CN115392808A - Building construction prediction progress and allocation system based on deep learning - Google Patents
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Abstract
The invention belongs to the technical field of building construction prediction progress and allocation, and particularly relates to a building construction prediction progress and allocation system based on deep learning; the prepared material and manpower data are input to each house building department through a house building department data module and are transmitted to a house building prediction module; after the house is built for a period of time, the pictures of the house under construction are collected through a picture collecting module and uploaded to a deep learning picture recognition platform; determining the data of the building construction completion degree according to the picture identification result and personnel; the data of the building construction completion degree is transmitted to a building construction allocation module; the method can effectively predict the completion degree of the building construction according to the existing data, and does not completely depend on the completion degree confirmation of the building construction by manpower; and a more accurate allocation mechanism is provided for the house construction, and the house construction can be allocated in more aspects when the difference between the prediction completion degree and the house construction is met.
Description
Technical Field
The invention belongs to the technical field of building construction prediction progress and allocation, and particularly relates to a building construction prediction progress and allocation system based on deep learning.
Background
Building construction is a project frequently encountered by construction units, but the building construction progress lacks an accurate prediction and allocation mechanism; the completion degree of house construction cannot be effectively predicted according to the existing data, and future projects are lack of grasp; high-tech application is not introduced in the house construction process, the completion degree is seriously determined by manpower, and an auxiliary mechanism is lacked; a detailed deployment mechanism cannot be implemented based on the difference between the predicted and actual completion levels.
On one hand, the existing data cannot be used for effectively predicting the completion degree of building construction on the one hand; the completion degree of house construction is confirmed by manpower; not only can the building construction completion degree be predicted, but also the building construction progress in the future can not be planned for a long time; on the other hand, the house construction does not have a more precise allocation mechanism for the house construction, and when the difference between the prediction completion degree and the house construction is met, the house construction cannot be allocated in more aspects, and an elastic mechanism for the house construction is lacked.
Disclosure of Invention
The invention is based on the technical problems, and the system for predicting the building construction progress and allocating is used for providing a system for predicting the building construction progress and allocating based on deep learning;
the method not only can effectively predict the completion degree of the building construction by the existing data and does not completely depend on manual confirmation of the completion degree of the building construction, but also can provide a more accurate allocation mechanism for the building construction.
The invention is realized by the following steps:
the invention provides a building construction prediction progress and allocation system based on deep learning, which applies a picture collection module, a building construction department data module, a deep learning picture identification platform, a building construction prediction module and a building construction allocation module; characterized in that the method comprises the following steps:
step1, each house building department inputs prepared material and manpower data through a house building department data module;
step2: transmitting the data collected by the data module of the house building department to the house building prediction module;
step3, after the house is built for a period of time, the photo of the house under construction is collected through a picture collecting module and uploaded to a deep learning picture recognition platform;
step 4, transmitting the completion data to a house construction allocation module according to the picture identification result and the house construction;
and 5, carrying out house building material and manpower data allocation through a house building allocation module.
According to one implementation manner of the aspect of the invention, the specific operation method for inputting the prepared material and human data by each department of building construction in the step1 comprises the following steps:
each department of house construction includes: reinforcing steel bar departments, human resource and cement departments and the like; marking phi for each department of building construction β Wherein β =0,1,2,. Cndot.n; n is a positive integer and represents the maximum value of the value of beta in each department of building construction; the prepared material and labor data represent the building materials required for the building construction and the number of people required to participate in the construction.
