CN117453923B - Method for optimizing relation between construction site construction equipment and building facilities - Google Patents
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Abstract
The invention provides a method for optimizing the relationship between construction equipment and building facilities on a construction site, which comprises the following steps: firstly, acquiring knowledge maps of construction equipment and building facilities of a plurality of construction sites; training the relationship of the knowledge graph through the M-DCN neural network to obtain a final scoring function of the trained M-DCN neural network; then, according to the knowledge graph of the new engineering project, extracting the final scoring function of the M-DCN neural network brought by the information in the knowledge graph, and calculating the scoring condition of the scoring function of the knowledge triplet; the obtained scoring condition of the knowledge triplet scoring function is respectively compared with scoring conditions of the knowledge triplet scoring functions of a plurality of construction sites, and the relationship between the knowledge maps of the new engineering projects is optimized; the method can optimize the knowledge graph of the construction site, and the knowledge graph is more perfect after the optimization; meanwhile, the three-dimensional dynamic model generated on the basis of the optimized knowledge graph is more perfect and accurate.
Description
Technical Field
The invention relates to the field of building construction and artificial intelligence, in particular to a method for optimizing the relationship between construction equipment and building facilities on a construction site.
Background
Before site construction is performed on a large-scale construction site, a three-dimensional dynamic model of the construction site is often required to be generated in order to monitor the construction progress and safety of the construction site in real time; but the generation of a three-dimensional dynamic model of a large-scale construction site faces the following problems: in the three-dimensional dynamic model, a great amount of corresponding relations between complex building structures and construction equipment are generated, and particularly, the changing relations between the building structures and the corresponding construction equipment on time sequence have certain difficulty.
The knowledge map is also called a scientific knowledge map, is called a knowledge domain visualization or knowledge domain mapping map in book emotion, is a series of different graphs for displaying the knowledge development process and the structural relationship, describes knowledge resources and carriers thereof by using a visualization technology, and excavates, analyzes, builds, draws and displays knowledge and the interrelationship between the knowledge resources and the carriers; the knowledge graph is applied to the field of building engineering, and can be used for describing the corresponding relation between the building and equipment on the construction site; therefore, it is necessary to describe the correspondence between the buildings and the devices on the construction site how to generate a knowledge graph with better effect.
In the existing research, the relationship among the research objects is processed through a neural network in artificial intelligence, and finally the relationship of the research objects is generated, which has proved to achieve great results; therefore, the interrelationship between the construction site building and the equipment is processed through the neural network, so that more specific interrelationship between the construction site building and the equipment can be generated; meanwhile, the three-dimensional dynamic model of the construction site can be optimized.
Disclosure of Invention
The invention aims to overcome the problems existing in the prior art and greatly improve the technical effect on the basis of the prior art; to this end, the present invention provides a method of optimizing the relationship of construction equipment and building facilities at a construction site, the method comprising:
acquiring actual conditions of a plurality of construction sites as much as possible from a historical construction data set; the actual situation refers to a knowledge graph of a plurality of construction sites generated by the relation between a plurality of construction site construction equipment and building facilities; the construction equipment is regarded as a tail entity of the knowledge graph and is represented by t; the building facilities are regarded as head entities of the knowledge graph and are represented by h; the construction operation of the tail entity on the head entity is regarded as the relation among the elements in the knowledge graph, and r is used for representing the relation;
extracting knowledge triples (h, r, t) of the knowledge maps corresponding to a plurality of construction sites, and taking all the obtained knowledge triples into an M-DCN neural network to train so as to obtain a final scoring function of the trained M-DCN neural network; the training formula of the M-DCN neural network is as follows:
wherein phi (h, r, t) is the final scoring function of the M-DCN neural network, sigma represents a logistic regression function, and the formula is:the logistic regression function σ is used to return confidence scores for the knowledge triples (h, r, t); f (x) represents a nonlinear activation function ReLU, vec (x) represents a fully connected operation, W i r Representing convolution kernels of different scales, each of the convolution kernels being represented by W 1 r 、W 2 r 、…W n r A representation; w represents a shared matrix for speaking that all feature maps are mapped to the same latitude as the entity; b represents a paranoid item;
the method comprises the steps of respectively recording scoring conditions of knowledge triples of a plurality of construction sites, firstly manufacturing a knowledge graph of a new engineering project before constructing the new engineering project, extracting information in the knowledge graph to bring the information into a final scoring function of an M-DCN neural network, and calculating the scoring condition of the scoring function of the knowledge triples; and comparing the obtained scoring condition of the knowledge triplet scoring function with the scoring condition of the knowledge triplet scoring function of a plurality of construction sites respectively to obtain a plurality of construction site knowledge maps closest to the new engineering project construction site knowledge map, and comparing and optimizing the new engineering project construction site knowledge map.
