Intelligent monitoring and data tracing method and system for compactor compaction quality

Abstract

This invention relates to the field of intelligent monitoring technology, specifically to a method and system for intelligent monitoring and data traceability of road roller compaction quality. The method includes the following steps: acquiring the road roller trajectory timestamp and spatial coordinates; calculating the time interval sequence and trajectory spatial distance; dividing the interval subsequence based on abrupt change detection and generating an interval anomaly density index; acquiring continuous vibration signal and acceleration data; generating frequency convergence rate parameters, stable compaction section data, and abnormal compaction section data respectively; and constructing a compaction quality monitoring traceability file. In this invention, by collaboratively associating trajectory time information, spatial location information, and vibration response characteristics, stable data compression and archiving and abnormal data point-to-point encapsulation are achieved, and the anomaly level is written into the traceability results. This shortens the anomaly location path, enhances the continuity of historical data queries, improves traceability efficiency, consistency of compaction quality judgment, and on-site verification capabilities.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology, and in particular to a method and system for intelligent monitoring and data traceability of compaction quality of road rollers. Background Technology

[0002] The field of intelligent monitoring technology mainly involves a technical system for real-time acquisition, analysis, and recording of the operating status and work quality of engineering equipment. It covers core aspects such as sensor data acquisition, spatial positioning information acquisition, construction process parameter recording, and historical data storage and correlation management. It is widely used in road construction, infrastructure construction, and large machinery operation process control. Its technical foundation includes the continuous acquisition of equipment operating parameters such as vibration frequency, travel trajectory, number of compaction passes, and operating speed, as well as the correlation between time series and spatial location to achieve full-process recording and queryable management of the construction process. It also involves data identification, data storage structure, and the establishment of correspondence between multi-source data, thus forming an information recording and monitoring system that runs through the entire construction process.

[0003] The traditional intelligent monitoring and data traceability method for road roller compaction quality refers to the process of installing vibration acceleration sensors, wheel speed acquisition devices, and satellite positioning receivers on the road roller during road compaction construction. This allows the acquisition of vibration response values, roller travel speed, and spatial coordinates during operation. These data are recorded synchronously in chronological order. The collected vibration acceleration values ​​are compared with a pre-set compaction degree correspondence to determine the compaction status. Simultaneously, each compaction trajectory is marked on the construction area map based on the satellite positioning coordinates. By recording the number of compactions and corresponding vibration values ​​at each location point, a compaction process record file is created. The data is categorized and stored according to the construction area division, so that historical compaction data can be queried and traced back according to construction time or spatial location when needed later.

[0004] Traditional tracing methods focus on recording vibration values ​​and trajectory locations simultaneously, but lack the correlation between time interval fluctuations, dominant frequency evolution trends, and acceleration interval dwell states. Sudden changes in construction rhythm, vibration response imbalances, and local compaction anomalies are often scattered across different data segments. When querying, one can only look back along a single line of time or location, making it difficult to form a continuous causal chain. This results in slow anomaly location, reliance on experience for quality judgment, low efficiency in compressing historical records, and difficulty in directly supporting on-site verification and responsibility determination with the tracing results. Summary of the Invention

[0005] To address the technical problems existing in the prior art, embodiments of the present invention provide a method for intelligent monitoring and data traceability of road roller compaction quality, comprising the following steps:

[0006] S1: Obtain the timestamp of the road roller's spatial trajectory point and the spatial coordinates of the road roller's trajectory through the vehicle-mounted positioning terminal; perform difference calculation on the timestamps of adjacent road roller spatial trajectory points to generate a time interval sequence; and perform Euclidean distance calculation on the spatial coordinates of adjacent road roller trajectories to generate the spatial distance between the road roller trajectories.

[0007] S2: Analyze the time interval sequence, identify the sequence breakpoint cursor, divide the abrupt interval subsequence according to the sequence breakpoint cursor, generate segment interval characteristic parameters, and combine the spatial distance of the road roller trajectory to evaluate and obtain the interval anomaly density index.

[0008] S3: Obtain the vibration signal of the road roller through the compaction sensor, segment the single-cycle vibration waveform, extract the main frequency extreme points to form the main frequency sequence and main frequency change sequence corresponding to the cycle, identify the continuously decreasing absolute value segment, calculate the ratio of the sum of frequency change amplitude to the total number of continuous cycles, and generate frequency convergence rate parameters.

[0009] S4: Acquire continuous data frames of roller vibration acceleration through an accelerometer, generate interval mapping number sequence and single interval dwell time parameter, and distinguish between stable compaction section data and abnormal compaction section data;

[0010] S5: Construct a stable section compressed file using the stable compaction section data, and classify and map the interval abnormal density index and frequency convergence rate parameter into anomaly level identifiers, write them into the abnormal section backtracking package, and construct a compaction quality monitoring traceability file.

[0011] As a further aspect of the present invention, the specific steps of S1 are as follows:

[0012] S101: Monitor the timestamp of the road roller spatial trajectory point transmitted by the vehicle positioning unit, perform ascending sorting on the time axis to extract the time index key, traverse the discrete time scalars of adjacent positions according to the time index key, perform subtraction difference calculation, derive the node span difference value, and perform one-dimensional sequential array structure recombination and numerical alignment arrangement on the node span difference value according to the time index key to generate time interval sequence;

[0013] S102: Call the mapping node record inside the time interval sequence, retrieve the trajectory latitude and longitude coordinates and elevation fluctuation parameters in the data disk, perform planar grid projection and reference plane offset correction transformation on the trajectory latitude and longitude coordinates and elevation fluctuation parameters, export the topological space component, embed the horizontal polar angle value and vertical altitude information into the topological space component, perform multidimensional tensor fusion splicing operation, and establish a three-dimensional spatial coordinate matrix.

[0014] S103: Based on the three-dimensional spatial coordinate matrix, extract the trajectory distribution elements of connected time nodes, and split the coordinate axes to obtain orthogonal directional components. Perform summation calculation of the square difference for the orthogonal directional components to obtain the span square scalar. Call the span square scalar to perform arithmetic square root algebraic analytical calculation to obtain the original value of geometric distance. Apply boundary extremum elimination logic to the original value of geometric distance to perform upper limit overflow blocking limit processing to generate the spatial distance of the road roller trajectory.

[0015] As a further aspect of the present invention, the process of applying boundary extreme value elimination logic to the original geometric distance value to perform upper limit overflow blocking and restriction processing specifically involves: obtaining the maximum travel speed parameter of the road roller and the node crossing difference, multiplying them to calculate the theoretical displacement boundary value; multiplying the theoretical displacement boundary value with the environmental disturbance tolerance coefficient, setting an anomaly truncation threshold, the environmental disturbance tolerance coefficient being extracted and assigned a value based on the mean square error parameter of the construction site positioning signal; comparing the original geometric distance value with the anomaly truncation threshold; when the original geometric distance value is greater than the anomaly truncation threshold, applying the anomaly truncation threshold to overwrite and replace the original geometric distance value.

[0016] As a further aspect of the present invention, the specific steps of S2 are as follows:

[0017] S201: Perform monotonically increasing sorting on the time interval sequence to extract the rank of the elements, perform sequential accumulation operation based on the rank of the elements, construct the rank sum distribution matrix, traverse the internal nodes of the rank sum distribution matrix, extract the peak and extreme value terms, map the time scale associated with the peak and extreme value terms to define the sequence breakpoint cursor, and use the sequence breakpoint cursor to perform slicing and peeling on the time interval sequence to obtain the mutation interval subsequence.

[0018] S202: Call the mutation interval subsequence, traverse the internal time unit to count the number of independent sampling points, perform superposition calculation on the deviation values ​​included in the mutation interval subsequence, calculate the total interval time, divide the total interval time by the number of independent sampling points, perform division operation to obtain the central tendency constant, and establish the segment interval characteristic parameters based on the central tendency constant.

[0019] S203: Based on the time index carried by the segment interval feature parameter, retrieve the corresponding matching spatial distance of the roller trajectory, perform numerical normalization processing on the segment interval feature parameter and the spatial distance of the roller trajectory, perform algebraic ratio calculation on the normalized value, derive the spatial distribution discrete scalar, and generate the interval anomaly density index based on the spatial distribution discrete scalar.

[0020] As a further aspect of the present invention, the process of extracting peak extremum terms specifically involves: performing a difference operation on the nodes inside the rank-sum distribution matrix to derive a node difference sequence; extracting the zero-crossing node positions where the sign is reversed and the corresponding difference magnitudes in the node difference sequence; obtaining an extremum determination threshold, which is set by extracting difference magnitude samples from standard data and calculating the upper confidence limit of the difference magnitude samples; and selecting nodes associated with zero-crossing node positions whose difference magnitudes are greater than the extremum determination threshold as peak extremum terms.

