Engineering cost dynamic management and control system based on cloud computing

Abstract

The invention discloses a project cost dynamic management and control system based on cloud computing, and relates to the technical field of data analysis, and the system comprises the steps: determining the upper limit of disposable funds of a project, the completion time limit of the project, and the application demands of the project; based on the construction process of the engineering project, performing completion time period division on the construction process according to the task type to obtain the construction time limit of each task type of the engineering project; on the basis of taking the construction time limit of each task type of the engineering project as a basic constraint and taking the upper limit of disposable funds of the engineering project as a variable constraint, generating a construction mode of each task type of the engineering project; and judging whether the construction mode of each task type of the engineering project meets the application requirements of the engineering project, if not, judging to re-screen, and if so, judging to normally execute. The method has the advantages that the manual intervention cost is reduced, the construction period is shortened, and the fund utilization rate control is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a dynamic engineering cost management and control system based on cloud computing. Background Art

[0002] Dynamic cost management and control of construction projects refers to a system of methods for refined and adaptive management of costs, schedules, and resources throughout the entire project lifecycle, based on real-time data collection, intelligent analysis, and dynamic optimization technologies. Its core approach is to use digital means to monitor cost fluctuations, resource consumption, and schedule deviations during the construction process in real time. Using optimization algorithms, the system dynamically adjusts construction plans to ensure efficient project progress within budget constraints.

[0003] Existing engineering cost control solutions generally suffer from insufficient capital control capabilities, which are mainly manifested in the following aspects: reliance on static budgets and manual experience makes it difficult to track capital flows in real time; lack of a dynamic response mechanism to market fluctuations, resulting in a high risk of budget overruns; capital allocation is disconnected from construction progress, and resource input cannot be dynamically optimized according to actual project progress, resulting in idle or insufficient funds, which ultimately affects project cost-effectiveness and schedule control. Summary of the Invention

[0004] In order to solve the above technical problems, a cloud computing-based dynamic engineering cost management and control system is provided. This technical solution solves the above problems.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is: A method for dynamic control of construction costs based on cloud computing, comprising: Determine the upper limit of available funds for the project, the time limit for completion of the project and the application requirements of the project; Based on the construction process of the project, the completion time limit is divided according to the task type, and the construction time limit of each task type of the project is obtained; Based on the construction time limit of each task type of the project as the basic constraint and the upper limit of the disposable funds of the project as the variable constraint, the construction method of each task type of the project is generated; Determine whether the construction methods of each task type of the project meet the application requirements of the project. If not, re-screen it. If so, execute it normally.

[0006] Furthermore, the construction progress of the project information is divided according to the task type to obtain the construction progress of several task types of the project; Verify the accessibility between the construction processes of several task types of the engineering project, and build a tree-like hierarchical structure task path dependency matrix for the engineering project; Obtain construction time limit parameters of several task types from historical engineering project information, and establish a reference database of construction time limits of task types of historical engineering projects; Marking the construction time limit of the task type of the historical engineering project and referring to the tasks of the same type as the several task types of the engineering project in the database, obtaining the construction process duration samples of the several task types of the engineering project; Based on the construction process duration samples of several task types of the engineering project as the reference objects of scatter point estimation, the expected duration vectors of the construction process of several task types of the engineering project are extracted.

[0007] Furthermore, based on the tree-structured task path dependency matrix of the engineering project, the expected duration vectors of the construction process of several task types of the engineering project are used to verify the forward execution duration path and reverse execution duration path of the construction process of several task types of the engineering project; By using the forward execution duration path and the reverse execution duration path of the construction process of several task types of the engineering project, the floating time allowed for the construction process of several task types of the engineering project is calculated, and the execution path of the construction process of several task types of the engineering project is determined.

[0008] Furthermore, based on the expected duration vectors of the construction processes of several task types of the engineering project and the allowed float times of the construction processes of several task types of the engineering project, the spatial distance of the construction processes of each task type in the tree-like hierarchical structure task path dependency matrix of the engineering project is calculated; The k-means clustering algorithm is used to minimize the spatial distance of the construction process of each task type in the tree-like hierarchical structure task path dependency matrix of the engineering project to obtain the construction process set of parallel and non-parallel task types of the engineering project.

