Earthwork rolling construction monitoring method and device, electronic equipment and storage medium

Abstract

The application relates to a kind of earthwork rolling construction monitoring method, device, electronic equipment and storage medium.The method comprises: the construction area is divided into multiple target block regions;During the construction of the construction area, real-time state data is collected for the target block region currently under construction;Based on the intelligent algorithm of rolling number, the real-time state data is analyzed to determine the target detection result;Based on the target detection result, a compensation rolling strategy is generated for the target block region that does not meet the rolling requirements;Wherein, the compensation rolling strategy includes at least one of the compensation path and the compensation parameter;The compensation rolling strategy is sent to the construction equipment;Wherein, the compensation rolling strategy is used to control the construction equipment to complete the rolling work.In this way, the granulation management of the construction area can be realized, and accurate coordinate positioning is provided for subsequent repair, ensuring timeliness;The optimal compensation path is planned through the algorithm, avoiding the repeated rolling of the road roller in the qualified area, reducing invalid fuel consumption and equipment wear.

Description

Technical Field

[0001] This application relates to the field of construction management technology, and in particular to a method, device, electronic equipment and storage medium for monitoring earthwork compaction construction. Background Technology

[0002] Large-scale airport earthwork engineering is the core of airfield construction, laying the foundation for subsequent pavement structures through site leveling. Currently, obtaining compaction parameters mainly relies on manual labor, which is not only time-consuming and labor-intensive, but also susceptible to human factors, and the parameter combination schemes are limited, making it difficult to adapt to digital intelligent monitoring technology. Furthermore, existing digital intelligent monitoring technology cannot directly detect the compaction density of the material in real time, making it difficult to guide on-site construction.

[0003] In terms of compaction quality inspection, compaction degree testing relies on manual operation, which has problems such as large human interference and low efficiency; the testing is based on points instead of areas, which cannot achieve full coverage of the construction area and is prone to missing weak areas; the post-construction test pit testing method cannot quickly respond to the on-site situation and is difficult to effectively guide construction.

[0004] Traditional monitoring methods are no longer sufficient to meet the efficiency and precision requirements of modern construction, making quality control difficult to guarantee. Therefore, how to achieve comprehensive and accurate construction monitoring at airports or other areas is an urgent problem to be solved. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, device, electronic equipment, and storage medium for monitoring earthwork compaction construction, addressing the aforementioned technical problems.

[0006] In a first aspect, this application provides a method for monitoring the compaction construction of earthwork projects, the method comprising:

[0007] The construction area is divided into multiple target block areas;

[0008] During the construction process in the construction area, real-time status data is collected for the target block area currently under construction.

[0009] Based on the intelligent algorithm for the number of compaction passes, the real-time status data is analyzed to determine the target detection result;

[0010] Based on the target block area whose target detection result is unqualified for compaction, a compensation compaction strategy is generated; wherein, the compensation compaction strategy includes at least one of compensation path and compensation parameters;

[0011] The compensating compaction strategy is sent to the construction equipment; wherein the compensating compaction strategy is used to control the construction equipment to complete the compaction operation.

[0012] In one embodiment, dividing the construction area into multiple target block areas includes:

[0013] The construction area is divided according to a preset ratio to obtain multiple grid units and corresponding area attributes; wherein, the area attributes include at least one of grid boundary coordinates and area identifier;

[0014] A trajectory surface is formed based on the operating trajectory of the construction equipment;

[0015] Based on the curvature change points of the trajectory line of the operation trajectory and / or the intersection points of the trajectory surface and the grid boundary as segmentation points, the trajectory surface is segmented to obtain a segmented trajectory surface;

[0016] Perform pairwise spatial intersection, difference, and complement operations on each of the segmented trajectory surfaces to determine non-overlapping candidate block regions;

[0017] During multiple compaction processes, when a new trajectory surface overlaps with an existing candidate block region, the overlapping region is obtained and the region attributes of the overlapping region are updated; wherein, the region attributes also include the number of compaction passes;

[0018] The process of performing pairwise spatial intersection, difference, and complement operations on each segmented trajectory surface to obtain overlapping regions and updating the number of compaction passes of the overlapping regions is repeated until the construction work is completed, resulting in non-overlapping target block regions and their corresponding number of compaction passes.

