Cloud Computing-Based Construction Project Management System

Abstract

The present invention relates to the technical field of construction project management, specifically a construction project management system based on cloud computing. The system includes: a construction path optimization module, a construction status analysis module, a construction environment monitoring module, a schedule plan adjustment module, and a green construction optimization module. In the present invention, by combining the distribution of construction task resources with three-dimensional spatial information, the resource path allocation logic is optimized. Using data on material consumption, equipment operation status, and key process deviations, precise positioning and prediction of construction dynamic problems are achieved, reducing construction interruptions. Based on on-site image data, the distribution characteristics of equipment and personnel are extracted, and the deviation degree of equipment layout and the superimposed value of safety hazards are quantified to improve the safety and efficiency of the construction site. By dynamically adjusting the process time and resource utilization rate, optimizing the relationship between task nodes and factors, the flexibility and response ability of the construction plan are improved. Through partition modeling of energy consumption data and multi-constraint optimization, the utilization rate of green construction resources is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of construction project management, and particularly to a construction project management system based on cloud computing. Background Art

[0002] The technical field of the construction project management system based on cloud computing includes a building construction management system based on cloud computing technology and its related applications. The core content of this technical field is to utilize the distributed storage, virtualization technology and efficient computing power of cloud computing to achieve digital and information-based management of the whole process of construction projects. Through this technical field, real-time monitoring and management can be carried out on core links such as construction progress, resource allocation, quality supervision and cost control, reducing problems such as information asymmetry and low management efficiency in traditional construction project management. The systematicness of the overall technical field covers multiple levels such as project plan formulation, task assignment, data storage, real-time monitoring and intelligent analysis. Relying on the technical architecture of cloud computing, the digital level of construction management is improved.

[0003] Among them, construction project management refers to the management of each stage of a project by using the data processing and analysis capabilities in a cloud computing environment for technical matters such as planning, organizing, coordinating and supervising during the construction process. Specifically, this patent theme covers the dynamic adjustment and real-time monitoring of project progress plans through cloud storage and distributed computing technologies, the optimization of resource allocation efficiency through reasonable scheduling and management methods of project resources, and the detection and statistics of construction quality data through model calibration and automated calculations based on big data analysis. In addition, it also includes using a cloud collaboration mechanism to achieve task division and communication management under the participation of multiple parties, and unified management of project documents and data throughout the process through data structured storage and retrieval technologies to improve the systematic and standardized level of building construction project management.

[0004] The prior art lacks a comprehensive analysis of resource circulation efficiency and three-dimensional space in resource path planning, resulting in uneven resource scheduling and path conflicts, which affects construction efficiency. In terms of anomaly analysis, it fails to dynamically track material consumption and equipment status, making it difficult to discover and predict key problems in a timely manner, increasing the risk of construction delays. On-site safety monitoring mainly relies on manual inspections, lacking quantitative analysis of personnel distribution and equipment layout, resulting in incomplete identification of safety hazards. In task plan adjustment, due to the lack of dynamic processing of real-time process and resource data, the plan execution lacks flexibility and is prone to wasting time and resources. For green construction, the prior art fails to systematically optimize energy consumption, water resources and waste management, making it difficult to meet environmental protection requirements. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art, and to propose a construction project management system based on cloud computing.

[0006] To achieve the above object, the present invention adopts the following technical solutions: The construction project management system based on cloud computing includes:

[0007] The construction path optimization module, based on the resource distribution of construction tasks and the three-dimensional space information in the BIM model, recomputes the allocation logic of resource paths by calculating the resource circulation efficiency within each region and the resource distribution ratio at path intersection points, and generates an optimized value for resource path allocation.

[0008] The construction status analysis module, based on the optimized value of resource path allocation, analyzes the material consumption, equipment operation status, and execution deviation of key processes. By calculating the deviation rate and screening abnormal distribution parameters, it establishes an abnormal trend model and calculates the dynamic abnormal distribution ratio in construction tasks, generating a construction dynamic abnormal distribution rate.

[0009] The construction environment monitoring module, based on the construction dynamic abnormal distribution rate, extracts the distribution characteristics of equipment and personnel in the UAV image data, combines with the task area information in the BIM model, calculates the superposition value of equipment layout deviation and potential safety hazard parameters, and generates a construction scenario deviation safety coefficient.

[0010] The progress plan adjustment module, based on the construction scenario deviation safety coefficient and the construction dynamic abnormal distribution rate, recomputes the process time and resource utilization rate of construction task nodes, constructs the relationship of dynamic adjustment factors during task execution, and generates a construction task time adjustment factor.

[0011] The green construction optimization module, based on the construction task time adjustment factor and the optimized value of resource path allocation, models the energy consumption data, waste recycling ratio, and water resource utilization rate by region, redistributes the resource usage parameters, calculates the optimization coefficient under multiple constraints, and generates a green construction resource optimization coefficient.

[0012] As a further solution of the present invention, the optimized value of resource path allocation specifically includes resource circulation efficiency, resource distribution ratio at path intersection points, and path allocation logic; the construction dynamic abnormal distribution rate specifically includes material consumption deviation rate, equipment operation status deviation, and key process execution deviation; the construction scenario deviation safety coefficient includes equipment layout deviation degree, superposition value of potential safety hazard parameters, and task area information matching degree; the construction task time adjustment factor specifically includes process time adjustment value, resource utilization rate adjustment value, and task dynamic adjustment factor; the green construction resource optimization coefficient includes optimized value of energy consumption data, optimized value of waste recycling ratio, and optimized value of water resource utilization rate.

