A control method and system for intelligent construction of construction projects

Abstract

The present invention discloses a management and control method and system for intelligent construction of a construction project, which belongs to the field of building management and control technology, and specifically comprises: collecting project data of the construction project, wherein the project data comprises project progress data, material information and human resource information; storing the project data of the construction project, and creating a project data model, wherein the project data model comprises a time dimension, a material dimension and a human resource dimension; based on the project data model, calculating key performance indicators of the construction project, including progress completion rate, material utilization rate and human resource efficiency; calculating deviation values ​​between the key performance indicators of the construction project and set target values, wherein the target values ​​comprise a progress target value, a material target value and a human resource target value; based on the deviation values ​​of the key performance indicators, applying a deep learning algorithm, and adjusting the management and control strategy of the project data corresponding to the deviation values; the present invention provides project managers with more efficient management and control means.

Description

Technical Field

[0001] The present invention relates to the technical field of building management and control, and in particular to a management and control method and system for intelligent construction of building projects. Background Art

[0002] In modern society, construction projects play a vital role in promoting urban development and meeting people's growing living needs. However, traditional construction project management and control methods often face a series of challenges and are difficult to adapt to the increasingly complex and changing construction environment. Traditional methods mainly rely on manual experience and static plans, and cannot flexibly respond to changes in project progress, material supply, and human resources, resulting in inefficiency, waste of resources, and project delays.

[0003] In construction projects, project progress, material utilization, and reasonable allocation of human resources are key factors to ensure the smooth progress of the project. However, due to the complexity and uncertainty of construction projects, it is difficult to achieve optimal management and control through traditional manual planning and adjustments. In addition, environmental factors, climate conditions, and natural disasters can also have a significant impact on the progress of construction projects, further increasing the difficulty of project management and control.

[0004] In order to solve the problems faced by traditional construction project management and control methods, the development of modern technology has provided new opportunities for the introduction of intelligent and data-driven methods. By using technologies such as big data analysis, artificial intelligence and deep learning algorithms, the actual situation of construction projects can be accurately analyzed and predicted, so as to carry out more refined management and control. However, in this field, there is still a lack of a comprehensive method that can fully combine project data, data models and deep learning algorithms to achieve intelligent construction management and control of construction projects. Summary of the invention

[0005] The purpose of the present invention is to provide a control method and system for intelligent construction of construction projects to solve the following technical problems:

[0006] In this field, there is still a lack of a comprehensive approach that can fully combine project data, data models and deep learning algorithms to achieve intelligent construction management and control of construction projects.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] A method for controlling intelligent construction of a construction project comprises the following steps:

[0009] Collecting project data of the construction project, wherein the project data includes project progress data PD, material information MI and human resource information LI;

[0010] Storing project data of the construction project and creating a project data model PM, wherein the project data model includes a time dimension, a material dimension and a human resource dimension;

[0011] Based on the project data model, key performance indicators of the construction project are calculated, including progress completion rate PCR, material utilization rate MUR and human resource efficiency LE;

[0012] Calculate the deviation between the key performance indicators of the construction project and the set target values, wherein the target values ​​include the progress target value PTV, the material target value MTV and the human resource target value LTV;

[0013] Based on the deviation value of the key performance indicator, a deep learning algorithm is applied to adjust the management and control strategy of the project data corresponding to the deviation value.

[0014] As a further solution of the present invention: the project data model also includes environmental dimensions, climate conditions, and natural disaster risks.

[0015] As a further solution of the present invention: the calculation process of the progress completion rate is as follows:

[0016] PCR = AP / PP;

[0017] Among them, AP stands for actual progress, PP stands for planned progress, the actual progress refers to the ratio of the actual completed engineering tasks to the total engineering tasks, and the planned progress refers to the pre-established engineering task plan.

[0018] As a further solution of the present invention: the calculation process of the material utilization rate is as follows:

[0019] MUR = AMU / TMR;

[0020] Among them, AMU represents the actual amount of materials used, and TMR represents the theoretical amount of materials required. The actual amount of materials used refers to the total amount of materials actually used in the project, and the theoretical amount of materials required is the estimated total amount of materials calculated based on the project plan and material consumption rate.

