Project progress prediction management method, system and device for multiple work areas

Abstract

The invention discloses a multi-work-area-oriented project progress prediction management method, system and device, and relates to the technical field of progress prediction management. According to the method, in a multi-work-area project execution period, contract construction periods and subitem projects are decomposed through WBS, and field return data are collected and processed in a standardized manner, so that a three-dimensional progress model is constructed, and a management party is assisted to control the whole situation; inputting field return data into the three-dimensional progress model, identifying cross operation conflicts under a critical path and a multi-constraint network, evaluating a delay risk in combination with a process amount completion rate and a material and equipment deviation, and predicting a work area progress level and a project delay degree; and verifying the validity of the progress change prediction process according to the delay risk assessment result in combination with a rolling window mechanism to judge whether to trigger risk early warning, thereby realizing quantitative analysis of multi-work-area cross-process coupling influence and progress risk early warning in advance, further effectively improving the reliability of multi-work-area collaborative management and the efficiency of complex project progress management and control, and improving the progress management and control efficiency. Projects are guaranteed to be propelled according to plans.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of progress prediction management, in particular to a multi-work-area-oriented engineering progress prediction management method, system and device. BACKGROUND

[0002] With the development of large-scale and cross-regional projects in fields such as infrastructure, energy and transportation, a single project is often divided into multiple work areas for simultaneous advancement, such as large hydropower stations and cross-provincial long-distance pipeline projects. There is a close connection between the process connection and resource allocation among multiple work areas, and the construction process is easily affected by factors such as weather, supply chain, personnel and equipment scheduling. The traditional single-work-area progress management mode has been difficult to adapt to the needs of multi-agent collaboration, and the demand for real-time monitoring of the overall progress of multiple work areas and early prediction of potential risks by project management parties is increasingly urgent, promoting the development of multi-work-area progress prediction management technology.

[0003] Existing devices usually include data collection terminals such as personnel attendance equipment, equipment operating state sensors, and construction quantity statistical instruments, which can upload progress data of a single work area to a local management system for recording and basic inquiry. The existing technology needs to rely on work breakdown structure (WBS) to decompose the overall project into specific processes of each work area according to contract duration and sub-projects, and on the other hand, to fill in the progress data of each work area regularly by artificial means, such as weekly and monthly reports, and then use Excel or simple management software to aggregate the data. By combining historical data of similar projects, the progress trend of each work area is estimated by the experience of management personnel, and a statistical model is introduced to calculate the progress deviation of each work area based on construction rate, duration node and other data of each work area, so as to realize the monitoring of each work area and assist the management personnel in judging whether there is a risk of duration lag in the work area.

[0004] For example, the engineering project progress intelligent monitoring method and system disclosed in the Chinese patent with publication number CN120106532B includes: real-time acquisition of progress parameters including task completion status and resource consumption through multi-source data collection terminals; inputting the parameters into a preset abnormality detection model to identify and mark abnormal data segments, the model dynamically adjusts the threshold value based on historical data characteristics; then inputting the marked parameters into a collaborative prediction model to generate progress trend prediction results, the model is fitted in multiple dimensions according to task dependency relationship and resource allocation weight; finally, using a priority scheduling algorithm to classify and integrate the prediction results and current progress parameters to form a dynamic monitoring data set for storage.

[0005] For example, Chinese invention patent application CN118967007A discloses an engineering progress management system and method based on a milestone prediction model, which includes: decomposing the entire project into projects, determining the objectives, scope of work, and planned project milestones for each project; recording the daily progress of the project and accumulating it to generate the actual project completion volume; comparing the planned project completion volume with the actual project completion volume data to generate a project completion progress chart; and comparing the project investment plan with the actual project completion volume to generate a project investment amount versus project completion progress comparison chart.

[0006] However, in complex scenarios involving simultaneous construction across multiple work zones and overlapping work processes, collaborative construction of the same project requires each work zone to not only complete its own independent tasks but also handle numerous cross-work zone procedures. This means that delays in one work zone's procedures can trigger a chain reaction, causing multiple related work zones to become idle. Existing methods, in the schedule prediction stage, focus only on the completion rate of work processes and material and equipment deviations in a single work zone, failing to fully consider the impact of such cross-work zone process coupling on the overall schedule. They neither quantify the transmission range of process delays nor assess the idle time of related work zones, resulting in significant discrepancies between the predicted and actual schedules. This leads to a lag in multi-work zone project schedule prediction, often only discovering problems when collaboration in multiple work zones has already stalled, thus resulting in low reliability of multi-work zone collaborative management and difficulty in supporting the efficient advancement of complex projects. Summary of the Invention

[0007] To address the low reliability of multi-work zone collaborative management in existing technologies, this invention provides a method, system, and apparatus for project schedule prediction and management across multiple work zones. The technical solution is as follows: On the one hand, a method for project progress prediction and management for multiple work zones is provided. This method includes: Step 1, during the multi-work zone project execution phase, acquiring field feedback data to reflect the real-time construction status of each work zone, and standardizing the data to construct a three-dimensional progress model. This model is used to visually present the progress status corresponding to the baseline planned progress, actual construction progress, and construction progress prediction process throughout the entire lifecycle of the multiple work zones. Step 2, inputting the currently acquired field feedback data into the three-dimensional progress model to identify cross-operation conflicts, outputting the cross-operation conflict identification results, and simultaneously conducting a delay risk assessment to predict the progress level of each work zone and the overall project delay degree. The cross-operation conflict identification results include process time difference anomalies and cross-work zone operation conflict points. Step 3, based on the delay risk assessment results, validating the progress change prediction process to determine whether to conduct early warnings of risky progress nodes, thereby achieving precise control of progress risks.

[0008] On the other hand, a project progress prediction and management system for multiple work zones is provided. This system includes: a data processing and model building module, used to acquire field feedback data reflecting the real-time construction status of each work zone during the multi-work zone project execution phase, and to perform standardized processing to build a three-dimensional progress model; a conflict identification and delay risk assessment module, used to input the currently acquired field feedback data into the three-dimensional progress model to identify cross-operation conflicts, output the cross-operation conflict identification results, and simultaneously conduct delay risk assessment to predict the progress level of each work zone and the overall project delay degree; and a prediction validity verification and early warning judgment module, used to verify the validity of the progress change prediction process based on the delay risk assessment results, to determine whether to issue an early warning for risky progress nodes, so as to achieve precise control of progress risks.

[0009] On the other hand, a project progress prediction and management device for multiple work zones is provided. This device includes: a data acquisition terminal, a data processing terminal, a project scheduling terminal, and a progress management terminal. The data acquisition terminal is used to collect construction status data of each work zone in real time and upload it to the data processing terminal. The construction status data of each work zone includes on-site feedback data, the proportion of initial abnormal data, core sub-indicators, baseline calculation progress, current construction progress, process path information, overlapping area of ​​work areas, actual completed process quantity of each work zone, actual arrival time of construction equipment, absolute value of slope, prediction deviation probability, average fluctuation range of process quantity completion rate of each work zone, and deviation trend of equipment arrival time. The data processing terminal is used to call the project construction database and process the construction status data of each work zone uploaded by the data acquisition terminal to generate structured processing results that can be directly called by the project scheduling terminal. The project scheduling terminal is used to receive the structured processing results generated by the data processing terminal and obtain the adjustment range of construction plan priority. The progress management terminal is used to manage the construction progress prediction process in the multi-work zone project execution phase. The construction progress prediction process includes data processing and model building process, conflict identification and delay risk assessment process, prediction effectiveness verification and early warning judgment process.