[0013] As a further solution of the present invention, the specific steps for obtaining the optimized value of resource path allocation are as follows:

[0014] Based on the resource distribution parameters of the construction tasks, call the resource circulation efficiency data in multiple regions and the resource distribution ratio at the path intersection points, analyze the weight relationship between the resource occupancy rate of each path and the circulation efficiency at the intersection points, and by calculating the weighted value of the resource distribution ratio and circulation efficiency at the intersection points, adjust the path parameter weights to generate the regional path resource distribution weights;

[0015] Call the regional path resource distribution weights, analyze the differences in resource utilization rates of all paths, and by comparing the average value and standard deviation of the path resource utilization rates, eliminate the paths below the standard range. Based on the data of the remaining paths, perform segmented weighted correction on the resource circulation efficiency, adjust the resource distribution ratio of the remaining paths, and generate the resource circulation correction coefficient;

[0016] Using the regional path resource distribution weights and the resource circulation correction coefficient, adopt the formula:

[0017] ;

[0018] Calculate the resource allocation efficiency of the path to generate the optimized value of resource path allocation;

[0019] Among them, represents the optimized value of resource path allocation, represents the regional path resource distribution weight of the th path, represents the resource circulation correction coefficient of the th path, represents the resource occupancy rate of the th path segment, is an adjustment coefficient used to optimize the balance of path weight allocation, represents the path length, represents the total number of paths, represents the total number of path segments, represents the path serial number, represents the path segment serial number.

[0020] As a further solution of the present invention, the specific steps for obtaining the construction dynamic abnormal distribution rate are as follows:

[0021] Based on the optimized value of resource path allocation, call the material consumption data and equipment operation status parameters, analyze the correlation between material consumption and equipment efficiency, and calculate the relative deviation of the material consumption rate and equipment operation efficiency by using the weight allocation comparison method to generate the preliminary screening result;

[0022] Call the preliminary screening results, calculate the execution deviation of the key processes, call the preset threshold to judge the deviation data item by item, perform hierarchical processing on the abnormal degree of deviation according to the deviation rate, extract all abnormal deviation data to establish an abnormal deviation screening matrix, and generate the analysis result of abnormal deviation;

[0023] Combined with the analysis result of abnormal deviation, combined with the abnormal distribution parameters, establish an abnormal trend model through multi-dimensional regression operation, using the formula:

[0024] ;

[0025] Calculate the dynamic abnormal distribution ratio in the construction task, and generate the construction dynamic abnormal distribution rate;

[0026] Among them, represents the construction dynamic abnormal distribution rate, represents the sample quantity, represents the th sample's abnormal deviation value, represents the mean value of the abnormal deviation value, represents the standard deviation of the abnormal deviation value, is an adjustment coefficient used to adjust the sensitivity of the abnormal deviation, is the sample index number.

[0027] As a further solution of the present invention, the specific steps for obtaining the construction scenario offset safety coefficient are as follows:

[0028] Based on the construction dynamic abnormal distribution rate, extract the distribution characteristics of equipment and personnel from the UAV image data, call the image analysis module to calculate the spatial distribution parameters of equipment and personnel in a specific task area, and generate equipment and personnel distribution characteristic data by calculating the regional density and position coordinates of each distribution point;

[0029] Combined with the equipment and personnel distribution characteristic data and the task area information in the BIM model, call the regional comparison module to perform spatial matching judgment on the actual equipment layout and the preset parameters of the BIM model, and calculate the layout error value and the total cumulative offset of each equipment according to the deviation amount of the equipment position, and generate the equipment layout deviation result;

[0030] Combined with the equipment layout deviation result and the potential safety hazard parameters, perform weighted superposition operation according to the offset risk and hazard factors of the equipment, using the formula:

[0031] ;

[0032] Calculate the construction scenario offset safety coefficient;

[0033] Among them, represents the construction scenario offset safety coefficient, represents the deviation of the th device, is the risk-weighted factor of the th device, represents the safety hazard parameter related to the th device, is the total number of devices, is the device index number.

[0034] As a further aspect of the present invention, the steps for obtaining the construction task time adjustment factor are specifically as follows:

[0035] Based on the construction scenario offset safety factor and the construction dynamic anomaly distribution rate, re-evaluate the key nodes affecting the construction task, calculate the resource allocation ratio and statistically analyze the fluctuation data of resource utilization rate by calling dynamic process data analysis for the time dependence of each process, and generate the time and resource utilization analysis result of the construction task;

[0036] Combined with the time and resource utilization analysis result of the construction task, through constructing a relationship model between process time and resource allocation, accurately calculate the adjustment of process time, and generate the adjustment data of process time and resource utilization rate by statistically analyzing the error distribution between the original plan and the actual execution item by item;

[0037] Using the adjustment data of process time and resource utilization rate, construct a dynamic adjustment factor relationship model during task execution, and adopt the formula:

[0038] ;

[0039] Calculate the construction task time adjustment factor;

[0040] where, represents the construction task time adjustment factor, and are adjustment coefficients, reflecting the sensitivity of time and resource impacts, represents the adjusted time of the th process, is the total planned time, represents the adjusted utilization rate of the th resource, is the total resource utilization rate, and are the number of processes and the number of resource types respectively.

[0041] As a further aspect of the present invention, the steps for obtaining the green construction resource optimization coefficient are specifically as follows:

[0042] Based on the construction task time adjustment factor and the optimized value of resource path allocation, re-evaluate the resource allocation situation at the construction site. By extracting energy consumption data, waste recycling ratio, and water resource utilization rate, calculate the resource utilization efficiency and analyze the optimization potential of each resource item to generate the resource utilization efficiency analysis result;

[0043] Combined with the resource utilization efficiency analysis result, analyze the utilization situation of each resource item by constructing a multi-dimensional data model, perform itemized statistics on resource usage deviations and potential improvement spaces, calibrate the resource usage parameters to be optimized, and integrate multiple resource attributes to generate the resource usage parameter adjustment result;

[0044] Utilize the resource usage parameter adjustment result and the multi-constraint conditions at the construction site, integrate the weights of various resource attributes, and calculate the optimization effect. Use the formula:

[0045] ;

[0046] Calculate the green construction resource optimization coefficient;

[0047] Wherein, represents the green construction resource optimization coefficient, is the optimization weight of the th resource, reflecting the impact degree of this resource on the overall optimization, represents the actual usage amount of the th resource in the optimization model, is the th resource's theoretical maximum usage amount, is the number of resource types, is the resource index number.

