Intelligent management method and system for civil engineering project

Abstract

The invention relates to the technical field of project management, in particular to an intelligent management method and system for a civil engineering project, and the method comprises the following steps: obtaining personnel distribution, equipment state and material inventory data, extracting a key task node and an execution state, recognizing a progress lagging task, screening a key path node, analyzing a time difference, and marking a progress trend. And identifying an equipment influence section, judging resource allocation imbalance, and generating a progress monitoring index. According to the invention, through multi-source data acquisition of personnel distribution, equipment state and material inventory, difference identification between a task state and a plan node is enhanced, progress lag early warning efficiency is improved, progress fluctuation dynamic classification and trend identification are realized, construction rhythm optimization is assisted, and through coincidence analysis of equipment operation and a task fluctuation area, the progress lag early warning efficiency is improved. The construction efficiency bottleneck is identified, the scheduling accuracy is improved, fine progress monitoring is realized through investment and period difference comparison, and the progress control capability and the collaboration of on-site resource allocation are comprehensively enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of project management, and particularly 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 whole process of engineering construction. The core contents of this technical field include project scope management, schedule management, cost management, quality management, human resource management, communication management, risk management, and procurement management, etc. Project management is particularly important in civil engineering. Its systematicness is reflected in improving the engineering implementation efficiency, ensuring the engineering quality, controlling the project cost, and reducing the project risk through standardized management processes and scientific management means. 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 links such as data collection, task scheduling, personnel allocation, progress tracking, and material control to improve the management level and execution efficiency of civil engineering projects.

[0003] Among them, the intelligent management method for civil engineering projects refers to achieving the dynamic coordination of construction management elements through information means for the key management matters in the process of civil engineering construction. The method mainly covers matters such as automated monitoring of construction progress, identification and scheduling of the trajectories of on-site operators, registration of building materials entering the site and calculation of remaining quantities, standardized verification of construction quality, and tracking of task process nodes, and completes the management work through a combination of preset rules, digital recognition methods, and task allocation mechanisms. For example, by deploying positioning devices at the construction site to identify the actual position trajectories of operators to dynamically coordinate their tasks, by recording material labels to compare the quantity of materials entering the warehouse and calculate the remaining quantities, and by setting standardized process nodes to conduct refined tracking of task progress, so as to realize the intelligent management and dynamic scheduling of various management elements of civil engineering projects.

[0004] The prior art generally relies on static node settings and pre-designed plans for task tracking, lacking a dynamic deviation identification mechanism driven by real-time data, resulting in an overly long response cycle for progress anomalies. For example, although the planned nodes are set, the actual task execution status cannot be immediately fed back. When there is a progress lag, relevant managers can only notice it within the weekly or monthly report cycle, missing the opportunity for intervention. Most existing construction scheduling processes are based on personnel experience and paper records, lacking precise comparison and analysis of equipment usage efficiency and on-site resource input, which easily leads to mismatches in equipment scheduling for key processes and affects the overall project duration. The attendance and task assignment of construction teams are recorded independently, making it difficult to map and analyze the correspondence between resource distribution and task execution. There is a lack of a scientific decision-making basis for resource scheduling, often resulting in phenomena such as idle manpower or local congestion. The progress monitoring indicator system is mainly result-oriented, lacking quantitative analysis of the resource-task matching process, and it is difficult to form a precise feedback loop during the construction execution stage, affecting the fine management level and risk control ability of construction projects. Summary of the Invention

[0005] An object of the present invention is to solve the deficiencies in the prior art and propose an intelligent management method and system for civil engineering projects.

[0006] To achieve the above object, the present invention adopts the following technical solutions: An intelligent management method for civil engineering projects includes the following steps: S1: Obtain the personnel distribution data, mechanical equipment operation status data, and material inventory records at the construction site, extract the key node information in the construction task breakdown structure, mark the task execution status and compare it with the node plan status, identify the task identifiers with progress lags, and generate a task node deviation list. S2: Based on the task node deviation list, screen the task nodes on the critical path, extract the real-time construction start and end times in combination with the construction process schedule, perform difference operations and classification processing with the planned time points, and obtain the critical path task progress trend labels. S3: Call the critical path task progress trend labels, extract the fluctuating task partition numbers, identify the regional mechanical equipment usage efficiency data, compare the equipment operation time periods with the fluctuating task time periods, record the number of overlapping time periods, and generate a list of sections of construction nodes affected by equipment. S4: Based on the list of sections of construction nodes affected by equipment, screen the task nodes of the undisturbed partition nodes, extract the segmented task records and attendance data of the construction teams, map the daily task assignment volume and the working hours of the attending teams, and determine whether there is an imbalance in resource allocation for the tasks to obtain the fluctuating group of completed tasks for the construction team nodes.

[0007] As a further solution of the present invention, the task node deviation list includes a task deviation number, an execution status label, a time deviation amount, and a task node classification. The critical path task progress trend label includes a progress deviation level, a trend change type, a plan comparison result, and a task association number. The list of sections of construction nodes affected by equipment includes equipment type, affected time sections, number of overlapping periods, and task numbers affected. The construction team node operation completion fluctuation group includes an uneven operation distribution number, a working hour usage record, a task completion deviation, and a team attendance matching degree.

[0008] As a further solution of the present invention, the steps for obtaining the task node deviation list are specifically as follows: S111: Obtain the personnel distribution data, mechanical equipment operation status data, and material inventory records at the construction site, extract the planned time and number of key nodes in the construction task breakdown structure, match the collection time and coordinates of the personnel distribution data, and compare the time range with the node planned time to generate a sectional node execution record period. S112: Based on the sectional node execution record period, extract the overlapping periods of the task nodes and the planned time intervals, calculate the ratio of the overlapping time to the total planned duration of the nodes, screen the task nodes with an overlapping ratio lower than the reference value, and obtain the sectional node progress coverage deviation rate according to the marked quantity of the node execution status. S113: According to the sectional node progress coverage deviation rate, determine the deviation status of the task node numbers, identify the task node numbers with a deviation rate exceeding the node synchronization threshold, integrate the node numbers, execution coverage information, and deviation rate values to generate a task node deviation list.

