A method and system for monitoring the compaction degree of concrete pouring of a rock-fill dam

Abstract

The application discloses a kind of rock-fill dam concrete pouring compactness monitoring method and system, it is related to building engineering field, comprising: calibration module, for in rock-fill gap inside arrangement with sensing installation site's pullable positioning component, calibrating the three-dimensional space coordinates of each component and sensing installation site and corresponding gap boundary parameter;Acquisition module is used to in the whole process of concrete pouring, according to preset fixed time interval, continuously acquisition each pullable positioning component transmits sensing unit and obtains the real-time data of medium characteristic in corresponding gap;The application is laid out in the field of rock-fill dam concrete pouring pullable positioning component and sensing unit, whole process synchronously acquires medium data, filters noise, eliminates temperature and pore wall scattering interference, identifies medium distribution and filling interface, quantifies compactness and generates space-time distribution cloud picture, data is real-time pushed to construction operation terminal and can be traced, regional compactness standard after controllable pullable component, dynamic speed regulation, guarantee compactness without attenuation, let pouring whole process visible controllable.

Description

Technical Field

[0001] This invention relates to the field of building engineering technology, specifically to a method and system for monitoring the compaction of concrete pouring in rockfill dams. Background Technology

[0002] Rockfill concrete dams combine the high strength of rockfill with the integrity of concrete. Due to their low cement consumption, low heat of hydration, convenient construction, and outstanding economic benefits, they are increasingly widely used in large and medium-sized water conservancy and hydropower projects. The density of the poured concrete is a core indicator for ensuring the dam's impermeability, durability, and overall load-bearing capacity. Its monitoring quality is directly related to the long-term safe and stable operation of rockfill dams and is one of the core aspects of engineering construction quality control.

[0003] The invention patent application with application number 202510741186.4 discloses a method and device for detecting the compactness of riprap concrete pouring. This application aims to solve the problem that "as riprap concrete dams are developed to the west and at higher altitudes, the impact of extreme low temperatures in high-altitude and cold regions on the fluidity of self-compacting concrete is becoming increasingly significant. Studying the liquid level rise law of self-compacting concrete in riprap can help adjust the pouring plan in a timely manner on site and prevent the occurrence of construction cold joints and non-compacted pouring caused by excessively long pouring intervals. However, there are currently few methods for real-time detection and evaluation of concrete fluidity, especially when small-diameter riprap and low-temperature environments coexist."

[0004] However, existing monitoring methods are either destructive testing after the fact or static monitoring at a single point, which cannot dynamically track the concrete filling status during the pouring process, and cannot accurately identify the key difference between unfilled and poorly compacted concrete in the riprap voids, thus failing to provide support for timely correction of construction deviations.

[0005] To address this, we propose a method and system for monitoring the compaction of concrete pouring in rockfill dams. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a method and system for monitoring the compactness of concrete pouring in rockfill dams, which can effectively solve the problems of the existing technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions; This invention discloses a system for monitoring the compaction of concrete pouring in rockfill dams, comprising: The system comprises the following modules: a calibration module for deploying removable positioning components with sensor mounting positions within the riprap voids, and for calibrating the three-dimensional spatial coordinates of each component and sensor mounting position relative to the corresponding void boundary parameters; an acquisition module for continuously acquiring real-time data on the characteristics of the medium within the corresponding voids from the sensor units on each removable positioning component at preset fixed time intervals throughout the concrete pouring process; an analysis module for analyzing the real-time acquired data, extracting the physical characteristic parameters of the medium within the corresponding voids, and distinguishing the type and spatial distribution of the medium within the voids; and a quantization module for quantizing the analyzed data. The physical characteristic parameters are quantified to obtain the density value of the concrete filling in the corresponding voids, and the filling density state of the concrete in the corresponding voids is calibrated; the tracking module is used to combine the three-dimensional spatial coordinates of each sensor installation position with the corresponding density value to generate spatiotemporal continuous distribution data of the concrete filling state in the rockfill in a time series, and track the filling process of the concrete in the voids; the output module is used to output the spatiotemporal continuous distribution data of the concrete filling state in the rockfill and the filling density state data of the corresponding voids to the construction operation terminal in real time according to a preset format. Among them, the void boundary parameters include at least the three-dimensional void boundary parameters, the effective void volume, and the inherent disturbance parameter values ​​of the void wall; The calibration module is interconnected with a data acquisition module and a parsing module via a wireless network. The data acquisition module and the parsing module are interconnected via a wireless network. The parsing module is interconnected with a quantization module via a wireless network. The quantization module is interconnected with a tracking module and an output module via a wireless network. The tracking module and the output module are interconnected via a wireless network.

[0008] Furthermore, the removable positioning component is a rigid hollow rod with multiple sets of sensor mounting positions arranged axially. The axial length of the rod matches the filling height of the rockfill. The spacing of the sensor mounting positions along the axial direction of the rod matches the axial dimension of the rockfill gap at the corresponding mounting point. All sensor mounting positions on a single rod correspond to unique three-dimensional spatial coordinates. Each sensor mounting position is embedded with a set of sensor units that correspond one-to-one with the mounting position. The sensor unit includes a dielectric impedance sensor subunit and an ultrasonic echo sensor subunit arranged in coaxial pairs. The acquisition surfaces of the two sets of subunits are coplanar and both face the internal cavity of the corresponding rockfill gap. The acquisition surfaces are flush with the outer wall of the rod. The removable positioning component is equipped with a closed-loop controlled removable execution unit, which is signal-connected to the quantization module and the tracking module. The extraction execution unit is allowed to be triggered only when the density measurement value of all gaps within the layout range of the corresponding extractable positioning component reaches the preset full compaction threshold and the concrete filling front has completely crossed the layout area of ​​the component. During the extraction process, the acquisition module continuously collects real-time data from all sensing units on the component, and the quantification module simultaneously verifies the density status of the corresponding gap. The extraction rate is positively correlated with the quantified value of the density of the corresponding gap until the rod is completely extracted from the rockfill.

[0009] Furthermore, during the operation of the acquisition module, a unique hardware address and a global synchronization acquisition clock are assigned to all sensing units, and the global synchronization acquisition clock is synchronized with the global clock of the pouring construction. Throughout the entire concrete pouring process, the acquisition module triggers all sensing units to synchronously start acquisition at the same clock node with a preset fixed time interval as the acquisition cycle. Within each acquisition cycle, a single sensing unit completes no less than two continuous acquisitions of the medium in the corresponding gap. After the acquisition is completed, the number of continuous acquisitions within a single acquisition cycle is used as the sliding window length to perform non-overlapping moving average filtering on multiple sets of raw data within the same cycle to remove random noise and abnormal jump values ​​in the acquired data. Then, the filtered data is bound to the corresponding hardware address, acquisition clock stamp, and three-dimensional spatial coordinates and synchronously uploaded to the parsing module.