[0010] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: 1. This invention, during the multi-work zone project execution phase, decomposes the contract duration and sub-projects based on a Work Breakdown Structure (WBS), collects and standardizes real-time on-site construction data, and constructs a three-dimensional progress model. This model can visually present the baseline plan, actual progress, and predicted progress by work zone, sub-project, and time node, solving the problem of scattered progress information in traditional management and helping managers intuitively control the overall dynamics. The on-site feedback data is input into the constructed three-dimensional progress model. On the one hand, it identifies critical paths and cross-operation conflicts under multi-constraint networks; on the other hand, it assesses delay risks by combining process completion rates and material and equipment deviations, predicting the progress level of the work zone and the degree of project delay. This step effectively solves the defects of cross-work zone process coupling, identifies potential collaborative problems in advance, and reduces the risk of cascading delays. Based on the delay risk assessment results, and using a rolling window mechanism, the effectiveness of the progress change prediction is verified to determine whether a risk warning has been triggered. This step achieves precise control of progress risks, avoids delayed problem discovery, and ultimately forms a closed-loop management system of identification and intervention, ensuring that multi-work zone projects proceed as expected and effectively improving the reliability of collaborative management and the efficiency of progress control in complex scenarios.

[0011] 2. The construction of the 3D progress model is carried out in two cycles: initial modeling and regular updates, ensuring that the model has both initial accuracy and dynamic timeliness. A 3D coordinate system is built with time as the horizontal axis, progress as the vertical axis, and work area / sub-project as the third dimension, solving the problem of the single dimension of traditional models and clearly distinguishing the progress differences of different work areas and sub-projects. In the initial modeling cycle, baseline duration, resource allocation, and other data are extracted from the engineering construction database. Core sub-indicators such as the on-time start rate of work processes are normalized, and the initial progress health is generated by fusion using the geometric mean method, ensuring accurate quantification of the initial construction status. The baseline planned progress and the initial health are mapped to the coordinate system according to time nodes to generate a construction progress benchmark curve and visualize it, allowing management to intuitively grasp the spatiotemporal distribution of the progress of multiple work areas throughout the entire cycle. In a regular update cycle, the baseline progress and current construction progress are first extracted from the standardized dataset. After normalization, the first-order difference is calculated to obtain the progress deviation value, accurately capturing the gap between the actual and planned progress. Then, the cumulative progress of the prediction cycle is calculated by definite integral, and the instantaneous progress change rate is obtained by differentiation, realizing multi-dimensional quantification of progress prediction. At the same time, the product of the two is coupled with the progress deviation value to obtain the predicted trend quantification value representing the correlation between deviation and trend. Finally, the baseline and current progress are mapped to a coordinate system to generate a progress prediction curve and visualize it, avoiding the problem of lag in traditional model updates and ensuring that the three-dimensional progress model reflects the dynamics of construction progress in real time, providing timely support for the identification and decision-making of delay risks.

[0012] 3. By first extracting process path information from the 3D progress model, the logic and time windows of key nodes are clarified. At the same time, the construction parameter thresholds of resource, space, and time constraint networks are obtained to provide clear standards for conflict judgment and avoid omissions due to ambiguous rules. Then, the actual progress is compared with the time window to mark process time differences and capture time-dimensional coordination problems in advance. Next, the equipment usage data is verified through resource constraints to mark resource conflicts and solve the problem of equipment competition in multiple work areas. Finally, the work area is converted into 3D coordinates, the overlapping area is calculated and spatial conflicts are marked. Combined with the work period, cross-work area conflict points are determined and classified conflict results are generated to comprehensively cover the three types of conflicts: time, resources, and space, and reduce the risk of cross-work area chain delays. At the end of the current assessment cycle, core data is obtained by calculating the completion rate of work processes and the deviation of equipment arrival, ensuring that the assessment basis is objective and quantifiable. Preset assessment levels are invoked, and the delay risk level is determined based on the dual-dimensional status of work processes and equipment, avoiding assessment bias caused by a single indicator. If there are conflict points in the work area, the risk level is increased to strengthen the correlation between conflict and risk and improve the accuracy of the assessment. Finally, the work area status, deviation days, and lag data are summarized to predict the degree of project delay, providing management with a holistic risk perspective. The entire process achieves a coherent analysis from conflict identification to risk assessment, solving the problem of traditional methods neglecting cross-work area coupling and improving the timeliness and accuracy of risk prediction.

[0013] 4. Implement a scenario-based approach: If the delay risk assessment indicates no delay risk, maintain the original plan priority to avoid excessive adjustments that could lead to resource waste. If a delay risk exists, use a preset time period as the prediction window, mark the risk results for each work area, and obtain the fluctuation range of process completion rate and the trend of equipment entry deviation. Map these values ​​to a two-dimensional coordinate system to generate a progress trend curve, which visually reflects the stability of the progress. Then, compare the absolute value of the curve slope with the predicted deviation probability: if the criteria are met, the prediction is deemed valid, and feedback is sent to the scheduling terminal to adjust the plan priority according to the original rules, ensuring efficient and compliant adjustments. If the criteria are not met, manual calibration is required to re-obtain the deviation probability, ensuring that the priority adjustment matches the level of risk. After adjusting the construction plan priority, determine whether to mark risk nodes by the ratio of process completion time to the total planned time: exceeding the first percentage triggers an early warning, retrieves historical early warnings and conflict information, and prompts for feedback on response plans within a preset period, enabling early detection and handling of risks. After the plan is implemented, calculate the total overdue time; exceeding the second percentage triggers an overall early warning to avoid missing risks; otherwise, an early warning is completed, and the system enters the next monitoring window, forming a continuous control closed loop. The entire process avoids misjudgment of risks and solves the problem of traditional verification lag through multi-layered verification and early warning, ensuring the timeliness and reliability of progress control. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A flowchart of a project progress prediction and management method for multiple work zones provided in an embodiment of the present invention; Figure 2 A flowchart for assessing the risk of delay provided in an embodiment of the present invention; Figure 3 This is one of the flowcharts for validating the schedule change prediction process provided in this embodiment of the invention; Figure 4 The second flowchart for verifying the effectiveness of the schedule change prediction process provided in this embodiment of the invention; Figure 5 This is a schematic diagram of the structure of a project progress prediction and management system for multiple work zones provided in an embodiment of the present invention. Detailed Implementation

[0016] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0017] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0018] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0019] In multi-work zone collaborative construction scenarios, the foundation, main structure, and electromechanical installation work zones each have independent operations. The foundation work zone is responsible for the excavation, pit support, and foundation bedding construction of the entire project; the main structure work zone is responsible for concrete pouring, steel structure erection, and wall construction; and the electromechanical installation work zone is responsible for pipeline pre-laying, equipment positioning, and pipeline connection. Furthermore, there are closely interconnected processes between these work zones. For example, if the foundation work zone's strength does not meet expectations, the main structure in the main structure work zone cannot be hoisted, which in turn affects the finishing work progress in the electromechanical installation work zone. In such cases, if the coupling effects between cross-work zone processes are not fully considered and progress analysis and prediction are only performed for a single work zone, it is easy to have inaccurate schedule predictions, making it difficult to identify cascading delay risks in advance. This leads to passive multi-work zone collaborative management and affects the overall project progress efficiency.

