Building construction site material demand sensing method and device based on AI prediction

Abstract

The invention provides a building construction site material demand sensing method and device based on AI prediction, and relates to the technical field of building data processing. The method comprises the following steps: obtaining construction plan information, construction site pictures, construction environment data and historical construction information of a historical period of time of a target building construction project; obtaining the actual construction progress of the target building construction project based on the construction site picture; performing construction progress prediction based on the actual construction progress, the construction environment data and the historical construction information to obtain a predicted construction progress of the target building construction project in a future time period; based on the predicted construction progress and the construction plan information, determining predicted construction resistance of the target building construction project in a future time period; and inputting the predicted construction progress and the predicted construction resistance into a preset material prediction model based on machine learning to obtain a material demand sensing result of the target building construction project in the future time period. According to the invention, material requirements can be accurately sensed in combination with specific building construction projects.

Description

Technical Field

[0001] The present invention relates to the technical field of building data processing, and particularly to a method and device for perceiving material requirements at a building construction site based on AI prediction. Background Art

[0002] Building construction is a production activity that uses various building materials and construction machinery and equipment to build various building products within a certain space and time according to a specific design blueprint. It is the construction process of various buildings, and can also be said to be a process of turning the various lines on the design drawings into physical objects at a designated location.

[0003] During the building construction process, material requirement management is a key link. However, in the actual construction production process, materials are usually allocated based on manual experience, and the degree of integration with specific building construction projects is not deep enough to accurately perceive the material requirements during the construction process. As a result, there are too many or too few materials at the construction site or in the material warehouse, affecting the continuity and efficiency of building construction. Summary of the Invention

[0004] Embodiments of the present invention provide a method and device for perceiving material requirements at a building construction site based on AI prediction to solve the problem that it is currently difficult to accurately perceive material requirements by deeply integrating with specific building construction projects.

[0005] In a first aspect, embodiments of the present invention provide a method for perceiving material requirements at a building construction site based on AI prediction, including:

[0006] Obtaining the construction plan information, construction site pictures, construction environment data of the target building construction project, and historical construction information of the target building construction project in a historical period;

[0007] Obtaining the actual construction progress of the target building construction project based on the construction site pictures;

[0008] Predicting the construction progress based on the actual construction progress, the construction environment data, and the historical construction information to obtain the predicted construction progress of the target building construction project in a future period;

[0009] Determining the predicted construction resistance of the target building construction project in a future period based on the predicted construction progress and the construction plan information;

[0010] Inputting the predicted construction progress and the predicted construction resistance into a preset material prediction model based on machine learning to obtain the material requirement perception result of the target building construction project in a future period.

[0011] In a possible implementation manner, obtaining the actual construction progress of the target building construction project based on the construction site pictures includes:

[0012] Perform construction target recognition and completion detection on the construction site pictures based on computer vision to obtain the construction completion degree of the target building construction project;

[0013] Obtain the actual construction progress of the target building construction project according to the construction completion degree.

[0014] In a possible implementation manner, perform construction progress prediction based on the actual construction progress, the construction environment data, and the historical construction information to obtain the predicted construction progress of the target building construction project in a future time period, including:

[0015] Perform construction progress prediction based on the actual construction progress and the historical construction information to obtain the initial predicted construction progress of the target building construction project in a future time period;

[0016] Modify the initial predicted construction progress based on the construction environment data to obtain the predicted construction progress of the target building construction project in a future time period.

[0017] In a possible implementation manner, modify the initial predicted construction progress based on the construction environment data to obtain the predicted construction progress of the target building construction project in a future time period, including:

[0018] Determine the environmental impact coefficient based on the construction environment data;

[0019] Modify the initial predicted construction progress according to the environmental impact coefficient to obtain the predicted construction progress of the target building construction project in a future time period.

[0020] In a possible implementation manner, determine the predicted construction resistance of the target building construction project in a future time period based on the predicted construction progress and the construction plan information, including:

[0021] Determine the conflicting construction processes of the target building construction project in a future time period based on the predicted construction progress and the construction plan information;

[0022] Determine the predicted construction resistance of the target building construction project in a future time period according to the conflicting construction processes and the time sequence of the conflicting construction processes.