[0010] Furthermore, the parsing module first performs environmental coupling interference decoupling processing on the uploaded filtered raw data to extract the effective physical characteristic parameters of the medium within the corresponding gap; Among them, the decoupling process follows the following rules: ; In the formula: For the first Effective values ​​of medium-like characteristic parameters; For the first The original acquired values ​​of the characteristic parameters of the medium-like material after filtering; For the first Temperature influence coefficient of characteristic parameters of media-like substances; The real-time ambient temperature collected by the temperature measurement node built into the sensing unit during the current acquisition cycle; The reference ambient temperature collected by the sensing unit during the calibration stage before pouring; For the first Pore ​​wall scattering interference coefficient, a characteristic parameter of a medium-like medium; This refers to the inherent interference parameter values ​​of the pore wall collected at the sensor installation location when there is no filling medium during the pre-pouring calibration stage. The analysis module uses various effective physical characteristic parameters obtained after decoupling to form a set of medium characteristic parameters at the corresponding sensing installation position. It distinguishes the medium type based on the preset threshold range of medium characteristic parameters. At the same time, it analyzes the echo delay data of the ultrasonic echo sensing subunit to obtain the three-dimensional spatial position of the medium filling interface and determine the spatial distribution state of the medium in the gap.

[0011] Furthermore, the quantization module calculates the quantified value of the concrete filling density within the target void for a single set of sensor mounting positions, based on the three-dimensional boundary parameters of the target void calibrated by the calibration module, the spatial distribution state of the medium output by the analysis module, and the effective physical characteristic parameters. ; In the formula: The compaction value of the concrete filling in the target void; The effective volume of the target void pre-calibrated for the calibration module; The effective filling volume of liquid-phase concrete within the target void; The first concrete medium within the target void output by the analysis module Class of effective physical characteristic parameters; For the preset standard liquid phase concrete corresponding to the first The baseline values ​​for physical characteristic parameters; The quantification module matches the calculated density quantification value with the threshold range based on the preset density threshold range, and calibrates the filling density state of the concrete in the corresponding void as unfilled, partially filled, basically dense or completely dense.

[0012] Furthermore, the tracking module uses the three-dimensional spatial coordinates of each sensor installation position calibrated by the calibration module as spatial nodes, and the density measurement values ​​corresponding to each node within the same acquisition cycle as node attribute values. It then uses a spatial interpolation algorithm to generate a spatial distribution cloud map of the concrete filling density of the entire cross section of the rockfill within a single acquisition cycle. The tracking module splices together the spatial distribution cloud maps of the compaction density of all collection periods according to the time sequence of the collection period to generate spatiotemporal continuous distribution data of the concrete filling state in the rockfill. The tracking module identifies the spatial surface of the concrete filling front based on spatiotemporally continuous distribution data and calculates the effective advancement speed of the filling front along the preset pouring direction. ; In the formula: The effective advancing speed of the pre-set pouring flow direction at the front edge of the concrete filling line; Arranged sequentially along the pre-set pouring direction The spatial displacement vector formed by the group of sensor mounting positions; The unit direction vector for the preset pouring flow direction; for The median time difference of the acquisition clock stamp when the density measurement value of the gap corresponding to the group sensor mounting position first reaches the preset filling threshold.

[0013] Furthermore, during the operation phase of the output module, the spatiotemporal continuous distribution data of the concrete filling state within the rockfill, the filling density data of the corresponding gaps, and the effective advancement speed data of the filling front are encapsulated in a preset structured format. Each set of data is bound to a unique three-dimensional spatial coordinate, a collection clock stamp, and a sensor unit hardware address, forming a traceable and complete data chain. The encapsulated structured data is then synchronously output to the construction operation terminal in real time according to the collection cycle, while all data is synchronously backed up to the local built-in storage unit.

[0014] On the other hand, a method for monitoring the compaction of concrete pouring in a rockfill dam includes: Rigid, hollow, removable positioning components with multiple axial sensor mounting positions are deployed within the riprap voids. The three-dimensional coordinates of each sensor mounting position and the corresponding void boundary parameters are calibrated. Each mounting position is embedded with a coaxial pair of dielectric impedance and ultrasonic echo sensing sub-units. A unique hardware address and a global synchronization clock are assigned to each sensing unit. Void dielectric characteristic data are synchronously collected at fixed time intervals. Non-overlapping sliding mean filtering is performed using the number of acquisitions per cycle as a window to remove noise and outliers. The data is then uploaded after binding coordinates and clock stamps. Temperature and pore wall scattering interference are decoupled from the filtered data, and effective physical characteristic parameters are extracted to distinguish the dielectric type. Based on ultrasonic echo time delay analysis, the three-dimensional position of the filling interface is determined, and the three-dimensional properties of the dielectric within the void are determined. The system calculates the density quantification value based on the effective void volume, concrete filling volume, and effective physical characteristic parameters. The void filling state is then categorized as unfilled, partially filled, basically dense, or completely dense according to a preset threshold range. Using the sensor installation coordinates as spatial nodes and the density value as an attribute, a single-cycle density spatial distribution cloud map is generated through spatial interpolation. This cloud map is then spliced ​​to obtain continuous spatiotemporal distribution data, while simultaneously calculating the concrete filling front advancement speed. The spatiotemporal distribution, density status, and advancement speed data are encapsulated and traced, then output to the construction operation terminal in real time. Once the density of the deployment area meets the standard and the filling front has passed the component, the extraction rate is dynamically adjusted according to the real-time density, completing the component extraction in a closed loop.

[0015] Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects: This invention accurately calibrates the three-dimensional spatial coordinates and void boundary parameters of each point by deploying removable positioning components in the voids of the riprap. Based on coaxial paired dielectric impedance and ultrasonic echo sensing units, it synchronously collects dielectric characteristic data within the voids throughout the process. After non-overlapping moving average filtering to remove random noise and abnormal jump values, and environmental coupling interference decoupling processing, it obtains the effective physical characteristic parameters of the dielectric, accurately distinguishes the dielectric type and determines its three-dimensional spatial distribution within the voids. The density value is quantified by the effective void volume and standard concrete physical parameters, clearly calibrating the concrete filling density. The system monitors the status of concrete filling and generates a continuous spatiotemporal distribution information based on spatial coordinates and temporal data. It tracks the filling process and the speed of the front advancement in real time. The monitoring data is bound to spatial coordinates, collected clock stamps, and hardware addresses to form a traceable chain. Simultaneously, the data is pushed to the construction operation terminal. After the concrete in the area is completely compacted and the front advancement of the filling crosses the deployment range, the positioning component can be controlled to be pulled out. The pulling rate is dynamically adjusted according to the real-time compaction to ensure that the compaction of the void filling does not decrease. This provides real-time operation support for the concrete pouring of the rockfill dam, thereby improving the accuracy of construction control and ensuring the quality of dam pouring. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0017] Figure 1 A schematic diagram of a concrete compaction monitoring system for rockfill dams; Figure 2 This is a flowchart illustrating a method for monitoring the compaction of concrete pouring in a rockfill dam. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0019] The present invention will be further described below with reference to embodiments.