[0020] Therefore, embodiments of the present invention provide a method for project progress prediction and management for multiple work zones, such as... Figure 1 The flowchart shown is for a multi-work zone project schedule forecasting and management method. The processing flow of this method may include the following steps: Step 1: During the multi-work zone project execution phase, the contract duration and sub-projects are decomposed layer by layer based on the Work Breakdown Structure (WBS) to obtain on-site feedback data that reflects the real-time construction status of each work zone. This data is then standardized to construct a three-dimensional progress model, which is used to visually present the baseline planned progress, actual construction progress, and construction progress prediction process corresponding to the work zone, sub-project, and time node throughout the entire multi-work zone cycle. This helps management to intuitively control the overall progress dynamics. On-site feedback data typically includes the number of procedures completed and quality acceptance results for each work zone each day, the actual arrival / use / departure time of construction equipment, the real-time occupancy of the work area, the quantity of materials brought in and consumed, the actual construction time of each sub-project, and records and handling results of on-site emergencies (such as personnel shortages).

[0021] Step two involves inputting the currently acquired field feedback data into the 3D progress model to identify cross-operation conflicts based on the critical path and multi-constraint networks. The results of the cross-operation conflict identification are then output. Simultaneously, a delay risk assessment is conducted in conjunction with the construction progress data to predict the progress level of each work area and the overall project delay. The cross-operation conflict identification results include process time difference anomalies and cross-work area operation conflict points. The multi-constraint network includes resource constraint networks, spatial constraint networks, and time constraint networks. The construction progress data includes the process completion rate and construction equipment arrival rate for each work area. The process completion rate for each work area indicates the completion rate of each work area at the end of the current assessment period. The ratio of actual completed work processes to planned work processes. For example, if the foundation work area actually completed 8 work processes and planned to complete 10 work processes within the current evaluation period, the corresponding work process completion rate for the work area is 80%. The construction equipment arrival rate represents the ratio of the actual arrival time of construction equipment to the planned arrival time at the end of the current evaluation period. For example, if a work area plans to bring in 5 tower cranes this week, with each crane planned to arrive in 2 days (i.e., a single crane needs to arrive within 2 days), the total planned arrival time is 5 cranes × 2 days = 10 days. In reality, only 1 crane arrives on time within 2 days, and the remaining 4 cranes do not arrive this week. The actual arrival time is 1 crane × 2 days = 2 days. At this time, the construction equipment arrival rate for the work area = 2 days ÷ 10 days × 100% = 20%.

[0022] Step 3: Based on the results of the delay risk assessment and in conjunction with the rolling window forecasting mechanism, verify the effectiveness of the schedule change forecasting process to determine whether to issue early warnings for risky schedule nodes. This will enable precise control of schedule risks, ensure that the overall progress of the multi-work area project proceeds as planned, and complete the management closed loop from risk identification to intervention. The rolling window forecasting mechanism means that a fixed duration (such as 1 week or 2 weeks) is used as a forecasting window. Within each window period, the schedule forecasting model is updated based on the latest acquired field feedback data and historical progress data. At the same time, the window slides to the next period, and the data update and forecasting are repeated to ensure that the schedule forecast is always based on the latest dynamic data, thereby improving the accuracy and timeliness of the forecast.

[0023] In a specific embodiment, a large residential project with a total construction area of ​​approximately 200,000 square meters includes three core work areas: foundation, main structure, and electromechanical installation. The work in each work area frequently overlaps. Specifically, the foundation work area needs to complete the excavation of the foundation pits and the pouring of the foundation for 12 buildings, the main structure work area simultaneously promotes the hoisting of steel structures and the pouring of concrete, and the electromechanical installation work area needs to insert pipelines for pre-embedding during the gaps in the main construction.

[0024] By constructing a 3D progress model, management can monitor the real-time progress of each work area's procedures, such as the foundation maintenance progress in the foundation work area and the daily hoisting volume in the main structure work area. The 3D progress model can identify in advance the spatial overlap and conflict between the steel structure hoisting area of ​​Building 1 in the main structure work area and the pipeline pre-laying work area in the electromechanical installation work area, thus avoiding the risk of mechanical collisions in a timely manner. At the same time, combined with the rolling window mechanism, based on the rainfall data of the past 10 days and the earthwork excavation efficiency in the foundation work area, it accurately predicts that the project may be delayed by 2 days due to rainfall. Based on this, the project team adjusts the equipment entry plan and the work process connection time in advance to ensure the orderly connection of operations in each work area. The efficiency of multi-work area project management has been greatly improved, which has effectively guaranteed the project to progress according to plan.

[0025] Furthermore, standardization is performed, specifically: The engineering construction database is accessed to validate the fields of the acquired field-transmitted data, filtering out initial abnormal data with incompatible formats or missing fields; the proportion of initial abnormal data in the field-transmitted data is calculated and recorded as the initial abnormal data proportion. If the initial abnormal data proportion is not greater than a preset acceptable proportion, the corresponding field-transmitted data is directly used as the standardized dataset for constructing the 3D progress model; otherwise, an anomaly correction judgment is performed. The process involves supplementing and correcting data with inconsistent formats in the initial abnormal data, and performing linear correction on data with missing fields. Supplementation and correction involve using data format templates from the engineering construction database and combining them with historical format characteristics of similar data from a preset historical period to obtain a unified format specification, thereby reconstructing the format of field-transmitted data with inconsistent formats. Linear correction involves fitting a corresponding linear trend based on the field change patterns of the same process in historical field-transmitted data from the same work area within a preset historical period, and dynamically correcting the linear trend fitting result using the current construction progress deviation coefficient to obtain missing field supplementary values, thus supplementing field-transmitted data with missing fields. The current construction progress deviation coefficient quantifies the deviation between the current actual construction progress and the planned completion progress, representing the ratio of the current construction progress completion rate to the calculated construction progress completion rate.

[0026] Within a preset number of iterations (usually set to 3), if the proportion of newly acquired initial abnormal data is not greater than the preset acceptable proportion, then the supplementary correction and linear correction are completed; otherwise, a data quality warning is issued to prompt the preset personnel to conduct on-site data collection process investigation and abnormal cause tracing. All initial abnormal data that have completed supplementary correction and linear correction are normalized to be converted into standardized values ​​in the 0-1 range, which serve as the standardized dataset for constructing the three-dimensional progress model.

[0027] Specifically, the engineering construction database is built by collecting historical data from similar projects (such as process duration, baseline planned progress during schedule forecasting, and records of schedule deviations), current construction specifications (such as foundation treatment and steel structure installation standards), and the project's design documents (drawings and schedule plans). It also connects to a real-time on-site data interface to ensure data accessibility and traceability. The preset acceptable percentage can be determined by referring to the data processing experience of multi-work area projects in the same industry, statistically analyzing the normal data fluctuation range in historical projects, and determining the initial abnormal data percentage threshold, typically preset to 5%-8%.

[0028] First, extract field data (such as daily rebar binding volume) for the same work area and process within a preset historical time period. Then, use the least squares method to fit a linear trend equation for the field's change over time, such as y=ax+b, where y is the field value, x is time, a represents the historical rate of progress change, and b represents the initial progress baseline value. Next, calculate the current construction progress deviation coefficient. For example, if the actual progress completion rate is 80% and the calculated completion rate is 90%, the coefficient is 0.89. Multiply this coefficient by a and b, using these as the updated parameters a and b in the linear trend equation to obtain the dynamically corrected trend equation. Finally, calculate the values ​​for the missing fields at the corresponding time points based on this equation and add them to the data to ensure field completeness.

[0029] In this embodiment, the standardized processing flow described above can significantly improve the quality and usability of data transmitted from the site. On the one hand, field validation and anomaly correction can accurately filter and repair data with incorrect formats or missing fields. Combined with the engineering construction database, this ensures that the data format is consistent and the content is complete, avoiding deviations in the construction of the 3D progress model due to data issues. On the other hand, the preset iterative correction and quality early warning mechanism can promptly detect loopholes in the data acquisition process, reducing the interference of invalid data on progress analysis. At the same time, normalization processing unifies the data to the 0-1 range, eliminating the dimensional differences between different dimensions of data. This allows indicators such as work area progress and process completion rate to be directly compared and analyzed, laying the foundation for the 3D progress model to accurately present the multi-dimensional progress status. Ultimately, this helps management to more accurately control the overall progress and improve the reliability and efficiency of multi-work area collaborative management.