[0009] Based on the construction process set of parallel and non-parallel task types of the engineering project, the construction time limit vector of each task type of the engineering project is assigned according to the expected construction period vector of the construction process of several task types of the engineering project, the forward execution period path and reverse execution period path of the construction process, and the allowed floating time and construction process execution path of the construction process.

[0010] Furthermore, based on several known construction methods of engineering buildings, the time series parameters of the historical cost changes of construction resources of each construction method are obtained; Stabilize the time series parameters of the historical cost changes of construction resources for each construction method; Based on AR time series autoregression, a construction cost prediction model for the construction method of engineering buildings is established; The time series parameters of the historical cost changes of construction resources of each construction method are substituted into the construction cost prediction model of the construction method of the engineering building. The time series parameters of the historical cost changes of construction resources of the construction method are used as the observation value input, and the predicted real-time construction costs of each construction method in the future are output as the predicted value.

[0011] Furthermore, the construction time requirement of each construction method and the construction cost requirement of the real-time construction method are determined, and a database of time limit-cost requirements of known construction methods is established; Establish an objective function to minimize the total construction period of the project; The construction time limit of each task type of the project is used as the basic constraint, and the upper limit of the disposable funds of the project is used as the variable constraint to establish the restriction conditions; Based on mixed integer linear programming, a database of known construction method time limits and costs is used as input. According to the constraints, several known construction methods that meet the objective function are screened out to form a set of known construction methods for each task type of the initial screening project.

[0012] Furthermore, based on the decision tree, the construction process of each task type of the engineering project is taken as the root node; the set of known construction methods of each task type of the engineering project that satisfies the initial screening of the construction process root node of each task type is taken as the branch node, and the information gain of each branch node for minimizing the total construction period of the engineering project is used as the branch decision to obtain the construction method of each task type of the engineering project.

[0013] Furthermore, based on the construction methods of each task type of the engineering project, the performance indicators of each construction method are quantified, and a performance scatter plot of the construction methods of each task type of the engineering project is established; Based on the application requirements of the engineering project, mark the construction method performance requirements of each task type of the engineering project; According to the cosine similarity formula, the matching degree between the performance requirements of the construction methods of each task type of the engineering project corresponding to each scatter point in the performance scatter diagram of the construction methods of each task type of the engineering project is calculated; According to the matching degree between the performance requirements of the construction methods of each task type of the engineering project corresponding to each scattered point, it is judged whether the construction methods of each task type of the engineering project meet the application requirements of the engineering project. If not, it is determined to be re-screened. If so, it is determined to be executed normally.

[0014] Furthermore, a cloud computing-based dynamic engineering cost management and control system includes: Initial module, construction time allocation module, construction method screening module, construction decision module; The initialization module is used to determine the upper limit of available funds for the project, the completion time limit for the project, and the application requirements of the project; The construction time limit allocation module is electrically connected to the initial module and is used to divide the completion time limit of the construction process according to the task type based on the construction progress of the project to obtain the construction time limit of each task type of the project; The construction method screening module is electrically connected to the initialization module and the construction time limit allocation module. The construction method screening module is used to generate a construction method for each task type of the project based on the construction time limit of each task type of the project as a basic constraint and the upper limit of the disposable funds of the project as a variable constraint; The construction decision module is electrically connected to the initial module and the construction method screening module. The construction decision module is used to determine whether the construction method of each task type of the engineering project meets the application requirements of the engineering project. If not, it is determined to be re-screened. If so, it is determined to be executed normally.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention proposes a cloud computing-based dynamic control solution for project costs, which achieves real-time optimization and control of project costs by dynamically integrating funding constraints, construction period requirements, and application needs. Its core principle is to utilize the distributed computing capabilities of cloud computing to quickly decompose the total project duration into each task node, combine historical cost time series forecasts with real-time resource price data, generate the optimal construction method combination under funding constraints through mixed integer programming, and use a decision tree algorithm to verify its compliance with requirements. Its beneficial effects include: 1. Improving the efficiency of multi-task parallel optimization; 2. Dynamically integrating market resource price fluctuations to enhance the accuracy of cost forecasts; 3. Reducing the cost of manual intervention through an automated decision chain, achieving shortened construction periods, and improving capital utilization control. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of a dynamic control method for engineering cost based on cloud computing; Figure 2 This is a framework diagram of a dynamic control system for engineering cost based on cloud computing; DETAILED DESCRIPTION