[0021] As a further solution of the present invention: the calculation process of the human resource efficiency is as follows:

[0022] LE = ALO / PLO;

[0023] Among them, ALO stands for actual manpower output, and PLO stands for planned manpower output. The actual manpower output refers to the number of human resources required to actually complete the workload, and the planned manpower output is the expected number of human resources based on the project plan and workload estimate.

[0024] As a further solution of the present invention: the specific steps of applying the deep learning algorithm include:

[0025] Construct a fitness function and comprehensively calculate the deviations of various key performance indicators and the weights of their impact on project management and control; use a deep learning algorithm to optimize the constructed fitness function, search for the optimal solution in the management and control strategy, and gradually approach the optimal management and control strategy through an iterative optimization process. The deep learning algorithm will evaluate the fitness function based on the results. When the evaluation results meet the preset standards, the management and control strategy will be marked as a candidate management and control strategy.

[0026] As a further solution of the present invention: the process of adjusting the control strategy is:

[0027] When the deviation between the progress completion rate and the progress target value is greater than the preset threshold a%, the number of human resources n in the project is obtained, and the number of human resources is adjusted to m, m=nPTV / PCR, and the human resource efficiency is recalculated based on m. If the deviation between the human resource efficiency and the human resource target value is greater than the preset threshold b%, employees are trained until their human resource efficiency is improved to b%. When the deviation between the material utilization rate and the material target value is greater than the preset threshold c%, additional materials of c% of the used materials are purchased, and the original material usage process is reviewed.

[0028] A management and control system for intelligent construction of construction projects, comprising:

[0029] A data collection module is used to collect project data of the construction project, wherein the project data includes project progress data PD, material information MI and human resource information LI;

[0030] A module building module is used to store project data of a construction project and create a project data model PM, wherein the project data model includes a time dimension, a material dimension and a human resource dimension;

[0031] An indicator calculation module, used for calculating key performance indicators of the construction project based on the project data model, including progress completion rate PCR, material utilization rate MUR and human resource efficiency LE;

[0032] Deviation monitoring module, used to calculate the deviation between the key performance indicators of the construction project and the set target values, wherein the target values ​​include the progress target value PTV, the material target value MTV and the human resource target value LTV;

[0033] The strategy optimization module is used to apply a deep learning algorithm based on the deviation value of the key performance indicator to adjust the management and control strategy of the project data corresponding to the deviation value.

[0034] Beneficial effects of the present invention:

[0035] The present invention collects and analyzes project data in real time, combined with the application of deep learning algorithms, to more accurately predict and identify deviations in project progress, material utilization, and human resources, so that adjustments can be made more quickly, improving the efficiency and accuracy of management and control, reducing delays and waste of resources, and automatically adjusting project management and control strategies based on deviations through the establishment and maintenance of project data models and the optimal application of deep learning algorithms, so as to achieve optimal management and control of project progress, materials, human resources, etc., and improve the overall performance of the project. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The present invention will be further described below in conjunction with the accompanying drawings.

[0037] Figure 1 It is a flow chart of a method for controlling intelligent construction of a construction project according to the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] See also Figure 1 As shown, the present invention is a method and system for controlling intelligent construction of a construction project, comprising the following steps:

[0040] Collecting project data of the construction project, wherein the project data includes project progress data PD, material information MI and human resource information LI;

[0041] Storing project data of the construction project and creating a project data model PM, wherein the project data model includes a time dimension, a material dimension and a human resource dimension;

[0042] Based on the project data model, key performance indicators of the construction project are calculated, including progress completion rate PCR, material utilization rate MUR and human resource efficiency LE;

[0043] Calculate the deviation between the key performance indicators of the construction project and the set target values, wherein the target values ​​include the progress target value PTV, the material target value MTV and the human resource target value LTV;

[0044] Based on the deviation value of the key performance indicator, a deep learning algorithm is applied to adjust the management and control strategy of the project data corresponding to the deviation value.

