Intelligent management method and system for civil engineering projects

Abstract

The present invention relates to the field of project management technology, specifically to an intelligent management method and system for civil engineering projects, comprising the following steps: acquiring personnel distribution, equipment status, and material inventory data, extracting key task nodes and execution status, identifying tasks with delayed progress, screening key path nodes and analyzing time differences, marking progress trends, identifying equipment impact sections, judging resource allocation imbalances, and generating progress monitoring indicators. In the present invention, through multi-source data collection of personnel distribution, equipment status, and material inventory, the difference identification between task status and planned nodes is enhanced, the efficiency of progress delay warning is improved, the dynamic classification and trend identification of progress fluctuations are achieved, and the optimization of construction rhythm is facilitated. Through the overlap analysis of equipment operation and task fluctuation areas, the bottleneck of construction efficiency is identified, the scheduling accuracy is improved, and then through the comparison of input and cycle differences, fine progress monitoring is achieved, and the synergy between progress control capabilities and on-site resource allocation is comprehensively enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of project management, and in particular to an intelligent management method and system for civil engineering projects. Background Art

[0002] The technical field of project management includes management activities such as planning, organizing, coordinating, and controlling the entire process of engineering construction. The core content of this technical field includes project scope management, schedule management, cost management, quality management, human resource management, communication management, risk management, and procurement management. Project management is particularly important in civil engineering projects. Its systematic nature is reflected in improving project implementation efficiency, ensuring project quality, controlling project costs, and reducing project risks through standardized management processes and scientific management methods. With the development of information technology, project management has gradually transformed towards digitalization and informatization. Especially in the execution of engineering projects, automated and intelligent management methods have been introduced in data collection, task scheduling, personnel deployment, progress tracking, material control and other links to improve the management level and execution efficiency of civil engineering projects.

[0003] Among them, the intelligent management method of civil engineering projects refers to the dynamic coordination of construction management elements through information technology for key management issues in the construction process of civil engineering projects. The method mainly covers matters such as automated monitoring of construction progress, on-site worker trajectory identification and scheduling, building material entry registration and balance calculation, construction quality standardization verification, task process node tracking, etc., and completes management work through a combination of preset rules, digital recognition methods and task allocation mechanisms. For example, positioning devices are deployed on the construction site to identify the actual location trajectory of workers in order to dynamically coordinate their task execution, material label recording is used to compare the quantity of materials entering the warehouse and calculate the balance, and standardized process node settings are used to track the progress of tasks in detail, thereby realizing intelligent management and dynamic scheduling of various management elements of civil engineering projects.

[0004] Existing technologies generally rely on static node settings and preset plans for task tracking, lacking a dynamic deviation identification mechanism driven by real-time data. This results in lengthy response times to progress anomalies. For example, while planned nodes may be set, actual task execution status cannot be immediately reflected. When progress lags occur, relevant managers may only notice them during weekly or monthly reporting cycles, missing the opportunity for intervention. Existing construction scheduling processes are largely based on personnel experience and paper records, lacking precise analysis of equipment utilization efficiency and on-site resource inputs. This can easily lead to equipment scheduling mismatches in key processes, impacting the overall construction schedule. Construction team attendance and task assignments are recorded independently, making it difficult to map and analyze the relationship between resource distribution and task execution. Resource scheduling lacks a scientific basis for decision-making, often leading to idle personnel and localized congestion. Progress monitoring indicator systems are primarily results-oriented and lack quantitative analysis of the resource-task matching process. This makes it difficult to establish a precise feedback loop during the construction execution phase, hindering the refined management and risk control capabilities of construction projects. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent management method and system for civil engineering projects.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for intelligent management of civil engineering projects, comprising the following steps:

[0007] S1: Obtain personnel distribution data, machinery and equipment operating status data, and material inventory records at the construction site, extract key node information from the construction task breakdown structure, mark the task execution status and compare it with the node plan status, identify tasks with delayed progress, and generate a task node deviation list;

[0008] S2: Based on the task node deviation list, the task nodes on the critical path are screened, and the real-time construction start and end times are extracted in combination with the construction process schedule. The real-time construction start and end times are then calculated and classified with the planned time points to obtain the progress trend labels of the critical path tasks;

[0009] S3: Call the progress trend tag of the critical path task, extract the fluctuation task partition number, identify the regional mechanical equipment utilization efficiency data, compare the equipment operation time period with the fluctuation task time period, record the number of overlapping time periods, and generate a list of sections of the construction node affected by the equipment;

[0010] S4: Based on the list of sections of the construction node affected by the equipment, filter the undisturbed partition node tasks, extract the construction team segment task records and attendance data, map the daily task assignments and the attendance team work periods, determine whether there is an imbalance in resource allocation for the task, and obtain the construction team node work completion fluctuation group.

[0011] As a further solution of the present invention, the task node deviation list includes the task deviation number, execution status label, time deviation amount, and task node classification; the critical path task progress trend label includes the progress deviation level, trend change type, plan comparison result, and task association number; the construction node equipment affected section list includes the equipment type, affected time section, number of overlapping time periods, and affected task number; the construction team node job completion fluctuation group includes the job uneven distribution number, working hour usage record, task completion deviation, and team attendance matching degree.

[0012] As a further solution of the present invention, the steps for obtaining the task node deviation list are specifically as follows:

[0013] S111: Obtain personnel distribution data, mechanical equipment operating status data, and material inventory records at the construction site, extract the planned time and number of key nodes in the construction task decomposition structure, match the collection time and coordinates of the personnel distribution data, and compare the time range with the node planned time to generate the partition node execution record period;

[0014] S112: Based on the partition node execution record period, extract the overlapping period of the task node and the planned time interval, and calculate the ratio of the overlapping time to the total planned duration of the node, filter out the task nodes with the overlapping ratio lower than the benchmark value, and obtain the partition node progress coverage deviation rate based on the number of node execution status annotations;

[0015] S113: Determine the deviation status of the task node number according to the partition node progress coverage deviation rate, identify the task node number whose deviation rate exceeds the node synchronization threshold, integrate the node number, execution coverage information and deviation rate value, and generate a task node deviation list.

