Intelligent construction supervision method and system for compaction and filling of inclined wall dam

Abstract

The invention relates to the technical field of construction monitoring, and discloses an intelligent construction supervision method and system for compaction and filling of an inclined wall dam, and the method sequentially comprises a test section model calibration step, a construction section intelligent supervision step, and a quality acceptance and filing step. The system corresponds to the method. According to the method, through performance calibration, continuous compaction and conventional acceptance inspection point position coincidence and test section multi-working-condition coverage, correlation coefficient verification optimization is combined, so that a correlation model is accurately matched with the difference between the inclined wall dam filler and the compaction process, the universality and precision of the model are improved, a compaction machine is integrated with multi-parameter acquisition equipment, vibration, track and environment data are synchronously obtained, and the accuracy of the model is improved. And the cloud platform calculates the compaction degree in real time based on a test section model, judges the uniformity according to the standard deviation of the compaction parameters of adjacent point locations, triggers early warning and links engineering treatment measures, so that the purposes of controllable compaction quality process and real-time intervention are achieved, and the detection timeliness is improved.

Description

Technical Field

[0001] This invention relates to the field of construction monitoring technology, specifically to an intelligent construction monitoring method and system for the compaction and filling of inclined wall dams. Background Technology

[0002] Traditional quality control methods have significant limitations in the compaction and filling construction of inclined wall dams:

[0003] Outdated testing methods: Relying on post-construction sampling inspections such as sand filling and ring cutter methods cannot reflect the quality of the construction process in real time, and there is an increase in costs due to destructive testing.

[0004] Insufficient data coverage: Point-based testing is difficult to represent the quality of large-area operations, and when the filling material is uneven, it is easy to miss detections, leading to potential quality problems;

[0005] Low coordination efficiency: When multiple machines are operating, they rely on manual coordination, which can easily lead to problems such as missed compaction and repeated compaction. Especially in complex environments such as deserts and wastelands, poor communication further exacerbates the management difficulty.

[0006] Water waste: Traditional construction requires repeated watering to reach the optimal moisture content, which leads to excessive water consumption in arid areas (such as the regulating reservoir of the Eastern Water Supply Project);

[0007] Data traceability is difficult: paper records are easily lost and cannot achieve accurate backtracking of the entire construction process, which is not conducive to the analysis of quality problems and the determination of responsibility.

[0008] While existing intelligent compaction technologies have partially solved the real-time monitoring problem, there is still room for improvement in multi-parameter fusion analysis, data transmission stability under extreme environments, and adaptability to dam-specific compaction models.

[0009] Therefore, there is an urgent need for a monitoring technology that integrates high-precision positioning, multi-dimensional data acquisition, intelligent analysis, and real-time feedback to meet the needs of large-scale, high-quality construction of inclined wall dam projects. Summary of the Invention

[0010] The purpose of this invention is to provide an intelligent construction supervision method and system for the compaction and filling of inclined wall dams, so as to solve the technical problems mentioned in the background art.

[0011] To achieve the above objectives, the present invention discloses the following technical solutions:

[0012] In a first aspect, this invention discloses an intelligent construction supervision method for the compaction and filling of inclined wall dams, comprising, in sequence: a test section model calibration step, a construction section intelligent supervision step, and a quality acceptance and archiving step; wherein:

[0013] The test section model calibration steps include:

[0014] S11 - Compliance Verification of Testing Equipment: Performance calibration of vibration sensing devices and GNSS positioning modules to ensure that amplitude acquisition error and positioning deviation meet the modeling accuracy requirements;

[0015] S12 - Correlation Test and Model Establishment:

[0016] S121 - Conduct continuous compaction testing and routine quality acceptance testing in the test section, so that the test points of continuous compaction testing and routine quality acceptance testing overlap. In this case, at least 10 test points covering different fillers and compaction parameters are selected in each test section.

[0017] S122 - Pearson correlation analysis is used to calculate the correlation coefficient r between the vibration response value and the actual compaction degree. If r ≥ 0.7, a correlation model between the two is established and the target thresholds for compaction degree and uniformity are determined. If r < 0.7, the detection range is expanded or the test section is reselected, and the correlation test is repeated until r ≥ 0.7.

[0018] The intelligent monitoring steps for the construction section include:

[0019] S21 - Multi-source data acquisition: A vibration sensor based on epoxy resin encapsulation is installed on the central shaft of the vibrating wheel of the compaction machinery; a GNSS positioning module is installed on the top of the operator's cab of the compaction machinery; and a main control unit is integrated into the operating platform of the compaction machinery. The vibration sensor and the GNSS positioning module are communicatively connected to the main control unit. The main control unit synchronously collects vibration parameters, position trajectories, and environmental parameters, and forms a structured data set.