[0045] In another preferred embodiment of the present invention, the project data model also includes environmental dimensions, climate conditions, and natural disaster risks.

[0046] In another preferred embodiment of the present invention, the calculation process of the progress completion rate is as follows:

[0047] PCR = AP / PP;

[0048] Among them, AP stands for actual progress, PP stands for planned progress, the actual progress refers to the ratio of the actual completed engineering tasks to the total engineering tasks, and the planned progress refers to the pre-established engineering task plan.

[0049] In another preferred embodiment of the present invention, the calculation process of the material utilization rate is as follows:

[0050] MUR = AMU / TMR;

[0051] Among them, AMU represents the actual amount of materials used, and TMR represents the theoretical amount of materials required. The actual amount of materials used refers to the total amount of materials actually used in the project, and the theoretical amount of materials required is the estimated total amount of materials calculated based on the project plan and material consumption rate.

[0052] In another preferred embodiment of the present invention, the calculation process of the human resource efficiency is as follows:

[0053] LE = ALO / PLO;

[0054] Among them, ALO stands for actual manpower output, and PLO stands for planned manpower output. The actual manpower output refers to the number of human resources required to actually complete the workload, and the planned manpower output is the expected number of human resources based on the project plan and workload estimate.

[0055] In another preferred embodiment of the present invention, the specific steps of applying the deep learning algorithm include:

[0056] Construct a fitness function and comprehensively calculate the deviations of various key performance indicators and the weights of their impact on project management and control; use a deep learning algorithm to optimize the constructed fitness function, search for the optimal solution in the management and control strategy, and gradually approach the optimal management and control strategy through an iterative optimization process. The deep learning algorithm will evaluate the fitness function based on the results. When the evaluation results meet the preset standards, the management and control strategy will be marked as a candidate management and control strategy.

[0057] In another preferred embodiment of the present invention, the process of adjusting the control strategy is:

[0058] When the deviation between the progress completion rate and the progress target value is greater than the preset threshold a%, the number of human resources n in the project is obtained, and the number of human resources is adjusted to m, m=nPTV / PCR, and the human resource efficiency is recalculated based on m. If the deviation between the human resource efficiency and the human resource target value is greater than the preset threshold b%, employees are trained until their human resource efficiency is improved to b%. When the deviation between the material utilization rate and the material target value is greater than the preset threshold c%, additional materials of c% of the used materials are purchased, and the original material usage process is reviewed.

[0059] A management and control system for intelligent construction of construction projects, comprising:

[0060] A data collection module is used to collect project data of the construction project, wherein the project data includes project progress data PD, material information MI and human resource information LI;

[0061] A module building module is used to store project data of a construction project and create a project data model PM, wherein the project data model includes a time dimension, a material dimension and a human resource dimension;

[0062] An indicator calculation module, used for calculating key performance indicators of the construction project based on the project data model, including progress completion rate PCR, material utilization rate MUR and human resource efficiency LE;

[0063] Deviation monitoring module, used to calculate the deviation between the key performance indicators of the construction project and the set target values, wherein the target values ​​include the progress target value PTV, the material target value MTV and the human resource target value LTV;

[0064] The strategy optimization module is used to apply a deep learning algorithm based on the deviation value of the key performance indicator to adjust the management and control strategy of the project data corresponding to the deviation value.