[0017] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0018] Reference Figure 1 As shown, a method for dynamic control of engineering cost based on cloud computing includes: Step 1: Determine the upper limit of available funds for the project, the completion time limit for the project, and the application requirements of the project.

[0019] Step 2: Based on the construction progress of the project, the completion time limit is divided according to the task type, and the construction time limit of each task type of the project is obtained; The second step includes the following: Step 201: Divide the construction progress of the project information by task type to obtain the construction progress of several task types of the project; Verify the accessibility between the construction processes of several task types of the engineering project, and build a tree-like hierarchical structure task path dependency matrix for the engineering project; Obtain construction time limit parameters of several task types from historical engineering project information, and establish a reference database of construction time limits of task types of historical engineering projects; Marking the construction time limit of the task type of the historical engineering project and referring to the tasks of the same type as the several task types of the engineering project in the database, obtaining the construction process duration samples of the several task types of the engineering project; Based on the construction duration samples of several task types of the engineering project as the reference objects of scatter point estimation, the expected duration vectors of the construction process of several task types of the engineering project are extracted as follows: , in, is the expected duration vector of the construction process of the i-th task type of the engineering project, is the shortest construction duration of the i-th task type of the project, is the possible duration of the construction process of the i-th task type of the project, is the longest construction period of the i-th task type of the project, is the calibration coefficient, Sample construction progress and duration for several task types of engineering projects; Step 202: Based on the tree-structured task path dependency matrix of the engineering project, and using the expected duration vectors of the construction processes of the several task types of the engineering project, verify the forward execution duration paths and reverse execution duration paths of the construction processes of the several task types of the engineering project; Using the forward execution duration paths and reverse execution duration paths of the construction process of several task types of the project, the allowed float time of the construction process of several task types of the project is calculated, and the execution paths of the construction process of several task types of the project are determined as follows: , in, Indicates that task type i is a predecessor task of task type j, is the earliest start time of the post-construction process of the j-th task type of the project, is the latest completion time of the preceding construction process of the i-th task type of the project, is the latest start time of the post-construction process of the j-th task type of the project, The float time allowed for the predecessor construction process of the i-th task type in the engineering project.

[0020] Step 203: Based on the expected duration vectors of the construction processes of the several task types of the engineering project and the allowed float times of the construction processes of the several task types of the engineering project, calculate the spatial distance of the construction processes of each task type in the tree-like hierarchical structure task path dependency matrix of the engineering project; Using the k-means clustering algorithm, we divide the tasks by minimizing the spatial distance of the construction process of each task type in the tree-like hierarchical task path dependency matrix of the project, and obtain the construction process set of parallel and non-parallel task types of the project as follows: , in, A set of construction processes of parallel and non-parallel task types for engineering projects. is the kth parallel-non-parallel task type cluster, is the spatial distance between the preceding construction process of the i-th task type and the following construction process of the j-th task type in the tree-structured task path dependency matrix of the engineering project, is the expected duration vector of the post-construction process of the j-th task type of the project, is the indicator function (if Right now yes construction progress), is the floating time weight coefficient, The float time allowed for the post-construction process of the j-th task type of the project, To rely on the penalty condition (if , forcing i and j not to be assigned to the same cluster).

[0021] Based on the construction process set of parallel and non-parallel task types of the project, the construction time limit vector of each task type of the project is assigned according to the expected construction period vector of the construction process of several task types of the project, the forward execution period path and reverse execution period path of the construction process, and the allowed float time and construction process execution path of the construction process. The method is as follows: , in, is the construction time limit vector of the i-th task type of the engineering project; When using, combine the contents in 201 to 203: As a further step, the expected construction period is calculated through structured task decomposition and dependency modeling, combined with the historical data-driven PERT three-point estimation method, and critical path analysis (CPM) is used to verify the task execution path and float time. On this basis, by integrating the spatial distance measurement of construction period, resources and dependency constraints and the k-means clustering algorithm, the parallel and non-parallel task sets are intelligently divided, and finally a reasonable construction time limit vector is generated.