[0019] In one embodiment, the analysis of the real-time status data based on the intelligent algorithm for the number of compaction passes to determine the target detection result includes:

[0020] The target block region is intersected and fitted with the grid cells to determine the distribution of the number of passes of the grid cells;

[0021] The real-time status data is associated with the corresponding grid cell using a spatial matching algorithm;

[0022] The core parameters affecting compaction quality are selected as network nodes; wherein, the network nodes include target nodes, observation nodes, and hidden nodes;

[0023] The dependencies between the network nodes are learned through a heuristic search algorithm to form a directed acyclic graph;

[0024] For each of the grid cells, the target detection result is determined based on the directed acyclic graph and Markov chain Monte Carlo algorithm; wherein the target detection result includes the probability of meeting the quality standard.

[0025] In one embodiment, the method further includes:

[0026] Establish a linear mapping relationship between the target detection results and the number of compaction passes and color;

[0027] Using visualization tools, each grid cell is filled with a corresponding color based on the linear mapping relationship to generate a compaction quality distribution map covering the construction area; wherein, the compaction quality distribution map is used to characterize the spatial distribution of the probability of quality compliance.

[0028] In one embodiment, generating a compensatory compaction strategy based on the target block area where the target detection result indicates non-compaction includes:

[0029] The target block area whose target detection result is unqualified for compaction is identified as the area to be compensated;

[0030] The compensation parameters are determined by regression analysis of the area to be compensated, combining historical status data of the labeled compliance results with the real-time status data.

[0031] With the goal of minimizing the total path length, a compensation path covering each of the areas to be compensated is generated based on an improved ant colony algorithm.

[0032] The compensation compaction strategy is determined based on the compensation path and the compensation parameters.

[0033] In one embodiment, the method further includes:

[0034] In response to a user interface viewing request, the user interface corresponding to the user interface viewing request is displayed; wherein, the user interface includes a monitoring platform interface and a vehicle terminal interface; the user interface is used to display the real-time status data of each of the grid cells; the monitoring platform interface includes a query bar and a details area; the query bar includes a selection area and query controls;

[0035] When an input operation for a target area is received through the query control, the data report names for each time point of the target area are displayed in the selection area.

[0036] When a selection operation for a target data report is received, detailed information about the target data report is displayed in the details area.

[0037] In one embodiment, the method further includes:

[0038] When the target inspection result is found to be unqualified for compaction, an early warning message is output; wherein, the early warning message is used to prompt the construction personnel to perform compensation operations on the area to be compensated corresponding to the target inspection result.

[0039] Secondly, this application also provides a monitoring device for earthwork compaction construction, the device comprising:

[0040] The partitioning module is used to divide the construction area into multiple target block areas;

[0041] The acquisition module is used to collect real-time status data of the target block area currently under construction during the construction process of the construction area.

[0042] The detection module is used to analyze the real-time status data based on the intelligent algorithm of the number of compaction passes to determine the target detection result;

[0043] The compensation module is used to generate a compensation compaction strategy based on the target block area whose compaction is deemed unqualified by the target detection result; wherein the compensation compaction strategy includes at least one of compensation path and compensation parameters;

[0044] A control module is used to send the compensating compaction strategy to the construction equipment; wherein the compensating compaction strategy is used to control the construction equipment to complete the compaction operation.

[0045] Thirdly, this application also provides an electronic device, including a processor and a memory; wherein the memory is used to store a computer program; and the processor is configured to, when executing the computer program, implement the steps of the method described in any embodiment of this application.

[0046] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the methods described in any embodiment of this application.

[0047] The aforementioned monitoring method for earthwork compaction construction achieves two key benefits. First, by dividing the construction area into multiple target block regions, granular management of the construction area is realized, providing precise coordinate positioning for subsequent repairs and ensuring timeliness. Second, a compensatory compaction strategy is generated for unqualified areas. The algorithm plans the optimal compaction path to avoid the roller repeatedly compacting qualified areas, reducing ineffective fuel consumption and equipment wear, while also shortening compaction time. Thus, intelligent methods reduce labor costs and resource waste, ultimately achieving the goals of more reliable construction quality, more controllable schedule, and optimized costs. Attached Figure Description

[0048] Figure 1 This is a flowchart illustrating a method for monitoring earthwork compaction construction according to an exemplary embodiment.

[0049] Figure 2 This is a schematic diagram of a grid cell according to an exemplary embodiment;

[0050] Figure 3 This is a schematic diagram of a target block-shaped region according to an exemplary embodiment;

[0051] Figure 4 This is a structural block diagram of a monitoring device for earthwork compaction construction according to an exemplary embodiment;

[0052] Figure 5 This is an internal structural diagram of an electronic device according to an exemplary embodiment. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0054] The terms "first," "second," and "third" used in the embodiments of this application are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0055] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0056] In some embodiments, the earthwork compaction construction monitoring method provided in this application can be applied to electronic devices. The electronic device can be any mobile terminal or fixed terminal. The terminal can be a device that provides voice and / or data connectivity to the user. For example, the terminal can be an Internet of Things (IoT) terminal, such as a sensor device, a mobile phone or so-called "cellular" phone, and a computer with an IoT terminal; for example, it can be a fixed, portable, pocket-sized, handheld, or computer-embedded device.