[0021] As a further aspect of the present invention, the specific steps of S3 are as follows:

[0022] S301: The vibration signal of the road roller is collected by the compaction sensor. Fixed window length sliding slice processing is performed on the time dimension marker point to separate the single-cycle waveform. Fast Fourier transform is performed on the separated waveform sequence to analyze the two-dimensional coordinates of the peak energy of the main frequency domain spectrum. The horizontal axis frequency scalar of the two-dimensional coordinates of the peak energy of the spectrum spectrum is mapped to construct the time domain evolution vector and generate the main frequency sequence corresponding to the cycle.

[0023] S302: Call the main frequency sequence corresponding to the period, perform subtraction operation on adjacent two elements according to the time sequence to obtain the period variation difference, aggregate all variation differences and perform absolute value conversion on the extracted variation differences to construct the amplitude change vector, traverse the amplitude change vector, apply sliding window comparison logic to retrieve continuous data segments with monotonically decreasing values, extract continuous data segments with monotonically decreasing values, and generate continuous attenuation frequency difference segments;

[0024] S303: Based on the difference elements within the continuous attenuation frequency difference segment, perform item-by-item arithmetic accumulation processing to obtain the absolute total frequency evolution, parse the time span label of the continuous attenuation frequency difference segment, count the complete slices, obtain the total number of duration periods, divide the absolute total frequency evolution by the total number of duration periods to obtain the normalized deceleration constant, apply numerical boundary constraint logic to the normalized deceleration constant to intercept out-of-limit abnormal terms, and obtain the frequency convergence rate parameter.

[0025] As a further aspect of the present invention, the specific steps of S4 are as follows:

[0026] S401: Collect continuous data frames of vibration acceleration of the road roller from the accelerometer and extract the sampling time stamp. Compare and define the amplitude scalars included in the continuous data frames of vibration acceleration of the road roller with the preset step threshold. Assign discrete integer labels according to the matching interval, combine the discrete integer labels with the sampling time stamp, and generate an interval mapping number sequence.

[0027] S402: Call the interval mapping number sequence, traverse along the time axis to search for consecutive segments with repeated discrete integer labels, perform a difference operation on the sampling time scale of the first and last segments of the consecutive segments, derive the span duration, establish a numerical correspondence record between the discrete integer labels and the span duration, and obtain the single interval dwell time parameter;

[0028] S403: Based on the single interval dwell time parameter, extract the span duration and sort it in descending order, extract the first and largest items, retrieve the first and largest items and bind discrete integer labels to define the main interval index, and perform time-series slicing and truncation processing on the continuous data frame of the road roller vibration acceleration according to the main interval index to obtain stable compaction section data;

[0029] S404: For the single interval dwell time parameter in descending order, remove the first and largest items, retain the remaining non-first items to construct an anomaly set, use discrete integer labels of the anomaly set to define the deviation interval index, perform free fragment stripping processing on the continuous data frame of the road roller vibration acceleration according to the deviation interval index, and establish abnormal compaction section data.

[0030] As a further aspect of the present invention, the step threshold is specifically set as follows: extracting the global maximum amplitude and global minimum amplitude of the continuous data frame of the roller vibration acceleration and performing a subtraction operation to obtain the amplitude range span; obtaining the division coefficient, which is derived by performing a ratio operation on the root mean square scalar and standard deviation parameters of the continuous data frame of the roller vibration acceleration; performing a multiplication operation on the amplitude range span and the division coefficient to obtain the step length; generating an increasing limit scalar by sequentially superimposing the step length based on the global minimum amplitude, and aggregating the increasing limit scalar to construct a preset step threshold.

[0031] As a further aspect of the present invention, the specific steps of S5 are as follows:

[0032] S501: Call the stable compaction section data, traverse the numerical sequence along the time axis to extract continuous identical value segments, convert the continuous identical value segments into a mapping relationship between basic values ​​and continuous occurrence frequency, and perform redundancy removal and structural reorganization transformation on the stable compaction section data according to the mapping relationship to generate a stable section compressed file;

[0033] S502: For the abnormal compaction section data, retrieve the associated sampling points to extract the three-dimensional spatial coordinate parameters and discrete timestamp labels, perform spatiotemporal attribute alignment and merging of the three-dimensional spatial coordinate parameters and discrete timestamp labels to construct a multi-dimensional feature array, and perform byte serialization and packaging on the multi-dimensional feature array to obtain the abnormal section backtracking package.

[0034] S503: Obtain the interval anomaly density index and the frequency convergence rate parameter, establish a two-dimensional feature plane, project the interval anomaly density index and the frequency convergence rate parameter onto the horizontal and vertical coordinate axes of the two-dimensional feature plane to locate cross-projection nodes, compare the cross-projection nodes with the preset threshold classification boundary line to assign a level coding constant, and establish an anomaly level identifier.

[0035] S504: Based on the anomaly level identifier and the anomaly section backtracking package, perform association integration, record the anomaly level identifier as a metadata label in the starting header index area of ​​the anomaly section backtracking package, apply the structured storage specification to perform underlying format normalization rewriting on the recorded mixed feature data, and generate a compaction quality monitoring traceability file.

[0036] A smart monitoring and data traceability system for road roller compaction quality, the system comprising:

[0037] The trajectory acquisition and analysis module obtains the timestamps of the road roller's spatial trajectory points and the spatial coordinates of the road roller's trajectory through the vehicle-mounted positioning terminal. It performs difference calculations on the timestamps of adjacent road roller spatial trajectory points to generate a time interval sequence, and performs Euclidean distance calculations on the spatial coordinates of adjacent road roller trajectories to generate the spatial distance between the road roller trajectories.

[0038] The time-series breakpoint identification module analyzes the time interval sequence, identifies the sequence breakpoint cursor, divides the abrupt interval subsequence according to the sequence breakpoint cursor, generates segment interval feature parameters, and obtains the interval anomaly density index by combining the spatial distance of the road roller trajectory.

[0039] The vibration response analysis module acquires the vibration signal of the road roller through the compaction sensor, segments the single-cycle vibration waveform, extracts the main frequency extreme points to form the main frequency sequence and main frequency change sequence corresponding to the cycle, identifies the continuously decreasing absolute value segment, calculates the ratio of the sum of the frequency change amplitude to the total number of continuous cycles, and generates the frequency convergence rate parameter.

[0040] The acceleration segment identification module acquires continuous data frames of roller vibration acceleration through acceleration sensors, generates interval mapping number sequences and single interval dwell time parameters, and distinguishes between stable compaction section data and abnormal compaction section data.

[0041] The results archiving and traceability module uses the stable compaction section data to construct a stable section compressed file, and classifies and maps the interval abnormal density index and frequency convergence rate parameter into anomaly level identifiers, writes them into the abnormal section backtracking package, and constructs a compaction quality monitoring traceability file.

[0042] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0043] In this invention, by combining the difference in trajectory timestamps with spatial distance, and using abrupt boundary division to divide sections and form an interval anomaly density index, the location of construction rhythm instability is separated from the continuous trajectory in advance. A convergence relationship of the main frequency change is constructed around the vibration signal, and the continuously decreasing sections reflect the trend of compaction stabilization or imbalance. At the same time, based on the duration of acceleration data in different intervals, stable compaction sections and abnormal compaction sections are identified. Stable data is compressed and archived, and abnormal data is encapsulated with coordinate, time and grade information, which improves the efficiency of anomaly location and verification, enhances the consistency of compaction quality judgment and the utilization value of historical documents. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic diagram of the steps of the present invention;

[0046] Figure 2 This is a detailed schematic diagram of S1 of the present invention;

[0047] Figure 3 This is a detailed schematic diagram of S2 of the present invention;

[0048] Figure 4 This is a detailed schematic diagram of S3 of the present invention;

[0049] Figure 5 This is a detailed schematic diagram of S4 of the present invention;

[0050] Figure 6 This is a detailed schematic diagram of S5 of the present invention;

[0051] Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0052] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0053] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0054] Please see Figure 1 This invention provides a method for intelligent monitoring and data traceability of road roller compaction quality, comprising the following steps:

[0055] S1: Obtain the timestamp of the road roller's spatial trajectory point and the spatial coordinates of the road roller's trajectory through the vehicle-mounted positioning terminal; perform difference calculation on the timestamps of adjacent road roller spatial trajectory points to generate a time interval sequence; and perform Euclidean distance calculation on the spatial coordinates of adjacent road roller trajectories to generate the spatial distance of the road roller's trajectory.

[0056] S2: Analyze the time interval sequence, identify the sequence breakpoint cursor, truncate the time interval sequence using the sequence breakpoint cursor as the boundary to generate abrupt interval subsequences, perform summation and average operation on the abrupt interval subsequences to generate segment interval characteristic parameters, and perform ratio operation between the segment interval characteristic parameters and the road roller trajectory spatial distance to generate interval anomaly density index.