Further, the treating the construction operation of the tail entity on the head entity as the relation among the elements in the knowledge graph includes: and generating the relation among the elements in the knowledge graph according to the specific operation matters and operation steps of the construction operation performed by the tail entity on the head entity.
Further, the extracting the knowledge triples (h, r, t) of the knowledge maps corresponding to the plurality of construction sites includes: extracting knowledge triples (h, r, t) of knowledge maps corresponding to each entity of a plurality of construction sites to form knowledge triples sets of the knowledge maps of the construction sites; and the step of bringing all the obtained knowledge triples into the M-DCN neural network for training refers to bringing the knowledge triples of the knowledge maps of a plurality of construction sites into the M-DCN neural network for training.
Further, the f (x) represents a nonlinear activation function ReLU including: the concept of the nonlinear activation function ReLU is: when the input is greater than 0, directly returning the value provided as input; if the input is not greater than 0, the return value is 0.
Further, the convolution kernels of different scales are respectively W 1 r 、W 2 r 、…W n r The representation includes: the convolution kernel dimensions associated with each relationship generated by the M-DCN model are equal to the convolution kernels W of different scales associated therewith 1 r 、W 2 r …, and W n r And the sum of dimensions.
Further, the comparing and optimizing the new engineering project construction site knowledge graph comprises the following steps: the knowledge patterns of the new engineering project are compared and optimized through a plurality of construction site knowledge patterns closest to the construction site knowledge patterns of the new engineering project, so that the relation between the head entity and the tail entity of the knowledge patterns of the new engineering project is optimized, and safer and smoother completion during engineering construction is ensured.
The beneficial effects of the invention are as follows:
the invention provides a method for optimizing the relation between construction equipment and building facilities on a construction site, which acquires knowledge maps of the construction equipment and the building facilities on the construction site by acquiring actual conditions of a plurality of construction sites; training the relationship of the knowledge graph through the M-DCN neural network to obtain a final scoring function of the trained M-DCN neural network; then, according to the knowledge graph of the new engineering project, extracting the final scoring function of the M-DCN neural network brought by the information in the knowledge graph, and calculating the scoring condition of the scoring function of the knowledge triplet; comparing the obtained scoring condition of the knowledge triplet scoring function with the scoring condition of the knowledge triplet scoring function of a plurality of construction sites respectively to obtain a plurality of construction site knowledge maps closest to the new engineering project construction site knowledge map; optimizing the knowledge graph of the new engineering project through the similar knowledge graph; the method can optimize the knowledge graph of the construction site, and the relation between construction equipment and building facilities in the optimized knowledge graph is more perfect; meanwhile, the three-dimensional dynamic model generated on the basis of the optimized knowledge graph is more perfect and accurate.
Drawings
Fig. 1: a flow chart of a method of optimizing the relationship of construction site construction equipment and building facilities of the present invention.
Detailed Description
Specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the particular embodiments presented herein are illustrative and explanatory only and are not restrictive of the invention.