[0048] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0049] In the present invention, by combining the resource distribution of construction tasks with three-dimensional space information, optimizing the resource path allocation logic, improving the resource circulation efficiency, and avoiding path conflicts and resource waste. Using material consumption, equipment operation status, and key process deviation data to establish an abnormal trend model to accurately locate and predict construction dynamic problems and reduce construction interruptions. Extracting equipment and personnel distribution characteristics based on on-site image data, quantifying the deviation degree of equipment layout and the superposition value of safety hazards to improve the safety and efficiency of the construction site. By dynamically adjusting the process time and resource utilization rate, optimizing the relationship between task nodes and factors, improving the flexibility and response ability of the construction plan. Through zoning modeling of energy consumption data and multi-constraint optimization, improving the utilization rate of green construction resources and reducing energy consumption and waste generation. Brief Description of the Drawings

[0050] Figure 1 is the system flowchart of the present invention;

[0051] Figure 2 Flow chart of the acquisition steps for the optimized value of the resource path allocation of the present invention;

[0052] Figure 3 Flow chart of the acquisition steps for the abnormal distribution rate of the construction dynamics of the present invention;

[0053] Figure 4 Flow chart of the acquisition steps for the offset safety factor of the construction scenario of the present invention;

[0054] Figure 5 Flow chart of the acquisition steps for the construction task time adjustment factor of the present invention;

[0055] Figure 6 Flow chart of the acquisition steps for the optimized coefficient of green construction resources of the present invention. Detailed implementation manners

[0056] In order to make the objectives, technical solutions and advantages of the present invention clearer, 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.

[0057] 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 accompanying drawings, and 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, and therefore should not be construed as a limitation of 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.

[0058] Embodiment 1

[0059] Please refer to Figure 1 , the construction project management system based on cloud computing includes:

[0060] The construction path optimization module, based on the resource distribution of the construction task and the three-dimensional space information in the BIM model, recomputes the allocation logic of the resource path by calculating the resource circulation efficiency in each area and the resource distribution ratio at the path intersection points, and generates an optimized value for the resource path allocation;

[0061] The construction status analysis module, based on the optimized value of the resource path allocation, analyzes the execution deviation of the material consumption, equipment operation status and key processes, and establishes an abnormal trend model and calculates the dynamic abnormal distribution ratio in the construction task by offset rate calculation and screening of abnormal distribution parameters, and generates an abnormal distribution rate of construction dynamics;

[0062] The construction environment monitoring module extracts the distribution characteristics of equipment and personnel in the UAV image data based on the construction dynamic abnormal distribution rate, combines the task area information in the BIM model, calculates the superposition value of the equipment layout deviation degree and safety hazard parameters, and generates the construction scene offset safety coefficient;

[0063] The progress plan adjustment module recalculates the process time and resource utilization rate of the construction task nodes based on the construction scene offset safety coefficient and the construction dynamic abnormal distribution rate, constructs the dynamic adjustment factor relationship during task execution, and generates the construction task time adjustment factor;

[0064] The green construction optimization module partitions and models the energy consumption data, waste recycling ratio, and water resource utilization rate based on the construction task time adjustment factor and the optimized value of resource path allocation, reallocates the resource usage parameters, calculates the optimization coefficient under multiple constraints, and generates the green construction resource optimization coefficient.

[0065] The optimized value of resource path allocation specifically includes resource circulation efficiency, resource distribution ratio at path intersections, and path allocation logic. The construction dynamic abnormal distribution rate specifically includes the offset rate of material consumption, deviation of equipment operation status, and deviation of key process execution. The construction scene offset safety coefficient includes equipment layout deviation degree, superposition value of safety hazard parameters, and task area information matching degree. The construction task time adjustment factor specifically includes process time adjustment value, resource utilization rate adjustment value, and task dynamic adjustment factor. The green construction resource optimization coefficient includes optimized value of energy consumption data, optimized value of waste recycling ratio, and optimized value of water resource utilization rate.

[0066] Please refer to Figure 2 , and the specific steps for obtaining the optimized value of resource path allocation are as follows:

[0067] Based on the resource distribution parameters of the construction task, call the resource circulation efficiency data and resource distribution ratio at path intersections in multiple regions, analyze the weight relationship between the resource occupancy rate of each path and the circulation efficiency at intersections, adjust the path parameter weights by calculating the weighted value of the resource distribution ratio at intersections and the circulation efficiency, and generate the regional path resource distribution weight;

[0068] Based on the resource distribution parameters of construction tasks, the call of resource circulation efficiency data within each region and the resource distribution ratio at path intersections. For the resource distribution parameters, it is necessary to first divide the construction area. The resource types and quantities within each region are used as basic data inputs. The resource circulation efficiency is quantified by monitoring the ratio of the time and quantity of resource inflows and outflows. For example, in a certain region, the resource circulation efficiency is the total amount of inflowing resources divided by the total time consumed, and then the parameter value is obtained by statistical calculation based on the average daily circulation efficiency. The resource distribution ratio at intersections is calculated by the proportion of the cumulative distribution amount of resources at the intersections of all paths within the region. The proportion value is obtained by dividing the cumulative distribution quantity of resources at the path intersections by the total amount of resources in the region. Subsequently, according to the weight relationship between the resource distribution ratio and circulation efficiency at intersections, by calculating the weighted value of the resource distribution ratio and circulation efficiency at intersections, combined with the resource distribution of the regional division, the parameter weights of the paths are further adjusted. For example, for a certain path, the resource distribution ratio is 0.6, the weight proportion of the circulation efficiency is 0.4, and the weighted value is 0.6×0.4. According to the weighted result, the priority of the path resources is re-evaluated, and finally the resource distribution weight of the regional path is generated.

[0069] Call the resource distribution weight of the regional path, analyze the differences in resource utilization rates of all paths. By comparing the average value and standard deviation of the path resource utilization rates, eliminate the paths below the standard range. Based on the data of the remaining paths, perform segmented weighted correction on the resource circulation efficiency, adjust the resource distribution ratio of the remaining paths, and generate a resource circulation correction coefficient.