According to one possible implementation of the aspect of the present invention, the method for transmitting the data collected by the house building department data module to the house building forecasting module in the step2 comprises:
calculating and analyzing the collected data through a building construction prediction module to obtain the required building construction completion degree according to the existing material and manpower data under the normal condition;
assuming that input data for each factor affecting house construction is set to x 1 ，x 2 ,x 3 ...x n (ii) a The weight of each influencing factor is set as a 1 ，a 2 ,a 3 ...a n (ii) a The estimated completion is set to y;
according to the mathematical formula:
an objective function:
the weight values are expressed as:
the house building prediction module mainly depends on the weight value of each factor influencing house building, the weight value is used as an independent variable, and the house building prediction result is a dependent variable; the existing completion degree representation occurring under similar conditions is based on historical experience data and the completion condition of building construction under the same condition, and new building construction is predicted; the two are rare in life based on the same situation, the similar situations are more, and the difference of the similar situations is not more than 5%;
different degrees of completeness are required, and the weight values are different;
predicting the final completion degree:
because each influence factor on the building construction is different, the corresponding percentage in the building construction is also different, although the percentage in the large-small range can be generated in the historical experience, the percentage of the maximum value is taken to ensure that the building construction is completed on time when the prediction condition of the building construction is carried out in practice, and the influence on the building construction under the extreme condition is avoided; thus, in the building construction prediction module, the percentage of the maximum value of the input data for each factor affecting building construction has been previously defined based on prior historical empirical data.
According to an implementation manner of the aspect of the present invention, the method for collecting the photo of the building under construction in step3 and uploading the photo to the deep learning picture recognition platform includes:
after the house is built for a period of time, the shot photo of the house under construction is collected through a picture collecting module and uploaded to a deep learning picture recognition platform;
the house photos mainly comprise multi-angle shooting scenes and construction photos concentrated on the building construction surface;
step1: shooting pictures from multiple angles and uploading the pictures to a deep learning picture identification platform;
step2: identifying the uploaded photos according to the photos with different completion degrees of the house construction trained by the deep learning picture identification platform;
step3: identifying a degree of completion of building construction occurring;
the method comprises the following specific steps: dividing the picture into N x N pixels, each pixel representing a neuron, all the pixels being arranged in a row as a first layer of a neural network, i.e. an input layer, only the data of the input layer being known for generating a first layer of the hidden layer;
secondly, hiding the first layer of the layer for identifying edges or edges, that is, generating the edges or edges of the image after the pixels of the first layer are input, that is, only identifying the least important places of the image;
then, the first layer of the hidden layer is used as input for the second layer of the hidden layer, generating angles and contours, the second layer of the hidden layer generating more detailed image content;
finally, assuming a total of three hidden layers, the second layer of the hidden layers is used as input for the third layer of the hidden layers, resulting in a more detailed picture, such as a local feature of the building construction. The characteristic pictures are used as input of an output layer to generate a target image; basic knowledge such as a sensor, a loss function, a back propagation algorithm, a learning rate, a filter, gradient descent, forward propagation and the like is used in the construction of the whole network;
preliminarily performing machine learning on the building construction completion degree, and determining and changing the building construction by combining construction site professionals; if the building construction completion degree is different from the data of the initial prediction, the building construction completion degree needs to be transmitted to the building construction deployment module.
One way in which aspects of the invention can be achieved is characterized in that: the concrete operation method of the house building and allocation module in the step 5 comprises the following steps:
assuming that the absolute value of the ratio of the difference between the actual completion value of the house construction and the predicted completion value is M, y is the predicted completion,
the actual completion degree is; the number of allocations for building construction is i =1,2,3.. N;
input value, x, for allocation of factors affecting building construction i Building actual factor occurrence values for the house;
according to the numerical value determined by M, under the allocation of i influence factors, the values of the two sides are converged to achieve the similarity or the equality of the predicted completion value and the actual completion value of the building construction;
increasing the number of people or the number of materials under the condition of meeting the determined value of M, and the like, wherein X is i The value of (a) is constantly changing; as long as X i When the values of (a) are entered, the left equation sums up cumulatively to equal M, indicating that the allocation of the building construction sections has been performed.
A cloud system predicts the completion degree of a house building prediction module according to data input by each department of the house building, and after the house building is carried out for a period of time, the completion degree recognition is carried out on the house building based on a deep learning picture recognition platform and the resource allocation is carried out through a house building allocation module; and predicting the progress and allocation of the building construction through cloud computing and analysis.
Based on any one of the aspects, the invention has the beneficial effects that:
1. the invention inputs prepared material and manpower data to each house building department through a house building department data module, and transmits the data to a house building prediction module; after the house is built for a period of time, the shot photo of the house under construction is collected through a picture collecting module and uploaded to a deep learning picture recognition platform; determining the data of the building construction completion degree according to the picture identification result and personnel; on one hand, the method can effectively predict the completion degree of building construction by the existing data; the completion degree of building construction is confirmed without completely depending on manpower; the method can not only predict the building construction completion degree, but also plan the building construction progress in the future.