[0016] As a further solution of the present invention, the steps for obtaining the progress trend label of the critical path task are specifically as follows:

[0017] S211: Based on the task node deviation list, identify the task nodes and construction process schedule on the critical path, extract the real-time construction start and completion time, calculate the start and end time difference of the task section, and compare it with the planned task node time difference to obtain the construction time deviation value;

[0018] S212: Call the construction time deviation value, combine the section distribution, deviation trend and adjustment frequency, uniformly collect task node deviation data, identify and calculate the trend deviation degree according to the section number, determine the trend direction according to the adjustment frequency, and obtain the critical path task progress trend label.

[0019] As a further solution of the present invention, the steps for obtaining the list of sections of the construction node affected by the equipment are specifically as follows:

[0020] S311: Call the progress trend tag of the critical path task, filter the fluctuation deviation task partition number, extract the construction time period according to the node association table, process the section task time period according to the time dimension, identify the construction time period index table, and obtain the fluctuation task construction time period set;

[0021] S312: Based on the fluctuating task construction period set, collect equipment operation efficiency data of the same period section, identify the equipment impact table, judge the daily equipment interference based on the interference threshold, match it with the fluctuating task construction period, judge whether there is any abnormal correlation with the construction progress, and generate a list of sections of the construction node affected by the equipment.

[0022] As a further solution of the present invention, the steps for obtaining the construction team node operation completion fluctuation group are specifically as follows:

[0023] S411: Based on the list of sections of the construction node affected by the equipment, filter out unmarked construction partition nodes, extract the work task list, record the planned process and start and end time of the task, and obtain the task set of nodes not affected by the equipment;

[0024] S412: Calling the node task set not affected by equipment interference, matching the construction team's daily segmented task records with attendance data, extracting the planned task volume by task number, counting the number of on-duty workers and real-time attendance periods, and generating a matching dataset for construction team cooperation execution;

[0025] S413: Match the data set according to the cooperation execution status of the construction team, evaluate the matching degree between task execution efficiency and manpower distribution, identify efficiency fluctuation nodes and calculate the degree of operation deviation, mark tasks whose fluctuation exceeds the baseline value as abnormal nodes, calculate the construction team task execution matching deviation value, and obtain the construction team node operation completion fluctuation group.

[0026] As a further embodiment of the present invention, the method further comprises step S5:

[0027] S5: Call the construction team node operation completion fluctuation group, extract the abnormal fluctuation task group, calculate the ratio of resource input to task volume in the construction model, record the difference distribution between resource input cycle and task execution cycle, and generate construction progress monitoring structure indicators;

[0028] The construction progress monitoring structure indicators include resource allocation ratio, task intensity level, execution cycle difference, and construction efficiency index.

[0029] As a further solution of the present invention, the steps for obtaining the construction progress monitoring structure indicators are specifically as follows:

[0030] S511: Calling the node numbers and corresponding operation time intervals in the construction team node operation completion fluctuation group, filtering out nodes that exceed the threshold, recording the time interval and task volume change range, and obtaining a progress fluctuation abnormality identification set;

[0031] S512: Based on the construction model resource input and task volume corresponding to the nodes in the progress fluctuation abnormality identification set, identify the node resource input ratio sequence, extract the abnormal distribution interval and compare the critical coefficient, record the ratio deviation direction and node number, and form a construction resource matching deviation indicator group;

[0032] S513: Extract the resource delivery and task execution time periods of the construction model according to the node numbers in the construction resource matching offset indicator group, identify the differences between the resource delivery cycle and the operation cycle, sort and mark them according to the progress benchmark, and generate construction progress monitoring structure indicators.

[0033] The intelligent management system for civil engineering projects is used to implement the intelligent management method for civil engineering projects, and the system includes:

[0034] The task status extraction module obtains personnel distribution data, mechanical equipment operation status data, and material inventory records on the construction site, extracts key node numbers and task node times in the construction task decomposition structure, compares the execution status with the task node identifier, marks component tasks with inconsistent times, and generates a node status deviation label group;

[0035] The node trend classification module locates the task nodes on the critical path based on the node status deviation label group, extracts the construction node identifiers, process nodes and planned time, calculates the difference between the on-site construction time and the planned time, classifies and marks the difference types, and generates node progress trend identifiers;

[0036] The equipment interference identification module selects the fluctuating and lagging task components based on the node progress trend identification, locates the corresponding partitions, extracts the mechanical equipment operation efficiency interference period, determines the overlap with the operation time period, selects the frequent interference sections, and generates the construction progress equipment interference mapping set;

[0037] The operation efficiency diagnosis module eliminates interfering section tasks based on the construction progress equipment interference mapping set, extracts construction team task records and attendance data, matches task assignments with operation time periods, calculates the ratio of attendance workload to tasks, identifies component tasks that are task-intensive and inefficient, and obtains a node operation execution deviation set;

[0038] The resource allocation analysis module locates the resource input records of the task in the construction model based on the node operation execution deviation set, extracts the ratio of task quantity to resource quantity configuration, compares the difference between the execution cycle and the input cycle, maps the task resource usage and progress status, and forms the construction progress monitoring structure indicator.

