An intelligent road construction control system and method

Abstract

The present invention relates to the technical field of resource management, and specifically provides an intelligent road construction control system and method. The system includes: A resource allocation optimization module, based on pavement materials and construction equipment, counts the equipment working hours and task requirements of divided sections of the road, matches the available working hours of the equipment with the task requirements, calculates the difference in the allocation of manual working hours, and compares the availability of equipment and labor. In the present invention, by matching the working hours and task requirements of pavement materials and construction equipment, resource allocation is optimized, resource waste and redundancy are reduced, the start times between tasks and the sorting of resource utilization rates are adjusted, the requirements of key tasks are preferentially met, the task execution efficiency is improved, the material supply cycle and equipment deployment conflicts are evaluated, the task intervals and equipment plans are dynamically adjusted, delays caused by insufficient resources are reduced, task coordination is optimized in combination with time nodes and equipment distribution, environmental changes and equipment deviations are monitored, and tasks and resource allocation are adjusted in real time to improve construction efficiency and continuity.

Description

Technical Field

[0001] The present invention relates to the technical field of resource management, and particularly to an intelligent road construction control system and method. Background Art

[0002] The technical field of resource management encompasses various systems and methods that are used to effectively allocate and utilize resources to support various operational activities. This generally includes, but is not limited to, the allocation of human resources, financial resources, materials, and equipment. The application of technology in this field aims to optimize resource usage, reduce waste, improve efficiency, and ensure that projects and activities can be successfully completed within the scheduled time and budget. Modern resource management technologies often also incorporate intelligent algorithms and data analysis capabilities to achieve more dynamic and responsive resource allocation.

[0003] Among them, an intelligent road construction control system refers to a system that integrates advanced computing technologies and automation tools for managing and controlling resources in road construction projects. The system automatically adjusts resource allocation, optimizes the construction process, reduces delays and costs during construction, and improves construction efficiency and safety through real-time data collection and analysis. The uses of such a system include monitoring the construction progress, predicting potential problems, and ensuring that construction activities comply with the plan and standards.

[0004] The prior art lacks the ability to dynamically adjust resource allocation and is difficult to handle resource conflicts caused by material supply delays or equipment anomalies. Task sequencing ignores the coordination between start intervals and resource utilization, resulting in non-critical tasks occupying resources or critical tasks being delayed. In construction progress management, equipment allocation conflicts are not evaluated in real time, making it difficult to adjust task intervals to ensure the progress. The capabilities of environmental monitoring and equipment status response are insufficient, increasing the risk of construction interruption, which in turn causes a decline in efficiency and an increase in costs. Summary of the Invention

[0005] The object of the present invention is to solve the drawbacks existing in the prior art, and to propose an intelligent road construction control system and method.

[0006] To achieve the above object, the present invention adopts the following technical solution: An intelligent road construction control system includes:

[0007] The resource allocation optimization module, based on the pavement materials and construction equipment, counts the equipment working hours and task requirements of the sectional road sections, matches the available working hours of the equipment with the task requirements, calculates the difference in the allocation of man-hours, compares the availability of the equipment and the labor, and generates the equipment allocation result for the road sections;

[0008] Based on the road section equipment allocation result, the task priority adjustment module calculates the start time and task interval of the key road section tasks, sorts the priority call order of machine working hours and manual working hours, calculates the task resource utilization rate and task interval weight, and generates the key road section task sorting result;

[0009] Based on the key road section task sorting result, the dynamic constraint evaluation module calculates the pavement material supply cycle and construction equipment working hours, calibrates the equipment allocation time conflict and resource shortage tasks, adjusts the task execution interval, and generates a task constraint adjustment parameter set;

[0010] Based on the task constraint adjustment parameter set, the construction progress optimization module counts the time node and task distribution difference of the construction road section, pairs the resource utilization rate and equipment available time, calculates the adjustment range of tasks and time nodes, reorganizes the task time and equipment resource distribution, and generates the road section construction progress plan;

[0011] Based on the road section construction progress plan, the real-time emergency response module monitors the environmental changes and equipment operation status of the construction road section, calculates the deviation between the task time and environmental changes, counts the equipment working hours and remaining material quantity, adjusts the equipment allocation and time node, and generates the construction deviation correction result.

[0012] The road section equipment allocation result includes the equipment working hour allocation ratio, the manual working hour matching result, and the analysis of the difference between equipment and task requirements. The key road section task sorting result includes the key road section task priority, the task resource utilization efficiency, and the task interval optimization order. The task constraint adjustment parameter set includes the equipment allocation time parameter, the resource shortage task identifier, and the task execution interval correction value. The road section construction progress plan includes the construction time node arrangement, the equipment resource allocation plan, and the task execution time adjustment. The construction deviation correction result includes the equipment operation status adjustment, the construction time deviation correction, and the remaining material quantity balance parameter.

