A Visual Analysis Method for Road Compaction Trajectory

Abstract

This invention discloses a method for visual analysis of road compaction trajectories, relating to the field of transportation engineering technology. The method involves collecting raw data, preprocessing the raw data to obtain a dataset, mapping the dataset to a preset range, establishing a visual mapping relationship, determining the HSL value of each rectangular unit, calculating the RGB value of each rectangular unit using the HSL value to obtain a foundation construction area map, setting a transparency channel on the foundation construction area map, and overlaying the adjusted transparency channel onto the foundation construction area map to obtain the final construction area map. An interactive interface is also provided. This invention, through a standardized data preprocessing process, employs a color mapping mechanism to achieve simultaneous visualization of multiple parameters, and combines color interpolation to eliminate visual discontinuities and adjust transparency to optimize layer display, providing reliable technical support for accurate visualization analysis of road compaction trajectories and construction quality control.

Description

Technical Field

[0001] This invention relates to the field of transportation engineering technology, and in particular to a method for visual analysis of road compaction trajectories. Background Technology

[0002] In recent years, in the field of road construction quality control, the visualization analysis of compaction trajectory has become of great significance for ensuring the quality of road compaction. With the development of GPS and sensor technologies, it has become possible to collect data such as the spatial location of compaction equipment, soil compaction degree, and compaction speed. However, these data are usually presented in the form of tables or simple charts, which cannot intuitively show the spatial distribution characteristics of compaction trajectory and the continuous changes in compaction quality, making it difficult to meet the needs of comprehensive and intuitive analysis in engineering practice.

[0003] In existing technologies, the data processing methods are not perfect, lacking effective data cleaning, time alignment, and gridding processes. This results in noise and bias in the visualization results. The color mapping scheme is too simplistic and cannot fully utilize the human eye's ability to perceive color to distinguish different degrees of compaction, making the boundaries between different compaction areas blurry. The transparency processing mechanism is not flexible enough and cannot dynamically adjust the display effect according to the number of data points, resulting in uneven visualization effects in data-dense and information-sparse areas. The interactive functions are limited, making it difficult for users to conduct in-depth data exploration and analysis. It cannot meet the diverse needs of engineering practice, such as detailed viewing, numerical querying, and area marking, thus limiting the application value of visualization analysis tools in actual engineering. Summary of the Invention

[0004] The technical problem solved by this invention is that: the data processing methods are not perfect, lacking effective data cleaning, time alignment, and gridding processes, resulting in noise and bias in the visualization results; the color mapping scheme is too simplistic, failing to fully utilize the human eye's ability to perceive color to distinguish different degrees of compaction, making the boundaries between different compaction areas blurry; the transparency processing mechanism is not flexible enough, unable to dynamically adjust the display effect according to the number of data points, resulting in uneven visualization effects in data-dense and information-sparse areas; the interactive functions are limited, making it difficult for users to conduct in-depth data exploration and analysis, and failing to meet the diverse needs of engineering practice for detailed viewing, numerical querying, and area marking, thus limiting the application value of visualization analysis tools in actual engineering.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for visual analysis of road compaction trajectories, comprising the following steps:

[0006] Step S1: Collect raw data and preprocess the raw data to obtain a dataset;

[0007] Step S2: Map the dataset to a preset range to obtain normalized data, and establish a visual mapping relationship based on the normalized data;

[0008] Step S3: Determine the HSL value of each rectangular unit based on the normalized data, and calculate the RGB value of each rectangular unit using the HSL value to obtain the foundation construction area map;

[0009] Step S4: Set a transparency channel on the basic construction area map, adjust the transparency of the transparency channel, and overlay the adjusted transparency channel onto the basic construction area map to obtain the final construction area map;

[0010] Step S5: Set up the interactive interface, and the user can explore the final construction area map through the different functions of the interactive interface.

[0011] As a preferred embodiment of the road compaction trajectory visualization analysis method described in this invention, step S1 specifically includes:

[0012] Raw data is collected using a GPS module, compaction sensor, and speed recorder on the compaction equipment. The raw data includes the spatial location information of the compaction, the compaction degree of the soil, and the compaction speed of the compaction equipment. The raw data is then preprocessed to obtain a dataset.

[0013] As a preferred embodiment of the road compaction trajectory visualization analysis method described in this invention, the preprocessing of the original data to obtain the dataset specifically includes:

[0014] The preprocessing includes data cleaning, data alignment, and gridding.

