Construction project management system based on cloud computing

Abstract

The invention relates to the technical field of construction project management, in particular to a construction project management system based on cloud computing, which comprises a construction path optimization module, a construction state analysis module, a construction environment monitoring module, a progress plan adjustment module and a green construction optimization module. According to the method, through combination of construction task resource distribution and three-dimensional space information, resource path distribution logic is optimized, material consumption, equipment operation states and key process deviation data are utilized, accurate positioning and prediction of construction dynamic problems are realized, construction interruption is reduced, and equipment and personnel distribution characteristics are extracted based on field image data, so that construction efficiency is improved. The equipment arrangement deviation degree and the potential safety hazard superposition value are quantified, the safety and efficiency of a construction site are improved, and the utilization rate of green construction resources is improved by dynamically adjusting the process time and the resource utilization rate, optimizing the relation between task nodes and factors, improving the flexibility and the response capability of a construction plan and conducting partition modeling energy consumption data and multi-constraint optimization.

Description

Technical Field

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

[0002] The technical field of cloud computing-based construction project management system includes cloud computing-based construction management system and its related applications. The core content of this technical field is to use cloud computing's distributed storage, virtualization technology and efficient computing power to achieve digital and information management of the entire construction project process. Through this technical field, real-time monitoring and management of core links such as construction progress, resource allocation, quality supervision and cost control can be carried out to reduce problems such as information asymmetry and low management efficiency in traditional construction project management. The overall technical field systematically covers multiple levels such as project planning, task allocation, data storage, real-time monitoring and intelligent analysis, relying on the technical architecture of cloud computing to improve the digital level of construction management.

[0003] Among them, construction project management refers to the use of data processing and analysis capabilities in a cloud computing environment to manage all stages of a project in response to technical matters such as planning, organization, coordination, and supervision during the construction process. Specifically, the subject of this patent covers the use of cloud storage and distributed computing technology to achieve dynamic adjustment and real-time monitoring of project schedules, optimize resource allocation efficiency through reasonable scheduling and management methods of project resources, and complete the detection and statistics of construction quality data through model calibration and automated calculations based on big data analysis. In addition, it also includes the use of cloud collaboration mechanisms to achieve task division and communication management with the participation of multiple parties, and unified management of documents and data throughout the project process through data structured storage and retrieval technology to improve the systematization and standardization of construction project management.

[0004] Existing technologies lack comprehensive analysis of resource circulation efficiency and three-dimensional space in resource path planning, resulting in uneven resource scheduling and path conflicts, affecting construction efficiency. In terms of abnormal analysis, the failure to dynamically track material consumption and equipment status makes it difficult to timely discover and predict key problems, increasing the risk of construction delays. On-site safety monitoring mainly relies on manual inspections, lacks quantitative analysis of personnel distribution and equipment layout, and leads to incomplete identification of safety hazards. In task plan adjustments, due to the lack of dynamic processing of real-time process and resource data, plan execution lacks flexibility, which easily leads to waste of time and resources. For green construction, existing technologies fail 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 shortcomings of the prior art and propose a construction project management system based on cloud computing.

[0006] In order to achieve the above purpose, the present invention adopts the following technical solution: The construction project management system based on cloud computing includes: 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.

[0007] As a further solution of the present invention, the resource path allocation optimization value is specifically the resource circulation efficiency, the resource distribution ratio of the path intersection, and the path allocation logic; the construction dynamic abnormal distribution rate is specifically the material consumption deviation rate, the equipment operation status deviation, and the key process execution deviation; the construction scene deviation safety factor includes the equipment layout deviation, the safety hazard parameter superposition value, and the task area information matching degree; the construction task time adjustment factor is specifically the process time adjustment value, the resource utilization adjustment value, and the task dynamic adjustment factor; the green construction resource optimization coefficient includes the energy consumption data optimization value, the waste recovery ratio optimization value, and the water resource utilization optimization value.

[0008] As a further solution of the present invention, the step of obtaining the resource path allocation optimization value is 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.

[0009] As a further solution of the present invention, 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.

[0010] As a further solution of the present invention, the step of obtaining the construction scene offset safety factor is 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.

[0011] As a further solution of the present invention, 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.

