance inside the concrete detected by electromagnetic wave resonance technology. Its physical essence is the dynamic modulation effect of the polarity of water molecules on the dielectric constant as the humidity diffuses; the phase locking technology refers to the method of using a closed-loop feedback system to track the target frequency offset and stably extract its time domain fluctuation characteristics. By establishing a synchronization mechanism between the oscillation signal and the reference benchmark, the environmental noise interference is filtered out. The diffusion characteristics finally obtained represent the specific response pattern caused by water migration in the dielectric spectrum.

[0069] In this embodiment, smoothed humidity diffusion data is first input into a microwave frequency band analysis device, which controls a ring waveguide resonant cavity to transmit a fixed-frequency electromagnetic beam to a concrete specimen. The resonant frequency signal of the electromagnetic wave penetrating the specimen is collected in real time, and its offset from the initial reference frequency is recorded. Secondly, the frequency offset data is input into a phase locking module. An error detection unit dynamically compares the phase difference between the signal and the local oscillator reference signal, generating a feedback voltage to adjust the output frequency of the oscillator. When the phase difference fluctuation remains below a threshold, synchronization is determined, and the locked instantaneous frequency fluctuation component is identified as a valid diffusion signature.

[0070] Step 104: performing spatiotemporal correlation processing on the diffusion characteristics to identify moisture migration hotspot patterns inside the concrete.

[0071] In this step, spatiotemporal correlation processing refers to coupling the temporal dynamics of diffusion characteristics with spatial distribution patterns, and identifying active paths of moisture migration by calculating the response synchronization of data points at different positions on the time axis and the correlation strength within the spatial neighborhood. The moisture migration hotspot pattern characterizes the local areas where moisture continuously accumulates or rapidly migrates inside the concrete. Its essence is a spatial aggregation unit with a diffusion characteristic intensity in the spatiotemporal dimension that is significantly higher than the background value and has a stable evolution law.

[0072] In an embodiment of the present application, the extracted diffusion feature data are first mapped to a three-dimensional concrete structure grid according to the spatial coordinates of the sensing points to generate a spatiotemporal feature matrix; secondly, a sliding time window is used to perform time series analysis on each grid unit, the cross-correlation strength of the diffusion feature fluctuations of adjacent sensing points is calculated, and response clusters with high synchronization are identified; then, a spatial clustering algorithm is used to classify continuous grid units with cross-correlation strength exceeding a threshold as candidate hotspot areas; finally, the evolution path of the candidate area is tracked along the time axis, and patterns with continuously increasing diffusion intensity and steadily expanding spatial range are screened, marked as humidity migration hotspots, and their spatial boundaries and migration rate parameters are output.

[0073] Step 105 : performing pattern clustering processing on the moisture migration hotspot patterns to group them into multiple levels, and outputting concrete permeability score grades according to the grouped multiple levels.

[0074] In this step, dynamic density clustering refers to a technology that performs multi-level grouping based on the similarity of the spatial distribution density and temporal evolution intensity of migration hotspots. Its core includes the neighborhood connectivity determination of hotspot spatial clusters, the persistence association in the time dimension, and the dynamic weight superposition analysis. The permeability rating is a comprehensive evaluation index generated by quantifying the migration scale, path continuity, and diffusion rate of each group after clustering, and is used to characterize the degree of danger of the capillary seepage capacity inside the concrete.

[0075] In an embodiment of the present application, the humidity migration hotspot pattern data output from step 104 is first imported into a spatial grid coordinate system, and the spatial density value and the active persistence coefficient in the time dimension of each hotspot unit within a preset neighborhood radius are calculated; secondly, a density clustering algorithm is used to scan all hotspot units, and units with continuously high spatial density values ​​and synchronously fluctuating active persistence coefficients are aggregated into initial clusters; then, through a time superposition verification mechanism, isolated clusters formed by short-term noise interference are filtered out, and stable migration groups that continue to expand are retained; finally, according to the spatial coverage range, path bifurcation complexity and migration intensity growth rate of each group, they are mapped to a preset permeability score grading table to output quantitative grade results.

[0076] In some embodiments, according to step 101, obtaining the moisture gradient change data inside the concrete includes:

[0077] Step 201 : using impedance sensing elements to synchronously collect raw humidity signals from multiple sensing points pre-deployed in the surface area, transition area, and core area of ​​a concrete specimen at preset time intervals. The raw humidity signals include impedance amplitude and phase angle.

[0078] In this step, synchronous acquisition at preset time intervals refers to triggering all sensor elements to capture data simultaneously according to unified timing instructions, ensuring that humidity signals at different depths have strictly comparable timestamps; the impedance sensor element is a detection unit based on the alternating current response principle. The original humidity signal it outputs contains two parameters: impedance amplitude and phase angle, which respectively characterize the resistance strength of concrete pore moisture to current and the time offset characteristics of the dielectric relaxation effect.