[0030] Example 1: The specific steps for constructing a three-dimensional progress model include: using time nodes as the horizontal axis and construction progress as the vertical axis to form a two-dimensional plane reflecting single-dimensional progress changes, and using each work area and sub-project as the third dimension of the two-dimensional plane to build a three-dimensional coordinate system framework to distinguish the progress differences of different work areas and sub-projects at the same time nodes; extracting baseline process durations, resource allocation baselines, and key milestone node data of the overall project schedule from the engineering construction database; normalizing the core sub-indicators of the current initial construction status of each work area; key milestone node data representing key time nodes and corresponding deliverables in the entire project cycle, such as the completion time of foundation acceptance, the capping time of the main structure, the pressure test qualification time of electromechanical pipelines, and the pre-acceptance time of project completion, etc. These nodes directly determine the conditions for starting subsequent processes and the overall project schedule; core sub-indicators are used to quantify the completeness and compliance of the initial construction preparation of each work area, including quantitative indicators reflecting the basic progress status such as the on-time start rate of processes, the matching degree of resource availability, and the quality compliance rate of the first process completion.

[0031] The on-time start rate of work processes is calculated by comparing the planned start times with the actual start times of work processes in each work area within the engineering construction database, counting the number of processes that started on time, and then dividing this number by the total number of processes in that work area. For example, if the foundation work area plans to start 5 processes and actually starts 4 on time, the on-time start rate is 80%.

[0032] Resource availability matching rate: Based on the project resource allocation baseline, the actual number and specifications of equipment, materials, and personnel arriving at each work area are statistically analyzed to match the planned quantity. The matching quantity divided by the planned total quantity is the resource availability matching rate. For example, if the main work area plans to bring in 10 cranes, and 8 cranes actually match the planned specifications, the matching rate is 80%.

[0033] First-stage process completion quality compliance rate: After each work area completes its first-stage process, quality inspection personnel conduct tests according to specifications, count the number of compliant processes, and divide the number of compliant processes by the total number of first-stage processes to obtain the first-stage process completion quality compliance rate. For example, in the electromechanical work area, the first-stage pipeline pre-laying consists of 3 sections, and 2 sections passed the inspection, resulting in a compliance rate of 67%.

[0034] After normalizing the core sub-indicators, the data is first integrated using the geometric mean method. First, weights are assigned to sub-indicators such as process start-up punctuality rate, resource availability matching degree, and first process completion quality compliance rate (e.g., set to 0.4, 0.3, and 0.3 respectively). Then, the normalized result of each sub-indicator is used as the base, and the corresponding weight is used as the exponent for multiplication. For example, if the normalized three indicators for a certain work area are 0.8, 0.7, and 0.9 respectively, the calculation result is... Finally, the square root of the product result (with the root number consistent with the number of sub-indicators) is taken to obtain the initial progress health data of the work area.

[0035] Next, the baseline planned progress (e.g., daily planned work volume) extracted from the engineering construction database and the calculated initial progress health data are mapped one-to-one according to time nodes (e.g., daily, weekly) and input into a three-dimensional coordinate system: the horizontal axis is the time node, the vertical axis is the construction progress, using percentage as the unit, and the third dimension is the work area / sub-item project, using classification codes as the unit, such as foundation work area (G01), main structure work area (Z02), and electromechanical work area (J03), to determine the specific coordinates of each work area under each time node. Subsequently, combined with the work process connection logic of each work area (e.g., the main structure work area can only start hoisting after the foundation curing of the foundation work area is completed), the progress data between adjacent time nodes is supplemented by linear interpolation to generate a continuous construction progress baseline curve. Finally, the preset dynamic chart tool is called to generate a three-dimensional surface map to intuitively display the spatial distribution of the progress of each work area, and a multi-dimensional line chart to compare the differences in the progress of different sub-items, fully presenting the spatiotemporal changes in the construction progress, completing the first construction of the three-dimensional progress model.

[0036] In this embodiment, a multi-dimensional coordinate system framework is used to clearly distinguish the progress differences of different work areas and sub-projects at the same time node, avoiding progress blind spots caused by single-dimensional analysis; the geometric mean method integrates core sub-indicators to quantify the initial progress health of each work area, providing objective data support for progress assessment and reducing subjective judgment errors; the coordinate mapping between baseline plan and health data, the generation of progress curves under the logic of process connection, and the visualization of dynamic charts allow managers to intuitively grasp the spatiotemporal distribution of construction progress and identify progress deviations in advance; the linear interpolation method completes the data to ensure the continuity of the progress curve, helps to accurately predict the risks of subsequent process connection, and effectively improves the efficiency of progress control and the stability of project progress.

[0037] Building upon Example 1, conventional 3D schedule models are largely based on static data—that is, built during the initial modeling cycle of a project. They can only present the progress status of each work area at a specific moment (such as the current completion rate of a process and the elapsed time), failing to capture the dynamic trends of progress over time. Secondly, they lack analysis of the correlation between schedule deviations and future trends. Conventional 3D schedule models can only calculate the deviation between actual and planned progress, but cannot determine whether this deviation will continue to widen or trigger a chain reaction. For example, whether a delay in a work area will lead to delays in the connection of processes in subsequent work areas makes it difficult for management to predict the scope of the risk impact.

[0038] The aforementioned limitations directly lead to insufficient accuracy in predicting the progress of the next cycle. During construction, variables such as material supply, weather changes, and equipment failures all affect the schedule. Static models cannot integrate these dynamic factors for prediction, resulting in limited reference value for the output progress data for the next cycle, making it difficult for management to formulate precise early intervention plans. Therefore, constructing a three-dimensional progress model also includes: The baseline calculated progress (planned construction progress value at each time point) and the current construction progress (actual construction progress value at each time point) for each time point are obtained from the standardized dataset and normalized respectively. The first difference between the two is calculated to obtain the progress deviation value for each node. Positive numbers represent progress ahead of schedule, and negative numbers represent progress lag. The current progress curve is integrally calculated with time as the integration variable over the prediction period (such as the next week) to calculate the cumulative progress reflecting the total amount expected to be completed in the next evaluation period. At the same time, the instantaneous progress change rate is obtained by differentiating the current progress curve to capture the dynamic trend of the predicted progress. A positive value indicates that the progress is accelerating, and a negative value indicates that the progress is slowing down.

[0039] The accumulated progress and instantaneous progress change rate are multiplied to reflect their synergistic impact on the predicted construction progress. The result of this multiplication is coupled with the progress deviation values ​​at each node to eliminate the bias of single data points. This ensures that the predicted trend quantification value reflects both the trend direction and historical deviations, thus obtaining a predicted trend quantification value that characterizes the correlation between progress deviations at each time node and the construction progress trend. The baseline planned progress and the current construction progress are mapped to corresponding coordinates in a three-dimensional coordinate system according to time nodes. Combined with the obtained predicted trend quantification value, a construction progress prediction curve is generated. A preset dynamic chart visualizes the spatiotemporal distribution of the construction progress, completing the construction of the three-dimensional progress model.