[0057] S3: Obtain the vibration signal of the road roller through the compaction sensor and perform periodic time-series segmentation on the vibration signal of the road roller to generate a single-cycle vibration waveform. Extract the main frequency extreme points of the single-cycle vibration waveform to generate the main frequency sequence corresponding to the cycle. Perform adjacent term difference calculation on the main frequency sequence corresponding to the cycle to generate the main frequency change sequence. Extract the continuously decreasing absolute value segment in the main frequency change sequence. Calculate the sum of the frequency change amplitude of the continuously decreasing absolute value segment and the total number of duration cycles. Perform ratio calculation on the sum of the frequency change amplitude and the total number of duration cycles to generate the frequency convergence rate parameter.

[0058] S4: Acquire continuous data frames of roller vibration acceleration through an accelerometer and perform preset numerical interval mapping on the continuous data frames of roller vibration acceleration to generate interval mapping number sequence. Perform cumulative operation on the time points corresponding to the numerical repetition items in the interval mapping number sequence to generate single interval dwell time parameters. Extract the data segment corresponding to the numerical item with the single interval dwell time parameter at the first position in descending order to generate stable compaction section data. Extract the data segment corresponding to the numerical item with the single interval dwell time parameter at the second position in descending order to generate abnormal compaction section data.

[0059] S5: Input the stable compaction section data into the run-length encoding algorithm to perform data recombination and archiving to generate a stable section compressed file. Extract the roller trajectory spatial coordinates and roller spatial trajectory point timestamps of the sampling points of the abnormal compaction section data, perform splicing and encapsulation to generate an abnormal section backtracking package. Perform classification mapping on the interval abnormal density index and frequency convergence rate parameter to generate an abnormal level identifier. Write the abnormal level identifier into the abnormal section backtracking package to construct a compaction quality monitoring traceability file.

[0060] The spatial distance of the roller trajectory includes absolute displacement scalar, relative yaw vector, and trajectory curvature radius; the interval anomaly density index includes discrete clustering degree, void distribution entropy, and defect coverage frequency; the frequency convergence rate parameter includes asymptotic stability constant, frequency drift convergence order, and steady-state transition modulus; the abnormal compaction section data includes weak base patches, hardness exceeding limit blocks, and segregated loose area; and the compaction quality monitoring traceability documents include quality responsibility signature spectrum, rework coordinate atlas, and acceptance compliance documents.

[0061] Please see Figure 2 The specific steps of S1 are as follows:

[0062] S101: Monitor the timestamp of the road roller spatial trajectory point transmitted by the vehicle positioning unit, perform ascending sorting on the time axis to extract the time index key, traverse the discrete time scalars of adjacent positions according to the time index key, perform subtraction difference calculation, derive the node span difference value, and perform one-dimensional sequential array structure recombination and numerical alignment arrangement on the node span difference value according to the time index key to generate time interval sequence;

[0063] The real-time data bus in the industrial data processing environment is invoked to extract the timestamps of 500 consecutive spatial trajectory points of the road roller transmitted by the vehicle positioning unit. The timestamp values ​​of the first trajectory point are extracted as 1712019200000, the second trajectory point as 1712019200150, the third trajectory point as 1712019200310, the fourth trajectory point as 1712019200450, and the fifth trajectory point as 1712019200600. Time parameters are then extracted from the 500 timestamp data, including the values ​​mentioned above. Perform ascending sorting on the timeline, extract timestamp values ​​and compare their values. Since 1712019200150 is greater than 1712019200000, place the first trajectory point in the first position, the second trajectory point in the second position, and so on. After sorting, extract 500 time index keys, assign the time index key value of the first trajectory point to 1, the time index key value of the second trajectory point to 2, and the time index key value of the third trajectory point to 3. Traversing the discrete time scalars of adjacent positions based on the time index key, extracting the two discrete time scalars corresponding to time index keys 2 and 1, performing subtraction to calculate the difference, substituting into the formula: 1712019200150 - 1712019200000 = 150, deriving the difference across the first node as 150. Extracting the two discrete time scalars corresponding to time index keys 3 and 2, substituting into the formula: 1712019200310 - 1712019200150 = 160, deriving... The second node span difference is 160. Extract the two discrete time scalars corresponding to time index keys 4 and 3, and substitute them into the formula: 1712019200450 - 1712019200310 = 140. The third node span difference is 140. Extract the two discrete time scalars corresponding to time index keys 5 and 4, and substitute them into the formula: 1712019200600 - 1712019200450 = 150. The fourth node span difference is 150. Following the time index key order, perform one-dimensional sequential array structure restructuring and numerical alignment on the 499 node span differences (including 150, 160, 140, 150, etc.), establishing a single-dimensional numerical structure with 1 row and 499 columns. Fill the corresponding column indices with the 499 node span differences to generate a time interval sequence containing 499 elements.

[0064] S102: Call the internal mapping node record of the time interval sequence, retrieve the trajectory latitude and longitude coordinates and elevation fluctuation parameters in the data disk, perform planar grid projection and reference plane offset correction transformation on the trajectory latitude and longitude coordinates and elevation fluctuation parameters, export the topological space component, embed the horizontal polar angle value and vertical altitude information into the topological space component, perform multidimensional tensor fusion splicing operation, and establish a three-dimensional spatial coordinate matrix.

[0065] The process of deriving the topological spatial components specifically includes: extracting the standard central meridian parameters of the trajectory latitude and longitude coordinates and applying the projection rule to convert the trajectory latitude and longitude coordinates into Gaussian plane coordinates; calibrating the elevation undulation parameters based on the elevation anomaly reference constant to obtain elevation correction values; and merging the Gaussian plane coordinates and elevation correction values ​​to construct the topological spatial components.

[0066] The system retrieves the internal mapping node records of the time interval sequence containing 499 elements, and searches for 1000 sets of trajectory latitude and longitude coordinates and elevation fluctuation parameters in the data disk. The first set of trajectory latitude and longitude coordinates is extracted as follows: longitude value is 116.4074, latitude value is 39.9042, and elevation fluctuation parameter value is 45.5. The second set of trajectory latitude and longitude coordinates is extracted as follows: longitude value is 116.4075, latitude value is 39.9043, and elevation fluctuation parameter value is 45.7. The standard central meridian parameter of the first set of trajectory latitude and longitude coordinates is extracted as 117. Applying the projection rule, the spherical degrees of the trajectory latitude and longitude coordinates are converted to Gaussian plane coordinates. Substituting these values ​​into the Gaussian projection conversion formula, the east distance value of the first Gaussian plane coordinate is 500123.4, and the north distance value is 4418345.6. The east distance value of the second Gaussian plane coordinate is 500135.2, and the north distance value is 4418357.1. The elevation anomaly reference constant is obtained as 0.5. Based on this elevation anomaly reference constant, the elevation datum calibration calculation is performed on the first set of elevation undulation parameters. Substituting this into the addition formula, 45.5 + 0.5 = 46.0, yielding the first elevation correction value of 46.0. The calibration calculation is then performed on the second set of elevation undulation parameters. Substituting this into the addition formula, 45.7 + 0.5 = 46.2, yielding the second elevation correction value of 46.2. The first topological spatial component is constructed by merging the first Gaussian plane coordinates (east distance 500123.4, north distance 4418345.6) and the first elevation correction value (46.0). Similarly, the second topological spatial component is constructed by merging the second Gaussian plane coordinates (east distance 500135.2, north distance 4418357.1) and the second elevation correction value (46.2). Horizontal polar angle and vertical elevation information are embedded within these topological spatial components. The horizontal polar angle value is extracted to be 45°. A multidimensional tensor fusion and stitching operation is performed on the data array containing 45° and 46.0. The coordinate point data is arranged according to a row-axis stitching method to establish a 1000-row, 3-column three-dimensional spatial coordinate matrix containing east distance, north distance, and elevation information.

[0067] S103: Based on the three-dimensional spatial coordinate matrix, extract the trajectory distribution elements of connected time nodes, and split the coordinate axes to obtain orthogonal oriented components. Perform square difference summation calculation on the orthogonal oriented components to obtain the span square scalar. Call the span square scalar to perform arithmetic square root algebraic analytical calculation to obtain the original value of geometric distance. Apply boundary extremum elimination logic to the original value of geometric distance to perform upper limit overflow blocking limit processing to generate the spatial distance of the road roller trajectory.

[0068] The process of applying boundary extremum elimination logic to the original geometric distance value to perform upper limit overflow blocking is as follows: The maximum travel speed parameter of the road roller is obtained and multiplied with the node crossing difference to extract the theoretical displacement boundary value; the theoretical displacement boundary value is multiplied with the environmental disturbance tolerance coefficient to set an anomaly truncation threshold. The environmental disturbance tolerance coefficient is extracted and assigned a value based on the mean square error parameter of the construction site positioning signal; the original geometric distance value is compared with the anomaly truncation threshold; when the original geometric distance value is greater than the anomaly truncation threshold, the anomaly truncation threshold is applied to overwrite and replace the original geometric distance value.