[0039] Compared with the prior art, the advantages and positive effects of the present invention are:

[0040] In the present invention, through the multi-source data collection of construction site personnel distribution, mechanical equipment status and material inventory, the ability to identify the difference between task execution status and planned nodes is enhanced, and the early warning efficiency of progress delay problems is significantly improved. With the help of the screening of key path task nodes and the extraction of real-time start and end times, the dynamic classification and trend identification of task progress fluctuations are achieved, making the construction rhythm adjustment more targeted. The overlap analysis of equipment operating efficiency and task fluctuation sections can effectively identify the bottleneck sections of construction efficiency and improve the accuracy of equipment scheduling. Through the mapping operation of task assignment records and attendance time periods, the imbalance of job resource allocation can be quickly discovered, and the actual reflection ability of team work behavior can be strengthened. Based on the difference calculation between task resource input and execution cycle, the refined measurement of progress monitoring is achieved, and the recognition accuracy of abnormal fluctuation nodes is enhanced. The above process comprehensively introduces quantitative comparison and dynamic screening logic in the aspects of task node identification, resource scheduling, time period matching and progress classification, and builds a refined management mechanism with data linkage as the core. During the construction process, it simultaneously improves the perception and control capabilities of task completion efficiency, equipment utilization level and resource allocation rationality, thereby greatly improving the overall construction progress control level and on-site organization coordination efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0042] Figure 2 This is a flowchart for obtaining a task node deviation list in the present invention;

[0043] Figure 3 This is a flowchart for obtaining progress trend labels for critical path tasks in the present invention;

[0044] Figure 4 This is a flowchart for obtaining a list of sections affected by equipment at a construction node in the present invention;

[0045] Figure 5 A flowchart for obtaining a construction team node operation completion fluctuation group in the present invention;

[0046] Figure 6 This is a flow chart for obtaining construction progress monitoring structural indicators in the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, 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 intended to limit the present invention.

[0048] 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, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0049] Example 1

[0050] See also Figure 1 The present invention provides a technical solution: an intelligent management method for civil engineering projects, comprising the following steps:

[0051] S1: Obtain personnel distribution data, machinery and equipment operating status data, and material inventory records at the construction site, extract key node information from the construction task breakdown structure, mark the task execution status and compare it with the node plan status, identify tasks with delayed progress, and generate a task node deviation list;

[0052] S2: Based on the task node deviation list, the task nodes on the critical path are screened. The real-time construction start and end times are extracted based on the construction process schedule. The difference between the real-time construction start and end times and the planned time points are calculated and classified to obtain the progress trend labels of the critical path tasks.

[0053] S3: Call the critical path task progress trend tag, extract the fluctuating task partition number, identify the regional mechanical equipment utilization efficiency data, compare the equipment operation time period with the fluctuating task time period, record the number of overlapping time periods, and generate a list of sections of the construction node affected by the equipment;

[0054] S4: Based on the list of sections of the construction node affected by the equipment, filter out the uninterrupted partition node tasks, extract the construction team segment task records and attendance data, map the daily task allocation with the attendance team work period, determine whether there is a resource allocation imbalance in the task, and obtain the construction team node task completion fluctuation group;

[0055] S5: Call the construction team node operation completion fluctuation group, extract the abnormal fluctuation task group, calculate the ratio of resource input and task volume in the construction model, record the difference distribution between the resource input cycle and the task execution cycle, and generate the construction progress monitoring structure indicator.

[0056] The task node deviation list includes the task deviation number, execution status label, time deviation amount, and task node classification. The critical path task progress trend label includes the progress deviation level, trend change type, plan comparison result, and task association number. The list of equipment-affected sections of the construction node includes the equipment type, affected time section, number of overlapping time periods, and affected task number. The construction team node operation completion fluctuation group includes the uneven distribution number of the operation, working hour usage record, task completion deviation, and team attendance matching degree. The construction progress monitoring structure indicators include resource allocation ratio, task intensity level, execution cycle difference, and construction efficiency index.

[0057] See also Figure 2 ,The specific steps for obtaining the task node deviation list are:

[0058] S111: Obtain personnel distribution data, mechanical equipment operating status data, and material inventory records at the construction site, extract the planned time and number of key nodes in the construction task decomposition structure, match the collection time and coordinates of the personnel distribution data, and compare the time range with the node planned time to generate the partition node execution record period;

[0059] First, real-time data related to the distribution of personnel on the construction site is collected, including the location of each worker or team and the specific tasks they are performing. This data can be collected through wireless positioning systems, sensors, or positioning devices. The operating status data of construction machinery and equipment must be obtained. Equipment monitoring systems (such as sensors or GPS systems) must record the equipment's location, operating status, and work content in real time. Material inventory records are collected from warehouse management, recording the quantity, storage location, and usage progress of each material. Based on the key node plan in the construction task breakdown structure, the planned time and task number of each task node are extracted. The collected personnel distribution data on site must be compared with the collection time of the machinery operation data to ensure that the data matches the planned time of the task node and avoid errors caused by data delays. For example, suppose a task node is scheduled for 9:00-11:00 on May 20th, and a worker collects location data at 10:00. Confirming that the data is within a reasonable range of the planned time and that the worker's location is within the task execution area, the data is used to generate partitioned node execution records for further analysis of task progress and coordination.

[0060] S112: Based on the partition node execution record period, the overlapping period between the task node and the planned time interval is extracted, and the ratio of the overlapping time to the total planned duration of the node is calculated. Task nodes with an overlapping ratio lower than a benchmark value are screened, and the partition node progress coverage deviation rate is obtained based on the number of node execution status annotations.

[0061] The actual execution period of each task node is extracted from the construction site data and compared with its planned period to determine if there is any overlap. The length of the overlapping period is the "overlap time." For example, if a task node is scheduled for 09:00-11:00 on May 20 and actually executed from 09:30-10:30 on May 20, the overlap time is 1 hour. The ratio of the overlap time to the node's planned duration is calculated. For example, if the node's planned duration is 2 hours and the actual overlap period is 1 hour, the ratio is 1 / 2 = 0.5, which means a 50% overlap. Based on a preset benchmark value (such as 0.8 or 80%), task nodes with an overlap ratio below this benchmark are selected. Assuming the benchmark value is set to 0.8, any task node with an overlap ratio below 80% will be marked as a progress deviation node. Furthermore, nodes are annotated to record their execution status, such as completion or delay. Based on these annotated statuses, the progress coverage deviation rate for each partitioned node is calculated. The deviation rate is calculated by comparing the actual progress with the planned progress. For example, if a task node is scheduled to complete at 5:00 PM on May 20th, but the actual completion time is 6:30 PM on May 20th, the deviation rate represents a 1.5-hour delay. This deviation is quantified relative to the total duration of the task.