[0013] As a further solution of the present invention, the steps for obtaining the road section equipment allocation result are specifically as follows:

[0014] Collect the task requirements and equipment working hours of the partitioned road sections, comprehensively count the available working hours of the multi-partition equipment, the matching degree with the task requirements, and calculate the deviation between the total equipment working hours and the task requirements to generate an equipment working hour deviation set;

[0015] Analyze the equipment working hour deviation set, and adjust the working hour allocation of equipment and manual according to the availability of human resources to establish a manual and equipment working hour adjustment set;

[0016] According to the manual and equipment working hour adjustment set, use the formula:

[0017]

[0018] Compare the equipment and labor allocation to obtain the equipment allocation results for each section;

[0019] Among them, DS represents the deviation of working hours allocation between equipment and labor, TQ c represents the task requirement of the c-th zone, RQ c represents the available working hours of the corresponding equipment, S c represents the adjustment coefficient, n D is the total number of zones, α is the weight parameter that adjusts the sensitivity of equipment allocation deviation to task requirements, γ d is the adjustment coefficient that controls the basic deviation of the allocation process, δ Q is the adjustment parameter that reflects the impact of the number of zones on the allocation flexibility.

[0020] As a further solution of the present invention, the steps for obtaining the task sorting results of the key sections are specifically as follows:

[0021] According to the equipment allocation results of each section, analyze the resource allocation of each key section, calculate the starting working time, and use the resource optimization algorithm to predict the optimal task sequence to generate the starting work schedule for each key section;

[0022] Through the starting work schedule of each key section, use the priority sorting model to sort the priorities of mechanical and labor resources, adjust the working hours allocation of mechanical and labor in combination with the urgency of tasks, and establish a task priority list;

[0023] Using the task priority list, combined with the interval requirements in actual task execution, use the resource utilization formula:

[0024]

[0025] Calculate the task resource utilization rate and task interval weight of the key section to generate the task sorting results of the key section;

[0026] Among them, P i represents the priority of the i-th task, VE i represents the interval weight of the task, HE i is the working hours required for the task, EU i is the environmental factor adjustment coefficient, n U is the total number of tasks, and UE represents the task resource utilization rate.

[0027] As a further solution of the present invention, the steps for obtaining the task constraint adjustment parameter set are specifically as follows:

[0028] Extract the start time and material cycle of each task from the task sorting results of the key section, analyze the equipment usage situation, apply the resource ratio algorithm to determine the resource requirements of each task, and generate the resource requirement analysis results;

[0029] According to the resource requirement analysis results, mark all time conflicts between resource supply and demand, as well as conflicts and resource shortages occurring in equipment allocation, and create a conflict and shortage index table;

[0030] Using the conflict and shortage index table, recalculate the execution interval of each task, using the formula:

[0031]

[0032] Optimize the task execution plan to generate a task constraint adjustment parameter set;

[0033] where D i is the allocation conflict factor, n U is the total number of tasks, AY represents the execution interval of the task, RY i is the resource requirement of each task, TY i is the resource supply cycle.

[0034] As a further solution of the present invention, the steps for obtaining the road section construction progress plan are specifically as follows:

[0035] Extract the construction road section time nodes and the distribution differences between tasks from the task constraint adjustment parameter set, and based on the comparison of task time and resource distribution, analyze the task execution order and resource utilization efficiency to generate a time node and task difference distribution table;

[0036] Using the time node and task difference distribution table, pair the resource utilization rate with the available time of the equipment, and based on the matching analysis of resource allocation and task requirements, calibrate the time conflict and resource shortage tasks to create a resource conflict and shortage task result;

[0037] Based on the resource conflict and shortage task result, calculate the adjustment range and time node correction value of task execution, using the formula:

[0038]

[0039] Optimize the task time distribution to generate a road section construction progress plan;

[0040] where TS is the adjusted time node, ES i represents the resource utilization rate of the i-th task, W i is the task priority weight, L i is the original planned time node, β i is the task adjustment factor, n U is the total number of tasks, indicating the total number of tasks that need to be adjusted.

[0041] As a further solution of the present invention, the steps for obtaining the construction offset correction result are specifically as follows:

[0042] Extract monitoring data from the construction progress plan of the section, including environmental changes in the construction section and the operating status of equipment. Combine the task time nodes and use data analysis methods to determine the impact of the environment and equipment status on the construction progress, and generate the analysis results of the environment and equipment status;

[0043] Utilize the analysis results of the environment and equipment status to calculate the deviation between the task time and environmental changes, and count the working hours and remaining material quantities of each equipment. Through quantitative analysis methods, calibrate the equipment allocation and time nodes that need to be adjusted, and create a task and environment deviation table;

[0044] Based on the task and environment deviation table, adjust the equipment allocation and time nodes to match the actual construction environment changes. Use the formula:

[0045]

[0046] Optimize the task allocation and generate the construction offset correction results;

[0047] where, RZ represents the total adjustment efficiency, G i represents the time deviation of the i-th task, VZ i is the operating efficiency of the equipment associated with the task, HZ i is the original planned working hours, δ R represents the global adjustment coefficient used to correct the time deviation caused by environmental changes, n U is the total number of tasks.