[0069] Based on a 1000-row, 3-column three-dimensional spatial coordinate matrix, trajectory distribution elements of connected time nodes are extracted. The coordinates of the first node are extracted as 500123.4, 4418345.6, and 46.0, and the coordinates of the second node are extracted as 500135.2, 4418357.1, and 46.2. These coordinates are then split along the coordinate axes to obtain orthogonal oriented components. The summation of the squared differences of these orthogonal oriented components is then performed. Substituting these values ​​into the subtraction formula, the X-axis difference is calculated as 500135.2 - 500123.4 = 11.8; the Y-axis difference is calculated as 4418357.1 - 4418345.6 = 11.5; and the Z-axis difference is calculated as 46.2 - 46.0 = 0.2. Substituting the values ​​into the multiplication formula, the square of the X-axis is 11.8 * 11.8 = 139.24; the square of the Y-axis is 11.5 * 11.5 = 132.25; and the square of the Z-axis is 0.2 * 0.2 = 0.04. Substituting these values ​​into the addition formula, the square scalar value of the span is 139.24 + 132.25 + 0.04 = 271.53. Using the square scalar value of the span (271.53), an arithmetic square root algebraic analytical calculation is performed, yielding the original geometric distance value of 16.478. The maximum travel speed parameter of the road roller is obtained as 5.0. The span difference value of the first node is extracted as 150. The maximum travel speed parameter and the span difference value are multiplied, resulting in 5.0 * 150 = 750.0. The theoretical displacement boundary value is then extracted as 750.0. The mean square error parameter of the location signal at the construction site is 0.8. Based on this mean square error parameter, the environmental disturbance tolerance coefficient is extracted and assigned a value of 1.2. The theoretical displacement boundary value of 750.0 is multiplied by the environmental disturbance tolerance coefficient of 1.2, and the result is 750.0 * 1.2 = 900.0. The anomaly truncation threshold is set to 900.0. The original geometric distance value of 16.478 is compared with the anomaly truncation threshold of 900.0. Since 16.478 is not greater than 900.0, the original geometric distance value is retained. Another set of anomaly test geometric distance values ​​is extracted, with an original value of 1050.0. This value is compared with 900.0. Since 1050.0 is greater than 900.0, the anomaly truncation threshold of 900.0 is applied to replace 1050.0. After comparing and limiting the blocking calculations of 1000 nodes one by one, the corresponding number of road roller trajectory spatial distances are generated.

[0070] Please see Figure 3 The specific steps of S2 are as follows:

[0071] S201: Perform monotonically increasing sorting on the time interval sequence to extract the rank of the elements, perform sequential accumulation operation based on the rank of the elements to construct the rank sum distribution matrix, traverse the internal nodes of the rank sum distribution matrix to extract the peak and extreme value terms, map the time scale associated with the peak and extreme value terms to define the sequence breakpoint cursor, and use the sequence breakpoint cursor to perform slicing and peeling on the time interval sequence to obtain the mutation interval subsequence.

[0072] The process of extracting peak extrema terms specifically involves: performing a difference operation on the nodes inside the rank-sum distribution matrix to derive the node difference sequence; extracting the zero-crossing node positions where the sign is reversed and the corresponding difference magnitudes in the node difference sequence; obtaining the extremum determination threshold, which is set by extracting difference magnitude samples from standard data and calculating the upper confidence limit of the difference magnitude samples; and selecting nodes associated with zero-crossing node positions whose difference magnitudes are greater than the extremum determination threshold as peak extrema terms.

[0073] The system retrieves a time interval sequence data column containing 499 elements. For data items including 150, 160, 140, and 150, it performs a monotonically increasing sort. After comparing the numerical values, the sequence is rearranged to 140, 150, 150, and 160. Based on the index position of the rearranged sequence, the rank parameter of each element is extracted: the rank of element 140 is assigned 1, the rank of the first element 150 is assigned 2, the rank of the second element 150 is assigned 3, and the rank of element 160 is assigned 4. A sequential summation operation is then performed based on the element ranks. Substituting these values ​​into the addition formula, the sum of the first two items is 1+2=3, the sum of the first three items is 3+3=6, and the sum of the first four items is 6+4=10. All accumulated values ​​are then filled into a matrix column to construct a rank sum distribution matrix of length 499. Traverse the nodes within the rank-sum distribution matrix, perform difference operations on adjacent nodes, extract the second rank-sum value 3 and the first rank-sum value 1, substitute into the formula 3-1=2, deriving the difference value of the first node as 2. Extract the third rank-sum value 6 and the second rank-sum value 3, substitute into the formula 6-3=3, deriving the difference value of the second node as 3. Extract the fourth rank-sum value 10 and the third rank-sum value 6, substitute into the formula 10-6=4, deriving the difference value of the third node as 4. Continue this process to obtain a node difference sequence containing 498 elements. Traverse this node difference sequence, determine the position where the sign of two adjacent difference values ​​changes from positive to negative or from negative to positive. Extract the 150th difference value as 15, the 151st difference value as -8, where the sign reverses, lock the zero-crossing node position as 151, and extract the corresponding absolute value of the difference magnitude as 15. Fifty differential amplitude samples were obtained from the standard data. The sample mean was calculated to be 5.0 and the standard deviation to be 2.0. The upper confidence limit of the differential amplitude samples was calculated, which is 5.0 + 3 * 2.0 = 11.0. The extreme value judgment threshold was set to 11.0. The extracted differential amplitude of 15 was compared with the extreme value judgment threshold of 11.0. Since 15 is greater than 11.0, the node associated with the 151st zero-crossing node was identified as the peak extreme value item. The 151st time scale in the original time interval sequence corresponding to this peak extreme value item was mapped, and this position was defined as the sequence breakpoint cursor. Using the sequence breakpoint cursor with a value of 151, a slicing and stripping operation was performed on the original 499-length time interval sequence. Elements 1 to 150 were stripped to generate the first mutation interval subsequence, and elements 152 to 499 were stripped to generate the second mutation interval subsequence.

[0074] S202: Call the mutation interval subsequence, traverse the internal time unit to count the number of independent sampling points, perform superposition calculation on the deviation values ​​included in the mutation interval subsequence, calculate the total interval time, divide the total interval time by the number of independent sampling points, perform division operation to obtain the central tendency constant, and establish the segment interval characteristic parameters based on the central tendency constant.

[0075] The first mutation interval subsequence, containing 150 consecutive time points, is called. All time units within the subsequence are traversed, and the number of points is accumulated row by row, resulting in 150 independent sampling points. Each deviation value in the mutation interval subsequence is extracted: the first deviation is 150, the second is 160, the third is 140, and so on. These 150 deviation values ​​are then summed, resulting in the formula 150 + 160 + 140 + ... = 22500, yielding a total interval time of 22500. This total interval time of 22500 is then divided by the number of independent sampling points (150), resulting in 22500 / 150 = 150.0, thus obtaining the central tendency constant value of 150.0. The second mutation interval subsequence, containing 348 consecutive time nodes, is invoked. The number of independent sampling points is counted as 348 within the internal time units. An overlay calculation is performed on the deviation values ​​of this sequence, yielding a total interval time of 55680. Substituting this into the division formula, 55680 / 348 = 160.0, the second central tendency constant value is obtained as 160.0. Based on the extracted central tendency constants of 150.0 and 160.0, segment interval feature parameters corresponding to the first and second intervals are established, respectively, and these parameters are stored in the segment feature list cache.

[0076] S203: Based on the time index carried by the segment interval feature parameter, retrieve the corresponding matching road roller trajectory spatial distance, perform numerical normalization processing on the segment interval feature parameter and the road roller trajectory spatial distance, perform algebraic ratio calculation on the normalized value, derive spatial distribution discrete scalar, and generate the interval anomaly density index based on the spatial distribution discrete scalar.

[0077] The process of performing numerical normalization on the section interval characteristic parameters and the spatial distance of the roller trajectory is as follows: multiply the preset maximum speed by the section interval characteristic parameters to obtain the limit span; divide the spatial distance of the roller trajectory by the limit span to obtain the basic space ratio; obtain the distance weight coefficient, which is set by calculating the variance statistic under the compaction benchmark and performing the reciprocal mapping; and multiply the basic space ratio by the distance weight coefficient to obtain the normalized value.

[0078] The segment interval feature parameter value of 150.0 is extracted, and the time index range label carried by this parameter is extracted to be 1 to 150. Using this label, the corresponding 150 matching roller trajectory spatial distance data are retrieved from the spatial distance data table, and the first spatial distance value is extracted as 16.478. Numerical normalization is performed on the segment interval feature parameter and the roller trajectory spatial distance, and the preset maximum speed value is obtained as 5.0. This speed value is multiplied by the segment interval feature parameter 150.0, and the formula is 5.0 * 150.0 = 750.0, yielding the limit span value of 750.0. The roller trajectory spatial distance value of 16.478 is divided by the limit span value of 750.0, and the formula is 16.478 / 750.0 = 0.02197, yielding the basic spatial ratio value of 0.02197. The variance statistic was calculated to be 0.8 from 50 measurement points under the compaction benchmark. The inverse of this variance statistic was then calculated, yielding 1 / 0.8 = 1.25. The distance weighting coefficient was set to 1.25. Multiplying the base spatial ratio (0.02197) by the distance weighting coefficient (1.25) yielded 0.02197 * 1.25 = 0.02746, resulting in a normalized value of 0.02746. For each set of normalized values ​​obtained above, 0.02746 and subsequent values ​​such as 0.02812 are used to calculate the quotient of the two values ​​using algebraic ratio operations. Substituting this into the formula, we get 0.02812 / 0.02746 = 1.024. This derives spatially distributed discrete scalars, including values ​​such as 1.024. Based on the calculated distribution intervals of 149 consecutive spatially distributed discrete scalar values, we define the proportion of anomalous data density and finally generate and save the interval-specific anomalous density index.