[0023] In a possible implementation manner, determine the predicted construction resistance of the target building construction project in a future time period according to the conflicting construction processes and the time sequence of the conflicting construction processes, including:

[0024] Determine the influence scope of the conflicting construction processes according to the conflicting construction processes and the time sequence of the conflicting construction processes;

[0025] Determine the predicted construction resistance of the target building construction project in the future period according to the influence scope of the conflict construction process.

[0026] In a second aspect, an embodiment of the present invention provides a device for perceiving the material requirements at a building construction site based on AI prediction, including:

[0027] A first processing module, configured to obtain the actual construction progress of the target building construction project based on the construction site pictures;

[0028] A second processing module, configured to perform construction progress prediction based on the actual construction progress, the construction environment data, and the historical construction information, and obtain the predicted construction progress of the target building construction project in the future period;

[0029] A third processing module, configured to determine the predicted construction resistance of the target building construction project in the future period based on the predicted construction progress and the construction plan information;

[0030] A demand perception module, configured to input the predicted construction progress and the predicted construction resistance into a preset material prediction model based on machine learning, and obtain the material demand perception result of the target building construction project in the future period.

[0031] In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the method in the first aspect or any possible implementation manner of the first aspect above is implemented.

[0032] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the method in the first aspect or any possible implementation manner of the first aspect above is implemented.

[0033] In a fifth aspect, an embodiment of the present invention provides a computer program product, including a computer program, and when the computer program is executed by a processor, the method in the first aspect or any possible implementation manner of the first aspect above is implemented.

[0034] In the embodiments of the present invention, the construction plan information, construction site pictures, construction environment data, and historical construction information of the target building construction project in the historical period are obtained; then, the actual construction progress of the target building construction project is obtained based on the construction site pictures, and the construction progress prediction is carried out based on the actual construction progress, construction environment data, and historical construction information to obtain the predicted construction progress of the target building construction project in the future period; then, based on the predicted construction progress and construction plan information, the predicted construction resistance of the target building construction project in the future period is determined; finally, the predicted construction progress and predicted construction resistance are input into a preset material prediction model based on machine learning to obtain the material demand perception result of the target building construction project in the future period. Historical construction information and real-time sensor information (i.e., construction site pictures, construction environment data) can be used to predict future material requirements through machine learning algorithms, and then the material supply plan can be dynamically adjusted according to the specific situation of the construction site to ensure that the materials are always at the optimal inventory level, and to ensure the continuity, efficiency, and safety of the construction process. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a flowchart of the implementation of the method for perceiving material requirements at a building construction site based on AI prediction provided by the embodiments of the present invention;

[0036] Figure 2 is a schematic structural diagram of the device for perceiving material requirements at a building construction site based on AI prediction provided by the embodiments of the present invention;

[0037] Figure 3 is a schematic diagram of an electronic device provided by the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] The embodiments of the present invention will be described in detail below with reference to the drawings.

[0039] See Figure 1 , which shows a flowchart of the implementation of the method for perceiving material requirements at a building construction site based on AI prediction provided by the embodiments of the present invention, and is described in detail as follows:

[0040] Step 101, obtain the construction plan information, construction site pictures, construction environment data, and historical construction information of the target building construction project in the historical period.

[0041] Exemplarily, the construction plan information can be obtained from the project duration information, construction schedule plan, and other preliminary documents of the target building construction project. The construction site pictures can be current multi-angle construction site pictures. The construction environment data can include the temperature, humidity, dust situation, construction site layout, etc. at the construction site. The historical construction information can include the labor situation, material usage situation, etc. in the historical period.

[0042] Obtaining the construction plan information, construction site pictures, construction environment data of the target building construction project, and the historical construction information of the target building construction project in the historical period helps to estimate the required materials by deeply combining the detailed information of the target building construction project.

[0043] Step 102, obtain the actual construction progress of the target building construction project based on the construction site pictures.

[0044] Optionally, obtaining the actual construction progress of the target building construction project based on the construction site pictures includes:

[0045] Perform construction target recognition and completion degree detection on the construction site pictures based on computer vision to obtain the construction completion degree of the target building construction project.

[0046] Obtain the actual construction progress of the target building construction project according to the construction completion degree.