[0022] Beneficial effects include: 1. The accuracy of construction period forecast driven by historical data is significantly improved; 2. Coupling analysis of dependency matrix and CPM to ensure critical path reliability; 3. Clustering algorithms are used to optimize the grouping of construction tasks. While ensuring the logic of the process, parallel scheduling is used to shorten the total construction period and improve resource utilization.

[0023] Step 3: Based on the construction time limit of each task type of the project as the basic constraint and the upper limit of the disposable funds of the project as the variable constraint, generate the construction method of each task type of the project; The step three includes the following: Step 301: Based on several known construction methods of engineering buildings, obtain the time series parameters of the historical cost changes of construction resources for each construction method; Stabilize the time series parameters of the historical cost changes of construction resources for each construction method; Based on AR time series autoregression, a construction cost prediction model for the construction method of engineering buildings is established; Substitute the time series parameters of the historical cost changes of construction resources of each construction method into the construction cost prediction model of the construction method of the engineering building, use the time series parameters of the historical cost changes of construction resources of the construction method as the observation value input, and use the predicted real-time construction cost of each construction method in the future as the prediction value output; Step 302: Determine the construction time requirement of each construction method and the construction cost requirement of the real-time construction method, and establish a time limit-cost requirement database for known construction methods; Establish an objective function to minimize the total construction period of the project; The construction time limit of each task type of the project is used as the basic constraint, and the upper limit of the disposable funds of the project is used as the variable constraint to establish the restriction conditions; Based on mixed integer linear programming, a database of known construction method time limits and costs is used as input. Under the constraints, several known construction methods that meet the objective function are screened out to form a set of known construction methods for each task type of the initial screening project. The method is as follows: , in, is the construction duration of the vth construction method of the i-th task type of the project, The vth construction method selected for the i-th task type of the project, is the construction cost of the vth construction method of the i-th task type of the project, is the total construction period of the project, is the vth construction method of the i-th task type in the time-cost requirement database of known construction methods, is the total number of task types, is the total number of construction methods that satisfy the i-th task type; Step 303: Based on the decision tree, the construction process of each task type of the project is used as the root node; the set of known construction methods for each task type of the project that initially screens the root node of the construction process of each task type is used as the branch nodes; the information gain of each branch node in minimizing the total construction period of the project is used as the branch decision, and the construction method for each task type of the project is obtained; When used, combine the content in 301 to 303: As a further content, by integrating the time series analysis of historical cost data of construction resources and the AR prediction model, a time limit-cost database of construction methods is established. Combined with mixed integer linear programming, the initial set of construction methods is screened under the constraints of funds and time. Then, based on the decision tree algorithm, the combination of construction methods is optimized with information gain as the criterion to achieve the minimization of project construction period and efficient allocation of funds.

[0024] The beneficial effects are: 1. Dynamic cost modeling: Real-time prediction of construction resource costs based on AR time series, building a time limit-cost database to ensure the timeliness of cost data; 2. Mixed Integer Programming (MILP): Using construction time limits and funding limits as constraints, and minimizing the total construction period as the goal, accurately screen a set of feasible construction methods; 3. Decision tree optimization: Quantify the impact of each construction method on the total construction period through information gain, select the optimal branch, and significantly reduce the total construction period.

[0025] Step 4: Determine whether the construction method of each task type of the project meets the application requirements of the project. If not, re-screen it. If so, determine that it is executed normally. The step 4 includes the following contents: Step 401: quantify the performance index of each construction method based on the construction methods of each task type of the engineering project, and create a performance scatter plot of the construction methods of each task type of the engineering project; Based on the application requirements of the engineering project, mark the construction method performance requirements of each task type of the engineering project; According to the cosine similarity formula, the matching degree between the performance requirements of the construction methods of each task type of the engineering project corresponding to each scatter point in the performance scatter diagram of the construction methods of each task type of the engineering project is calculated; According to the matching degree between the performance requirements of the construction methods of each task type of the engineering project corresponding to each scattered point, it is judged whether the construction methods of each task type of the engineering project meet the application requirements of the engineering project. If not, it is determined to be re-screened. If so, it is determined to be executed normally.