[0057] In some embodiments, such as Figure 1 As shown, a method for monitoring the compaction construction of earthwork projects is provided, the method comprising the following steps:

[0058] S101 divides the construction area into multiple target block areas.

[0059] In this embodiment of the application, the construction area may include, but is not limited to, at least one of the following: airport flight area / terminal, highway / tunnel entrance, service area, port terminal, city square, etc.

[0060] In some embodiments, the electronic device obtains multiple target block areas by meshing the construction area.

[0061] In some embodiments, dividing the construction area into multiple target block areas includes:

[0062] The construction area is divided according to a preset ratio to obtain multiple grid units and corresponding area attributes; wherein, the area attributes include at least one of grid boundary coordinates and area identifier;

[0063] A trajectory surface is formed based on the operating trajectory of the construction equipment;

[0064] Based on the curvature change points of the trajectory line of the operation trajectory and / or the intersection points of the trajectory surface and the grid boundary as segmentation points, the trajectory surface is segmented to obtain a segmented trajectory surface;

[0065] Perform pairwise spatial intersection, difference, and complement operations on each of the segmented trajectory surfaces to determine non-overlapping candidate block regions;

[0066] During multiple compaction processes, when a new trajectory surface overlaps with an existing candidate block region, the overlapping region is obtained and the region attributes of the overlapping region are updated; wherein, the region attributes also include the number of compaction passes;

[0067] The process of performing pairwise spatial intersection, difference, and complement operations on each segmented trajectory surface to obtain overlapping regions and updating the number of compaction passes of the overlapping regions is repeated until the construction work is completed, resulting in non-overlapping target block regions and their corresponding number of compaction passes.

[0068] In this embodiment, the preset ratio can be determined based on the area of ​​the construction area and construction requirements. For example, the preset ratio can be 20m×20m or 10m×15m, etc.

[0069] In one embodiment, the grid boundary coordinates may include, but are not limited to, the top corner coordinates of each grid, such as the coordinates of the bottom left and top right corners.

[0070] In this embodiment of the application, the area identifier may be, but is not limited to, at least one of a string, a numeric code, or a combination of a pattern and a character.

[0071] For example, such as Figure 2 As shown, Figure 2 This is a schematic diagram of grid cell division.

[0072] In this application embodiment, the construction equipment may include, but is not limited to, at least one of a single-drum vibratory roller, a double-drum vibratory roller, a rubber-tired roller, and an impact roller.

[0073] In this embodiment, the curvature abrupt change point can characterize the turning point of the construction equipment during construction operations.

[0074] In one embodiment, the electronic device can determine the unit tangent vector at any point on the trajectory line of the construction equipment's operation trajectory; determine the direction vector perpendicular to the tangent (wheel width direction) based on the unit tangent vector; determine the boundary lines (left and right boundaries) of the trajectory surface based on the direction vector perpendicular to the tangent and the trajectory line; and determine the area enclosed by the trajectory line and the boundary lines as the trajectory surface.

[0075] Based on curvature abrupt change points and / or intersections between the trajectory surface and the grid boundary, the trajectory surface is segmented. For the segmented trajectory surface, it can be decomposed into non-overlapping candidate block regions through polygon Boolean operations. During multiple compaction processes, the new trajectory surface is gradually interactively calculated with the processed candidate block regions to obtain overlapping regions, and the corresponding compaction pass count is incremented by 1 until the construction work is completed. That is, the pairwise spatial intersection, difference, and complement operations are repeatedly performed on each segmented trajectory surface to obtain overlapping regions and update the compaction pass count of the overlapping regions. Finally, a non-overlapping target block region covering all trajectory surfaces is obtained, and the number of passes for each target block region is equal to the total number of covered trajectory surfaces.

[0076] For example, such as Figure 3 As shown, Figure 3 This is a schematic diagram of the target block region.

[0077] S102, During the construction process of the construction area, real-time status data is collected for the target block area currently under construction;

[0078] In this embodiment of the application, the real-time status data includes at least one of coordinate parameters, compaction parameters, moisture content parameters, temperature parameters, and vibration frequency.

[0079] In some embodiments, electronic devices may deploy a sensor network based on the gridding results of the construction area, continuously or at predetermined frequencies collecting real-time status data by embedding multi-parameter sensors in the construction equipment (such as a road roller) or the grid area.