[0047] Among them, the construction target recognition of the construction site pictures can be performed based on a preset construction target recognition model. After the construction targets in the construction site pictures are recognized, the completion degree detection can be performed based on the standard completion situation of the construction targets, and then the actual construction progress of the target building construction project can be determined based on its completion degree.

[0048] Step 103, perform construction progress prediction based on the actual construction progress, construction environment data, and historical construction information to obtain the predicted construction progress of the target building construction project in the future period.

[0049] Optionally, performing construction progress prediction based on the actual construction progress, construction environment data, and historical construction information to obtain the predicted construction progress of the target building construction project in the future period includes:

[0050] Perform construction progress prediction based on the actual construction progress and historical construction information to obtain the initial predicted construction progress of the target building construction project in the future period.

[0051] Correct the initial predicted construction progress based on the construction environment data to obtain the predicted construction progress of the target building construction project in the future period.

[0052] Exemplarily, correcting the initial predicted construction progress based on the construction environment data to obtain the predicted construction progress of the target building construction project in the future period includes:

[0053] Determine the environmental impact coefficient based on the construction environment data.

[0054] Correct the initial predicted construction progress according to the environmental impact coefficient to obtain the predicted construction progress of the target building construction project in the future period.

[0055] In this embodiment, based on the actual construction progress and historical construction information, the actual construction speed of the target building construction project can be evaluated, and then the initial predicted construction progress of the target building construction project in the future period can be obtained based on the actual construction speed of the target building construction project. On this basis, considering the influence of construction environment data on the construction speed, where the construction environment data may include the construction environment data of the current period and may also include the predicted construction environment data in the future, so as to comprehensively evaluate the environmental impact coefficient in the future period by combining the construction environment data of the current period and the predicted construction environment data in the future, and to correct the initial predicted construction progress according to the environmental impact coefficient to obtain the predicted construction progress of the target building construction project in the future period.

[0056] Step 104, based on the predicted construction progress and construction plan information, determine the predicted construction resistance of the target building construction project in the future period.

[0057] Optionally, based on the predicted construction progress and construction plan information, determining the predicted construction resistance of the target building construction project in the future period includes:

[0058] Based on the predicted construction progress and construction plan information, determine the conflicting construction processes of the target building construction project in the future period.

[0059] According to the conflicting construction processes and the time sequence of the conflicting construction processes, determine the predicted construction resistance of the target building construction project in the future period.

[0060] Exemplarily, according to the conflicting construction processes and the time sequence of the conflicting construction processes, determining the predicted construction resistance of the target building construction project in the future period includes:

[0061] According to the conflicting construction processes and the time sequence of the conflicting construction processes, determine the influence scope of the conflicting construction processes.

[0062] According to the influence scope of the conflicting construction processes, determine the predicted construction resistance of the target building construction project in the future period.

[0063] In this embodiment, after obtaining the predicted construction progress, it is also considered that there may be conflicting construction processes during the construction process, and the conflicting construction processes will affect the construction speed and thus affect the materials required during the construction process.

[0064] Therefore, first, according to the predicted construction progress and construction plan information, screen out the conflicting construction processes that may occur in the future period of the target building construction project, and then based on the conflicting construction processes and the time sequence of the conflicting construction processes (that is, the position where the conflicting construction processes appear in the predicted construction progress, such as on the first day or the third day, the fifth day, etc.), determine the influence scope of the conflicting construction processes. The larger the influence scope, the greater the predicted construction resistance.

[0065] Step 105: Input the predicted construction progress and predicted construction resistance into a preset material prediction model based on machine learning to obtain the material demand perception result of the target building construction project in the future time period.

[0066] In the embodiment of the present invention, by obtaining the construction plan information, construction site pictures, construction environment data of the target building construction project, and the historical construction information of the target building construction project in the historical time period; then first obtaining the actual construction progress of the target building construction project based on the construction site pictures, and predicting the construction progress based on the actual construction progress, construction environment data, and historical construction information to obtain the predicted construction progress of the target building construction project in the future time period; then determining the predicted construction resistance of the target building construction project in the future time period based on the predicted construction progress and construction plan information; finally, inputting the predicted construction progress and predicted construction resistance into a preset material prediction model based on machine learning to obtain the material demand perception result of the target building construction project in the future time period. It is possible to utilize historical construction information and real-time sensor information (i.e., construction site pictures, construction environment data), predict future material requirements through machine learning algorithms, and then dynamically adjust the material replenishment plan according to the specific situation of the construction site to ensure that the materials are always at the optimal inventory level, and guarantee the continuity, efficiency, and safety of the construction process.