[0070] Call the resource distribution weight of the regional path. By analyzing the differences in resource utilization rates of all paths, perform quantitative processing on the path resource utilization rates. First, calculate the resource utilization rate of each path, which is equal to the percentage of the resource flow on the path in the total amount of resources in the region. For example, if the resource flow of a certain path is 200 and the total amount of resources is 1000, the utilization rate is 20%. Subsequently, statistically analyze the resource utilization rate data of all paths, calculate the average value and standard deviation of the path resource utilization rates. For example, if the utilization rates of five paths are 10%, 20%, 30%, 40%, and 50% respectively, the average value is 30%, and the standard deviation is obtained by taking the square root of the sum of the squares of the differences between the utilization rates of each path and the average value. When screening paths, consider the paths with utilization rates lower than the average value minus the standard deviation as low-utilization paths and eliminate them. For example, if the average value is 30% and the standard deviation is 10%, then eliminate the paths with utilization rates lower than 20%. Next, perform segmented weighted correction on the resource circulation efficiency data of the remaining paths, and adjust the correction weight according to the fluctuation range of the path resource flow at different time periods. For example, if the fluctuation range of the resource flow is larger during peak hours, the correction weight during peak hours is higher. After correction calculation, adjust the resource distribution ratio of the remaining paths, and finally generate a resource circulation correction coefficient.

[0071] Using the regional path resource distribution weight and the resource circulation correction coefficient, the formula is adopted:

[0072] ;

[0073] Calculate the resource allocation efficiency of the path and generate an optimized value for resource path allocation;

[0074] Among them, represents the optimized value of resource path allocation, represents the regional path resource distribution weight of the th path, represents the resource circulation correction coefficient of the th path, represents the resource occupancy rate of the th path segment, is an adjustment coefficient used to optimize the balance of path weight allocation, represents the path length, represents the total number of paths, represents the total number of path segments, represents the path serial number, represents the path segment serial number.

[0075] Formula:

[0076] ;

[0077] The benefit of the formula is that by introducing the resource distribution weight , the resource circulation correction coefficient , the sum of squares of resource occupancy rates , the adjustment coefficient and the maximum path length for joint calculation, the resource allocation of the path can be comprehensively optimized, improving the rationality of path planning and resource utilization efficiency.

[0078] Detailed explanation of the formula and the derivation process of formula calculation:

[0079] Suppose there are three paths in total, with weights , , , corresponding correction coefficients , , , and the resource occupancy rates of the path segments are , , , the maximum path length is , and the adjustment coefficient .

[0080] The calculation process is as follows:

[0081] Calculate the numerator part:

[0082] ;

[0083] Calculate the denominator part:

[0084] ;

[0085] ;

[0086] Sum of denominators: ;

[0087] Calculate the final result: ;

[0088] This result indicates that the comprehensive resource allocation efficiency of the path is 0.0626. A relatively low value may indicate that the resource distribution and circulation efficiency of the current path need to be further optimized. This result can be used as a reference basis for subsequent resource allocation adjustments.

[0089] Please refer to Figure 3 , and the specific steps for obtaining the abnormal distribution rate of construction dynamics are as follows:

[0090] Based on the optimized value of resource path allocation, call the material consumption data and equipment operation status parameters, analyze the correlation between material consumption and equipment efficiency, and calculate the relative deviation of material consumption rate and equipment operation efficiency using the weight allocation comparison method to generate a preliminary screening result;

[0091] Based on the optimized value of resource path allocation, first call the material consumption data and group and statistically analyze the data according to the time dimension. By comparing the change rate of material consumption in each time period, judge the fluctuation range of each time period, screen out the data of the time periods with significant fluctuations, and extract the equipment operation status parameters within these time periods. Then decompose the extracted equipment operation parameters into core index groups (such as operation duration, operation power, and temperature fluctuation), perform normalization processing on these index groups, calculate the standardized values of each index in different time periods to make the index groups comparable. Subsequently, calculate the correlation coefficient between the equipment operation status and material consumption. The correlation coefficient is calculated through the Pearson correlation formula where and represent the values of material consumption and equipment operation parameters in the th time period respectively, and represent the means of material consumption and equipment operation parameters respectively. By sorting and comparing the absolute values of the correlation coefficients, screen out the time periods and corresponding equipment operation parameters with significant correlations, and finally generate a preliminary screening result based on these screened time periods and parameters.

[0092] Call the preliminary screening results, calculate the execution deviation of the key processes, call the preset threshold to judge the deviation data item by item, perform hierarchical processing on the abnormal degree of deviation according to the deviation rate, extract all abnormal deviation data to establish an abnormal deviation screening matrix, and generate the analysis result of deviation abnormality;

[0093] Call the preliminary screening results, first perform statistical analysis on the execution deviation of the key processes, call the data of the execution time of the key processes and the pre-designed planned time, and calculate the deviation value through the formula (where is the actual completion time, is the pre-designed planned time) to obtain the time deviation value of each process. Then, group the time deviation values according to the preset threshold range, establish a deviation matrix for the deviation data exceeding the threshold. Each column of this matrix represents the change of the deviation value of a certain key process. Compare the deviation values in each column of the matrix with the preset deviation standard range, screen out all abnormal deviation data, sort the influence degree of the deviation by constructing a weight parameter matrix. The setting basis of the weight parameter is the influence degree of the deviation on the overall construction plan progress, and quantify the influence weight of the weight parameter through the normalization processing formula (where is the weight value, is the influence value of the th deviation) to finally generate the analysis result of deviation abnormality in combination with the screened abnormal deviation data and the weight parameter matrix.

[0094] Combine the analysis result of deviation abnormality, combine the abnormal distribution parameters, establish an abnormal trend model through multi-dimensional regression operation, and use the formula:

[0095] ;

[0096] Calculate the dynamic abnormal distribution ratio in the construction task and generate the construction dynamic abnormal distribution rate;

[0097] Among them, represents the construction dynamic abnormal distribution rate, represents the sample quantity, represents the abnormal deviation value of the th sample, represents the mean value of the abnormal deviation value, represents the standard deviation of the abnormal deviation value, is the adjustment coefficient used to adjust the sensitivity of the abnormal deviation, is the sample serial number.

[0098] Formula:

[0099] ;

[0100] The advantage of the formula is that by introducing parameters it adjusts the sensitivity of abnormal deviation and the mean and the standard deviation and the ability to describe the characteristics of the sample distribution, improving the calculation accuracy of the abnormal distribution ratio, so that the calculation results can better reflect the dynamic abnormal distribution characteristics in the construction task.