[0079] In the embodiment of the present application, the central controller first sends a synchronous acquisition instruction to the sensing points on each layer, triggering all impedance sensing elements to obtain the original electrical response signal at exactly the same time; secondly, through the distributed signal conversion module, the analog electrical signal collected by each sensing point is converted into a digital signal packet containing the impedance amplitude oscillation waveform and phase angle timing in real time; then, position codes and unified timestamps are added to all signal packets to form the original humidity signal with the spatial coordinates and time dimensions strictly aligned.

[0080] Step 202: Convert the impedance amplitude and phase angle into initial physical quantities associated with humidity to form humidity gradient change data inside the concrete.

[0081] In this step, the initial physical quantity associated with humidity refers to mapping the electrical parameters into standardized indicators that can quantify the moisture content through the calibration relationship between the dielectric properties of concrete and the moisture content.

[0082] In an embodiment of the present application, the original signal collected in step 201 is first classified according to the position of the sensor point and input into the humidity conversion model. Based on the mapping relationship between the dielectric response of concrete and the moisture content, the model jointly analyzes the current obstruction strength of the impedance amplitude and the time offset characteristics of the phase angle, and outputs the humidity physical quantity of each sensor point; secondly, the humidity physical quantities of all sensor points in the same batch are aligned using the timestamp as the index, and the humidity difference between the surface area, transition area, and core area is calculated; finally, the spatial humidity difference is integrated according to the time series to generate the internal humidity gradient change data of the concrete including the position coordinates, time dimension, and humidity value.

[0083] In some embodiments, according to step 102, performing moving average processing on the moisture gradient change data inside the concrete to generate smoothed moisture diffusion data includes:

[0084] Step 301: Determine a time window size associated with the attenuation law of the moisture gradient change data inside the concrete.

[0085] In this step, the attenuation law refers to the dynamic pattern of moisture gradient data weakening over time, reflecting the process characteristics of moisture diffusion within concrete shifting from active to stable. The time window size is the interception period length, adaptively determined based on the attenuation dynamic characteristics, used to match the staged changes in diffusion rate in subsequent processing. The core logic is to shorten the window during the attenuation acceleration period to preserve the details of the sudden change, and expand the window during the attenuation stable period to enhance signal stability.

[0086] In the embodiment of the present application, the time dimension evolution curve of the concrete internal moisture gradient change data set is first extracted to analyze the variation law of the gradient attenuation rate; for the area with obvious signal fluctuations when the gradient decays rapidly in the initial stage, the shortest duration of maintaining high change intensity is calculated; for the signal stable stage where the gradient slows down in the later stage, the characteristic period range of its low-frequency fluctuation is counted; finally, based on the dynamic characteristics of the accelerated decay period and the stable decay period, the time window size matching each decay stage is generated.

[0087] Step 302: extract a continuous subsequence of the concrete internal humidity gradient change data based on the time window size.

[0088] In this step, a continuous subsequence is a local dataset with a uniform time span, cut from the concrete internal moisture gradient data according to a defined window size. Its time span is dynamically adjusted according to the decay phase, but the internal time points are continuous and complete. This truncation operation is performed by sliding the subsequence along the time axis, ensuring that the spatiotemporal correlation of the data points within the window period is not destroyed.

[0089] In an embodiment of the present application, the output time window size is first loaded into the data processing module, and the first interception interval is located according to the preset starting time node; the window size is equidistantly slid along the time axis, and the three-dimensional matrix slices of the corresponding time period in the humidity gradient change data are extracted segment by segment; the continuity of the data of each sensor point in the window is verified at each interception, and abnormal segments with missing timestamps are discarded; finally, a continuous subsequence is obtained.

[0090] Step 303: Calculate the data average of each continuous subsequence as a discrete point.

[0091] In this step, the data average refers to the aggregated feature quantity obtained by arithmetic averaging the humidity gradient values ​​of all spatial positions in a single continuous subsequence in the corresponding time window; the discrete point is a simplified data point generated by associating the average value as the unique representation value to the central time node of the subsequence.

[0092] In the embodiment of the present application, the subsequences are first loaded, and the humidity measurement values ​​of all sensing points within the time window are extracted for the three-dimensional humidity gradient matrix (spatial coordinate × time point × humidity value) of each subsequence; secondly, an arithmetic average operation is performed on all humidity values ​​to eliminate the dispersion effect caused by spatial position differences; then, the operation result is used as a quantitative indicator of the overall diffusion intensity, marked to the time midpoint position of the subsequence window, and finally, discrete points are output in time order.

[0093] Step 304: Connect all discrete points to generate time-series continuous smoothed humidity diffusion data.

[0094] In this step, the time-series continuous smoothed humidity diffusion data refers to the humidity evolution curve with continuous first-order derivative formed by connecting a sequence of discrete points through a curve fitting algorithm.