[0048] An intelligent road construction control method, which is executed based on the above intelligent road construction control system, and includes the following steps:

[0049] S1: Based on the working hour records of road surface materials and construction equipment, count the task requirements and equipment working hours of the partition section, match the equipment working hours with the task requirements, calculate the difference in manual working hours, and compare the availability of equipment and labor item by item to generate a section equipment resource allocation table;

[0050] S2: Based on the section equipment resource allocation table, calculate the start time and task interval of the tasks in the key section, sort the calling order of mechanical working hours and manual working hours, count the task resource utilization rate and interval weight, and combine the statistical data with the calling order to generate a key section task priority table;

[0051] S3: Based on the key section task priority table, calculate the supply cycle of road surface materials and the working hours of construction equipment, calibrate the time conflict of equipment allocation, and combine the allocation time with the distribution of tasks with insufficient resources to adjust the task execution interval and establish a task execution constraint parameter set;

[0052] S4: Based on the task execution constraint parameter set, count the difference between the time nodes and task distribution in the construction section, pair the resource utilization rate with the equipment time, adjust the task and time node range, reorganize the equipment resource distribution, obtain the optimized construction progress plan for the section, monitor the environmental changes and the operating status of the equipment, calculate the deviation between the task time and the environmental changes, count the equipment working hours and the remaining material quantity, adjust the equipment allocation and time nodes, and generate the construction offset correction result.

[0053] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0054] In the present invention, by matching the working hours of the pavement materials and construction equipment with the task requirements, the resource allocation is optimized, and the resource waste and redundancy are reduced. The start time between tasks and the sorting of resource utilization rates are adjusted to give priority to meeting the requirements of key tasks and improve the task execution efficiency. The material supply cycle and equipment allocation conflicts are evaluated, and the task intervals and equipment plans are dynamically adjusted to reduce the delays caused by insufficient resources. The task coordination is optimized by combining the time nodes and equipment distribution, the environmental changes and equipment deviations are monitored, and the tasks and resource allocation are adjusted in real time to improve the construction efficiency and continuity. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is the system flowchart of the present invention;

[0056] Figure 2 is the flowchart of the steps for obtaining the equipment allocation result of the section of the present invention;

[0057] Figure 3 is the flowchart of the steps for obtaining the task sorting result of the key section of the present invention;

[0058] Figure 4 is the flowchart of the steps for obtaining the task constraint adjustment parameter set of the present invention;

[0059] Figure 5 is the flowchart of the steps for obtaining the construction progress plan of the section of the present invention;

[0060] Figure 6 is the flowchart of the steps for obtaining the construction offset correction result of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0062] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0063] Embodiment 1

[0064] Please refer to Figure 1 , the present invention provides a technical solution: an intelligent road construction control system includes:

[0065] The resource allocation optimization module, based on the pavement materials and construction equipment, counts the equipment working hours and task requirements of the partitioned road sections, matches the available working hours of the equipment with the task requirements, calculates the difference in manual working hour allocation, compares the availability of equipment and labor, and generates the equipment allocation result for the road sections;

[0066] The task priority adjustment module, based on the equipment allocation result for the road sections, calculates the start time and task interval of the tasks in the key road sections, sorts the priority call order of mechanical working hours and manual working hours, calculates the task resource utilization rate and the task interval weight, and generates the task sorting result for the key road sections;

[0067] The dynamic constraint evaluation module, based on the task sorting result for the key road sections, calculates the supply cycle of the pavement materials and the working hours of the construction equipment, calibrates the time conflicts in equipment allocation and the tasks with insufficient resources, adjusts the task execution interval, and generates a set of task constraint adjustment parameters;

[0068] The construction progress optimization module, based on the set of task constraint adjustment parameters, counts the time nodes and the distribution difference between tasks in the construction road sections, pairs the resource utilization rate with the available time of the equipment, calculates the adjustment range of tasks and time nodes, reorganizes the task time and the distribution of equipment resources, and generates the construction progress plan for the road sections;

[0069] The real-time emergency response module, based on the construction progress plan for the road sections, monitors the environmental changes and the operating status of the equipment in the construction road sections, calculates the deviation between the task time and the environmental changes, counts the equipment working hours and the remaining amount of materials, adjusts the equipment allocation and the time nodes, and generates the construction deviation correction result.

[0070] The results of section equipment allocation include the allocation ratio of equipment working hours, the matching results of manual working hours, the analysis of the differences between equipment and task requirements. The results of key section task sequencing include the priorities of key section tasks, the utilization efficiency of task resources, and the optimized order of task intervals. The set of task constraint adjustment parameters includes equipment deployment time parameters, task identifiers with insufficient resources, and correction values for task execution intervals. The section construction progress plan includes the arrangement of construction time nodes, the equipment resource allocation plan, and the adjustment of task execution time. The construction offset correction results include the adjustment of equipment operation status, the correction of construction time deviation, and the balance parameters of remaining material quantities.

