Project progress and resource early warning method and system based on dynamic regulation and control

Abstract

The invention discloses a project progress and resource early warning method and system based on dynamic regulation and control, and the method comprises the steps: 1) collecting project progress and resource data in real time, and constructing a multi-source data fusion model; 2) based on the constructed multi-source data fusion model, judging whether the construction progress and the resource consumption are abnormal or not, and if so, executing the step 3); otherwise, continuing conventional construction progress tracking and resource management; 3) triggering a grading early warning mechanism, generating and executing an optimization regulation and control strategy, and adjusting the construction progress and resource configuration; and 4) evaluating the regulation and control effect, and returning to the step 2) after collecting feedback data. According to the method, the actual construction progress deviation and the resource supply and demand imbalance condition are comprehensively considered, the current overall operation condition of the construction project is analyzed, the construction management strategies are flexibly switched, the management parameters are flexibly adjusted, the construction progress can be rapidly and effectively guaranteed, resource utilization is optimized, and the construction efficiency is improved. And the intelligent level and the response efficiency of construction management are obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of civil engineering management, and specifically to a method and system for project progress and resource early warning based on dynamic regulation. Background Art

[0002] Traditional project management relies on static plans and manual experience, and it is difficult to cope with sudden resource shortages, schedule delays, and external risks. In the prior art, although project management software can provide basic data analysis, it lacks real-time early warning and dynamic regulation capabilities, resulting in the following problems: 1. Coarse schedule management: Traditional schedule plans are based on static assumptions, and do not dynamically consider resource constraints (such as the finiteness of manpower, materials, and equipment) and uncertain factors (such as weather changes and design changes), resulting in a lack of effective regulation means after key processes are delayed; 2. Lagging resource early warning: The contradiction between resource supply and demand is usually discovered only after the problem occurs, lacking real-time monitoring and early warning of resource consumption trends, which is likely to cause work stoppages due to material shortages or resource waste; 3. Data island problem: Construction progress data (such as the completion rate of processes) and resource data (such as material inventory and equipment utilization rate) are scattered in different systems, without forming collaborative analysis, making it difficult to support scientific decision-making.

[0003] With the popularization of smart construction site technology, the massive data generated during the construction process (such as BIM model data and Internet of Things sensor data) provides a data basis for dynamic regulation. However, how to convert these data into executable early warning strategies is still a technical problem to be solved urgently. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a method and system for project progress and resource early warning based on dynamic regulation. Through critical chain analysis driven by real-time data and dynamic adjustment of buffers, the early identification of construction risks and the optimal allocation of resources are realized. It can automatically identify potential risks and take timely regulation measures, effectively reducing the risk of project duration delay, improving resource utilization efficiency, and ensuring the smooth progress of construction projects as planned.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions.

[0006] A method for project progress and resource early warning based on dynamic regulation includes the following steps: Step S1, collect construction progress data and resource consumption data in real time, and construct a multi-source data fusion model; Step S2: Based on the constructed multi-source data fusion model, dynamically analyze the construction progress deviation and resource consumption anomaly, and determine whether there are anomalies in the construction progress and resource consumption according to the preset threshold and warning level. If so, execute Step S3; otherwise, continue to monitor the construction progress data and resource consumption data for regular construction progress tracking and resource management; Step S3: Trigger the hierarchical warning mechanism, generate and execute the optimization control strategy, and adjust the construction progress and resource allocation; Step S4: Evaluate the control effect, collect feedback data, and then return to Step S2 to form a closed-loop management of monitoring - warning - control - feedback until the completion of the project.