[0020] Example 1:

[0021] This embodiment provides a concrete compaction monitoring system for rockfill dams, such as... Figure 1 As shown, it includes: The calibration module is used to deploy removable positioning components with sensor mounting positions in the gaps of the riprap, and to calibrate the three-dimensional spatial coordinates of each component and sensor mounting position and the corresponding gap boundary parameters. Among them, the void boundary parameters include at least the three-dimensional void boundary parameters, the effective void volume, and the inherent disturbance parameter values ​​of the void wall; The removable positioning component is a rigid hollow rod with multiple sets of sensor mounting positions arranged axially. The axial length of the rod matches the filling height of the rockfill. The spacing of the sensor mounting positions along the axial direction of the rod matches the axial dimension of the rockfill gap at the corresponding mounting point. All sensor mounting positions on a single rod correspond to unique three-dimensional spatial coordinates. Each sensor mounting position is embedded with a set of sensor units that correspond one-to-one with the mounting position. The sensor units include a dielectric impedance sensor sub-unit and an ultrasonic echo sensor sub-unit arranged in a coaxial pair. The acquisition surfaces of the two sets of sub-units are coplanar and both face the internal cavity of the corresponding rockfill gap. The acquisition surfaces are flush with the outer wall of the rod. The key performance and operating parameters of the dielectric impedance sensing subunit and the ultrasonic echo sensing subunit of the sensing unit are set as follows: The dielectric impedance sensing subunit adopts a high-frequency impedance test mode, with a test frequency adjustable from 10MHz to 100MHz, a range covering relative permittivity of 1 to 80 and conductivity of 0 to 10S / m, and a test accuracy of not less than ±1% of the full scale; The ultrasonic echo sensing subunit adopts a piezoelectric ceramic transducer, with a center frequency adjustable from 500kHz to 2MHz, a sampling rate of not less than 100MS / s, an emission voltage of not less than 100Vpp, a longitudinal wave velocity test range covering 100m / s to 6000m / s, and a test accuracy of not less than ±2%; The diameter of the acquisition surface of both subunits is not less than 10mm, and the center deviation when coaxially paired is not greater than 0.5mm, ensuring that the acquisition surfaces are completely coplanar and that the spatial consistency of the acquired data is maintained. Among them, the corresponding void boundary parameters include the three-dimensional envelope boundary coordinates of the rockfill void corresponding to the sensor mounting position, the effective volume of the void, the axial length of the void, the maximum radial cross-sectional size of the void, the aspect ratio of the void, the roughness of the inner wall of the void, the wave impedance value of the inner wall of the void, and the effective scattering area of ​​the inner wall of the void. The removable positioning component is equipped with a closed-loop control removable execution unit, which is connected to the quantization module and the tracking module via signals. The extraction execution unit is allowed to be triggered only when the density measurement value of all gaps within the layout range of the corresponding extractable positioning component reaches the preset full compaction threshold and the concrete filling front has completely crossed the layout area of ​​the component. During the extraction process, the acquisition module continuously collects real-time data from all sensing units on the component, and the quantification module simultaneously verifies the density status of the corresponding gap. The extraction rate is positively correlated with the quantified value of the density of the corresponding gap until the rod is completely extracted from the rock pile. In the process of quantification, when the density state of the corresponding void is verified, the acquisition module synchronously acquires real-time data of the medium characteristics of the corresponding sensor unit at each sensor mounting position on the component at a preset fixed time interval. After binding the real-time data with the three-dimensional spatial coordinates of the corresponding sensor mounting position and the acquisition clock stamp, it is synchronously transmitted to the analysis module to complete the environmental coupling interference decoupling processing and output the effective physical characteristic parameter set of the medium in the corresponding void. Based on the effective physical characteristic parameter set output by the analysis module and the three-dimensional boundary parameters of the corresponding void pre-calibrated by the calibration module, the quantification module calculates the real-time density quantification value of the corresponding void during the extraction process. The real-time density quantification value is continuously compared and verified with the preset complete compaction threshold to confirm that the filling density state of the concrete in the corresponding void does not decrease during the extraction process. The quantification module synchronously transmits the verified real-time density quantification value to the extraction execution unit. The extraction execution unit dynamically adjusts the extraction rate based on the real-time density quantification value to complete the closed-loop control of the entire extraction process. The specific functional relationship and rate adjustment method of the positive-phase closed-loop control rule between the extraction rate of the extraction execution unit and the density quantified value are as follows: The extraction rate is controlled according to a piecewise linear positive correlation rule, using the minimum real-time compactness measurement value of all voids within the corresponding component layout range as the control benchmark. When the minimum real-time compactness measurement value of all voids is ≥0.95 and the extraction start condition is met, the initial extraction rate is set to 0.5 m / min. When the minimum real-time compactness measurement value of all voids remains within the range of 0.95 ≤ minimum real-time compactness measurement value of all voids ≤0.97, the extraction rate is increased by 0.01 for every 0.01 increase in the minimum real-time compactness measurement value of all voids. The linear adjustment rule is increased by 0.2 m / min, and the maximum extraction rate does not exceed 2 m / min. When the minimum value of the real-time density measurement of all voids drops below 0.95 during the extraction process, the extraction rate is linearly reduced in sync with the decrease of the minimum value of the real-time density measurement of all voids. When the minimum value of the real-time density measurement of all voids is <0.92, the extraction operation is immediately suspended. The extraction can only be resumed after the re-pouring operation is completed and the minimum value of the real-time density measurement of all voids rises back to above 0.95, ensuring that the concrete density does not decrease during the extraction process. The data acquisition module is used to continuously acquire real-time data on the characteristics of the medium in the corresponding gaps of each removable positioning component by the uploading sensor unit at preset fixed time intervals throughout the entire concrete pouring process. During the operation of the acquisition module, a unique hardware address and a global synchronous acquisition clock are assigned to each sensing unit. The global synchronous acquisition clock is synchronized with the global clock of the pouring construction. Throughout the entire concrete pouring process, the acquisition module triggers all sensing units to synchronously start acquisition at the same clock node with a preset fixed time interval as the acquisition cycle. Within each acquisition cycle, a single sensing unit completes no less than two continuous acquisitions of the medium in the corresponding gap. After the acquisition is completed, the number of continuous acquisitions within a single acquisition cycle is used as the sliding window length to perform non-overlapping moving average filtering on multiple sets of raw data within the same cycle to remove random noise and abnormal jump values ​​in the acquired data. Then, the filtered data is bound to the corresponding hardware address, acquisition clock stamp, and three-dimensional spatial coordinates and synchronously uploaded to the parsing module. After data acquisition, the total number of consecutive acquisitions by a single group of sensing units within a single acquisition cycle is used as the fixed sliding window length. Then, non-overlapping moving average filtering is performed on the time-series sorted raw data of the same cycle to remove random noise and abnormal jump values ​​from the acquired data, following the following rules: The sorted raw data is continuously grouped according to the order of collection time. The length of each group of data is exactly the same as the length of the fixed sliding window. There is no overlap or cross-use of data between adjacent groups of data in the time series. Calculate the arithmetic mean of all the raw data within each sliding window, and use this arithmetic mean as the intermediate value of the filtering for the corresponding window time interval; All raw data in each sliding window are compared with the corresponding filtered median value of that window. Raw data with a numerical deviation exceeding a preset deviation threshold are identified as abnormal jump values ​​and removed. After removal, the corresponding abnormal jump value is replaced by the filtered median value of that window. After completing the filtering calculation and outlier removal for all sliding