[0015] The data cleaning process is used to remove outliers and noisy data from the original data to obtain the first processed data;

[0016] The data alignment process is used to align the first processed data according to a unified timestamp to obtain the second processed data;

[0017] The gridding process is used to divide the construction area into rectangular units of a preset size based on the second processed data, and to count the number of times of compaction, the degree of compaction and the compaction speed of each rectangular unit to obtain a dataset.

[0018] As a preferred embodiment of the road compaction trajectory visualization analysis method described in this invention, step S2 specifically includes:

[0019] The dataset is mapped to a preset range using the min-max normalization method to obtain normalized data. A visual mapping relationship is then established based on the normalized data. The visual mapping relationship includes mapping the number of compaction cycles to hue, the compaction degree to brightness, and the compaction speed to saturation. Relevant parameters for the visual mapping are set, including hue mapping parameters, brightness mapping parameters, and saturation mapping parameters.

[0020] As a preferred embodiment of the road compaction trajectory visualization analysis method described in this invention, the setting of relevant parameters for the visual mapping specifically includes:

[0021] Regarding the hue mapping parameter: when the number of rolling cycles is less than the first threshold, the hue is set to blue;

[0022] When the number of compaction cycles is greater than or equal to the first threshold and less than the second threshold, the hue is set to green.

[0023] When the number of compaction cycles is greater than or equal to the second threshold, the hue is set to red;

[0024] Regarding the brightness mapping parameters: when the compaction degree is less than the third threshold, the brightness is set to the first brightness value;

[0025] When the compaction degree is greater than or equal to the third threshold and less than the fourth threshold, the brightness is set to the second brightness value;

[0026] When the compaction degree is greater than or equal to the fourth threshold, the brightness is set to the third brightness value;

[0027] The third brightness value is greater than the second brightness value, which is greater than the first brightness value;

[0028] Regarding the saturation mapping parameters: when the compaction speed is less than the fifth threshold or greater than the sixth threshold, the saturation is set to the first saturation value; when the compaction speed is greater than the fifth threshold and less than the sixth threshold, the saturation is set to the second saturation value.

[0029] The first saturation value is greater than the second saturation value.

[0030] As a preferred embodiment of the road compaction trajectory visualization analysis method described in this invention, step S3 specifically includes:

[0031] Based on the normalized data, the HSL value of each rectangular unit is determined. The HSL value includes hue, brightness, and saturation. The RGB value of each rectangular unit is calculated using the HSL to RGB conversion formula. Linear interpolation is performed on the RGB values ​​between adjacent rectangular units using color interpolation to generate a first construction area map. An explanatory legend is added to the first construction area map to obtain a second construction area map. The second construction area map is rendered using a rendering engine to output a third construction area map, which is used as the base construction area map.

[0032] The explanatory illustrations include the physical meaning and numerical range of hue, brightness, and saturation.

[0033] As a preferred embodiment of the road compaction trajectory visualization analysis method described in this invention, the step of using color interpolation to perform linear interpolation calculation of the RGB values ​​between each adjacent rectangular unit specifically includes:

[0034] The RGB values ​​of adjacent first and second rectangular units are obtained respectively to obtain first and second RGB values. The geometric center point of the first rectangular unit is used as the first reference point, and the geometric center point of the second rectangular unit is used as the second reference point. A straight line is formed by connecting the first and second reference points. Interpolation points are obtained on the straight line at preset intervals. The distance from the interpolation point to the first rectangular unit is obtained. According to the ratio of the distance to the total distance between the first and second rectangular units, linear interpolation calculation is performed on the first and second RGB values ​​to obtain the RGB values ​​of the interpolation points between the first and second rectangular units. The RGB values ​​of the interpolation points are then applied to the area corresponding to the interpolation points.

[0035] As a preferred embodiment of the road compaction trajectory visualization analysis method described in this invention, step S4 specifically includes:

[0036] A transparency channel is set on the basic construction area map. The transparency channel includes a rolling number channel, a compaction degree channel, and a speed channel. The number of data points within a preset range of each rectangular unit is obtained. The transparency of the transparency channel is adjusted according to the number of data points. The data points include coordinate position information, rolling construction parameters, and construction equipment information. An Alpha mixing algorithm is used to overlay the transparency channel with the adjusted transparency onto the basic construction area map to obtain the final construction area map.

[0037] As a preferred embodiment of the road compaction trajectory visualization analysis method described in this invention, the step of adjusting the transparency of the transparency channel according to the number of data points specifically includes:

[0038] When the number of data points is greater than the first preset number, the transparency is set to the first preset value.