[0040] In this embodiment, normalization and first-order difference calculations can accurately quantify the progress deviations at each node, making it clear whether the progress is ahead or behind, and avoiding misjudgments due to differences in data units. The definite integral calculation of the cumulative progress and the derivative calculation of the instantaneous rate of change not only clarify the total amount expected to be completed in the next cycle, but also capture the dynamic trend of progress, overcoming the limitation of traditional models that can only statically present progress. The product and coupling processes further establish the correlation between progress deviations, cumulative amounts, and rates of change. The generated predicted trend quantification value makes the impact of deviations on future progress quantifiable, improving prediction accuracy. The progress curve generated by combining the predicted values ​​and dynamic visualization can intuitively show the spatiotemporal distribution and future trends of progress, helping managers to identify potential delay risks in advance.

[0041] Furthermore, cross-operation conflict identification based on critical path and multi-constraint network is conducted. Specifically, the process involves extracting the work sequence information of each work area from the 3D progress model to clarify the sequential logical relationship and time window of the corresponding critical nodes on the critical path of each work area. For example, after the concrete pouring in the foundation work area is completed, subsequent overlapping operations can only be carried out in the main structure work area. Simultaneously, the construction parameter thresholds of the multi-constraint network are obtained. These thresholds include the maximum daily usage time of the same equipment and the safe interval time for cross-operations of the same equipment. These are extracted from the engineering construction database using equipment usage records of similar projects and industry safety standard data, combined with recommendations from equipment manufacturers. The daily operating limit is determined after referring to the project's workload and safety standards. The predicted time corresponding to the current field data is compared with the time window. If the actual start / end time of a process exceeds the time window and the next adjacent process does not start as originally planned, the corresponding process is marked as a process time difference abnormality. Otherwise, the equipment usage plan and actual occupancy data of each work area are matched and verified based on the resource constraint network. If the same equipment is applied for by multiple work areas during overlapping periods and exceeds the maximum daily usage time or does not meet the safety interval time, the corresponding process is marked as a resource conflict. Otherwise, there is no resource conflict.

[0042] Based on the spatial constraint network, the spatial coordinate data of each work area is verified, and the work area of ​​each process is transformed into a three-dimensional coordinate boundary, such as the polygon coordinates formed by the operating radius of construction machinery and the outline of the material stacking area. The overlapping area of ​​the work area is obtained by obtaining the overlapping area of ​​the work area of ​​different work areas. The overlapping area of ​​the work area represents the actual area occupied by the intersection of the work areas of different work areas in the same time and space range. That is, the projected area of ​​the overlapping area calculated by the spatial superposition of the three-dimensional coordinate boundaries, which reflects the degree of conflict between multiple work areas in terms of construction space resources. If the overlapping area of ​​the work areas of different work areas is greater than the preset allowable overlapping area, the corresponding process is marked as a spatial conflict. Otherwise, there is no spatial conflict. The preset allowable overlapping area is directly retrieved from the engineering construction database. For the marked resource conflict and spatial conflict, the cross-work area operation conflict point is determined by combining the planned operation time period of each work area project. Based on the obtained process time difference anomalies and cross-work area operation conflict points, the cross-operation conflict identification result is generated and the corresponding conflict type is marked. The conflict types include resource conflict, spatial conflict and time conflict.

[0043] It's important to understand that time conflicts refer to a type of conflict caused by abnormal time differences in work processes. Specifically, it manifests as follows: the actual start or completion time of a work process exceeds the critical node time window specified in the 3D schedule model, and the adjacent next work process cannot start as originally planned due to the preceding process's timeout, resulting in a break in the overall work process chain and affecting the progress of subsequent work areas. For example, if the concrete pouring process in the foundation work area is planned to be completed within 3 days (time window 1-3), but actually takes 5 days (exceeding the time window), the rebar tying process in the main work area (the adjacent next work process) cannot start as originally planned on the 4th day. In this case, it is determined that there is a time conflict between the concrete pouring process in the foundation work area and the rebar tying process in the main work area.

[0044] In this embodiment, by extracting the sequence logic and time windows of key nodes, the critical nodes connecting the work processes in each work area are accurately identified, avoiding overall project delays caused by delays in key nodes, such as clearly defining the overlap nodes between the foundation and main structure work areas. Relying on a multi-constraint network, comprehensive control over resources, space, and time dimensions is achieved. Simultaneously, for resource conflicts, it can prevent the safety risks of excessive equipment use or overlapping operations; for spatial conflicts, it can proactively avoid construction interference caused by overlapping work areas; and for time conflicts, it can promptly detect work process overtime issues. The accurate identification and marking of these three types of conflicts allows management to quickly locate the root cause of the conflict, improving construction safety and efficiency, and providing strong support for the smooth progress of multi-work area projects.

[0045] like Figure 2 The flowchart for the delay risk assessment shown is designed as follows: First, assess the progress status of each work area. Based on whether the workload and equipment status meet standards, require improvement, or are lagging, the risk is classified into three levels: Level 3, None, Level 1, and Level 2 delay risks. Next, summarize the risk assessment levels. If there are abnormal work time differences or cross-work area operation conflicts, the progress status is upgraded by one level and summarized again; otherwise, it is summarized directly. Finally, the delay risk assessment result is output based on the summarized results. This logic comprehensively considers various factors affecting progress, can accurately assess delay risks, and provides an effective basis for project scheduling.

[0046] Further understanding is needed regarding the process of assessing delay risks based on construction progress data. Specifically, this involves retrieving preset work quantity assessment levels and preset equipment assessment levels from the project construction database. The preset work quantity assessment levels include: work quantity met, work quantity needs improvement, and work quantity is lagging. The preset equipment assessment levels include: equipment met, equipment needs improvement, and equipment is lagging. If both the work quantity and equipment statuses corresponding to the current progress status of each work area are met, then the corresponding work area's progress status is determined to have no delay risk. If one of the work quantity and equipment statuses corresponding to the current progress status of each work area is "needs improvement" while the other is met, then the corresponding work area's progress status is determined to have a Level 1 delay risk. If one of the work quantity and equipment statuses corresponding to the current progress status of each work area is "needs improvement" while the other is lagging, then the corresponding work area's progress status is determined to have a Level 2 delay. Risk; If the current progress status of each work area corresponds to both the process quantity status and equipment status being lagging, then the corresponding work area progress status is judged as Level 3 delay risk; the delay risk levels corresponding to Level 1 delay risk, Level 2 delay risk, and Level 3 delay risk increase sequentially; if there are process time difference abnormalities or cross-work area operation conflicts in the corresponding work area at this time, then the corresponding progress status is upgraded by one level; if it is Level 3 delay risk, then manual investigation and early warning are carried out; if there are no process time difference abnormalities or cross-work area operation conflicts in the corresponding work area at this time, then the delay risk assessment result is directly output; summarize the progress status and material and equipment deviation days of each work area, and at the same time count the number of lagging work areas and the average number of lagging days, output the delay risk assessment result to predict the overall project delay degree. For example, if all 3 key work areas are Level 2 delay risk with an average delay of 4 days, the overall project is expected to be delayed by 3-5 days.

[0047] It is important to note that if either the process quantity status or the equipment status in each work area is up to standard while the other is lagging behind, an anomaly warning will be issued during the delay risk assessment process. This is because there is a clear contradiction between the standard-compliant and lagging statuses. Meeting the process quantity standard indicates that the current work area is completing the expected amount of work, while a lagging equipment status means that equipment support cannot support subsequent construction. The two cannot be matched to the corresponding risk level according to the preset risk assessment rules. Furthermore, this contradiction may stem from data collection errors (such as incorrect equipment status statistics) or rule loopholes. Therefore, an anomaly warning needs to be triggered to check the authenticity of the data and the applicability of the judgment logic, avoiding the output of erroneous assessment results.