[0020] S22-Dual-channel reliable transmission: The main control unit transmits data through dual-channel communication consisting of a GPRS network and a data transmission radio, wherein each data segment is set with a timestamp and a checksum; the cloud platform receives the transmitted data and verifies the data integrity, and automatically requests retransmission of missing data segments; when the network is interrupted, the main control unit locally caches the data and resumes transmission from the breakpoint after the network is restored.

[0021] S23 - Intelligent Data Analysis and Control:

[0022] S231-Data Analysis: The cloud platform preprocesses the data, calculates the real-time compaction degree based on the correlation model, and generates compaction cloud map, pass heat map and velocity distribution map;

[0023] S232 - Compaction Status Analysis: Calculate the standard deviation of compaction parameters at 10 adjacent points to determine compaction uniformity. If the degree of compaction or compaction uniformity does not meet the target threshold, an early warning is triggered, and engineering treatment measures are implemented. Otherwise, the rolling operation is terminated, continuous compaction quality testing is carried out, and the compaction status distribution is determined.

[0024] The quality acceptance and archiving steps include:

[0025] S31 - Routine Inspection: After continuous compaction of the construction section, routine quality inspection is carried out in the weak areas marked on the compaction cloud map and at the preset designated locations.

[0026] S32 - Report Generation: Compaction quality report is generated, which includes parameters of the test section associated model, key compaction indicators of the construction section, a comparison table of routine quality inspection results, and targeted improvement suggestions.

[0027] Preferably, the performance calibration of the vibration sensing device and the GNSS positioning module includes the following steps:

[0028] The vibration sensing device is calibrated for amplitude.

[0029] A static deviation test was performed on the GNSS positioning module.

[0030] Preferably, in the correlation test and model establishment, the rules for arranging the detection points include:

[0031] The locations of continuous compaction testing and conventional quality acceptance testing overlap spatially, and cover different filler stratification zones, compaction pass variation zones, and mechanical travel boundary zones within the test section.

[0032] Preferably, in step S122, when r < 0.7, the expanded detection range includes detection points for supplementary filler gradation abrupt layer and mechanical overlap blind zone; when the correlation coefficient is still r < 0.7 after expanding the detection range, the test section is replaced and the filler type or compaction process is adjusted, and the correlation test is repeated.

[0033] Preferably, the target thresholds of the correlation model include: compaction threshold and uniformity threshold.

[0034] Preferably, the calculation of the standard deviation of compaction parameters at 10 adjacent points to determine compaction uniformity includes the following steps:

[0035] The compaction degree is calculated from 10 consecutive adjacent compaction points, and its standard deviation σ is calculated. The calculated compaction degree is compared with the preset compaction degree threshold, and the calculated standard deviation σ is compared with the preset uniformity threshold. If the compaction degree is greater than or equal to the compaction degree threshold and σ is less than or equal to the uniformity threshold, then the uniformity is determined to meet the target threshold; otherwise, it is determined to not meet the target threshold.

[0036] Preferably, the engineering treatment measures include the following steps:

[0037] When the compaction degree is less than the compaction threshold, increase the number of rolling passes or decrease the rolling speed to perform additional compaction;

[0038] When σ > uniformity threshold, compaction correction measures are implemented through local replacement or densification of the compaction trajectory.

[0039] Preferably, the selection rules for the locations of the routine quality inspection are as follows:

[0040] Select at least 3 points in the continuous area of ​​the lowest compaction value in the compaction cloud map;

[0041] At least two points were selected at each of the pre-designed dam joints, slope toe area, and mechanical overlap area to verify the quality of the key structural areas.

[0042] Preferably, the parameters of the test segment correlation model include the correlation coefficient r, the fitting equation, and the target threshold;

[0043] The key compaction indicators for the construction section include average compaction degree, pass rate, uniformity standard deviation, and spatial distribution heat map;

[0044] The comparison table of routine quality inspection results includes the deviation between the measured values ​​and the model calculated values;

[0045] The targeted improvement suggestions include pressure compensation parameters, processing range, and re-inspection requirements.

[0046] Secondly, this invention discloses an intelligent construction monitoring system for the compaction and filling of inclined wall dams, applying the intelligent construction monitoring method for the compaction and filling of inclined wall dams as described above. The system includes:

[0047] A front-end sensing and control subsystem deployed on compaction machinery, the front-end sensing and control subsystem comprising: a vibration sensor, a GNSS positioning module, an environmental parameter acquisition module, and a main control unit;

[0048] A dual-channel communication subsystem connected to the front-end sensing and control subsystem, wherein the dual-channel communication subsystem is integrated into the main control unit;

[0049] The cloud platform, which is connected to the front-end sensing and control subsystem through the dual-path communication subsystem, includes a test section model calibration module, a construction section real-time analysis module, an early warning module, and a quality report generation module.