[0039] When the number of data points is less than a first preset number but greater than a second preset number, the second preset transparency is set.

[0040] When the number of data points is less than the second preset number, the third preset transparency is set.

[0041] As a preferred embodiment of the road compaction trajectory visualization analysis method described in this invention, step S5 specifically includes:

[0042] An interactive interface is set up so that users can explore the final construction area map through different functions of the interactive interface, including zoom function, pan function, numerical query function and area marking function;

[0043] Zoom function: Users can zoom in and out of the final construction area map using the mouse wheel to view details of local areas of the final construction area map;

[0044] Panning function: Users can pan the final construction area map by dragging the mouse to view the compaction degree at different locations on the final construction area map;

[0045] Numerical query function: When the user hovers the mouse cursor over one of the rectangular units in the final construction area map, the number of compaction times, compaction degree and compaction speed of the one rectangular unit are displayed;

[0046] Area marking function: Users can manually mark areas of interest on the final construction area map using the mouse.

[0047] The beneficial effects of this invention are as follows: By standardizing the data preprocessing process, this invention cleans and removes abnormal noise, aligns and unifies the time dimension, and accurately statistically counts rectangular unit parameters using a grid. This solves the problems of low data quality and coarse spatial mapping, providing a high-precision dataset for analysis. A color mapping mechanism is used to achieve synchronous visualization of multiple parameters. Color interpolation is combined to eliminate visual discontinuities, and transparency is adjusted to optimize layer display. This breaks through the limitations of traditional single-parameter mapping and information interference, making the distribution of compaction quality more intuitive. Simultaneously, relying on rich interactive functions, it facilitates managers to quickly locate hidden dangers and trace their root causes. This comprehensively improves the accuracy, visualization effect, and practicality of compaction trajectory analysis, providing reliable technical support for the accurate visualization analysis of road compaction trajectories and construction quality control. Attached Figure Description

[0048] Figure 1 This is a basic flowchart illustrating a method for visualizing and analyzing road compaction trajectories, provided as an embodiment of the present invention. Detailed Implementation

[0049] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0050] Example, refer to Figure 1 As an embodiment of the present invention, a method for visual analysis of road compaction trajectories is provided, comprising the following steps:

[0051] Step S1: Collect raw data, preprocess the raw data, and obtain the dataset.

[0052] Step S2: Map the dataset to a preset range to obtain normalized data, and establish a visual mapping relationship based on the normalized data.

[0053] Step S3: Determine the HSL value of each rectangular unit based on the normalized data, and calculate the RGB value of each rectangular unit using the HSL value to obtain the foundation construction area map.

[0054] Step S4: Set a transparency channel on the foundation construction area map, adjust the transparency of the transparency channel, and overlay the adjusted transparency channel onto the foundation construction area map to obtain the final construction area map.

[0055] Step S5: Set up the interactive interface, allowing users to explore the final construction area map through different functions of the interactive interface.

[0056] Step S1 specifically includes:

[0057] Raw data is collected using GPS modules, compaction sensors, and speed recorders on the compaction equipment. The raw data includes spatial location information of the compaction, soil compaction, and compaction speed of the compaction equipment. The raw data is then preprocessed to obtain a dataset.

[0058] Precise spatial location information pinpoints the spatial distribution of compaction trajectories; soil compaction degree directly reflects core indicators of construction quality; and compaction speed is linked to construction efficiency and compaction effect. The combination of these three elements forms a complete data chain for the construction process. Preprocessing of the raw data removes outliers and noise data generated during collection due to equipment errors and environmental interference, preventing invalid data from interfering with subsequent analysis. Simultaneously, data alignment and gridding transform the scattered raw data into a unified, formatted dataset, providing standardized data input for subsequent data normalization and visual mapping steps. This ensures that the visualization results accurately and clearly reflect the actual situation of road compaction construction.

[0059] The raw data is preprocessed to obtain the dataset, which specifically includes:

[0060] Preprocessing includes data cleaning, data alignment, and gridding.

[0061] Data cleaning is used to remove outliers and noisy data from the original data to obtain the first processed data.

[0062] Outliers in the raw data are initially screened using box plots based on industry standards for soil type (e.g., compaction degree ≥93% for cohesive soil) and equipment parameter settings. These outliers are then further verified in conjunction with the construction scenario to avoid mistakenly deleting valid data.