[0062] S113: Determine the deviation status of the task node number based on the partition node progress coverage deviation rate, identify the task node number whose deviation rate exceeds the node synchronization threshold, integrate the node number, execution coverage information and deviation rate value, and generate a task node deviation list;

[0063] The progress of each task node is evaluated based on the progress coverage deviation rate mentioned above. The progress coverage deviation rate is obtained by comparing the difference between the actual progress and the planned progress. This difference is expressed as a time difference (such as hours) or a percentage. If the progress deviation of a task node exceeds the preset node synchronization threshold (such as 10%), it is considered to be a progress deviation and is in a deviation state. For example, suppose the task of a node is scheduled for 09:00-10:00 on May 20, and the actual execution time is 09:00-10:30 on May 20, the progress deviation time is 30 minutes, which is equivalent to a deviation rate of 50%. If this deviation rate is higher than the set synchronization threshold of 10%, the node is marked as a deviation state. All nodes with large progress deviations, that is, task nodes with deviation rates above the synchronization threshold, will be identified and numbered. To achieve this process, it's necessary to compare the difference between the actual execution time and the planned time of each task node in real time, perform precise time calculations for each task node, and integrate the task node number, actual execution period versus planned period, deviation rate, and execution status information (such as the work content being performed and the distribution of construction personnel). This creates a deviation list for the task node. This list allows project managers to track task progress based on the node number, quickly identify delayed or deviated task nodes, and make corresponding adjustments and improvements. For example, if the deviation list shows a node numbered "TN003" with a deviation rate of 15%, indicating that the task was not completed on schedule, the project manager can further analyze the cause of the node to determine whether the delay was caused by insufficient construction personnel, equipment failure, or other factors.

[0064] See also Figure 3 ,The specific steps for obtaining the progress trend label of the critical path task are as follows:

[0065] S211: Based on the task node deviation list, identify the task nodes and construction process schedule on the critical path, extract the real-time construction start and completion time, and use the formula:

[0066] ;

[0067] Calculate the start and end time difference of the task section, compare it with the time difference of the planned task node, and obtain the construction time deviation value;

[0068] in, Represents the start and end time difference of the task segment, Representative The construction completion time of the task, Representative The construction start time of the task, Representative The construction workload of the task section, Representative The construction intensity coefficient of the task, Represents the total number of task segments;

[0069] Identify the task nodes on the critical path and ensure the accuracy of the task node deviation data. Combined with the construction process schedule, determine the various task nodes in the construction process, and then extract the real-time construction start and completion times. The process requires real-time tracking of the time information of each task node during the construction process to ensure the accurate entry of real-time data. Taking a specific construction project as an example, for example, the foundation construction process in a building project, sensors are used to capture the construction start and end times in real time for subsequent calculation and evaluation. The start and end time difference of the task segment is calculated and compared with the planned task node time difference. During the calculation process, the start and end time of the task segment must be accurately extracted. For example, if the planned completion time of a task is 12:00 and the actual completion time is 12:30, the task segment time difference is 30 minutes. By comparing the actual time difference with the planned time difference, the construction time deviation value is obtained, and the deviation of the construction progress from the plan is further determined. If the deviation value exceeds a certain set threshold (such as 10%), it means that there is a significant delay in the construction progress and timely adjustment is required.

[0070] The start-end time difference of a task segment refers to the length of time between the actual start and completion of a specific construction task. This time reflects the complete time period occupied by the task during its actual on-site execution. It not only reflects the duration of the construction activity itself, but is also influenced by factors such as resource allocation, work organization, site environment, and coordination between processes. Therefore, it is an important basic data for measuring construction task execution efficiency and schedule deviation. By calculating the start-end time difference of a task segment, it provides a quantitative basis for subsequent construction plan comparison, delay analysis, and critical path correction.

[0071] Time deviation value of the task segment Reflects the The deviation of the construction progress of a section from the overall average level. This formula combines the two dimensions of duration difference and construction workload and intensity, and establishes a dynamic difference expression in the form of root mean square;

[0072] : Actual completion time, recorded in hours by the construction site’s intelligent time recording terminal, extracted from the time when the worker swipes their card to complete the task;

[0073] Normalization processing: recorded as ;

[0074] : Actual start time, also automatically collected by the construction record system, in hours, refers to the valid construction record of the worker's first entry into the task area;

[0075] Normalization processing: recorded as ;

[0076] : Construction workload, derived from the BIM model to derive individual construction quantity table data, combined with on-site measurements, in square meters;

[0077] Normalization: Due to the inconsistency of units and time, it is necessary to set the maximum reference working area Normalization: ;

[0078] : Construction density coefficient. The density evaluation model is established based on the original process efficiency data. The value for standard work types under standard conditions is 1.0. If the work requires collaboration among multiple people or is limited by construction space, the density coefficient increases.

[0079] Standardized scoring is based on the following: single person work, no obstruction, , two-person collaborative construction, , the working surface is limited and there are obstacles, , comprehensive rating: , unit dimensionless;

[0080] :Total number of tasks, source: imported from the construction plan master control table, each task segment that can independently define the construction start and end nodes is defined as a task, and the total number of statistics is given directly without normalization. ;

[0081] Numerical setting and normalization:

[0082] Hour, , , after normalization: ;

[0083] Hour, , , after normalization: ;

[0084] , , after normalization: ;

[0085] , for two-person collaboration + mild obstacle construction, ;

[0086] The operation process is as follows:

[0087] Calculate the construction time difference (unnormalized): ;

[0088] Calculate the product of workload and density (unnormalized): ;

[0089] Compute the sum of squares and take the root: ;

[0090] The difference in average construction time for all tasks is: ;

[0091] Final time deviation value calculation: ;

[0092] This result shows that the The comprehensive progress characteristics of each task section deviate by about 1.45 hours compared to the average status of all tasks. This value is affected by the construction time span and workload intensity. The result can be used as a basis for construction scheduling deviation alarm or node tracking correction parameter.