[0050] An interactive terminal connected to the cloud platform and the front-end perception and control subsystem;

[0051] in:

[0052] The vibration sensing device is configured to be fixed to the central shaft of the vibrating wheel of the compaction machine and to collect the amplitude and frequency data of the compaction machine.

[0053] The GNSS positioning module is configured to be installed on the top of the driver's cab of the compaction machinery to acquire real-time coordinates, driving speed and compaction trajectory;

[0054] The environmental parameter acquisition module is configured to integrate an IoT sensor for collecting environmental data, including paving temperature and compaction temperature.

[0055] The main control unit is configured to connect to the data interface of the vibration sensing device, the GNSS positioning module and the environmental parameter acquisition module, and to perform structured integration of amplitude, frequency data, position trajectory and environmental parameters to generate structured data groups.

[0056] The dual-channel communication subsystem is configured to transmit data via a GPRS network and a data radio, wherein each data segment is equipped with a timestamp and a checksum; when the network is interrupted, the main control unit locally caches the data and resumes transmission from the point of interruption after the network is restored;

[0057] The test section model calibration module is configured to: receive test section data uploaded by the front-end sensing and control subsystem, and use Pearson correlation analysis to calculate the correlation coefficient r between the vibration response value and the actual compaction degree. If r ≥ 0.7, then establish a correlation model between the two and determine the target thresholds for compaction degree and uniformity; if r < 0.7, then expand the detection range or reselect the test section and repeat the correlation test until r ≥ 0.7.

[0058] The real-time analysis module for the construction section is configured to: preprocess the received structured data set, calculate the real-time compaction degree based on the correlation model, and generate compaction cloud map, pass heat map and velocity distribution map; simultaneously calculate the standard deviation of compaction parameters at 10 adjacent points to determine the compaction uniformity.

[0059] The early warning module is configured to trigger an early warning and associate engineering treatment measures if the compaction degree or compaction uniformity does not meet the target threshold.

[0060] The quality report generation module is configured to: compile a compaction quality report after continuous compaction construction of the construction section. The compaction quality report includes the parameters of the test section correlation model, the key compaction indicators of the construction section, the comparison table of routine quality inspection results, and targeted improvement suggestions.

[0061] The interactive terminal is configured to display in real time the vibration sensing device, the GNSS positioning module, the environmental parameter acquisition module, the early warning information of the early warning module, the comparison table of routine quality inspection results, and the targeted improvement suggestions.

[0062] Beneficial Effects: The intelligent construction supervision method and system for compacted filling of inclined wall dams of this invention, through performance calibration of sensing and positioning equipment, overlap of continuous compaction and conventional acceptance testing points, and multi-condition coverage of test sections, combined with correlation coefficient verification and optimization, enables the associated model to accurately match the differences between the inclined wall dam filling material and compaction process, improving the model's versatility and accuracy. Simultaneously, the compaction machinery integrates multi-parameter acquisition equipment to synchronously acquire vibration, trajectory, and environmental data, and employs dual-path communication and breakpoint resume transmission to achieve comprehensive data acquisition and stable transmission. Furthermore, the cloud platform calculates the compaction degree in real time based on the test section model, judges uniformity using the standard deviation of compaction parameters at adjacent points, triggers early warnings, and links engineering treatment measures, achieving controllable and real-time intervention in the compaction quality process and improving testing timeliness. Finally, acceptance is carried out by locating weak areas and structurally critical areas based on the compaction cloud map, compiling and archiving a report containing model parameters and multi-dimensional compaction indicators to ensure data traceability and the integrity of construction data throughout its entire lifecycle. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 A flowchart illustrating the intelligent construction supervision method for compacted filling of inclined wall dams provided in this application embodiment. Detailed Implementation

[0065] The technical solutions in the embodiments of this application will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0066] In this document, the term "comprising" is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0067] This embodiment discloses, in a first aspect, a method as follows: Figure 1The intelligent construction supervision method for compacted filling of inclined wall dams shown is designed to meet the needs of large-scale and high-quality construction of inclined wall dam projects. This method includes, in sequence: test section model calibration step, construction section intelligent supervision step, and quality acceptance and archiving step.

[0068] In detail

[0069] The test section model calibration steps include:

[0070] S11 - Compliance Verification of Testing Equipment: The performance of vibration sensing devices and GNSS positioning modules is calibrated to ensure that amplitude acquisition errors and positioning deviations meet the modeling accuracy requirements.