[0048] In this embodiment, by calling the preset delay risk levels in the engineering construction database, the completion rate of work processes and the equipment arrival rate in the construction progress data are accurately matched with the preset delay risk levels, thereby standardizing risk assessment and avoiding subjective judgment bias. The graded assessment rules (from no delay risk to level three delay risk) clearly quantify the risk level of each work area. Combined with abnormal work process time differences and cross-work area conflicts, the level is dynamically adjusted to further improve the accuracy of the assessment and can promptly capture potential delay risks. The data such as work area status and deviation days are summarized to predict the overall delay level of the project, allowing the management to focus on high-risk work areas (such as work areas corresponding to level three delay risk) and prompt the preset personnel to formulate intervention measures in advance (such as adjusting the equipment arrival plan and optimizing work process connection), effectively reducing the probability of project delay and ensuring that the project progress is controllable.

[0049] like Figure 3 , 4 The flowchart illustrating the effectiveness verification of the schedule change prediction process is designed as follows: First, based on the delay risk assessment results, cases with delay risk and cases without delay risk are processed separately. The former is marked, while the latter maintains the original construction plan priority. Next, a unified two-dimensional coordinate system is established to generate a progress trend curve. The effectiveness of the prediction process is determined by judging whether the absolute value of the slope and the predicted deviation probability are within the allowable range. If effective, the adjustment range is obtained according to the rules; if ineffective, manual calibration is required, data is re-acquired, and the adjustment range is mapped again. After the construction plan priority is adjusted, whether to trigger an early warning for risky progress nodes is determined by judging whether the completion time of designated processes in each work area exceeds the first percentage of the total planned completion time. If not, manual verification is performed and the next window is monitored; if it exceeds, an early warning is pushed and a response plan is implemented. Then, the total overdue time is calculated, and whether it exceeds the second percentage of the total planned completion time determines whether to issue an overall project progress warning. This logic can effectively and timely identify and respond to construction progress risks.

[0050] Further understanding is needed regarding the validity verification of the schedule change prediction process. Specifically, this includes: if the delay risk assessment result is that there is no delay risk, the original construction plan priority is maintained, and no additional adjustments are triggered; if the delay risk assessment result is that there is a delay risk, a preset monitoring period is used as a prediction window, and the delay risk assessment results of each work area are marked within the prediction window. At the same time, the daily or weekly work completion rate of each work area within a preset quantity window (such as the past 4 weeks) is obtained, the difference between its maximum and minimum values ​​is calculated, and the average value is taken after obtaining the single-cycle fluctuation range, which is the average fluctuation range of the work completion rate. At the same time, the number of days of deviation between the actual arrival time of equipment and the planned time of each work area is counted, and the increase or decrease of the number of deviation days is sorted out in chronological order to form the equipment arrival time deviation trend. The two are mapped to a two-dimensional coordinate system according to the time node, with the horizontal axis set as the time node (such as every Monday), and the vertical axis set as the fluctuation range (percentage) and the number of deviation days (days). The average fluctuation range and deviation trend data are mapped to the coordinate system one by one according to the time node and marked as discrete points.

[0051] Discrete points are plotted with fluctuation amplitude as the vertical axis and time as the horizontal axis. Using a moving average method, with three time nodes as a moving window, the mean of the discrete points within each window is calculated as a smoothed data point. Connecting all smoothed data points generates a progress trend curve. This curve visually reflects the stability of process progress (changes in fluctuation amplitude) and equipment support capability (changes in deviation trend). Within a preset window, the vertical axis values ​​corresponding to the start and end time nodes of the curve are selected. The ratio of the difference between the end and start values ​​to the corresponding time interval is used as the absolute value of the slope to quantify the rate of trend change. This value is then compared with the current progress change prediction results. The predicted deviation probability is compared: if the absolute value of the slope is not greater than the preset allowable absolute value of the slope, and the predicted deviation probability is not greater than the preset allowable deviation probability, then the current progress change prediction process is deemed valid, and feedback is sent to the project scheduling terminal to obtain the adjustment range of the construction plan priority according to the initial mapping rule. The initial mapping rule means that the corresponding adjustment range of the construction plan priority is obtained by mapping the obtained predicted deviation probability in the engineering construction database. The preset allowable absolute value of the slope and the preset allowable deviation probability need to be further determined by the preset personnel after extracting historical progress data of similar projects from the engineering construction database and combining them with the project schedule requirements.

[0052] Otherwise, the current progress change prediction process is determined to require manual calibration. After manual calibration, the data acquisition terminal is fed back to re-acquire the prediction deviation probability. Based on the project scheduling terminal, the corresponding construction plan priority adjustment range is mapped in the engineering construction database to ensure that the construction plan priority adjustment range matches the degree of delay risk. After the construction plan priority is adjusted, if the completion time of the specified process in each work area exceeds the first percentage of the total planned completion time of the corresponding project period, the corresponding process and work area are marked as risk progress nodes, and an early warning for risk progress nodes is triggered to ensure that the corresponding risk progress nodes are controllable. Otherwise, the early warning for risk progress nodes is not triggered for the time being, and the preset personnel are prompted to conduct a second verification to determine whether the early warning for risk progress nodes has been triggered, while the monitoring process for the next prediction window is carried out.

[0053] The triggering of early warnings for risk progress milestones involves: retrieving historical construction progress warning information and cross-operation conflict information for the corresponding process and work area to push early warnings for risk progress milestones, prompting designated personnel to verify the reasons for overdue progress and provide preliminary response solutions within a predetermined period (generally within 24 hours); after the preliminary response solutions are implemented, the total overdue time corresponding to the completion time of designated processes in all work areas exceeding the total planned completion time is calculated. If the total overdue time is greater than the second percentage of the total planned completion time, an overall project progress warning is required to ensure that no risk progress milestones are missed; otherwise, an early warning for risk progress milestones is completed; the first percentage value is greater than the second percentage value.

[0054] In this embodiment, delay risks are handled in different scenarios to avoid blindly adjusting the construction plan and ensure targeted progress control. A progress trend curve is generated by combining the fluctuation range of the number of work processes and the trend of equipment deviation, and the rate of change is quantified by the absolute value of the slope, which can intuitively reflect the construction stability and equipment support capabilities of the work area. By verifying the absolute value of the slope and the predicted deviation probability with dual indicators, prediction results that require manual calibration are accurately screened to ensure that the adjustment range of the construction plan priority matches the delay risk and avoids insufficient or excessive adjustment. In the risk node early warning stage, the push of early warnings based on historical early warnings and conflict information, and the determination of the degree of overdueness by percentage (first / second percentage), can not only lock individual risk nodes in a timely manner, but also prevent the overall project progress from getting out of control. This achieves full-process control from prediction verification to risk early warning, which greatly improves the controllability and risk resistance of the progress of multi-work area projects.

[0055] This invention provides a project progress prediction and management system for multiple work zones, such as... Figure 5The diagram shows the structure of a multi-work zone project progress prediction management system. This system can include: a data processing and model building module, used to acquire real-time field feedback data reflecting the construction status of each work zone during the multi-work zone project execution phase, and perform standardized processing to build a three-dimensional progress model; a conflict identification and delay risk assessment module, used to input the currently acquired field feedback data into the three-dimensional progress model to identify cross-operation conflicts, output the cross-operation conflict identification results, and simultaneously perform delay risk assessment to predict the progress level of each work zone and the overall project delay degree; and a prediction validity verification and early warning determination module, used to verify the validity of the progress change prediction process based on the delay risk assessment results, to determine whether to issue early warnings for risky progress nodes, thereby achieving precise control of progress risks.

