A dynamic regulation-based engineering progress and resource early warning method and system

Abstract

The application discloses a kind of engineering progress and resource early warning method and system based on dynamic regulation, method includes:1) real-time acquisition engineering progress and resource data, constructs multi-source data fusion model;2) based on the multi-source data fusion model constructed, whether there is abnormality in construction progress and resource consumption is judged, if yes then execute step 3);Otherwise, continue to carry out normal construction progress tracking and resource management;3) trigger hierarchical early warning mechanism, generate and execute optimization control strategy, adjust construction progress and resource allocation;4) the effect of regulation is evaluated, and after collecting feedback data, return to step 2).The application considers actual construction progress deviation and resource supply and demand imbalance condition by comprehensively, analyzes the overall operation of current construction project, flexibly carries out the switching of construction management strategy and the adjustment of management parameter, can quickly and effectively guarantee construction progress, optimizes resource utilization, significantly improves the intelligent level and response efficiency of construction management.

Description

Technical Field

[0001] This invention relates to the field of civil engineering management technology, specifically a method and system for early warning of project progress and resources based on dynamic control. Background Technology

[0002] Traditional project management relies on static planning and manual experience, making it difficult to cope with unexpected resource shortages, schedule delays, and external risks. While existing project management software can provide basic data analysis, it lacks real-time early warning and dynamic control capabilities, leading to the following problems:

[0003] 1. Inefficient schedule management: Traditional schedule planning is based on static assumptions and does not dynamically consider resource constraints (such as the finiteness of manpower, materials and equipment) and uncertainties (such as weather changes and design changes), resulting in a lack of effective control measures after key processes are delayed;

[0004] 2. Delayed resource early warning: Resource supply and demand contradictions are usually only discovered after the problem occurs. The lack of real-time monitoring and early warning of resource consumption trends can easily lead to work stoppages due to material shortages or resource waste.

[0005] 3. Data silo problem: Construction progress data (such as process completion rate) 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.

[0006] With the popularization of smart construction site technology, the massive amounts of data generated during construction (such as BIM model data and IoT sensor data) provide a data foundation for dynamic control. However, how to transform this data into actionable early warning strategies remains a technical problem that urgently needs to be solved. Summary of the Invention

[0007] The purpose of this invention is to address the shortcomings of existing technologies by providing a method and system for early warning of project progress and resources based on dynamic control. Through real-time data-driven critical chain analysis and dynamic adjustment of buffer zones, it enables early identification of construction risks and optimized allocation of resources. It can automatically identify potential risks and take timely control measures to effectively reduce the risk of project delays, improve resource utilization efficiency, and ensure that construction projects proceed smoothly as planned.

[0008] To achieve the above objectives, the present invention adopts the following technical solution.

[0009] A method for early warning of project progress and resources based on dynamic control includes the following steps:

[0010] Step S1: Collect construction progress data and resource consumption data in real time, and build a multi-source data fusion model;

[0011] Step S2: Based on the constructed multi-source data fusion model, dynamically analyze the deviation of construction progress and abnormal resource consumption, and determine whether there are abnormalities in construction progress and resource consumption according to the preset threshold and warning level. If so, proceed to step S3; otherwise, continue to monitor construction progress data and resource consumption data, and carry out routine construction progress tracking and resource management.

[0012] Step S3: Trigger the graded early warning mechanism, and generate and execute optimized control strategies to adjust the construction schedule and resource allocation;

[0013] Step S4: Evaluate the control effect, collect feedback data, and return to step S2 to form a closed-loop management of monitoring, early warning, control, and feedback until completion.

[0014] Specifically, the construction progress data 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 rate, the expected completion time, and the progress deviation. The risk-related parameters include the coordinates of potential risk areas, the expected 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 proportion of the project buffer and the incoming buffer. The construction progress data is obtained through real-time monitoring of the smart construction site platform, or generated by combining real-time construction logs, historical progress data, and the project WBS structure through a progress prediction model, Bayesian network, or machine learning algorithm.

[0015] The resource consumption data includes the actual usage, remaining amount, 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.

[0016] Furthermore, the process of constructing the multi-source data fusion model described in step S1 is as follows:

[0017] Step S11: Calculate the process schedule deviation rate (ASDR).