[0007] Specifically, the construction progress data described in Step S1 includes basic progress parameters and risk-related parameters. The basic progress parameters include the actual progress, progress completion ratio, estimated completion time, and progress deviation of each process; the risk-related parameters include the coordinates of potential risk areas, the estimated impact time of risks, the historical progress deviation rate, and the consumption rate of the critical path buffer area, and the consumption rate of the critical path buffer area is obtained by calculating the remaining ratios of the project buffer area and the incoming buffer area; the construction progress data is obtained through the real-time monitoring of the intelligent construction site platform, or generated through the progress prediction model, Bayesian network, or machine learning algorithm in combination with real-time construction logs, historical progress data, and the project WBS structure; The resource consumption data includes the actual usage, remaining quantity, consumption rate, emergency reserve resources, and supply data of various resources; the resource consumption data is obtained through the resource management system and on-site feedback.

[0008] Further, the process of constructing the multi-source data fusion model in Step S1 is as follows: Step S11: Calculate the progress deviation rate ASDR of the process; ; ; In the above formula, is the progress deviation rate of process ; is the progress delay of process . If , it means the process progress is lagging behind. If , it means the process progress is ahead; is the actual duration of process ; is the planned duration of process ; Step S12: Calculate the resource consumption imbalance rate RIR; ; In the above formula, is the resource The resource consumption imbalance rate. If , it indicates that the resources are overspent. If , it indicates that there is a surplus of resources; is the actual consumption of the resource ; is the baseline consumption of the resource ; Step S13: Calculate the dynamic weight allocation; The dynamic weight allocation includes the process weight and the resource weight , where: ; ; Step S14: Construct a multi-source data fusion model. The mathematical expression of the multi-source data fusion model is as follows: ; In the above formula, is the comprehensive warning index; is the dynamic adjustment coefficient of the construction progress, is the dynamic adjustment coefficient of the resource consumption, , the initial value , .

[0009] Specifically, in step S2, based on the constructed multi-source data fusion model, the real-time collected construction progress data and resource consumption data are input into the multi-source data fusion model. According to the output value, dynamically analyze the construction progress deviation and resource consumption anomaly, and judge whether there are anomalies in the construction progress and resource consumption according to the preset thresholds and warning levels. The corresponding relationship between the warning levels and the preset thresholds is as follows: Level A warning: IWI < 33%; Level B warning: 33% ≤ IWI ≤ 66%; Level C warning: IWI > 66%.

[0010] Specifically, in step S3, trigger the hierarchical warning mechanism and generate and execute the optimization control strategy. The corresponding relationship between the warning levels and the generated and executed optimization control strategies is as follows: Level A warning: The construction progress is normal and the resources are sufficient. Maintain the original construction plan and keep regular monitoring; Level B warning: The construction progress is slightly behind or the resources are used relatively quickly. Start the resource adjustment strategy, reduce the resource consumption priority of non-critical processes, and compress the critical chain buffer; Level C warning: The construction progress is seriously behind or the resources are seriously short. Suspend the construction and start cross-regional support and emergency control measures.

[0011] Specifically, the preset thresholds of 33% and 66% are obtained by referring to the commonly used 3:7 method in civil engineering. Considering the influence of the buffer consumption rate (BCR) of the critical chain, the more the critical chain buffer is consumed, the stricter the warning level should be. Therefore, an engineering management coupling mechanism is introduced to correct the preset thresholds, values, and the multi-source data fusion model respectively. The correction methods are as follows: Every time 5% of the total project progress is completed, the preset threshold is adjusted according to the average project duration deviation rate; ; When the buffer consumption rate (BCR) of the critical chain > 30%, trigger the IWI correction: ; After each execution of the optimization and regulation strategy, record the resource saving rate and the project duration recovery rate , and update the weights: ; ; In the above formula, is the updated resource weight; is the resource weight before update; is the updated process weight; is the process weight before update.

[0012] Furthermore, the dynamic adjustment coefficients and are equipped with a dynamic adjustment mechanism. The triggering conditions of the dynamic adjustment mechanism are as follows: When the cumulative resource shortage exceeds 10%, that is, the critical resource consumption rate ≥ 110% of the planned value for 3 consecutive days, the dynamic adjustment coefficient is adjusted to , , to balance the construction progress and the priority of resource consumption; When the buffer consumption rate (BCR) of the critical chain > 30%, the dynamic adjustment coefficient is adjusted to , , to strengthen the response to construction progress risks and reduce the priority of resource consumption for non-critical processes; When the buffer consumption rate (BCR) of the critical chain > 66% and the project completion ratio < 33%, the dynamic adjustment coefficient is adjusted to , , and start the emergency construction progress strengthening.