[0009] As a further solution of the present invention, the steps for obtaining the critical path task progress trend label are specifically as follows: S211: Based on the task node deviation list, identify the task nodes and construction process schedules on the critical path, extract the real-time construction start and completion times, calculate the difference in the start and end times of the task sections, and compare it with the planned task node time difference to obtain the construction time deviation value. S212: Invoke the construction time deviation value, combine the section distribution, deviation trend, and adjustment frequency, uniformly collect the task node deviation data, identify and calculate the trend deviation degree according to the section number, and judge the trend direction based on the adjustment frequency to obtain the critical path task progress trend label.

[0010] As a further solution of the present invention, the steps for obtaining the list of sections of construction nodes affected by equipment are specifically as follows: S311: Invoke the key path task progress trend label, filter the task partition numbers with fluctuating deviations, extract the construction time period according to the node association table, process the task time periods of the sections in the time dimension, identify the construction time period index table, and obtain the set of construction time periods for fluctuating tasks; S312: According to the set of construction time periods for fluctuating tasks, collect the equipment operation efficiency data of the same-period sections, identify the equipment impact table, judge the daily equipment interference according to the interference threshold, match it with the construction time periods of the fluctuating tasks, judge whether there is an abnormal association of construction progress, and generate a list of sections affected by equipment at construction nodes.

[0011] As a further solution of the present invention, the steps for obtaining the completion fluctuation group of the construction team node operations are specifically as follows: S411: Based on the list of sections affected by equipment at construction nodes, filter the unmarked construction partition nodes, extract the operation task list, record the planned processes, start and end times of the tasks, and obtain the set of tasks for nodes not affected by equipment interference; S412: Invoke the set of tasks for nodes not affected by equipment interference, match the daily segmented task records of the construction team with the attendance data, extract the planned task volume according to the task number, count the number of people on duty and the real-time attendance time periods, and generate a matching data set for the cooperation execution situation of the construction team; S413: According to the matching data set for the cooperation execution situation of the construction team, evaluate the matching degree between the task execution efficiency and the manpower distribution, identify the nodes with efficiency fluctuations and calculate the operation deviation degree, mark the tasks with fluctuations exceeding the benchmark value as abnormal nodes, calculate the matching deviation value of the task execution of the construction team, and obtain the completion fluctuation group of the construction team node operations.

[0012] As a further solution of the present invention, the method further includes step S5: S5: Invoke the completion fluctuation group of the construction team node operations, extract the abnormal fluctuation task group, calculate the ratio of the resource input volume to the task volume in the construction model, record the difference distribution between the resource release period and the task execution period, and generate the construction progress monitoring structure index; The construction progress monitoring structure index includes the resource allocation ratio, the task intensity level, the execution period difference, and the construction efficiency index.

[0013] As a further solution of the present invention, the steps for obtaining the construction progress monitoring structure index are specifically as follows: S511: Invoke the node numbers and the corresponding operation time intervals in the completion fluctuation group of the construction team node operations, filter the nodes exceeding the threshold, record the time interval and the change range of the task volume, and obtain the set of progress fluctuation abnormal identifiers; S512: Based on the construction model resource input quantity and task quantity corresponding to the nodes in the progress fluctuation anomaly identification set, identify the node resource input ratio sequence, extract the abnormal distribution interval and compare it with the critical coefficient, record the ratio deviation direction and node number, and form a construction resource matching deviation index group; S513: According to the node numbers in the construction resource matching deviation index group, extract the construction model resource input and task execution time periods, identify the differences between the resource input cycle and the operation cycle, and perform sorting and marking according to the progress benchmark to generate a construction progress monitoring structure index.