[0095] In an embodiment of the present application, the generated discrete points are first read and arranged in ascending order by timestamp to form a scattered distribution; secondly, the piecewise cubic spline interpolation algorithm is used to connect the adjacent discrete points with a curve to ensure that the slope of the connection is continuous and the curvature changes smoothly; when it is detected that the interval time of the discrete points exceeds the threshold, it is automatically switched to linear interpolation to avoid overfitting; finally, the fitting curve is checked for residuals with the original discrete points, and the abnormal points with significant deviations are eliminated and re-fitted to output smoothed humidity diffusion data covering the entire monitoring period.

[0096] In some embodiments, according to step 103, based on the smoothed humidity diffusion data, measuring frequency offset data caused by humidity influence in a microwave frequency band includes:

[0097] Step 401: Determine the local area to be scanned and the scanning path inside the concrete according to the smoothed moisture diffusion data.

[0098] In this step, the local scanning area refers to the abnormally active area identified by analyzing the spatial heterogeneity of humidity diffusion data, which is defined as a continuous spatial cluster with a diffusion intensity significantly higher than the surrounding background value; the scanning path is the route of the microwave resonant cavity scanning device dynamically generated based on the topological distribution and spatial connectivity of the local area with the goal of minimizing the equipment movement distance, ensuring complete coverage and optimal efficiency of physical field detection in key areas.

[0099] In an embodiment of the present application, first, based on the diffusion activity of each spatial position in the smoothed humidity diffusion data, abnormal points whose humidity change rate is significantly higher than the surrounding background value are screened; secondly, abnormal points with adjacent physical positions are merged into continuous regional blocks; then, based on the geometric distribution characteristics of the regional blocks (such as strips are suitable for serpentine paths, and dotted areas are suitable for star paths), the shortest serial path covering all blocks is generated; finally, the path trajectory is optimized in combination with the mobility of the detection equipment (such as the minimum turning radius) to ensure efficient and complete scanning.

[0100] Step 402 : Directly projecting the continuous wave signal emitted by the microwave scanning unit to the local area along the scanning path, and receiving the microwave echo signal reflected from the interior of the concrete.

[0101] In this step, the microwave scanning unit refers to a mobile probe with integrated electromagnetic wave transmitting and receiving functions, which radiates and captures microwave signals through a waveguide cavity; the continuous wave signal refers to an electromagnetic oscillation waveform with a constant frequency and no interruption, which is used to penetrate the interior of the concrete to produce steady-state resonance; directional projection refers to controlling the microwave beam to focus and project it to a preset point at a vertical angle of incidence to ensure that the energy is concentrated to penetrate the target area; the microwave echo signal refers to the electromagnetic wave feedback signal formed by reflection at the humidity gradient interface inside the concrete, whose amplitude attenuation represents the moisture absorption intensity, and the phase delay reflects the humidity diffusion depth.

[0102] In the embodiment of the present application, the microwave scanning unit is first driven to move along the path planned in step 401, and the transmitting module is started each time a scanning node is reached to project a continuous microwave beam of a fixed frequency band vertically onto the concrete surface; secondly, when the microwave penetrates the surface and enters the diffusion active area (such as the seepage zone of the aggregate interface), a reflected echo is generated due to the sudden change in the dielectric constant; the echo signal is captured synchronously by the high-sensitivity antenna array of the receiving module, and its amplitude attenuation rate and phase offset are recorded in real time; after completing the current node acquisition, it moves to the next path point for iterative operation, and finally generates an echo feature data set bound to the scanning path coordinates.

[0103] Step 403 : performing frequency mixing processing on the microwave echo signal and a preset reference oscillation signal to generate a mixed signal containing a frequency difference.

[0104] In this step, the reference oscillation signal refers to the standard frequency oscillation source built into the device, which provides a pure reference electromagnetic wave; the frequency difference signal refers to the low-frequency component output after mixing, whose frequency value is equal to the frequency difference between the echo signal and the reference signal, and carries the modulation information of humidity diffusion.

[0105] In an embodiment of the present application, the received microwave echo signal is first input into the first input end of the mixer, and the preset reference oscillation signal is input into the second input end; the instantaneous amplitude multiplication operation of the two signals is performed by the multiplier to generate a mixed signal containing the sum frequency and the difference frequency.

[0106] Step 404: Record the real-time oscillation frequency of the mixing signal in the local area through a frequency counting unit.

[0107] In this step, the frequency counting unit refers to a digital signal processing module that accurately calculates the frequency value by measuring the signal period or number of pulses; the real-time oscillation frequency refers to the number of fluctuations of the mixing signal per unit time, and its numerical change directly reflects the modulation intensity of the electromagnetic wave by the humidity gradient inside the concrete.

[0108] In an embodiment of the present application, the mixing signal is first input into the frequency counting unit, which uses a high-speed clock signal to time the waveform period of the mixing signal, and counts the number of complete waveform periods in a fixed time window, thereby calculating the real-time oscillation frequency of the current mixing signal; secondly, the frequency value is continuously monitored during the scanning process, and when it is detected that the frequency jump exceeds the preset tolerance range, the data verification mechanism is automatically triggered, and the frequency value of the current point is re-collected and calculated; finally, the real-time oscillation frequency corresponding to the scanning node is obtained.