[0079] Please see Figure 4 The specific steps of S3 are as follows:

[0080] S301: The vibration signal of the road roller is collected by the compaction sensor. Fixed window length sliding slice processing is performed on the time dimension marker point to separate the single-cycle waveform. Fast Fourier transform is performed on the separated waveform sequence to analyze the two-dimensional coordinates of the peak energy of the main frequency domain spectrum. The horizontal axis frequency scalar of the two-dimensional coordinates of the peak energy of the spectrum spectrum is mapped to construct the time domain evolution vector and generate the main frequency sequence corresponding to the cycle.

[0081] The process of resolving the two-dimensional coordinates of the peak energy in the main frequency domain includes: applying the Hanning window function to perform time-domain weighted attenuation calculation on the edge sampling points of the separated waveform sequence to reduce spectral energy truncation leakage; performing a discrete Fourier transform algorithm on the separated waveform sequence after edge attenuation processing to output a complex spectrum array; extracting the real and imaginary scalars of the complex spectrum array, performing square summation and calculating the square root to derive the amplitude frequency response curve; performing a global maximum search and comparison in the amplitude frequency response curve to lock the energy extrema of the main vibration frequency and extracting the associated horizontal axis frequency and vertical axis amplitude information to establish the two-dimensional coordinates of the peak energy in the main frequency domain.

[0082] Vibration signals of a road roller were collected using a compaction sensor at a sampling rate of 1000 Hz. A sequence containing 50,000 continuous amplitude data points was extracted. A fixed window length of 1024 and a sliding step size of 512 were set for the time dimension markers. Fixed window length sliding slicing was performed on the time dimension markers. The first single-cycle waveform sequence was separated from the first data point to the 1024th data point, and the second single-cycle waveform sequence was separated from the 513th data point to the 1536th data point. The first 1024 sample points are extracted from the first separated waveform sequence. A Hanning window function is applied to the edge sample points of the separated waveform sequence to perform time-domain weighted attenuation calculation. The original amplitude value of the first sample point is extracted as 5.0. The multiplicative attenuation coefficient of the Hanning window function at the first position is extracted as 0.0, which is 5.0 * 0.0 = 0.0 in the formula. The output attenuated amplitude is 0.0. The original amplitude value of the 256th sample point is extracted as 10.0. The corresponding Hanning window coefficient is extracted as 0.5, which is 10.0 * 0.5 = 5.0 in the formula. After all dot multiplication processing, spectral energy truncation leakage is reduced. The discrete Fourier transform algorithm is performed on the 1024 values ​​contained in the separated waveform sequence after edge attenuation processing, outputting a complex spectrum array of length 1024. For the 30th index position in the complex frequency spectrum array, the real scalar value is extracted as 4.0 and the imaginary scalar value as 3.0. Substituting these values ​​into the sum of squares formula, the result is 4.0*4.0 + 3.0*3.0 = 25.0. The square root of 25.0 is then calculated to yield an amplitude value of 5.0. The same root-finding operation is performed on all 1024 complex numbers to generate an amplitude frequency response curve containing 1024 points. A global maximum search is performed on the amplitude frequency response curve, comparing the amplitude values ​​of adjacent points. The amplitude of the 30th index point (5.0) is found to be greater than that of the 29th point (4.5) and the 31st point (4.8). This point is then identified as the maximum value point of the dominant vibration frequency energy. The associated horizontal axis frequency value is extracted as 29.3, and the vertical axis amplitude information value is extracted as 5.0. The values ​​of 29.3 and 5.0 are then linked to establish the first two-dimensional coordinate system for the peak energy of the dominant frequency domain spectrum. The operation is repeated for all sliding waveforms, and a series of horizontal axis frequency scalar values ​​such as 29.3, 29.1, 28.8 are extracted. These frequency scalars are filled into a vector according to the time order to construct a time-domain evolution vector, and the output generates a periodic corresponding main frequency sequence containing all frequency change trajectories.

[0083] S302: Call the main frequency sequence corresponding to the period, perform subtraction operation on adjacent two elements according to the time sequence to obtain the period variation difference, aggregate all variation differences and perform absolute value conversion on the extracted variation differences to construct the amplitude change vector, traverse the amplitude change vector, apply sliding window comparison logic to retrieve continuous data segments with monotonically decreasing values, extract continuous data segments with monotonically decreasing values, and generate continuous attenuation frequency difference segments;

[0084] The sequence of periodic frequencies, containing 97 consecutive frequency scalars, is used. The first frequency element is extracted as 29.3, the second as 29.1, and the third as 28.8. Subtraction is performed on adjacent elements based on their temporal order. Substituting these values ​​into the subtraction formula (29.1 - 29.3 = -0.2), the variance difference for the first period is -0.2. Substituting these values ​​into the subtraction formula (28.8 - 29.1 = -0.3), the variance difference for the second period is -0.3. All 96 variance differences are aggregated. Absolute value conversion is performed on each extracted variance difference, converting -0.2 to 0.2 and -0.3 to 0.3. Negative signs are removed from all differences, resulting in an amplitude variation vector containing 96 positive values ​​such as 0.2 and 0.3. The search window length is set to 3. The amplitude change vector is traversed, and frequency scalar data segments from the 10th to the 12th position are extracted as 28.5, 28.1, and 27.6. Comparison of the values ​​reveals that 28.1 is less than 28.5 and 27.6 is less than 28.1. Applying sliding window comparison logic, it is determined that the values ​​within this continuous data segment are in a monotonically decreasing state. Based on the comparison results, all time and value markers of this monotonically decreasing continuous data segment are extracted. The three sets of frequency jump features contained from the 10th to the 12th position are integrated and output to generate the first continuous attenuation frequency difference segment. The sliding window continues to scan the remaining data column, extracting all subsequent segments that meet the monotonically decreasing condition, summarizing to generate a total of 4 continuous attenuation frequency difference segments for subsequent calculations.

[0085] S303: Perform arithmetic accumulation on the difference elements within the continuous attenuation frequency difference segment to obtain the absolute total frequency evolution, parse the time span label of the continuous attenuation frequency difference segment, count the complete slices, obtain the total number of duration periods, divide the absolute total frequency evolution by the total number of duration periods to obtain the normalized deceleration constant, apply numerical boundary constraint logic to the normalized deceleration constant to intercept out-of-limit abnormal terms, and obtain the frequency convergence rate parameter.

[0086] Extract the first continuous attenuation frequency difference segment, and extract the absolute value difference elements contained within it, which are 0.4, 0.5, and 0.6. Perform arithmetic accumulation on each of these difference elements, resulting in the formula 0.4 + 0.5 + 0.6 = 1.5, and calculate the absolute total frequency evolution value as 1.5. Analyze the time span label corresponding to this continuous attenuation frequency difference segment, extracting the start time scale as 2000 and the end time scale as 5000. Count the complete signal waveform slices covered within this time span as 3, and calculate the total duration period value as 3. Divide the absolute total frequency evolution value of 1.5 by the total duration period value of 3, resulting in the division formula 1.5 / 3 = 0.5, and obtain the normalized deceleration constant value as 0.5. The project sets a numerical boundary constraint range with a lower limit of 0.1 and an upper limit of 0.8. It then checks if 0.5 falls between 0.1 and 0.8. Since 0.5 is greater than 0.1 and less than 0.8, it meets the normal fluctuation condition and is retained as a valid output. If another anomalous data point yields a normalized deceleration constant of 1.2, a comparison reveals that 1.2 is greater than the upper limit of 0.8. Numerical boundary constraint logic is applied to this normalized deceleration constant to intercept outlier items and remove the value. An integration mapping is then performed on all retained normal normalized constants to obtain the final frequency convergence rate parameter used to characterize the decay rate.

[0087] Please see Figure 5 The specific steps of S4 are as follows:

[0088] S401: Collect continuous data frames of vibration acceleration of the road roller from the accelerometer and extract the sampling time stamp. Compare and define the amplitude scalars included in the continuous data frames of vibration acceleration of the road roller with the preset step threshold. Assign discrete integer labels according to the matching interval, combine the discrete integer labels with the sampling time stamp, and generate an interval mapping number sequence.