2. According to the invention, the data of the building construction completion degree is transmitted to the building construction allocation module, and the building construction materials and the manpower data are allocated through the building construction allocation module; preliminarily performing machine learning on the building construction completion degree, and determining and changing the building construction by combining construction site professionals; if the building construction completion degree is different from the data of starting prediction, the building construction completion degree needs to be transmitted to a building construction allocation module; the house is built and a more accurate allocation mechanism is provided for the house, and the house can be allocated in more aspects when the difference between the predicted completion degree and the house building is met.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a flow chart of the method implementation steps of the present invention.
Detailed Description
While the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.
With reference to fig. 1, a deep learning based system for predicting progress and deployment of building construction employs a picture collection module, a data module of a building construction department, a deep learning picture recognition platform, a building construction prediction module, and a building construction deployment module; characterized in that the method comprises the following steps:
step1, each house building department inputs prepared material and manpower data through a house building department data module;
in a specific embodiment of the present invention, the specific operation method for inputting prepared material and human data by each department of building construction in step1 comprises:
various departments of building construction include: reinforcing steel bar department, human resource and cement department; marking phi for each department of building construction β Wherein β =0,1,2,. Cndot.n; n is a positive integer and represents the maximum value of the value of beta in each department of building construction; the prepared material and labor data represent the building materials required for the building construction and the number of people required to participate in the construction.
Step2: transmitting the data collected by the data module of the house building department to the house building prediction module;
in an embodiment of the present invention, the operation method of transmitting the data collected by the building construction department data module in the step2 to the building construction prediction module includes:
calculating and analyzing the collected data through a house construction prediction module to obtain the required house construction completion degree under normal conditions according to the existing material and manpower data during house construction;
assuming that input data for each factor affecting house construction is set to x 1 ，x 2 ,x 3 ...x n (ii) a The weight of each influencing factor is set as a 1 ，a 2 ,a 3 ...a n (ii) a The estimated completion is set to y;
according to the mathematical formula:
an objective function:
the weight values are expressed as:
the house building prediction module mainly depends on the weight value of each factor influencing the house building, the weight value is used as an independent variable, and the result of the house building prediction is a dependent variable; the existing completion degree representation occurring under similar conditions is based on historical experience data and the completion condition of building construction under the same condition, and new building construction is predicted; the two are rare in life based on the same situation, the similar situations are more, and the difference of the similar situations is not more than 5%;
different degrees of completeness are required, and the weight values are different;
predicting the final completion degree:
because each influence factor on the building construction is different, the corresponding percentage in the building construction is also different, although the historical experience can generate the percentage in the size range, the percentage of the maximum value is taken to ensure that the building construction is finished on time when the prediction condition of the building construction is realized in practice, and the influence on the building construction under the extreme condition is avoided; thus, in the building construction prediction module, the percentage of the maximum value of the input data for each factor affecting building construction has been previously defined based on the existing historical empirical data.
Step3, after the house is built for a period of time, the photo of the house under construction is collected through a picture collecting module and uploaded to a deep learning picture recognition platform;
in a specific embodiment of the present invention, the method for collecting the photo of the building under construction by the photo collection module in step3 and uploading the photo to the deep learning photo recognition platform includes:
after the house is built for a period of time, the pictures of the house under construction are collected through a picture collecting module and uploaded to a deep learning picture recognition platform;
the house photos mainly focus on multi-angle shooting scenes and construction photos on the building construction surface;
step1: shooting pictures from multiple angles and uploading the pictures to a deep learning picture identification platform;
step2: identifying the uploaded photos according to the photos with different completion degrees of the house construction trained by the deep learning picture identification platform;
step3: identifying a degree of completion of building construction occurring;
the method comprises the following specific steps: dividing the picture into N x N pixels, each pixel representing a neuron, all the pixels being arranged in a row as a first layer of a neural network, i.e. an input layer, only data of the input layer being known for generating a first layer of the hidden layer;
secondly, hiding the first layer of the layer for identifying edges or edges, that is, generating the edges or edges of the image after the pixels of the first layer are input, that is, only identifying the least important places of the image;
then, the first layer of the hidden layer is used as input for the second layer of the hidden layer, generating angles and contours, the second layer of the hidden layer generating more detailed image content;
finally, assuming a total of three hidden layers, the second layer of the hidden layers is used as input for the third layer of the hidden layers, resulting in a more detailed picture, such as a local feature of the building construction. The characteristic pictures are used as input of an output layer to generate a target image; basic knowledge such as a sensor, a loss function, a back propagation algorithm, a learning rate, a filter, gradient descent, forward propagation and the like is used in constructing the whole network;
preliminarily performing machine learning on the building construction completion degree, and determining and changing the building construction by combining construction site professionals; if the building construction completion degree is different from the data of the initial prediction, the building construction completion degree needs to be transmitted to the building construction deployment module.