[0093] S212: Call the construction time deviation value, combine the section distribution, deviation trend and adjustment frequency, uniformly collect task node deviation data, identify and calculate the trend deviation degree by section number, determine the trend direction based on the adjustment frequency, and obtain the critical path task progress trend label;

[0094] According to the actual construction progress of each section of the construction project, its regional distribution is analyzed, and the construction data of the section where each task node is located is divided and analyzed by region to distinguish the impact of the construction progress of different regions on the overall project progress. For example, in a building construction project, the construction progress of the southern area is obviously lagging behind, while the construction progress of the eastern area is faster, and adjustments of different priorities are required. Combined with the deviation trend, the changing pattern of the construction progress deviation in different time periods is evaluated. Specifically, the trend of increasing deviation value in a certain period of time is analyzed, indicating that there are problems with construction quality control in certain links, or bottlenecks in material distribution. For example, if in the past three days, the construction If the deviation gradually increases, it can be inferred that the problem has accumulated and forward-looking adjustments can be made. The adjustment frequency can be aggregated to obtain the adjustment situation and frequency of each section during the construction process, and the frequency of changes in the deviation trend during the construction process and its volatility can be evaluated. For example, a project has experienced frequent deviations and large fluctuations in the past five adjustments, indicating that the adjustment measures have not yet achieved the expected results and need further optimization. The trend direction can be judged based on the adjustment frequency, and the adjustment measures for the construction progress can be further determined. If the adjustment frequency is high and the trend tends to lag, it means that a more timely and effective adjustment strategy needs to be adopted to achieve the smooth progress of the overall project construction plan, and finally obtain the trend label of the critical path task progress.

[0095] See also Figure 4 The specific steps for obtaining the list of sections affected by equipment at a construction node are as follows:

[0096] S311: Call the critical path task progress trend tag, filter the fluctuation deviation task partition number, extract the construction time period based on the node association table, process the segment task time period according to the time dimension, identify the construction time period index table, and obtain the fluctuation task construction time period set;

[0097] Extract the progress trend labels of critical path tasks. The steps mainly rely on the various tasks and milestone time nodes in the construction plan. According to the information, a task sequence diagram or Gantt chart is formed, and subsequent fluctuation deviation analysis is carried out based on this. In order to complete this step, it is first necessary to use project management tools to collect and organize the planned start and end times of each critical path task, and indicate each stage of construction through the Gantt chart. The tasks will be marked as "critical path tasks" and sorted according to their importance in the overall project. The parts with fluctuation deviations in the task sequence are screened out, and the relevant data of the construction time period are extracted by using the node association table. The node association table lists the association information between different construction nodes and tasks in the project. The associated data in the table can be used to further identify the dependencies between tasks and tasks that may affect the construction progress. For example, in a certain construction project, assuming that there is a direct dependency between critical path task A and task B, and if the construction period of task A deviates, the start time of task B will also change, which will eventually affect the progress of the entire project. In the process of identifying the construction time period, it is necessary to clarify the location and type of the fluctuating deviation task, use the time dimension for analysis, and adjust the section task time period according to the requirements of the construction progress. Further, through the construction time period index table, select specific fluctuating task construction time periods to form a fluctuating task construction time period set.

[0098] S312: Based on the fluctuating task construction period set, collect equipment operating efficiency data for the same period of the section, identify the equipment impact table, determine daily equipment interference based on the interference threshold, match it with the fluctuating task construction period, determine whether there is any abnormal construction progress correlation, and generate a list of sections of the construction node affected by the equipment;

[0099] Collecting equipment operating efficiency data for the same period is crucial. Equipment operating efficiency directly impacts the completion time of construction tasks, especially those requiring heavy machinery or high-efficiency equipment. Equipment operating status is determined by identifying the equipment impact table. This table records the efficiency of each piece of equipment at different time periods and its impact on construction, which in turn affects the progress of construction nodes. Daily equipment interference is determined based on an interference threshold, a value determined by the equipment's operating status, task type, and their mutual influence. For example, for construction equipment operation, the interference threshold is set to indicate a significant impact on the construction progress if the equipment is in use for more than 8 hours and has a failure rate exceeding 5%. Equipment interference is then matched to fluctuating task construction periods to analyze whether there are any correlations with abnormal construction progress. For example, if a piece of equipment operates for 10 hours in a construction section and has a failure rate of 6%, exceeding the interference threshold, the equipment's operating status will directly impact the progress of the construction task and will be listed in the equipment impact section list. This process creates a list of equipment-affected sections for construction nodes, providing a basis for subsequent adjustments to the construction progress.

[0100] Table 1: Equipment operating efficiency and interference threshold parameters

[0101] ;

[0102] As shown in Table 1, the interference threshold analysis results of equipment A and equipment C show that they have a significant impact on the progress during the construction process.

[0103] See also Figure 5 The specific steps for obtaining the construction team node operation completion fluctuation group are as follows:

[0104] S411: Based on the list of sections of the construction node affected by the equipment, filter out the unmarked construction partition nodes, extract the work task list, record the planned process and start and end time of the task, and obtain the task set of the node not affected by the equipment;

[0105] Unmarked construction zone nodes are screened out. Nodes are construction tasks that are not affected by equipment interference and are progressing normally. To screen out unmarked nodes, the task number is first compared with the equipment impact record to ensure that the selected nodes are indeed not affected by equipment interference. A list of work tasks associated with the unmarked nodes is then extracted. This list includes detailed information for each task, such as the planned process, planned start time, and planned end time. The start and end times of each task are recorded and monitored as key data to facilitate subsequent construction progress analysis. This detailed data on planned tasks allows for the identification, statistics, and tracking of node task sets that are not affected by equipment interference. The core task of this process is to identify and extract those tasks from the entire construction plan that are not affected by equipment interference, providing accurate data support for subsequent progress assessment and optimization. For example, suppose a construction zone task is numbered T001, with a planned start time of May 1st and an end time of May 10th. After screening for equipment impact, it is found that this task is not affected by equipment inefficiency. Therefore, this task is listed as a node task set that is not affected by equipment interference.