[0018] ;

[0019] ;

[0020] In the above formula, For process Schedule deviation rate; For process The amount of delay in progress, if This indicates that the process is behind schedule. This indicates that the process is ahead of schedule; For process The actual time spent; For process The plan takes time;

[0021] Step S12: Calculate the resource consumption imbalance rate (RIR);

[0022] ;

[0023] In the above formula, For resources The resource consumption imbalance rate, if This indicates that resources have been overspent. This indicates a resource surplus. For resources The actual consumption; For resources The baseline consumption;

[0024] Step S13: Calculate the dynamic weight allocation;

[0025] Dynamic weight allocation includes process weights and resource weight ,in:

[0026] ;

[0027] ;

[0028] Step S14: Construct a multi-source data fusion model. The mathematical expression of the multi-source data fusion model is as follows:

[0029] ;

[0030] In the above formula, This is a comprehensive early warning index; This is a dynamic adjustment coefficient for the construction progress. This is a dynamic adjustment coefficient for resource consumption. initial value , .

[0031] 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 is used to determine the final result. The system dynamically analyzes deviations in construction progress and anomalies in resource consumption, and determines whether there are any anomalies in construction progress and resource consumption based on preset thresholds and warning levels. The correspondence between warning levels and preset thresholds is as follows:

[0032] Level A warning: IWI < 33%;

[0033] Level B warning: 33% ≤ IWI ≤ 66%;

[0034] Level C alert: IWI > 66%.

[0035] Specifically, in step S3, a tiered early warning mechanism is triggered, and optimized control strategies are generated and executed. The correspondence between the early warning levels and the generated and executed optimized control strategies is as follows:

[0036] Level A Warning: Construction progress is normal and resources are sufficient. Maintain the original construction plan and continue routine monitoring.

[0037] Level B warning: Construction progress is slightly behind schedule or resources are being used too quickly. Initiate resource adjustment strategies, reduce the priority of resource consumption in non-critical processes, and compress the critical chain buffer.

[0038] Level C Warning: Construction progress is severely delayed or resources are severely scarce. Construction is suspended, and cross-regional support and emergency control measures are initiated.

[0039] Specifically, the preset thresholds of 33% and 66% are derived with reference to the 30 / 70 split method commonly used in civil engineering. Considering the impact of the critical chain buffer consumption rate (BCR), the higher the critical chain buffer consumption, the stricter the warning level should be. Therefore, an engineering management coupling mechanism is introduced to adjust the preset thresholds. The values ​​and multi-source data fusion models are corrected using the following methods:

[0040] For every 5% completion of the total project progress, the preset threshold is adjusted based on the average schedule deviation rate.

[0041] ;

[0042] When the critical chain buffer consumption rate (BCR) is greater than 30%, IWI correction is triggered.

[0043] ;

[0044] Record the resource saving rate after each execution of the optimization and control strategy. With project recovery rate And update the weights:

[0045] ;

[0046] ;

[0047] In the above formula, The updated resource weights; The resource weights before the update; The updated process weights; The process weights before the update.

[0048] Furthermore, the dynamic adjustment coefficient and A dynamic adjustment mechanism is provided, and the triggering conditions for the dynamic adjustment mechanism are as follows:

[0049] When the cumulative resource shortage exceeds 10%, that is, when the critical resource consumption rate is ≥110% of the planned value for three consecutive days, the dynamic adjustment coefficient will be adjusted to... , In order to balance the priorities of construction progress and resource consumption;

[0050] When the critical chain buffer consumption rate (BCR) > 30%, the dynamic adjustment coefficient is adjusted to... , Strengthen the response to construction progress risks and reduce the priority of resource consumption in non-critical processes;

[0051] When the critical chain buffer consumption rate (BCR) is greater than 66% and the project completion rate is less than 33%, the dynamic adjustment factor is adjusted to: , Emergency construction progress has been accelerated.

[0052] Based on the above technical solutions, the present invention also provides an engineering progress and resource early warning system based on dynamic control, including a data acquisition 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 control module, and an effect evaluation and optimization module;

[0053] The data acquisition and fusion module is used to acquire construction progress and resource data from different data sources and perform fusion processing;

[0054] The critical chain and resource analysis module is used to decompose the construction project, identify critical chain processes, analyze the logical relationships and resource constraints of the processes, determine the resource requirements and time parameters of the critical chain processes, and generate executable control strategies.