[0014] The intelligent management system for civil engineering projects is used to execute the above-mentioned intelligent management method for civil engineering projects. The system includes: The task status extraction module obtains the personnel distribution data, mechanical equipment operation status data, and material inventory records at the construction site, extracts the key node numbers and task node times in the construction task breakdown structure, compares the execution status with the task node identification, marks the component tasks with inconsistent times, and generates a node status deviation label group; The node trend classification module, based on the node status deviation label group, locates the task nodes on the critical path, extracts the construction node identification, 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 a node progress trend identification; The equipment interference identification module, based on the node progress trend identification, filters out the tasks and components with fluctuating lags, locates the corresponding partitions, extracts the mechanical equipment operation efficiency interference time periods, and determines whether they coincide with the operation time periods, filters out the frequently interfered sections, and generates a construction progress equipment interference mapping set; The operation efficiency diagnosis module, based on the construction progress equipment interference mapping set, eliminates the tasks in the interfered sections, extracts the construction team task records and attendance data, matches the task assignment and the operation time periods, calculates the ratio of the attendance operation volume to the task, identifies the component tasks with intensive tasks and low execution efficiency, and obtains a node operation execution deviation set; The resource allocation analysis module, based on the node operation execution deviation set, locates the resource input records of the tasks in the construction model, extracts the ratio of the task quantity to the resource quantity configuration, compares the difference between the execution cycle and the input cycle, maps the task resource usage and progress status, and forms a construction progress monitoring structure index.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, through the multi-source data collection of the distribution of on-site personnel, the status of mechanical equipment, and the material inventory, the ability to identify the differences between the task execution status and the planned nodes is strengthened, the early warning efficiency for progress lag problems is significantly improved. By screening the key path task nodes and extracting the real-time start and end times, the dynamic classification and trend identification of task progress fluctuations are realized, making the adjustment of the construction rhythm more targeted. Through the coincidence analysis of the equipment operation efficiency and the task fluctuation section, the bottleneck section of construction efficiency can be effectively identified, and the accuracy of equipment scheduling can be improved. By mapping the task assignment records to the attendance periods, the imbalance in the allocation of operation resources can be quickly discovered, and the actual reflection ability of the team operation behavior can be strengthened. Based on the difference calculation between the task resource input and the execution cycle, the refined measurement of progress monitoring is realized, and the identification accuracy of abnormal fluctuation nodes is enhanced. The above processes comprehensively introduce the quantitative comparison and dynamic screening logic in the links of task node identification, resource scheduling, time period matching, and progress classification, and build a fine management mechanism with data linkage as the core, synchronously improving the perception and control ability of task completion efficiency, equipment utilization level, and resource allocation rationality during the construction process, thereby greatly improving the overall construction progress control level and the on-site organization and coordination efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a schematic diagram of the work flow of the present invention; Figure 2 is a flow chart for obtaining the task node deviation list in the present invention; Figure 3 is a flow chart for obtaining the key path task progress trend label in the present invention; Figure 4 is a flow chart for obtaining the list of sections affected by equipment at the construction nodes in the present invention; Figure 5 is a flow chart for obtaining the construction team node operation completion fluctuation group in the present invention; Figure 6 is a flow chart for obtaining the construction progress monitoring structure index in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0017] In order to make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0018] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0019] Embodiment 1 Please refer to Figure 1 , the present invention provides a technical solution: an intelligent management method for civil engineering projects, including the following steps: S1: Obtain the personnel distribution data, mechanical equipment operation status data, and material inventory records at the construction site, extract the key node information in the work breakdown structure of the construction task, mark the task execution status and compare it with the node plan status, identify the task identifiers with lagging progress, and generate a task node deviation list; S2: Based on the task node deviation list, screen the task nodes on the critical path, extract the real-time construction start and end times in combination with the construction process schedule, perform difference calculation and classification processing with the planned time points, and obtain the critical path task progress trend label; S3: Call the critical path task progress trend label, extract the fluctuating task partition numbers, identify the regional mechanical equipment usage 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 affected by equipment at construction nodes; S4: Based on the list of sections affected by equipment at construction nodes, screen the task nodes of the undisturbed partition nodes, extract the segmented task records and attendance data of the construction teams, map the daily task assignment volume and the working hours of the attending teams, and judge whether there is an imbalance in resource allocation for the tasks, and obtain the fluctuating group of completed tasks at the construction team nodes; S5: Call the fluctuating group of completed tasks at the construction team nodes, extract the abnormally fluctuating task groups, calculate the ratio of the resource input volume to the task volume in the construction model, record the difference distribution of the resource delivery cycle and the task execution cycle, and generate the construction progress monitoring structure index.

[0020] The task node deviation list includes the task deviation number, execution status label, time deviation amount, task node classification. The critical path task progress trend label includes the progress deviation level, trend change type, planned comparison result, and task association number. The list of sections of construction nodes affected by equipment includes equipment type, affected time period, number of overlapping periods, and task numbers affected. The construction team node operation completion fluctuation group includes uneven operation distribution numbers, man-hour usage records, task completion deviations, and team attendance matching degrees. The construction progress monitoring structure indicators include resource allocation ratios, task intensity levels, execution cycle differences, and construction efficiency indicators.

[0021] Please refer to Figure 2 , and the specific steps for obtaining the task node deviation list are as follows: S111: Obtain the personnel distribution data, mechanical equipment operation status data, and material inventory records at the construction site, extract the planned time and number of key nodes in the construction task breakdown structure, match the collection time and coordinates of the personnel distribution data, and compare the time range with the planned time of the nodes to generate the execution record period of the partition nodes; First, obtain real-time data related to the personnel distribution at the construction site, including the location information of each worker or team and the specific tasks being performed. The data can be collected through wireless positioning systems, sensors, or positioning devices. It is necessary to obtain the operation status data of the construction machinery and equipment, and record the location, operation status, and operation content of the equipment in real time through an equipment monitoring system (such as sensors or GPS systems). The material inventory records come from the warehouse management, recording the quantity, storage location, and usage progress of each material. According to the key node plan in the construction task breakdown structure, extract the planned time and task number of each task node. It is necessary to compare the collection time of the on-site personnel distribution data and the mechanical operation data to ensure that the data matches the planned time of the task node and avoid errors caused by data delay. For example, assume that the planned time of a certain task node is from 09:00 to 11:00 on May 20th, and the staff conducts a location data collection at 10:00. Confirm that the data is within a reasonable range compared to the planned time, and the location of the staff is within the task execution area. Based on the data, generate the execution record period of the partition nodes and further analyze the task progress and coordination.

[0022] S112: Based on the execution record period of the partition nodes, extract the overlapping periods between the task nodes and the planned time intervals, calculate the ratio of the overlapping time to the total planned duration of the nodes, screen the task nodes with overlapping ratios lower than the benchmark value, and mark the quantity according to the node execution status to obtain the progress coverage deviation rate of the partition nodes; Extract the actual execution period of each task node from the construction site data and compare it with its planned period to confirm whether there is an overlapping part. The length of the overlapping period is the "overlapping time". For example, if the planned time of a task node is from 09:00 to 11:00 on May 20th and the actual execution time is from 09:30 to 10:30 on May 20th, then the overlapping time is 1 hour. Calculate the ratio of the overlapping time to the planned duration of the node. For example, if the planned duration of this node is 2 hours and the actual overlapping period is 1 hour, then the ratio is 1 / 2 = 0.5, that is, 50% overlap. According to the preset benchmark value (such as 0.8 or 80%), filter out the task nodes with an overlapping ratio lower than this benchmark value. Assuming the benchmark value is set to 0.8, then any task node with an overlapping ratio lower than 80% will be marked as a progress deviation node. Further, label the status of the node, record its execution status, such as whether it is completed, whether it is delayed, etc., and calculate the progress coverage deviation rate of the partition node based on the label. The calculation method of the deviation rate is to compare the actual progress with the planned progress to calculate the deviation. For example, assume that the planned completion time of a task node is 17:00 on May 20th, and the actual completion time is 18:30 on May 20th. The deviation rate is a 1.5-hour delay, which is quantified relative to the deviation of the total task duration.