windows within the acquisition cycle, the intermediate values ​​of the filters for each window are sorted according to the acquisition sequence to generate the effective filtered data for the group of sensing units within the acquisition cycle. Among them, the non-overlapping moving average filtering process is used to determine the preset deviation threshold for abnormal jump values. The determination method and value rules for this process also include: The deviation threshold is set to ±15% based on the intermediate value of the filter corresponding to the sliding window within a single acquisition cycle. That is, when the relative deviation between a single set of raw data within the window and the intermediate value of the filter exceeds ±15%, it is judged as an abnormal jump value and is removed, and replaced by the intermediate value of the filter. For deployment points with greater signal interference, such as deep and edge areas of the rockfill, the deviation threshold can be relaxed to ±20%. However, the threshold must be preset and fixed before construction and must not be arbitrarily adjusted during the pouring and monitoring process to ensure the consistency and feasibility of the filtering process. The parsing module is used to parse the real-time acquired data, extract the physical characteristic parameters of the medium in the corresponding void, and distinguish the type of medium and the spatial distribution state of the medium in the void. The parsing module first performs environmental coupling interference decoupling processing on the uploaded filtered raw data, and extracts the effective physical characteristic parameters of the medium in the corresponding gap; Among them, the decoupling process follows the following rules: ; In the formula: For the first Effective values ​​of medium-like characteristic parameters These correspond to four types of physical characteristic parameters of the medium, namely, relative permittivity, conductivity, longitudinal wave velocity, and acoustic attenuation coefficient. For the first The original acquired values ​​of the characteristic parameters of the medium-like material after filtering; For the first The temperature influence coefficient of the characteristic parameter of the medium is dimensionless and is a fixed value determined in advance by calibration test before pouring, corresponding to the deviation of the parameter under unit temperature change. The real-time ambient temperature collected by the temperature measurement node built into the sensing unit during the current acquisition cycle; The reference ambient temperature collected by the sensing unit during the calibration stage before pouring; For the first The pore wall scattering interference coefficient of the characteristic parameter of the medium is dimensionless and is a fixed value determined in advance by calibration test before pouring. It corresponds to the proportion of scattering interference of the pore wall of the rockfill on this parameter. This refers to the inherent interference parameter values ​​of the pore wall collected at the sensor installation location when there is no filling medium during the pre-pouring calibration stage. The above formula corrects the original collected data into effective characteristic parameters of the medium by eliminating the coupling interference caused by temperature fluctuations and scattering from the pore walls of the rockfill. It effectively removes the interference from the environment and the structure itself, making the extraction of physical characteristics of the medium more in line with the actual state, and providing a data foundation for subsequent medium type differentiation and spatial distribution analysis. The temperature influence coefficient of the i-th type of medium characteristic parameter involved in the decoupling process formula Pore ​​wall scattering interference coefficient This section supplements the complete implementation method of its calibration test: temperature influence coefficient. The calibration was performed in a laboratory environment before pouring, using the same batch of sensing units as those used on-site. These units were placed in a high-low temperature alternating test chamber, with the extreme temperature range of the on-site construction environment as the calibration range. A temperature step of 5°C was set, and after holding at each temperature for 30 minutes, the characteristic parameters of the i-th type of medium corresponding to standard air and standard liquid concrete were tested. The slope of the linear fit of the parameter with temperature was used as the corresponding parameter for that type of medium. Where i = 1, 2, 3, 4, corresponding to four types of parameters: relative permittivity, conductivity, longitudinal wave velocity, and acoustic attenuation coefficient, respectively; pore wall scattering interference coefficient. The calibration process involves using the same type of sensor unit as the one used on-site, placed close to the typical pore walls of the riprap in the area to be deployed. In an air environment without concrete filling, the pore wall interference values ​​of the i-th type of medium characteristic parameters are collected. Simultaneously, the baseline values ​​of the same parameters in a free air environment without pore wall interference are collected. The percentage difference between the two values ​​is used as the corresponding value for that type of parameter. The calibration test for each type of parameter should be repeated no less than 5 times, and the arithmetic mean should be taken as the final calibration value to ensure the repeatability of the decoupling process. The analysis module uses various effective physical characteristic parameters obtained after decoupling to form a set of medium characteristic parameters at the corresponding sensing installation position. Based on the preset threshold range of medium characteristic parameters, it completes the differentiation of medium type. At the same time, based on the echo delay data of the ultrasonic echo sensing subunit, it analyzes the three-dimensional spatial position of the medium filling interface and determines the spatial distribution state of the medium in the gap. The analysis module, based on the echo delay data of the ultrasonic echo sensing subunit, analyzes the three-dimensional spatial position of the medium-filled interface and determines the spatial distribution of the medium within the void. First, it identifies the peaks of the full wave train data collected by a single ultrasonic echo sensing subunit, removing the inherent echoes of the pore walls and environmental noise echoes pre-calibrated by the calibration module. Then, it extracts the effective reflected echo of the medium-filled interface and records the echo delay of the effective reflected echo relative to the emitted wave. Using the effective longitudinal wave velocity of the corresponding medium obtained through decoupling as the calculation benchmark, it calculates the vertical distance between the medium-filled interface and the acquisition surface of the sensing subunit, combined with the echo delay. Finally, it calculates the three-dimensional spatial coordinates of the feature points of the medium-filled interface corresponding to the acquisition point, based on the three-dimensional spatial coordinates of the sensing installation location and the normal direction vector of the sensing subunit acquisition surface pre-calibrated by the calibration module. The analysis module integrates the three-dimensional coordinates of the filling interface feature points of all sensor mounting positions corresponding to the same target gap, and combines them with the three-dimensional boundary parameters of the target gap pre-calibrated by the calibration module to fit and generate a continuous three-dimensional spatial surface of the medium filling interface; taking the fitted three-dimensional spatial surface as the boundary, and combining the distinguished medium types, the occupied areas of different media in the target gap are divided to determine the three-dimensional spatial distribution state of the medium in the gap. The quantization module is used to quantify the parsed physical characteristic parameters, obtain the quantified value of the density of the concrete filling in the corresponding voids, and calibrate the density state of the concrete filling in the corresponding voids. The quantization module calculates the quantified value of the concrete filling density within the target void for a single set of sensor mounting positions, based on the three-dimensional boundary parameters of the target void calibrated by the calibration module and the spatial distribution state and effective physical characteristic parameters of the medium output by the analysis module. ; In the formula: This is a quantitative value for the density of concrete filling within the target voids, with a range of values. When there is no concrete filling When the standard liquid phase concrete is completely filled and homogeneous ; The effective volume of the target void, which is pre-calibrated for the calibration module, is calculated by integrating the three-dimensional boundary parameters of the target void through the spatial envelope volume. The effective filling volume of liquid-phase concrete within the target void is obtained by spatial reconstruction integration from the three-dimensional spatial position of the medium filling interface and the three-dimensional boundary parameters of the void, as output by the analytical module. The first concrete medium within the target void output by the analysis module Class of effective physical characteristic parameters; For the preset standard liquid phase concrete corresponding to the first The baseline values ​​for physical characteristic parameters are fixed values ​​determined by standard sample tests before casting. The benchmark values ​​for the i-th type of physical characteristic