[0106] S412: Call the task set of the node not affected by the equipment, match the daily segmented task records of the construction team with the attendance data, extract the planned task volume by task number, count the number of people on duty and the real-time attendance period, calculate the construction team task execution matching deviation value, and generate the construction team cooperation execution matching data set;

[0107] Task data from the task set of nodes not affected by equipment interference is retrieved and matched with the construction team's daily segmented task records. This matching process relies on the construction team's attendance data, which includes information such as the number of construction team members present and their actual daily attendance periods. By matching this data, the planned workload for each task number can be accurately calculated, thereby determining the corresponding task execution progress. For example, suppose the planned workload for task T001 is 1,000 square meters of construction area. The construction team had 10 members present on May 1st, and each member was actually present for 8 hours. The team's work efficiency can be calculated using this data. By tallying the actual number of people present and the actual attendance periods each day, a matching dataset for construction team collaboration can be generated. This dataset includes the actual execution progress of each task and the construction team's attendance, helping to determine whether the construction team's execution is in line with the original plan. For example, insufficient actual number of people present and insufficient attendance periods will affect the progress of the construction task, and the discrepancy will be recorded and analyzed.

[0108] The construction team task execution matching deviation value uses the formula:

[0109] ;

[0110] in, Represents the construction team task execution matching deviation value, The representative task number is The corresponding planned task volume, The representative task number is The number of people on duty in this team is counted. The representative task number is The corresponding attendance period of the team, The representative task number is Attendance period within the same time period as the original task, The representative task number is The number of execution delays of the associated task, The representative task number is The actual recorded amount of construction completed;

[0111] The construction team task execution matching deviation value refers to the absolute difference between the task volume completed by the construction team during the actual execution process under a specific task number and the task volume that should be completed after comprehensive adjustment based on the planned task volume, the number of people on duty, the real-time attendance period and its fluctuation, the original attendance pattern, and the task execution delay. This deviation value reflects the degree of deviation between the construction team's actual execution efficiency and the planned expectations. The larger the value, the more significant the difference between the construction execution status and the planned arrangement. It can be used to evaluate the coordination of the construction organization, the rationality of resource allocation, and the responsiveness during the execution process.

[0112] (Task number is Planned task volume): Obtained through the planned task data recorded in the project management system. For example, the planned task volume of a construction task is 1,000 cubic meters;

[0113] (Task number is Number of people on duty): Use the attendance system to count the number of workers on duty every day. For example, the number of people on duty on a certain day is 20;

[0114] (Task number is Attendance period): By analyzing the attendance time of each worker in the attendance data, the variance is calculated. For example, the variance of the attendance time of workers on a certain day is 1.5 hours;

[0115] (Task number is Original attendance period): Calculate the variance of attendance time of the same task in the original attendance data. For example, the variance of the original attendance time 2.0 hours;

[0116] (Task number is The number of task execution delays is obtained through the number of task delays recorded in the project management system. For example, the number of delays for a task is 2;

[0117] (Task number is Actual completion volume of a task): obtained through on-site construction records or progress reports. For example, the actual completion volume of a task is 950 cubic meters;

[0118] Substitute specific values ​​for calculation:

[0119] ;

[0120] The results show that after taking into account the fluctuations in the number of posts, attendance periods, and original attendance periods, there is a deviation of 6340 between the adjusted planned task volume and the actual completion volume.

[0121] S413: Matching the dataset based on the collaborative execution of the construction teams, evaluating the matching degree between task execution efficiency and manpower distribution, identifying efficiency fluctuation nodes and calculating the degree of operation deviation, marking tasks with fluctuations exceeding the baseline value as abnormal nodes, and obtaining the construction team node operation completion fluctuation group;

[0122] Evaluate the degree of alignment between task execution efficiency and human resource distribution. By analyzing task execution efficiency and combining it with human resource allocation, we can determine whether the construction task's progress is affected by human resource distribution. For example, suppose task T001 is scheduled to complete 1,000 square meters of construction area. However, the construction team's actual work efficiency on a particular day is 150 square meters, and the human resource distribution is 10 people. In this case, the actual work efficiency for that day is calculated and compared with the task's planned progress to assess the construction team's work efficiency. For nodes experiencing efficiency fluctuations, the degree of deviation is calculated by comparing the actual completed task volume with the planned task volume. For example, if the actual completed task volume is less than 30% of the planned task volume, the daily deviation is 30%. Tasks that fluctuate beyond a baseline value are marked as abnormal nodes. The baseline value is set based on multiple factors, including the original data, task type, construction area, and construction team capabilities. If the baseline value is set at 5%, then if the daily deviation exceeds 5%, the task will be marked as an abnormal node. This ultimately forms a construction team node completion fluctuation group for subsequent analysis and processing.

[0123] See also Figure 6 ,The specific steps for obtaining the construction progress monitoring structure indicators are as follows:

[0124] S511: Call the node numbers and corresponding operation time intervals in the construction team node operation completion fluctuation group, filter out nodes that exceed the threshold, record the time interval and task volume change range, and obtain the progress fluctuation abnormality identification set;

[0125] The node numbers and corresponding time intervals in the construction team's node completion fluctuation group are retrieved to determine the operation interval for each node and examine its progress fluctuation. By comparing the time interval with the magnitude of the task volume change, nodes whose progress fluctuation exceeds a predetermined threshold are screened out. The key step in this process is to calculate the magnitude of the task volume change for each node and compare it with the set threshold. For example, if the planned duration of a task is 10 days and the task volume for each node in the construction plan is 500 square meters, but during actual execution, only 300 square meters of a node are completed in 7 days, resulting in a 40% magnitude of task volume change, the node's progress fluctuation exceeds the set threshold. Nodes with fluctuations exceeding the threshold are marked as abnormal progress fluctuations, and their time intervals and magnitude of task volume change are recorded to form a set of abnormal progress fluctuation identifications. In this case, the threshold can be set to 20%, meaning that any task volume change exceeding 20% ​​is marked as an abnormal node. By performing the same judgment on multiple nodes, tasks whose construction progress deviates from the normal track can be efficiently identified, providing basic data for subsequent analysis.