[0071] The test employed a standard vibration table to simulate the vibration conditions of compaction machinery (amplitudes of 5mm, 10mm, and 15mm, and frequencies of 20Hz, 30Hz, and 40Hz). Vibration sensors were fixed to the test position on the vibration table, and 100 sets of vibration data were collected. The error formula was used to... This calculation ensures that the amplitude acquisition error is ≤ ±5%.

[0072] Secondly, concrete marker stakes (coordinates known, error ≤ ±1cm) were placed in the test section. The GNSS module was installed in the compaction machinery's cab and left to stand for 10 minutes, recording the real-time coordinates. The static positioning deviation (the distance between the marker stake coordinates and the GNSS recorded coordinates) was calculated, ensuring the deviation was ≤ ±2cm.

[0073] S12 - Correlation Test and Model Establishment:

[0074] S121 - At least 10 test points covering different fillers and compaction parameters are selected in each test section. The arrangement rules for the test points include: spatial overlap between the points for continuous compaction testing and routine quality acceptance testing, and coverage of different filler stratification areas, compaction pass variation areas, and machinery travel boundary areas within the test section. In this embodiment, three typical filler areas (clay sloping wall, gravel dam shell, transition material area), three compaction pass areas (3 passes, 5 passes, 7 passes), and two machinery boundary areas (single machinery operation area, double machinery overlap area) are selected within the test section, for a total of 12 test points. When the compaction machinery compacts according to the set parameters, the vibration sensing device collects vibration response values ​​(such as vibration energy ECV) in real time for continuous compaction testing. At the same time, routine quality acceptance testing is performed at the corresponding points using the sand cone method for gravel and the ring cutter method for clay to determine the actual compaction degree, ensuring spatial overlap and time synchronization ≤ ±5 seconds.

[0075] S122 - Pearson correlation analysis is performed using SPSS software to calculate the correlation coefficient r between the vibration response value and the actual compaction degree. If r ≥ 0.7, a correlation model (such as a linear regression model) is established between the two using the least squares method. The target thresholds of the correlation model include: compaction degree threshold and uniformity threshold. The compaction degree threshold (e.g., clay ≥ 96%, gravel ≥ 94%) and uniformity threshold (e.g., for the standard deviation σ, the preset threshold for gravel is 3%, and the preset threshold for clay is 5%) are determined based on the design requirements. If r < 0.7, five additional detection points are added in the filler gradation abrupt layer (e.g., clay-gravel transition zone) and the mechanical overlap blind zone (untested area of ​​double machine overlap). The correlation test steps (including the analysis content in S121 and S122) are repeated. If the results are still not satisfactory, the test section is reselected until r ≥ 0.7.

[0076] The intelligent monitoring steps for the construction section include:

[0077] S21 - Multi-source data acquisition: A nitrile rubber pad (5mm thick, 1.5GPa elastic modulus) is used for shock absorption and fixed to the central shaft of the compaction machine's vibratory wheel. The exterior is protected with epoxy resin potting (3mm thick) to ensure vibration resistance and waterproofing. It is adapted to the machine's vibration frequency, collecting ≥3 sets of data per vibration cycle. A GNSS positioning module is installed in an unobstructed area on the top of the compaction machine's cab. The GNSS positioning module interacts with the ground reference station in real time via 4G or 5G modules using RTK differential signals. A main control unit is integrated into the compaction machine's operating platform, where the vibration sensor and GNSS positioning module communicate with the main control unit. The main control unit synchronously collects vibration parameters (amplitude, frequency), position trajectory (real-time coordinates, travel speed, number of compaction passes), and environmental parameters (paving temperature, compaction temperature) at a 10Hz frequency, forming structured data sets (data encapsulated in JSON format).

[0078] S22 - Dual-channel Reliable Transmission: The main control unit slices structured data groups into 5-second segments (each segment ≤ 1MB), generates checksums using the CRC-32 algorithm to ensure data integrity, and each segment is timestamped. Then, data is transmitted via dual-channel communication consisting of a GPRS network (priority transmission, suitable for areas with good signal) and a data radio (as backup, automatically switching in weak network environments, transmission distance ≥ 5km). The cloud platform receives the transmitted data via dual-channel communication, verifies data integrity, and automatically requests retransmission of missing data segments. In the event of a network interruption, the main control unit locally caches the data (temporarily storing the data on an SD card) and resumes transmission after network recovery.