[0026] Reference Figure 2 As shown, a cloud computing-based dynamic engineering cost management and control system includes: Initial module, construction time allocation module, construction method screening module, construction decision module; The initialization module is used to determine the upper limit of available funds for the project, the completion time limit for the project, and the application requirements of the project; The construction time limit allocation module is electrically connected to the initial module and is used to divide the completion time limit of the construction process according to the task type based on the construction progress of the project to obtain the construction time limit of each task type of the project; The construction method screening module is electrically connected to the initialization module and the construction time limit allocation module. The construction method screening module is used to generate a construction method for each task type of the project based on the construction time limit of each task type of the project as a basic constraint and the upper limit of the disposable funds of the project as a variable constraint; The construction decision module is electrically connected to the initial module and the construction method screening module. The construction decision module is used to determine whether the construction method of each task type of the engineering project meets the application requirements of the engineering project. If not, it is determined to be re-screened. If so, it is determined to be executed normally.

[0027] In summary, the advantages of the present invention are: by dynamically integrating financial constraints, construction period requirements and application needs, it can achieve real-time optimization and control of project costs. Its core principle is: using the distributed computing power of cloud computing to quickly decompose the total project period into each task node, combining historical cost time series forecasts and real-time resource price data, generating the optimal construction method combination under financial constraints through mixed integer programming, and using a decision tree algorithm to verify its compliance with requirements. Its beneficial effects include: 1. Improving the efficiency of multi-task parallel optimization; 2. Dynamically integrating market resource price fluctuations to enhance cost forecast accuracy; 3. Reducing the cost of manual intervention through automated decision-making chains, achieving shortened construction periods, and improving capital utilization control.

[0028] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for dynamic control of construction cost based on cloud computing, characterized in that: include: Determine the upper limit of available funds for the project, the time limit for completion of the project and the application requirements of the project; Based on the construction process of the project, the completion time limit is divided according to the task type, and the construction time limit of each task type of the project is obtained; Based on the construction time limit of each task type of the project as the basic constraint and the upper limit of the disposable funds of the project as the variable constraint, the construction method of each task type of the project is generated; Determine whether the construction methods of each task type of the project meet the application requirements of the project. If not, re-screen it. If so, execute it normally.

2. The method for dynamic control of construction cost based on cloud computing according to claim 1, characterized in that: The construction progress of the project information is divided according to the task type to obtain the construction progress of several task types of the project; Verify the accessibility between the construction processes of several task types of the engineering project, and build a tree-like hierarchical structure task path dependency matrix for the engineering project; Obtain construction time limit parameters of several task types from historical engineering project information, and establish a reference database of construction time limits of task types of historical engineering projects; Marking the construction time limit of the task type of the historical engineering project and referring to the tasks of the same type as the several task types of the engineering project in the database, obtaining the construction process duration samples of the several task types of the engineering project; Based on the construction process duration samples of several task types of the engineering project as the reference objects of scatter point estimation, the expected duration vectors of the construction process of several task types of the engineering project are extracted.

3. The method for dynamic control of construction cost based on cloud computing according to claim 2, characterized in that: Based on the tree-structured task path dependency matrix of the engineering project, the expected duration vectors of the construction process of several task types of the engineering project are used to verify the forward execution duration path and reverse execution duration path of the construction process of several task types of the engineering project; By using the forward execution duration path and the reverse execution duration path of the construction process of several task types of the engineering project, the floating time allowed for the construction process of several task types of the engineering project is calculated, and the execution path of the construction process of several task types of the engineering project is determined.