[0023] S113: Determine the deviation status of the task node number according to the progress coverage deviation rate of the partition node, identify the task node numbers with a deviation rate exceeding the node synchronization threshold, integrate the node numbers, execution coverage information, and deviation rate values to generate a task node deviation list; Evaluate the progress of each task node 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, which is expressed in the form of 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 a progress deviation and belongs to the deviation state. For example, assume that the planned time for a task at a certain node is from 09:00 to 10:00 on May 20th, and the actual execution time is from 09:00 to 10:30 on May 20th. 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 the deviation state. All nodes with a large progress deviation, that is, task nodes with a deviation rate higher than the synchronization threshold, will be identified and numbered. To achieve this process, it is necessary to compare the difference between the actual execution time and the planned time of the task node in real-time, perform precise time calculations for each task node, and integrate the task node number, actual execution period and planned period, deviation rate, and execution status information (such as the work content executed, distribution of construction personnel, etc.) to form a deviation list of task nodes. The role of this list is to facilitate project managers to track the task progress according to the node number, quickly identify the delayed or deviated task nodes, and make corresponding adjustments and improvements. For example, if the list shows that the node numbered "TN003" has a deviation rate of 15% and the task is not completed within the preset duration, the project manager can further analyze the reasons for this node to determine whether it is due to insufficient construction personnel, equipment failure, or other factors causing the delay.

[0024] Please refer to Figure 3 , and the specific 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 schedules on the critical path, extract the real-time construction start and completion times, and use the formula: ; Calculate the start and end time difference of the task section, compare it with the planned task node time difference, and obtain the construction time deviation value; Among them, represents the start and end time difference of the task section, represents the construction completion time of the th task, represents the construction start time of the th task, represents the total number of task sections; Identify the task nodes on the critical path, ensure the accuracy of the deviation data of the task nodes, determine each task node in the construction process in combination with the construction process schedule, and then extract the start and completion times of the real-time construction. 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, such as the foundation construction process in a building project, the start and end times of the construction are captured in real time through sensors for subsequent calculation and evaluation. Calculate the time difference between the start and end times of the task section, and compare this difference with the planned task node time difference. During the process, the start and completion times of the task section need to be accurately extracted during the calculation. For example, assume that the planned completion time of a task is 12:00 and the actual completion time is 12:30, then the time difference of the task section is 30 minutes. By comparing the actual time difference with the planned time difference, the deviation value of the construction time is obtained, and further judge the deviation situation between the construction progress and the plan. If the deviation value exceeds a certain set threshold (such as 10%), it means that there is a large lag in the construction progress and needs to be adjusted in time; The time difference between the start and end times of the task section refers to the time length experienced from the actual start to the actual completion of a specific construction task. This time reflects the complete time cycle occupied by the task during the actual execution on site. It not only reflects the duration of the construction activity itself but is also affected by factors such as resource allocation, operation organization, site environment, and coordination between processes. Therefore, it is an important basic data for measuring the execution efficiency and progress deviation of construction tasks. By calculating the time difference between the start and end times of the task section, it can provide a quantitative basis for subsequent comparison of construction plans, delay analysis, and critical path correction; The time deviation value of the task section reflects the deviation of the construction progress of the : The actual completion time, which is extracted from the time node when the worker swipes the card to end the task by the intelligent working hours recording terminal on the construction site, in hours. Normalization processing: denoted as ; : The actual start time, which is also automatically collected by the construction record system, in hours, referring to the effective construction record when the worker first enters the task area. Normalization processing: denoted as ; : The construction workload, which is exported from the BIM model's single - body construction quantity table data and combined with on - site measurements, in square meters. Normalization processing: Since the units and time are inconsistent, it is necessary to set the maximum reference working surface Normalization: ; : Construction intensity coefficient. An intensity evaluation model is established from the original process efficiency data, and it is 1.0 for standard work types in a standard environment. If the operation requires multi-person collaboration or there are construction space limitations, the intensity coefficient increases; The basis for standardized scoring is as follows: single-person operation, no obstruction, , two-person collaborative construction, , limited working surface, presence of obstacles, , Comprehensive score: , unitless; : Total number of tasks. Source: Imported from the overall construction plan control table. Each task section that can independently define the start and end nodes of construction is defined as one task, and the total count is directly given without normalization, ; Value setting and normalization: hours, , , after normalization: ; hours, , , after normalization: ; , , after normalization: ; , for two-person collaboration + construction with minor obstacles, ; The operation process is as follows: Calculate the construction duration difference (not normalized): ; Calculate the product of workload and intensity (not normalized): ; Calculate the sum of squares term and take the square root: ; The average construction duration difference for all tasks is: ; Final calculation of the time deviation value: ; This result indicates that there is a deviation of approximately 1.45 hours in the comprehensive progress characteristics of the th task section compared to the average state of all tasks. This value is jointly affected by the construction time span and the workload intensity. The result can be used as a basis for construction scheduling deviation alarm or a parameter for node tracking and correction.