[0013] Based on the above technical solutions, the present invention further provides an engineering progress and resource early warning system based on dynamic regulation, including a data collection and fusion module, a critical chain and resource analysis module, a buffer setting module, a real-time monitoring and risk assessment module, an early warning and regulation module, and an effect evaluation and optimization module; The data collection and fusion module is used to obtain construction progress and resource data from different data sources and perform fusion processing; The critical chain and resource analysis module is used to decompose construction projects, identify critical chain processes, analyze process logical relationships and resource constraint conditions, determine the resource requirements and time parameters of critical chain processes, and generate executable regulation strategies; The buffer setting module is used to set project buffers, merge buffers and resource buffers according to the resource requirements and time uncertainties of critical chain processes, and dynamically adjust the sizes of each buffer as the construction progress advances; The real-time monitoring and risk assessment module is used to monitor the construction progress and resource status in real time, calculate the construction progress deviation and resource consumption deviation of each process, convert them into a comprehensive early warning index through a built-in multi-source data fusion model, and determine the early warning level; The early warning and regulation module is used to start the corresponding early warning mechanism according to the early warning classification results, and automatically adjust the prompt color in the device according to different early warning levels, where the A-level early warning is green, the B-level early warning is yellow, and the C-level early warning is red; The effect evaluation and optimization module is used to evaluate the effect of the implemented dynamic regulation strategy, collect relevant data and feedback it to the system, which is used to optimize the subsequent risk assessment model and regulation strategy, form a closed-loop management, and improve the accuracy of subsequent regulation.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. The engineering progress and resource early warning method based on dynamic regulation of the present invention, based on real-time construction data and the concept of dynamic risk regulation, when a certain process or construction area in a civil engineering construction project is about to face the risk of schedule delay or resource shortage, by adjusting the overall management strategy of the construction project, while ensuring the stable progress of the construction project, it can effectively reduce the negative impact caused by potential risks and realize the smooth progress of the construction process under complex conditions.

[0015] 2. The engineering progress and resource early warning system based on dynamic regulation of the present invention, by comprehensively considering the actual construction progress deviation and the imbalance between resource supply and demand, analyzes the current overall operation situation of the construction project, flexibly switches the construction management strategy and adjusts the management parameters, quickly and effectively guarantees the construction progress, optimizes resource utilization, and reduces the losses caused by risks; it can be widely applied to various civil engineering construction management scenarios, significantly improving the intelligent level and response efficiency of construction management. Brief Description of the Drawings

[0016] Figure 1 is a flowchart of a method for engineering progress and resource early warning based on dynamic regulation according to the present invention; Figure 2 is an architecture diagram of a system for engineering progress and resource early warning based on dynamic regulation according to the present invention; Figure 3 is a schematic diagram of the system processing flow adopted in the embodiments of the present invention; Figure 4 is a schematic diagram of the structure of a certain tunnel construction project adopted in the embodiments of the present invention; Figure 5 is a path diagram of resource status perception and dynamic scheduling in the embodiments of the present invention. Detailed Embodiments

[0017] To facilitate the understanding and implementation of the present invention by those of ordinary skill in the art, the following provides a detailed description of each step of the method proposed by the present invention. It should be understood that these embodiments are only for illustrating the present invention and not for limiting the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of this application.

[0018] Embodiments As Figure 1 shown, the present invention discloses a method for engineering progress and resource early warning based on dynamic regulation, including the following steps: Step S1: Real-time collect construction progress data and resource consumption data, and construct a multi-source data fusion model; Step S2: Based on the constructed multi-source data fusion model, dynamically analyze the construction progress deviation and resource consumption anomaly, and judge whether there is an anomaly in the construction progress and resource consumption according to the preset threshold and early warning level. If so, execute Step S3; otherwise, continue to monitor the construction progress data and resource consumption data for conventional construction progress tracking and resource management; Step S3: Trigger a hierarchical early warning mechanism, generate and execute an optimized regulation strategy, and adjust the construction progress and resource allocation; Step S4: Evaluate the regulation effect, collect feedback data, and then return to Step S2 to form a closed-loop management of monitoring - early warning - regulation - feedback until completion.

