A method and system for rolling data fusion and compaction quality analysis

Abstract

This invention relates to the field of construction status assessment technology, specifically a method and system for fusion of compaction data and analysis of compaction quality. This invention accurately captures the soil impact response at the moment of maximum excitation energy by monitoring the rotation phase of the vibrating wheel. It utilizes a time-adaptive weighted information entropy calculation method to deeply analyze the micro-fluctuation patterns of the acceleration sequence, quantitatively assessing the orderliness and stability of soil particle arrangement. This allows for the precise elimination of unstable areas in a falsely solidified or loose state. Simultaneously, it combines historical rebound data with second-order difference operations to construct a compaction growth acceleration sequence, keenly capturing polarity reversals or abrupt changes in soil strength growth trends. It provides real-time warnings of the risk of shear failure induced by excessive compaction, achieving a leap from a single strength index to a multi-dimensional coupled analysis of structural stability and evolution trends. This ensures accurate identification of the true and false compaction quality under various geological conditions, effectively avoiding potential structural hazards and improving the level of refined management in dam filling operations.

Description

Technical Field

[0001] This invention relates to the field of construction status assessment technology, and in particular to a method and system for fusion of compaction data and analysis of compaction quality. Background Technology

[0002] The field of construction status assessment technology refers to a set of technologies related to the acquisition, identification, judgment, and description of various operational states during engineering construction. This includes the collection of operating parameters of construction equipment, the characterization of operational process states, the establishment of the correspondence between construction behavior and the state of the engineering entity, and the comprehensive analysis of construction process information.

[0003] Among them, the compaction data fusion and compaction quality analysis method refers to the process of controlling the compaction quality of embankment filling. It involves taking into account the multi-source data generated during compaction construction, such as the walking trajectory of compaction machinery, compaction speed, number of compaction cycles, vibration amplitude, and thickness of the filling layer. By aligning and matching various types of data in time and space, a comprehensive data set is formed within the compaction operation area. The compaction status is then analyzed and judged based on single or combined indicators.

[0004] Existing technologies often rely on macroscopic statistical indicators such as the number of compaction passes or vibration amplitude for quality assessment. In data processing, they only form a comprehensive dataset through simple time alignment and spatial matching, ignoring the dynamic evolution characteristics of the microscopic dynamic response waveform of the soil under continuous high-frequency excitation. They evaluate based solely on the static magnitude of the final rebound value, making it difficult to effectively distinguish between the true tight interlocking between soil particles and the false setting phenomenon caused by the accumulation of pore water pressure. Furthermore, they lack in-depth analysis of the nonlinear changes in the soil strength growth rate during compaction, and cannot identify soil dilatation or shear slip failure caused by excessive compaction based on historical growth trends. This leads to quality misjudgment when facing complex geological conditions, and structural hazards may still exist in areas that have been accepted, making it difficult to ensure the overall compaction uniformity and long-term structural stability of the filling project. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and to propose a method and system for rolling data fusion and compaction quality analysis.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for fusion of rolling data and analysis of compaction quality, comprising the following steps: S1: Monitor the rotation angle of the vibratory roller during the operation of the vibratory roller, trigger the rebound hammer to collect the soil impact response acceleration value of each compaction point according to the rotation angle, calculate the corresponding rebound strength value, and obtain the positioning coordinates of each compaction point to obtain the basic data of the compaction point; S2: Based on the soil impact response acceleration value sequence of all compaction points in the basic data of the compaction points, convert it into a symbol combination representing the fluctuation mode of the soil impact response acceleration value sequence, and classify the soil structure state identifier. S3: Collect the historical rebound strength value of the positioning coordinates of each compaction point in the basic data of the compaction points, compare it with the rebound strength value of the current corresponding compaction point, and generate a soil shear failure early warning signal; S4: Compare the rebound strength value of each compaction point in the basic data of the compaction points with the preset rebound value and compaction degree quantitative correlation table, calculate the soil compaction degree percentage value of each compaction point, combine the soil structure state identifier and soil shear failure early warning signal, and construct a compaction quality feature set. S5: Map the compaction quality feature set to the corresponding grid of the preset electronic map, set the highlight display command, and draw the compaction quality heat map.

[0007] The present invention is improved in that the basic data of the compaction points includes the positioning coordinates of each compaction point and the rebound strength value of each compaction point; the soil structure state identifier includes an abnormal state identifier representing that the soil particles are in a loose or pseudo-coagulated state, and a normal state identifier representing that the soil is in a stable and dense state; the soil shear failure early warning signal is specifically generated by detecting the sign polarity change and absolute value of the difference value arranged in the last position in the second-order difference sequence; the compaction quality feature set includes the soil compaction percentage value, soil structure state identifier, and soil shear failure early warning signal of each compaction point; and the compaction quality heat map includes a highlighting instruction for the grid area containing the soil shear failure early warning signal, a soil structure anomaly prompt instruction for the grid area where the soil structure state identifier is an abnormal state identifier, and a compaction quality heat map drawing instruction based on the soil compaction percentage value.

[0008] The present invention is improved in that step S1 is specifically as follows: S101: Monitor the rotation angle of the vibratory roller during the operation of the vibratory roller, match the rotation angle with the preset ground contact angle threshold, and trigger the rebound meter when the match is consistent. Collect the soil impact response acceleration value sequence at each compaction point, extract the acceleration value with the largest amplitude from the soil impact response acceleration value sequence, and obtain the peak value of soil impact response acceleration. S102: Select the benchmark acceleration value with the smallest difference from the peak value of the soil impact response acceleration in the preset rebound modulus calibration reference table, extract the calibration rebound modulus value corresponding to the benchmark acceleration value as the rebound strength value of each compaction point, and use the GPS receiver to extract the positioning coordinates of each compaction point from the satellite positioning signal. S103: Using the positioning coordinates of each compaction point as an index reference for spatial location, the rebound strength value and the soil impact response acceleration value sequence are associated with the positioning coordinates of the compaction point to generate basic compaction point data.