[0109] Step 405 : Compare the real-time oscillation frequency with a preset reference frequency of the concrete drying state, and generate frequency offset data based on the comparison result.

[0110] In this step, the reference frequency of the dry concrete state refers to the electromagnetic wave resonance frequency obtained by calibration of a standard laboratory specimen under a constant temperature and humidity environment, which serves as a reference value when the humidity is zero.

[0111] In the embodiment of the present application, a pre-stored reference frequency of the concrete dry state is first called (this frequency is obtained by calibration of a standard laboratory specimen under a constant temperature and humidity environment). Next, the real-time oscillation frequency data recorded in step 404 is read, and the algebraic difference between the reference frequency and the real-time oscillation frequency data is calculated for each scan node to generate an original frequency offset. Then, a sliding window filter is used to eliminate instantaneous measurement noise, retaining a stable offset component that reflects the actual humidity change. Finally, frequency offset data containing the scan path position coordinates and the corresponding frequency offset is output. This data directly represents the difference in humidity diffusion intensity between different areas within the concrete.

[0112] In some embodiments, according to step 103, extracting the diffusion feature from the frequency offset data using a phase locking technique includes:

[0113] Step 501: Input frequency offset data into a phase-locked loop, and continuously compare the phase difference between the frequency offset data and a preset reference signal through an error signal generating unit within the phase-locked loop.

[0114] In this step, the error signal generation unit refers to the circuit module in the phase-locked loop that is responsible for quantifying the timing deviation between the input signal and the reference signal. It converts the time difference of the waveform zero crossing point into a pulse width modulation signal through time-to-digital conversion technology; the preset reference signal is a reference waveform with stable frequency and phase provided inside the loop, and its frequency value corresponds to the calibrated resonant frequency when the concrete is completely dry.

[0115] In the embodiment of the present application, the frequency offset data is first connected to the signal input terminal of the phase-locked loop, and the internal oscillator generates a preset reference signal and inputs it into the error detection channel. Secondly, a high-speed comparator is used to capture the rising edge time difference of the two signals in real time. When the frequency offset data waveform lags, a positive timing pulse is triggered, and when it advances, a negative timing pulse is generated. Then, a time-to-voltage conversion module linearly converts the pulse width into an analog voltage signal, whose voltage amplitude is proportional to the degree of phase deviation. The error voltage signal finally output will serve as the basis for frequency adjustment in step 502.

[0116] Step 502: Dynamically adjust the output frequency of the oscillating unit inside the phase-locked loop according to the phase difference, and monitor the variation amplitude of the phase difference.

[0117] In this step, the oscillation unit is a signal generator using voltage-controlled oscillation technology, and its output frequency changes linearly with the input voltage of the control end; the lock state judgment module refers to a logic unit that analyzes the phase difference fluctuation amplitude in real time through a digital signal processor, and determines that the system has completed synchronous tracking when the fluctuation continues to be lower than the threshold value.

[0118] In an embodiment of the present application, the error voltage signal output from step 501 is first input into the control port of the oscillation unit to drive its output frequency to be adjusted in the direction of offsetting the phase difference; secondly, the timing deviation between the current oscillation signal and the reference signal is continuously collected through the phase difference monitoring channel, and the fluctuation variance within the sliding time window is calculated; when the fluctuation variance of multiple consecutive monitoring cycles is lower than the stability threshold, the locking state flag signal is triggered; finally, the instantaneous frequency fluctuation component of the oscillation signal in the locked state is turned off.

[0119] Step 503: When the variation amplitude of the phase difference is continuously lower than a first set threshold, it is determined that the phase-locked loop enters a locked state.

[0120] In this step, the first set threshold refers to a pre-configured phase difference fluctuation tolerance value, which is used to determine whether the loop has reached a stable tracking state; the locked state indicates that the phase-locked loop has completed the frequency and phase synchronization of the input signal, and at this time the time domain characteristics of the output signal maintain a fixed relationship with the input signal.

[0121] In an embodiment of the present application, first, the sliding window variance of the phase difference sequence is calculated in real time by the digital signal processing unit to monitor its fluctuation amplitude; secondly, when the variance values ​​of multiple consecutive monitoring cycles are lower than the first set threshold, a locking state trigger signal is sent to the system control unit; finally, the control unit freezes the current loop parameters and starts the data recording process of step 504.

[0122] Step 504 : In a locked state, the output frequency of the internal oscillating unit of the phase-locked loop is recorded, and the instantaneous frequency fluctuation component of the output frequency is extracted as a diffusion feature.

[0123] In this step, the instantaneous frequency fluctuation component refers to the rapid fluctuation component in the output signal of the oscillation unit that deviates from its central frequency, and its spectral characteristics are directly related to the moisture migration rate inside the concrete.