[0019] Specifically, the construction progress data described in step S1 includes basic progress parameters and risk correlation parameters. The basic progress parameters include the actual progress of each process, the progress completion ratio, the estimated completion time, and the progress deviation. The risk correlation parameters include the coordinates of potential risk areas, the estimated impact time of risks, the historical progress deviation rate, and the critical path buffer consumption rate. The critical path buffer consumption rate is obtained by calculating the remaining ratios of the project buffer and the merging buffer. The construction progress data is obtained through real-time monitoring of the intelligent construction site platform, or generated through a progress prediction model, Bayesian network, or machine learning algorithm in combination with real-time construction logs, historical progress data, and the project WBS structure. The resource consumption data includes the actual usage, remaining quantity, consumption rate, emergency reserve resources, and supply data of various resources. The resource consumption data is obtained through the resource management system and on-site feedback.

[0020] Furthermore, the process of constructing the multi-source data fusion model in step S1 is as follows: Step S11: Calculate the process progress deviation rate ASDR; ; ; In the above formula, is the progress deviation rate of process ; is the progress delay of process . If , it indicates that the process progress is lagging behind. If , it indicates that the process progress is ahead; is the actual duration of process ; is the planned duration of process ; Step S12: Calculate the resource consumption imbalance rate RIR; ; In the above formula, is the resource consumption imbalance rate of resource . If , it indicates that the resource is overspent. If , it indicates that the resource has a surplus; is the actual consumption of resource ; is the benchmark consumption of resource ; Step S13: Calculate the dynamic weight allocation; The dynamic weight allocation includes the process weight and the resource weight , where: ; ; Step S14: construct a multi-source data fusion model. The mathematical expression of the multi-source data fusion model is as follows: ; In the above formula, is the comprehensive early warning index; is the dynamic adjustment coefficient of construction progress, is the dynamic adjustment coefficient of resource consumption, , initial value , .

[0021] Specifically, in step S2, based on the constructed multi-source data fusion model, the real-time collected construction progress data and resource consumption data are input into the multi-source data fusion model, and the output The system dynamically analyzes the deviation of construction progress and abnormal resource consumption, and determines whether there are abnormalities in construction progress and resource consumption based on the preset thresholds and warning levels. The corresponding relationship between the warning level and the preset thresholds is as follows: Level A warning: IWI < 33%; Level B warning: 33%≤IWI≤66%; Level C warning: IWI>66%.

[0022] Specifically, in step S3, a graded warning mechanism is triggered, and an optimization control strategy is generated and executed. The corresponding relationship between the warning level and the generation and execution of the optimization control strategy is as follows: Level A warning: The construction progress is normal and resources are sufficient. The original construction plan is maintained and regular monitoring is maintained; Level B warning: The construction progress is slightly behind schedule or resources are used quickly. The resource adjustment strategy is initiated to reduce the resource consumption priority of non-critical processes and compress the critical chain buffer. Level C warning: The construction progress is seriously delayed or resources are seriously short. Construction is suspended and cross-regional support and emergency control measures are initiated.

[0023] In this embodiment, as shown in Table 1 below, the optimization control strategy is divided into 7 response levels, and the resource consumption ratio and construction progress ratio corresponding to each response level are shown in Table 2 below.

[0024] Table 1. Response levels of optimized control strategies .

[0025] Table 2. Comparison table of response level, resource consumption ratio and construction progress ratio .

[0026] In this embodiment, response levels I to III correspond to Class A alarms; response levels IV to V correspond to Class B alarms; and response levels VI to VII correspond to Class C alarms.