[0056] In this embodiment, the data processing and model building module standardizes on-site data and builds a 3D progress model, providing a precise data foundation and visualization platform for subsequent analysis; the conflict identification and delay risk assessment module quickly identifies cross-operation conflicts, predicts the progress level of work areas and the degree of project delay, and avoids potential collaboration risks in advance; the prediction validity verification and early warning judgment module verifies the effectiveness of the prediction and accurately triggers risk warnings to avoid risk omissions. Through the coordinated operation of these three modules, the system achieves closed-loop control of the entire process of multi-work area progress management, effectively ensuring that the project progresses as planned.

[0057] This invention provides a project progress prediction and management device for multiple work zones. The device may include: a data acquisition terminal, a data processing terminal, a project scheduling terminal, and a progress management terminal. The data acquisition terminal collects construction status data from each work zone in real time and uploads it to the data processing terminal. The construction status data for each work zone includes on-site feedback data, the proportion of initial abnormal data, core sub-indicators, baseline calculation progress, current construction progress, process path information, overlapping area of ​​work areas, actual completed processes in each work zone, actual arrival time of construction equipment, absolute value of slope, prediction deviation probability, average fluctuation range of process completion rate in each work zone, and deviation of equipment arrival time. The data processing terminal is used to access the engineering construction database and process the construction status data of each work area uploaded by the data acquisition terminal to generate structured processing results that can be directly accessed by the project scheduling terminal, including normalization, supplementary correction, and linear correction. The project scheduling terminal is used to receive the structured processing results generated by the data processing terminal and obtain the adjustment range of the construction plan priority. The progress management terminal is used to manage the construction progress prediction process in the multi-work area engineering execution phase. The construction progress prediction process includes data processing and model building, conflict identification and delay risk assessment, prediction effectiveness verification and early warning determination.

[0058] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0059] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0060] In various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0061] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0062] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for project schedule forecasting and management across multiple work zones, characterized in that: The method includes: Step 1: During the multi-work zone project execution phase, acquire on-site feedback data that reflects the construction status of each work zone in real time, and perform standardized processing to build a three-dimensional progress model for visually presenting the progress status corresponding to the baseline planned progress, actual construction progress, and construction progress prediction process throughout the entire cycle of the multi-work zone. Step 2: Input the currently acquired field feedback data into the 3D progress model to identify cross-operation conflicts, output the cross-operation conflict identification results, and conduct a delay risk assessment to predict the progress level of each work area and the overall project delay. The cross-operation conflict identification results include process time difference anomalies and cross-work area operation conflict points. Step 3: Based on the results of the delay risk assessment, verify the effectiveness of the schedule change prediction process to determine whether to issue early warnings for risky schedule milestones, so as to achieve precise control of schedule risks.

2. The project progress prediction and management method for multiple work zones as described in claim 1, characterized in that, The standardization process specifically involves: The project construction database is accessed to perform field validation on the acquired field-transmitted data in order to filter out initial abnormal data with incorrect data format and missing data fields. The percentage of initial abnormal data in the field-transmitted data is calculated and recorded as the initial abnormal data percentage. If the initial abnormal data percentage is not greater than the preset acceptable percentage, the corresponding field-transmitted data is directly used as the standardized dataset for constructing the 3D progress model; otherwise, an anomaly correction judgment is performed. Supplement and correct data with incorrect data format in the initial abnormal data, and perform linear correction on data with missing data fields in the initial abnormal data; The supplementary correction means: based on the data format template in the engineering construction database, and combined with the historical format characteristics of the same type of data in the preset historical period, a unified format specification is obtained to reconstruct the format of the field-transmitted data with inconsistent data format; The linear correction means: based on the field change pattern of the same process in the historical field feedback data of the same work area in the preset historical period, the corresponding linear trend is fitted, and the linear trend fitting result is dynamically corrected in combination with the current construction progress deviation coefficient to obtain missing field supplement values, so as to supplement the field feedback data with missing data fields. The current construction progress deviation coefficient is used to quantify the degree of deviation between the current actual construction progress and the planned completion progress. If the proportion of newly acquired initial abnormal data is not greater than the preset acceptable proportion within the preset number of iterations, the supplementary correction and linear correction are completed; otherwise, a data quality warning is issued to prompt the preset personnel to conduct on-site data collection process investigation and abnormal cause tracing. All initial outlier data that have undergone supplementary and linear corrections are normalized to convert them into standardized values, which serve as a standardized dataset for constructing the 3D progress model.

3. The project progress prediction and management method for multiple work zones as described in claim 2, characterized in that, The specific steps for constructing the three-dimensional progress model include: Using time nodes as the horizontal axis and construction progress as the vertical axis, a two-dimensional plane is constructed to reflect single-dimensional progress changes. Each work area and sub-project is used as the third dimension of the two-dimensional plane to build a three-dimensional coordinate system framework, so as to distinguish the progress differences of different work areas and sub-projects at the same time node. If the current evaluation period is the first modeling period of the project, the baseline process duration, resource allocation baseline and key milestone node data of the overall project schedule are extracted from the engineering construction database. The core sub-indicators of the initial construction status of each work area are normalized. The core sub-indicators are used to quantify the completeness and compliance of the initial construction preparation of each work area. Initial progress health data is generated by fusing normalized results using the geometric mean method. The baseline project progress and initial progress health data are mapped to the corresponding coordinates in the three-dimensional coordinate system according to the time nodes. The construction progress benchmark curve is generated by combining the process connection logic of each work area. The spatiotemporal distribution of the construction progress is visualized through preset dynamic charts, thus completing the first construction of the three-dimensional progress model.

4. The project progress prediction and management method for multiple work zones as described in claim 3, characterized in that, The construction of the three-dimensional progress model also includes: If the current assessment cycle is the project's regular update cycle, the baseline calculation progress and the current construction progress of each time node are obtained from the standardized dataset, and normalized respectively. The first difference between the two is calculated to obtain the progress deviation value of each node. The cumulative progress within the forecast period is calculated by definite integrals to reflect the total expected completion amount for the next assessment period. At the same time, the instantaneous progress change rate is obtained by differentiation to capture the dynamic trend of the forecast progress. The accumulated progress amount and the instantaneous progress change rate are multiplied to reflect the synergistic effect of the two on the predicted construction progress. At the same time, the result of the multiplication is coupled with the progress deviation value of each node to obtain the predicted trend quantification value used to characterize the correlation between the progress deviation of each time node and the construction progress trend. The baseline planned progress and the current construction progress are mapped to the corresponding coordinates in the three-dimensional coordinate system according to the time nodes. The construction progress prediction curve is generated by combining the obtained prediction trend quantification value. The spatiotemporal distribution of the construction progress is visualized through preset dynamic charts, thus completing the construction of the three-dimensional progress model.

5. The project progress prediction and management method for multiple work zones as described in claim 1, characterized in that, The specific process for identifying cross-operation conflicts is as follows: The process path information of each work area is extracted from the three-dimensional progress model to clarify the sequential logical relationship and time window of the corresponding key nodes on the critical path of each work area. At the same time, the construction parameter thresholds of the multi-constraint network are obtained. The multi-constraint network includes resource constraint network, spatial constraint network and time constraint network. The construction parameter thresholds include the maximum daily usage time of the same equipment and the safe interval time for cross-operation of the same equipment. Compare the predicted time corresponding to the current field data with the time window. If the actual start / end time of a process exceeds the time window and the next adjacent process does not start as originally planned, then mark the corresponding process as a process time difference abnormality. Otherwise, the resource constraint network is used to match and verify the equipment usage plan and actual occupancy data of each work area. If the same equipment is applied for by multiple work areas during overlapping periods and exceeds the maximum daily usage time or does not meet the safety interval time, the corresponding process is marked as a resource conflict. Otherwise, there is no resource conflict. Based on the spatial constraint network, the spatial coordinate data of each work area is verified, and the work area of ​​each process is transformed into a three-dimensional coordinate boundary to obtain the overlapping area of ​​the work areas of different work area coordinate boundaries. The overlapping area of ​​the work areas represents the actual land area of ​​the intersection of the work areas of different work areas in the same time and space range. If the overlapping area of ​​different work areas is greater than the preset allowable overlapping area, the corresponding process will be marked as a spatial conflict; otherwise, there is no spatial conflict. For marked resource and spatial conflicts, determine cross-work area conflict points by combining the planned work periods of each work area project; Based on the acquired process time difference anomalies and cross-work area operation conflict points, cross-operation conflict identification results are generated, and the corresponding conflict types are marked. The conflict types include resource conflicts, spatial conflicts, and time conflicts.