[0101] Detailed explanation of the formula and the derivation process of formula calculation:

[0102] Among them, represents the construction dynamic abnormal distribution rate, represents the number of samples, represents the th abnormal deviation value of the sample, represents the mean of the sample, represents the standard deviation of the sample, is a adjustment coefficient used to adjust the sensitivity of the deviation. Suppose , the sample data is , the mean and the standard deviation are calculated through the formula

[0103] ;

[0104] Calculating, we get , substituting these values into the formula:

[0105] ;

[0106] ;

[0107] ;

[0108] The result shows that the construction dynamic abnormal distribution rate is 1.1715. Comparing it with the benchmark value range (assuming the reasonable range is 0.8 to 1.2), it shows that the abnormal distribution in the construction task belongs to the normal deviation range. This result is further used to optimize the subsequent parameter settings of the abnormal trend model and provide a basis for construction adjustment.

[0109] Please refer to Figure 4 , the specific steps for obtaining the construction scenario deviation safety factor are as follows:

[0110] Based on the construction dynamic abnormal distribution rate, extract the distribution characteristics of equipment and personnel from the UAV image data, call the image analysis module to calculate the spatial distribution parameters of equipment and personnel in a specific task area, and generate equipment and personnel distribution characteristic data by calculating the regional density and position coordinates of each distribution point;

[0111] Based on the construction dynamic anomaly distribution rate, extract the distribution characteristics of equipment and personnel from the UAV image data. Call the image analysis module to solve the images of the task area obtained by the UAV. Classify the spatial distribution data of equipment and personnel into two categories: independent points and adjacent aggregation points. By extracting the coordinate information of each equipment and personnel, combined with the image segmentation method, quantitatively analyze the distribution characteristics of different regions. Among them, the equipment distribution quantification is calculated based on the ratio of the geometric boundary size of the equipment to the area of the distribution region, and the personnel distribution is quantified according to the ratio of the individual volume parameter of the personnel to the distribution distance of the nearest surrounding equipment. Subsequently, based on the image region segmentation results, calculate the regional density for each distribution point. The regional density is obtained by calculating the ratio of the number of distribution points to the area of the distribution region. Through these calculations, further obtain the position information coordinates of each equipment and personnel and the distribution density parameters of the regions where they are located. For example, a group of equipment collected by the UAV is located at the coordinate point (100, 200), the distribution region area is 500 square meters, and there are 10 pieces of equipment, then the regional density is 10 / 500 = 0.02. Based on the collation and summary of all distribution point data, finally generate the equipment and personnel distribution characteristic data.

[0112] Combined with the equipment and personnel distribution characteristic data and the task area information in the BIM model, call the regional comparison module to perform spatial matching judgment on the actual equipment layout and the preset parameters of the BIM model. Calculate the layout error value and the total cumulative offset of each equipment based on the deviation amount of the equipment position, and generate the equipment layout deviation degree result;

[0113] Combined with the equipment and personnel distribution characteristic data and the task area information in the BIM model, call the regional comparison module to perform spatial matching judgment on the actual equipment layout and the preset parameters of the BIM model. Take the equipment layout position in the BIM model as the benchmark, compare the actual distribution coordinates of each equipment with the BIM preset position one by one, and calculate the deviation amount of each equipment layout. The deviation amount is calculated by the Euclidean distance between the actual coordinates and the preset coordinates of the equipment. For example, the actual coordinates of a certain equipment are (150, 250), and its preset position in the BIM model is (100, 200), and its deviation amount is , and then summarize the deviation amount results of all equipment, and further perform distribution analysis on the equipment layout error value. The distribution analysis divides the deviation amount into two parts: a high deviation interval and a low deviation interval. The high deviation interval is screened by setting a regional matching threshold (such as the deviation amount is greater than 50). Sum the total cumulative offset of the equipment within the high deviation interval. For example, the deviation amounts of multiple pieces of equipment are 70.71, 60.5, and 40 respectively, then the total cumulative offset is 70.71 + 60.5 + 40 = 171.21. Finally, generate the equipment layout deviation degree result.

[0114] Combine the equipment layout deviation results with the safety hazard parameters, and perform a weighted superposition operation based on the offset risk and hazard factors of the equipment. Use the formula:

[0115] ;

[0116] Calculate the offset safety factor of the construction scenario;

[0117] Among them, represents the offset safety factor of the construction scenario, represents the deviation of the th equipment, is the risk weighting factor of the th equipment, represents the safety hazard parameter related to the th equipment, is the total number of equipment, is the equipment index number.

[0118] Formula:

[0119] ;

[0120] The benefit of the formula is that by comprehensively superimposing and calculating the equipment deviation, risk weighting factor, and safety hazard parameter, it can accurately reflect the overall safety deviation degree caused by equipment layout deviation and safety hazard accumulation in the construction scenario, providing a quantitative basis for optimizing the construction site layout.

[0121] Detailed explanation of the formula and the derivation process of formula calculation:

[0122] Suppose the total number of equipment is 3, and the deviation values are , , , the risk weighting factors are , , , and the safety hazard parameters are , , .

[0123] First, calculate the offset superposition value of each equipment one by one:

[0124] For the first equipment: ;

[0125] For the second equipment: ;

[0126] For the third equipment: ;

[0127] Then, sum up the offset superposition values of all equipment to obtain the offset safety factor of the construction scenario:

[0128] ;

[0129] The result shows that the construction scenario offset safety factor is 213.402. This value reflects the deviation degree of the layout of all equipment in the scenario and the overall cumulative impact of relevant potential safety hazards, indicating that the overall safety of the scenario needs further evaluation or optimization. The accumulation of the value represents the severity of the offset impact.