[0009] The present invention is improved in that step S2 is specifically as follows: S201: Divide the soil impact response acceleration value sequence in the basic data of the compaction point into multiple continuous data segments according to the set time window, calculate the average value of the acceleration value in each data segment, reorganize the average value into an average acceleration value sequence according to the time order, extract the order of adjacent values ​​in the average acceleration value sequence, and convert it into a combination of permutation symbols representing the fluctuation mode. S202: Statistically calculate the frequency proportion of the permutation symbol combination in the average acceleration value sequence, construct the frequency distribution probability based on the frequency proportion, calculate the information entropy of the frequency distribution probability, obtain the permutation entropy value that characterizes the fluctuation of the average acceleration value sequence, and perform weighted summation calculation on the permutation entropy values ​​at multiple times based on preset weight coefficients to obtain the stability evaluation value. S203: Compare the stability evaluation value with a preset discrete threshold. If the stability evaluation value is greater than the discrete threshold, an abnormal state identifier is generated indicating that the soil particles are in a loose or pseudo-coagulated state. If the stability evaluation value is less than or equal to the discrete threshold, a normal state identifier is generated indicating that the soil is in a stable and dense state, thus obtaining a soil structure state identifier.

[0010] The present invention is improved in that step S3 is specifically as follows: S301: Based on the rolling point location coordinates in the rolling point basic data, retrieve the historical rebound strength values ​​of all rolling passes, sort the historical rebound strength values ​​and the rebound strength values ​​in the rolling point basic data according to the rolling time sequence, and construct a rebound growth trend sequence. S302: Perform differential calculation on the rebound strength values ​​corresponding to adjacent rolling passes in the rebound growth trend sequence to obtain the first-order differential value representing the compaction growth rate. Perform secondary differential calculation on adjacent values ​​in the first-order differential value sequence to obtain the second-order differential value representing the change in compaction growth acceleration, and form a second-order differential sequence. S303: Detect the sign polarity change and absolute value of the last difference value in the second-order difference sequence to determine whether the soil structure has undergone shear failure and generate a soil shear failure early warning signal.

[0011] The present invention is improved in that step S4 is specifically as follows: S401: Call the preset quantitative correlation table between rebound value and compaction degree, compare the rebound strength value in the basic data of the rolling point with the calibrated rebound strength value in the quantitative correlation table of compaction degree, and determine the numerical mapping range of the rebound strength value in the quantitative correlation table of rebound value and compaction degree. S402: Perform linear interpolation calculation based on the relative position ratio of the rebound strength value within the numerical mapping interval to obtain the compaction value under the corresponding ratio, and use the compaction value as the soil compaction percentage value for each compaction point. S403: Integrate the soil compaction percentage value, the soil structure status indicator, and the soil shear failure early warning signal, and fuse and package the multi-dimensional compaction quality parameters to generate a compaction quality feature set.

[0012] The present invention is improved in that step S5 is specifically as follows: S501: Call the compaction point positioning coordinates in the compaction point basic data, map the compaction quality feature set to the corresponding grid coordinate position of the preset electronic map, and generate electronic map grid data; S502: Analyze the electronic map grid data, determine whether each grid contains the soil shear failure early warning signal and the type of the soil structure status identifier, set a highlight display instruction for grids containing soil shear failure early warning signals, set an abnormal prompt instruction for grids that do not contain soil shear failure early warning signals and whose soil structure status identifier is an abnormal status identifier, and generate a grid display instruction set; S503: For grids that do not contain the soil shear failure warning signal and whose soil structure status is marked as normal, generate multiple rendering instructions with varying shades of color according to the value of the soil compaction percentage. Combine the grid display instruction set to draw a visualization layer on the electronic map and generate a compaction quality heat map.

[0013] A rolling data fusion and compaction quality analysis system, the system comprising: The compaction point data acquisition module monitors the rotation angle of the vibratory roller during the operation of the vibratory roller, triggers the rebound hammer to collect the soil impact response acceleration value of each compaction point based on the rotation angle, calculates the corresponding rebound strength value, and obtains the positioning coordinates of each compaction point to obtain the basic data of the compaction point. The soil structure state identification module converts the soil impact response acceleration value sequence of all compaction points in the basic data of compaction points into a symbol combination representing the fluctuation mode of the soil impact response acceleration value sequence, and classifies the soil structure state identifier. The shear failure early warning module collects the historical rebound strength value of the positioning coordinates of each compaction point in the basic data of the compaction points, compares it with the rebound strength value of the current corresponding compaction point, and generates a soil shear failure early warning signal. The compaction quality assessment module compares the rebound strength value of each compaction point in the basic data of the compaction points with the preset rebound value and compaction degree quantitative correlation table, calculates the soil compaction degree percentage value of each compaction point, combines the soil structure state identifier and soil shear failure early warning signal, and constructs a compaction quality feature set. The compaction quality visualization module maps the compaction quality feature set to the corresponding grid of a preset electronic map, sets a highlight display command, and draws a compaction quality heat map.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, the soil impact response at the moment of maximum excitation energy is accurately captured by monitoring the rotation phase of the vibrating wheel. The micro-fluctuation mode of the acceleration sequence is deeply analyzed by using the time-adaptive weighted information entropy calculation method, and the orderliness and stability of soil particle arrangement are quantitatively evaluated. This allows for the precise elimination of unstable areas in a falsely solidified or loose state. At the same time, the second-order difference operation of historical rebound data is combined to construct a compaction growth acceleration sequence, which can keenly capture the polarity reversal or abrupt change characteristics in the soil strength growth trend. It provides real-time warning of the risk of shear failure induced by excessive compaction, realizing a leap from a single strength index to a multi-dimensional coupled analysis of structural stability and evolution trend. This ensures that the authenticity of compaction quality can be accurately identified under various geological conditions, effectively avoiding potential structural hazards and improving the level of refined management of dam filling operations. Attached Figure Description