[0079] S23 - Intelligent Data Analysis and Control:

[0080] S231 - Data Analysis: The cloud platform preprocesses the data (removing outliers), such as using the 3σ principle to remove outliers (e.g., abrupt changes in vibration parameters exceeding the mean ± 3 times the standard deviation), ensuring the reliability of the analysis basis. Based on the correlation model, the vibration response values ​​are substituted into the calculation of real-time compaction degree, generating compaction cloud maps, pass-through heat maps, and velocity distribution maps.

[0081] S232 - Compaction State Analysis: Calculate the standard deviation of compaction parameters at 10 adjacent points to determine compaction uniformity. Specifically, select the compaction degree calculation values ​​of 10 consecutive adjacent compaction points, calculate their standard deviation σ, and compare the calculated compaction degree with a preset compaction degree threshold, and the calculated standard deviation σ with a preset uniformity threshold. If the compaction degree ≥ the compaction degree threshold and σ ≤ the uniformity threshold, then the uniformity is determined to meet the target threshold; otherwise, it is determined not to meet the target threshold. The formula for calculating the standard deviation σ is:

[0082]

[0083] Among them, Y i Let i be the compaction degree at compaction point i. The average compaction degree of all selected compaction points is n = 10.

[0084] If the compaction degree or compaction uniformity does not meet the target threshold, an early warning is triggered (e.g., flat panel notification, audible and visual alarm, and push notification on the interactive terminal), and engineering treatment measures are implemented (e.g., additional compaction, replacement, and trajectory adjustment); otherwise, the compaction operation is terminated, continuous compaction quality testing is conducted, and the compaction state distribution is determined. The selection rules for routine quality inspection points are as follows:

[0085] Select at least 3 points in the continuous area of ​​the lowest compaction value in the compaction cloud map;

[0086] At least two points were selected at each of the pre-designed dam joints, slope toe area, and mechanical overlap area to verify the quality of the key structural areas.

[0087] The engineering treatment measures include the following steps:

[0088] When the compaction degree is less than the compaction degree threshold, increase the number of rolling passes (adjusted by 1 to 2 passes) or reduce the rolling speed (1 to 2 km / h) to perform additional compaction;

[0089] When σ > uniformity threshold, compaction correction measures are implemented through local replacement (replacement depth ≥ 30cm, matching the original filler gradation) or densification of compaction trajectory (spacing ≤ 10cm).

[0090] The quality acceptance and archiving steps include:

[0091] S31 - Routine Inspection: After continuous compaction of the construction section, routine quality inspection (ring cutter method, water injection method) is carried out on the weak areas marked on the compaction cloud map (lowest value area ≥3 points) and the pre-set designated locations (joints, slope toe, etc. ≥2 points).

[0092] S32 - Report Generation: Compaction quality report is generated, which includes parameters of the test section associated model, key compaction indicators of the construction section, comparison table of routine quality inspection results, and targeted improvement suggestions.

[0093] Among them, the parameters of the test section association model include the correlation coefficient r, the fitting equation (i.e., the regression equation corresponding to the association model), and the target threshold;

[0094] Key compaction indicators for the construction section include average compaction degree, pass rate, standard deviation of uniformity, and spatial distribution heat map;

[0095] The comparison table of routine quality inspection results includes the deviation between measured values ​​and model calculated values;

[0096] Targeted improvement suggestions include pressure compensation parameters (such as number of passes and speed), processing range (coordinate interval), and re-inspection requirements (time and location).

[0097] In summary, the intelligent construction supervision method for the compaction and filling of inclined wall dams in this embodiment ensures the accuracy of modeling data by calibrating the performance of vibration sensing devices and GNSS positioning modules. Simultaneous continuous compaction testing and routine quality acceptance testing are conducted, requiring overlapping testing points for both types of tests and selecting at least 10 testing points covering different fillers and compaction parameters in the test section. Combined with correlation coefficient verification, this ensures that the established correlation model can accurately adapt to the diverse fillers and compaction processes of inclined wall dams, solving the problems of poor versatility and insufficient accuracy of existing models. Furthermore, the compaction machinery integrates epoxy resin-encapsulated vibration sensing devices and GNSS positioning modules. The main control unit simultaneously collects vibration, position trajectory, and environmental parameters, forming comprehensive structured data. Combined with dual-channel communication of GPRS and data transmission radio, comprehensive data acquisition and stable transmission are achieved, overcoming the quality control failures caused by traditional single-parameter monitoring and data transmission interruptions. Furthermore, the cloud platform calculates compaction degree in real time based on the test section model, judges uniformity by the standard deviation of compaction parameters at 10 adjacent points, and triggers early warnings and implements engineering measures such as additional compaction / replacement for cases that do not meet the target threshold. This achieves controllable and real-time intervention in the compaction quality process, avoiding the situation where problems are discovered only after traditional post-inspection and have already formed large-scale quality hazards. During quality acceptance, routine inspections are conducted at designated locations such as continuous areas with the lowest values ​​and joints and slope toes, based on the compaction cloud map, solving the problems of random inspection points and missed inspections of weak areas in traditional methods. A report is compiled that includes model parameters, multi-dimensional compaction indicators (average compaction degree, uniformity standard deviation), and inspection comparisons, overcoming the shortcomings of traditional paper records, such as difficulty in traceability and fragmented quality data.