6. The project progress prediction and management method for multiple work zones as described in claim 5, characterized in that, The specific process for conducting the delay risk assessment is as follows: At the end of the current evaluation period, based on the actual number of completed procedures and the planned number of procedures in each work area, the procedure completion rate of each work area is obtained. At the same time, based on the actual arrival time and the planned arrival time of construction equipment, the construction equipment arrival rate is obtained. The preset process quantity assessment level and preset equipment assessment level are retrieved from the engineering construction database. The preset process quantity assessment level includes process quantity meeting the standard, process quantity needing improvement, and process quantity lagging behind. The preset equipment assessment level includes equipment meeting the standard, equipment needing improvement, and equipment lagging behind. If the current progress status of each work area corresponds to both the process quantity status and the equipment status, then the progress status of the corresponding work area is judged to be without risk of delay. If there are any issues that need to be addressed or met in the current progress status of each work area, the corresponding work area progress status will be classified as a Level 1 delay risk. If there are any delays or deficiencies in the process quantity status and equipment status corresponding to the current progress status of each work area, the corresponding work area progress status will be judged as a level 2 delay risk. If the current progress status of each work area corresponds to both the process quantity status and the equipment status that are lagging behind, then the progress status of the corresponding work area will be judged as a level three delay risk. If there are instances of compliance and lag in the process quantity status and equipment status corresponding to the current progress status of each work area, an abnormal warning will be issued during the delay risk assessment process. The delay risk levels corresponding to the Level 1 delay risk, the Level 2 delay risk, and the Level 3 delay risk increase sequentially. If there are abnormalities in the process time or conflicts in cross-work areas in the corresponding work area at this time, the corresponding progress status will be upgraded by one level. If it is a level three delay risk, manual investigation and early warning will be carried out. If there are no abnormalities in process timing or cross-work area conflicts in the corresponding work area at this time, the delay risk assessment result will be output directly. Summarize the progress status and material and equipment deviation days of each work area, and at the same time, count the number of lagging work areas and the average number of lagging days, and output the delay risk assessment results.

7. The project progress prediction and management method for multiple work zones as described in claim 1, characterized in that, The validity verification of the process for predicting schedule changes specifically includes: If the risk assessment result is that there is no risk of delay, the original construction plan priority will be maintained and no additional adjustments will be triggered. If the delay risk assessment result indicates that there is a delay risk, then the preset monitoring period is used as a prediction window, and the delay risk assessment result of each work area is marked within the prediction window. At the same time, the average fluctuation range of the completion rate of each work area and the deviation trend of equipment arrival time are obtained within the preset quantity window, and the two are mapped to a two-dimensional coordinate system according to the time node. Discrete points are plotted with fluctuation amplitude as the vertical axis and time as the horizontal axis. After smoothing by the moving average method, a progress trend curve is generated to reflect the changes in the stability of process advancement and equipment support capability. Within the preset window, obtain the absolute value of the slope of the progress trend curve and compare it with the predicted deviation probability in the current progress change prediction result: If the absolute value of the slope is not greater than the preset allowable absolute value of the slope, and the predicted deviation probability is not greater than the preset allowable deviation probability, then the current progress change prediction process is deemed valid, and feedback is sent to the project scheduling terminal to obtain the construction plan priority adjustment range according to the initial mapping rule. The initial mapping rule means that the corresponding construction plan priority adjustment range is obtained by mapping the obtained predicted deviation probability in the engineering construction database. Otherwise, the current progress change prediction process is determined to require manual calibration, and after manual calibration, the prediction deviation probability is re-acquired at the data acquisition terminal. Based on the project scheduling terminal, the corresponding construction plan priority adjustment range is mapped in the engineering construction database to ensure that the construction plan priority adjustment range matches the degree of delay risk.

8. The project progress prediction and management method for multiple work zones as described in claim 7, characterized in that, The validity verification of the process for predicting schedule changes also includes: After the construction plan priority is adjusted, if the completion time of the designated process in each work area exceeds the first percentage of the total planned completion time of the corresponding project period, the corresponding process and work area will be marked as a risk progress node, and an early warning of the risk progress node will be triggered to ensure that the corresponding risk progress node is controllable. Conversely, if the risk progress node early warning is not triggered, the preset personnel will be prompted to conduct a second verification to determine whether the risk progress node early warning has been triggered, and the monitoring of the next prediction window will be carried out at the same time. The triggering of early warning for risk progress nodes is specifically as follows: Retrieve historical construction progress warning information and cross-operation conflict information for the corresponding process and work area to push warnings for risk progress nodes, prompting the designated personnel to check the reasons for the delay within the designated time limit and provide preliminary response solutions. After the initial response plan is implemented, the total overdue time corresponding to the completion time of the designated process in all work areas exceeding the total planned completion time is counted. If the total overdue time accounts for more than the second percent of the total planned completion time, an overall project progress warning is issued to ensure that no risky progress nodes are missed. Conversely, early warnings are issued for key risk milestones. The value of the first percentage is greater than the value of the second percentage.

9. A project schedule prediction and management system for multiple work areas, employing the project schedule prediction and management method for multiple work areas as described in any one of claims 1-8, characterized in that, include: The data processing and model building module is used to acquire field feedback data that reflects the construction status of each work area in real time during the multi-work area project execution phase, and to perform standardized processing to build a three-dimensional progress model. The conflict identification and delay risk assessment module is used to input the currently acquired field feedback data into the three-dimensional progress model to identify cross-operation conflicts, output the cross-operation conflict identification results, and at the same time conduct delay risk assessment to predict the progress level of each work area and the overall project delay level. The prediction validity verification and early warning judgment module is used to verify the validity of the schedule change prediction process based on the delay risk assessment results, in order to determine whether to issue an early warning for risky schedule nodes, so as to achieve precise control of schedule risks.

10. A project schedule prediction and management device for multiple work zones, employing the project schedule prediction and management method for multiple work zones as described in any one of claims 1-8, characterized in that, include: Data acquisition terminal, data processing terminal, project scheduling terminal, and progress management terminal; The data acquisition terminal is used to collect construction status data of each work area in real time and upload it to the data processing terminal. The construction status data of each work area includes on-site feedback data, initial abnormal data ratio, core sub-indicators, baseline calculation progress, current construction progress, process path information, overlapping area of ​​work area, actual completed process quantity of each work area, actual arrival time of construction equipment, absolute value of slope, predicted deviation probability, average fluctuation range of process quantity completion rate of each work area, and deviation trend of equipment arrival time. The data processing terminal is used to call the engineering construction database and process the construction status data of each work area uploaded by the data acquisition terminal to generate a structured processing result that can be directly called by the project scheduling terminal. The project scheduling terminal is used to receive the structured processing results generated by the data processing terminal and obtain the adjustment range of the construction plan priority; The progress management terminal is used to manage the construction progress prediction process in the multi-work zone project execution phase. The construction progress prediction process includes data processing and model building, conflict identification and delay risk assessment, prediction effectiveness verification and early warning determination.

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