[0015] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a detailed flowchart of step S1 of the present invention; Figure 3 This is a detailed flowchart of step S2 of the present invention; Figure 4 This is a detailed flowchart of step S3 of the present invention; Figure 5 This is a detailed flowchart of step S4 of the present invention; Figure 6 This is a detailed flowchart of step S5 of the present invention; Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] Please see Figure 1 This invention provides a technical solution: a method for fusion of rolling data and analysis of compaction quality, comprising the following steps: S1: Monitor the rotation angle of the vibratory roller during the operation of the vibratory roller, trigger the rebound hammer to collect the soil impact response acceleration value of each compaction point according to the rotation angle, calculate the corresponding rebound strength value, and obtain the positioning coordinates of each compaction point to obtain the basic data of the compaction point; S2: Based on the soil impact response acceleration value sequence of all compaction points in the basic data of compaction points, convert it into a symbol combination representing the fluctuation mode of the soil impact response acceleration value sequence, and classify the soil structure state identifier. S3: Collect the historical rebound strength value of each compaction point's location coordinate in the basic data of compaction points, compare it with the rebound strength value of the current corresponding compaction point, and generate a soil shear failure early warning signal; S4: Compare the rebound strength value of each compaction point in the basic data of compaction points with the preset rebound value and compaction degree quantitative correlation table, calculate the soil compaction degree percentage value of each compaction point, combine the soil structure status identifier and soil shear failure early warning signal, and construct a compaction quality feature set. S5: Map the compaction quality feature set to the corresponding grid of the preset electronic map, set the highlight display command, and draw the compaction quality heat map.

[0018] The basic data for compaction points includes the location coordinates of each compaction point and the rebound strength value of each compaction point. The soil structure status identifiers include anomaly status identifiers representing soil particles in a loose or pseudo-coagulated state and normal status identifiers representing soil in a stable and dense state. The soil shear failure early warning signal is specifically generated by detecting the sign polarity change and absolute value of the last difference value in the second-order difference sequence. The compaction quality feature set includes the soil compaction percentage value, soil structure status identifiers, and soil shear failure early warning signal for each compaction point. The compaction quality heatmap includes instructions for highlighting grid areas containing soil shear failure early warning signals, instructions for soil structure anomaly prompts for grid areas where the soil structure status identifier is an anomaly status identifier, and instructions for drawing the compaction quality heatmap based on the soil compaction percentage value.

[0019] Please see Figure 2 Step S1 is as follows: S101: Monitor the rotation angle of the vibratory roller during the operation of the vibratory roller, match the rotation angle with the preset ground contact angle threshold, and trigger the rebound meter when the match is consistent. Collect the soil impact response acceleration value sequence at each compaction point, extract the acceleration value with the largest amplitude from the soil impact response acceleration value sequence, and obtain the peak value of soil impact response acceleration. In the initial monitoring phase of vibratory compaction operations in the dam filling area, the physical state capture process is initiated first, monitoring the rotational motion of the vibratory roller in real time. This process utilizes a high-frequency sensor installed at the axle end to continuously read the rotation angle value and compare the real-time reading angle with a preset ground contact angle threshold. This ground contact angle threshold is determined based on the geometric kinematic model of the eccentric block of the vibratory roller, and the determination process involves the following operations: First, a two-dimensional coordinate system for the rotational motion of the eccentric block is established, the mass of the eccentric block is set as the physical parameter input, and the magnitude of the centrifugal force vector generated by the eccentric block is calculated in conjunction with the rated rotational speed of the vibratory roller; then, trigonometric function decomposition logic is used to calculate the component value of the centrifugal force vector in the vertically downward direction; by simulating degree by degree within the range of 0 degrees to 360 degrees, the phase angle corresponding to the maximum value of the vertical component (e.g., 180 degrees) is identified, and this angle is fixed as the ground contact angle threshold to ensure that the acquisition timing corresponds to the moment when the excitation force has the strongest effect on the dam soil. When the real-time monitored rotation angle perfectly matches this threshold, the rebound hammer is immediately triggered to start the acquisition command. The data acquisition process continuously records the sequence of soil impact response acceleration values ​​at each compaction point, and the sequence is traversed using a peak extraction algorithm. For example, in a compaction of an impermeable clay layer, the acquired sequence contains multiple discrete sampling points. After comparing the values ​​one by one, the maximum amplitude in the sequence is identified as 15.6 gravitational acceleration, which is then locked as the peak value of the soil impact response acceleration at the current compaction point.