It should be noted that numerous specific details are set forth in the following description in order to provide a thorough understanding of the present invention, however, that other embodiments of the invention and variations thereof are possible and, therefore, the scope of the invention is not limited by the specific examples disclosed below.
As shown in fig. 1, a method of optimizing a relationship between construction site construction equipment and a building facility according to an embodiment of the present invention includes: step S100, obtaining actual conditions of a plurality of construction sites as much as possible from a historical construction data set; the actual situation refers to a knowledge graph of a plurality of construction sites generated by the relation between a plurality of construction site construction equipment and building facilities; the construction equipment is regarded as a tail entity of the knowledge graph and is represented by t; the building facilities are regarded as head entities of the knowledge graph and are represented by h; the construction operation of the tail entity on the head entity is regarded as the relation among the elements in the knowledge graph, and r is used for representing the relation; step S101, extracting knowledge triples (h, r, t) of knowledge maps corresponding to a plurality of construction sites, and taking all the obtained knowledge triples into an M-DCN neural network to train, so as to obtain a final scoring function of the trained M-DCN neural network; step S102, respectively recording scoring conditions of knowledge triples of a plurality of construction sites, firstly manufacturing a knowledge graph of a new engineering project before constructing the new engineering project, extracting information in the knowledge graph to bring the information into a final scoring function of an M-DCN neural network, and calculating scoring conditions of the scoring functions of the knowledge triples; and comparing the obtained scoring condition of the knowledge triplet scoring function with the scoring condition of the knowledge triplet scoring function of a plurality of construction sites respectively to obtain a plurality of construction site knowledge maps closest to the new engineering project construction site knowledge map, and comparing and optimizing the new engineering project construction site knowledge map.
Specifically, the method comprises the steps of firstly acquiring actual conditions of a plurality of construction sites, and acquiring knowledge maps of construction equipment and building facilities of the construction sites; training the relationship of the knowledge graph through an M-DCN neural network, and obtaining a final scoring function of the trained M-DCN neural network; then, according to the knowledge graph of the new engineering project, extracting the final scoring function of the M-DCN neural network brought by the information in the knowledge graph, and calculating the scoring condition of the scoring function of the knowledge triplet; comparing the obtained scoring condition of the knowledge triplet scoring function with the scoring condition of the knowledge triplet scoring function of a plurality of construction sites respectively to obtain a plurality of construction site knowledge maps closest to the new engineering project construction site knowledge map; and optimizing the knowledge graph of the new engineering project through the similar knowledge graph.
Step S100, obtaining actual conditions of a plurality of construction sites as much as possible from a historical construction data set; the actual situation refers to a knowledge graph of a plurality of construction sites generated by the relation between a plurality of construction site construction equipment and building facilities; the construction equipment is regarded as a tail entity of the knowledge graph and is represented by t; the building facilities are regarded as head entities of the knowledge graph and are represented by h; and (3) regarding the construction operation of the tail entity on the head entity as the relation among the elements in the knowledge graph, wherein r is used for representing the relation.
In the foregoing embodiment, specifically, the treating the construction operation performed by the tail entity on the head entity as the relationship between the elements in the knowledge graph includes: generating a relation among elements in the knowledge graph according to specific operation matters and operation steps of construction operation performed on the head entity by the tail entity; the operation steps include that the operation of the tail entity on the head entity is performed in steps, and include the time required for each step when the tail entity operates on the head entity.
And S101, extracting knowledge triples (h, r and t) of knowledge maps corresponding to a plurality of construction sites, and taking all the obtained knowledge triples into the M-DCN neural network to train, so as to obtain a final scoring function of the trained M-DCN neural network.