[0130] Please refer to Figure 5 , and the specific steps for obtaining the construction task time adjustment factor are as follows:

[0131] Based on the construction scenario offset safety factor and the construction dynamic anomaly distribution rate, re-evaluate the key nodes affecting the construction task. By calling the dynamic process data to analyze the time dependence of each process, calculate the resource allocation ratio and count the fluctuation data of the resource utilization rate, and generate the time and resource utilization analysis results of the construction task;

[0132] Based on the construction scenario offset safety factor and the construction dynamic anomaly distribution rate, first call the deviation value parameter and the anomaly distribution rate parameter in the construction scenario offset safety factor, combine them with the node information of the construction task, and analyze the process time arrangement and resource allocation ratio of each task node one by one. For the process time arrangement, by extracting the difference between the planned process time and the actual consumption time, calculate the process time deviation value of the task node, and use the formula to calculate the time deviation value of each node , where is the actual consumption time of the process, is the planned consumption time. By applying this formula to the time arrangement records of each process, calculate the deviation value list of multiple nodes in sequence, and obtain the time fluctuation range by analyzing the difference between the maximum deviation value and the deviation mean value in this list; secondly, for the resource allocation ratio, call the node resource usage record, calculate the resource utilization rate by counting the ratio of the usage amount of each resource to the total allocated amount, and use the formula , where is the actual resource usage quantity, is the total allocated resource amount, calculate the utilization rate of each resource type, and group the utilization rate data by node to generate a time-resource allocation matrix for the resource allocation data of each node; thirdly, conduct a joint analysis of the time deviation value and the resource utilization rate, correlate the fluctuation values of the time deviation value and the resource utilization rate respectively, and screen out the task nodes with significant differences in time fluctuation and resource allocation by comparing the data points in the time deviation list and the resource utilization rate matrix. Take these nodes as high-priority adjustment nodes and generate the time and resource utilization analysis results of the construction task.

[0133] Combined with the analysis results of the time and resource utilization of the construction tasks, by constructing a relationship model between the process time and resource allocation, the adjustment of the process time is accurately calculated. By statistically analyzing the error distribution between the original plan and the actual execution item by item, the adjustment data of the process time and resource utilization rate are generated;

[0134] Combined with the analysis results of the time and resource utilization of the construction tasks, by performing segmented calculations on the significant nodes of the time deviation and resource allocation problems, calling the time fluctuation data and resource allocation ratios, and analyzing the dependencies between processes for each node one by one. For the time fluctuation data, the time dependence intensity between nodes is calculated by using the method of segmented integration, and the influence range of each time deviation value on the subsequent nodes is set. Through the accumulation formula Calculate the time dependence intensity , where is the time deviation value of the th node, is the corresponding time deviation weight. Calculate the time adjustment requirements between each process through the time dependence intensity between nodes; at the same time, for the resource allocation ratio, by normalizing the utilization rate data of each resource and the node demand, the nodes and resource types with low resource allocation efficiency are screened out. Using the normalization formula , where is the actual resource usage quantity, is the maximum resource usage quantity, calculate the normalized utilization rate , and sort the normalized data to mark the nodes with low resource utilization; combining the time dependence intensity and the marking of nodes with low resource allocation efficiency, establish a time adjustment data and resource optimization plan. By optimizing and adjusting the time and resource allocation ratio one by one, the adjustment data of the process time and resource utilization rate are generated.

[0135] Using the adjustment data of the process time and resource utilization rate, construct a dynamic adjustment factor relationship model during the task execution, and use the formula:

[0136] ;

[0137] Calculate the time adjustment factor of the construction task;

[0138] Among them, represents the time adjustment factor of the construction task, and are adjustment coefficients, reflecting the sensitivity of the time and resource impacts, represents the adjusted time of the th process, is the total planned time, represents the adjusted utilization rate of the th resource, is the total resource utilization rate, and are the number of processes and the number of resource types respectively.

[0139] Formula:

[0140] ;

[0141] The advantage of the formula is that by combining the time deviation data with the resource allocation utilization data, the weighted parameter reflects the comprehensive impact of time and resource utilization, and can dynamically adjust the priority of the process time and the resource allocation plan to adapt to the real-time changes of the construction plan.

[0142] Detailed explanation of the formula and the derivation process of the formula calculation:

[0143] For the time data, set as the adjusted time of each process, call the time adjustment data result list in paragraph 2, and set as the total construction plan time (in hours), and the adjusted time of each process is , substitute it into the formula:

[0144] ;

[0145] For the resource allocation data, set as the adjusted utilization rate of each resource, call the resource utilization optimization result list in paragraph 2, and set as the total normalized resource allocation ratio, and the utilization rate of each resource is , substitute it into the formula:

[0146] ;

[0147] Set , substitute it into the formula:

[0148] ;

[0149] The result shows that the construction task time adjustment factor is 0.64, which is related to the comprehensive efficiency of time and resource adjustment, indicating that the adjustment priority of the construction plan can reflect the optimization degree of time and resource allocation, and the overall construction efficiency is improved through dynamic adjustment.

[0150] Please refer to Figure 6 , and the specific steps for obtaining the green construction resource optimization coefficient are as follows:

[0151] Based on the construction task time adjustment factor and the resource path allocation optimization value, re-evaluate the resource configuration situation at the construction site, calculate the resource use efficiency and analyze the optimization potential of each resource item by extracting energy consumption data, waste recycling ratio and water resource utilization rate, and generate the resource use efficiency analysis result;

[0152] Based on the construction task time adjustment factor and the optimized value of resource path allocation, re-evaluate the resource allocation situation at the construction site. First, extract the energy usage records from the energy consumption data collected by the construction site monitoring equipment, statistically analyze the energy consumption distribution of each stage by construction stage area, and compare with the theoretical maximum energy consumption required for each stage in combination with the construction planning documents to obtain the actual usage ratio and deviation of energy consumption in each stage. Subsequently, for the waste recycling ratio, obtain the waste weight data through on-site waste classification and recycling equipment, and regularly record the types and quantities of waste through weight sensors. Compare the recycled weight of various wastes with their theoretical production weights to quantify the waste recycling efficiency. Finally, extract the water utilization rate data from the construction water meter, combine the operating time of water-using equipment recorded at the construction site with the theoretical water consumption, calculate the actual water usage efficiency and loss ratio of each equipment, and mark the equipment with water usage efficiency lower than the standard value. Obtain the usage efficiency parameters of each resource item at the construction site through the above operations; then, analyze the obtained resource usage efficiency parameters, list the stages with energy consumption distribution deviation greater than the standard value as the key analysis objects, mark the projects with waste recycling ratio lower than the industry standard as resource waste points, construct a resource optimization analysis matrix for these key objects and waste points, quantify the optimization potential and possible improvement directions of each resource, and establish a resource distribution evaluation table in combination with the theoretical distribution of resource usage in the construction task plan. Through the interactive comparison of the resource evaluation table and the optimization analysis matrix, finally generate the resource usage efficiency analysis result.