4. The method for dynamic control of construction cost based on cloud computing according to claim 3, characterized in that: Based on the expected duration vectors of the construction processes of several task types of the engineering project and the allowed floating times of the construction processes of several task types of the engineering project, the spatial distance of the construction processes of each task type in the tree-like hierarchical structure task path dependency matrix of the engineering project is calculated; Using the k-means clustering algorithm, tasks are divided by minimizing the spatial distance of the construction process of each task type in the tree-like hierarchical structure task path dependency matrix of the engineering project, and the construction process set of parallel and non-parallel task types of the engineering project is obtained; Based on the construction process set of parallel and non-parallel task types of the engineering project, the construction time limit vector of each task type of the engineering project is assigned according to the expected construction period vector of the construction process of several task types of the engineering project, the forward execution period path and reverse execution period path of the construction process, and the allowed floating time and construction process execution path of the construction process.

5. The method for dynamic control of construction cost based on cloud computing according to claim 4, characterized in that: Based on several known construction methods of engineering buildings, obtain the time series parameters of the historical cost changes of construction resources for each construction method; Stabilize the time series parameters of the historical cost changes of construction resources for each construction method; Based on AR time series autoregression, a construction cost prediction model for the construction method of engineering buildings is established; The time series parameters of the historical cost changes of construction resources of each construction method are substituted into the construction cost prediction model of the construction method of the engineering building. The time series parameters of the historical cost changes of construction resources of the construction method are used as the observation value input, and the predicted real-time construction costs of each construction method in the future are output as the predicted value.

6. The method for dynamic control of construction cost based on cloud computing according to claim 5, characterized in that: Determine the construction time requirements and construction cost requirements of each construction method in real time, and establish a database of time-cost requirements for known construction methods; Establish an objective function to minimize the total construction period of the project; The construction time limit of each task type of the project is used as the basic constraint, and the upper limit of the disposable funds of the project is used as the variable constraint to establish the restriction conditions; Based on mixed integer linear programming, a database of known construction method time limits and costs is used as input. According to the constraints, several known construction methods that meet the objective function are screened out to form a set of known construction methods for each task type of the initial screening project.

7. The method for dynamic control of construction cost based on cloud computing according to claim 6, characterized in that: Based on the decision tree, the construction process of each task type of the engineering project is taken as the root node; the known construction method set of each task type of the engineering project that satisfies the initial screening of the construction process root node of each task type is taken as the branch node, and the information gain of each branch node for minimizing the total construction period of the engineering project is used as the branch decision to obtain the construction method of each task type of the engineering project.

8. The method for dynamic control of construction cost based on cloud computing according to claim 7, characterized in that: Based on the construction methods of each task type of the project, quantify the performance indicators of each construction method and establish a performance scatter plot of the construction methods of each task type of the project; Based on the application requirements of the engineering project, mark the construction method performance requirements of each task type of the engineering project; According to the cosine similarity formula, the matching degree between the performance requirements of the construction methods of each task type of the engineering project corresponding to each scatter point in the performance scatter diagram of the construction methods of each task type of the engineering project is calculated; According to the matching degree between the performance requirements of the construction methods of each task type of the engineering project corresponding to each scattered point, it is judged whether the construction methods of each task type of the engineering project meet the application requirements of the engineering project. If not, it is determined to be re-screened. If so, it is determined to be executed normally.

9. A cloud computing-based dynamic engineering cost management and control system, comprising: Initial module, construction time allocation module, construction method screening module, construction decision module; The initialization module is used to determine the upper limit of available funds for the project, the completion time limit for the project, and the application requirements of the project; The construction time limit allocation module is electrically connected to the initial module and is used to divide the completion time limit of the construction process according to the task type based on the construction progress of the project to obtain the construction time limit of each task type of the project; The construction method screening module is electrically connected to the initialization module and the construction time limit allocation module. The construction method screening module is used to generate a construction method for each task type of the project based on the construction time limit of each task type of the project as a basic constraint and the upper limit of the disposable funds of the project as a variable constraint; The construction decision module is electrically connected to the initial module and the construction method screening module. The construction decision module is used to determine whether the construction method of each task type of the engineering project meets the application requirements of the engineering project. If not, it is determined to be re-screened. If so, it is determined to be executed normally.

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