[0071] Please refer to Figure 2 , and the specific steps for obtaining the results of section equipment allocation are as follows:

[0072] Collect the task requirements and equipment working hours of the sectionalized roads, comprehensively count the available working hours of multi-sectionalized equipment, the matching degree with task requirements, and calculate the deviation between the total equipment working hours and task requirements to generate a set of equipment working hour deviations;

[0073] Obtain equipment working hour data through a real-time monitoring system, including the startup time and fault time records of each equipment. These data are transmitted to the central data processing center in real time through on-site data collectors. The data processing center uses a preset algorithm to clean and preliminarily analyze the data, calculate the actual available working hours of each equipment, statistically analyze the working hour utilization rate and task completion rate of each equipment, and analyze the optimal path of working hour utilization by comparing the working hour data of each equipment with task requirement data, calculate the deviation between the total equipment working hours and task requirements, and generate a set of equipment working hour deviations.

[0074] Analyze the set of equipment working hour deviations, adjust the allocation of equipment and manual working hours according to the availability of human resources, and establish a set of adjustments for manual and equipment working hours;

[0075] Analyze the set of equipment working hour deviations, conduct a detailed analysis of the equipment working hour deviations through the equipment scheduling system. The system updates the equipment status and task progress according to real-time data, monitors the working hour deviations of each equipment in real time, and adjusts the equipment working hours in real time through a dynamic adjustment algorithm to match the availability of human resources, ensuring the optimal matching of equipment use and manual allocation. In addition, the system will also predict the maintenance and fault time of the equipment based on historical data to optimize the overall cooperation between manual and equipment, thereby establishing a comprehensive set of adjustments for manual and equipment working hours.

[0076] According to the set of adjustments for manual and equipment working hours, use the formula:

[0077]

[0078] Compare the allocation of equipment and manual to obtain the results of section equipment allocation;

[0079] Among them, DS represents the deviation of the man-hour allocation between the equipment and the labor, TQ c represents the task requirement of the c-th partition, RQ c represents the corresponding available man-hours of the equipment, S c represents the adjustment coefficient, n D is the total number of partitions, α is the weight parameter, which adjusts the sensitivity of the equipment allocation deviation to the task requirement, γ d is the adjustment coefficient, which controls the basic deviation of the allocation process, δ Q is the adjustment parameter, which reflects the influence of the number of partitions on the allocation flexibility.

[0080] Formula:

[0081]

[0082] The benefit of the formula is that by considering the deviation between the task requirement and the available man-hours of the equipment in each partition and introducing an adjustment coefficient to balance the influence of the deviation, the influence of the deviation on the total allocation result can be flexibly adjusted, and the accuracy and efficiency of the equipment and labor resource allocation can be improved.

[0083] Detailed explanation of the formula and the derivation process of the formula calculation:

[0084] Set the specific values as: TQ c = 50 hours (task requirement), RQ c = 30 hours (available man-hours of the equipment), S c = 1.5 (adjustment coefficient), n D = 10 (total number of partitions), α = 2, γ d = 5, δ Q = 0.3.

[0085] The calculation steps are as follows:

[0086] 1. Calculate the deviation ratio of a single partition:

[0087] |TQ c -RQ c | / S c = |50 - 30| / 1.5 = 13.33

[0088] 2. Multiply the deviation ratio by the adjustment factor:

[0089]

[0090] 3. Sum over all partitions:

[0091] DS = 10 × 6.665 = 66.65

[0092] The result shows that the deviation of man-hour allocation between the overall equipment and human resources is 66.65, and this value can be used as a reference for adjusting the allocation strategy of labor and equipment to achieve more efficient resource utilization.

[0093] Please refer to Figure 3 , and the specific steps for obtaining the task sequencing result of the critical section are as follows:

[0094] According to the equipment allocation result of the section, analyze the resource allocation of each critical section, calculate the starting working time, and use the resource optimization algorithm to predict the optimal task sequence to generate the starting work schedule for each critical section;

[0095] Based on the equipment allocation result of the section, analyze the resource allocation of each critical section in detail to ensure that each section has the required mechanical and human resources before the planned start. Through the resource optimization algorithm, predict the best task sequence to reduce waiting time and improve operation efficiency. The prediction model obtains the start time of each task based on previous data analysis, so as to accurately arrange the work sequence. The optimization process includes evaluating the specific resource requirements of each task, considering geographical location, task complexity and urgency, taking these factors as input parameters, and determining the optimal resource allocation and task start time through linear programming method. The generated starting work schedule provides a basis for the subsequent steps.

[0096] Through the starting work schedule of each critical section, use the priority ranking model to rank the mechanical and human resources, adjust the man-hour allocation of machinery and labor in combination with the task urgency, and establish a task priority list;

[0097] Through the starting work schedule, use the priority ranking model to rank the mechanical and human resources. The model is dynamically adjusted according to the urgency of the task and the availability of resources. This process includes collecting real-time data on the current status of various resources, such as equipment availability, human skill level and performance. This data is automatically collected and updated through Internet of Things devices to ensure the timeliness and accuracy of the data. During the ranking process, it is also necessary to evaluate the impact of resource scheduling on the project schedule to minimize task delays and costs, and give priority to scheduling those resources that can maximize utilization and productivity to ensure the smooth completion of tasks.