[0080] In this embodiment of the application, the multi-parameter sensor may include, but is not limited to, at least one of the following:

[0081] Status sensors are used to collect vibration frequency, travel speed, number of compaction passes, excitation force, and / or travel speed.

[0082] Mass sensors are used to collect compaction parameters, moisture content parameters, and / or density.

[0083] Positioning sensors are used to collect real-time location data and associate it with the corresponding grid cell.

[0084] In one embodiment, real-time status data is uploaded to the cloud platform in real time via wireless communication technologies such as 5G / LoRa, with each data entry including a timestamp and real-time location. The real-time status data undergoes data preprocessing, for example, abnormal data is removed using the 3σ criterion, and missing data is supplemented by interpolation using the real-time status data corresponding to adjacent grid cells. The preprocessed real-time status data is then associated with the corresponding grid cells using a spatial matching algorithm.

[0085] S103, based on the intelligent algorithm of compaction pass count, analyze the real-time status data to determine the target detection result.

[0086] In this embodiment of the application, the intelligent algorithm for determining the number of compaction passes indicates whether the design requirements are met by collecting real-time status data, counting the number of compaction passes in each area, and determining whether the design requirements are met.

[0087] In one embodiment, the electronic device uses a rolling pass intelligent algorithm to analyze and process real-time status data. Specific steps may include: recording the number of rolling passes and the operation trajectory in real time through sensors installed on the roller, and automatically calculating the number of rolling passes required for each grid cell and the number of rolling passes already completed, based on the size of the grid cell and the compaction degree required by the design, thereby evaluating the rolling quality of the construction area and obtaining the target detection result.

[0088] S104, based on the target block area whose target detection result is unqualified for compaction, a compensation compaction strategy is generated; wherein, the compensation compaction strategy includes at least one of compensation path and compensation parameters.

[0089] In some embodiments, generating a compensatory compaction strategy based on the target block area where the target detection result indicates non-compliance compaction includes:

[0090] The target block area whose target detection result is unqualified for compaction is identified as the area to be compensated;

[0091] The compensation parameters are determined by regression analysis of the area to be compensated, combining historical status data of the labeled compliance results with the real-time status data.

[0092] With the goal of minimizing the total path length, a compensation path covering each of the areas to be compensated is generated based on an improved ant colony algorithm.

[0093] The compensation compaction strategy is determined based on the compensation path and the compensation parameters.

[0094] In one embodiment, a regression model of the probability of meeting quality standards and the number of compaction passes can be established based on historical state data to infer the additional number of passes required to make the probability of meeting quality standards greater than the target probability in the design requirements; and if the current moisture content deviates from the optimal moisture content, the vibration frequency and / or excitation force can be corrected using historical state data.

[0095] In one embodiment, the electronic device can be based on an improved ant colony algorithm, setting the center coordinates of the area to be compensated as a necessary node and the center of the already qualified area as a forbidden node; setting M ants to simulate M road rollers, and the path of each ant is a sequence of nodes; in order to guide the ants to prioritize the area to be compensated where the quality qualification probability is lower, the pheromone concentration on the path between nodes is negatively correlated with the regional quality qualification probability, and decays rapidly in the already qualified area.

[0096] For example, a pheromone update formula is established based on the basic decay coefficient and the pheromone increment left by the ant on the path; the probability of the ant moving from the first node to the second node is determined by the unvisited compensation area and the heuristic function; when the compensation area is fully covered and / or the number of iterations reaches a preset upper limit, the compensation path is output.

[0097] In this way, by combining historical and real-time status data of the marked compliance results, compensation parameters are derived through regression analysis to achieve dynamic correction of real-time data; by combining the improved ant colony algorithm to generate paths covering the areas to be compensated, the shortest path is planned to reduce energy consumption and time, and the path is ensured to prioritize covering non-compliant areas and automatically avoid compliant areas, thereby improving the reliability of pressure compensation quality.

[0098] S105, the compensating compaction strategy is sent to the construction equipment; wherein, the compensating compaction strategy is used to control the construction equipment to complete the compaction operation.

[0099] In one embodiment, electronic equipment can precisely control the construction equipment to complete the compaction operation by sending a compensating compaction strategy to the equipment. For example, it can dynamically prompt the operator of the machine to indicate the required number of compaction passes for each area, ensuring that the soil in each grid cell meets the predetermined compaction standard. When the compaction degree of a certain cell does not meet the standard, the system will automatically indicate the number of additional compaction passes required for that area, guiding the operator to operate precisely until the design requirements are met.