[0012] As a further solution of the present invention, 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.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by combining the resource distribution of construction tasks with three-dimensional spatial information, the resource path allocation logic is optimized, the resource circulation efficiency is improved, and path conflicts and resource waste are avoided. By using material consumption, equipment operating status and key process deviation data, an abnormal trend model is established to achieve accurate positioning and prediction of construction dynamic problems and reduce construction interruptions. Based on the on-site image data, the equipment and personnel distribution characteristics are extracted, the equipment layout deviation and the safety hazard superposition value are quantified, and the safety and efficiency of the construction site are improved. By dynamically adjusting the process time and resource utilization, the relationship between task nodes and factors is optimized, and the flexibility and responsiveness of the construction plan are improved. By partitioning modeling energy consumption data and multi-constraint optimization, the utilization rate of green construction resources is improved, and energy consumption and waste generation are reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a system flow chart of the present invention; Figure 2 A flow chart of the steps for obtaining the resource path allocation optimization value of the present invention; Figure 3 The flowchart of the steps for obtaining the construction dynamic abnormal distribution rate of the present invention is as follows; Figure 4 A flowchart of the steps for obtaining the offset safety factor of the construction scene of the present invention; Figure 5 A flowchart of the steps for obtaining the construction task time adjustment factor of the present invention; Figure 6 This is a flow chart of the steps for obtaining the green construction resource optimization coefficient of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 intended to limit the present invention.

[0016] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are 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 cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0017] Embodiment 1

[0018] See also Figure 1 , the cloud computing-based construction project management system includes: 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. By calculating the deviation rate and screening the abnormal distribution parameters, it establishes an abnormal trend model and calculates the dynamic abnormal distribution ratio in the construction task, generating 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 nodes based on the construction scenario offset safety factor and the construction dynamic abnormal distribution rate, builds 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.

[0019] The resource path allocation optimization values ​​include resource circulation efficiency, resource distribution ratio at path intersections, and path allocation logic. The construction dynamic abnormal distribution rate 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 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 recycling ratio optimization value, and water resource utilization optimization value.

[0020] See also Figure 2 ,The specific steps for obtaining the resource path allocation optimization value are: 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. Based on the resource distribution parameters of the construction tasks, the resource circulation efficiency data in each area and the resource distribution ratio of the path intersection are called. For the resource distribution parameters, the construction area needs to be divided first, and the resource type and quantity in each area are input as basic data. The resource circulation efficiency is quantified by monitoring the time and quantity ratio of resource inflow and outflow. For example, in a certain area, the resource circulation efficiency is the total amount of inflow resources divided by the total time consumption, and then the parameter value is obtained by statistics based on the daily average circulation efficiency. The intersection resource distribution ratio is calculated by the cumulative distribution ratio of the intersection resources of all paths in the area, and the cumulative distribution number of path intersection resources is divided by the total amount of resources in the area to obtain the ratio value. Then, according to the weight relationship between the intersection resource distribution ratio and the circulation efficiency, the weighted value of the intersection resource distribution ratio and the circulation efficiency is calculated, and the parameter weight of the path is further adjusted by combining the resource distribution of the regional division. For example, for a certain path, the resource distribution ratio is 0.6, the weight ratio of the circulation efficiency is 0.4, and the weighted value is 0.6×0.4. The priority of the path resources is re-evaluated according to the weighted result, and finally the regional path resource distribution weight is generated.

[0021] Call the regional path resource distribution weights to analyze the resource utilization differences of all paths. By comparing the average and standard deviation of the path resource utilization, eliminate the paths below the standard range, perform segmented weighted correction on the resource circulation efficiency based on the data of the remaining paths, adjust the resource distribution ratio of the remaining paths, and generate the resource circulation correction coefficient; The regional path resource distribution weight is called, and the path resource utilization is quantified by analyzing the resource utilization differences of all paths. First, the resource utilization of each path is calculated. This value is equal to the percentage of the resource flow on the path to the total resource volume of the region. For example, if the resource flow of a path is 200 and the total resource volume is 1000, the utilization rate is 20%. Then, the resource utilization data of all paths are counted, and the average and standard deviation of the path resource utilization are calculated. 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 calculated by each path. The square root of the difference between the path utilization and the average value is taken. When screening paths, the paths that are lower than the average value minus the standard deviation are regarded as low-utilization paths and eliminated. For example, if the average value is 30% and the standard deviation is 10%, the paths with utilization rates lower than 20% are eliminated. Next, the resource flow efficiency data of the remaining paths are weighted and corrected in sections. The correction weights are adjusted according to the fluctuation range of the path resource flow in different time periods. For example, if the fluctuation range of resource flow is large during peak hours, the correction weight during peak hours is higher. After the correction calculation, the resource distribution ratio of the remaining paths is adjusted to finally generate the resource flow correction coefficient.