[0098] Using the task priority list, combined with the interval requirements in the actual task execution, adopt the resource utilization formula:

[0099]

[0100] Calculate the task resource utilization rate and task interval weight of the critical section to generate the task sequencing result of the critical section;

[0101] Among them, P i represents the priority of the i-th task, VEi Represents the interval weight of the task, HE i Is the man-hour required for the task, EU i Is the environmental factor adjustment coefficient, n U Is the total number of tasks, UE represents the task resource utilization rate.

[0102] Formula:

[0103]

[0104] The benefit of the formula is that by integrating the task priority P i And the task interval weight VE i , the weight of each task is adjusted, and according to the man-hour required for the task HE i And the environmental factor adjustment coefficient EU i , the resource utilization rate of the entire system is maximized. The formula allows the project manager to flexibly adjust the task plan under different environmental conditions, thereby improving efficiency.

[0105] Detailed explanation of the formula and the derivation process of the formula calculation:

[0106] Suppose there are three tasks, the task priority P = [0.9, 0.75, 0.6], the task interval weight VE i = [1, 0.8, 0.9], the man-hour required for the task HE i = [2, 3, 1.5], the environmental factor adjustment coefficient EU i = [0.1, 0.2, 0.15],

[0107] Calculate the numerator:

[0108] 0.9×1 + 0.75×0.8 + 0.6×0.9 = 0.9 + 0.6 + 0.54 = 2.04

[0109] Calculate the denominator:

[0110] 2×(1 + 0.1) + 3×(1 + 0.2) + 1.5×(1 + 0.15) = 2.2 + 3.6 + 1.725 = 7.525

[0111]

[0112] This result shows that the resource utilization rate after comprehensively considering the priority and interval weight is about 27.1%. This value reflects the effectiveness of resource allocation. How to further process this result depends on the analysis of the specific project and the actual allocation of resources.

[0113] Please refer to Figure 4 , the specific steps for obtaining the task constraint adjustment parameter set are as follows:

[0114] Extract the start time and material cycle of each task from the critical section task sorting result, analyze the equipment usage, apply the resource ratio algorithm to determine the resource requirements of each task, and generate the resource requirement analysis result;

[0115] Based on the start time and material cycle of the critical section task sorting result, first collect the specific requirements and material usage cycles of each task, analyze the equipment usage and the existing deployment plan, then use the resource ratio algorithm to analyze the material usage and the work plan of the equipment, optimize the efficiency of equipment usage and the schedule of material supply, improve the resource utilization rate by adjusting the material usage of each task and the work plan of the equipment, and generate the resource requirement analysis result. This analysis result provides data support and strategic direction for the next task deployment and resource plan.

[0116] According to the resource requirement analysis result, mark all time conflicts between resource supply and demand, as well as conflicts and resource shortages that occur in equipment deployment, and create a conflict and shortage index table;

[0117] After completing the resource requirement analysis, further mark all the time conflicts and resource shortage problems that occur, especially those key equipment that may cause significant delays during peak periods. This includes a specific analysis of the shortage of equipment during high-demand periods. By establishing a detailed conflict and shortage index table, it provides a basis for deployment decisions. These index tables list in detail the specific problems that each task may encounter in resource allocation, as well as the high-risk areas that require additional attention.

[0118] Use the conflict and shortage index table to recalculate the execution interval of each task, using the formula:

[0119]

[0120] Optimize the task execution plan and generate a set of task constraint adjustment parameters;

[0121] where D i is the deployment conflict factor, n U is the total number of tasks, AY represents the execution interval of the task, RY i is the resource requirement of each task, and TY i is the resource supply cycle.

[0122] Formula:

[0123]

[0124] The benefit of the formula is that it optimizes the efficiency of task execution and resource utilization rate by adjusting the time interval between tasks and resource allocation. Especially in the case of limited resources and tight time, it can effectively balance resource supply and demand.

[0125] Detailed Explanation of Formulas and Derivation Process of Formula Calculations:

[0126] Suppose there are three tasks, and the resource requirements RY for each task i are 20, 30, and 25 respectively, and the supply cycle TY i is 5 days, 3 days, and 4 days respectively, and the deployment conflict factor D i are 2, 1, and 3 respectively. Calculate the resource utilization rate of each task as follows:

[0127] Task 1:

[0128]

[0129] Task 2:

[0130]

[0131] Task 3:

[0132]

[0133] Then calculate the average resource utilization rate:

[0134]

[0135] This result shows that by adjusting the time intervals of task execution and resource allocation, the system can optimize resource utilization among different tasks, ensuring the continuous operation and efficiency improvement of the project.

[0136] Please refer to Figure 5 , and the specific steps for obtaining the road section construction progress plan are as follows:

[0137] Extract the construction road section time nodes and the distribution differences between tasks from the task constraint adjustment parameter set. Based on the comparison of task time and resource distribution, analyze the task execution order and resource utilization efficiency, and generate a time node and task difference distribution table;

[0138] Based on the time node and task difference distribution table, which clearly shows the start time of each task and the required resources, through comprehensive analysis of the data in the table, determine the time differences and resource allocation situations between tasks. This process involves detailed time calculations and resource comparisons to determine which tasks need to adjust their time nodes due to insufficient resources, so as to ensure that all tasks are properly arranged within the scope of available resources. By adjusting the time nodes, resource waste and time conflicts can be effectively avoided, thus enabling the smooth progress of the work on the construction road section and ensuring that each task can be executed in the planned order. This method not only improves the utilization efficiency of resources but also guarantees the possibility of completing the project on schedule.