[0055] The buffer setting module is used to set up project buffers, inflow buffers, and resource buffers according to the resource requirements and time uncertainties of critical chain processes, and dynamically adjust the size of each buffer as the construction progresses.

[0056] The real-time monitoring and risk assessment module is used to monitor 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 the built-in multi-source data fusion model, and determine the early warning level.

[0057] The warning and control module is used to activate the corresponding warning mechanism according to the warning classification results, and automatically adjust the prompt color in the device according to different warning levels, where level A warning is green, level B warning is yellow, and level C warning is red.

[0058] The effect evaluation and optimization module is used to evaluate the effectiveness of the implemented dynamic control strategy, collect relevant data and feed it back to the system to optimize the subsequent risk assessment model and control strategy, form a closed-loop management, and improve the accuracy of subsequent control.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] 1. This invention is based on a dynamic control method for project progress and resource early warning. Based on real-time construction data and the concept of dynamic risk control, when a certain process or construction area in a civil engineering construction project is about to face the risk of progress delay or resource shortage, the overall management and control strategy of the construction project is adjusted to ensure the stable progress of the construction project while effectively reducing the negative impact of potential risks and achieving the smooth progress of the construction process under complex conditions.

[0061] 2. This invention is based on a dynamic control engineering progress and resource early warning system. By comprehensively considering the deviation of actual construction progress and the imbalance between resource supply and demand, it analyzes the current overall operation of the construction project, flexibly switches construction management strategies and adjusts management parameters, and quickly and effectively ensures construction progress, optimizes resource utilization, and reduces losses caused by risks. It can be widely applied to various civil engineering construction management scenarios, significantly improving the intelligence level and response efficiency of construction management. Attached Figure Description

[0062] Figure 1 This is a flowchart of a dynamic control-based early warning method for engineering progress and resources according to the present invention;

[0063] Figure 2 This is an architecture diagram of an engineering progress and resource early warning system based on dynamic control according to the present invention;

[0064] Figure 3 This is a schematic diagram of the system processing flow used in the embodiments of the present invention;

[0065] Figure 4 This is a structural schematic diagram of a tunnel construction project used in an embodiment of the present invention;

[0066] Figure 5 This is a resource status perception and dynamic scheduling path diagram in an embodiment of the present invention. Detailed Implementation

[0067] To facilitate understanding and implementation of the present invention by those skilled in the art, the various steps of the method proposed in this invention are described in detail below. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various modifications or alterations to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0068] Example

[0069] like Figure 1 As shown, this invention discloses a method for early warning of engineering progress and resources based on dynamic control, comprising the following steps:

[0070] Step S1: Collect construction progress data and resource consumption data in real time, and build a multi-source data fusion model;

[0071] Step S2: Based on the constructed multi-source data fusion model, dynamically analyze the deviation of construction progress and abnormal resource consumption, and determine whether there are abnormalities in construction progress and resource consumption according to the preset threshold and warning level. If so, proceed to step S3; otherwise, continue to monitor construction progress data and resource consumption data, and carry out routine construction progress tracking and resource management.

[0072] Step S3: Trigger the graded early warning mechanism, and generate and execute optimized control strategies to adjust the construction schedule and resource allocation;

[0073] Step S4: Evaluate the control effect, collect feedback data, and return to step S2 to form a closed-loop management of monitoring, early warning, control, and feedback until completion.

[0074] Specifically, the construction progress data 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 rate, the expected completion time, and the progress deviation. The risk-related parameters include the coordinates of potential risk areas, the expected 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 proportion of the project buffer and the incoming buffer. The construction progress data is obtained through real-time monitoring of the smart construction site platform, or generated by combining real-time construction logs, historical progress data, and the project WBS structure through a progress prediction model, Bayesian network, or machine learning algorithm.

[0075] The resource consumption data includes the actual usage, remaining amount, 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.

[0076] Furthermore, the process of constructing the multi-source data fusion model described in step S1 is as follows:

[0077] Step S11: Calculate the process schedule deviation rate (ASDR).