[0153] Combined with the resource usage efficiency analysis result, analyze the utilization situation of each resource item by constructing a multi-dimensional data model, perform itemized statistics on the resource usage deviation and potential improvement space, calibrate the resource usage parameters to be optimized, and integrate multiple resource attributes to generate the resource usage parameter adjustment result;

[0154] Combined with the resource usage efficiency analysis result, first, for the usage deviation data of each resource item at the construction site, use the multi-dimensional data model of resource allocation for itemized analysis, integrate the usage data of each resource item in different construction stages, and identify the stages with abnormal resource allocation by calculating the variance and mean deviation of resource allocation. Next, for the points with waste recycling ratio lower than the industry standard, analyze the sources of waste and the main construction processes, construct a waste utilization regression model based on the types and weights of waste in different construction processes, calculate the recyclable potential of waste and the priority parameters for waste treatment. Subsequently, for the equipment with water usage efficiency lower than the standard, analyze its operating time and actual water consumption, establish a prediction model for the water usage efficiency of the equipment through a simple linear regression formula, and mark the inefficient equipment in the model result as the key point for water resource optimization. Calibrate the usage parameters of each resource item through the above process, integrate the optimization directions and potential improvement points of all resources, form a complete resource usage parameter adjustment table, and finally generate the resource usage parameter adjustment result.

[0155] Adjust the result of resource usage parameters and multiple constraints on the construction site, integrate the weight of various resource attributes, and calculate the optimization effect. Use the formula:

[0156] ;

[0157] Calculate the green construction resource optimization coefficient;

[0158] Among them, represents the green construction resource optimization coefficient, is the optimization weight of the th resource, reflecting the impact of this resource on the overall optimization, represents the th actual usage amount of the resource in the optimization model, is the th theoretical maximum usage amount of the resource, is the number of resource types, is the resource index number.

[0159] Formula:

[0160] ;

[0161] The benefit of the formula is that by introducing the resource optimization weight parameter , the influence factors of different resources can be quantified. At the same time, through the ratio of the actual usage amount and the theoretical maximum usage amount , the deviation between the resource utilization rate and the theoretical efficiency can be dynamically reflected, thus providing a quantitative basis for the resource optimization of green construction.

[0162] Detailed explanation of the formula and the derivation process of formula calculation:

[0163] First, set (that is, there are 3 types of resources, namely energy consumption resources, waste resources, and water resources), , , (the weights are adjusted according to the importance of each resource in construction and the optimization requirements, obtained through comprehensive calculation of expert scoring and resource attribute-related data). Then, set the actual usage amount of each resource , , (the unit is unified as tons and directly obtained through on-site construction monitoring equipment), and the corresponding theoretical maximum usage amounts , , (the unit is unified as tons and obtained through the resource allocation plan document in the construction stage).

[0164] Substitute into the formula for calculation:

[0165] 1. First, calculate the utilization ratio of each resource:

[0166] ;

[0167] 2. Multiply each utilization ratio by the corresponding weight:

[0168] ,

[0169] ;

[0170] 3. Sum up the weighted utilizations:

[0171] ;

[0172] The result shows that, considering the current usage and optimization requirements of comprehensive energy consumption resources, waste resources, and water resources, the on-site resource optimization coefficient is , close to the theoretical optimal value of 1, indicating a relatively high utilization rate of construction resources. However, there is still room for optimization. Attention can be focused on the parts with relatively low waste recycling ratio and water resource utilization rate to further improve the green level of construction.

[0173] The above is only a preferred embodiment of the present invention, and it does not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content 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. A cloud computing-based construction project management system, characterized in that: The system comprises: The construction path optimization module is based on the resource distribution of the construction task and the three-dimensional space information in the BIM model. By calculating the resource circulation efficiency in each area and the resource distribution ratio at the intersection of the path, the resource path allocation logic is replanned to generate the resource path allocation optimization value. The construction status analysis module analyzes the material consumption, equipment operation status and execution deviation of key processes based on the resource path allocation optimization value, establishes an abnormal trend model and calculates the dynamic abnormal distribution ratio in the construction task through deviation rate calculation and abnormal distribution parameter screening, and generates the construction dynamic abnormal distribution rate; The construction environment monitoring module extracts the equipment and personnel distribution characteristics in the drone image data based on the construction dynamic abnormal distribution rate, combines the task area information in the BIM model, calculates the equipment layout deviation and the safety hazard parameter superposition value, and generates the construction scene deviation safety factor; The schedule adjustment module recalculates the process time and resource utilization rate of the construction task node based on the construction scene offset safety factor and the construction dynamic abnormal distribution rate, constructs the dynamic adjustment factor relationship in task execution, and generates the construction task time adjustment factor; The green construction optimization module is based on the construction task time adjustment factor and resource path allocation optimization value, partitions the modeling energy consumption data, waste recycling ratio and water resource utilization rate, reallocates resource usage parameters, calculates the optimization coefficient under multiple constraints, and generates the green construction resource optimization coefficient.

2. The cloud computing-based construction project management system according to claim 1, characterized in that: The resource path allocation optimization value specifically includes resource circulation efficiency, resource distribution ratio of path intersections, and path allocation logic. The construction dynamic abnormal distribution rate specifically includes material consumption deviation rate, equipment operation status deviation, and key process execution deviation. The construction scene deviation safety factor includes equipment layout deviation, safety hazard parameter superposition value, and task area information matching degree. The construction task time adjustment factor specifically includes process time adjustment value, resource utilization adjustment value, and task dynamic adjustment factor. The green construction resource optimization coefficient includes energy consumption data optimization value, waste recovery ratio optimization value, and water resource utilization optimization value.