[0126] S512: Based on the construction model resource input and task volume corresponding to the nodes in the progress fluctuation abnormality identification set, identify the node resource input ratio sequence, extract the abnormal distribution interval and compare the critical coefficient, record the ratio deviation direction and node number, and form a construction resource matching deviation indicator group;

[0127] Based on the node information in the abnormal progress fluctuation identification set and the corresponding construction model resource input and task volume, a sequence of resource input ratios for each node is identified. This process is performed by calculating the proportional relationship between the node resource input and task volume. In practice, construction model resource input involves resources such as manpower, equipment, and materials, while the task volume refers to the construction workload for each node in the plan. By calculating the ratio between the node resource input and task volume, it is possible to determine whether the node is over- or under-invested in resources during the construction process. For example, if the resource input for a task node is 10,000 yuan and the task volume is 500 square meters, the resource input ratio is 20 yuan / square meter. A higher ratio indicates excessive resource investment at the node, while a lower ratio indicates insufficient resource investment. This ratio is then compared with a critical coefficient to determine whether there is an abnormal distribution interval. If the ratio of a certain node differs too much from the set critical coefficient, it indicates that the distribution of resource input is unreasonable. The node will be marked as an abnormal node, and the ratio offset direction and node number will be recorded to form a construction resource matching offset indicator group. The critical coefficient is set to 0.15, that is, if the difference between the ratio and this coefficient exceeds 15%, it will be judged as abnormal.

[0128] S513: Extract the resource deployment and task execution time periods of the construction model based on the node numbers in the construction resource matching offset indicator group, identify the differences between the resource deployment cycle and the operation cycle, sort and annotate them according to the progress benchmark, and generate construction progress monitoring structure indicators;

[0129] According to the node number in the construction resource matching offset indicator group, the construction model resource allocation and task execution time period of each node are further extracted. By comparing the difference between the resource allocation cycle and the operation cycle, it is possible to identify which nodes have time mismatches in resource allocation. For example, assuming that the resource allocation cycle of a task node is 10 days and the operation cycle is 8 days, calculate the difference between the two and analyze whether there is a lag in resource allocation. In this way, nodes where resources are not in place in time during the operation cycle can be identified, thereby helping project managers to make timely adjustments. Nodes will also be sorted and labeled according to the construction progress benchmark to accurately evaluate the construction progress of each node. The progress benchmark can be set by the critical path tasks in the project plan and the start and end times of each task node. Finally, by sorting and labeling the nodes, construction progress monitoring structure indicators are generated to help managers track and adjust the construction progress in real time;

[0130] Table 2: Example table of resource input ratio and task volume

[0131] ;

[0132] As shown in Table 2, by calculating the resource input ratio of each node and comparing it with the set critical coefficient, node C is determined to be an abnormal node because its resource input ratio is greater than the range set by the critical coefficient.

[0133] The intelligent management system for civil engineering projects is used to implement the intelligent management method for civil engineering projects. The system includes:

[0134] The task status extraction module obtains personnel distribution data, mechanical equipment operation status data, and material inventory records on the construction site, extracts key node numbers and task node times in the construction task decomposition structure, compares the execution status with the task node identifier, marks component tasks with inconsistent times, and generates a node status deviation label group;

[0135] The node trend classification module locates the task nodes on the critical path based on the node status deviation label group, extracts the construction node identifiers, process nodes and planned time, calculates the difference between the on-site construction time and the planned time, classifies and annotates the difference type, and generates the node progress trend identifier;

[0136] The equipment interference identification module, based on the node progress trend identification, screens the fluctuating and lagging task components, locates the corresponding partitions, extracts the time periods of mechanical equipment operation efficiency interference, determines the overlap with the operation time period, screens the frequent interference sections, and generates the construction progress equipment interference mapping set;

[0137] The operation efficiency diagnosis module is based on the construction progress equipment interference mapping set, eliminates interfering section tasks, extracts construction team task records and attendance data, matches task assignments with operation time periods, calculates the ratio of attendance workload to tasks, identifies task-intensive and inefficient component tasks, and obtains node operation execution deviation sets.

[0138] The resource allocation analysis module locates the resource input records of tasks in the construction model based on the node job execution deviation set, extracts the ratio of task quantity to resource quantity configuration, compares the difference between execution cycle and input cycle, maps task resource usage and progress status, and forms construction progress monitoring structure indicators.

[0139] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. An intelligent management method for civil engineering projects, characterized in that: The following steps are involved: S1: Obtain personnel distribution data, machinery and equipment operating status data, and material inventory records at the construction site, extract key node information from the construction task breakdown structure, mark the task execution status and compare it with the node plan status, identify tasks with delayed progress, and generate a task node deviation list; S2: Based on the task node deviation list, the task nodes on the critical path are screened, and the real-time construction start and end times are extracted in combination with the construction process schedule. The real-time construction start and end times are then calculated and classified with the planned time points to obtain the progress trend labels of the critical path tasks; S3: Call the progress trend tag of the critical path task, extract the fluctuation task partition number, identify the regional mechanical equipment utilization efficiency data, compare the equipment operation time period with the fluctuation task time period, record the number of overlapping time periods, and generate a list of sections of the construction node affected by the equipment; S4: Based on the list of sections of the construction node affected by the equipment, filter the undisturbed partition node tasks, extract the construction team segment task records and attendance data, map the daily task assignments and the attendance team work periods, determine whether there is an imbalance in resource allocation for the task, and obtain the construction team node work completion fluctuation group.

2. The intelligent management method for civil engineering projects according to claim 1, characterized in that: The task node deviation list includes the task deviation number, execution status label, time deviation amount and task node classification; the critical path task progress trend label includes the progress deviation level, trend change type, plan comparison result and task association number; the construction node equipment affected section list includes the equipment type, affected time section, number of overlapping time periods and affected task number; the construction team node job completion fluctuation group includes the job uneven distribution number, working hour usage record, task completion deviation and team attendance matching degree.