[0078] ;

[0079] ;

[0080] In the above formula, For process Schedule deviation rate; For process The amount of delay in progress, if This indicates that the process is behind schedule. This indicates that the process is ahead of schedule; For process The actual time spent; For process The plan takes time;

[0081] Step S12: Calculate the resource consumption imbalance rate (RIR);

[0082] ;

[0083] In the above formula, For resources The resource consumption imbalance rate, if This indicates that resources have been overspent. This indicates a resource surplus. For resources The actual consumption; For resources The baseline consumption;

[0084] Step S13: Calculate the dynamic weight allocation;

[0085] Dynamic weight allocation includes process weights and resource weight ,in:

[0086] ;

[0087] ;

[0088] Step S14: Construct a multi-source data fusion model. The mathematical expression of the multi-source data fusion model is as follows:

[0089] ;

[0090] In the above formula, This is a comprehensive early warning index; This is a dynamic adjustment coefficient for the construction progress. This is a dynamic adjustment coefficient for resource consumption. initial value , .

[0091] 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 is used to determine the final result. The system dynamically analyzes deviations in construction progress and anomalies in resource consumption, and determines whether there are any anomalies in construction progress and resource consumption based on preset thresholds and warning levels. The correspondence between warning levels and preset thresholds is as follows:

[0092] Level A warning: IWI < 33%;

[0093] Level B warning: 33% ≤ IWI ≤ 66%;

[0094] Level C alert: IWI > 66%.

[0095] Specifically, in step S3, a tiered early warning mechanism is triggered, and optimized control strategies are generated and executed. The correspondence between the early warning levels and the generated and executed optimized control strategies is as follows:

[0096] Level A Warning: Construction progress is normal and resources are sufficient. Maintain the original construction plan and continue routine monitoring.

[0097] Level B warning: Construction progress is slightly behind schedule or resources are being used too quickly. Initiate resource adjustment strategies, reduce the priority of resource consumption in non-critical processes, and compress the critical chain buffer.

[0098] Level C Warning: Construction progress is severely delayed or resources are severely scarce. Construction is suspended, and cross-regional support and emergency control measures are initiated.

[0099] In this embodiment, as shown in Table 1 below, the optimization and 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.

[0100] Table 1. Response Levels of Optimized Control Strategies

[0101] .

[0102] Table 2. Comparison Table of Response Level, Resource Consumption Ratio, and Construction Progress Ratio

[0103] .

[0104] In this embodiment, response levels I to III correspond to Level A alarms; response levels IV to V correspond to Level B alarms; and response levels VI to VII correspond to Level C alarms.

[0105] Specifically, the preset thresholds of 33% and 66% are derived with reference to the 30 / 70 split method commonly used in civil engineering. Considering the impact of the critical chain buffer consumption rate (BCR), the higher the critical chain buffer consumption, the stricter the warning level should be. Therefore, an engineering management coupling mechanism is introduced to adjust the preset thresholds. The values ​​and multi-source data fusion models are corrected using the following methods:

[0106] For every 5% completion of the total project progress, the preset threshold is adjusted based on the average schedule deviation rate.

[0107] ;

[0108] When the critical chain buffer consumption rate (BCR) is greater than 30%, IWI correction is triggered.

[0109] ;

[0110] Record the resource saving rate after each execution of the optimization and control strategy. With project recovery rate And update the weights:

[0111] ;

[0112] ;

[0113] In the above formula, The updated resource weights; The resource weights before the update; The updated process weights; The process weights before the update.

[0114] Furthermore, the dynamic adjustment coefficient and A dynamic adjustment mechanism is provided, and the triggering conditions for the dynamic adjustment mechanism are as follows:

[0115] When the cumulative resource shortage exceeds 10%, that is, when the critical resource consumption rate is ≥110% of the planned value for three consecutive days, the dynamic adjustment coefficient will be adjusted to... , In order to balance the priorities of construction progress and resource consumption;

[0116] When the critical chain buffer consumption rate (BCR) > 30%, the dynamic adjustment coefficient is adjusted to... , Strengthen the response to construction progress risks and reduce the priority of resource consumption in non-critical processes;

[0117] When the critical chain buffer consumption rate (BCR) is greater than 66% and the project completion rate is less than 33%, the dynamic adjustment factor is adjusted to: , Emergency construction progress has been accelerated.