[0100] The aforementioned monitoring method for earthwork compaction construction achieves two key benefits. First, by dividing the construction area into multiple target block regions, granular management of the construction area is realized, providing precise coordinate positioning for subsequent repairs and ensuring timeliness. Second, a compensatory compaction strategy is generated for unqualified areas. The algorithm plans the optimal compaction path to avoid the roller repeatedly compacting qualified areas, reducing ineffective fuel consumption and equipment wear, while also shortening compaction time. Thus, intelligent methods reduce labor costs and resource waste, ultimately achieving the goals of more reliable construction quality, more controllable schedule, and optimized costs.

[0101] In some embodiments, the analysis of the real-time status data based on the intelligent algorithm for the number of compaction passes to determine the target detection result includes:

[0102] The target block region is intersected and fitted with the grid cells to determine the distribution of the number of passes of the grid cells;

[0103] The real-time status data is associated with the corresponding grid cell using a spatial matching algorithm;

[0104] The core parameters affecting compaction quality are selected as network nodes; wherein, the network nodes include target nodes, observation nodes, and hidden nodes;

[0105] The dependencies between the network nodes are learned through a heuristic search algorithm to form a directed acyclic graph;

[0106] For each of the grid cells, the target detection result is determined based on the directed acyclic graph and Markov chain Monte Carlo algorithm; wherein the target detection result includes the probability of meeting the quality standard.

[0107] In this embodiment of the application, the target node can be used to characterize whether the compaction quality meets the standard.

[0108] In this embodiment of the application, the observation node can be established based on the number of compaction passes, compaction parameters, vibration frequency and / or moisture content parameters in the real-time status data.

[0109] In this embodiment of the application, the hidden nodes can be established based on the initial density and soil type in historical data.

[0110] In this embodiment of the application, the heuristic search algorithm refers to an algorithm that uses heuristic information to guide the search direction during the search process in order to improve search efficiency.

[0111] In some embodiments, the heuristic search algorithm may include, but is not limited to, at least one of the greedy best-first search algorithm and the genetic algorithm.

[0112] In some embodiments, the electronic device can also combine expert knowledge and historical data to automatically learn the Bayesian network structure based on the K2 algorithm, that is, the dependencies between network nodes. For example, the number of rolling passes affects the compaction quality (target detection result), and the moisture content parameter affects the compaction degree parameter, etc. The conditional probability of each network node is calculated by maximum likelihood estimation, and a directed acyclic graph is formed based on each network node and its dependencies.

[0113] In one embodiment, the electronic device randomly generates a state vector that satisfies the conditional probability constraints of a Bayesian network. For the current state, candidate states are sampled using the MH (Metropolis-Hastings) algorithm, and the acceptance probability is calculated. A random number uniformly distributed on [0,1] is generated. If the acceptance probability is greater than the random number, the candidate state is retained. If the acceptance probability is less than or equal to the random number, the current state is retained. This process is repeated until the chain converges, and the quality compliance probability is calculated.

[0114] In this embodiment of the application, by using conditional probability inference through Bayesian networks, the probability of quality compliance in some areas can be inferred based on adjacent data when sensors fail or are not sampled in some areas, thus reducing blind spots; by using unified modeling based on trajectory data, sensor data and / or geological data, misjudgments caused by a single indicator can be reduced, thus improving the accuracy of the probability of quality compliance.

[0115] In some embodiments, the method further includes:

[0116] Establish a linear mapping relationship between the target detection results and the number of compaction passes and color;

[0117] Using visualization tools, each grid cell is filled with a corresponding color based on the linear mapping relationship to generate a compaction quality distribution map covering the construction area; wherein, the compaction quality distribution map is used to characterize the spatial distribution of the probability of quality compliance.

[0118] In some embodiments, the quality compliance probability of each grid cell is associated with spatial coordinates to establish a linear mapping relationship between the probability value and RGB color; for example, if the probability value is less than a first threshold, the quality compliance probability is determined to be low probability, and low probability is mapped to green; if the probability value is greater than a second threshold or greater than the first threshold, the quality compliance probability is determined to be high probability, and high probability is mapped to red.

[0119] For example, the compaction quality distribution map can be a heat map. Electronic devices can process the blank areas between grid cells using inverse distance weighted interpolation, and then fill each grid cell with the corresponding color using visualization tools (such as a GIS platform) to generate a compaction quality heat map covering the entire construction area, intuitively displaying the spatial distribution of the probability of quality compliance. Based on a predetermined period or after the data collected by sensors is updated, the probability of quality compliance for each grid cell is automatically recalculated and the corresponding heat map is refreshed.

[0120] In this embodiment, the construction process involves multiple roles, including equipment operators, quality inspectors, and managers, whose understanding of data varies considerably. The compaction quality distribution map, as a standardized visualization, can intuitively show which areas are stable and compliant, and which areas are areas of fluctuating risk, providing efficient support for construction decisions, process monitoring, and quality traceability.