parameters of standard liquid phase concrete involved in the density calculation formula. This section supplements the complete implementation rules for the standard specimen test: Standard specimens are prepared using concrete mix proportions identical to those used in on-site pouring. The specimen size is a standard cube block of 150mm × 150mm × 150mm. After preparation, the specimens are left to stand for 2 hours under the same temperature and humidity conditions as the on-site construction environment to ensure the specimen state is consistent with the state of the liquid-phase concrete poured on-site. During testing, the same type of sensor unit as used on-site is used, closely attached to the specimen test surface. Four parameters are tested: relative permittivity, conductivity, longitudinal wave velocity, and sound attenuation coefficient. At least six parallel specimens are prepared for each mix proportion. After discarding test data with a dispersion coefficient greater than 5%, the arithmetic mean of the remaining valid data is taken as the corresponding parameter. The baseline value refers to the ambient temperature during the testing process compared to the baseline ambient temperature during the calibration phase before pouring. Maintain consistency and eliminate the impact of temperature differences on the baseline value; The above formula combines the effective filling volume ratio of concrete with the average standard deviation of four physical characteristic parameters to comprehensively calculate the density value. It not only reflects the volume filling of concrete in the voids, but also reflects the homogeneity of the medium itself, making the density judgment more accurate and objective, and clearly distinguishing different concrete filling density states. The quantification module matches the calculated quantified value of the density with the threshold range based on the preset density threshold range, and calibrates the filling density state of the concrete in the corresponding void as unfilled, partially filled, basically dense or completely dense. For the preset density threshold range corresponding to the compaction state calibration of concrete, combined with the quality control standards for concrete construction of rockfill dams in water conservancy and hydropower projects, the value range of the compaction quantification value D [0,1] is divided into four continuous intervals, corresponding to four types of compaction states: when D=0, it is calibrated as unfilled state; when 0<D<0.7, it is calibrated as partially filled state; when 0.7≤D<0.95, it is calibrated as basically compacted state; when D≥0.95, it is calibrated as fully compacted state. Among them, the threshold of 0.95 for fully compacted state corresponds to the minimum quality requirement for concrete compaction in the core area of ​​the dam body in the rockfill concrete construction specification of the water conservancy industry. The division of the threshold interval can be finely adjusted by ±0.02 according to the specific quality requirements of the engineering design. It needs to be preset before construction and written into the special construction plan to ensure the uniformity of the state calibration. The tracking module is used to combine the three-dimensional spatial coordinates of each sensor installation position with the corresponding compaction measurement value to generate spatiotemporal continuous distribution data of the concrete filling state in the rockfill in a time series, and to track the filling process of concrete in the voids. The tracking module uses the three-dimensional spatial coordinates of each sensor installation position calibrated by the calibration module as spatial nodes, and the density measurement values ​​corresponding to each node within the same acquisition cycle as node attribute values. It generates a spatial distribution cloud map of the concrete filling density of the entire cross section of the rockfill within a single acquisition cycle through a spatial interpolation algorithm. The tracking module stitches together the spatial distribution cloud maps of compaction from all acquisition periods according to the time sequence of the acquisition period to generate spatiotemporal continuous distribution data of the concrete filling state within the rockfill. The tracking module identifies the spatial surface of the concrete infill front based on spatiotemporally continuous distribution data and calculates the effective advancement speed of the infill front along the preset pouring direction. ; In the formula: The effective advancing speed of the pre-set pouring flow direction at the front edge of the concrete filling line; Arranged sequentially along the pre-set pouring direction The spatial displacement vector formed by the group of sensor mounting positions It is calculated from the three-dimensional spatial coordinates of the first and last sets of sensor mounting positions; The unit direction vector for the preset pouring flow direction; for The median time difference of the acquisition clock stamp when the density measurement value of the gap corresponding to the group of sensor installation positions first reaches the preset filling threshold is calculated from the difference between the median arrival time of the first group of sensor installation positions and the median arrival time of the last group of sensor installation positions. The above formula is based on the difference between the spatial displacement of the sensor point, the preset pouring flow direction, and the median time to achieve the compaction standard. It calculates the effective advancing speed of the concrete filling front, avoids the error caused by single-point data anomalies, and truly reflects the dynamic advancing law of concrete during the pouring process, providing accurate speed data reference and support for real-time construction control. When the tracking module generates a spatial distribution cloud map of concrete filling density of the entire cross section of the rockfill body within a single acquisition cycle by executing the spatial interpolation algorithm, it first verifies the validity of all spatial nodes within the current acquisition cycle, and removes abnormal nodes whose density measurement values ​​exceed the preset reasonable range or whose values ​​deviate from those of adjacent nodes in the same area by a preset deviation threshold. Based on the verified valid spatial nodes, a full-section three-dimensional unstructured spatial grid matching the rockfill body filling boundary is constructed. Then, the density measurement values ​​of each valid spatial node are used as the attribute reference values ​​of the grid nodes. Based on the spatial Euclidean distance between nodes and the corresponding void boundary parameters synchronously calibrated by the calibration module, the density values ​​of each non-node position in the grid are interpolated and fitted to obtain continuous density spatial distribution data of the entire cross section of the rockfill body. Finally, based on the preset one-to-one mapping rule between density values ​​and color levels, the continuous density spatial distribution data is converted into a visualized spatial distribution cloud map, so that each pixel in the cloud map is bound to a unique three-dimensional spatial coordinate and a density fitting value. Regarding the preset filling threshold involved in the calculation formula for the effective advancement speed of the concrete filling front, this section supplements its specific value and determination basis: The preset filling threshold is set to 0.7, which corresponds to the basic compaction state starting threshold in the compaction state calibration. When the compaction quantification value of the void corresponding to the sensor installation position reaches 0.7 for the first time, it is determined that the concrete filling front has reached the location of the sensor installation position, and the corresponding acquisition clock stamp is recorded. The setting of this threshold matches the medium characteristics of the liquid phase concrete filling front, which can effectively distinguish the effective front of concrete filling from the slurry wetting zone, ensuring the accuracy of the calculation of the filling front advancement speed. For concrete with special fluidity requirements in the design, the threshold can be finely adjusted within the range of 0.6 to 0.8, and the preset curing needs to be completed before construction. The output module is used to output the spatiotemporal continuous distribution data of the concrete filling state in the rockfill body and the filling density data of the corresponding voids to the construction operation terminal in real time according to the preset format. During the output module operation phase, the spatiotemporal continuous distribution data of the concrete filling state in the rockfill, the filling density data of the corresponding voids, and the effective advancement speed data of the filling front are encapsulated in a preset structured format. Each set of data is bound to a unique three-dimensional spatial coordinate, acquisition clock stamp, and sensor unit hardware address to form a traceable complete data chain. The encapsulated structured data is then synchronously output to the construction operation terminal in real time according to the acquisition cycle, while all data is synchronously backed up to the local built-in storage unit. The calibration module is interconnected with the acquisition module and the analysis module via a wireless network. The acquisition module and the analysis module are interconnected via a wireless network. The analysis module is interconnected with the quantization module via a wireless network. The quantization module is interconnected with the tracking module and the output module via a wireless network. The tracking module and the output module are interconnected via a wireless network.