[0022] Using the regional path resource distribution weight and 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.

[0023] formula: ; The benefit of the formula is that by introducing resource distribution weights , Resource circulation correction coefficient , the sum of squares of resource utilization , adjustment coefficient Maximum path length The joint calculation can comprehensively optimize the resource allocation of the path, improving the rationality of path planning and the efficiency of resource utilization.

[0024] Detailed explanation of the formula and the process of formula calculation and derivation: Assume there are three paths with weights , , , corresponding to the correction factor , , , the resource occupancy rates of the path segments are , , , the maximum path length is , adjustment coefficient .

[0025] The calculation process is as follows: Calculate the numerator part: ; Calculate the denominator: ; ; Sum of denominators: ; Calculate the final result: ; The result shows that the comprehensive resource allocation efficiency of the path is 0.0626. The 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 for subsequent resource allocation adjustments.

[0026] See also Figure 3 , the specific steps for obtaining the construction dynamic abnormal distribution rate are: Based on the resource path allocation optimization value, the material consumption data and equipment operation status parameters are called to analyze the correlation between material consumption and equipment efficiency. The relative deviation between material consumption rate and equipment operation efficiency is calculated by weight allocation comparison to generate preliminary screening results. Based on the resource path allocation optimization value, the material consumption data is first called and the data is grouped and counted according to the time dimension. The fluctuation range of each time period is determined by comparing the change rate of material consumption in each time period, and the time period data with significant fluctuations are screened out. The equipment operation status parameters in these time periods are extracted, and then the extracted equipment operation parameters are decomposed into core indicator groups (such as operating time, operating power and temperature fluctuation). These indicator groups are normalized, and the standardized values ​​of each indicator in different time periods are calculated to make the indicator groups comparable. Then the correlation coefficient between the equipment operation status and material consumption is calculated. The correlation coefficient is calculated using the Pearson correlation formula. Calculate, where and Represents material consumption and equipment operating parameters in the The value of the time period, and They represent the means of material consumption and equipment operating parameters respectively. By sorting and comparing the absolute values ​​of the correlation coefficients, the time periods with significant correlation and the corresponding equipment operating parameters are screened out, and finally the preliminary screening results are generated based on these screened time periods and parameters.

[0027] Call the preliminary screening results, calculate the execution deviation of key processes, call the preset threshold to judge the deviation data one by one, perform graded processing on the degree of deviation abnormality according to the deviation rate, extract all abnormal deviation data to establish an abnormal deviation screening matrix, and generate deviation abnormality analysis results; Call the preliminary screening results, first perform statistical analysis on the execution deviation of the key process, call the data of the key process execution time and the preset plan time, and calculate the deviation value formula (in is the actual completion time, The time deviation value of each process is obtained (preset planned time), and then the time deviation values ​​are grouped according to the preset threshold range. A deviation matrix is ​​established for the deviation data exceeding the threshold. Each column of the matrix represents the change of the deviation value of a key process. The deviation value of each column in the matrix is ​​compared with the preset deviation standard range, and all abnormal deviation data are screened out. The influence degree of the deviation is ranked 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 the normalization processing formula is used. (in is the weight value, For the The influence value of each deviation) is used to quantify the influence weight of the weight parameter, and finally the deviation anomaly analysis result is generated by combining the screened abnormal deviation data and the weight parameter matrix.

[0028] 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.

[0029] formula: ; The benefit of the formula is that by introducing the parameter Adjust the sensitivity and mean of abnormal deviations and standard deviation The ability to describe sample distribution characteristics improves the calculation accuracy of abnormal distribution ratio, so that the calculation results can better reflect the dynamic abnormal distribution characteristics in construction tasks.