[0118] like Figure 2 As shown, the present invention also provides an engineering progress and resource early warning system based on dynamic control, including a data acquisition 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 control module, and an effect evaluation and optimization module;

[0119] The data acquisition and fusion module is used to acquire construction progress and resource data from different data sources and perform fusion processing;

[0120] The critical chain and resource analysis module is used to decompose the construction project, identify critical chain processes, analyze the logical relationships and resource constraints of the processes, determine the resource requirements and time parameters of the critical chain processes, and generate executable control strategies.

[0121] The buffer setting module is used to set up project buffers, inflow buffers, and resource buffers according to the resource requirements and time uncertainties of critical chain processes, and dynamically adjust the size of each buffer as the construction progresses.

[0122] The real-time monitoring and risk assessment module is used to monitor 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 the built-in multi-source data fusion model, and determine the early warning level.

[0123] The warning and control module is used to activate the corresponding warning mechanism according to the warning classification results, and automatically adjust the prompt color in the device according to different warning levels: green for level A warning, yellow for level B warning, and red for level C warning.

[0124] The effect evaluation and optimization module is used to evaluate the effectiveness of the implemented dynamic control strategy, collect relevant data and feed it back to the system to optimize the subsequent risk assessment model and control strategy, form a closed-loop management, and improve the accuracy of subsequent control.

[0125] like Figure 3 The diagram shown is a schematic representation of the processing flow of the dynamic control-based project progress and resource early warning system used in this embodiment. The diagram illustrates the core architecture and data processing flow of the system, specifically divided into the following five layers:

[0126] Data acquisition layer: responsible for collecting construction progress data and resource consumption data in real time from external systems (such as smart construction site terminals, resource management systems, on-site feedback, etc.) to form raw data input;

[0127] Data processing layer: Standardizes raw data, including data cleaning, format conversion, and outlier correction, to ensure data consistency and reliability;

[0128] Model Analysis Layer: Based on a multi-source data fusion model, calculates the Integrated Early Warning Index (IWI), dynamically analyzes construction progress deviations and resource consumption anomalies, and outputs risk levels and optimization control strategies;

[0129] Early warning and control layer: Generates execution instructions based on risk level, triggers a graded early warning mechanism (A / B / C level), and provides intuitive notifications to project managers through a three-color early warning system, where level A is green, level B is yellow, and level C is red;

[0130] Execution feedback layer: Feeds back the execution effect of the control strategy to the system to form closed-loop management; the feedback data is used to optimize model parameters and subsequent strategies to improve the accuracy of system control.

[0131] The system interacts with external devices such as construction equipment terminals and project management screens to achieve real-time data sharing and command issuance, ensuring the timeliness and effectiveness of dynamic control.

[0132] like Figure 3 The diagram clearly illustrates the entire process from data collection to feedback optimization, demonstrating the closed-loop management logic of "monitoring-early warning-control-feedback" in this invention.

[0133] The technical effects of the method and system of the present invention will be further illustrated below through a specific construction example.

[0134] like Figure 4 The diagram shows a tunnel construction project, which includes three independent construction sections, each approximately 500 meters long. Each section is supported by an independent tunneling team, equipment resources, and material supply system, forming a relatively independent work unit.

[0135] Taking the segmented collaborative construction management strategy as an example, the three segments can be set as progress-leading segments or resource-priority guarantee segments according to project needs. However, at the same time, only one segment is allowed to lead the overall tunneling rhythm as the leading segment, while the other segments are used as collaborative segments. Through resource sharing and process collaboration, the continuity of the overall tunnel construction progress is ensured.

[0136] When the tunneling efficiency of the main section is hindered by special construction conditions (such as encountering weak surrounding rock, sudden water inrush, mudslide and other geological disasters), the cooperating section can be switched to the main section, and the overall tunneling progress of the project can be kept stable through local accelerated construction strategies.

[0137] Upon receiving tunnel excavation progress data and resource consumption status, the dynamic control-based project progress and resource early warning system automatically executes the construction section management and control algorithm, formulates dynamic response strategies, and outputs the overall project control operation sequence, thereby achieving autonomous system scheduling.