[0067] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution is prior or subsequent. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0068] The following is the device embodiment of the present invention. For the details not described in detail herein, reference can be made to the corresponding method embodiments above.

[0069] Figure 2 The structure diagram of the material demand perception device for a building construction site based on AI prediction provided by the embodiment of the present invention is shown. For the sake of convenience of description, only the parts related to the embodiment of the present invention are shown and are described in detail as follows:

[0070] As Figure 2 shown, the material demand perception device for a building construction site based on AI prediction includes: an acquisition module 21, a first processing module 22, a second processing module 23, a third processing module 24, and a demand perception module 25.

[0071] The acquisition module 21 is used to acquire the construction plan information, construction site pictures, construction environment data of the target building construction project, and the historical construction information of the target building construction project in the historical time period;

[0072] The first processing module 22 is used to obtain the actual construction progress of the target building construction project based on the construction site pictures;

[0073] A second processing module 23, configured to perform construction progress prediction based on the actual construction progress, the construction environment data, and the historical construction information, so as to obtain the predicted construction progress of the target building construction project in a future time period;

[0074] A third processing module 24, configured to determine the predicted construction resistance of the target building construction project in a future time period based on the predicted construction progress and the construction plan information;

[0075] A demand perception module 25, configured to input the predicted construction progress and the predicted construction resistance into a preset material prediction model based on machine learning, so as to obtain the material demand perception result of the target building construction project in a future time period.

[0076] In a possible implementation manner, the first processing module 22 may be configured to perform construction target recognition and completion degree detection on the construction site pictures based on computer vision, so as to obtain the construction completion degree of the target building construction project; and obtain the actual construction progress of the target building construction project according to the construction completion degree.

[0077] In a possible implementation manner, the second processing module 23 may be configured to perform construction progress prediction based on the actual construction progress and the historical construction information, so as to obtain the initial predicted construction progress of the target building construction project in a future time period; and correct the initial predicted construction progress based on the construction environment data, so as to obtain the predicted construction progress of the target building construction project in a future time period.

[0078] In a possible implementation manner, the second processing module 23 may be configured to determine an environmental impact coefficient based on the construction environment data; and correct the initial predicted construction progress according to the environmental impact coefficient, so as to obtain the predicted construction progress of the target building construction project in a future time period.

[0079] In a possible implementation manner, the third processing module 24 may be configured to determine the conflicting construction processes of the target building construction project in a future time period based on the predicted construction progress and the construction plan information; and determine the predicted construction resistance of the target building construction project in a future time period according to the conflicting construction processes and the time sequence of the conflicting construction processes.

[0080] In a possible implementation manner, the third processing module 24 may be configured to determine the influence range of the conflicting construction process according to the conflicting construction process and the time sequence of the conflicting construction process; and determine the predicted construction resistance of the target building construction project in a future time period according to the influence range of the conflicting construction process.

[0081] Figure 3 It is a schematic diagram of an electronic device provided by an embodiment of the present invention. AsFigure 3 As shown, the electronic device 3 of this embodiment includes: a processor 30 and a memory 31. The memory 31 stores a computer program 32. When the processor 30 executes the computer program 32, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor 30 executes the computer program 32, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0082] Exemplarily, the computer program 32 can be divided into one or more modules / units. The one or more modules / units are stored in the memory 31 and executed by the processor 30 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program 32 in the electronic device 3.

[0083] The electronic device 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art can understand that Figure 3 merely examples of the electronic device 3, which do not constitute a limitation on the electronic device 3. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, the electronic device 3 may further include input / output devices, network access devices, buses, etc.

[0084] The processor 30 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0085] The memory 31 can be an internal storage unit of the electronic device 3, such as the hard disk or memory of the electronic device 3. The memory 31 can also be an external storage device of the electronic device 3, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the electronic device 3. Further, the memory 31 can also include both the internal storage unit of the electronic device 3 and the external storage device. The memory 31 is used to store the computer program 32 and other programs and data required by the electronic device 3. The memory 31 can also be used to temporarily store the data that has been output or will be output.