[0063] Data cleaning can remove outliers (such as compaction values ​​that exceed reasonable ranges) and noisy data (such as instantaneous fluctuations in speed records) caused by equipment errors and environmental interference, thus preventing these invalid data from affecting the accuracy of subsequent analysis and ensuring the authenticity and reliability of the first-processed data.

[0064] Data alignment is used to align the first-processed data according to a unified timestamp to obtain the second-processed data.

[0065] The time is synchronized with the satellite time synchronization device in advance, and the deviation is controlled to be ≤1 second. If there is drift, it is corrected by the common time point. If the deviation exceeds 5 seconds, it will cause data mismatch, and the time consistency is checked.

[0066] Data alignment processing solves the time synchronization problem that may exist between different acquisition devices (GPS module, compaction sensor, velocity recorder) by using a unified timestamp. It enables spatial location, compaction and velocity data under the same time dimension to form a precise matching relationship, providing time consistency guarantee for subsequent multi-parameter joint analysis. The generated second-processed data has stronger correlation and analytical value.

[0067] The gridding process is used to divide the construction area into rectangular units of a preset size based on the second processing data, and to count the number of times of compaction, the degree of compaction and the compaction speed of each rectangular unit to obtain a dataset.

[0068] The size of the rectangular unit is set according to the construction area (1m×1m for large areas and 0.5m×0.5m for small areas) and the accuracy requirements.

[0069] The compaction degree is taken as the arithmetic mean of all collected data within the unit. The number of rolling operations is set with a clear counting standard (e.g., if the rolling equipment wheel tracks completely cover the unit area, it is counted as 1 time; if it is partially covered, the counting ratio is set according to the coverage area ratio, such as 0.5 times for coverage ≥ 50%). The rolling speed is taken as the average or median of all speed data within the unit (to avoid the influence of extreme speed values).

[0070] Grid processing transforms continuous construction areas into quantifiable rectangular units. By statistically analyzing key construction parameters (number of compaction passes, compaction degree, and compaction speed) within each unit, the scattered raw data is transformed into structured data directly related to the spatial area. This reduces the complexity of subsequent visualization rendering and allows the analysis results to more intuitively reflect the differences in construction quality in different areas. The resulting dataset can directly support subsequent visual mapping and chart generation.

[0071] Step S2 specifically includes:

[0072] The minimum-maximum normalization method is used to map the dataset to a preset range to obtain normalized data. Based on the normalized data, a visual mapping relationship is established. The visual mapping relationship includes mapping the number of compaction times to hue, the compaction degree to brightness, and the compaction speed to saturation. Relevant parameters for visual mapping are set, including hue mapping parameters, brightness mapping parameters, and saturation mapping parameters.

[0073] Min-max normalization maps datasets of different magnitudes (e.g., the number of compaction cycles may be 1-20, the compaction degree may be 60%-100%, and the speed may be 1-10 km / h) to a preset range (0-1), eliminating visual expression bias caused by differences in data magnitude. This ensures that subsequent mapping of hue, brightness, and saturation can fairly reflect the characteristics of each data dimension. It establishes a visual mapping relationship of "number of compaction cycles - hue, compaction degree - brightness, and compaction speed - saturation," transforming abstract data into the core of intuitive color information. This allows users to quickly identify data differences through color—for example, different hues distinguish the number of compaction cycles, different brightness levels indicate the degree of compaction, and different saturations indicate whether the speed is normal, significantly reducing the threshold for data interpretation.

[0074] The specific parameters for setting the visual mapping include:

[0075] Regarding the hue mapping parameter: when the number of rolling cycles is less than the first threshold, the hue is set to blue.

[0076] When the number of compaction cycles is greater than or equal to the first threshold and less than the second threshold, the hue is set to green.

[0077] When the number of compaction cycles is greater than or equal to the second threshold, the hue is set to red.

[0078] Regarding the brightness mapping parameters: when the compaction degree is less than the third threshold, the brightness is set to the first brightness value.

[0079] When the compaction degree is greater than or equal to the third threshold and less than the fourth threshold, the brightness is set to the second brightness value.

[0080] When the compaction degree is greater than or equal to the fourth threshold, the brightness is set to the third brightness value.

[0081] The third brightness value is greater than the second brightness value, which in turn is greater than the first brightness value.

[0082] Regarding the saturation mapping parameters: when the compaction speed is less than the fifth threshold or greater than the sixth threshold, the saturation is set to the first saturation value; when the compaction speed is greater than the fifth threshold but less than the sixth threshold, the saturation is set to the second saturation value.