Step 4, the data of the completion degree of the building construction is transmitted to a building construction allocation module according to the picture recognition result and the building construction;
and 5, carrying out house building material and manpower data allocation through a house building allocation module.
In an embodiment of the present invention, the specific operation method of the house building deployment module in step 5 includes:
assuming that the absolute value of the ratio of the difference between the actual completion value of the house construction and the predicted completion value is M, y is the predicted completion,
the actual completion degree is; the number of allocations to the building construction is i =1,2,3.. N;
input value, x, for allocation of factors affecting building construction i Values of actual factors occurring for building a house;
according to the numerical value determined by M, under the allocation of i influence factors, the values of the two sides are converged to achieve the similarity or the equality of the predicted completion value and the actual completion value of the building construction;
increasing the number of people or the number of materials under the condition of meeting the determined value of M, and the like, X i The value of (a) is constantly changing; as long as X i When the values of (a) are entered, the left equation sums up cumulatively to equal M, indicating that the allocation of the building construction sections has been performed.
A cloud system predicts the completion degree of building construction in a building construction prediction module according to data input by each department for building construction, and after the building construction is carried out for a period of time, the completion degree recognition is carried out on the building construction based on a deep learning picture recognition platform and the resource allocation is carried out through a building construction allocation module; and predicting the progress and allocation of the house construction through cloud computing and analysis.
Example 2 assuming an actual completion value of 20% and a predicted completion value of 30%; the distance difference is between the predicted completion degree and the predicted completion degree, and the allocation is needed;
according to the mathematical formula:
m =50%, M being the difference between the actual completion value and the predicted completion value of the building construction, accounting for the absolute value of the ratio of the actual completion values; i.e. the progress that needs to be increased; then the above mathematical formula is substituted;
suppose that 50 persons are actually used for building a house;
calculate out
=25 persons, so we need to be peopleThe number is increased to a value of 75 people making up the predicted completion;
in real life, the construction progress of building construction is allocated with various numerical changes, such as the number of people, materials and the like; as long as the value obtained by calculating and adding up the allocated values is equal to or approximately equal to the value of M, the allocation of the building construction is accurate;
the invention inputs prepared material and manpower data to each house building department through a house building department data module and transmits the data to a house building prediction module; after the house is built for a period of time, the shot photo of the house under construction is collected through a picture collecting module and uploaded to a deep learning picture recognition platform; determining the data of the building construction completion degree according to the picture identification result and personnel; the data of the building construction completion degree is transmitted to a building construction allocation module, and the building construction materials and the manpower data are allocated through the building construction allocation module; preliminarily performing machine learning on the building construction completion degree, and determining and changing the building construction by combining construction site professionals; if the building construction completion degree is different from the data of starting prediction, the building construction completion degree needs to be transmitted to a building construction allocation module; on one hand, the method can effectively predict the completion degree of building construction by the existing data; the completion degree of building construction is confirmed without completely depending on manpower; not only can the building construction completion degree be predicted, but also the future building construction progress can be planned for a long time; the house is built and a more accurate allocation mechanism is provided for the house, and the house can be allocated in more aspects when the difference between the predicted completion degree and the house building is met.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (7)
1. A building construction prediction progress and allocation system based on deep learning is disclosed, wherein a picture collection module, a building construction department data module, a deep learning picture identification platform, a building construction prediction module and a building construction allocation module are applied in the method; characterized in that the method comprises the following steps:
step1, each house building department inputs prepared material and manpower data through a house building department data module;
and 2, step: transmitting the data collected by the data module of the house building department to the house building prediction module;
step3, after the house is built for a period of time, the photo of the house under construction is collected through the picture collecting module and uploaded to the deep learning picture recognition platform;
step 4, transmitting the completion data to a house construction allocation module according to the picture identification result and the house construction;
and 5, carrying out house building material and manpower data allocation through a house building allocation module.