[0121] In some embodiments, the method further includes:

[0122] In response to a user interface viewing request, the user interface corresponding to the user interface viewing request is displayed; wherein, the user interface includes a monitoring platform interface and a vehicle terminal interface; the user interface is used to display the real-time status data of each of the grid cells; the monitoring platform interface includes a query bar and a details area; the query bar includes a selection area and query controls;

[0123] When an input operation for a target area is received through the query control, the data report names for each time point of the target area are displayed in the selection area.

[0124] When a selection operation for a target data report is received, detailed information about the target data report is displayed in the details area.

[0125] For example, when a user or construction worker wants to view the real-time status data and / or historical status data of a target area, they can enter the identifier of the target area in the query bar of the monitoring platform interface using the query control. The selection area of ​​the query bar will synchronously display the data report names of the target area at each time point. By clicking on the data report name to be viewed, the detailed information corresponding to the data report will be displayed in the details area of ​​the monitoring platform interface.

[0126] In this embodiment, the user interface is divided into a monitoring platform interface and a vehicle-mounted interface. The monitoring platform interface helps to grasp the overall status of the construction area, while the vehicle-mounted interface can focus on the real-time data of the current work area, allowing different users to quickly find core information and improve work efficiency.

[0127] In some embodiments, the method further includes:

[0128] When the target inspection result is found to be unqualified for compaction, an early warning message is output; wherein, the early warning message is used to prompt the construction personnel to perform compensation operations on the area to be compensated corresponding to the target inspection result.

[0129] In one embodiment, when it is detected that the number of rolling passes or the degree of compaction of a certain grid cell does not meet the preset standard, an alarm mechanism will be automatically triggered, and the alarm information will be displayed in real time through the user interface to prompt construction personnel to take targeted measures in a timely manner.

[0130] In this embodiment, insufficient compaction passes or substandard compaction, if not detected in time, may gradually evolve into serious quality problems in subsequent processes. By automatically triggering early warnings, alerts can be issued immediately upon the occurrence of a problem, allowing construction personnel to promptly re-compact or adjust parameters of the affected grid unit, preventing small defects from accumulating into major hidden dangers and reducing the risk of rework from the outset.

[0131] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0132] Based on the same inventive concept, this application also provides an earthwork compaction construction monitoring device for implementing the aforementioned earthwork compaction construction monitoring method. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more earthwork compaction construction monitoring device embodiments provided below can be found in the limitations of the earthwork compaction construction monitoring method described above, and will not be repeated here.

[0133] In one embodiment, such as Figure 4 As shown, a monitoring device for earthwork compaction construction is provided, the device comprising:

[0134] The division module 10 is used to divide the construction area into multiple target block areas;

[0135] The acquisition module 20 is used to collect real-time status data of the target block area currently under construction during the construction process of the construction area.

[0136] The detection module 30 is used to analyze the real-time status data based on the intelligent algorithm of the number of compaction passes to determine the target detection result;

[0137] The compensation module 40 is used to generate a compensation compaction strategy based on the target block area whose target detection result is unqualified for compaction; wherein the compensation compaction strategy includes at least one of compensation path and compensation parameters;

[0138] The control module 50 is used to send the compensating compaction strategy to the construction equipment; wherein the compensating compaction strategy is used to control the construction equipment to complete the compaction operation.

[0139] In one embodiment, the partitioning module 10 is configured to perform the following steps:

[0140] The construction area is divided according to a preset ratio to obtain multiple grid units and corresponding area attributes; wherein, the area attributes include at least one of grid boundary coordinates and area identifier;

[0141] A trajectory surface is formed based on the operating trajectory of the construction equipment;

[0142] Based on the curvature change points of the trajectory line of the operation trajectory and / or the intersection points of the trajectory surface and the grid boundary as segmentation points, the trajectory surface is segmented to obtain a segmented trajectory surface;

[0143] Perform pairwise spatial intersection, difference, and complement operations on each of the segmented trajectory surfaces to determine non-overlapping candidate block regions;

[0144] During multiple compaction processes, when a new trajectory surface overlaps with an existing candidate block region, the overlapping region is obtained and the region attributes of the overlapping region are updated; wherein, the region attributes also include the number of compaction passes;

[0145] The process of performing pairwise spatial intersection, difference, and complement operations on each segmented trajectory surface to obtain overlapping regions and updating the number of compaction passes of the overlapping regions is repeated until the construction work is completed, resulting in non-overlapping target block regions and their corresponding number of compaction passes.