[0025] S212: Invoke the construction time deviation value, combine the section distribution, deviation trend, and adjustment frequency, uniformly collect the deviation data of task nodes, identify and calculate the trend deviation degree according to the section number, and judge the trend direction based on the adjustment frequency to obtain the progress trend label of the critical path task; According to the actual construction progress of each section of the construction project, analyze its regional distribution, divide and analyze the construction data of the sections where each task node is located according to the region, and 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 in the south area is significantly lagging behind, while the construction progress in the east area is relatively fast, and adjustments with different priorities are required. Combine the deviation trend to evaluate the change law of the construction progress deviation in different time periods. Specifically, analyze the trend of the deviation value increasing in a certain period of time, indicating that there are problems in the construction quality control of some links or bottlenecks in material distribution. For example, if the construction deviation has gradually increased in the past three days, it can be inferred that the problem has accumulated and forward-looking adjustments can be made. Collect according to the adjustment frequency to obtain the adjustment situation and frequency of each section during the construction process, and evaluate the change frequency and volatility of the deviation trend during the construction process. For example, in a certain project, the deviation is frequent and fluctuates greatly in the past five adjustments, indicating that the adjustment measures have not achieved the expected effect and need to be further optimized. Judge the trend direction based on the adjustment frequency, and further determine the adjustment measures for the construction progress. If the adjustment frequency is high and the trend is towards lagging, it means that more timely and effective adjustment strategies need to be adopted to smoothly promote the overall construction plan of the project, and finally obtain the trend label of the critical path task progress.

[0026] Please refer to Figure 4 , the steps for obtaining the list of sections affected by equipment for construction nodes are specifically as follows: S311: Invoke the progress trend label of the critical path task, filter the partition numbers of tasks with fluctuating deviations, extract the construction time period according to the node association table, process the section task time period in the time dimension, identify the construction time period index table, and obtain the set of construction time periods for fluctuating tasks; Extract the progress trend labels of the critical path tasks. The steps mainly rely on the tasks and milestone time nodes in the construction plan, form a task time sequence diagram or Gantt chart based on the information, and conduct subsequent fluctuation deviation analysis based on this. To complete this step, first, through the project management tool, collect and sort out the planned start time and end time of each critical path task, and mark each stage of the construction through the Gantt chart. The tasks will be marked as "critical path tasks" and sorted according to their importance in the overall project. Screen out the part with fluctuation deviation in the task sequence. Extract relevant data of the construction time period by using the node association table. The node association table will list the association information between different construction nodes and tasks in the project. The dependency relationship between tasks and the tasks that may affect the construction progress can be further identified through the associated data in the table. For example, in a certain building construction project, assume that there is a direct dependency relationship between critical path task A and task B, and the construction period of task A deviates, then the start time of task B will also change accordingly, ultimately affecting the progress of the entire project. During the process of identifying the construction time period, it is necessary to clarify the location and type of the fluctuation deviation tasks, conduct analysis using the time dimension, 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 construction time periods of the fluctuation tasks to form a set of construction time periods of the fluctuation tasks.

[0027] S312: According to the set of construction time periods of the fluctuation tasks, collect the 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 construction time periods of the fluctuation tasks, judge whether there is an abnormal association of the construction progress, and generate a list of sections affected by equipment at the construction nodes; Collecting the equipment operation efficiency data in the same period section is crucial. The operation efficiency of the equipment directly affects the completion time of the construction tasks, especially for some tasks that rely on heavy machinery or high-efficiency equipment. By identifying the equipment impact table to judge the operation status of the equipment, the equipment impact table will record the working efficiency of each equipment in different time periods and its impact degree on the construction, thus affecting the progress of the construction nodes. It is necessary to judge the daily equipment interference situation according to the interference threshold, which is a value jointly determined by the equipment operation status, task type and their mutual influence relationship. Taking the operation of construction equipment as an example, when the equipment usage duration exceeds 8 hours and the equipment failure rate exceeds 5%, it is considered that the equipment interference has a significant impact on the construction progress. The equipment interference will be matched with the construction period of the fluctuating tasks to analyze whether there is an associated situation of abnormal construction progress. Suppose a mechanical equipment works for 10 hours in a certain construction section and its failure rate is 6%, exceeding the interference threshold, then the operation status of this equipment will directly affect the progress of this construction task and will be listed in the equipment impact section list. Through the steps, the equipment impact section list of the construction nodes can be formed, providing a basis for subsequent adjustment of the construction progress; Table 1: Equipment Operation Efficiency and Interference Threshold Parameter Table ; As shown in Table 1, through the analysis results of the interference thresholds of Equipment A and Equipment C, it shows that they have a greater impact on the progress during the construction process.

[0028] Please refer to Figure 5 , the specific steps for obtaining the fluctuating group of the construction team node operation completion are as follows: S411: Based on the equipment impact section list of the construction nodes, screen the unmarked construction partition nodes, extract the operation task list, record the planned processes, start and end times of the tasks, and obtain the task set of the nodes not affected by equipment interference; Filter out those unmarked construction partition nodes. Nodes refer to those construction tasks that are not affected by equipment interference and have normal progress. To filter out unmarked nodes, first, the task numbers will be compared with the equipment impact records to ensure that the selected nodes are indeed not affected by equipment interference. A list of operation tasks related to the unmarked nodes will be extracted. The operation task list includes detailed information for each task, such as planned processes, planned start time, and planned end time, etc. The start and end times of each task will be recorded and monitored as key data to assist in subsequent analysis of the construction progress. Through the detailed data of the planned tasks, it is possible to further identify the set of node tasks that are not affected by equipment interference and conduct statistics and tracking on them. The core task of the process is to identify and extract those construction tasks that are not affected by equipment interference from the entire construction plan to provide accurate data support for subsequent progress evaluation and optimization. For example, assume that the task number for a certain construction area is T001, the planned start time is May 1st, and the end time is May 10th. After screening for equipment impact, it is found that this task is not affected by low equipment operation efficiency. Therefore, this task will be listed in the set of node tasks not affected by equipment interference.