[0065] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for controlling intelligent construction of a building project, characterized in that: The following steps are involved: Collecting project data of the construction project, wherein the project data includes project progress data PD, material information MI and human resource information LI; Storing project data of the construction project and creating a project data model PM, wherein the project data model includes a time dimension, a material dimension and a human resource dimension; Based on the project data model, key performance indicators of the construction project are calculated, including progress completion rate PCR, material utilization rate MUR and human resource efficiency LE; Calculate the deviation between the key performance indicators of the construction project and the set target values, wherein the target values ​​include the progress target value PTV, the material target value MTV and the human resource target value LTV; Based on the deviation value of the key performance indicator, a deep learning algorithm is applied to adjust the control strategy of the project data corresponding to the deviation value; The specific steps of applying the deep learning algorithm include: Construct an adaptive function to comprehensively calculate the deviations of various key performance indicators and the weights of their impact on project management and control; Use deep learning algorithms to optimize the constructed fitness function, search for the optimal solution in the management and control strategy, and gradually approach the optimal management and control strategy through an iterative optimization process. The deep learning algorithm will evaluate the fitness function based on the results. When the evaluation results meet the preset standards, the control strategy will be marked as a candidate control strategy.

2. A method for controlling intelligent construction of a construction project according to claim 1, characterized in that: The project data model also includes environmental dimensions, climate conditions, and natural disaster risks.

3. The method for controlling intelligent construction of a construction project according to claim 1, characterized in that: The calculation process of the progress completion rate is as follows: PCR = AP / PP; Among them, AP stands for actual progress, PP stands for planned progress, the actual progress refers to the ratio of the actual completed engineering tasks to the total engineering tasks, and the planned progress refers to the pre-established engineering task plan.

4. The method for controlling the intelligent construction of a construction project according to claim 1, characterized in that: The calculation process of the material utilization rate is as follows: MUR = AMU / TMR; Among them, AMU represents the actual amount of materials used, and TMR represents the theoretical amount of materials required. The actual amount of materials used refers to the total amount of materials actually used in the project, and the theoretical amount of materials required is the estimated total amount of materials calculated based on the project plan and material consumption rate.

5. A method for controlling intelligent construction of a construction project according to claim 1, characterized in that: The calculation process of the human resource efficiency is as follows: LE = ALO / PLO; Among them, ALO stands for actual manpower output, and PLO stands for planned manpower output. The actual manpower output refers to the number of human resources required to actually complete the workload, and the planned manpower output is the expected number of human resources based on the project plan and workload estimate.

6. A method for controlling intelligent construction of a construction project according to claim 1, characterized in that: The process of adjusting the control strategy of the project data corresponding to the deviation value is: When the deviation between the progress completion rate and the progress target value is greater than the preset threshold a%, the number of human resources n in the project is obtained, and the number of human resources is adjusted to m, m=nPTV / PCR, and the human resource efficiency is recalculated based on m. If the deviation between the human resource efficiency and the human resource target value is greater than the preset threshold b%, employees are trained until their human resource efficiency is improved to b%. When the deviation between the material utilization rate and the material target value is greater than the preset threshold c%, additional materials of c% of the used materials are purchased, and the original material usage process is reviewed.

7. A management and control system for intelligent construction of construction projects, characterized in that: include: A data collection module is used to collect project data of the construction project, wherein the project data includes project progress data PD, material information MI and human resource information LI; A module building module is used to store project data of a construction project and create a project data model PM, wherein the project data model includes a time dimension, a material dimension and a human resource dimension; An indicator calculation module, used for calculating key performance indicators of the construction project based on the project data model, including progress completion rate PCR, material utilization rate MUR and human resource efficiency LE; Deviation monitoring module, used to calculate the deviation between the key performance indicators of the construction project and the set target values, wherein the target values ​​include the progress target value PTV, the material target value MTV and the human resource target value LTV; A strategy optimization module, for applying a deep learning algorithm based on the deviation value of the key performance indicator to adjust the control strategy of the project data corresponding to the deviation value; The specific steps of applying the deep learning algorithm include: Construct an adaptive function to comprehensively calculate the deviations of various key performance indicators and the weights of their impact on project management and control; Use deep learning algorithms to optimize the constructed fitness function, search for the optimal solution in the management and control strategy, and gradually approach the optimal management and control strategy through an iterative optimization process. The deep learning algorithm will evaluate the fitness function based on the results. When the evaluation results meet the preset standards, the control strategy will be marked as a candidate control strategy.

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