[0086] For the convenience and simplicity of description, only the above division of each functional module / unit is used as an example for illustration. In actual applications, the above functions can be assigned to different functional modules / units according to needs. The above modules / units can be implemented in the form of hardware, or in the form of software, or in the form of a combination of hardware and software.

[0087] The embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the methods in the above method embodiments are implemented.

[0088] The embodiment of the present invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, the methods in the above method embodiments are implemented.

[0089] Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0090] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments. Without special instructions and logical conflicts, the terms and / or descriptions between different embodiments are consistent and can be mutually referred to. The technical features in different embodiments can be combined to form new embodiments according to their internal logical relationships.

[0091] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for sensing material demand at a construction site based on AI prediction, characterized in that: include: Obtaining construction plan information, construction site pictures, construction environment data and historical construction information of the target building construction project in historical periods; Obtaining the actual construction progress of the target building construction project based on the construction site picture; Perform construction progress forecasting based on the actual construction progress, the construction environment data and the historical construction information to obtain a forecasted construction progress of the target building construction project in a future period; Determining predicted construction resistance of a target building construction project in a future period based on the predicted construction progress and the construction plan information; The predicted construction progress and the predicted construction resistance are input into a preset material prediction model based on machine learning to obtain material demand perception results for the target construction project in the future period.

2. The method for sensing material demand at a construction site based on AI prediction according to claim 1 is characterized in that: The actual construction progress of the target building construction project is obtained based on the construction site picture, including: Based on computer vision, the construction site image is used to identify construction targets and detect completion, so as to obtain the construction completion degree of the target building construction project; The actual construction progress of the target building construction project is obtained according to the construction completion degree.

3. The method for sensing material demand at a construction site based on AI prediction according to claim 1 is characterized in that: The construction progress is predicted based on the actual construction progress, the construction environment data and the historical construction information to obtain the predicted construction progress of the target building construction project in the future period, including: Perform construction progress forecasting based on the actual construction progress and the historical construction information to obtain an initial forecast construction progress of the target building construction project in the future period; The initial predicted construction progress is corrected based on the construction environment data to obtain a predicted construction progress of the target building construction project in a future period.

4. The method for sensing material demand at a construction site based on AI prediction according to claim 3 is characterized in that: The initial predicted construction progress is corrected based on the construction environment data to obtain the predicted construction progress of the target building construction project in the future period, including: Determining an environmental impact coefficient based on the construction environment data; The initial predicted construction progress is corrected according to the environmental impact coefficient to obtain the predicted construction progress of the target building construction project in the future period.

5. The method for sensing material demand at a construction site based on AI prediction according to claim 1 is characterized in that: Determining the predicted construction resistance of the target building construction project in a future period based on the predicted construction progress and the construction plan information includes: Determine conflicting construction processes of the target building construction project in a future period based on the predicted construction progress and the construction plan information; According to the conflicting construction processes and the time sequence of the conflicting construction processes, the predicted construction resistance of the target building construction project in the future period is determined.

6. The method for sensing material demand at a construction site based on AI prediction according to claim 5 is characterized in that: According to the conflicting construction process and the time sequence of the conflicting construction process, the predicted construction resistance of the target building construction project in the future period is determined, including: Determining the impact scope of the conflicting construction process according to the conflicting construction process and the time sequence of the conflicting construction process; According to the impact scope of the conflicting construction processes, the predicted construction resistance of the target building construction project in the future period is determined.

7. A material demand sensing device for a construction site based on AI prediction, characterized in that: include: An acquisition module is used to acquire construction plan information, construction site pictures, construction environment data and historical construction information of the target building construction project in historical periods; A first processing module, configured to obtain an actual construction progress of a target building construction project based on the construction site picture; A second processing module is used to predict the construction progress based on the actual construction progress, the construction environment data and the historical construction information, and obtain the predicted construction progress of the target building construction project in the future period; A third processing module is used to determine the predicted construction resistance of the target building construction project in a future period based on the predicted construction progress and the construction plan information; The demand perception module is used to input the predicted construction progress and the predicted construction resistance into a preset material prediction model based on machine learning to obtain the material demand perception results of the target construction project in the future period.

8. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The method comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.

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