[0020] S102: Select the benchmark acceleration value with the smallest difference from the peak value of soil impact response acceleration in the preset resilient modulus calibration reference table, extract the calibration resilient modulus value corresponding to the benchmark acceleration value as the resilient strength value of each compaction point, and use the GPS receiver to extract the positioning coordinates of each compaction point from the satellite positioning signal. After obtaining the peak acceleration, the screening process calls a pre-set resilient modulus calibration reference table. This reference table is constructed based on data from field test sections during the pre-construction phase. The construction process involves the following steps: Test areas with different compaction levels (e.g., 10 areas) are selected within the test section. Static plate load tests (PLT) and dynamic acceleration data acquisition are simultaneously performed in each area. The standard resilient modulus value is calculated from the PS curve (pressure-settlement curve) of the plate load test, and the corresponding peak acceleration value is extracted. 100 sets of "acceleration-resilient modulus" data pairs are obtained. The acceleration values ​​are divided into intervals according to a fixed step size (e.g., 0.5 times the gravitational acceleration), and the arithmetic mean of all resilient modulus data within each interval is calculated. This average value is used as the calibration value for that interval and stored in the table. During the screening process, the minimum absolute difference method is used to calculate the absolute value of the difference between the currently measured peak soil impact response acceleration and the benchmark acceleration values ​​in the table. For example, the reference table contains two sets of baseline data: 15.0 gravitational acceleration corresponds to 50 MPa, and 16.0 gravitational acceleration corresponds to 60 MPa. For the measured value of 15.6 gravitational acceleration, calculate the absolute value of the difference between it and 15.0: Calculate the absolute value of the difference between it and 16.0: .because The process determines that the difference between the gravitational acceleration of 15.6 and the baseline value of 16.0 is the smallest, and then extracts the corresponding 60 MPa as the rebound strength value of the current compaction point. At the same time, the positioning and analysis process uses a GPS receiver to solve the satellite positioning signal and extract the high-precision longitude (e.g., 116.354 degrees) and latitude (e.g., 39.905 degrees) coordinates of the compaction point.

[0021] S103: Using the location coordinates of each compaction point as the index reference for spatial location, the rebound strength value and soil impact response acceleration value sequence are associated with the location coordinates of the compaction point to generate basic data of the compaction point; The data association process uses the extracted compaction point coordinates as the core index key to construct a spatial database record. This process structurally binds the rebound strength value (60 MPa) determined in step S102, the sequence of raw soil impact response acceleration values ​​collected in step S101, and their corresponding spatial coordinates. Through this association, each discrete compaction point is no longer an isolated data silo, but rather a physical entity with precise spatial attributes, ultimately generating basic compaction point data containing both mechanical and spatial attributes. Please see Figure 3 Step S2 is as follows: S201: Divide the soil impact response acceleration value sequence in the basic data of the compaction point into multiple continuous data segments according to the set time window, calculate the average value of the acceleration value in each data segment, reorganize the average value into an average acceleration value sequence according to the time order, extract the order of the size of adjacent values ​​in the average acceleration value sequence, and convert it into a combination of permutation symbols representing the fluctuation mode. To assess the dynamic stability of soil structure, the preprocessing process first slices the soil impact response acceleration value sequence from the compaction point baseline data along the time dimension. Based on the natural vibration frequency of the vibrating wheel (e.g., 30 Hz), the single vibration period is calculated, and the time window length is set to an integer multiple of the period (e.g., 0.1 seconds), dividing the long sequence into multiple continuous and non-overlapping data segments. For each independent data segment, the arithmetic mean of all sampling points within it is calculated, and these averages are recombined in chronological order to generate an average acceleration value sequence. Subsequently, the process performs symbolic transformation on this sequence, extracting the magnitude arrangement relationship between adjacent values ​​and converting it into a combination of symbols representing the fluctuation pattern. For example, for the sequence segment [10.2, 12.5, 11.1], its magnitude relationship is identified as "small-large-medium," mapped to the preset symbol code "0-2-1." This step aims to filter out high-frequency noise interference and preserve the macroscopic fluctuation characteristics of the soil's structural response under continuous impact.

[0022] S202: Statistically calculate the frequency proportion of the combination of symbols in the average acceleration value sequence, construct the frequency distribution probability based on the frequency proportion, calculate the information entropy of the frequency distribution probability, obtain the permutation entropy value that characterizes the fluctuation of the average acceleration value sequence, and calculate the weighted sum of the permutation entropy values ​​at multiple time points based on the preset weight coefficients to obtain the stability evaluation value. The preset weighting coefficients are positively correlated with the time order of each average acceleration value in the average acceleration value sequence. In the average acceleration value sequence, the later the time order, that is, the closer the corresponding time is to the current time, the larger the corresponding weighting coefficient is. After symbolization, the statistical process calculates the frequency proportion of each permutation symbol combination in the sequence, constructs the frequency distribution probability, and calculates the permutation entropy value accordingly. To accurately quantify the dynamic stability of the compaction process, a time-adaptive weighted information entropy fusion algorithm is introduced. This algorithm is based on rheological principles and considers the thixotropic recovery characteristics of soil under continuous excitation. It assumes that the soil state closer to the current moment contributes more to the final stability, therefore, an exponential decay mechanism is used to weight the permutation entropy values ​​at different time points. The formula for calculating the stability evaluation value is as follows: In the formula, This represents the final calculated stability evaluation value (dimension 1). This is a discrete correction factor (dimension 1). The parameter is set based on multiple (e.g., 50) stability tests of dam soil under standard compaction conditions, calculating the arithmetic mean of these test values, and taking the reciprocal of this mean as the correction benchmark. Here it is set to 1.0. The total number of data segments included in the calculation (dimension 1) is set to 3 based on the time window length of real-time monitoring. The time series index (dimension 1) in the outer summation operation represents the sequence number of the specific time segment currently undergoing weighted calculation, with a value ranging from 1 to... ; The traversal index (dimension 1) used in the inner denominator calculation represents the sequence number used when summing all time segments to calculate the denominator for the normalized weights; its value range is also from 1 to... ; It is a natural constant; The time-focusing coefficient (dimension 1) is determined based on the experimental measurement of the rate at which soil viscosity recovers over time. The larger the value, the faster the weight decays over time, meaning the model assigns a greater weight to the latest data. Here, it is set to 0.5. For the first The permutation entropy value (dimension 1) calculated from each data segment.

[0023] Suppose we measure the permutation entropy values ​​of three consecutive segments. The values ​​are 0.5, 0.6, and 0.8 respectively. The calculation process is as follows: First, calculate the non-normalized weights of each node: hour, ; hour, ; hour, Calculate the weighted normalized denominator: Substitute the values ​​into the formula to sum: The final stability evaluation value was calculated to be 0.683.