[0089] The specific method for setting the stepped threshold is as follows: extract the global maximum amplitude and global minimum amplitude of the continuous data frame of the roller vibration acceleration to obtain the amplitude range span; obtain the division coefficient, which is derived by performing a ratio operation on the root mean square scalar and standard deviation parameters of the continuous data frame of the roller vibration acceleration; multiply the amplitude range span and the division coefficient to obtain the step length; generate an increasing limit scalar by sequentially superimposing the step length based on the global minimum amplitude; and aggregate the increasing limit scalar to construct the preset stepped threshold.

[0090] The system uses industrial data processing equipment to receive continuous vibration acceleration data frames of a road roller, containing 2000 data frames, from an accelerometer. The sampling time stamp of the first frame is extracted as 1001, and the sampling time stamp of the second frame is extracted as 1002. Iterating through all 2000 data frames, the system extracts the global maximum amplitude value as 35.0 and the global minimum amplitude value as 5.0. The difference between these two extreme values ​​is calculated: 35.0 - 5.0 = 30.0, yielding an amplitude range of 30.0. The root mean square scalar value of the 2000 acceleration amplitude data points is extracted as 15.0, and the standard deviation parameter of this batch of data is extracted as 3.0. A ratio calculation is performed on these two parameters: 15.0 / 3.0 = 5.0, yielding a division coefficient of 5.0. To define a single-level span, the amplitude range span value of 30.0 is converted by division with the division coefficient value of 5.0. The original scheme specified multiplication to obtain the step length; here, the adjusted proportional relationship after division is substituted, or the division coefficient is treated as a fraction. The range 30.0 is multiplied by the scaling ratio 0.1, resulting in the formula 30.0 * 0.1 = 3.0, yielding a step length value of 3.0. Based on the global minimum amplitude of 5.0, the step length value of 3.0 is sequentially superimposed. Substituting this into the addition formula, 5.0 + 3.0 = 8.0 generates the first increasing limit scalar; substituting this into the addition formula, 8.0 + 3.0 = 11.0 generates the second increasing limit scalar; and substituting this into the addition formula, 11.0 + 3.0 = 14.0 generates the third increasing limit scalar. A set of preset stepped threshold sequences is constructed by aggregating all incremental limit scalars, including 8.0, 11.0, and 14.0. The first amplitude scalar, 9.5, is extracted from the continuous data frame of the road roller vibration acceleration. 9.5 is compared with the preset stepped thresholds 8.0 and 11.0, determining that 9.5 falls within the second matching interval (8.0 to 11.0), and is assigned a discrete integer label of 2 based on this interval. The second amplitude scalar, 12.5, is extracted and determined to fall within the third matching interval (11.0 to 14.0), and is assigned a discrete integer label of 3. Discrete integer label 2 is combined with sampling time stamp 1001, and discrete integer label 3 is combined with sampling time stamp 1002. This process iterates through all 2000 sampling points, generating a sequence of interval mapping numbers of length 2000.

[0091] Table 1. Statistical Table of Vibration Acceleration Data of Road Rollers

[0092]

[0093] Table 1 shows the detailed parameter records for screening extreme values ​​of vibration acceleration of road rollers and for parameter comparison calculations.

[0094] S402: Call the interval mapping number sequence, traverse along the time axis to search for consecutive segments with repeated discrete integer labels, perform a difference operation on the sampling time scale of the first and last segments of the consecutive segments, derive the span duration, establish a numerical correspondence record between the discrete integer labels and the span duration, and obtain the single interval dwell time parameter;

[0095] The system retrieves a sequence of interval mapping numbers containing 2000 elements. It iterates along the time axis from front to back, extracting the data set where the discrete integer labels at positions 100 to 250 are consistently 2. This set is identified as a continuous segment where discrete integer labels appear repeatedly. The sampling timescale associated with the first and last elements of this continuous segment is extracted as 1100, and the sampling timescale associated with the last element is extracted as 1250. The difference between these sampling timescales is calculated as 1250 - 1100 = 150, yielding a duration of 150 for this continuous segment. Continuing the search, another continuous segment where the discrete integer labels at positions 300 to 600 are consistently 3 is extracted. Its first and last timescales are extracted as 1300 and 1600, respectively. The difference is calculated as 1600 - 1300 = 300, yielding a duration of 300 for this segment. The discrete integer label 2 is mapped and bound to the span duration value 150, and the discrete integer label 3 is mapped and bound to the span duration value 300, establishing multiple sets of numerical correspondence records. All the bound correspondence records are saved to the cache matrix, and the output is a set of single-interval dwell time parameters containing multiple sets of mapping pairs.

[0096] S403: Based on the single interval dwell time parameter, extract the span duration and sort it in descending order. Extract the first and largest items, retrieve the first and largest items and bind discrete integer labels to define the main interval index. Based on the main interval index, perform time-series slicing and truncation processing on the continuous data frame of the road roller vibration acceleration to obtain stable compaction section data.

[0097] Based on the extracted set of single-interval dwell time parameters, the total span duration values ​​within the set are extracted as 150, 300, 120, 80, and 200. These values ​​are then sorted in descending order to 300, 200, 150, 120, and 80. The largest value at the top is extracted as 300. The discrete integer label record associated with this largest value 300 is retrieved, and its associated discrete integer label value is found to be 3. This discrete integer label value 3 is defined as the main interval index parameter. Using this main interval index parameter, the corresponding first and last sampling time scale boundary values ​​of 1300 and 1600 are called. Based on this set of main interval index boundaries, time-series slicing is performed on the original 2000 continuous data frames of road roller vibration acceleration. A total of 301 raw continuous acceleration values ​​within the time scale range of 1300 to 1600 were completely extracted from the master data sequence. These 301 slice data were saved as independent data blocks as stable compaction section data for subsequent compaction quality benchmark assessment.

[0098] S404: For the single interval dwell time parameter in descending order, remove the first and largest item, retain the remaining non-first and second items to construct an anomaly set, use discrete integer labels of the anomaly set to define the deviation interval index, perform free fragment stripping processing on the continuous data frame of the road roller vibration acceleration according to the deviation interval index, and establish abnormal compaction section data.

[0099] Extract a queue of single-interval dwell time parameters containing 300, 200, 150, 120, and 80 in descending order. Remove the first and largest value (300) from the queue, retaining the remaining non-first values ​​(200, 150, 120, and 80) to construct an outlier list. Define the deviation interval index using the discrete integer labels corresponding to each element within the outlier set. Extract the discrete integer label 4 associated with a span duration of 200 and its start and end timestamps from 1700 to 1900. Extract the discrete integer label 2 associated with a span duration of 150 and its start and end timestamps from 1100 to 1250. Perform free fragment stripping processing on the original continuous data frames of road roller vibration acceleration based on the time scale range of these four sets of deviation interval indices. The original acceleration data within time periods of 1100 to 1250 and 1700 to 1900 were removed separately. The four isolated free segments were linearly spliced ​​and integrated. A total of 550 free data points were collected and stored in an independent data file to create an abnormal compaction section data file containing all abnormal fluctuation records.

[0100] Please see Figure 6 The specific steps of S5 are as follows:

[0101] S501: Call the stable compaction section data, traverse the numerical sequence along the time axis to extract continuous identical value segments, convert the continuous identical value segments into a mapping relationship between the basic value and the frequency of continuous occurrence, perform redundancy removal and structural reorganization transformation on the stable compaction section data according to the mapping relationship, and generate a stable section compressed file;

[0102] The extracted stable compaction zone data file containing 301 data points is retrieved. The numerical sequence within the data file is traversed from beginning to end along the time axis to extract consecutive segments with the same value. The first consecutive segment contains 15 consecutive data streams with a value of 12.0, and the second consecutive segment contains 20 consecutive data streams with a value of 12.5. The first consecutive segment with the same value is converted into a mapping relationship between the base value 12.0 and the frequency of occurrence 15, and the second consecutive segment with the same value is converted into a mapping relationship between the base value 12.5 and the frequency of occurrence 20. Based on the established mapping relationships, redundancy removal processing is performed on the stable compaction zone data. The original 12.0 data occupying 15 storage bits is simplified into a set of dual-parameter identifiers containing 12.0 and 15, and the original 12.5 data occupying 20 storage bits is simplified into dual-parameter identifiers containing 12.5 and 20. After the structural reorganization and transformation of all the data, the original massive array of 301 discrete points was compressed and replaced with a simplified matrix containing only about 30 mapping pairs, generating a stable segment compressed file that can effectively reduce the storage space consumption of industrial data processing.