[0030] Detailed explanation of the formula and the process of formula calculation and derivation: in, represents the abnormal distribution rate of construction dynamics, represents the number of samples, Indicates The abnormal deviation value of samples, represents the sample mean, represents the standard deviation of the sample, is the adjustment coefficient, which is used to adjust the sensitivity of the deviation. , the sample data is , mean and standard deviation By formula ; Calculate and get , substituting these values ​​into the formula: ; ; ; The result shows that the dynamic anomaly distribution rate in the construction task is 1.1715, which is compared with the benchmark value range (assuming a reasonable range of 0.8 to 1.2), indicating that the anomaly distribution in the construction task is within the normal deviation range. This result is further used to optimize the subsequent parameter settings of the anomaly trend model and provide a basis for construction adjustments.

[0031] See also Figure 4 ,The specific steps for obtaining the offset safety factor of the construction scene are: Based on the abnormal distribution rate of construction dynamics, the distribution characteristics of equipment and personnel are extracted from the drone image data. 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. Based on the abnormal distribution rate of construction dynamics, the distribution characteristics of equipment and personnel are extracted from the UAV image data. The image analysis module is called to solve the mission area image obtained by the UAV, and the spatial distribution data of equipment and personnel are divided into two categories: independent points and adjacent clustered points. By extracting the coordinate information of each equipment and personnel, the distribution characteristics of different areas are quantitatively analyzed in combination with the image segmentation method. The equipment distribution is quantified based on the ratio of the geometric boundary size of the equipment and the area of ​​the distribution area. The personnel distribution is quantified based on the distribution distance between the individual volume parameter of the personnel and the nearest neighboring equipment around. Then, based on the image area segmentation result, the regional density is calculated for each distribution point. The regional density is calculated by the ratio of the number of distribution points to the area of ​​the distribution area. Through these calculations, the location information coordinates of each equipment and personnel and the distribution density parameters of the area where they are located are further obtained. For example, a group of equipment collected by the UAV is located at the coordinate point (100, 200), the distribution area is 500 square meters, and it contains 10 equipment. The regional density is 10 / 500=0.02. Based on the collation and summary of all distribution point data, the equipment and personnel distribution feature data are finally generated.

[0032] 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. The layout error value and the cumulative offset of each device are calculated based on the deviation of the equipment position to generate the equipment layout deviation result; 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 on the actual equipment layout and the preset parameters of the BIM model. The equipment layout position in the BIM model is used as a benchmark, and the actual distribution coordinates of each device are compared with the BIM preset position one by one. The deviation of each equipment layout is calculated by the Euclidean distance between the actual coordinates of the equipment and the preset coordinates. For example, the actual coordinates of a certain equipment are (150, 250), and its BIM model preset position is (100, 200). Its deviation is , and then the deviation results of all devices are summarized, and the distribution analysis of the equipment layout error value is further performed. The distribution analysis is performed by dividing the deviation into a high deviation interval and a low deviation interval. The high deviation interval is obtained by screening by setting a regional matching threshold (such as a deviation greater than 50). The total cumulative deviation of the equipment in the high deviation interval is summed. For example, if the offsets of multiple devices are 70.71, 60.5, and 40 respectively, the total cumulative offset is 70.71+60.5+40=171.21, and the equipment layout deviation result is finally generated.

[0033] Combine the equipment layout deviation results with the safety hazard parameters, and perform weighted superposition calculations based on the equipment's deviation risk and hazard factors, 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.

[0034] formula: ; The benefit of the formula is that, by comprehensively superimposing the equipment deviation, risk weighting factor and safety hazard parameters, 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.

[0035] Detailed explanation of the formula and the process of formula calculation and derivation: Assume the total number of devices is 3, and the deviation values ​​are , , The risk weighting factors are , , The safety hazard parameters are , , .

[0036] First, calculate the offset stacking value for each device one by one: For the first device: ; For the second device: ; For the 3rd device: ; Then, sum the offset superposition values ​​of all equipment to obtain the offset safety factor of the construction scenario: ; The results show that the construction scene offset safety factor is 213.402, which reflects the degree of deviation of all equipment layouts in the scene and the overall cumulative impact of related safety hazards, indicating that the overall safety of the scene needs further evaluation or optimization, and the accumulation of values ​​represents the severity of the offset impact.