[0139] Using the time node and task difference distribution table, pair the resource utilization rate with the available equipment time. Based on the matching analysis of resource allocation and task requirements, calibrate time conflict and resource shortage tasks, and create the results of resource conflict and shortage tasks;

[0140] Extract key data from the results of resource conflict and shortage tasks. This result details the resource shortages and time allocation problems that each construction task may encounter during execution. By analyzing this data, it is possible to identify which tasks are the most urgent and require priority resource allocation. At the same time, the result also helps the construction team anticipate potential problems, allowing them to make plan adjustments in advance to prevent work stoppages or resource waste during construction. This early warning mechanism is achieved by comparing the resources required for each task with the available time of the equipment, ensuring that each construction stage receives the necessary resource support, thereby effectively improving construction efficiency and reducing costs.

[0141] Based on the results of resource conflict and shortage tasks, calculate the adjustment range and time node correction value for task execution, using the formula:

[0142]

[0143] Optimize the task time distribution to generate the construction progress plan for the road section;

[0144] where TS is the adjusted time node, ES i represents the resource utilization rate of the i-th task, W i is the task priority weight, L i is the original planned time node, β i is the task adjustment factor, n U is the total number of tasks, representing the total number of tasks that need to be adjusted.

[0145] Formula:

[0146]

[0147] The benefit of the formula is that it can optimize the task time arrangement by considering the resource utilization rate and priority weight of each task, and combining the time adjustment factors, ensuring the maximum efficient use of resources while reducing time waste caused by improper resource allocation.

[0148] Detailed explanation of the formula and the formula calculation derivation process:

[0149] Suppose there are three tasks, where ES1 = 0.9, W1 = 1.2, L1 = 4 hours, adjustment factor β1 = 0.5, the second task ES2 = 0.85, W2 = 1.0, L2 = 3 hours, β2 = 0.3, and the third task ES3 = 0.75, W3 = 1.5, L3 = 5 hours, β3 = 0.7. The formula calculation is as follows:

[0150]

[0151] TS = 0.509 + 0.467 + 0.471

[0152] TS = 1.447

[0153] The result shows that after optimization and adjustment, the total time node adjustment value is 1.447, which means that all tasks can be completed within this adjusted time node after considering resource efficiency and priority weights. This calculation method effectively balances the urgency of tasks and the availability of resources.

[0154] Please refer to Figure 6 , and the specific steps for obtaining the construction offset correction result are as follows:

[0155] Extract monitoring data from the road section construction progress plan, including environmental changes in the construction section and the operating status of equipment. Combine the task time nodes and use data analysis methods to determine the impact of the environment and equipment status on the construction progress, and generate the analysis result of the environment and equipment status;

[0156] Obtain it in real time through sensors and monitoring equipment. The data covers environmental indicators such as temperature, humidity, and noise, as well as the operating conditions of various mechanical equipment such as startup time, shutdown time, and failure rate. Use data analysis software to preliminarily screen and process these data, excluding obvious outliers or incorrect data, such as data points with extremely high or low temperatures, equipment failure data, etc. Analyze how these data affect the construction progress and safety, and calculate the specific impact of environmental factors on the construction progress through an algorithm model to generate the analysis result of the environment and equipment status, providing data support for the next construction adjustment.

[0157] Using the analysis result of the environment and equipment status, calculate the deviation between the task time and environmental changes, and count the working hours and remaining material quantities of each equipment. Through quantitative analysis methods, calibrate the equipment allocation and time nodes that need to be adjusted, and create a task and environment deviation table;

[0158] Using the analysis result of the environment and equipment status, continue to calculate the deviation between the task time and environmental changes. By comparing the time nodes in the actual construction with the environmental records, identify the time periods with large deviations from the plan, and then count the actual working hours and remaining material quantities of each equipment. This statistics involves parsing the operation logs of each equipment, including startup, operation, and shutdown times, as well as monitoring data on the material consumption speed. Based on these data, use statistical analysis software for data fusion and processing to obtain the resource utilization rate of each task and the time efficiency of equipment scheduling, providing a basis for the final adjustment of equipment and time nodes.

[0159] Based on the task and environment deviation table, adjust the equipment allocation and time nodes to match the actual construction environment changes. Use the formula:

[0160]

[0161] Optimize the task allocation to generate the construction offset correction result;

[0162] Among them, RZ represents the total adjustment efficiency, G i represents the time deviation of the i-th task, VZ i is the equipment operation efficiency associated with the task, HZ i is the original planned working hours, δ R represents the global adjustment coefficient, used to correct the time deviation caused by environmental changes, n U is the total number of tasks.

[0163] Formula:

[0164]

[0165] The benefit of the formula is that it optimizes the construction allocation, reduces resource waste, and improves construction efficiency by considering the actual data of environmental factors and equipment efficiency.