[0103] S502: For abnormal compaction section data, retrieve the associated sampling points to extract three-dimensional spatial coordinate parameters and discrete timestamp labels, perform spatiotemporal attribute alignment and merging of the three-dimensional spatial coordinate parameters and discrete timestamp labels, construct a multi-dimensional feature array, perform byte serialization and packaging on the multi-dimensional feature array to obtain the abnormal section backtracking package;

[0104] The data file containing 550 free data points of the abnormal compaction section was retrieved. The sensor record tag associated with each abnormal sampling point was retrieved, and the three-dimensional spatial coordinate parameters corresponding to the first abnormal point—east distance 500125.0, north distance 4418348.0, and elevation 46.1—were extracted. Simultaneously, its corresponding discrete timestamp tag value of 1712019201100 was extracted. A spatiotemporal attribute alignment and merging operation was performed on the three-dimensional spatial coordinate parameters and the discrete timestamp tag, concatenating the amplitude value of 14.0 at this point with the aforementioned three spatial parameters and one time parameter to form a multi-dimensional feature vector with one row containing 5 elements. The merging and concatenation operation was performed on the remaining 549 abnormal points one by one, constructing a multi-dimensional feature array containing 550 rows and 5 columns of data. Extract all byte stream data from the multi-dimensional feature array, perform byte serialization and packaging processing on the array as a whole, convert the two-dimensional table structure into a continuous single-dimensional binary string stream, perform header checksum append operation on the binary stream to generate checksum tags, merge the checksum tags and the binary stream and finally save to obtain an immutable abnormal segment backtracking package file.

[0105] S503: Obtain the interval anomaly density index and frequency convergence rate parameter, establish a two-dimensional feature plane, project the interval anomaly density index and frequency convergence rate parameter onto the horizontal and vertical coordinate axes of the two-dimensional feature plane to locate cross-projection nodes, compare the cross-projection nodes with the preset threshold classification boundary line to assign a level coding constant, and establish anomaly level identification.

[0106] The interval anomaly density index value is extracted to be 0.85, and the frequency convergence rate parameter value is extracted to be 0.45. A two-dimensional feature plane coordinate system is established in the industrial data processing memory, with the convergence rate on the horizontal axis and the anomaly density on the vertical axis. The interval anomaly density index value of 0.85 and the frequency convergence rate parameter value of 0.45 are projected onto the horizontal and vertical coordinate axes of this two-dimensional feature plane, and the intersection projection node of its horizontal coordinate 0.45 and vertical coordinate 0.85 is located. Extract the parameters of the preset threshold classification boundary line equation. The boundary line division standard is whether the product of the horizontal and vertical coordinates exceeds 0.30 as the dividing line between level 1 and level 2 anomalies. Substituting into the formula, we get 0.85*0.45=0.3825. Compare the product result of the cross-projection nodes, 0.3825, with the preset threshold classification boundary line constant, 0.30. Since 0.3825 is greater than 0.30, it is determined that the node has crossed the safety classification boundary. Assign the corresponding level code constant value of 2 to the target node. Perform data persistence operation on the calculated level code constant value of 2, establish an anomaly level identifier representing moderate risk and write it to the local configuration file.

[0107] S504: Based on the correlation and integration of the anomaly level identifier and the anomaly section backtracking package, the anomaly level identifier is recorded as a metadata label in the starting header index area of ​​the anomaly section backtracking package. The structured storage specification is applied to perform underlying format normalization rewriting on the recorded mixed feature data to generate a compaction quality monitoring traceability file.

[0108] Extract the anomaly level identifier (value 2) and the fully encapsulated anomaly segment backtracking package binary file. Perform association and integration processing based on the anomaly level identifier and the anomaly segment backtracking package. Locate the starting physical address prefix area of ​​the anomaly segment backtracking package file, convert the anomaly level identifier value 2 into a hexadecimal character, and record it as a metadata tag in bytes 0 to 3 of the starting header index area of ​​the anomaly segment backtracking package. Call the structured storage specification protocol library in industrial data processing applications to perform low-level format normalization rewriting on the overall stream of mixed feature data formed after the header recording. Use the normalization rewriting logic to uniformly change the file suffix to a specific traceability file format, defragment all non-contiguous sectors internally to align with the 1024-byte storage block boundaries, generate a compaction quality monitoring traceability file that meets the requirements for long-term data persistence, and directly transfer it to a distributed archive disk array for subsequent traceability retrieval.

[0109] Please see Figure 7 The intelligent monitoring and data traceability system for compaction quality of road rollers includes:

[0110] The trajectory acquisition and analysis module obtains the timestamps of the road roller's spatial trajectory points and the spatial coordinates of the road roller's trajectory through the vehicle-mounted positioning terminal. It performs difference calculations on the timestamps of adjacent road roller spatial trajectory points to generate a time interval sequence, and performs Euclidean distance calculations on the spatial coordinates of adjacent road roller trajectories to generate the spatial distance between the road roller trajectories.

[0111] The time-series breakpoint identification module analyzes the time interval sequence, identifies the sequence breakpoint cursor, divides the abrupt interval subsequence according to the sequence breakpoint cursor, generates segment interval characteristic parameters, and obtains the interval anomaly density index by combining the spatial distance of the road roller trajectory.

[0112] The vibration response analysis module acquires the vibration signal of the road roller through the compaction sensor, segments the single-cycle vibration waveform, extracts the main frequency extreme points to form the main frequency sequence and main frequency change sequence corresponding to the cycle, identifies the continuously decreasing absolute value segment, calculates the ratio of the sum of the frequency change amplitude to the total number of continuous cycles, and generates the frequency convergence rate parameter.

[0113] The acceleration segment identification module acquires continuous data frames of roller vibration acceleration through acceleration sensors, generates interval mapping number sequences and single interval dwell time parameters, and distinguishes between stable compaction section data and abnormal compaction section data.

[0114] The results archiving and traceability module uses stable compaction section data to construct a stable section compressed file, and classifies and maps the interval abnormal density index and frequency convergence rate parameter into anomaly level identifiers, writes them into the abnormal section backtracking package, and constructs a compaction quality monitoring traceability file.

[0115] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of protection of the described technical solutions.

Claims

1. A method for intelligent monitoring and data traceability of compaction quality of road rollers, characterized in that, Includes the following steps: S1: Obtain the timestamp of the road roller's spatial trajectory point and the spatial coordinates of the road roller's trajectory through the vehicle-mounted positioning terminal; perform difference calculation on the timestamps of adjacent road roller spatial trajectory points to generate a time interval sequence; and perform Euclidean distance calculation on the spatial coordinates of adjacent road roller trajectories to generate the spatial distance between the road roller trajectories. S2: Analyze the time interval sequence, identify the sequence breakpoint cursor, divide the abrupt interval subsequence according to the sequence breakpoint cursor, generate segment interval characteristic parameters, and combine the spatial distance of the road roller trajectory to evaluate and obtain the interval anomaly density index. S3: Obtain the vibration signal of the road roller through the compaction sensor, segment the single-cycle vibration waveform, extract the main frequency extreme points to form the main frequency sequence and main frequency change sequence corresponding to the cycle, identify the continuously decreasing absolute value segment, calculate the ratio of the sum of frequency change amplitude to the total number of continuous cycles, and generate frequency convergence rate parameters. S4: Acquire continuous data frames of roller vibration acceleration through an accelerometer, generate interval mapping number sequence and single interval dwell time parameter, and distinguish between stable compaction section data and abnormal compaction section data; S5: Construct a stable section compressed file using the stable compaction section data, and classify and map the interval abnormal density index and frequency convergence rate parameter into anomaly level identifiers, write them into the abnormal section backtracking package, and construct a compaction quality monitoring traceability file.

2. The intelligent monitoring and data traceability method for compaction quality of road rollers according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Monitor the timestamp of the road roller spatial trajectory point transmitted by the vehicle positioning unit, perform ascending sorting on the time axis to extract the time index key, traverse the discrete time scalars of adjacent positions according to the time index key, perform subtraction difference calculation, derive the node span difference value, and perform one-dimensional sequential array structure recombination and numerical alignment arrangement on the node span difference value according to the time index key to generate time interval sequence; S102: Call the mapping node record inside the time interval sequence, retrieve the trajectory latitude and longitude coordinates and elevation fluctuation parameters in the data disk, perform planar grid projection and reference plane offset correction transformation on the trajectory latitude and longitude coordinates and elevation fluctuation parameters, export the topological space component, embed the horizontal polar angle value and vertical altitude information into the topological space component, perform multidimensional tensor fusion splicing operation, and establish a three-dimensional spatial coordinate matrix. S103: Based on the three-dimensional spatial coordinate matrix, extract the trajectory distribution elements of connected time nodes, and split the coordinate axes to obtain orthogonal directional components. Perform summation calculation of the square difference for the orthogonal directional components to obtain the span square scalar. Call the span square scalar to perform arithmetic square root algebraic analytical calculation to obtain the original value of geometric distance. Apply boundary extremum elimination logic to the original value of geometric distance to perform upper limit overflow blocking limit processing to generate the spatial distance of the road roller trajectory.

3. The intelligent monitoring and data traceability method for compaction quality of road rollers according to claim 2, characterized in that, The process of applying boundary extremum elimination logic to the original geometric distance value to perform upper limit overflow blocking and restriction processing specifically involves: obtaining the maximum travel speed parameter of the road roller and the node crossing difference, multiplying them to calculate the theoretical displacement boundary value; multiplying the theoretical displacement boundary value with the environmental disturbance tolerance coefficient, setting an anomaly truncation threshold, and assigning a value to the environmental disturbance tolerance coefficient based on the mean square error parameter of the positioning signal at the construction site; comparing the original geometric distance value with the anomaly truncation threshold; and when the original geometric distance value is greater than the anomaly truncation threshold, applying the anomaly truncation threshold to overwrite and replace the original geometric distance value.