[0024] S203: Compare the stability evaluation value with the preset discrete threshold. If the stability evaluation value is greater than the discrete threshold, an abnormal state label is generated indicating that the soil particles are in a loose or pseudo-coagulated state. If the stability evaluation value is less than or equal to the discrete threshold, a normal state label is generated indicating that the soil is in a stable and dense state, thus obtaining the soil structure state label. The judgment process compares the calculated stability evaluation value with a preset discrete threshold. This discrete threshold is determined by conducting full-coverage monitoring of the accepted dam area, collecting a massive number of stability evaluation value samples, constructing a probability density function distribution, and selecting the 95th percentile of this distribution as the critical threshold (e.g., 0.85), representing the maximum allowable fluctuation under normal compaction conditions. The judgment logic includes two possibilities: First, if... This indicates drastic data fluctuations and unstable contact between soil particles, generating an "abnormal state marker" that suggests the soil may be in a loose or pseudo-coagulated state; the second type, if This indicates that the data fluctuations have converged and the structure has stabilized, and the process generates a "normal state identifier". In this example, the calculated stability evaluation value of 0.683 is less than the discrete threshold of 0.85, so the determination process determines that the soil in the current area is in a stable and compacted state and outputs a normal state identifier.

[0025] Please see Figure 4 Step S3 is as follows: S301: Based on the rolling point location coordinates in the rolling point base data, retrieve the historical rebound strength values ​​of all rolling passes, sort the historical rebound strength values ​​and the rebound strength values ​​in the rolling point base data according to the rolling time sequence, and construct a rebound growth trend sequence. First, based on the current compaction point's coordinates, the rebound strength records for that location in all previous compaction passes are retrieved from the historical database. After the retrieval, the historical data is sorted according to the chronological order of compaction occurrence, and the rebound strength value (60 MPa) measured in the current 5th pass is combined to construct a rebound growth trend sequence. For example, if the retrieved data from the previous four passes are 40 MPa, 50 MPa, 56 MPa, and 58 MPa, combined with the current 60 MPa, the ordered sequence is: 40, 50, 56, 58, 60. This sequence visually reflects the physical process of soil strength gradually increasing with the accumulation of compaction work.

[0026] S302: Perform differential calculation on the rebound strength values ​​corresponding to adjacent compaction passes in the rebound growth trend sequence to obtain the first-order differential value representing the compaction growth rate. Perform second-order differential calculation on adjacent values ​​in the first-order differential value sequence to obtain the second-order differential value representing the change in compaction growth acceleration, and form a second-order differential sequence. To quantify the changing patterns of compaction effects, the calculation process performs a first-order difference operation on the rebound growth trend sequence. The specific calculation is as follows: , , , This yields a first-order difference sequence [10, 6, 2, 2], representing the compaction increments for each pass. Subsequently, the process performs a second-order difference operation on this first-order difference sequence, calculating the rate of change between adjacent increments. The calculation process is as follows: , , The second-order difference sequence [-4, -4, 0] is obtained. This sequence reveals the acceleration of the compaction growth rate. Negative values ​​usually indicate that the compaction efficiency naturally decreases with the number of passes, which is consistent with the compaction law.

[0027] S303: Detects the sign polarity change and absolute value of the last difference value in the second-order difference sequence to determine whether the soil structure has undergone shear failure and generates a soil shear failure early warning signal. The process of determining whether a soil structure has experienced shear failure is as follows: Extract the difference value of the last element in chronological order from the second-order difference sequence; Identify the sign polarity of the last difference value and calculate its absolute value. Call the preset mutation threshold that characterizes acceleration anomalies, and compare the absolute value of the last difference value with the preset mutation threshold. Determine whether the sign polarity of the last difference value exhibits a polarity reversal phenomenon, shifting from the negative value region to the positive value region; When the monitoring results show that the sign polarity of the last difference value changes from negative to positive, or the absolute value is greater than the preset mutation threshold, it is determined that the soil in the current compaction area can no longer withstand the compaction work, and the soil structure is judged to have undergone shear failure. The shear failure discrimination process focuses on the last value in the second-order difference sequence (0 in this example). The judgment logic aims to identify whether soil structural failure (dilatation or shear slip) is caused by excessive compaction. The process calls a pre-set abrupt change threshold (e.g., 5.0), which is determined by conducting numerous triaxial shear tests on soil in a laboratory, recording stress-strain data at the moment of shear failure, calculating the peak values ​​of the second derivatives, and taking the minimum of these peak values ​​as the safety threshold. The judgment includes two parallel conditions: first, checking whether the sign polarity of the last difference value has changed from negative to positive (i.e., a "bounce"), and second, checking whether its absolute value exceeds the abrupt change threshold. In this example, the sign of the last value 0 did not reverse from negative to positive (remaining in the non-positive range), and its absolute value... The value is less than the mutation threshold of 5.0. Based on these two judgments, the process determines that the soil structure in the current compaction area has not undergone shear failure, and the soil is still in the normal compaction and hardening stage. Therefore, no warning signal is generated.

[0028] Please see Figure 5 Step S4 is as follows: S401: Call the preset quantitative correlation table between rebound value and compaction degree, compare the rebound strength value in the basic data of the rolling point with the calibrated rebound strength value in the quantitative correlation table of compaction degree, and determine the numerical mapping range of the rebound strength value in the quantitative correlation table of rebound value and compaction degree. To achieve accurate quantification of compaction degree, the calculation process calls a pre-set correlation table between rebound value and compaction degree. This table is based on standard compaction tests, and its construction process is as follows: typical soil samples used for dam filling are collected, samples with different moisture contents are prepared in the laboratory, and compaction tests are conducted to determine the maximum dry density and calculate the corresponding compaction degree; simultaneously, the rebound modulus is measured in the compaction mold using a micro-penetrator, thereby establishing a mapping relationship between "rebound modulus-compaction degree". The data in the table are set as follows: 50 MPa corresponds to 90% compaction degree, and 70 MPa corresponds to 96% compaction degree. The process takes the measured rebound strength value of 60 MPa obtained in step S1 as input and compares and searches in the correlation table. Through comparison, it is found that 60 MPa is between 50 MPa and 70 MPa, therefore, it is determined that the measured value falls within the numerical mapping range of [50, 70].