3. The intelligent management method for civil engineering projects according to claim 1, characterized in that: The steps for obtaining the task node deviation list are specifically as follows: S111: Obtain personnel distribution data, mechanical equipment operating status data, and material inventory records at the construction site, extract the planned time and number of key nodes in the construction task decomposition structure, match the collection time and coordinates of the personnel distribution data, and compare the time range with the node planned time to generate the partition node execution record period; S112: Based on the partition node execution record period, extract the overlapping period of the task node and the planned time interval, and calculate the ratio of the overlapping time to the total planned duration of the node, filter out the task nodes with the overlapping ratio lower than the benchmark value, and obtain the partition node progress coverage deviation rate based on the number of node execution status annotations; S113: Determine the deviation status of the task node number according to the partition node progress coverage deviation rate, identify the task node number whose deviation rate exceeds the node synchronization threshold, integrate the node number, execution coverage information and deviation rate value, and generate a task node deviation list.

4. The intelligent management method for civil engineering projects according to claim 3 is characterized in that: The steps for obtaining the progress trend label of the critical path task are as follows: S211: Based on the task node deviation list, identify the task nodes and construction process schedule on the critical path, extract the real-time construction start and completion time, calculate the start and end time difference of the task section, and compare it with the planned task node time difference to obtain the construction time deviation value; S212: Call the construction time deviation value, combine the section distribution, deviation trend and adjustment frequency, uniformly collect task node deviation data, identify and calculate the trend deviation degree according to the section number, determine the trend direction according to the adjustment frequency, and obtain the critical path task progress trend label.

5. The intelligent management method for civil engineering projects according to claim 4 is characterized in that: The specific steps for obtaining the list of sections affected by the equipment at the construction node are as follows: S311: Call the progress trend tag of the critical path task, filter the fluctuation deviation task partition number, extract the construction time period according to the node association table, process the section task time period according to the time dimension, identify the construction time period index table, and obtain the fluctuation task construction time period set; S312: Based on the fluctuating task construction period set, collect equipment operation efficiency data of the same period section, identify the equipment impact table, judge the daily equipment interference based on the interference threshold, match it with the fluctuating task construction period, judge whether there is any abnormal correlation with the construction progress, and generate a list of sections of the construction node affected by the equipment.

6. The intelligent management method for civil engineering projects according to claim 5, characterized in that: The specific steps for obtaining the construction team node operation completion fluctuation group are: S411: Based on the list of sections of the construction node affected by the equipment, filter out unmarked construction partition nodes, extract the work task list, record the planned process and start and end time of the task, and obtain the task set of nodes not affected by the equipment; S412: Calling the node task set not affected by equipment interference, matching the construction team's daily segmented task records with attendance data, extracting the planned task volume by task number, counting the number of on-duty workers and real-time attendance periods, and generating a matching dataset for construction team cooperation execution; S413: Match the data set according to the cooperation execution status of the construction team, evaluate the matching degree between task execution efficiency and manpower distribution, identify efficiency fluctuation nodes and calculate the degree of operation deviation, mark tasks whose fluctuation exceeds the baseline value as abnormal nodes, calculate the construction team task execution matching deviation value, and obtain the construction team node operation completion fluctuation group.

7. The intelligent management method for civil engineering projects according to claim 1, characterized in that: The method further comprises step S5: S5: Call the construction team node operation completion fluctuation group, extract the abnormal fluctuation task group, calculate the ratio of resource input to task volume in the construction model, record the difference distribution between resource input cycle and task execution cycle, and generate construction progress monitoring structure indicators; The construction progress monitoring structure indicators include resource allocation ratio, task intensity level, execution cycle difference and construction efficiency index.

8. The intelligent management method for civil engineering projects according to claim 7, characterized in that: The steps for obtaining the construction progress monitoring structure indicators are specifically as follows: S511: Calling the node numbers and corresponding operation time intervals in the construction team node operation completion fluctuation group, filtering out nodes that exceed the threshold, recording the time interval and task volume change range, and obtaining a progress fluctuation abnormality identification set; S512: Based on the construction model resource input and task volume corresponding to the nodes in the progress fluctuation abnormality identification set, identify the node resource input ratio sequence, extract the abnormal distribution interval and compare the critical coefficient, record the ratio deviation direction and node number, and form a construction resource matching deviation indicator group; S513: Extract the resource delivery and task execution time periods of the construction model according to the node numbers in the construction resource matching offset indicator group, identify the differences between the resource delivery cycle and the operation cycle, sort and mark them according to the progress benchmark, and generate construction progress monitoring structure indicators.

9. An intelligent management system for civil engineering projects, characterized in that: The system is used to implement the intelligent management method for civil engineering projects according to any one of claims 1 to 8, and the system includes: The task status extraction module obtains personnel distribution data, mechanical equipment operation status data, and material inventory records on the construction site, extracts key node numbers and task node times in the construction task decomposition structure, compares the execution status with the task node identifier, marks component tasks with inconsistent times, and generates a node status deviation label group; The node trend classification module locates the task nodes on the critical path based on the node status deviation label group, extracts the construction node identifiers, process nodes and planned time, calculates the difference between the on-site construction time and the planned time, classifies and marks the difference types, and generates node progress trend identifiers; The equipment interference identification module selects the fluctuating and lagging task components based on the node progress trend identification, locates the corresponding partitions, extracts the mechanical equipment operation efficiency interference period, determines the overlap with the operation time period, selects the frequent interference sections, and generates the construction progress equipment interference mapping set; The operation efficiency diagnosis module eliminates interfering section tasks based on the construction progress equipment interference mapping set, extracts construction team task records and attendance data, matches task assignments with operation time periods, calculates the ratio of attendance workload to tasks, identifies component tasks that are task-intensive and inefficient, and obtains a node operation execution deviation set; The resource allocation analysis module locates the resource input records of the task in the construction model based on the node operation execution deviation set, extracts the ratio of task quantity to resource quantity configuration, compares the difference between the execution cycle and the input cycle, maps the task resource usage and progress status, and forms the construction progress monitoring structure indicator.

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