[0098] In a second aspect, this embodiment provides an intelligent construction monitoring system for the compaction and filling of inclined wall dams, applying the intelligent construction monitoring method for the compaction and filling of inclined wall dams as described above. The system includes:

[0099] The front-end sensing and control subsystem deployed on compaction machinery includes: a vibration sensor, a GNSS positioning module, an environmental parameter acquisition module, and a main control unit.

[0100] A dual-channel communication subsystem connected to the front-end sensing and control subsystem is integrated into the main control unit.

[0101] The cloud platform, which is connected to the front-end sensing and control subsystem via a dual-path communication subsystem, includes a test section model calibration module, a construction section real-time analysis module, an early warning module, and a quality report generation module.

[0102] An interactive terminal that connects to the cloud platform and the front-end perception and control subsystem;

[0103] in:

[0104] The vibration sensing device is configured to be fixed to the central shaft of the vibrating wheel of the compaction machine and to collect the amplitude and frequency data of the compaction machine for continuous compaction detection.

[0105] The GNSS positioning module is configured to be installed on the top of the driver's cab of the compaction machinery to acquire real-time coordinates, driving speed and compaction trajectory, with an accuracy that meets the centimeter-level positioning requirements;

[0106] The environmental parameter acquisition module is configured to integrate IoT sensors to collect environmental data, including paving temperature and compaction temperature.

[0107] The main control unit is configured to connect to the data interface of the vibration sensing device, GNSS positioning module and environmental parameter acquisition module, and to perform structured integration of amplitude, frequency data, position trajectory and environmental parameters to generate structured data groups, supporting local caching and breakpoint resume logic.

[0108] The dual-channel communication subsystem is configured to transmit data via a GPRS network and a data radio, with each data segment having a timestamp and a checksum; in the event of a network interruption, the main control unit locally caches the data and resumes transmission from the point of interruption after network recovery.

[0109] The test section model calibration module is configured to: receive test section data uploaded by the front-end sensing and control subsystem, and use Pearson correlation analysis to calculate the correlation coefficient r between the vibration response value and the actual compaction degree. If r ≥ 0.7, then establish a correlation model between the two and determine the target thresholds for compaction degree and uniformity; if r < 0.7, then expand the detection range or reselect the test section and repeat the correlation test until r ≥ 0.7.

[0110] The real-time analysis module for the construction section is configured to: preprocess the received structured data set, calculate the real-time compaction degree based on the correlation model, and generate compaction cloud map, pass heat map and velocity distribution map;

[0111] Simultaneously calculate the standard deviation of compaction parameters at 10 adjacent points to determine the uniformity of compaction;

[0112] The early warning module is configured to trigger an early warning and associate engineering treatment measures if the compaction degree or compaction uniformity does not meet the target threshold.

[0113] The quality report generation module is configured to: compile a compaction quality report after continuous compaction of the construction section. The compaction quality report includes the parameters of the test section associated model, the key compaction indicators of the construction section, a comparison table of routine quality inspection results, and targeted improvement suggestions.

[0114] The interactive terminal is configured to display in real time the warning information from the vibration sensing device, GNSS positioning module, environmental parameter acquisition module, and early warning module, as well as a comparison table of routine quality inspection results and targeted improvement suggestions.

[0115] It should be noted that the intelligent construction supervision system for compacted filling of inclined wall dams in this embodiment corresponds to the aforementioned intelligent construction supervision method for compacted filling of inclined wall dams. Therefore, the parts of the intelligent construction supervision system for compacted filling of inclined wall dams that are not described in detail in this embodiment (including but not limited to specific technical means and technical effects) can be referred to the description in the aforementioned intelligent construction supervision method for compacted filling of inclined wall dams, and will not be repeated here.

[0116] In the embodiments provided in this application, it should be understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code, or any suitable combination thereof. For hardware implementation, the processor may be implemented in one or more of the following: application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, other electronic units designed to implement the functions described herein, or combinations thereof. For software implementation, some or all of the processes of the embodiments may be performed by a computer program instructing the associated hardware. During implementation, the program may be stored in a computer-readable storage medium or transmitted as one or more instructions or code on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of a computer program from one place to another. Storage media may be any available medium accessible to a computer. Computer-readable storage media may include, but are not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code having the form of instructions or data structures and accessible to a computer.