[0029] S402: Perform linear interpolation calculation based on the relative position ratio of the rebound strength value within the numerical mapping interval to obtain the compaction value under the corresponding ratio, and use the compaction value as the soil compaction percentage value for each rolling point. After determining the mapping interval, the calculation process executes a linear interpolation algorithm to obtain the accurate percentage of compaction. The algorithm logic is based on the relative proportion of the measured value within the interval. First, the relative proportion of the measured value to the lower limit of the interval is calculated: Next, calculate the compaction span value corresponding to the interval: Finally, the span value is multiplied by the relative proportion and accumulated to the lower limit compaction benchmark value of the interval. The calculation process is as follows: This means that the calculated soil compaction percentage at the current compaction point is 93%.

[0030] S403: Integrates soil compaction percentage values, soil structure status indicators, and soil shear failure early warning signals, and fuses and packages multi-dimensional compaction quality parameters to generate a compaction quality feature set; The calculated soil compaction percentage (93%), the soil structure status indicator (normal status indicator) generated in step S2, and the result determined in step S3 (no shear failure warning signal) are comprehensively packaged. This step integrates a single-dimensional physical quantity into a multi-dimensional compaction quality feature set that includes quality grade, structural stability, and risk warning. This feature set, as the final analysis product, forms comprehensive and standardized data support.

[0031] Please see Figure 6 Step S5 is as follows: S501: Call the compaction point positioning coordinates in the compaction point basic data, map the compaction quality feature set to the corresponding grid coordinate position of the preset electronic map, and generate electronic map grid data; The positioning coordinates from the compaction point baseline data are retrieved and projected onto a pre-defined electronic map of the embankment. The electronic map is divided into a high-resolution grid matrix. The grid size is determined through the following calculation process: First, the physical width of the vibratory roller (e.g., 2.3 meters) and the minimum wheel track overlap width mandated by construction specifications (e.g., 0.3 meters) are obtained; then, a subtraction operation is used to obtain the effective net width for a single compaction pass. Meters; to ensure that each grid is completely covered by a single compaction trajectory and has sufficient spatial resolution, the effective net width is divided by a resolution factor (e.g., 2.0) to obtain the calculation result. The grid size is determined to be 1 meter by 1 meter. The process uses a coordinate transformation algorithm to map each specific latitude and longitude coordinate to the corresponding grid index, and fills the data attributes of the grid with the compaction quality feature set generated in step S403, thereby generating electronic map grid data containing rich construction information.

[0032] S502: Analyze the electronic map grid data, determine whether each grid contains soil shear failure warning signals and the type of soil structure status identifier, set a highlighting instruction for grids containing soil shear failure warning signals, set an abnormal prompt instruction for grids that do not contain soil shear failure warning signals and whose soil structure status identifier is an abnormal status identifier, and generate a grid display instruction set. The electronic map grid data is scanned one by one, and the display strategy for each grid is determined based on logical priority. The logical judgment includes the following possibilities: First, it checks whether the grid attributes contain a "soil shear failure early warning signal." If this signal is present, it is classified as the first scenario, triggering the highest priority "highlight display instruction" (such as red flashing) to warn of soil failure. If no early warning signal is present, it proceeds to the second level of judgment, checking whether the soil structure status indicator is an "abnormal status indicator." If so, it is classified as the second scenario, triggering an "abnormal prompt instruction" (such as yellow fill) to indicate insufficient stability. If neither of these negative scenarios occurs, it is classified as the third scenario, entering the color rendering process based on compaction degree. Through this filtering mechanism, a grid display instruction set covering the entire map is generated.

[0033] S503: For grids that do not contain soil shear failure warning signals and whose soil structure status is marked as normal, multiple rendering instructions with varying shades of color are generated according to the size of the soil compaction percentage value. Combined with the grid display instruction set, a visualization layer is drawn on the electronic map to generate a compaction quality heat map. For mesh regions classified as falling into the third category (i.e., possessing normal structural condition indicators and lacking shear failure warning signals), the process performs graded thermal rendering based on compaction degree values. This step first loads the chromatographic mapping rules stored in the system configuration file, which defines the logical correspondence between compaction degree value ranges and visualization colors. The boundary thresholds of 93% and 96% set in the rules are directly extracted from the dam filling engineering construction quality acceptance specification document, where 93% is defined as the lower limit critical value of qualified compaction degree, and 96% is defined as the boundary line of excellent compaction degree. The rendering and parsing process uses the compaction percentage calculated in step S402 as an input variable, and sequentially iterates through the following three mutually exclusive numerical judgment logics: First possibility: If the input compaction percentage is less than 93% (e.g., 91%), this value physically indicates that although the soil structure is stable, its dry density has not yet reached the minimum acceptance standard, indicating an under-compacted state. The process maps this to the "light green" category (e.g., RGB color value #90EE90) and generates the corresponding low-saturation color fill instruction; Second possibility: If the input compaction percentage is between 93% and 96%... Within a closed interval of 94% (e.g., 94%), this value indicates that the soil compaction quality meets the basic design requirements and is in a qualified state. The process maps it to a "medium green" category (e.g., RGB color value #3CB371) and generates a corresponding medium saturation color fill instruction. The third possibility is that if the input compaction value is strictly greater than 96% (e.g., 97%), this value indicates that the soil has reached an extremely high degree of density and is in an excellent state. The process maps it to a "dark green" category (e.g., RGB color value #006400) and generates a corresponding high saturation color fill instruction. In this example, since the calculated compaction value is exactly 93%, the numerical comparison results show... and Therefore, it was determined that the value precisely fell within the defined range of the second possibility. Based on this, the process ultimately determined to output a "medium green" rendering instruction. The drawing engine then responded by performing a pixel fill operation at the corresponding grid coordinate position on the electronic map, rendering the grid as medium green, thereby updating the mass status of that point in the macroscopic compaction mass heat map.