2. The deep learning based predictive scheduling and deployment system for house construction according to claim 1, wherein: the concrete operation method for inputting prepared material and manpower data by each department of building construction in the step1 comprises the following steps:
various departments of building construction include: reinforcing steel bar department, human resource and cement department; marking phi for each department of building construction β Wherein β =0,1,2, ·, n; n is a positive integer and represents the maximum value of the value of beta in each department of building construction; the prepared material and labor data represent the building materials required for the building construction and the number of people required to participate in the construction.
3. The deep learning based prediction schedule and deployment system for house construction according to claim 1, wherein: the operation method for transmitting the data collected by the data module of the house building department in the step2 to the house building forecasting module comprises the following steps:
calculating and analyzing the collected data through a building construction prediction module to obtain the required building construction completion degree according to the existing material and manpower data under the normal condition;
assuming that input data for each factor affecting house construction is set to x 1 ，x 2 ,x 3 ...x n (ii) a The weight of each influencing factor is set as a 1 ，a 2 ,a 3 ...a n (ii) a The estimated completion is set to y;
according to the mathematical formula:
an objective function:
predicting the final completion degree:
4. The deep learning based prediction schedule and deployment system for house construction according to claim 1, wherein: the operation method that the picture collecting module collects the shot house pictures which are being constructed in the step3 and uploads the pictures to the deep learning picture recognition platform comprises the following steps:
after the house is built for a period of time, the shot photo of the house under construction is collected through a picture collecting module and uploaded to a deep learning picture recognition platform;
the house photos mainly focus on multi-angle shooting scenes and construction photos on the building construction surface;
step1: shooting pictures from multiple angles and uploading the pictures to a deep learning picture identification platform;
step2: identifying the uploaded photos according to the photos with different completion degrees of the house construction trained by the deep learning picture identification platform;
step3: identifying a degree of completion of building construction occurring;
preliminarily performing machine learning on the building construction completion degree, and determining and changing the building construction by combining construction site professionals; if the current building construction completion is different from the data of the initial forecast, the current building construction completion needs to be transmitted to the building construction deployment module.
5. The deep learning based prediction schedule and deployment system for house construction according to claim 1, wherein: the concrete operation method of the house building and allocation module in the step 5 comprises the following steps:
assuming that the difference between the actual completion degree value and the predicted completion degree value of the house construction is M in absolute value of the ratio of the actual completion degree value, y is the predicted completion degree,
the actual completion degree is; the number of allocations to the building construction is i =1,2,3.. N;
input value, x, for allocation of factors affecting building construction i Building actual factor occurrence values for the house;
according to the numerical value determined by M, under the allocation of i influence factors, the values of the two sides are converged to achieve the similarity or the equivalence of the predicted completion value and the actual completion value of the building construction.
6. A cloud system, characterized in that: the method comprises the steps that the completion degree is predicted in a house building prediction module according to data input by each department for building, after a period of house building, the completion degree recognition is carried out on the house building based on a deep learning picture recognition platform, and resources are allocated through a house building allocation module; the system for predicting the progress and allocation of building construction based on deep learning as claimed in any one of claims 1 to 5 is implemented by calculating and analyzing the progress and allocation of building construction through a cloud.
7. A cloud system, characterized in that: the cloud end realizes the deep learning-based house construction prediction progress and allocation system of any one of claims 1 to 5 on the basis of the prediction progress and allocation of house construction by means of a cloud end calculation and analysis service program under the network.
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