[0027] Specifically, the preset thresholds of 33% and 66% are obtained by referring to the commonly used 30-70 method in civil engineering. Considering the impact of the Buffer Consumption Rate (BCR) of the critical chain buffer, the more the critical chain buffer is consumed, the stricter the warning level should be. Therefore, an engineering management coupling mechanism is introduced to correct the preset thresholds, values, and the multi-source data fusion model. The correction method is as follows: Every time 5% of the total project progress is completed, the preset threshold is adjusted according to the average project duration deviation rate; ; When the Buffer Consumption Rate (BCR) of the critical chain buffer > 30%, trigger the IWI correction: ; After each execution of the optimization control strategy, record the resource savings rate and the project duration recovery rate , and update the weights: ; ; In the above formula, is the updated resource weight; is the resource weight before update; is the updated process weight; is the process weight before update.

[0028] Furthermore, the dynamic adjustment coefficients and are provided with a dynamic adjustment mechanism. The triggering conditions of the dynamic adjustment mechanism are as follows: When the cumulative resource shortage exceeds 10%, that is, the critical resource consumption rate ≥ 110% of the planned value for 3 consecutive days, the dynamic adjustment coefficient is adjusted to , , to balance the construction progress and the priority of resource consumption; When the Buffer Consumption Rate (BCR) of the critical chain buffer > 30%, the dynamic adjustment coefficient is adjusted to , , to strengthen the response to construction progress risks and reduce the priority of resource consumption for non-critical processes; When the Buffer Consumption Rate (BCR) of the critical chain buffer > 66% and the project completion ratio < 33%, the dynamic adjustment coefficient is adjusted to , , to initiate emergency construction progress enhancement.

[0029] Such as Figure 2As shown in the figure, the present invention also provides an engineering progress and resource early warning system based on dynamic regulation, including a data collection and fusion module, a critical chain and resource analysis module, a buffer setting module, a real-time monitoring and risk assessment module, an early warning and regulation module, and an effect evaluation and optimization module; The data collection and fusion module is used to obtain construction progress and resource data from different data sources and perform fusion processing; The critical chain and resource analysis module is used to decompose construction projects, identify critical chain processes, analyze process logical relationships and resource constraint conditions, determine the resource requirements and time parameters of critical chain processes, and generate executable regulation strategies; The buffer setting module is used to set project buffers, merge buffers and resource buffers according to the resource requirements and time uncertainties of critical chain processes, and dynamically adjust the sizes of each buffer as the construction progress advances; The real-time monitoring and risk assessment module is used to monitor the construction progress and resource status in real time, calculate the construction progress deviation and resource consumption deviation of each process, convert them into a comprehensive early warning index through a built-in multi-source data fusion model, and determine the early warning level; The early warning and regulation module is used to start the corresponding early warning mechanism according to the early warning classification result, and automatically adjust the prompt color in the device according to different early warning levels. The A-level early warning is green, the B-level early warning is yellow, and the C-level early warning is red; The effect evaluation and optimization module is used to evaluate the effect of the implemented dynamic regulation strategy, collect relevant data and feedback it to the system, which is used to optimize the subsequent risk assessment model and regulation strategy, form a closed-loop management, and improve the accuracy of subsequent regulation.

[0030] As Figure 3 shown is a schematic diagram of the processing flow of the engineering progress and resource early warning system based on dynamic regulation adopted in this embodiment. This figure shows the core architecture and data processing flow of the system, which is specifically divided into the following five levels: Data collection layer: responsible for real-time collection of construction progress data and resource consumption data from external systems (such as intelligent construction site terminals, resource management systems, on-site feedback, etc.) to form raw data input; Data processing layer: perform standardization processing on the raw data, including data cleaning, format conversion and outlier correction, to ensure the consistency and reliability of the data; Model analysis layer: based on the multi-source data fusion model, calculate the comprehensive early warning index (IWI), dynamically analyze the construction progress deviation and resource consumption anomalies, and output the risk level and optimized regulation strategy; Early warning and control layer: Generate execution instructions according to the risk level, trigger the hierarchical early warning mechanism (A / B / C levels), and visually prompt project managers through three-color early warnings, where the A-level early warning is green, the B-level early warning is yellow, and the C-level early warning is red; Execution feedback layer: Feed back the execution effect of the control strategy to the system to form a closed-loop management; the feedback data is used to optimize the model parameters and subsequent strategies to improve the system control accuracy.