[0117] Finally, it should be noted that the above description is only a preferred embodiment of this application and is not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A smart construction monitoring method for compacted filling of inclined wall dams, characterized in that, In order, they include: The test section model calibration steps, the construction section intelligent monitoring steps, and the quality acceptance and archiving steps; among which: The test section model calibration steps include: S11 - Compliance Verification of Testing Equipment: Performance calibration of vibration sensing devices and GNSS positioning modules to ensure that amplitude acquisition error and positioning deviation meet the modeling accuracy requirements; S12 - Correlation Test and Model Establishment: S121 - Conduct continuous compaction testing and routine quality acceptance testing in the test section, so that the test points of continuous compaction testing and routine quality acceptance testing overlap. In this case, at least 10 test points covering different fillers and compaction parameters are selected in each test section. S122 - Pearson correlation analysis is used to calculate the correlation coefficient r between the vibration response value and the actual compaction degree. If r ≥ 0.7, a correlation model between the two is established and the target thresholds for compaction degree and uniformity are determined. If r < 0.7, the detection range is expanded or the test section is reselected, and the correlation test is repeated until r ≥ 0.

7. The intelligent monitoring steps for the construction section include: S21 - Multi-source data acquisition: A vibration sensor based on epoxy resin encapsulation is installed on the central shaft of the vibrating wheel of the compaction machinery; a GNSS positioning module is installed on the top of the operator's cab of the compaction machinery; and a main control unit is integrated into the operating platform of the compaction machinery. The vibration sensor and the GNSS positioning module are communicatively connected to the main control unit. The main control unit synchronously collects vibration parameters, position trajectories, and environmental parameters, and forms a structured data set. S22-Dual-channel reliable transmission: The main control unit transmits data through dual-channel communication consisting of a GPRS network and a data transmission radio, wherein each data segment is set with a timestamp and a checksum; the cloud platform receives the transmitted data and verifies the data integrity, and automatically requests retransmission of missing data segments; when the network is interrupted, the main control unit locally caches the data and resumes transmission from the breakpoint after the network is restored. S23 - Intelligent Data Analysis and Control: S231-Data Analysis: The cloud platform preprocesses the data, calculates the real-time compaction degree based on the correlation model, and generates compaction cloud map, pass heat map and velocity distribution map; S232 - Compaction Status Analysis: Calculate the standard deviation of compaction parameters at 10 adjacent points to determine compaction uniformity. If the degree of compaction or compaction uniformity does not meet the target threshold, an early warning is triggered, and engineering treatment measures are implemented. Otherwise, the rolling operation is terminated, continuous compaction quality testing is carried out, and the compaction status distribution is determined. The quality acceptance and archiving steps include: S31 - Routine Inspection: After continuous compaction of the construction section, routine quality inspection is carried out in the weak areas marked on the compaction cloud map and at the preset designated locations. S32 - Report Generation: Compaction quality report is generated, which includes parameters of the test section associated model, key compaction indicators of the construction section, a comparison table of routine quality inspection results, and targeted improvement suggestions.

2. The intelligent construction supervision method for compacted filling of inclined wall dams according to claim 1, characterized in that, The performance calibration of the vibration sensing device and GNSS positioning module includes the following steps: The vibration sensing device is calibrated for amplitude. A static deviation test was performed on the GNSS positioning module.

3. The intelligent construction supervision method for compacted filling of inclined wall dams according to claim 1, characterized in that, In the correlation test and model building process, the rules for arranging the detection points include: The locations of continuous compaction testing and conventional quality acceptance testing overlap spatially, and cover different filler stratification zones, compaction pass variation zones, and mechanical travel boundary zones within the test section.

4. The intelligent construction supervision method for compacted filling of inclined wall dams according to claim 3, characterized in that, In step S122, when r < 0.7, the expanded detection range includes detection points for the filler gradation mutation layer and mechanical overlap blind zone; when the correlation coefficient is still r < 0.7 after expanding the detection range, the test section is replaced and the filler type or compaction process is adjusted, and the correlation test is repeated.

5. The intelligent construction supervision method for compacted filling of inclined wall dams according to claim 1, characterized in that, The target thresholds of the correlation model include: compaction threshold and uniformity threshold.