[0029] S412: Invoke the set of node tasks not affected by equipment interference, match the daily segmented task records of the construction team with the attendance data, extract the planned task volume according to the task number, count the number of people on duty and the real-time attendance period, calculate the matching deviation value of the construction team's task execution, and generate the matching data set of the construction team's cooperative execution situation; Invoke the task data in the set of node tasks not affected by equipment interference and match it with the daily segmented task records of the construction team. This matching process depends on the attendance data of the construction team. The attendance data includes information such as the number of people on duty and the actual daily attendance period of the construction team members. Through the matching data, the planned task volume for each task number can be accurately calculated, and then the corresponding task execution progress can be obtained. For example, assume that the planned task volume for task T001 is a construction area of 1000 square meters. The number of people on duty in the construction team on May 1st is 10, and the actual attendance period for each person is 8 hours. Then the work efficiency of the construction team can be calculated through the data. By counting the actual number of people on duty and the actual attendance period every day, a matching data set of the construction team's cooperative execution situation can be generated. This data set will include the actual execution progress of each task and the attendance situation of the construction team to help determine whether the execution situation of the construction team meets the original plan. For example, if the actual number of people on duty and the attendance period are insufficient, it will affect the progress of the construction task, and the differences will be recorded and analyzed; The matching deviation value of the construction team's task execution adopts the formula: ; Among them, represents the matching deviation value of the construction team's task execution, Represents the task number as The corresponding planned task volume, Represents the task number as The number of people on duty in this work team counted, Represents the task number as The attendance period of this work team corresponding to it, Represents the task number as The attendance period during the same time period as the original task, Represents the task number as The number of times of task execution delay associated, Represents the task number as The actually recorded construction completion volume; The matching deviation value of the task execution of the construction work team refers to the absolute difference between the task volume completed by the construction work team during the actual execution process and the task volume that should be completed after comprehensive adjustment according to the planned task volume, the number of people on duty, the real-time attendance period and its fluctuations, the original attendance pattern, and the task execution delay situation under a specific task number. This deviation value reflects the degree of deviation between the actual execution efficiency of the construction work team and the planned expectation. The larger the value, the more significant the difference between the construction execution status and the planned arrangement, and it can be used to evaluate the coordination of construction organization, the rationality of resource allocation, and the response ability during the execution process; (The planned task volume with the task number ): Obtained from the planned task data recorded in the project management system. For example, the planned task volume of a certain construction task is 1000 cubic meters; (The number of people on duty with the task number ): The number of workers on duty every day is counted through the attendance system. For example, the number of people on duty on a certain day is 20; (The attendance period with the task number ): By analyzing the attendance time of each worker in the attendance data, calculate its variance. For example, the variance of the workers' attendance time on a certain day is 1.5 hours; (The original attendance period with the task number ): By calculating the variance of the attendance time of the same task in the original attendance data. For example, the variance of the original attendance time is 2.0 hours; (The number of times of task execution delay with the task number ): Obtained from the number of task delays recorded in the project management system. For example, the number of delays of a certain task is 2 times; (The task number is Actual completed quantity: Obtained through on-site construction records or progress reports. For example, the actual completed quantity of a certain task is 950 cubic meters; Substitute specific values for calculation: ; This result indicates that after considering the fluctuations in the number of people on duty, the attendance time period, and the fluctuations in the original attendance time period, there is a deviation of 6340 between the adjusted planned task quantity and the actual completed quantity.

[0030] S413: Match the data set according to the cooperation execution situation of the construction team, evaluate the matching degree between the task execution efficiency and the manpower distribution, identify the efficiency fluctuation nodes and calculate the operation deviation degree, mark the tasks with fluctuations exceeding the benchmark value as abnormal nodes, and obtain the construction team node operation completion fluctuation group; Evaluate the matching degree between the task execution efficiency and the manpower distribution. By analyzing the task execution efficiency and combining the allocation of human resources, it can be judged whether the progress of the construction task is affected by the manpower distribution. For example, assume that task T001 plans to complete a construction area of 1000 square meters, and the actual work efficiency of the construction team on a certain day is 150 square meters, and the manpower distribution is 10 people in attendance. In this case, calculate the actual work efficiency on that day and compare it with the task planned progress to evaluate the work efficiency of the construction team. For the nodes with efficiency fluctuations, calculate the operation deviation degree. The calculation method of the operation deviation degree can be obtained by comparing the actual completed task quantity with the planned task quantity. For example, if the actual completed task quantity is less than 30% of the planned task quantity, the operation deviation degree for one day is 30%. Mark the tasks with fluctuations exceeding the benchmark value as abnormal nodes. The benchmark value is set according to multiple factors such as the original data, task type, construction area, and the ability of the construction team. If the benchmark value is set to 5%, then if the operation deviation degree on a certain day exceeds 5%, the task will be marked as an abnormal node, and finally form the construction team node operation completion fluctuation group for subsequent analysis and processing.

[0031] Please refer to Figure 6 , and the specific steps for obtaining the construction progress monitoring structure indicators are as follows: S511: Call the node numbers and corresponding operation time intervals in the construction team node operation completion fluctuation group, screen the nodes exceeding the threshold, record the time interval and the change range of the task quantity, and obtain the progress fluctuation abnormal identification set; Call the node numbers in the node operation completion fluctuation group of the construction team and the corresponding operation time intervals, determine the operation intervals of each node and check their progress fluctuations. By comparing the time intervals with the change range of the task volume, screen out those nodes whose progress fluctuations exceed the predetermined threshold. The key action in the process is to calculate the change range of the task volume of each node and compare it with the set threshold. For example, if the planned construction period of a task is 10 days and the task volume of each node in the construction plan is 500 square meters, but in the actual execution process, a certain node only completes 300 square meters in 7 days, and the change range of the task volume is 40%, then the progress fluctuation of this node exceeds the set threshold. The nodes with fluctuations exceeding the threshold will be marked as abnormal progress fluctuations, record their time intervals and the change range of the task volume, and finally form a set of abnormal progress fluctuation identifiers. At this time, the threshold can be set to 20%, that is, if the change range of the task volume exceeds 20%, it is marked as an abnormal node. By making the same judgment on multiple nodes, those tasks whose construction progress deviates from the normal track can be efficiently identified, providing basic data for subsequent analysis.