[0037] See also Figure 5 , the specific steps for obtaining the construction task time adjustment factor are: Based on the construction scenario deviation safety factor and the construction dynamic abnormal distribution rate, the key nodes that affect the construction task are re-evaluated. By calling the dynamic process data, the time dependency of each process is analyzed, the resource allocation ratio is calculated, and the resource utilization fluctuation data is counted to generate the time and resource utilization analysis results of the construction task. Based on the construction scene offset safety factor and the construction dynamic abnormal distribution rate, the deviation value parameter and the abnormal distribution rate parameter in the construction scene offset safety factor are first called, and then combined with the node information of the construction task, and the process time arrangement and resource allocation ratio of each task node are analyzed one by one. For the process time arrangement, the process time deviation value in the task node is calculated by extracting the difference between the process plan time and the actual time consumption, and the formula is used. Calculate the time deviation value of each node ,in The actual time taken by the process. The time consumption is planned. By applying this formula to the time schedule record of each process, the deviation value list of multiple nodes is calculated in turn, and the time fluctuation range is obtained by analyzing the difference between the maximum deviation value and the mean deviation in the list; secondly, for the resource allocation ratio, the node resource usage record is called, and the resource utilization rate is calculated by counting the ratio of the usage of each resource to the total allocation, using the formula ,in is the actual amount of resources used, The first step is to allocate the total amount of resources, calculate the utilization rate of each resource type, group the utilization data by node, and generate a time-resource allocation matrix for the resource allocation data of each node; secondly, conduct a joint analysis of the time deviation value and resource utilization rate, associate the time deviation value with the fluctuation value of resource utilization rate, 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 matrix. These nodes are used as high-priority adjustment nodes to generate the time and resource utilization analysis results of the construction tasks.

[0038] Combined with the time and resource utilization analysis results of the construction task, the process time adjustment is accurately calculated by building a process time and resource allocation relationship model. The error distribution between the original plan and the actual execution is statistically analyzed by item, and the adjustment data of process time and resource utilization is generated. Combined with the time and resource utilization analysis results of the construction task, by segmenting the calculation of the nodes with significant time deviation and resource allocation problems, calling the time fluctuation data and resource allocation ratio, the inter-process dependency of each node is analyzed one by one. For the time fluctuation data, the time dependency intensity between nodes is calculated by segmented integration, and the influence range of each time deviation value on the subsequent nodes is set. The cumulative formula is used to calculate the time dependency intensity between nodes. Calculate the time-dependent intensity ,in It is The time deviation value of each node, is the corresponding time deviation weight, and the time adjustment requirements between each process are calculated by the time dependency intensity between nodes; at the same time, for the resource allocation ratio, the utilization data of each resource is normalized with the node demand, and the nodes and resource types with inefficient resource allocation are screened out, and the normalized formula is used ,in is the actual amount of resources used, is the maximum resource usage, calculate the normalized utilization , and sort the normalized data, mark the nodes with inefficient resource utilization; combine the time dependence intensity with the marking of inefficient nodes in resource allocation, establish time adjustment data and resource optimization plans, and generate adjustment data for process time and resource utilization by optimizing and adjusting the time and resource allocation ratio one by one.

[0039] 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.

[0040] formula: ; The benefit of the formula is that, by combining time deviation data with resource allocation utilization data, weighted parameters are used to reflect the comprehensive impact of time and resource utilization, and the priority of process time and resource allocation plan can be dynamically adjusted to adapt to real-time changes in the construction plan.

[0041] Detailed explanation of the formula and the process of formula calculation and derivation: For time data, set For the adjusted time of each process, call the time adjustment data result list in paragraph 2, set is the total construction plan time (in hours), and the adjustment time for each process is , put it into the formula: ; For resource allocation data, set For the adjusted utilization of each resource, call the resource utilization optimization result list in paragraph 2 and set is the normalized total resource allocation ratio, and the utilization rate of each resource is , put it into the formula: ; set up , put it into the formula: ; 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 degree of optimization of time and resource allocation, and improve the overall construction efficiency through dynamic adjustment.