[0034] Please see Figure 7 A rolling data fusion and compaction quality analysis system, the system comprising: The compaction point data acquisition module monitors the rotation angle of the vibratory roller during the operation of the vibratory roller, triggers the rebound hammer to collect the soil impact response acceleration value of each compaction point based on the rotation angle, calculates the corresponding rebound strength value, and obtains the positioning coordinates of each compaction point to obtain the basic data of the compaction point. The soil structure state identification module converts the soil impact response acceleration value sequence of all compaction points in the basic data of compaction points into a symbol combination representing the fluctuation mode of the soil impact response acceleration value sequence, and classifies the soil structure state identifier. The shear failure early warning module collects the historical rebound strength value of the location coordinates of each compaction point in the basic data of compaction points, compares it with the rebound strength value of the current corresponding compaction point, and generates a soil shear failure early warning signal. The compaction quality assessment module compares the rebound strength value of each compaction point in the basic data of compaction points with the preset rebound value and compaction degree quantitative correlation table, calculates the soil compaction degree percentage value of each compaction point, combines soil structure status identifier and soil shear failure early warning signal, and constructs a compaction quality feature set. The compaction quality visualization module maps the compaction quality feature set to the corresponding grid in the preset electronic map, sets the highlight display command, and draws a compaction quality heat map.

[0035] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for fusion of rolling data and analysis of compaction quality, characterized in that, Includes the following steps: S1: Monitor the rotation angle of the vibratory roller during the operation of the vibratory roller, trigger the rebound hammer to collect the soil impact response acceleration value of each compaction point according to the rotation angle, calculate the corresponding rebound strength value, and obtain the positioning coordinates of each compaction point to obtain the basic data of the compaction point; S2: Based on the soil impact response acceleration value sequence of all compaction points in the basic data of the compaction points, convert it into a symbol combination representing the fluctuation mode of the soil impact response acceleration value sequence, and classify the soil structure state identifier. S3: Collect the historical rebound strength value of the positioning coordinates of each compaction point in the basic data of the compaction points, compare it with the rebound strength value of the current corresponding compaction point, and generate a soil shear failure early warning signal; S4: Compare the rebound strength value of each compaction point in the basic data of the compaction points with the preset rebound value and compaction degree quantitative correlation table, calculate the soil compaction degree percentage value of each compaction point, combine the soil structure state identifier and soil shear failure early warning signal, and construct a compaction quality feature set. S5: Map the compaction quality feature set to the corresponding grid of the preset electronic map, set the highlight display command, and draw the compaction quality heat map.

2. The method for fusion of compaction data and analysis of compaction quality according to claim 1, characterized in that, The basic data of the compaction points includes the location coordinates of each compaction point and the rebound strength value of each compaction point. The soil structure state identifier includes an abnormal state identifier representing that the soil particles are in a loose or pseudo-coagulated state, and a normal state identifier representing that the soil is in a stable and dense state. The soil shear failure early warning signal is specifically generated by detecting the sign polarity change and absolute value of the difference value arranged in the last position of the second-order difference sequence. The compaction quality feature set includes the soil compaction percentage value, soil structure state identifier, and soil shear failure early warning signal for each compaction point. The compaction quality heat map includes a highlighting instruction for the grid area containing the soil shear failure early warning signal, a soil structure anomaly prompt instruction for the grid area where the soil structure state identifier is an abnormal state identifier, and a compaction quality heat map drawing instruction based on the soil compaction percentage value.

3. The method for fusion of compaction data and analysis of compaction quality according to claim 1, characterized in that, Step S1 is as follows: S101: Monitor the rotation angle of the vibratory roller during the operation of the vibratory roller, match the rotation angle with the preset ground contact angle threshold, and trigger the rebound meter when the match is consistent. Collect the soil impact response acceleration value sequence at each compaction point, extract the acceleration value with the largest amplitude from the soil impact response acceleration value sequence, and obtain the peak value of soil impact response acceleration. S102: Select the benchmark acceleration value with the smallest difference from the peak value of the soil impact response acceleration in the preset rebound modulus calibration reference table, extract the calibration rebound modulus value corresponding to the benchmark acceleration value as the rebound strength value of each compaction point, and use the GPS receiver to extract the positioning coordinates of each compaction point from the satellite positioning signal. S103: Using the positioning coordinates of each compaction point as an index reference for spatial location, the rebound strength value and the soil impact response acceleration value sequence are associated with the positioning coordinates of the compaction point to generate basic compaction point data.

4. The method for fusion of compaction data and analysis of compaction quality according to claim 1, characterized in that, Step S2 is as follows: S201: Divide the soil impact response acceleration value sequence in the basic data of the compaction point into multiple continuous data segments according to the set time window, calculate the average value of the acceleration value in each data segment, reorganize the average value into an average acceleration value sequence according to the time order, extract the order of adjacent values ​​in the average acceleration value sequence, and convert it into a combination of permutation symbols representing the fluctuation mode. S202: Statistically calculate the frequency proportion of the permutation symbol combination in the average acceleration value sequence, construct the frequency distribution probability based on the frequency proportion, calculate the information entropy of the frequency distribution probability, obtain the permutation entropy value that characterizes the fluctuation of the average acceleration value sequence, and perform weighted summation calculation on the permutation entropy values ​​at multiple times based on preset weight coefficients to obtain the stability evaluation value. S203: Compare the stability evaluation value with a preset discrete threshold. If the stability evaluation value is greater than the discrete threshold, an abnormal state identifier is generated indicating that the soil particles are in a loose or pseudo-coagulated state. If the stability evaluation value is less than or equal to the discrete threshold, a normal state identifier is generated indicating that the soil is in a stable and dense state, thus obtaining a soil structure state identifier.