[0022] In this embodiment, the calibration module deploys removable positioning components with sensor mounting positions within the riprap voids, calibrating the three-dimensional spatial coordinates of each component and sensor mounting position, as well as the corresponding void boundary parameters. During the entire concrete pouring process, the acquisition module continuously collects real-time data on the medium characteristics within the corresponding voids from the sensor units on each removable positioning component at preset fixed time intervals. The analysis module analyzes the real-time collected data, extracting the physical characteristic parameters of the medium within the corresponding voids, distinguishing the type and spatial distribution of the medium. The quantization module simultaneously quantifies the analyzed physical characteristic parameters, obtaining quantified values ​​of the concrete compaction within the corresponding voids, and calibrating the concrete compaction state within the corresponding voids. Then, the tracking module combines the three-dimensional spatial coordinates of each sensor mounting position with the corresponding compaction quantification values ​​to generate spatiotemporal continuous distribution data of the concrete filling state within the riprap in a time series, tracking the concrete filling process within the voids. Finally, the output module synchronously outputs the spatiotemporal continuous distribution data of the concrete filling state within the riprap and the corresponding void compaction state data to the construction operation terminal in real-time according to a preset format.

[0023] In the above embodiments, the system deploys removable positioning components and matching sensing units at the concrete pouring site of the rockfill dam. It synchronously collects medium data within the voids throughout the process, filters data noise and eliminates environmental interference from temperature and pore wall scattering, accurately identifies the medium distribution and concrete filling interface, quantifies and calculates the filling density, generates a spatiotemporal distribution cloud map and pushes it to the construction operation terminal in real time, and the monitoring data can be completely traced. After the pouring area reaches the density standard and the filling front crosses the deployment range, the removable components are controllably pulled out and the pulling speed is dynamically adjusted to ensure that the density does not decrease. This makes the entire pouring construction process visible and controllable, improves the accuracy of on-site management, and stably ensures the quality of dam pouring construction.

[0024] Application examples of the system in the above embodiments: In a large-scale rockfill dam project in a river basin, the maximum dam height is 86m. The rockfill area in the middle of the dam body is constructed using graded rockfill material in layers. After the filling is completed, the natural voids inside the rockfill body need to be filled with C25 concrete. To accurately control the compaction of the concrete filling and monitor the pouring progress in real time, this system was used for construction monitoring in this project. The specific implementation process is as follows: In the early stages of construction, technicians deployed 12 rigid, hollow, removable positioning rods in the core monitoring area of ​​the rockfill voids, ranging from 42m to 58m in elevation. The axial length of the rods perfectly matched the 16m filling height of the area. Each rod had eight sensor mounting positions evenly distributed along its axial direction according to the axial dimensions of the corresponding rockfill void. Each sensor mounting position on a single rod was calibrated with its own unique three-dimensional spatial coordinates. Subsequently, the three-dimensional spatial coordinates of all positioning components and sensor mounting positions were calibrated. Simultaneously, the three-dimensional envelope boundary, effective volume, axial length, maximum radial cross-sectional dimension, aspect ratio, inner wall roughness, inner wall wave impedance value, and effective scattering area of ​​the rockfill voids corresponding to each sensor mounting position were measured. This completed the initial calibration of the system, establishing a solid data benchmark for subsequent monitoring.

[0025] Once the concrete pouring officially begins, the system's data acquisition module assigns a unique hardware address to each sensor unit and synchronously connects to the global clock for the pouring process to generate a unified acquisition clock. With a fixed acquisition cycle of 30 seconds, all sensor units are triggered to synchronously acquire data at the same clock node. Within each acquisition cycle, a single sensor unit completes three consecutive acquisitions of the medium within the corresponding gap. After acquisition, the three acquisitions are used as a sliding window to perform non-overlapping moving average filtering, removing random noise and abnormal jump values ​​from the data. The filtered data is then bound to the corresponding hardware address, acquisition clock stamp, and three-dimensional spatial coordinates, and uploaded to the system's parsing module in real time.

[0026] After receiving the filtered data, the analysis module first performs environmental coupling interference decoupling processing on temperature and pore wall scattering, extracting four types of effective physical characteristic parameters: relative permittivity, conductivity, longitudinal wave velocity, and sound attenuation coefficient. Then, based on preset parameter threshold ranges, it accurately distinguishes the medium within the pores into three types: air, cement mortar, and liquid-phase concrete. Simultaneously, it identifies effective reflected echoes through ultrasonic echo data, calculates the vertical distance between the medium filling interface and the sensing acquisition surface, and combines this with the three-dimensional coordinates of the sensor mounting position to generate a continuous three-dimensional surface of the filling interface, clearly determining the three-dimensional spatial distribution of different media within each pore.

[0027] The quantification module calculates the quantitative values ​​of concrete filling density for each void based on void boundary parameters, media spatial distribution results, and effective physical characteristic parameters. Calculations show that in the initial stage of pouring, the void density values ​​are between 0.15 and 0.38, indicating a partially filled state. During the middle stage of pouring, the density values ​​gradually increase to between 0.72 and 0.89, indicating a basically dense state. In the final stage of pouring, the void density values ​​in the core area reach 0.96 to 0.99, indicating a fully dense state, while the void density values ​​in the edge areas are between 0.85 and 0.92, still indicating a basically dense state. Based on this, construction personnel promptly carry out supplementary pouring operations.

[0028] The tracking module uses the three-dimensional coordinates of each sensor installation position as spatial nodes, combined with the compaction values ​​from a single acquisition cycle, to generate a spatial distribution cloud map of the compaction density across the entire cross-section of the rockfill using a spatial interpolation algorithm. Then, all cloud maps are stitched together according to the acquisition cycle time sequence to form continuous spatiotemporal distribution data of the concrete filling state within the rockfill. Simultaneously, the effective advance speed of the concrete filling front is calculated. The calculation shows that the effective advance speed of the filling front along the preset pouring direction in this monitoring area is 1.2 meters per minute, achieving real-time tracking of the entire concrete filling process.

[0029] Specifically, the spatial interpolation algorithm used to generate the density spatial distribution cloud map has the following specific type and key parameters: This scheme adopts the ordinary Kriging interpolation algorithm, which is suitable for fitting the density distribution of irregular three-dimensional space of the rockfill. During algorithm implementation, the three-dimensional spatial coordinates of each calibrated sensor installation position are used as nodes to construct a tetrahedral three-dimensional unstructured mesh that matches the boundary of the rockfill. The maximum cell size of the mesh is no greater than 1 / 2 of the minimum spacing between adjacent sensor installation positions to ensure interpolation accuracy. The variogram adopts a spherical model, and the range is set to the maximum spacing between adjacent sensor installation positions. The ratio of the nugget value to the abutment value is no greater than 25%. After the interpolation calculation is completed, the interpolation results of the boundary area of ​​the rockfill are corrected by boundary constraints to ensure that the generated density spatial distribution cloud map is consistent with the actual filling shape of the rockfill. Each interpolation mesh cell is bound to the corresponding three-dimensional spatial coordinates and density fitting values.

[0030] The output module encapsulates the spatiotemporal distribution data of the concrete filling in the rockfill, the compaction status data of each void, and the advance speed data of the filling front in a preset structured format. It binds three-dimensional spatial coordinates, acquisition clock stamps, and sensor unit hardware addresses to each set of data, forming a complete and traceable data chain. The data is pushed to the construction site operation terminal in real time, while the entire data is backed up to the local storage unit. Construction personnel can intuitively view the pouring status, compaction distribution, and filling speed through the terminal and adjust the construction strategy in a timely manner.