[0042] See also Figure 6 ,The specific steps for obtaining the green construction resource optimization coefficient are: 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; Based on the construction task time adjustment factor and the resource path allocation optimization value, the resource allocation of the construction site is re-evaluated. First, the energy use records are extracted from the energy consumption data collected by the construction site monitoring equipment, and the energy consumption distribution of each stage is statistically analyzed by construction stage. The theoretical maximum value of the energy consumption required for each stage is compared with the construction planning documents to obtain the actual usage ratio and deviation of the energy consumption in each stage. Then, for the waste recycling ratio, the waste weight data is obtained through the on-site waste classification and recycling equipment, and the type and quantity of waste are regularly recorded through the weight sensor. The recycled weight of various wastes is compared with their theoretical production weight to quantify the waste recycling efficiency. Finally, the water resource utilization rate data is extracted from the construction water meter and combined with the usage records of the construction site. The operation time of water equipment and theoretical water consumption are calculated, the actual water use efficiency and loss ratio of each equipment are calculated, and the equipment with water use efficiency lower than the standard value is marked. The utilization efficiency parameters of each resource item on the construction site are obtained through the above operations; then, the obtained resource utilization efficiency parameters are analyzed, and the stages where the energy consumption distribution deviation is greater than the standard value are listed as key analysis objects, and the projects where the waste recovery ratio is lower than the industry standard are marked as resource waste points. A resource optimization analysis matrix is ​​constructed for these key objects and waste points, and the optimization potential and possible improvement directions of each resource are quantified. In combination with the theoretical distribution of resource use in construction task planning, a resource distribution evaluation table is established, and the resource utilization efficiency analysis results are finally generated through interactive comparison between the resource evaluation table and the optimization analysis matrix.

[0043] Combined with the results of resource utilization efficiency analysis, 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. The resource utilization parameters that need to be optimized are calibrated and multiple resource attributes are integrated to generate resource utilization parameter adjustment results. Combined with the results of resource utilization efficiency analysis, firstly, the usage deviation data of each resource item at the construction site is analyzed by using the multidimensional data model of resource allocation, and the usage data of each resource item at different construction stages is integrated. By calculating the variance and mean deviation of resource allocation, the stage of abnormal resource allocation is identified. Then, for the points where the waste recovery ratio is lower than the industry standard, the source of the waste and the main construction process are analyzed, and a waste utilization regression model is constructed according to the type and weight of waste in different construction processes. The recycling potential of the waste and the priority parameters of waste treatment are calculated. Subsequently, for equipment with water use efficiency lower than the standard, its operating time and actual water consumption are analyzed, and a prediction model for equipment water use efficiency is established through a simple linear regression formula. The inefficient equipment in the model results is marked as the focus of water resource optimization. The usage parameters of each resource item are calibrated through the above process, and the optimization direction and potential improvement points of all resources are integrated to form a complete resource use parameter adjustment table, and finally the resource use parameter adjustment results are generated.

[0044] 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.

[0045] formula: ; The benefit of the formula is that by introducing the resource optimization weight parameter , so that the impact factors of different resources can be quantified, and at the same time, the actual usage and theoretical maximum usage The ratio of resource utilization to theoretical efficiency can dynamically reflect the deviation between resource utilization and theoretical efficiency, thus providing a quantitative basis for resource optimization in green construction.

[0046] Detailed explanation of the formula and the process of formula calculation and derivation: First set (That is, there are three types of resources, namely energy consumption resources, waste resources, and water resources), , , (The weight is adjusted according to the importance of each resource in the construction and the optimization needs. It is obtained by comprehensive calculation of expert scores and resource attribute-related data), and then the actual usage of each resource is set , , (Units are unified in tons, directly obtained through construction site monitoring equipment), corresponding to the theoretical maximum usage , , (The unit is uniformly tons, obtained through the resource allocation plan document during the construction phase).

[0047] Enter the formula for calculation: 1. First calculate the utilization ratio of each resource: ; 2. Multiply each utilization ratio by its corresponding weight: , ; 3. Sum the weighted utilization rates: ; The results show that considering the current usage and optimization requirements of energy consumption resources, waste resources and water resources, the construction site resource optimization coefficient is , which is close to the theoretical optimal value of 1, indicating that the resource utilization rate of construction is high, but there is still room for optimization. We can focus on the waste recycling ratio and the parts with low water resource utilization rate to further improve the green level of construction.

[0048] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls 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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