In the above embodiment, specifically, the formula for training the M-DCN neural network is:
wherein phi (h, r, t) is the final scoring function of the M-DCN neural network, sigma represents a logistic regression function, and the formula is:logistic regressionThe function σ is used to return confidence scores for the knowledge triples (h, r, t); f (x) represents a nonlinear activation function ReLU, vec (x) represents a fully connected operation, W i r Representing convolution kernels of different scales, each of the convolution kernels being represented by W 1 r 、W 2 r 、…W n r A representation; w represents a shared matrix for speaking that all feature maps are mapped to the same latitude as the entity; b represents a paranoid item.
In the above embodiment, specifically, the extracting the knowledge triples (h, r, t) of the knowledge maps corresponding to the plurality of construction sites includes: extracting knowledge triples (h, r, t) of knowledge maps corresponding to each entity of a plurality of construction sites to form knowledge triples sets of the knowledge maps of the construction sites; and the step of bringing all the obtained knowledge triples into the M-DCN neural network for training refers to bringing all the knowledge triples of the knowledge maps of a plurality of construction sites into the M-DCN neural network for training.
In the above embodiment, specifically, the f (x) represents a nonlinear activation function ReLU including: the concept of the nonlinear activation function ReLU is: when the input is greater than 0, directly returning the value provided as input; if the input is not greater than 0, the return value is 0.
Further, the convolution kernels of different scales are respectively W 1 r 、W 2 r 、…W n r The representation includes: the convolution kernel dimensions associated with each relationship generated by the M-DCN model are equal to the convolution kernels W of different scales associated therewith 1 r 、W 2 r …, and W n r And the sum of dimensions.
Step S102, respectively recording scoring conditions of the knowledge triples of all the multiple construction sites, firstly manufacturing a knowledge graph of a new engineering project before constructing the new engineering project, extracting information in the knowledge graph to bring the information into a final scoring function of an M-DCN neural network, and calculating the scoring conditions of the scoring functions of the knowledge triples; and comparing the obtained scoring condition of the knowledge triplet scoring function with the scoring condition of the knowledge triplet scoring function of a plurality of construction sites respectively to obtain a plurality of construction site knowledge maps closest to the new engineering project construction site knowledge map, and comparing and optimizing the new engineering project construction site knowledge map.
In the above embodiment, specifically, the comparing and optimizing the new engineering project construction site knowledge graph includes: the knowledge patterns of the new engineering project are compared and optimized through a plurality of construction site knowledge patterns closest to the construction site knowledge patterns of the new engineering project, so that the relation between the head entity and the tail entity of the knowledge patterns of the new engineering project is optimized, and safer and smoother completion during engineering construction is ensured.
In the above embodiment, preferably, the knowledge patterns of the new engineering project may be compared and optimized by simultaneously selecting a plurality of construction site knowledge patterns closest to the construction site knowledge patterns of the new engineering project; obtaining a first optimized construction site knowledge graph after optimization, then, carrying information of the first optimized construction site knowledge graph into a final scoring function of an M-DCN neural network, calculating scoring conditions of the first optimized construction site knowledge graph, respectively comparing the scoring conditions of the obtained first optimized construction site knowledge graph with scoring conditions of the knowledge triples of a plurality of construction sites to obtain a plurality of construction site knowledge graphs closest to the first optimized construction site knowledge graph, and optimizing the first optimized construction site knowledge graph again; the process is iterated continuously until the last iteration, the scoring condition of the optimized construction site knowledge patterns is almost the same as the scoring condition of one or more knowledge patterns of a plurality of construction site knowledge patterns, the iteration is ended, and the optimization of the relation between construction equipment and building facilities in the construction site of a new engineering project is completed by the method.
It is to be understood that the above-described embodiments are one or more embodiments of the invention, and that many other embodiments and variations thereof are possible in accordance with the invention; variations and modifications of the invention, which are intended to be within the scope of the invention, will occur to those skilled in the art without any development of the invention.