[0124] In an embodiment of the present application, the high-speed data acquisition module is first started in a locked state to record the instantaneous frequency value of the output signal of the oscillation unit at an ultra-high sampling rate; secondly, the fluctuation component of a specific frequency band is separated by a digital bandpass filter to eliminate the background noise interference of the equipment; then, the filtered signal is subjected to a joint time-frequency analysis to extract its envelope waveform characteristics and harmonic distribution pattern; finally, the processed feature data is bound to the spatial coordinates to form a standardized diffusion feature.

[0125] In some embodiments, according to step 104, performing spatiotemporal correlation processing on the diffusion features to identify moisture migration hotspot patterns within the concrete includes:

[0126] Step 601 : mapping the diffusion features to the spatial coordinate positions and corresponding time nodes inside the concrete to generate a spatiotemporal index framework.

[0127] In this step, the spatiotemporal indexing framework is a multidimensional data structure that systematically associates diffusion characteristics with the spatial position of the concrete structure and the monitoring time nodes. It realizes the traceability analysis of the moisture migration process through spatial grid coding and timestamp sequence. The association mapping refers to the conversion process of relocating abstract signal characteristics to the spatial coordinates of the concrete entity based on the topological relationship of sensor deployment.

[0128] In an embodiment of the present application, the diffusion feature is first read and the sensor number and acquisition time information contained therein are parsed; secondly, the pre-stored sensor spatial coordinate library is called to convert the data record of each feature point into a specific position in the three-dimensional structure grid; then, the feature points are sequentially numbered according to the monitoring time sequence, and a mapping relationship table of spatial coordinates-timestamps-feature values ​​is established; finally, a spatiotemporal index framework that supports rapid retrieval by region is generated, and each entry contains structured fields such as position code, time node, and feature intensity.

[0129] Step 602: Calculate the migration intensity similarity between each spatial coordinate position and adjacent spatial coordinate positions in the spatiotemporal index framework.

[0130] In this step, migration intensity similarity is an indicator that quantifies the consistency of moisture diffusion behavior between adjacent spatial points. The continuity of the infiltration path is evaluated by comparing the matching degree of characteristic waveforms in the time domain and frequency domain. Adjacent spatial coordinates refer to the set of grid points within a preset radius that meet the structural connectivity conditions.

[0131] In an embodiment of the present application, first, all feature data within the target point and its neighborhood are extracted from the spatiotemporal index framework; secondly, time domain waveform cross-correlation calculation is performed on each adjacent point pair to obtain the waveform shape similarity coefficient; then, the frequency domain energy distribution characteristics are compared through fast Fourier transform to calculate the spectrum overlap; finally, the time domain and frequency domain similarity indicators are weighted and fused to obtain the migration intensity similarity, and a migration intensity similarity matrix is ​​constructed.

[0132] Step 603: When the migration intensity similarity of adjacent spatial coordinate positions exceeds a second set threshold, the adjacent spatial coordinate positions are associated as the same migration unit.

[0133] In this step, the second set threshold refers to the pre-configured migration intensity similarity critical value, which is used to determine whether the spatial points belong to the same infiltration path; the migration unit represents a set of continuous spatial regions with high correlation, and its internal points show synchronous moisture diffusion behavior characteristics, reflecting the potential seepage channels inside the concrete.

[0134] In an embodiment of the present application, the migration intensity similarity matrix is ​​first loaded and all non-diagonal elements therein are scanned; secondly, each element value is compared with a second set threshold value, and when the similarity exceeds the threshold value, a connecting edge between corresponding points is added to the adjacency relationship graph; then, through the graph theory connected component algorithm, all points with direct or indirect connection relationships are classified as the same migration unit; finally, a list of migration units containing unit number, member point coordinates and average similarity is output to provide a structured partition basis for subsequent permeability evaluation.

[0135] Step 604: traverse all migration units and calculate the cumulative value of the diffusion characteristic intensity of each migration unit.

[0136] In this step, the cumulative value of the diffusion characteristic intensity refers to the sum of the diffusion characteristic intensities of all spatial coordinate points within a single migration unit, which is used to quantify the overall moisture migration activity of the unit. Its physical meaning is that the higher the cumulative value, the more active the moisture infiltration path within the migration unit and the more significant the concrete durability risk.

[0137] In an embodiment of the present application, the migration unit list is first loaded, and the member point set of each unit is traversed in turn; secondly, all the diffusion feature intensity values ​​corresponding to these points are extracted from the spatiotemporal index framework; then, the feature intensities of all points in the unit are arithmetic summed up through the accumulator module to generate the cumulative value of the diffusion feature intensity of the unit; finally, the calculation result is bound to the migration unit number and the spatial boundary coordinates to form a structured summary table containing quantitative indicators of the unit penetration intensity.