[0083] The first saturation value is greater than the second saturation value.

[0084] By using hue to differentiate the number of compaction cycles (a gradient change from blue to green to red), users can quickly identify differences in compaction frequency in different areas. Red areas intuitively indicate sufficient compaction, while blue areas suggest potential undercompaction. Brightness variations reflect compaction degree (higher brightness indicates better compaction). The contrast between light and dark clearly presents the spatial distribution of construction quality; bright areas quickly locate areas that meet compaction standards, while darker areas highlight areas with insufficient compaction. Saturation differences indicate whether the compaction speed is within a reasonable range (low saturation corresponds to an appropriate speed, while high saturation indicates too fast or too slow speed), visually identifying areas with abnormal speed. The combination of these three elements allows the final visualization to not only reflect the distribution characteristics of each parameter individually but also to present the synergistic relationship between multiple parameters through comprehensive color changes. This enables users to quickly grasp the overall construction situation and potential problem areas through visual perception, improving analysis efficiency and decision-making accuracy.

[0085] Step S3 specifically includes:

[0086] The HSL value of each rectangular unit is determined based on normalized data. The HSL value includes hue, brightness, and saturation values. The RGB value of each rectangular unit is calculated using the HSL to RGB conversion formula. The RGB values ​​between adjacent rectangular units are linearly interpolated using color interpolation to generate a first construction area map. An explanatory legend is added to the first construction area map to obtain a second construction area map. The second construction area map is rendered using a rendering engine to output a third construction area map, which is used as the base construction area map.

[0087] The illustrations include the physical meaning and numerical range of hue, brightness, and saturation.

[0088] The HSL value of each rectangular unit is determined based on normalized data. The standardized data is precisely matched with the three dimensions of HSL. The hue value needs to be combined with the normalized number of compaction times. First, it is back-correlated to the original number range, and then the corresponding hue is assigned according to the preset mapping parameters (e.g., hue 240° for compaction times <3 times, hue 120° for 3-7 times, and hue 0° for ≥7 times). This intuitively reflects the difference in the number of times. The brightness value is based on the normalized compaction degree and is matched to the preset compaction degree range (e.g., <80%, 80%-90%, ≥90%). Then, it is assigned according to the parameters (corresponding to 30%, 60%, and 90% brightness), and the brightness and compaction degree are kept positively correlated, which is in line with the understanding that "brightness equals quality". The saturation value is associated with the normalized compaction speed to determine the speed range to which the unit belongs (e.g., <2km / h or >5km / h is abnormal, 2-5km / h is normal). The abnormal range is assigned high saturation (e.g., 100%), and the normal range is assigned medium saturation (e.g., 50%) to highlight the abnormal speed area.

[0089] Hue: Physically, it distinguishes the number of times the vehicle has been rolled. Numerical range (example): number of rolls < 3 (blue), 3 ≤ number of rolls < 6 (green), number of rolls ≥ 6 (red).

[0090] Brightness: Physically, it reflects the degree of compaction. The numerical range (example) is: compaction degree < 90% (brightness 30%), 90% ≤ compaction degree < 95% (brightness 60%), and compaction degree ≥ 95% (brightness 90%).

[0091] Saturation: Physically, it indicates whether the compaction speed is reasonable. The numerical range (example) is: compaction speed < 2 km / h or > 4 km / h (saturation 80%), 2 km / h < compaction speed < 4 km / h (saturation 30%).

[0092] By converting HSL values ​​to RGB, an accurate correspondence between data and color is established, allowing the color of each rectangular unit to precisely reflect the number of compaction cycles, compaction degree, and speed information. The application of color interpolation eliminates color abrupt changes between adjacent units, resulting in a smooth transition visual effect in the construction area map, which is more in line with the human eye's perception of continuous space. Adding explanatory legends provides a basis for image interpretation, allowing users to quickly understand the physical meaning and numerical range of hue, brightness, and saturation, avoiding misinterpretation of color information. The rendering engine further optimizes the image display quality. The final basic construction area map not only retains the spatial distribution characteristics of each parameter but also has good visual coherence and readability, laying a clear and accurate visual foundation for subsequent overlay of transparency channels and interactive analysis.