[0031] The system interacts with external devices such as construction equipment terminals and project management large screens to achieve real-time data sharing and instruction issuance, ensuring the timeliness and effectiveness of dynamic control.

[0032] As Figure 3 shown, it clearly demonstrates the entire process from data collection to feedback optimization, reflecting the closed-loop management logic of "monitoring - early warning - control - feedback" of the present invention.

[0033] Next, through a specific construction example, the technical effects of the method and system of the present invention will be further described.

[0034] As Figure 4 shown is a schematic diagram of a tunnel engineering construction project, including three independent construction sections, each section being approximately 500 meters long. Each section is supported by an independent tunneling team, equipment resources, and material supply system to form a relatively independent operation unit.

[0035] Taking the section collaborative construction management strategy as an example, the three sections can be set as the progress-dominated section or the resource-priority guarantee section according to the project requirements. However, only one section is allowed to be the leading section to lead the overall tunneling rhythm at the same time, and the remaining sections are used as collaborative sections to ensure the continuity of the overall tunnel construction progress through resource sharing and process coordination.

[0036] When the leading section is hindered in tunneling efficiency due to special construction conditions (such as sudden encounter of soft surrounding rock, sudden water inrush, sudden mud inrush, etc.), the collaborative section can be switched to the leading section to maintain the stability of the overall project tunneling progress through local acceleration construction strategies.

[0037] After receiving the tunnel tunneling progress data and resource consumption status, the engineering progress and resource early warning system based on dynamic control automatically executes the construction section management control algorithm, formulates dynamic response strategies, and outputs the overall project control operation sequence to achieve system autonomous scheduling.

[0038] As Figure 5As shown in the figure, in this example, after receiving the construction status information, the project progress and resource early warning system based on dynamic regulation automatically identifies potential risk areas and activates resource and process regulation strategies to prevent the impact of tunneling delays and material shortages on the main control path. The system comprehensively considers factors such as construction progress deviation, actual supply and demand of resources, and equipment status, intelligently analyzes the current operation situation of the tunnel project, and adjusts construction organization parameters based on key processes, material reserves, and unit utilization rates. After the risk is mitigated, it automatically switches to the conventional regulation mode. The specific method steps are as follows: Step 1: Taking the construction data received by the system three days ago as an example, the potential risk area is located in the No. 2 tunneling section. After detection, the cumulative tunneling progress of the No. 2 tunnel face is 145m, the planned completion should be 170m, with a lag of 25m; in terms of materials, the initial support concrete reserve is 95t, and the remaining amount according to the original plan should be 140t, with a shortage of 45t; in terms of equipment, the current operating efficiency of the main tunneling jumbo is 55%, which is lower than the standard operating efficiency of 75%. Step 2: According to the preliminary assessment of the regulation system, the current progress lag has not triggered the delay of key path processes, and the material gap can be supplemented through coordination with surrounding construction sections. There is still room for adjustment in the equipment load status; therefore, the system determines that there is no need to make large-scale plan adjustments, and only needs to activate the local resource scheduling and shift arrangement optimization plan. Step 3: Further evaluate the execution of the process chain of face blasting - mucking - initial support. It is found that the lag of a certain process is converted into time for about 2 days, which is expected to cause a 5-day delay to the breakthrough plan, but according to the overall construction control plan, the allowable deviation is 10 days, and the current situation is still within the tolerance; the system proposes suggestions to synchronously optimize the work shift and the vehicle organizational structure of the mucking channel, including: Adjust the No. 2 tunneling section to the "progress priority control mode" to increase the density of human-machine collaboration; at the same time, switch the No. 3 tunneling section far from the risk section to the "resource redundancy support mode" to release some spare materials and schedulable equipment to the No. 2 section to ensure the balanced regulation of overall resources and progress. Step 4: When the potential risk in the No. 2 section is lifted and the early warning period ends, after the system determines that the construction status has returned to normal, the current dynamic regulation mode is restored to the "conventional progress control state", and the regulation records and rule base are updated to provide decision support for subsequent similar situations.