[0032] S512: Based on the resource input volume and task volume of the construction model corresponding to the nodes in the abnormal progress fluctuation identifier set, identify the sequence of node resource input ratios, extract the abnormal distribution intervals and compare the critical coefficients, record the ratio deviation direction and node numbers, and form a set of construction resource matching deviation indicators; According to the node information in the abnormal progress fluctuation identifier set, combined with the resource input volume and task volume of the construction model corresponding to the nodes, identify the sequence of resource input ratios of each node. The process is carried out by calculating the proportional relationship between the node resource input volume and the task volume. In actual operation, the resource input volume of the construction model involves resources in different aspects such as manpower, equipment, and materials, while the task volume refers to the construction workload of each node in the plan. By calculating the ratio between the node resource input volume and the task volume, it can be determined whether there is an over-investment or shortage of resources during the construction process of this node. For example, assume that the resource input volume of a task node is 10,000 yuan and the task volume is 500 square meters, then the resource input ratio is 20 yuan / square meter. If the ratio is high, it means that the resource input of this node is excessive, while if the ratio is low, it means that the resource input is insufficient. The ratio will be compared with the critical coefficient to determine whether there is an abnormal distribution interval. If the ratio of a certain node is too different from the set critical coefficient, it indicates that the distribution of resource input is unreasonable, and this node will be marked as an abnormal node, record the ratio deviation direction and node numbers, and form a set of construction resource matching deviation indicators. The critical coefficient is set to 0.15, that is, if the difference between the ratio and this coefficient exceeds 15%, it is determined as abnormal.

[0033] S513: Match the node numbers in the offset index group according to the construction resources, extract the construction model resource allocation and task execution time periods, identify the differences between the resource allocation cycle and the operation cycle, and perform sorting and annotation according to the progress benchmark to generate the construction progress monitoring structure index; According to the node numbers in the offset index group of construction resources, further extract the construction model resource allocation and task execution time periods of each node. By comparing the differences between the resource allocation cycle and the operation cycle, it is possible to identify which nodes have a time mismatch in resource allocation. For example, assume 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 situation of resource allocation lag. In this way, it is possible to identify the nodes where resources are not in place in a timely manner during the operation cycle, which helps project managers make adjustments in a timely manner. The nodes will also be sorted and annotated according to the construction progress benchmark to accurately evaluate the construction progress of each node. The progress benchmark can be set through the critical path tasks in the project plan and the start time and end time of each task node. Finally, through the sorting and annotation of the nodes, the construction progress monitoring structure index is generated to help managers track and adjust the construction progress in real time; Table 2: Example Table of Resource Input Ratio and Task Quantity ; 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.

[0034] The intelligent management system for civil engineering projects is used to execute the above intelligent management method for civil engineering projects. The system includes: The task status extraction module obtains the personnel distribution data, mechanical equipment operation status data, and material inventory records at the construction site, extracts the key node numbers and task node times in the construction task breakdown structure, compares the execution status with the task node identifiers, marks the component tasks with inconsistent times, and generates a node status deviation label group; The node trend classification module, based on the node status deviation label group, locates the task nodes on the critical path, extracts the construction node identifiers, process nodes, and planned times, calculates the difference between the on-site construction time and the planned time, and classifies and annotates the difference types to generate a node progress trend identifier; The equipment interference identification module, based on the node progress trend identifier, screens the fluctuating and lagging task components, locates the corresponding zones, extracts the mechanical equipment operation efficiency interference time periods, determines whether they coincide with the operation time periods, and screens the frequently interfered sections to generate a construction progress equipment interference mapping set; The operation efficiency diagnosis module, based on the construction progress equipment interference mapping set, eliminates the tasks in the interference sections, extracts the task records and attendance data of the construction teams, matches the task assignment with the operation time period, calculates the ratio of the attendance operation volume to the task volume, identifies the component tasks with intensive tasks and low execution efficiency, and obtains the node operation execution deviation set; The resource allocation analysis module, based on the node operation execution deviation set, locates the resource input records of the tasks in the construction model, extracts the ratio of the task volume to the resource volume configuration, compares the differences between the execution period and the input period, maps the task resource usage and progress status, and forms the construction progress monitoring structure index.

[0035] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications 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 content of the present invention still belong to the protection scope of the technical solution of the present invention.

Claims

1. An intelligent management method for civil engineering projects, characterized in that, It includes the following steps: S1: Obtain the personnel distribution data, mechanical equipment operation status data, and material inventory records at the construction site, extract the key node information in the construction task breakdown structure, mark the task execution status and compare it with the node planned status, identify the task identifiers with lagging progress, and generate a task node deviation list; S2: Based on the task node deviation list, screen the task nodes on the critical path, extract the real-time construction start and end times in combination with the construction process schedule, perform difference calculation and classification processing with the planned time points, and obtain the critical path task progress trend label; S3: Invoke the critical path task progress trend label, extract the fluctuating task partition numbers, identify the regional mechanical equipment usage efficiency data, compare the equipment operation time periods with the fluctuating task time periods, record the number of overlapping time periods, and generate a list of sections of construction nodes affected by equipment; S4: Based on the list of sections of construction nodes affected by equipment, screen the task nodes of the undisturbed partition nodes, extract the segmented task records and attendance data of the construction teams, map the daily task assignment volume and the working hours of the attending teams, and determine whether there is an imbalance in resource allocation for the tasks to obtain the fluctuating group of task completion at the construction team nodes.

2. The intelligent management method for civil engineering projects according to claim 1, wherein The task node deviation list includes task deviation numbers, execution status labels, time deviation amounts, and task node classifications. The critical path task progress trend label includes progress deviation levels, trend change types, planned comparison results, and task association numbers. The list of sections of construction nodes affected by equipment includes equipment types, affected time periods, number of overlapping time periods, and task numbers affected. The fluctuating group of task completion at the construction team nodes includes uneven operation distribution numbers, working hour usage records, task completion deviations, and team attendance matching degrees.