[0093] The linear interpolation calculation of the RGB values ​​between adjacent rectangular units using color interpolation specifically includes:

[0094] The RGB values ​​of adjacent first and second rectangular units are obtained respectively, resulting in first and second RGB values. The geometric center point of the first rectangular unit is used as the first reference point, and the geometric center point of the second rectangular unit is used as the second reference point. A straight line is formed by connecting the first and second reference points. Interpolation points are obtained on the straight line at preset intervals. The distance from the interpolation point to the first rectangular unit is obtained. Based on the ratio of the distance to the total distance between the first and second rectangular units, linear interpolation calculation is performed on the first and second RGB values ​​to obtain the RGB values ​​of the interpolation points between the first and second rectangular units. The RGB values ​​of the interpolation points are then applied to the areas corresponding to the interpolation points.

[0095] The preset interval is determined in conjunction with the size of the rectangular unit. If the unit size is 1m×1m, the interval can be set to 0.1-0.2m. This ensures a smooth color transition without increasing the amount of calculation due to too small an interval. At the same time, it verifies the interpolation effect and avoids uneven color transitions between adjacent units (such as obvious color bands), ensuring a coherent and natural overall visual presentation.

[0096] The linear interpolation formula calculates the values ​​for each of the three RGB components separately. The formula is: Target component value = Starting unit component value + (Ending unit component value - Starting unit component value) × (Distance from interpolation point to starting unit ÷ Total distance). For example, for R component interpolation, if R1=100, R2=200, and the interpolation point distance accounts for 0.3, then R=100+(200-100)×0.3=130. This ensures synchronous transition of the three channels and avoids color cast.

[0097] The total distance is based on the straight-line distance between the geometric center points of the two units. For example, if the center coordinates of the first unit are (1,1) and the center coordinates of the second unit are (2,1), the total distance is 1m. This ensures the accuracy of subsequent scale calculations and avoids imbalance in color transition scales due to incorrect distance references.

[0098] The area corresponding to the interpolation point is defined, and the spatial range corresponding to the interpolation point is clearly defined. The transition area is divided into continuous small areas according to the interpolation interval, with the boundary of adjacent units as the boundary. Each small area corresponds to the RGB value of an interpolation point. For example, when the distance between two units is 1m and the interval is 0.1m, 10 transition small areas will be formed, and the gradient color from the first unit to the second unit will be applied in sequence.

[0099] By eliminating color abrupt changes between adjacent rectangular units and calculating the gradient colors in the transition area, the construction area map presents a natural and smooth visual effect, which is more in line with the human eye's perception of continuous space and avoids the visual fragmentation caused by color block splicing, thereby improving the readability of the overall visualization image.

[0100] Step S4 specifically includes:

[0101] A transparency channel is set on the basic construction area map. The transparency channel includes a rolling number channel, a compaction degree channel, and a speed channel. The number of data points within a preset range of each rectangular unit is obtained. The transparency of the transparency channel is adjusted according to the number of data points. The data points include coordinate position information, rolling construction parameters, and construction equipment information. An Alpha mixing algorithm is used to overlay the transparency channel with the adjusted transparency onto the basic construction area map to obtain the final construction area map.

[0102] The preset range is centered on a rectangular cell, and the statistical boundary is defined by a radius of 1-2 cells. The dense data area uses a small range (1 radius), while the sparse area can be expanded to 2 radii. This avoids the inclusion of irrelevant data due to an excessively large range or insufficient statistics due to an excessively small range.

[0103] The transparency channels, designed for compaction frequency, compaction degree, and compaction speed, adjust transparency based on the number of data points within a preset range for each rectangular unit. More data points (indicating more comprehensive data collection in the area) result in lower transparency (clearer image), while fewer data points (sparser data collection) result in higher transparency (more transparent image). This design allows users to quickly identify "high-confidence areas" (low transparency, dense data) and "low-confidence areas" (high transparency, sparse data), avoiding misjudgments due to insufficient data. The application of the Alpha blending algorithm achieves a natural integration of the transparency channels with the base map. This prevents transparency adjustments from obscuring color information in the base map (such as brightness corresponding to compaction degree and hue corresponding to compaction frequency), while simultaneously conveying data density information through transparency. This creates a dual information expression where "color reflects parameters, and transparency reflects confidence," enriching the information content of the final construction area map.

[0104] Adjusting the transparency of the transparency channel based on the number of data points specifically includes:

[0105] When the number of data points is greater than the first preset number, set the transparency to the first preset value.

[0106] When the number of data points is less than the first preset number but greater than the second preset number, set the transparency to the second preset value.

[0107] When the number of data points is less than the second preset number, set the transparency to the third preset.