6. The intelligent construction supervision method for compacted filling of inclined wall dams according to claim 5, characterized in that, The calculation of the standard deviation of compaction parameters at 10 adjacent points to determine compaction uniformity includes the following steps: The compaction degree is calculated from 10 consecutive adjacent compaction points, and its standard deviation σ is calculated. The calculated compaction degree is compared with the preset compaction degree threshold, and the calculated standard deviation σ is compared with the preset uniformity threshold. If the compaction degree is greater than or equal to the compaction degree threshold and σ is less than or equal to the uniformity threshold, then the uniformity is determined to meet the target threshold; otherwise, it is determined to not meet the target threshold.

7. The intelligent construction supervision method for compacted filling of inclined wall dams according to claim 6, characterized in that, The engineering treatment measures include the following steps: When the compaction degree is less than the compaction threshold, increase the number of rolling passes or decrease the rolling speed to perform additional compaction; When σ > uniformity threshold, compaction correction measures are implemented through local replacement or densification of the compaction trajectory.

8. The intelligent construction supervision method for compacted filling of inclined wall dams according to claim 1, characterized in that, The selection rules for the locations of routine quality inspections are as follows: Select at least 3 points in the continuous area of ​​the lowest compaction value in the compaction cloud map; At least two points were selected at each of the pre-designed dam joints, slope toe area, and mechanical overlap area to verify the quality of the key structural areas.

9. The intelligent construction supervision method for compacted filling of inclined wall dams according to claim 1, characterized in that, The parameters of the test segment correlation model include the correlation coefficient r, the fitting equation, and the target threshold; The key compaction indicators for the construction section include average compaction degree, pass rate, uniformity standard deviation, and spatial distribution heat map; The comparison table of routine quality inspection results includes the deviation between the measured values ​​and the model calculated values; The targeted improvement suggestions include pressure compensation parameters, processing range, and re-inspection requirements.

10. An intelligent construction monitoring system for the compaction and filling of inclined wall dams, employing the intelligent construction monitoring method for the compaction and filling of inclined wall dams as described in any one of claims 1-9, characterized in that, The system includes: A front-end sensing and control subsystem deployed on compaction machinery, the front-end sensing and control subsystem comprising: a vibration sensor, a GNSS positioning module, an environmental parameter acquisition module, and a main control unit; A dual-channel communication subsystem connected to the front-end sensing and control subsystem, wherein the dual-channel communication subsystem is integrated into the main control unit; The cloud platform, which is connected to the front-end sensing and control subsystem through the dual-path communication subsystem, includes a test section model calibration module, a construction section real-time analysis module, an early warning module, and a quality report generation module. An interactive terminal connected to the cloud platform and the front-end perception and control subsystem; in: The vibration sensing device is configured to be fixed to the central shaft of the vibrating wheel of the compaction machine and to collect the amplitude and frequency data of the compaction machine. The GNSS positioning module is configured to be installed on the top of the driver's cab of the compaction machinery to acquire real-time coordinates, driving speed and compaction trajectory; The environmental parameter acquisition module is configured to integrate an IoT sensor for collecting environmental data, including paving temperature and compaction temperature. The main control unit is configured to connect to the data interface of the vibration sensing device, the GNSS positioning module and the environmental parameter acquisition module, and to perform structured integration of amplitude, frequency data, position trajectory and environmental parameters to generate structured data groups. The dual-channel communication subsystem is configured to transmit data via a GPRS network and a data radio, wherein each data segment is equipped with a timestamp and a checksum; when the network is interrupted, the main control unit locally caches the data and resumes transmission from the point of interruption after the network is restored; The test section model calibration module is configured to: receive test section data uploaded by the front-end sensing and control subsystem, and use Pearson correlation analysis to calculate the correlation coefficient r between the vibration response value and the actual compaction degree. If r ≥ 0.7, then establish a correlation model between the two and determine the target thresholds for compaction degree and uniformity; if r < 0.7, then expand the detection range or reselect the test section and repeat the correlation test until r ≥ 0.

7. The real-time analysis module for the construction section is configured to: preprocess the received structured data set, calculate the real-time compaction degree based on the correlation model, and generate compaction cloud map, pass heat map and velocity distribution map; simultaneously calculate the standard deviation of compaction parameters at 10 adjacent points to determine the compaction uniformity. The early warning module is configured to trigger an early warning and associate engineering treatment measures if the compaction degree or compaction uniformity does not meet the target threshold. The quality report generation module is configured to: compile a compaction quality report after continuous compaction construction of the construction section. The compaction quality report includes the parameters of the test section correlation model, the key compaction indicators of the construction section, the comparison table of routine quality inspection results, and targeted improvement suggestions. The interactive terminal is configured to display in real time the vibration sensing device, the GNSS positioning module, the environmental parameter acquisition module, the early warning information of the early warning module, the comparison table of routine quality inspection results, and the targeted improvement suggestions.

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