3. The cloud computing-based construction project management system according to claim 2, characterized in that: The steps for obtaining the resource path allocation optimization value are specifically as follows: Based on the resource distribution parameters of the construction tasks, the resource circulation efficiency data in multiple regions and the resource distribution ratio of the path intersections are called to analyze the weight relationship between the resource occupancy rate of each path and the intersection circulation efficiency. By calculating the weighted value of the intersection resource distribution ratio and the circulation efficiency, the path parameter weight is adjusted to generate the regional path resource distribution weight. The resource distribution weight of the regional path is called to analyze the resource utilization differences of all paths, and the paths below the standard range are eliminated by comparing the average and standard deviation of the path resource utilization. The resource circulation efficiency is corrected in sections according to the data of the remaining paths, and the resource distribution ratio of the remaining paths is adjusted to generate a resource circulation correction coefficient. Using the regional path resource distribution weight and the resource circulation correction coefficient, the formula is adopted: ； Calculate the resource allocation efficiency of the path and generate the resource path allocation optimization value; in, Represents the resource path allocation optimization value, Representative The regional path resource distribution weight of the path, Representative The resource flow correction coefficient of the path, Representative The resource usage of each path segment, The adjustment coefficient is used to optimize the balance of path weight distribution. represents the path length, Represents the total number of paths, Represents the total number of path segments, Represents the path number, Represents the path segment number.

4. The cloud computing-based construction project management system according to claim 3, characterized in that: The steps for obtaining the construction dynamic abnormal distribution rate are specifically as follows: Based on the resource path allocation optimization value, material consumption data and equipment operation status parameters are called, the correlation between material consumption and equipment efficiency is analyzed, and the relative deviation between material consumption rate and equipment operation efficiency is calculated by weight allocation comparison method to generate preliminary screening results; Calling the preliminary screening results, calculating the execution deviation of the key process, calling the preset threshold to judge the deviation data one by one, performing classification processing on the degree of deviation abnormality according to the deviation rate, extracting all abnormal deviation data to establish an abnormal deviation screening matrix, and generating deviation abnormality analysis results; Combined with the deviation anomaly analysis results and the anomaly distribution parameters, an anomaly trend model is established through multidimensional regression calculation, using the formula: ； Calculate the dynamic abnormal distribution ratio in the construction task and generate the construction dynamic abnormal distribution rate; in, represents the abnormal distribution rate of construction dynamics, represents the sample size, Representative The abnormal deviation value of samples, represents the mean of the abnormal deviation values, represents the standard deviation of the abnormal deviation value, is the adjustment coefficient used to adjust the sensitivity of abnormal deviation. is the sample index number.

5. The cloud computing-based construction project management system according to claim 4, characterized in that: The steps for obtaining the offset safety factor of the construction scene are specifically as follows: Based on the abnormal distribution rate of construction dynamics, the distribution characteristics of equipment and personnel are extracted from the drone image data, and the image analysis module is called to solve the spatial distribution parameters of equipment and personnel in a specific task area. By calculating the regional density and position coordinates of each distribution point, the distribution characteristic data of equipment and personnel are generated; Combined with the equipment and personnel distribution feature data and the task area information in the BIM model, the regional comparison module is called to perform spatial matching judgment between the actual equipment layout and the preset parameters of the BIM model, and the layout error value and the cumulative total offset of each equipment are calculated according to the deviation of the equipment position to generate the equipment layout deviation result; Combined with the equipment layout deviation results and safety hazard parameters, a weighted superposition operation is performed based on the equipment's deviation risk and hazard factor, using the formula: ； Calculate the offset safety factor of the construction scenario; in, represents the offset safety factor of the construction scenario, Representative The deviation of the device, For the The risk weighting factor for each device, Representatives and Safety risk parameters related to each device, is the total number of devices, The device index number.

6. The cloud computing-based construction project management system according to claim 5, characterized in that: The steps for obtaining the construction task time adjustment factor are specifically as follows: Based on the construction scene deviation safety factor and the construction dynamic abnormal distribution rate, the key nodes affecting the construction task are re-evaluated, the time dependency of each process is analyzed by calling the dynamic process data, the resource allocation ratio is calculated and the resource utilization fluctuation data is counted, and the time and resource utilization analysis results of the construction task are generated; Combined with the time and resource utilization analysis results of the construction task, by building a process time and resource allocation relationship model, the process time adjustment is accurately calculated, and the error distribution between the original plan and the actual execution is statistically analyzed by item, and the adjustment data of the process time and resource utilization rate are generated; Using the adjustment data of process time and resource utilization, a dynamic adjustment factor relationship model in task execution is constructed using the formula: ； Calculate construction task time adjustment factors; in, represents the construction task time adjustment factor, and To adjust the coefficients to reflect the sensitivity of time and resource impacts, Representative The adjusted time of each process is is the total planned time, Representative The adjusted utilization of resources, is the total resource utilization, and are the number of processes and the number of resource types respectively.

7. The cloud computing-based construction project management system according to claim 6, characterized in that: The steps for obtaining the green construction resource optimization coefficient are specifically as follows: Based on the construction task time adjustment factor and resource path allocation optimization value, re-evaluate the resource allocation of the construction site, extract energy consumption data, waste recycling ratio and water resource utilization rate, calculate resource utilization efficiency and analyze the optimization potential of each resource item to generate resource utilization efficiency analysis results; In combination with the resource utilization efficiency analysis results, a multidimensional data model is constructed to analyze the utilization of each resource item, and sub-item statistics are performed on resource utilization deviations and potential improvement spaces, and resource utilization parameters that need to be optimized are calibrated and multiple resource attributes are integrated to generate resource utilization parameter adjustment results; By using the resource usage parameter adjustment results and the multiple constraints of the construction site, the weights of various resource attributes are integrated and the optimization effect is calculated using the formula: ； Calculate the green construction resource optimization coefficient; in, Represents the green construction resource optimization coefficient, For the The optimization weight of a resource reflects the impact of the resource on the overall optimization. Representative The actual usage of resources in the optimization model, For the The theoretical maximum usage of resources, is the number of resource types, The resource index number.

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