5. The method for fusion of compaction data and analysis of compaction quality according to claim 1, characterized in that, Step S3 is as follows: S301: Based on the rolling point location coordinates in the rolling point basic data, retrieve the historical rebound strength values ​​of all rolling passes, sort the historical rebound strength values ​​and the rebound strength values ​​in the rolling point basic data according to the rolling time sequence, and construct a rebound growth trend sequence. S302: Perform differential calculation on the rebound strength values ​​corresponding to adjacent rolling passes in the rebound growth trend sequence to obtain the first-order differential value representing the compaction growth rate. Perform secondary differential calculation on adjacent values ​​in the first-order differential value sequence to obtain the second-order differential value representing the change in compaction growth acceleration, and form a second-order differential sequence. S303: Detect the sign polarity change and absolute value of the last difference value in the second-order difference sequence to determine whether the soil structure has undergone shear failure and generate a soil shear failure early warning signal.

6. The method for fusion of compaction data and analysis of compaction quality according to claim 1, characterized in that, Step S4 is as follows: S401: Call the preset quantitative correlation table between rebound value and compaction degree, compare the rebound strength value in the basic data of the rolling point with the calibrated rebound strength value in the quantitative correlation table of compaction degree, and determine the numerical mapping range of the rebound strength value in the quantitative correlation table of rebound value and compaction degree. S402: Perform linear interpolation calculation based on the relative position ratio of the rebound strength value within the numerical mapping interval to obtain the compaction value under the corresponding ratio, and use the compaction value as the soil compaction percentage value for each compaction point. S403: Integrate the soil compaction percentage value, the soil structure status indicator, and the soil shear failure early warning signal, and fuse and package the multi-dimensional compaction quality parameters to generate a compaction quality feature set.

7. The method for fusion of compaction data and analysis of compaction quality according to claim 1, characterized in that, Step S5 is as follows: S501: Call the compaction point positioning coordinates in the compaction point basic data, map the compaction quality feature set to the corresponding grid coordinate position of the preset electronic map, and generate electronic map grid data; S502: Analyze the electronic map grid data, determine whether each grid contains the soil shear failure early warning signal and the type of the soil structure status identifier, set a highlight display instruction for grids containing soil shear failure early warning signals, set an abnormal prompt instruction for grids that do not contain soil shear failure early warning signals and whose soil structure status identifier is an abnormal status identifier, and generate a grid display instruction set; S503: For grids that do not contain the soil shear failure warning signal and whose soil structure status is marked as normal, generate multiple rendering instructions with varying shades of color according to the value of the soil compaction percentage. Combine the grid display instruction set to draw a visualization layer on the electronic map and generate a compaction quality heat map.

8. The method for fusion of compaction data and analysis of compaction quality according to claim 4, characterized in that, The preset weighting coefficient is positively correlated with the time order of each average acceleration value in the average acceleration value sequence. In the average acceleration value sequence, the later the time order, that is, the closer the corresponding time is to the current time, the larger the corresponding weighting coefficient is.

9. The method for fusion of compaction data and analysis of compaction quality according to claim 5, characterized in that, The process for determining whether a soil structure has experienced shear failure is as follows: Extract the difference value of the last element in chronological order from the second-order difference sequence; Identify the sign polarity of the last difference value and calculate its absolute value. Call the preset mutation threshold that characterizes acceleration anomalies, and compare the absolute value of the last difference value with the preset mutation threshold. Determine whether the sign polarity of the last difference value exhibits a polarity reversal phenomenon, shifting from the negative value region to the positive value region; When the monitoring results show that the sign polarity of the last difference value changes from negative to positive, or the absolute value is greater than the preset mutation threshold, it is determined that the soil in the current compaction area can no longer withstand the compaction work, and the soil structure is judged to have undergone shear failure.

10. A rolling data fusion and compaction quality analysis system, characterized in that, The system comprising the following components is executed according to any one of claims 1-9: (The method for fusion of compaction data and analysis of compaction quality is described in any one of claims 1-9) The compaction point data acquisition module monitors the rotation angle of the vibratory roller during the operation of the vibratory roller, triggers the rebound hammer to collect the soil impact response acceleration value of each compaction point based on the rotation angle, calculates the corresponding rebound strength value, and obtains the positioning coordinates of each compaction point to obtain the basic data of the compaction point. The soil structure state identification module converts the soil impact response acceleration value sequence of all compaction points in the basic data of compaction points into a symbol combination representing the fluctuation mode of the soil impact response acceleration value sequence, and classifies the soil structure state identifier. The shear failure early warning module collects the historical rebound strength value of the positioning coordinates of each compaction point in the basic data of the compaction points, compares it with the rebound strength value of the current corresponding compaction point, and generates a soil shear failure early warning signal. The compaction quality assessment module compares the rebound strength value of each compaction point in the basic data of the compaction points with the preset rebound value and compaction degree quantitative correlation table, calculates the soil compaction degree percentage value of each compaction point, combines the soil structure state identifier and soil shear failure early warning signal, and constructs a compaction quality feature set. The compaction quality visualization module maps the compaction quality feature set to the corresponding grid of a preset electronic map, sets a highlight display command, and draws a compaction quality heat map.

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