[0146] In one embodiment, the detection module 30 is configured to perform the following steps:

[0147] The target block region is intersected and fitted with the grid cells to determine the distribution of the number of passes of the grid cells;

[0148] The real-time status data is associated with the corresponding grid cell using a spatial matching algorithm;

[0149] The core parameters affecting compaction quality are selected as network nodes; wherein, the network nodes include target nodes, observation nodes, and hidden nodes;

[0150] The dependencies between the network nodes are learned through a heuristic search algorithm to form a directed acyclic graph;

[0151] For each of the grid cells, the target detection result is determined based on the directed acyclic graph and Markov chain Monte Carlo algorithm; wherein the target detection result includes the probability of meeting the quality standard.

[0152] In one embodiment, the apparatus further includes:

[0153] A module is established to create a linear mapping relationship between the target detection results and the number of compaction passes and color.

[0154] The mapping module is used to fill each grid cell with a corresponding color using a visualization tool and in conjunction with the linear mapping relationship, thereby generating a compaction quality distribution map covering the construction area; wherein, the compaction quality distribution map is used to characterize the spatial distribution of the probability of meeting the quality standards.

[0155] In one embodiment, the compensation module 40 is configured to perform the following steps:

[0156] The target block area whose target detection result is unqualified for compaction is identified as the area to be compensated;

[0157] The compensation parameters are determined by regression analysis of the area to be compensated, combining historical status data of the labeled compliance results with the real-time status data.

[0158] With the goal of minimizing the total path length, a compensation path covering each of the areas to be compensated is generated based on an improved ant colony algorithm.

[0159] The compensation compaction strategy is determined based on the compensation path and the compensation parameters.

[0160] In one embodiment, the apparatus further includes:

[0161] The display module is used to respond to a user interface viewing request and display the user interface corresponding to the user interface viewing request; wherein, the user interface includes a monitoring platform interface and a vehicle terminal interface; the user interface is used to display the real-time status data of each of the grid cells; the monitoring platform interface includes a query bar and a details area; the query bar includes a selection area and query controls;

[0162] The display module is used to display the data report names of each time node of the target area in the selection area when an input operation for the target area is received through the query control.

[0163] The display module is used to display detailed information of the target data report in the details area when a selection operation for the target data report is received.

[0164] In one embodiment, the apparatus further includes:

[0165] The early warning module is used to output early warning information when the detection result of the target is unqualified for compaction; wherein, the early warning information is used to prompt the construction personnel to perform compensation operation on the area to be compensated corresponding to the detection result of the target.

[0166] Each module in the aforementioned earthwork compaction construction monitoring device can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in the processor of the electronic device in hardware form or independent of the processor, or stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of each module.

[0167] In one embodiment, an electronic device is provided, the internal structure of which can be shown as follows: Figure 5 As shown, the electronic device includes a processor, memory, communication interface, display unit, and input device connected via a method bus. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores operating methods and computer programs. The internal memory provides an environment for the operation of the operating methods and computer programs stored in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for monitoring earthwork compaction construction. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the device's casing, or an external keyboard, touchpad, or mouse.

[0168] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0169] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0170] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps performed by the processor of the electronic device of any of the above.

[0171] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0172] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, compilable logic units, quantum computing-based earthwork compaction construction monitoring logic units, etc., and are not limited to these.

[0173] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0174] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for monitoring earthwork rolling construction, characterized by, The method comprises: dividing a construction area into a plurality of target block regions, comprising: dividing the construction area according to a preset ratio to obtain a plurality of grid units and corresponding region attributes; wherein the region attributes comprise at least one of grid boundary coordinates and region identifiers; forming a trajectory surface based on a working trajectory of a construction device; segmenting the trajectory surface based on a curvature mutation point of a trajectory line of the working trajectory and / or an intersection point of the trajectory surface and a grid boundary as a segmentation point to obtain a segmented trajectory surface; performing two-by-two spatial intersection, difference set and complement set operations on each of the segmented trajectory surfaces to determine candidate block regions that do not overlap with each other; during multiple rolling processes, when a new trajectory surface overlaps with an existing candidate block region, obtaining an overlapping region and updating the region attributes of the overlapping region; wherein the region attributes further comprise a rolling pass number; repeating the process of performing two-by-two spatial intersection, difference set and complement set operations on each of the segmented trajectory surfaces until the rolling pass number of the overlapping region is obtained and updated, until the construction work is completed, to obtain the target block regions that do not overlap with each other and the corresponding rolling pass numbers; During the construction of the construction area, collecting real-time state data for the target block region currently under construction; Based on a rolling pass number intelligent algorithm, analyzing the real-time state data to determine a target detection result, comprising: intersecting and fitting the target block region with the grid units to determine the distribution of the pass number of the grid units; associating the real-time state data to the corresponding grid units through a spatial matching algorithm; selecting a core parameter affecting the compaction quality as a network node; wherein the network node comprises a target node, an observation node and an implicit node; learning the dependency relationship between each network node through a heuristic search algorithm to form a directed acyclic graph; for each grid unit, determining the target detection result based on the directed acyclic graph and a Markov chain Monte Carlo algorithm; wherein the target detection result comprises a quality compliance probability; Based on the target detection result, generating a compensation rolling strategy for the target block region that fails to roll, comprising: determining the target block region that fails to roll as a region to be compensated; determining a compensation parameter through regression analysis on the region to be compensated in combination with historical state data of labeled compliance results and real-time state data; generating a compensation path covering each region to be compensated based on an improved ant colony algorithm with the shortest total path length as the target; determining the compensation rolling strategy based on the compensation path and the compensation parameter; Sending the compensation rolling strategy to a construction device; wherein the compensation rolling strategy is used to control the construction device to complete the rolling work.