[0031] Once the density of all gaps within the area where the removable positioning rods are positioned reaches the full compaction threshold and the concrete filling front completely crosses the positioned area, the extraction execution unit is automatically triggered. During the extraction process, the acquisition module continuously collects real-time sensor data, and the quantification module simultaneously verifies the density of the gaps. Verification shows that the density of each gap does not decrease throughout the extraction process and remains above 0.95. The extraction execution unit dynamically adjusts the extraction rate based on the real-time density, ultimately successfully extracting all positioning rods completely from the rockfill, completing the closed-loop extraction operation.

[0032] During its application in this project, the system enabled real-time monitoring of the compaction of the rockfill dam concrete throughout the entire pouring process and precise control of the filling progress. It promptly identified two issues with delayed filling of edge voids and guided the construction to make up for the deficiencies. Ultimately, the compaction of the concrete filling in all voids of the rockfill body met the design standards, and the dam filling quality met the requirements of the water conservancy project construction specifications. This significantly improved the accuracy and quality control efficiency of the rockfill dam concrete pouring construction.

[0033] Example 2:

[0034] At the implementation level, based on Example 1, this example refers to... Figure 2 A further detailed description of the concrete compaction monitoring system for rockfill dams in Example 1 is provided below: A method for monitoring the compaction of concrete pouring in a rockfill dam includes: Rigid hollow removable positioning components with multiple sets of axial sensor mounting positions are arranged in the gaps of the rock pile. The three-dimensional coordinates of each sensor mounting position and the corresponding gap boundary parameters are calibrated. Each mounting position is embedded with a coaxial pair of dielectric impedance and ultrasonic echo sensing sub-units. All sensor units are assigned a unique hardware address and a global synchronization clock. The characteristic data of the void medium are collected synchronously at fixed time intervals. The non-overlapping sliding mean filter is performed with the number of collections per cycle as a window to remove noise and outliers. The data is then uploaded after binding coordinates and clock stamps. Temperature and pore wall scattering interference are decoupled from the filtered data, and effective physical feature parameters are extracted to distinguish the medium type. Based on the ultrasonic echo time delay analysis of the three-dimensional position of the filling interface, the three-dimensional spatial distribution of the medium in the pore is determined. Based on the effective volume of voids, the volume of concrete filling, and the effective physical characteristic parameters, the density quantification value is calculated, and the void filling state is calibrated as unfilled, partially filled, basically dense, or completely dense according to the preset threshold range. Using the sensor installation position coordinates as spatial nodes and the compaction value as an attribute, a single-cycle compaction spatial distribution cloud map is generated through spatial interpolation, and spatiotemporal continuous distribution data is obtained by time-series splicing. At the same time, the advance speed of the concrete filling front is calculated. After the spatiotemporal distribution, compaction status, and advancement speed data are encapsulated and traced, they are output to the construction operation terminal in real time. Once the compaction of the deployment area meets the standard and the front edge of the filling crosses the component, the extraction rate is dynamically adjusted according to the real-time compaction, and the component extraction is completed in a closed loop.

[0035] In summary, the system and method in the above embodiments accurately calibrate the three-dimensional spatial coordinates and void boundary parameters of each point by deploying removable positioning components in the voids of the riprap. Based on coaxial paired dielectric impedance and ultrasonic echo sensing units, the system synchronously collects dielectric characteristic data within the voids. After non-overlapping moving average filtering to remove random noise and abnormal jump values, and environmental coupling interference decoupling processing, the system obtains the effective physical characteristic parameters of the dielectric, accurately distinguishes the dielectric type and determines its three-dimensional spatial distribution within the voids, quantifies the density value using the effective void volume and standard concrete physical parameters, and clearly calibrates the concrete density. The system monitors the compaction status of the concrete filling and generates continuous spatiotemporal distribution information of the concrete filling status by combining spatial coordinates and temporal data. It tracks the filling process and the speed of the front advancement in real time. The monitoring data is bound to spatial coordinates, collected clock stamps and hardware addresses to form a traceable chain, and is simultaneously pushed to the construction operation terminal. After the concrete in the area is completely compacted and the front advancement of the filling crosses the deployment range, the positioning component can be controlled to be pulled out. The pulling rate is dynamically adjusted according to the real-time compaction to ensure that the compaction of the void filling does not decrease. This provides real-time operation support for the concrete pouring of the rockfill dam, thereby improving the accuracy of construction control and ensuring the quality of dam pouring.

[0036] 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. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A system for monitoring the compaction of concrete pouring in rockfill dams, characterized in that, include: The calibration module is used to deploy removable positioning components with sensor mounting positions in the gaps of the riprap, and to calibrate the three-dimensional spatial coordinates of each component and sensor mounting position and the corresponding gap boundary parameters. The data acquisition module is used to continuously acquire real-time data on the characteristics of the medium in the corresponding gaps of each removable positioning component at preset fixed time intervals throughout the entire concrete pouring process. The parsing module is used to parse the real-time acquired data, extract the physical characteristic parameters of the medium in the corresponding void, and distinguish the type of medium and the spatial distribution state of the medium in the void. The quantization module is used to quantify the parsed physical characteristic parameters, obtain the quantified value of the density of the concrete filling in the corresponding voids, and calibrate the filling density state of the concrete in the corresponding voids. The tracking module is used to combine the three-dimensional spatial coordinates of each sensor installation position with the corresponding compaction measurement value to generate spatiotemporal continuous distribution data of the concrete filling state in the rockfill in a time series, and to track the filling process of concrete in the voids. The output module is used to output the spatiotemporal continuous distribution data of the concrete filling state in the rockfill body and the filling density data of the corresponding voids to the construction operation terminal in real time according to the preset format. Among them, the void boundary parameters include at least the three-dimensional void boundary parameters, the effective void volume, and the inherent interference parameter values ​​of the void wall.

2. The concrete compaction monitoring system for rockfill dams according to claim 1, characterized in that, The removable positioning component is a rigid hollow rod with multiple sets of sensor mounting positions arranged axially. The axial length of the rod matches the filling height of the rockfill. The spacing of the sensor mounting positions along the axial direction of the rod matches the axial dimension of the rockfill gap at the corresponding mounting point. All sensor mounting positions on a single rod correspond to unique three-dimensional spatial coordinates. Each sensor mounting position is embedded with a set of sensor units that correspond one-to-one with the mounting position. The sensor unit includes a dielectric impedance sensor subunit and an ultrasonic echo sensor subunit arranged in coaxial pairs. The acquisition surfaces of the two sets of subunits are coplanar and both face the internal cavity of the corresponding rockfill gap. The acquisition surfaces are flush with the outer wall of the rod.

3. The concrete compaction monitoring system for rockfill dams according to claim 2, characterized in that, During the operation of the acquisition module, a unique hardware address and a global synchronization acquisition clock are assigned to each sensing unit. The global synchronization acquisition clock is synchronized with the global clock of the pouring construction. Throughout the entire concrete pouring process, the acquisition module triggers all sensing units to synchronously start acquisition at the same clock node with a preset fixed time interval as the acquisition cycle. Within each acquisition cycle, a single sensing unit completes no less than two continuous acquisitions of the medium in the corresponding gap. After the acquisition is completed, the number of continuous acquisitions within a single acquisition cycle is used as the sliding window length to perform non-overlapping moving average filtering on multiple sets of raw data within the same cycle to remove random noise and abnormal jump values ​​in the acquired data. Then, the filtered data is bound to the corresponding hardware address, acquisition clock stamp, and three-dimensional spatial coordinates and synchronously uploaded to the parsing module.