3. The intelligent management method for civil engineering projects according to claim 1, characterized in that The specific steps for obtaining the task node deviation list are as follows: S111: Obtain the personnel distribution data, mechanical equipment operation status data, and material inventory records at the construction site, extract the planned time and numbers of the key nodes in the construction task breakdown 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 execution record time period of the partition nodes; S112: Based on the execution record time period of the partition nodes, extract the overlapping time periods between the task nodes and the planned time intervals, calculate the ratio of the overlapping time to the total planned duration of the nodes, screen the task nodes with overlapping ratios lower than the reference value, and obtain the progress coverage deviation rate of the partition nodes according to the marked quantity of the node execution status; S113: According to the progress coverage deviation rate of the partition nodes, determine the deviation status of the task node numbers, identify the task node numbers with deviation rates exceeding the node synchronization threshold, and integrate the node numbers, execution coverage information, and deviation rate values to generate a task node deviation list.

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

5. The intelligent management method for civil engineering projects according to claim 4, characterized in that The specific steps for obtaining the list of sections affected by equipment at the construction nodes are as follows: S311: Invoke the progress trend label of the critical path task, filter the partition numbers of tasks with fluctuating deviations, extract the construction time period according to the node association table, process the section task time period in the time dimension, identify the construction time period index table, and obtain the set of construction time periods for fluctuating tasks. S312: According to the set of construction time periods for fluctuating tasks, collect the equipment operation efficiency data of the same-period sections, identify the equipment impact table, judge the daily equipment interference based on the interference threshold, match it with the construction time period of the fluctuating tasks, judge whether there is an abnormal association with the construction progress, and generate the list of sections affected by equipment at the construction nodes.

6. The intelligent management method for civil engineering projects according to claim 5, characterized in that, The specific steps for obtaining the fluctuation group of completed operations at the construction team nodes are as follows: S411: Based on the list of sections affected by equipment at the construction nodes, filter the unmarked construction partition nodes, extract the operation task list, record the planned processes, start and end times of the tasks, and obtain the set of tasks at nodes not affected by equipment interference. S412: Invoke the set of tasks at nodes not affected by equipment interference, match the daily segmented task records of the construction team with the attendance data, extract the planned task volume according to the task number, and count the number of people on duty and the real-time attendance time period to generate the matching data set of the cooperation execution situation of the construction team. S413: According to the matching data set of the cooperation execution situation of the construction team, evaluate the matching degree between the task execution efficiency and the manpower distribution, identify the nodes with efficiency fluctuations and calculate the operation deviation degree, mark the tasks with fluctuations exceeding the benchmark value as abnormal nodes, calculate the matching deviation value of the task execution of the construction team, and obtain the fluctuation group of completed operations at the construction team nodes.

7. The intelligent management method for civil engineering projects according to claim 1, characterized in that, The method further includes step S5: S5: Invoke the fluctuation group of completed operations at the construction team nodes, extract the abnormal fluctuation task group, calculate the ratio of the resource input amount to the task amount in the construction model, record the difference distribution of the resource delivery period and the task execution period, and generate the structural index for construction progress monitoring. The structural index for construction progress monitoring includes the resource allocation ratio, task intensity level, execution period difference, and construction efficiency index.

8. The intelligent management method for civil engineering projects according to claim 7, characterized in that, The specific steps for obtaining the structural index for construction progress monitoring are as follows: S511: Invoke the node numbers and corresponding operation time intervals in the fluctuation group of completed operations at the construction team nodes, filter the nodes exceeding the threshold, record the time interval and the change range of the task amount, and obtain the set of progress fluctuation anomaly identifiers. S512: Based on the resource input amount and task amount of the construction model corresponding to the nodes in the set of progress fluctuation anomaly identifiers, identify the sequence of node resource input ratios, extract the abnormal distribution interval and compare the critical coefficient, record the ratio deviation direction and the node number, and form the construction resource matching deviation index group. S513: According to the node numbers in the construction resource matching offset index group, extract the construction model resource investment and task execution time periods, identify the differences between the resource investment cycle and the operation cycle, and perform sorting and marking according to the progress benchmark to generate the construction progress monitoring structure index.

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 described in any one of claims 1-8. The system includes: The task status extraction module obtains the personnel distribution data, mechanical equipment operation status data, and material inventory records at the construction site, extracts the key node numbers and task node times in the construction task breakdown structure, compares the execution status with the task node identifiers, marks the component tasks with inconsistent times, and generates a node status deviation label group. The node trend classification module, based on the node status deviation label group, locates the task nodes on the critical path, extracts the construction node identifiers, process nodes, and planned times, calculates the difference between the on-site construction time and the planned time, classifies and marks the difference types, and generates a node progress trend identifier. The equipment interference identification module, based on the node progress trend identifier, filters the fluctuating and lagging task components, locates the corresponding zones, extracts the mechanical equipment operation efficiency interference time periods, determines the coincidence with the operation time periods, filters the frequently interfered zones, and generates a construction progress equipment interference mapping set. The operation efficiency diagnosis module, based on the construction progress equipment interference mapping set, eliminates the tasks in the interfered zones, extracts the construction team task records and attendance data, matches the task assignment with the operation time periods, calculates the ratio of the attendance operation volume to the task, identifies the component tasks with intensive tasks and low execution efficiency, and obtains a node operation execution deviation set. The resource allocation analysis module, based on the node operation execution deviation set, locates the resource investment records of the tasks in the construction model, extracts the ratio of the task volume to the resource volume configuration, compares the differences between the execution cycle and the investment cycle, maps the task resource usage and progress status, and forms a construction progress monitoring structure index.

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