2. The method of claim 1, wherein, The method further comprises: establishing a linear mapping relationship between the target detection result and the rolling pass number and color; The method further comprises:

3. The method of claim 1, wherein, In response to a user interface viewing request, a user interface corresponding to the user interface viewing request is displayed; wherein the user interface comprises a monitoring platform interface and a vehicle-mounted terminal interface; the user interface is used to display the real-time state data of each grid cell; the monitoring platform interface comprises a query bar and a detail area; the query bar comprises a selection area and a query control; When an input operation on a target area is received through the query control, data report names of each time node of the target area are displayed in the selection area; When a selection operation on a target data report is received, detail information of the target data report is displayed in the detail area. The method further comprises:

4. The method of claim 1, wherein, When it is detected that the target detection result is unqualified rolling, an early warning information is output; wherein the early warning information is used to prompt a construction personnel to perform a compensation operation on a to-be-compensated area corresponding to the target detection result. The device comprises:

5. A device for monitoring the compaction of earthwork, characterized in that, A division module is configured to divide a construction area into a plurality of target block-shaped areas, including: dividing the construction area according to a preset proportion to obtain a plurality of grid cells and corresponding area attributes; wherein the area attributes include at least one of grid boundary coordinates and area identifiers; a trajectory surface is formed based on a work trajectory of a construction device; a segmented trajectory surface is obtained by segmenting the trajectory surface based on curvature mutation points of a trajectory line of the work trajectory and / or intersection points of the trajectory surface and grid boundaries; mutual non-overlapping candidate block-shaped areas are determined by performing two-by-two spatial intersection, difference set and complement set operations on the segmented trajectory surfaces; during multiple rolling processes, when a new trajectory surface coincides with an existing candidate block-shaped area, a coincident area is obtained and the area attributes of the coincident area are updated; wherein the area attributes further include rolling passes; the process of performing two-by-two spatial intersection, difference set and complement set operations on the segmented trajectory surfaces is repeatedly executed until the rolling passes of the coincident area are updated until construction work is completed, and the target block-shaped areas and corresponding rolling passes that do not overlap each other are obtained; An acquisition module is configured to, during construction of the construction area, collect real-time state data for a target block-shaped area that is currently under construction; ​ The detection module is configured to analyze the real-time state data based on a rolling pass intelligent algorithm to determine a target detection result, including: fitting the target block region with the grid cells to determine the pass distribution of the grid cells; correlating the real-time state data to the corresponding grid cells through a spatial matching algorithm; selecting a core parameter affecting the compaction quality as a network node; wherein the network node includes a target node, an observation node, and an implicit node; learning the dependency relationship between each network node through a heuristic search algorithm to form a directed acyclic graph; for each grid cell, determining the target detection result based on the directed acyclic graph and a Markov chain Monte Carlo algorithm; wherein the target detection result includes a quality compliance probability; The compensation module is configured to generate a compensation rolling strategy for the target block region that fails to pass the rolling based on the target detection result, including: determining the target block region that fails to pass the rolling as a target compensation region based on the target detection result; determining a compensation parameter through regression analysis on the target compensation region combined with historical state data of labeled compliance results and the real-time state data; generating a compensation path covering each target compensation region based on an improved ant colony algorithm with the shortest total path length as the target; and determining the compensation rolling strategy based on the compensation path and the compensation parameter. The control module is configured to send the compensation rolling strategy to a construction device; wherein the compensation rolling strategy is used to control the construction device to complete the rolling operation.

6. An electronic device, comprising: The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 4.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 4.

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