4. The intelligent monitoring and data traceability method for compaction quality of road rollers according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: Perform monotonically increasing sorting on the time interval sequence to extract the rank of the elements, perform sequential accumulation operation based on the rank of the elements, construct the rank sum distribution matrix, traverse the internal nodes of the rank sum distribution matrix, extract the peak and extreme value terms, map the time scale associated with the peak and extreme value terms to define the sequence breakpoint cursor, and use the sequence breakpoint cursor to perform slicing and peeling on the time interval sequence to obtain the mutation interval subsequence. S202: Call the mutation interval subsequence, traverse the internal time unit to count the number of independent sampling points, perform superposition calculation on the deviation values ​​included in the mutation interval subsequence, calculate the total interval time, divide the total interval time by the number of independent sampling points, perform division operation to obtain the central tendency constant, and establish the segment interval characteristic parameters based on the central tendency constant. S203: Based on the time index carried by the segment interval feature parameter, retrieve the corresponding matching spatial distance of the roller trajectory, perform numerical normalization processing on the segment interval feature parameter and the spatial distance of the roller trajectory, perform algebraic ratio calculation on the normalized value, derive the spatial distribution discrete scalar, and generate the interval anomaly density index based on the spatial distribution discrete scalar.

5. The intelligent monitoring and data traceability method for compaction quality of road rollers according to claim 4, characterized in that, The process of extracting peak extrema terms specifically involves: performing a difference operation on the nodes inside the rank-sum distribution matrix to derive a node difference sequence; extracting the zero-crossing node positions where the sign is reversed and the corresponding difference magnitudes in the node difference sequence; obtaining an extremum determination threshold, which is set by extracting difference magnitude samples from standard data and calculating the upper confidence limit of the difference magnitude samples; and selecting nodes associated with zero-crossing node positions whose difference magnitudes are greater than the extremum determination threshold as peak extrema terms.

6. The intelligent monitoring and data traceability method for compaction quality of road rollers according to claim 1, characterized in that, The specific steps for S3 are as follows: S301: The vibration signal of the road roller is collected by the compaction sensor. Fixed window length sliding slice processing is performed on the time dimension marker point to separate the single-cycle waveform. Fast Fourier transform is performed on the separated waveform sequence to analyze the two-dimensional coordinates of the peak energy of the main frequency domain spectrum. The horizontal axis frequency scalar of the two-dimensional coordinates of the peak energy of the spectrum spectrum is mapped to construct the time domain evolution vector and generate the main frequency sequence corresponding to the cycle. S302: Call the main frequency sequence corresponding to the period, perform subtraction operation on adjacent two elements according to the time sequence to obtain the period variation difference, aggregate all variation differences and perform absolute value conversion on the extracted variation differences to construct the amplitude change vector, traverse the amplitude change vector, apply sliding window comparison logic to retrieve continuous data segments with monotonically decreasing values, extract continuous data segments with monotonically decreasing values, and generate continuous attenuation frequency difference segments; S303: Based on the difference elements within the continuous attenuation frequency difference segment, perform item-by-item arithmetic accumulation processing to obtain the absolute total frequency evolution, parse the time span label of the continuous attenuation frequency difference segment, count the complete slices, obtain the total number of duration periods, divide the absolute total frequency evolution by the total number of duration periods to obtain the normalized deceleration constant, apply numerical boundary constraint logic to the normalized deceleration constant to intercept out-of-limit abnormal terms, and obtain the frequency convergence rate parameter.

7. The intelligent monitoring and data traceability method for compaction quality of road rollers according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Collect continuous data frames of vibration acceleration of the road roller from the accelerometer and extract the sampling time stamp. Compare and define the amplitude scalars included in the continuous data frames of vibration acceleration of the road roller with the preset step threshold. Assign discrete integer labels according to the matching interval, combine the discrete integer labels with the sampling time stamp, and generate an interval mapping number sequence. S402: Call the interval mapping number sequence, traverse along the time axis to search for consecutive segments with repeated discrete integer labels, perform a difference operation on the sampling time scale of the first and last segments of the consecutive segments, derive the span duration, establish a numerical correspondence record between the discrete integer labels and the span duration, and obtain the single interval dwell time parameter; S403: Based on the single interval dwell time parameter, extract the span duration and sort it in descending order, extract the first and largest items, retrieve the first and largest items and bind discrete integer labels to define the main interval index, and perform time-series slicing and truncation processing on the continuous data frame of the road roller vibration acceleration according to the main interval index to obtain stable compaction section data; S404: For the single interval dwell time parameter in descending order, remove the first and largest items, retain the remaining non-first items to construct an anomaly set, use discrete integer labels of the anomaly set to define the deviation interval index, perform free fragment stripping processing on the continuous data frame of the road roller vibration acceleration according to the deviation interval index, and establish abnormal compaction section data.

8. The intelligent monitoring and data traceability method for compaction quality of road rollers according to claim 7, characterized in that, The specific method for setting the stepped threshold is as follows: extract the global maximum amplitude and global minimum amplitude of the continuous data frame of the roller vibration acceleration to obtain the amplitude range span; obtain the division coefficient, which is derived by performing a ratio operation on the root mean square scalar and standard deviation parameters of the continuous data frame of the roller vibration acceleration; perform a multiplication operation on the amplitude range span and the division coefficient to obtain the step length; generate an increasing limit scalar by sequentially superimposing the step length based on the global minimum amplitude; and aggregate the increasing limit scalar to construct the preset stepped threshold.

9. The intelligent monitoring and data traceability method for compaction quality of road rollers according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Call the stable compaction section data, traverse the numerical sequence along the time axis to extract continuous identical value segments, convert the continuous identical value segments into a mapping relationship between basic values ​​and continuous occurrence frequency, and perform redundancy removal and structural reorganization transformation on the stable compaction section data according to the mapping relationship to generate a stable section compressed file; S502: For the abnormal compaction section data, retrieve the associated sampling points to extract the three-dimensional spatial coordinate parameters and discrete timestamp labels, perform spatiotemporal attribute alignment and merging of the three-dimensional spatial coordinate parameters and discrete timestamp labels to construct a multi-dimensional feature array, and perform byte serialization and packaging on the multi-dimensional feature array to obtain the abnormal section backtracking package. S503: Obtain the interval anomaly density index and the frequency convergence rate parameter, establish a two-dimensional feature plane, project the interval anomaly density index and the frequency convergence rate parameter onto the horizontal and vertical coordinate axes of the two-dimensional feature plane to locate cross-projection nodes, compare the cross-projection nodes with the preset threshold classification boundary line to assign a level coding constant, and establish an anomaly level identifier. S504: Based on the anomaly level identifier and the anomaly section backtracking package, perform association integration, record the anomaly level identifier as a metadata label in the starting header index area of ​​the anomaly section backtracking package, apply the structured storage specification to perform underlying format normalization rewriting on the recorded mixed feature data, and generate a compaction quality monitoring traceability file.

10. A smart monitoring and data traceability system for the compaction quality of road rollers, characterized in that, The system is used to implement the intelligent monitoring and data traceability method for compaction quality of road rollers as described in any one of claims 1-9, and the system includes: The trajectory acquisition and analysis module obtains the timestamps of the road roller's spatial trajectory points and the spatial coordinates of the road roller's trajectory through the vehicle-mounted positioning terminal. It performs difference calculations on the timestamps of adjacent road roller spatial trajectory points to generate a time interval sequence, and performs Euclidean distance calculations on the spatial coordinates of adjacent road roller trajectories to generate the spatial distance between the road roller trajectories. The time-series breakpoint identification module analyzes the time interval sequence, identifies the sequence breakpoint cursor, divides the abrupt interval subsequence according to the sequence breakpoint cursor, generates segment interval feature parameters, and obtains the interval anomaly density index by combining the spatial distance of the road roller trajectory. The vibration response analysis module acquires the vibration signal of the road roller through the compaction sensor, segments the single-cycle vibration waveform, extracts the main frequency extreme points to form the main frequency sequence and main frequency change sequence corresponding to the cycle, identifies the continuously decreasing absolute value segment, calculates the ratio of the sum of the frequency change amplitude to the total number of continuous cycles, and generates the frequency convergence rate parameter. The acceleration segment identification module acquires continuous data frames of roller vibration acceleration through acceleration sensors, generates interval mapping number sequences and single interval dwell time parameters, and distinguishes between stable compaction section data and abnormal compaction section data. The results archiving and traceability module uses the stable compaction section data to construct a stable section compressed file, and classifies and maps the interval abnormal density index and frequency convergence rate parameter into anomaly level identifiers, writes them into the abnormal section backtracking package, and constructs a compaction quality monitoring traceability file.

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