[0039] The above is only a preferred embodiment of the present invention, and it is not a limitation to the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes. However, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution content of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for early warning of project progress and resources based on dynamic regulation, characterized in that, It includes the following steps: Step S1: Collect construction progress data and resource consumption data in real time, and build a multi-source data fusion model; Step S2: Based on the built multi-source data fusion model, dynamically analyze construction progress deviations and abnormal resource consumption, and judge whether there are abnormalities in construction progress and resource consumption according to preset thresholds and warning levels. If so, execute Step S3; otherwise, continue to monitor construction progress data and resource consumption data for regular construction progress tracking and resource management; Step S3: Trigger a hierarchical warning mechanism, generate and execute an optimization control strategy to adjust construction progress and resource allocation; Step S4: Evaluate the control effect, collect feedback data, and return to Step S2 to form a closed-loop management of monitoring-warning-control-feedback until completion.

2. The method for engineering progress and resource warning based on dynamic regulation according to claim 1, wherein, The construction progress data described in Step S1 includes basic progress parameters and risk-related parameters. The basic progress parameters include the actual progress of each process, the progress completion ratio, the estimated completion time, and the progress deviation; the risk-related parameters include the coordinates of potential risk areas, the estimated impact time of risks, the historical progress deviation rate, and the consumption rate of the critical path buffer. The consumption rate of the critical path buffer is obtained by calculating the remaining ratios of the project buffer and the merge buffer; the construction progress data is obtained through the real-time monitoring of the intelligent construction site platform, or generated through a progress prediction model, Bayesian network, or machine learning algorithm in combination with real-time construction logs, historical progress data, and the project WBS structure; The resource consumption data includes the actual usage, remaining quantity, consumption rate, emergency reserve resources, and supply data of various resources; the resource consumption data is obtained through the resource management system and on-site feedback.

3. A method for engineering progress and resource early warning based on dynamic regulation according to claim 1, characterized in that, The process of building the multi-source data fusion model described in Step S1 is as follows: Step S11: Calculate the process progress deviation rate ASDR; ； ； In the above formula, is the schedule deviation rate of process ; is the schedule delay of process . If , it indicates that the process schedule is behind schedule. If , it indicates that the process schedule is ahead of schedule; is the actual duration of process ; is the planned duration of process . Step S12: Calculate the resource consumption imbalance rate RIR; ； In the above formula, is the resource consumption imbalance rate. If , it means the resource is overspent. If , it means there is a resource surplus; is the actual consumption of the resource ; is the baseline consumption of the resource . Step S13: Calculate the dynamic weight allocation; Dynamic weight assignment includes process weights and resource weights , where: ； ； Step S14: Build a multi-source data fusion model. The mathematical expression of the multi-source data fusion model is as follows: ； In the above formula, is the comprehensive warning index; is the dynamic adjustment coefficient of the construction progress, is the dynamic adjustment coefficient of the resource consumption, , the initial value , .

4. A method for engineering progress and resource early warning based on dynamic regulation according to claim 3, characterized in that, In step S2, based on the constructed multi-source data fusion model, the real-time collected construction progress data and resource consumption data are input into the multi-source data fusion model, and according to the output value, dynamically analyze the construction progress deviation and resource consumption anomaly, and judge whether there is an anomaly in the construction progress and resource consumption according to the preset threshold and warning level. The corresponding relationship between the warning level and the preset threshold is as follows: Level A warning: IWI < 33%; Level B warning: 33% ≤ IWI ≤ 66%; Level C warning: IWI > 66%.