[0108] When the number of data points exceeds the first preset number (most sufficient data), the first preset transparency (usually lower, resulting in a clearer image) highlights the area, intuitively conveying the information that "the data is reliable and the analysis results are credible." When the number of data points is between the first and second preset numbers (relatively sufficient data), the second preset transparency (medium value) acts as a transition, indicating to the user that the data credibility in this area is moderate. When the number of data points is less than the second preset number (sparse data), the third preset transparency (higher, resulting in a more transparent image) serves as a warning, indicating that the data in this area is insufficient and the analysis results should be carefully considered. This hierarchical adjustment avoids the problem of a single transparency level failing to distinguish data density, and lowers the threshold for users to judge data credibility through simple visual differences. This allows the final construction area map to simultaneously convey data quality information while displaying construction parameters, providing a more comprehensive basis for decision-making.

[0109] Step S5 specifically includes:

[0110] The interactive interface allows users to explore the final construction area map through various functions, including zoom, pan, numerical query, and area marking.

[0111] Zoom function: Users can zoom in and out of the final construction area map using the mouse wheel to view details of local areas.

[0112] Panning function: Users can pan the final construction area map by dragging the mouse to view the compaction degree at different locations on the final construction area map.

[0113] Numerical query function: When the user hovers the mouse cursor over one of the rectangular units in the final construction area map, the number of compaction times, compaction degree and compaction speed of one of the rectangular units are displayed.

[0114] Area marking function: Users can manually mark areas of interest on the final construction area map using the mouse.

[0115] These interactive features allow users to flexibly manipulate the final construction area map, zoom in and out to view global and local details, pan to browse different locations, hover to obtain specific values, and manually mark areas of interest, forming a complete exploration experience. This not only improves the efficiency of information acquisition but also meets personalized analysis needs, greatly enhancing the practical value of visualization results.

[0116] This invention solves the problems of low data quality and coarse spatial mapping by standardizing data preprocessing, cleaning and removing abnormal noise, aligning and unifying the time dimension, and accurately statistically analyzing rectangular unit parameters through gridding. It provides a high-precision dataset for analysis, and uses a color mapping mechanism to achieve synchronous visualization of multiple parameters. Combined with color interpolation to eliminate visual discontinuities and adjusting transparency to optimize layer display, it breaks through the limitations of traditional single-parameter mapping and information interference, making the distribution of compaction quality more intuitive. At the same time, relying on rich interactive functions, it facilitates managers to quickly locate hidden dangers and trace their root causes, comprehensively improving the accuracy of compaction trajectory analysis, visualization effect and control practicality, and providing reliable technical support for the accurate visualization analysis of road compaction trajectories and construction quality control.

[0117] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0118] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for visual analysis of road compaction trajectories, characterized in that, Includes the following steps: Step S1: Collect raw data and preprocess the raw data to obtain a dataset; Step S2: Map the dataset to a preset range to obtain normalized data, and establish a visual mapping relationship based on the normalized data; Step S3: Determine the HSL value of each rectangular unit based on the normalized data, and calculate the RGB value of each rectangular unit using the HSL value to obtain the foundation construction area map; Step S4: Set a transparency channel on the basic construction area map, adjust the transparency of the transparency channel, and overlay the adjusted transparency channel onto the basic construction area map to obtain the final construction area map; Step S5: Set up the interactive interface, and the user can explore the final construction area map through the different functions of the interactive interface; Step S1 specifically includes: Raw data is collected using a GPS module, compaction sensor, and speed recorder on the compaction equipment. The raw data includes the spatial location information of the compaction, the compaction degree of the soil, and the compaction speed of the compaction equipment. The raw data is then preprocessed to obtain a dataset. Step S2 specifically includes: The dataset is mapped to a preset range using the minimum-maximum normalization method to obtain normalized data. A visual mapping relationship is established based on the normalized data. The visual mapping relationship includes mapping the number of compaction times to hue, the compaction degree to brightness, and the compaction speed to saturation. Relevant parameters of the visual mapping are set, including hue mapping parameters, brightness mapping parameters, and saturation mapping parameters. Step S4 specifically includes: A transparency channel is set on the basic construction area map. The transparency channel includes a rolling number channel, a compaction degree channel, and a speed channel. The number of data points within a preset range of each rectangular unit is obtained. The transparency of the transparency channel is adjusted according to the number of data points. The data points include coordinate position information, rolling construction parameters, and construction equipment information. An Alpha mixing algorithm is used to overlay the transparency channel with the adjusted transparency onto the basic construction area map to obtain the final construction area map.