[0166] Detailed explanation of the formula and the derivation process of formula calculation:

[0167] Assume that in actual construction, it is known that the reduction in work efficiency caused by environmental factors such as temperature change is G i = 1.5, the equipment operation efficiency VZ i = 0.8, the original planned working hours HZ i = 100 hours, and the adjustment factor δ R = 5. Insert these values into the formula, and the calculation process is as follows:

[0168]

[0169] The result shows that under the current environment and equipment status, the adjusted work efficiency is approximately 1.14%, indicating that construction managers need to further adjust their strategies to improve efficiency.

[0170] An intelligent road construction control method, the intelligent road construction control method is executed based on the above intelligent road construction control system, and includes the following steps:

[0171] S1: Based on the working hour records of pavement materials and construction equipment, count the task requirements and equipment working hours of the partitioned road sections, match the equipment working hours with the task requirements, calculate the difference in manual working hours, compare the availability of equipment and labor item by item, and generate a road section equipment resource allocation table;

[0172] S2: Based on the road section equipment resource allocation table, calculate the start time and task interval of the key road section tasks, sort the call order of mechanical working hours and manual working hours, count the task resource utilization rate and interval weight, and combine the statistical data with the call order to generate the key road section task priority table;

[0173] S3: Based on the key road section task priority table, calculate the pavement material supply cycle and construction equipment working hours, calibrate the time conflict of equipment allocation, and combine the allocation time with the distribution of tasks with insufficient resources to adjust the task execution interval and establish a task execution constraint parameter set;

[0174] S4: Based on the task execution constraint parameter set, count the difference between the time nodes and task distribution of the construction road section, pair the resource utilization rate with the equipment time, adjust the task and time node range, reorganize the equipment resource distribution, obtain the optimized construction progress plan for the road section, monitor the environmental changes and equipment operation status, calculate the deviation between the task time and environmental changes, count the equipment working hours and the remaining material quantity, adjust the equipment allocation and time nodes, and generate the construction offset correction result.

[0175] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. An intelligent road construction control system, characterized in that: The system comprises: The resource allocation optimization module is based on pavement materials and construction equipment. It counts the equipment hours and task requirements of the divided road sections, matches the available equipment hours with the task requirements, calculates the difference in labor hours allocation, compares the equipment and labor availability, and generates the equipment allocation results for the road sections. The task priority adjustment module calculates the start time and task interval of the key section tasks based on the section equipment allocation result, sorts the priority calling order of mechanical working hours and manual working hours, calculates the task resource utilization rate and task interval weight, and generates the key section task sorting result; The dynamic constraint evaluation module calculates the pavement material supply cycle and the construction equipment working hours based on the task sorting results of the key road section, calibrates the equipment allocation time conflict and resource shortage tasks, adjusts the task execution interval, and generates a task constraint adjustment parameter set; The construction progress optimization module is based on the task constraint adjustment parameter set, counts the distribution difference between the construction section time node and the task, matches the resource utilization rate and the equipment available time, calculates the adjustment range of the task and the time node, reorganizes the task time and the equipment resource distribution, and generates the section construction schedule; The real-time emergency response module monitors the environmental changes and equipment operation status of the construction section based on the construction progress plan of the section, calculates the deviation between the task time and the environmental changes, counts the equipment working hours and the remaining amount of materials, adjusts the equipment deployment and time nodes, and generates the construction deviation correction results; The equipment allocation results of the road section include the equipment working time allocation ratio, the labor working time matching results, and the analysis of the difference between equipment and task requirements. The task sorting results of the key section include the key section task priority, task resource utilization efficiency, and task interval optimization order. The task constraint adjustment parameter set includes equipment deployment time parameters, resource-deficient task identification, and task execution interval correction value. The road section construction schedule includes construction time node arrangement, equipment resource allocation plan, and task execution time adjustment. The construction offset correction results include equipment operation status adjustment, construction time deviation correction, and material remaining balance parameters.

2. The intelligent road construction control system according to claim 1 is characterized in that: The steps for obtaining the road section equipment allocation result are specifically as follows: Collect the task requirements and equipment working hours of the partitioned sections, comprehensively count the available working hours of multi-partitioned equipment, and the matching degree with the task requirements, and calculate the deviation between the total equipment working hours and the task requirements to generate the equipment working hours deviation set; Analyze the equipment working time deviation set, adjust the working time allocation of equipment and labor according to the availability of labor resources, and establish a labor and equipment working time adjustment set; Based on the labor and equipment time adjustment set, the formula is used: Compare equipment and manual allocation to obtain the equipment allocation results for the road section; Among them, DS represents the deviation of time allocation between equipment and labor, TQ c represents the task demand of the cth partition, RQ c Represents the available working hours of the corresponding equipment, S c represents the adjustment coefficient, n D is the total number of partitions, α is a weight parameter that adjusts the sensitivity of device allocation deviation to task requirements, and γ d is the adjustment coefficient, controlling the basic deviation of the allocation process, δ Q It is a tuning parameter that reflects the impact of the number of partitions on allocation flexibility.