5. The method for engineering progress and resource early warning based on dynamic regulation according to claim 4, characterized in that In Step S3, trigger a hierarchical warning mechanism, generate and execute an optimization control strategy. The corresponding relationship between the warning level and the generated and executed optimization control strategy is as follows: Level A warning: The construction progress is normal and resources are sufficient. Maintain the original construction plan and keep regular monitoring; Level B warning: The construction progress is slightly behind or resources are used up quickly. Start a resource adjustment strategy, reduce the resource consumption priority of non-critical processes, and compress the critical chain buffer; Level C warning: The construction progress is seriously behind or resources are seriously short. Suspend construction and start cross-regional support and emergency control measures.

6. The method for engineering progress and resource early warning based on dynamic regulation according to claim 5, wherein The preset thresholds of 33% and 66% are obtained by referring to the commonly used 30-70 method in civil engineering. Considering the influence of the buffer consumption rate (BCR) of the critical chain, the more the critical chain buffer is consumed, the stricter the warning level should be. Therefore, an engineering management coupling mechanism is introduced to correct the preset thresholds, values and the multi-source data fusion model respectively. The correction method is as follows: Every time 5% of the total project progress is completed, adjust the preset threshold according to the average project duration deviation rate; ； When the critical chain buffer consumption rate BCR > 30%, trigger IWI correction: ； After each execution of the optimization and control strategy, record the resource saving rate and the project duration recovery rate , and update the weights: ； ； In the above formula, is the updated resource weight; is the resource weight before update; is the updated process weight; is the process weight before update.

7. A method for engineering progress and resource early warning based on dynamic regulation according to claim 3, characterized in that The dynamic adjustment coefficient and are provided with a dynamic adjustment mechanism, and the triggering conditions of the dynamic adjustment mechanism are as follows: When the cumulative resource shortage exceeds 10%, that is, the critical resource consumption rate has been ≥ 110% of the planned value for three consecutive days, the dynamic adjustment coefficient is adjusted to , , to balance the construction progress and the priority of resource consumption; When the buffer consumption rate of the critical chain BCR > 30%, the dynamic adjustment coefficient is adjusted to , , strengthen the response to the risk of construction progress, and reduce the resource consumption priority of non-critical processes; When the buffer consumption rate of the critical chain BCR > 66% and the project completion ratio < 33%, the dynamic adjustment coefficient is adjusted to , , and the emergency construction schedule is strengthened.

8. An engineering progress and resource early warning system based on dynamic regulation, which adopts the engineering progress and resource early warning method based on dynamic regulation as described in any one of claims 1-7, is characterized in that It includes a data collection and fusion module, a critical chain and resource analysis module, a buffer setting module, a real-time monitoring and risk assessment module, a warning and control module, and an effect evaluation and optimization module; The data acquisition and fusion module is used to obtain construction progress and resource data from different data sources and perform fusion processing; The critical chain and resource analysis module is used to decompose construction projects, identify critical chain processes, analyze the logical relationships of processes and resource constraint conditions, determine the resource requirements and time parameters of critical chain processes, and generate executable control strategies; The buffer setting module is used to set project buffers, merge buffers and resource buffers according to the resource requirements and time uncertainties of critical chain processes, and dynamically adjust the sizes of each buffer as the construction progress advances; The real-time monitoring and risk assessment module is used to monitor the construction progress and resource status in real time, calculate the construction progress deviation and resource consumption deviation of each process, convert them into a comprehensive early warning index through a built-in multi-source data fusion model, and determine the early warning level; The early warning and control module is used to start the corresponding early warning mechanism according to the early warning classification results, and automatically adjust the prompt color in the device according to different early warning levels, where the A-level early warning is green, the B-level early warning is yellow, and the C-level early warning is red; The effect evaluation and optimization module is used to evaluate the effect of the implemented dynamic control strategy, collect relevant data and feedback it to the system for optimizing the subsequent risk assessment model and control strategy, forming a closed-loop management and improving the accuracy of subsequent control.

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