[0138] like Figure 5 As shown in the example, the dynamic control-based engineering progress and resource early warning system automatically identifies potential risk areas after receiving construction status information and activates resource and process control 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 deviations, actual resource supply and demand, and equipment status, intelligently analyzes the current tunnel project operation status, 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 control mode. The specific steps are as follows:

[0139] Step 1: Taking the construction data received by the system three days ago as an example, the potential risk area is located in tunneling section No. 2. Testing shows that the cumulative tunneling progress at the No. 2 face is 145m, while the planned progress is 170m, lagging behind by 25m. Regarding materials, the initial support concrete reserve is 95t, while the original plan was to have 140t remaining, resulting in a shortage of 45t. Regarding equipment, the current operating efficiency of the main tunneling trolley is 55%, lower than the standard operating efficiency of 75%.

[0140] Step 2: According to the preliminary assessment of the control system, the current progress lag has not yet triggered the delay of the critical path process, and the material shortage can be supplemented by coordination with the surrounding construction sections. The equipment load status still has room for adjustment. Therefore, the system determines that there is no need to adjust the plan on a large scale. It is only necessary to start a local resource scheduling and shift optimization plan.

[0141] Step 3: Further evaluate the execution of the blasting-muck removal-initial support process chain at the tunnel face. It was found that a certain process was delayed by approximately 2 days, which is expected to cause a 5-day delay to the completion plan. However, according to the overall construction control plan, the allowable deviation is 10 days, and the current situation is still within the tolerance limit. The system proposes suggestions for simultaneously optimizing the work shifts and vehicle organization structure of the muck removal channel, including:

[0142] The No. 2 tunneling section was adjusted to "progress priority control mode" to increase the density of human-machine collaboration; at the same time, the No. 3 tunneling section, which is far away from the risk section, was switched to "resource redundancy support mode" to release some spare materials and dispatchable equipment to the No. 2 section to ensure the overall resource and progress are balanced.

[0143] Step 4: When the potential risks in section 2 are eliminated, the warning period ends, and the system determines that the construction status has returned to normal, the current dynamic control mode will be restored to "normal progress control status", and the control records and rule base will be updated to provide decision support for similar situations in the future.

[0144] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for early warning of project progress and resources based on dynamic control, characterized in that, 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 S11: Calculate the process schedule deviation rate (ASDR). ； ； In the above formula, For process Schedule deviation rate; For process The amount of delay in progress, if This indicates that the process is behind schedule. This indicates that the process is ahead of schedule; For process The actual time spent; For process The plan takes time; Step S12: Calculate the resource consumption imbalance rate (RIR); ； In the above formula, For resources The resource consumption imbalance rate, if This indicates that resources have been overspent. This indicates a resource surplus. For resources The actual consumption; For resources The baseline consumption; Step S13: Calculate the dynamic weight allocation; Dynamic weight allocation includes process weights and resource weight ,in: ； ； 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, This is a comprehensive early warning index; This is a dynamic adjustment coefficient for the construction progress. This is a dynamic adjustment coefficient for resource consumption. initial value , ; Step S2: Based on the constructed multi-source data fusion model, dynamically analyze the deviation of construction progress and abnormal resource consumption, and determine whether there are abnormalities in construction progress and resource consumption according to the preset threshold and warning level. If so, proceed to step S3; otherwise, continue to monitor construction progress data and resource consumption data, and carry out routine construction progress tracking and resource management. Step S3: Trigger the graded early warning mechanism, and generate and execute optimized control strategies to adjust the construction schedule 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, early warning, control, and feedback until completion.

2. The method for early warning of project progress and resources based on dynamic control according to claim 1, characterized in that, The construction progress data mentioned 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 rate, the expected completion time, and the progress deviation. The risk-related parameters include the coordinates of potential risk areas, the expected 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 proportion of the project buffer and the incoming buffer. The construction progress data is obtained through real-time monitoring of the smart construction site platform, or generated by combining real-time construction logs, historical progress data, and the project WBS structure through a progress prediction model, Bayesian network, or machine learning algorithm. The resource consumption data includes the actual usage, remaining amount, 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. The method for early warning of project progress and resources based on dynamic control according to claim 1, 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 the output is used to... The system dynamically analyzes deviations in construction progress and anomalies in resource consumption, and determines whether there are any anomalies in construction progress and resource consumption based on preset thresholds and warning levels. The correspondence between warning levels and preset thresholds is as follows: Level A warning: IWI < 33%; Level B warning: 33% ≤ IWI ≤ 66%; Level C alert: IWI > 66%.