[0138] Step 605 : Based on the diffusion feature intensity accumulation value, a migration unit whose diffusion feature intensity accumulation value exceeds twice the average intensity value of surrounding migration units is selected as a target migration unit, and all spatially adjacent target migration units are merged into a continuous spatial region.

[0139] In this step, the target migration unit refers to a set of migration units whose cumulative diffusion characteristic intensity significantly exceeds the average level of the surrounding units. The judgment is based on a preset comparison multiple relationship. The continuous spatial area is a geometric continuum formed by fusing adjacent high-risk target units through spatial adjacency analysis, which is used to identify the core area of ​​the leakage channel inside the concrete that needs to be treated first.

[0140] In an embodiment of the present application, first, the cumulative value of the diffusion characteristic intensity of all migration units is calculated, and the average value of the units within their spatial neighborhood is counted; secondly, abnormal units with cumulative values ​​exceeding 2 times the surrounding average value are screened out and marked as target migration units; then, an adjacency relationship network between target units is established in the spatial topology map, and a graph traversal algorithm is used to search for connected subgraphs; finally, all target units contained in each connected subgraph are geometrically merged to generate a continuous risk area containing spatial boundary coordinates.

[0141] Step 606: Mark the continuous spatial area as a humidity migration hotspot pattern.

[0142] In an embodiment of the present application, the geometric data of a continuous spatial region (boundary coordinate set, diffusion intensity distribution) is first loaded, and a unique pattern identifier is assigned to each region; secondly, the diffusion characteristic intensity of all spatial coordinate points in the region is analyzed, and the deviation level from the preset risk threshold is calculated; then, the spatial boundary coordinates, intensity deviation level, main migration direction angle and other attributes are encapsulated as structured metadata and bound to the corresponding pattern identifier; finally, the marked pattern identifier and metadata are written into the hotspot pattern storage partition of the global database to complete the conversion from the original spatial data to the marked hotspot pattern.

[0143] In some embodiments, according to step 105, performing pattern clustering processing on the moisture migration hotspot patterns to group them into multiple levels, and outputting a concrete permeability score based on the grouped multiple levels, including:

[0144] Step 701 : extract the spatial distribution characteristics and intensity distribution characteristics of each humidity migration hotspot pattern respectively.

[0145] In this step, the spatial distribution characteristics refer to the geometric shape and positional attributes of the humidity migration hotspot pattern, including regional area, boundary curvature, spatial orientation, and spatial relationship with key structural components (such as joints and steel bars); the intensity distribution characteristics characterize the statistical laws and changing trends of the intensity of the internal diffusion characteristics of the hotspot, covering core parameters such as intensity extreme points, gradient change direction, and discrete fluctuation degree.

[0146] In the application embodiment, spatial distribution features are first extracted. The spatial boundary coordinate set is read from the hotspot marking mode in step 606. A convex hull algorithm is used to generate a minimum circumscribed polygon. The curvature of each boundary point is analyzed using a curvature calculation module. Simultaneously, the distances between all sensor points and key concrete structure components (rebars and joints) within the hotspot region are scanned to establish a spatial relationship matrix. Next, intensity distribution features are independently extracted. The diffusion intensity data corresponding to the hotspot is loaded, and the region is divided into small cells using a three-dimensional grid discretization. Within each cell, the maximum, median, and variance of the diffusion intensity are calculated. The intensity change rate and principal direction vector between cells are calculated using a gradient field algorithm.

[0147] Step 702: Based on the similarity of the spatial distribution characteristics and the intensity distribution characteristics, the humidity migration hotspot patterns with migration synergy effects are associated into a migration group.

[0148] In this step, the migration synergy effect refers to the correlation characteristics between different hotspot patterns, such as spatial proximity, intensity evolution synchronization, and diffusion direction complementarity, which manifests as the continuous transport of water along a specific path inside the concrete. The migration group is a functional unit formed by aggregating multiple hotspot patterns with synergistic effects through similarity measurement. Its essence is the abstract representation of the interconnected seepage network inside the concrete in the analysis model.

[0149] In an embodiment of the present application, first, based on the spatial distribution characteristics of each hotspot pattern (including parameters such as the boundary curvature distribution pattern and the distance relationship with the key components), a shape context algorithm is used to generate a spatial feature descriptor, and the matching degree of different hotspot boundary descriptors is measured by cosine similarity, and the continuity condition of the main migration direction angle between adjacent hotspots is verified; then, based on the intensity distribution characteristics, the gradient change trend and fluctuation period characteristics are extracted, and the time domain dynamic time warping algorithm is used to align the intensity evolution curves of different hotspots, and the morphological similarity coefficient after the curve alignment is calculated; then, the spatial similarity and the intensity similarity are weighted and fused into a comprehensive synergy index; when the index exceeds the preset synergy threshold, an associated edge is established between the hotspot patterns; finally, the hotspot sets with connection relationships are classified by a graph clustering algorithm to form a migration group that characterizes the percolation network.