4. The concrete compaction monitoring system for rockfill dams according to claim 2, characterized in that, The analysis module first performs environmental coupling interference decoupling processing on the uploaded filtered raw data to extract the effective physical characteristic parameters of the medium in the corresponding gap; The decoupling process follows the following rules: ； In the formula: For the first Effective values ​​of medium-like characteristic parameters; For the first The original acquired values ​​of the characteristic parameters of the medium-like material after filtering; For the first Temperature influence coefficient of characteristic parameters of media-like substances; The real-time ambient temperature collected by the temperature measurement node built into the sensing unit during the current acquisition cycle; The reference ambient temperature collected by the sensing unit during the calibration stage before pouring; For the first Pore ​​wall scattering interference coefficient, a characteristic parameter of a medium-like medium; This refers to the inherent interference parameter values ​​of the pore wall collected at the sensor installation location when there is no filling medium during the pre-pouring calibration stage. The analysis module uses various effective physical characteristic parameters obtained after decoupling to form a set of medium characteristic parameters at the corresponding sensing installation position. It distinguishes the medium type based on the preset threshold range of medium characteristic parameters. At the same time, it analyzes the echo delay data of the ultrasonic echo sensing subunit to obtain the three-dimensional spatial position of the medium filling interface and determine the spatial distribution state of the medium in the gap.

5. The concrete compaction monitoring system for rockfill dams according to claim 1, characterized in that, The quantization module calculates the quantified value of the concrete filling density within the target void for each set of sensor mounting positions, based on the three-dimensional boundary parameters of the target void calibrated by the calibration module and the spatial distribution state and effective physical characteristic parameters of the medium output by the analysis module. ； In the formula: The compaction value of the concrete filling in the target void; The effective volume of the target void pre-calibrated for the calibration module; The effective filling volume of liquid-phase concrete within the target void; The first concrete medium within the target void output by the analysis module Class of effective physical characteristic parameters; For the preset standard liquid phase concrete corresponding to the first Baseline values ​​for physical characteristic parameters; The quantification module matches the calculated density quantification value with the threshold range based on the preset density threshold range, and calibrates the filling density state of the concrete in the corresponding void as unfilled, partially filled, basically dense or completely dense.

6. The concrete compaction monitoring system for rockfill dams according to claim 1, characterized in that, The tracking module uses the three-dimensional spatial coordinates of each sensor installation position calibrated by the calibration module as spatial nodes, and the density measurement values ​​corresponding to each node within the same acquisition cycle as node attribute values. It generates a spatial distribution cloud map of the concrete filling density of the entire cross section of the rockfill within a single acquisition cycle through a spatial interpolation algorithm. The tracking module splices together the spatial distribution cloud maps of the compaction density of all collection periods according to the time sequence of the collection period to generate spatiotemporal continuous distribution data of the concrete filling state in the rockfill. The tracking module identifies the spatial surface of the concrete filling front based on spatiotemporally continuous distribution data and calculates the effective advancement speed of the filling front along the preset pouring direction. ； In the formula: The effective advancing speed of the pre-set pouring flow direction at the front edge of the concrete filling line; Arranged sequentially along the pre-set pouring direction The spatial displacement vector formed by the group of sensor mounting positions; The unit direction vector for the preset pouring flow direction; for The median time difference of the acquisition clock stamp when the density measurement value of the gap corresponding to the group sensor mounting position first reaches the preset filling threshold.

7. A concrete compaction monitoring system for rockfill dams according to claim 6, characterized in that, During the operation phase of the output module, the spatiotemporal continuous distribution data of the concrete filling state within the rockfill, the filling density data of the corresponding voids, and the effective advancement speed data of the filling front are encapsulated in a preset structured format. Each set of data is bound to a unique three-dimensional spatial coordinate, a collection clock stamp, and a sensor unit hardware address, forming a traceable and complete data chain. The encapsulated structured data is then synchronously output to the construction operation terminal in real time according to the collection cycle, while all data is synchronously backed up to the local built-in storage unit.

8. A concrete compaction monitoring system for rockfill dams according to claim 6, characterized in that, The removable positioning component is equipped with a closed-loop controlled removable execution unit, which is signal-connected to the quantization module and the tracking module. The extraction execution unit is allowed to be triggered only when the density measurement value of all gaps within the layout range of the corresponding extractable positioning component reaches the preset full compaction threshold and the concrete filling front has completely crossed the layout area of ​​the component. During the extraction process, the acquisition module continuously collects real-time data from all sensing units on the component, and the quantification module simultaneously verifies the density status of the corresponding gap. The extraction rate is positively correlated with the quantified value of the density of the corresponding gap until the rod is completely extracted from the rockfill.

9. A concrete compaction monitoring system for rockfill dams according to claim 1, characterized in that, The calibration module is interconnected with a data acquisition module and a parsing module via a wireless network. The data acquisition module and the parsing module are interconnected via a wireless network. The parsing module is interconnected with a quantization module via a wireless network. The quantization module is interconnected with a tracking module and an output module via a wireless network. The tracking module and the output module are interconnected via a wireless network.

10. A method for monitoring the density of concrete pouring in a rockfill dam, wherein the method is an implementation method of the concrete pouring density monitoring system for rockfill dams as described in any one of claims 1-9, characterized in that, include: Rigid hollow removable positioning components with multiple sets of axial sensor mounting positions are arranged in the gaps of the rock pile. The three-dimensional coordinates of each sensor mounting position and the corresponding gap boundary parameters are calibrated. Each mounting position is embedded with a coaxial pair of dielectric impedance and ultrasonic echo sensing sub-units. All sensor units are assigned a unique hardware address and a global synchronization clock. The characteristic data of the void medium are collected synchronously at fixed time intervals. The non-overlapping sliding mean filter is performed with the number of collections per cycle as a window to remove noise and outliers. The data is then uploaded after binding coordinates and clock stamps. Temperature and pore wall scattering interference are decoupled from the filtered data, and effective physical feature parameters are extracted to distinguish the medium type. Based on the ultrasonic echo time delay analysis of the three-dimensional position of the filling interface, the three-dimensional spatial distribution of the medium in the pore is determined. Based on the effective volume of voids, the volume of concrete filling, and the effective physical characteristic parameters, the density quantification value is calculated, and the void filling state is calibrated as unfilled, partially filled, basically dense, or completely dense according to the preset threshold range. Using the sensor installation position coordinates as spatial nodes and the compaction value as an attribute, a single-cycle compaction spatial distribution cloud map is generated through spatial interpolation, and spatiotemporal continuous distribution data is obtained by time-series splicing. At the same time, the advance speed of the concrete filling front is calculated. After the spatiotemporal distribution, compaction status, and advancement speed data are encapsulated and traced, they are output to the construction operation terminal in real time. Once the area to be filled meets the required density and the filling front has passed the component, the extraction rate is dynamically adjusted according to the real-time density to complete the component extraction in a closed loop.

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