4. The method for early warning of project progress and resources based on dynamic control according to claim 3, characterized in that, In step S3, a tiered early warning mechanism is triggered, and optimized control strategies are generated and executed. The correspondence between the early warning levels and the generated and executed optimized control strategies is as follows: Level A Warning: Construction progress is normal and resources are sufficient. Maintain the original construction plan and continue routine monitoring. Level B warning: Construction progress is slightly behind schedule or resources are being used too quickly. Initiate resource adjustment strategies, reduce the priority of resource consumption in non-critical processes, and compress the critical chain buffer. Level C Warning: Construction progress is severely delayed or resources are severely scarce. Construction is suspended, and cross-regional support and emergency control measures are initiated.

5. The method for early warning of project progress and resources based on dynamic control according to claim 4, characterized in that, The preset thresholds of 33% and 66% are derived from the 30 / 70 rule commonly used in civil engineering. Considering the impact of the critical chain buffer consumption rate (BCR), the higher the BCR consumption, the stricter the warning level should be. Therefore, an engineering management coupling mechanism is introduced to adjust the preset thresholds. The values ​​and multi-source data fusion models are corrected using the following methods: For every 5% completion of the total project progress, the preset threshold is adjusted based on the average schedule deviation rate. ； When the critical chain buffer consumption rate (BCR) > 30%, IWI correction is triggered. ； Record the resource saving rate after each execution of the optimization and control strategy. With project recovery rate And update the weights: ； ； In the above formula, The updated resource weights; The resource weights before the update; The updated process weights; The process weights before the update.

6. The method for early warning of project progress and resources based on dynamic control according to claim 5, characterized in that, The dynamic adjustment coefficient and A dynamic adjustment mechanism is provided, and the triggering conditions for the dynamic adjustment mechanism are as follows: When the cumulative resource shortage exceeds 10%, i.e., the critical resource consumption rate is ≥110% of the planned value for three consecutive days, the dynamic adjustment coefficient will be adjusted to... , In order to balance the priorities of construction progress and resource consumption; When the critical chain buffer consumption rate (BCR) > 30%, the dynamic adjustment coefficient is adjusted to... , Strengthen the response to construction progress risks and reduce the priority of resource consumption in non-critical processes; When the critical chain buffer consumption rate (BCR) is greater than 66% and the project completion rate is less than 33%, the dynamic adjustment factor is adjusted to: , Emergency construction progress has been accelerated.

7. A dynamic control-based project progress and resource early warning system, employing the dynamic control-based project progress and resource early warning method as described in any one of claims 1-6, characterized in that, It includes a data acquisition and fusion module, a critical chain and resource analysis module, a buffer zone setting module, a real-time monitoring and risk assessment module, an early warning and control module, and an effect evaluation and optimization module; The data acquisition and fusion module is used to acquire construction progress and resource data from different data sources and perform fusion processing; The critical chain and resource analysis module is used to decompose the construction project, identify critical chain processes, analyze the logical relationships and resource constraints of the processes, determine the resource requirements and time parameters of the critical chain processes, and generate executable control strategies. The buffer setting module is used to set up project buffers, inflow buffers, and resource buffers according to the resource requirements and time uncertainties of critical chain processes, and dynamically adjust the size of each buffer as the construction progresses. The real-time monitoring and risk assessment module is used to monitor 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 the built-in multi-source data fusion model, and determine the early warning level. The warning and control module is used to activate the corresponding warning mechanism according to the warning classification results, and automatically adjust the prompt color in the device according to different warning levels, where level A warning is green, level B warning is yellow, and level C warning is red. The effect evaluation and optimization module is used to evaluate the effectiveness of the implemented dynamic control strategy, collect relevant data and feed it back to the system to optimize the subsequent risk assessment model and control strategy, form a closed-loop management, and improve the accuracy of subsequent control.

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