[0150] Step 703: Outputting a permeability score according to preset rules based on the spatial coverage and intensity stability of the migration group.

[0151] In this step, the spatial coverage refers to the geometric extension range of the migration group within the concrete structure, which is quantified by the surface area or projected area of ​​its circumscribed polygon, reflecting the breadth of the seepage impact; the intensity stability characterizes the fluctuation amplitude of the diffusion characteristic intensity within the migration group in the time series, which is obtained by variance analysis and trend smoothness evaluation. The higher the value, the more continuous and stable the seepage behavior; the preset rule is a mapping relationship library established based on engineering experience, which maps the characteristic combination of coverage and stability to a qualitative risk level (such as AE level).

[0152] In the embodiment of the present application, the spatial coverage of the migration group is first calculated, the spatial boundary coordinates of all hot spot patterns in the group are extracted, and the minimum circumscribed geometric body is generated by the convex hull algorithm and its surface area is calculated; at the same time, the overlapping area ratio of the migration group and key structural components (such as steel mesh and construction joints) is evaluated. Secondly, the strength stability is analyzed: the time series strength data of the migration group is loaded, the fluctuation amplitude is quantified using the variance calculation module, and the continuity of the strength change trend is evaluated based on the sliding window smoothness test. Then, the spatial coverage (with a higher weight) and the strength stability are weighted and fused to generate a comprehensive penetration index. Finally, the preset rule library is called, and the comprehensive penetration index of the migration group is input into the multi-dimensional decision engine of the rule library. The engine constructs a nine-square mapping matrix based on the spatial coverage (divided into three levels: high / medium / low) and the strength stability (divided into three levels: continuous rise / fluctuation stability / decline), and determines the basic risk level code (such as AE level) by cross-positioning the row and column coordinates. Next, a dynamic correction process is implemented: historical assessment records for the migration group are retrieved. If there are three consecutive upward rating trends, the risk level is automatically upgraded to one level. The group is also verified to see if any spatial coordinates marked as "critical weak points" (such as joint intersections) are present. If so, an additional "+" risk indicator is added. A structured report is then generated: based on the final risk code, a corresponding description template is extracted and filled with specific parameters, including the permeability rating (e.g., "Level_E+" indicates extremely high risk, with a water content greater than 15%), the coordinate set of the impact area diagram, the required timeliness (e.g., "intervention required within 48 hours"), and the recommended action code (referenced to the grouting process number in the standard construction method library). Finally, multi-system collaborative output is implemented: the main control platform pushes the report to the maintenance decision-making system, automatically generates a work order, synchronizes it with the BIM model, issues a red flashing warning for the risk area, and sends a digitally signed PDF assessment certificate to the supervisor's terminal, completing the closed-loop control from quantitative analysis to engineering response.

[0153] Figure 2 A structural diagram of a concrete moisture permeability evaluation system is provided for an embodiment of the present application. Figure 2 As shown, the system includes:

[0154] An acquisition module 21 is used to obtain the moisture gradient change data inside the concrete;

[0155] The processing module 22 is used to perform moving average processing on the moisture gradient change data inside the concrete to generate smoothed moisture diffusion data;

[0156] An extraction module 23 is configured to measure frequency offset data caused by humidity in a microwave frequency band based on the smoothed humidity diffusion data, and extract diffusion features from the frequency offset data using a phase locking technique;

[0157] an identification module 24 for performing spatiotemporal correlation processing on the diffusion characteristics to identify a moisture migration hotspot pattern inside the concrete;

[0158] The output module 25 is configured to perform pattern clustering processing on the moisture migration hotspot patterns to group them into multiple levels, and output the permeability score of the concrete according to the grouped multiple levels.

[0159] Figure 2 The concrete moisture permeability evaluation system can be executed Figure 1 The implementation principles and technical effects of the concrete moisture permeability evaluation method described in the illustrated embodiment are not further elaborated. The specific manner in which each module and unit performs operations in the concrete moisture permeability evaluation system in the aforementioned embodiment has been described in detail in the related embodiments of the method and will not be further elaborated here.

[0160] In one possible design, Figure 2 A concrete moisture permeability evaluation system of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0161] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0162] The processing component 32 is used for the above Figure 1 The embodiment provides a method for evaluating moisture permeability of concrete.

[0163] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0164] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0165] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0166] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0167] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0168] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0169] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 A method for evaluating moisture permeability of concrete according to the illustrated embodiment.

[0170] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0171] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0172] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0173] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for evaluating concrete moisture permeability, characterized in that: include: Obtain the moisture gradient change data inside the concrete; Performing moving average processing on the moisture gradient change data inside the concrete to generate smoothed moisture diffusion data; Based on the smoothed humidity diffusion data, measuring frequency shift data caused by humidity influence in the microwave frequency band, and extracting diffusion characteristics from the frequency shift data using phase locking technology; performing spatiotemporal correlation processing on the diffusion characteristics to identify moisture migration hotspot patterns within the concrete; A pattern clustering process is performed on the moisture migration hotspot patterns to group them into multiple levels, and a permeability score grade of the concrete is output according to the grouped multiple levels.