3. The intelligent road construction control system according to claim 2 is characterized in that: The steps for obtaining the ranking results of the key road section tasks are specifically as follows: According to the equipment allocation results of the road sections, the resource configuration of each key road section is analyzed, the starting working time is calculated, and the optimal task sequence is predicted by using the resource optimization algorithm to generate a start timetable for each key road section; By using the construction schedule of each key section, the priority sorting model is used to sort the priority of mechanical and human resources, and the allocation of mechanical and human time is adjusted according to the urgency of the task to establish a task priority list; Using the task priority list and the interval requirements in actual task execution, the resource utilization formula is adopted: Calculate the task resource utilization and task interval weight of key sections, and generate the task sorting results of key sections; Among them, P i Represents the priority of the i-th task, VE i represents the interval weight of the task, HE i is the work time required for the task, EU i is the environmental factor adjustment coefficient, n U is the total number of tasks, and UE represents the task resource utilization.

4. The intelligent road construction control system according to claim 3 is characterized in that: The steps for obtaining the task constraint adjustment parameter set are specifically as follows: Extracting the start time and material cycle of each task from the key section task sorting results, analyzing equipment usage, applying a resource allocation algorithm to determine the resource requirements of each task, and generating a resource requirement analysis result; According to the resource demand analysis results, mark all time conflicts of resource supply and demand as well as conflicts and resource shortages occurring in equipment allocation, and create a conflict and shortage index table; Using the conflict and shortage index table, recalculate the execution interval of each task using the formula: Optimize the task execution plan and generate a set of task constraint adjustment parameters; Among them, D i is the coordination conflict factor, n U is the total number of tasks, AY represents the execution interval of the task, and RY i The resource requirement for each task, TY i For the resource supply cycle.

5. The intelligent road construction control system according to claim 4 is characterized in that: The specific steps for obtaining the construction schedule of the road section are as follows: Extracting the distribution difference between the time nodes and tasks of the construction section from the task constraint adjustment parameter set, analyzing the task execution sequence and resource utilization efficiency based on the comparison between the task time and resource distribution, and generating a distribution table of the time nodes and task differences; By using the time node and task difference distribution table, resource utilization and equipment available time are matched, and based on the matching analysis of resource allocation and task requirements, time conflict and resource shortage tasks are calibrated, and resource conflict and resource shortage task results are created; Based on the resource conflicts and insufficient task results, the adjustment range and time node correction value of task execution are calculated using the formula: Optimize task time distribution and generate road section construction schedule; Among them, TS is the adjusted time node, ES i represents the resource utilization of the i-th task, W i is the task priority weight, L i is the original planned time node, β i is the task adjustment factor, n U is the total number of tasks, indicating the total number of tasks that need to be adjusted.

6. The intelligent road construction control system according to claim 5, characterized in that: The steps for obtaining the construction offset correction result are specifically as follows: Extract monitoring data from the construction schedule of the road section, including environmental changes of the construction section and equipment operating status, combine the task time nodes, use data analysis methods to determine the impact of the environment and equipment status on the construction progress, and generate environmental and equipment status analysis results; Using the environmental and equipment status analysis results, calculate the deviation between task time and environmental changes, and count the working hours and material remaining of each equipment. Through quantitative analysis methods, calibrate the equipment deployment and time nodes that need to be adjusted, and create a task and environment deviation table; Based on the task and environment deviation table, adjust the equipment deployment and time nodes to match the actual construction environment changes, using the formula: Optimize task allocation and generate construction offset correction results; Among them, RZ represents the total adjustment efficiency, G i represents the time deviation of the ith task, VZ i is the equipment operation efficiency associated with the task, HZ i is the original planned working hours, δ R Represents the global adjustment coefficient, which is used to correct the time deviation caused by environmental changes. U is the total number of tasks.

7. An intelligent road construction control method, characterized in that: According to any one of claims 1 to 6, the intelligent road construction control system is implemented. The following steps are involved: Based on the working time records of pavement materials and construction equipment, the task requirements and equipment working hours of the divided sections are counted, the equipment working hours are matched with the task requirements, the difference in labor working hours is calculated, the equipment and labor availability are compared item by item, and the equipment resource allocation table of the section is generated; Based on the road section equipment resource allocation table, calculate the start time and task interval of key road section tasks, sort the calling order of mechanical working hours and manual working hours, count the task resource utilization rate and interval weight, and generate a key road section task priority table by combining the statistical data and the calling order; Based on the task priority table of the key road section, the pavement material supply cycle and the construction equipment working hours are calculated, the equipment deployment time conflict is calibrated, and the task execution interval is adjusted in combination with the deployment time and the resource shortage task distribution, and a task execution constraint parameter set is established; Based on the task execution constraint parameter set, the difference between the construction section time node and the task distribution is counted, the resource utilization rate and equipment time are paired, the task and time node ranges are adjusted, the equipment resource distribution is reorganized, the section construction optimization schedule is obtained, the environmental changes and equipment operating status are monitored, the deviation between the task time and the environmental changes is calculated, the equipment working hours and the remaining materials are counted, the equipment deployment and time nodes are adjusted, and the construction offset correction results are generated.

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