Claims (6)
1. A method of optimizing a relationship between a job site construction equipment and a building facility, the method comprising:
acquiring actual conditions of a plurality of construction sites as much as possible from a historical construction data set; the actual situation refers to a knowledge graph of a plurality of construction sites generated by the relation between a plurality of construction site construction equipment and building facilities; the construction equipment is regarded as a tail entity of the knowledge graph and is represented by t; the building facilities are regarded as head entities of the knowledge graph and are represented by h; the construction operation of the tail entity on the head entity is regarded as the relation among the elements in the knowledge graph, and r is used for representing the relation;
extracting knowledge triples (h, r, t) of the knowledge maps corresponding to a plurality of construction sites, and taking all the obtained knowledge triples into an M-DCN neural network to train so as to obtain a final scoring function of the trained M-DCN neural network; the training formula of the M-DCN neural network is as follows:
wherein phi (h, r, t) is the final scoring function of the M-DCN neural network, sigma represents a logistic regression function, and the formula is:the logistic regression function σ is used to return confidence scores for the knowledge triples (h, r, t); f (x) represents a nonlinear activation function ReLU, vec (x) represents a fully connected operation, W i r Representing convolution kernels of different scales, each of the convolution kernels being represented by W 1 r 、W 2 r 、…W n r A representation; w represents a shared matrix for speaking that all feature maps are mapped to the same latitude as the entity; b represents a paranoid item;
the method comprises the steps of respectively recording scoring conditions of knowledge triples of a plurality of construction sites, firstly manufacturing a knowledge graph of a new engineering project before constructing the new engineering project, extracting information in the knowledge graph to bring the information into a final scoring function of an M-DCN neural network, and calculating the scoring condition of the scoring function of the knowledge triples; and comparing the obtained scoring condition of the knowledge triplet scoring function with the scoring condition of the knowledge triplet scoring function of a plurality of construction sites respectively to obtain a plurality of construction site knowledge maps closest to the new engineering project construction site knowledge map, and comparing and optimizing the new engineering project construction site knowledge map.
2. A method of optimizing a relationship between construction equipment and a building facility at a construction site according to claim 1, wherein the treating the construction operation performed by the tail entity on the head entity as a relationship between elements in the knowledge graph comprises: and generating the relation among the elements in the knowledge graph according to the specific operation matters and operation steps of the construction operation performed by the tail entity on the head entity.
3. A method of optimizing the relationship of construction equipment and building facilities at a construction site according to claim 1, wherein said extracting knowledge triples (h, r, t) of knowledge maps corresponding to a plurality of construction sites comprises: extracting knowledge triples (h, r, t) of knowledge maps corresponding to each entity of a plurality of construction sites to form knowledge triples sets of the knowledge maps of the construction sites; and the step of bringing all the obtained knowledge triples into the M-DCN neural network for training refers to bringing the knowledge triples of the knowledge maps of a plurality of construction sites into the M-DCN neural network for training.
4. A method of optimizing a relationship between construction equipment and a building facility in a construction site according to claim 1, wherein said f (x) represents a nonlinear activation function ReLU comprising: the concept of the nonlinear activation function ReL U is: when the input is greater than 0, directly returning the value provided as input; if the input is not greater than 0, the return value is 0.
5. A method of optimizing a relationship between construction equipment and a building facility in a construction site as claimed in claim 1, wherein said convolution kernels of different scales are each of W 1 r 、W 2 r 、…W n r The representation includes: the convolution kernel dimensions associated with each relationship generated by the M-DCN model are equal to the convolution kernels W of different scales associated therewith 1 r 、W 2 r …, and W n r And the sum of dimensions.
6. A method of optimizing a relationship between construction equipment and building equipment at a construction site as claimed in claim 1, wherein said comparing and optimizing new engineering project construction site knowledge patterns comprises: the knowledge patterns of the new engineering project are compared and optimized through a plurality of construction site knowledge patterns closest to the construction site knowledge patterns of the new engineering project, so that the relation between the head entity and the tail entity of the knowledge patterns of the new engineering project is optimized, and safer and smoother completion during engineering construction is ensured.
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