2. The method according to claim 1, characterized in that Obtaining data on moisture gradient changes inside concrete, including: The original humidity signal of the multi-layer sensing points pre-deployed in the surface area, transition area and core area of ​​the concrete specimen is synchronously collected at preset time intervals by the impedance sensing element, wherein the original humidity signal includes the impedance amplitude and phase angle; The impedance amplitude and phase angle are converted into initial physical quantities associated with humidity to form humidity gradient change data inside the concrete.

3. The method according to claim 1, characterized in that Performing moving average processing on the moisture gradient change data inside the concrete to generate smoothed moisture diffusion data, including: Determining a time window size associated with an attenuation law of the moisture gradient change data inside the concrete; intercepting a continuous subsequence of the concrete internal moisture gradient change data based on the time window size; Calculating the data average of each of the continuous subsequences as a discrete point; Connect all discrete points to generate time-series continuous smooth humidity diffusion data.

4. The method according to claim 1, wherein Based on the smoothed humidity diffusion data, frequency shift data caused by humidity influence is measured in the microwave frequency band, including: Determine the local area and scanning path to be scanned inside the concrete based on the smoothed moisture diffusion data; Directing the continuous wave signal emitted by the microwave scanning unit to the local area along the scanning path, and receiving the microwave echo signal reflected by the interior of the concrete; Mixing the microwave echo signal with a preset reference oscillation signal to generate a mixed signal containing a frequency difference; Recording the real-time oscillation frequency of the mixing signal in the local area by a frequency counting unit; The real-time oscillation frequency is compared with a preset reference frequency of the concrete drying state, and frequency offset data is generated according to the comparison result.

5. The method according to claim 1, wherein Extracting diffusion features from the frequency offset data using a phase locking technique includes: Inputting the frequency offset data into a phase-locked loop, and continuously comparing the phase difference between the frequency offset data and a preset reference signal through an error signal generating unit within the phase-locked loop; Dynamically adjusting the output frequency of an oscillating unit within a phase-locked loop according to the phase difference, and monitoring the amplitude of the change in the phase difference; When the change amplitude of the phase difference is continuously lower than a first set threshold, determining that the phase-locked loop enters a locked state; In a locked state, the output frequency of the internal oscillation unit of the phase-locked loop is recorded, and the instantaneous frequency fluctuation component of the output frequency is extracted as a diffusion feature.

6. The method according to claim 1, wherein Performing spatiotemporal correlation processing on the diffusion characteristics to identify moisture migration hotspot patterns within the concrete, including: The diffusion characteristics are mapped to the spatial coordinate positions and corresponding time nodes inside the concrete to generate a spatiotemporal index framework; Calculating the migration intensity similarity between each spatial coordinate position and adjacent spatial coordinate positions in the spatiotemporal index framework; When the migration intensity similarity of adjacent spatial coordinate positions exceeds a second set threshold, the adjacent spatial coordinate positions are associated as the same migration unit; Traverse all migration units and count the cumulative value of the diffusion characteristic intensity of each migration unit; Based on the diffusion feature intensity accumulation value, a migration unit whose diffusion feature intensity accumulation value exceeds twice the average intensity value of the surrounding migration units is taken as a target migration unit, and all spatially adjacent target migration units are merged into a continuous spatial region; Marking continuous spatial regions as moisture migration hotspot patterns.

7. The method according to claim 1, characterized in that Performing pattern clustering processing on the moisture migration hotspot pattern to group it into multiple levels, and outputting a concrete permeability score grade based on the grouped multiple levels, including: The spatial distribution characteristics and intensity distribution characteristics of each humidity migration hotspot pattern are extracted respectively; Based on the similarity of the spatial distribution characteristics and the intensity distribution characteristics, the humidity migration hotspot patterns with migration synergy effects are associated into migration groups; According to the spatial coverage and intensity stability of the migration group, the permeability score level is output according to preset rules.

8. A concrete moisture permeability evaluation system, characterized in that: include: An acquisition module is used to obtain the moisture gradient change data inside the concrete; a processing module, configured to perform moving average processing on the moisture gradient change data inside the concrete to generate smoothed moisture diffusion data; an extraction module, configured to measure frequency offset data caused by humidity influence in a microwave frequency band based on the smoothed humidity diffusion data, and extract diffusion features from the frequency offset data using a phase locking technique; an identification module for performing spatiotemporal correlation processing on the diffusion characteristics to identify a moisture migration hotspot pattern inside the concrete; The output module is used to perform pattern clustering processing on the moisture migration hotspot pattern to group it into multiple levels, and output the permeability score grade of the concrete according to the multiple levels after grouping.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a concrete moisture permeability evaluation method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, a method for evaluating moisture permeability of concrete according to any one of claims 1 to 7 is implemented.

Images