2. The road compaction trajectory visualization analysis method as described in claim 1, characterized in that: The preprocessing of the original data to obtain the dataset specifically includes: The preprocessing includes data cleaning, data alignment, and gridding. The data cleaning process is used to remove outliers and noisy data from the original data to obtain the first processed data; The data alignment process is used to align the first processed data according to a unified timestamp to obtain the second processed data; The gridding process is used to divide the construction area into rectangular units of a preset size based on the second processed data, and to count the number of times of compaction, the degree of compaction and the compaction speed of each rectangular unit to obtain a dataset.

3. The road compaction trajectory visualization analysis method as described in claim 2, characterized in that: The specific parameters for setting the visual mapping include: Regarding the hue mapping parameter: when the number of rolling cycles is less than the first threshold, the hue is set to blue; When the number of compaction cycles is greater than or equal to the first threshold and less than the second threshold, the hue is set to green. When the number of compaction cycles is greater than or equal to the second threshold, the hue is set to red; Regarding the brightness mapping parameters: when the compaction degree is less than the third threshold, the brightness is set to the first brightness value; When the compaction degree is greater than or equal to the third threshold and less than the fourth threshold, the brightness is set to the second brightness value; When the compaction degree is greater than or equal to the fourth threshold, the brightness is set to the third brightness value; The third brightness value is greater than the second brightness value, which is greater than the first brightness value; Regarding the saturation mapping parameters: when the compaction speed is less than the fifth threshold or greater than the sixth threshold, the saturation is set to the first saturation value; when the compaction speed is greater than the fifth threshold and less than the sixth threshold, the saturation is set to the second saturation value. The first saturation value is greater than the second saturation value.

4. The road compaction trajectory visualization analysis method as described in claim 3, characterized in that: Step S3 specifically includes: Based on the normalized data, the HSL value of each rectangular unit is determined. The HSL value includes hue, brightness, and saturation. The RGB value of each rectangular unit is calculated using the HSL to RGB conversion formula. Linear interpolation is performed on the RGB values ​​between adjacent rectangular units using color interpolation to generate a first construction area map. An explanatory legend is added to the first construction area map to obtain a second construction area map. The second construction area map is rendered using a rendering engine to output a third construction area map, which is used as the base construction area map. The explanatory illustrations include the physical meaning and numerical range of hue, brightness, and saturation.

5. The road compaction trajectory visualization analysis method as described in claim 4, characterized in that: The step of using color interpolation to perform linear interpolation calculations on the RGB values ​​between adjacent rectangular units specifically includes: The RGB values ​​of adjacent first and second rectangular units are obtained respectively to obtain first and second RGB values. The geometric center point of the first rectangular unit is used as the first reference point, and the geometric center point of the second rectangular unit is used as the second reference point. A straight line is formed by connecting the first and second reference points. Interpolation points are obtained on the straight line at preset intervals. The distance from the interpolation point to the first rectangular unit is obtained. According to the ratio of the distance to the total distance between the first and second rectangular units, linear interpolation calculation is performed on the first and second RGB values ​​to obtain the RGB values ​​of the interpolation points between the first and second rectangular units. The RGB values ​​of the interpolation points are then applied to the area corresponding to the interpolation points.

6. The road compaction trajectory visualization analysis method as described in claim 5, characterized in that: The step of adjusting the transparency of the transparency channel based on the number of data points specifically includes: When the number of data points is greater than the first preset number, the transparency is set to the first preset value. When the number of data points is less than a first preset number but greater than a second preset number, the second preset transparency is set. When the number of data points is less than the second preset number, the third preset transparency is set.

7. The road compaction trajectory visualization analysis method as described in claim 6, characterized in that: Step S5 specifically includes: An interactive interface is set up so that users can explore the final construction area map through different functions of the interactive interface, including zoom function, pan function, numerical query function and area marking function; Zoom function: Users can zoom in and out of the final construction area map using the mouse wheel to view details of local areas of the final construction area map; Panning function: Users can pan the final construction area map by dragging the mouse to view the compaction degree at different locations on the final construction area map; Numerical query function: When the user hovers the mouse cursor over one of the rectangular units in the final construction area map, the number of compaction times, compaction degree and compaction speed of the one rectangular unit are displayed; Area marking function: Users can manually mark areas of interest on the final construction area map using the mouse.

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