Node deepening design method and system for digital construction based on deep learning

Abstract

The present invention provides a node in-depth design method and system for digital construction based on deep learning, including: obtaining a number of construction projects of this construction and a construction method corresponding to each of the construction projects, constructing corresponding digital construction nodes, constructing a construction model of this construction based on the digital construction nodes, simulating a simulated construction method corresponding to each of the construction methods, performing in-depth analysis on each of the simulated construction methods, allocating a corresponding construction execution method and a construction supervision method to the corresponding simulated construction method according to the analysis results, and constructing a construction plan for this construction using the construction execution method and the construction supervision method. The model can be used to predict the construction results in advance, reasonably organize the construction, avoid rework, and ensure the construction period and quality.

Description

Technical Field

[0001] The present invention relates to the technical field of digital construction analysis, and in particular to a node deepening design method and system for digital construction based on deep learning. Background Art

[0002] The construction industry is known as the second most dangerous industry after mining operations. For construction projects with frequent safety accidents, how to manage the construction site and control the frequency of accidents has always been the focus of government management departments and construction companies. However, the construction industry has a long construction cycle and a series of problems such as long time spent on rework when encountering quality problems. Therefore, it is an urgent need for construction companies to improve the current status of the construction industry and improve the level of construction and management in the industry.

[0003] Therefore, the present invention provides a node deepening design method and system for digital construction based on deep learning. Summary of the invention

[0004] The node deepening design method of digital construction based on deep learning of the present invention can use the model to predict the construction results in advance, reasonably organize the construction, avoid rework, and ensure the construction period and quality.

[0005] The present invention provides a node deepening design method for digital construction based on deep learning, comprising:

[0006] Step 1: Obtain several construction projects of this construction and the construction methods corresponding to each of the construction projects, and construct corresponding digital construction nodes;

[0007] Step 2: constructing a construction model of this construction based on the digital construction nodes, and simulating the simulated construction method corresponding to each of the construction methods;

[0008] Step 3: Conduct an in-depth analysis on each of the simulated construction methods, and assign corresponding construction execution methods and construction supervision methods to the corresponding simulated construction methods according to the analysis results;

[0009] Step 4: Use the construction execution method and the construction supervision method to construct a construction plan for this construction.

[0010] In one practicable manner,

[0011] Also includes:

[0012] The construction works include: foundation treatment works, earthwork works, drainage works, fire protection pipe network works, pavement works and perimeter works.

[0013] In one practicable manner,

[0014] The step 1 comprises:

[0015] Step 11: construct multiple sets of construction data for this construction according to the construction information uploaded by the user, establish multiple construction projects for this construction according to the construction data, and establish a sequence between the construction projects according to the data connection relationship between different construction data;

[0016] Step 12: Analyze each of the construction projects to obtain the construction specifications corresponding to each of the construction projects, and construct a construction switching method between different construction projects based on the sequence;

[0017] Step 13: Decompose each of the construction switching modes into a plurality of switching actions, obtain the action correlation degree between each of the switching actions and the corresponding construction specifications, integrate each of the switching actions into the corresponding construction specifications according to the action correlation degree, and generate the construction mode corresponding to each of the construction projects;

[0018] Step 14: Simulate each of the construction methods in a preset simulation space to obtain digital construction information corresponding to each of the construction projects, and use the digital construction information to construct digital construction nodes for each of the construction projects.

[0019] In one practicable manner,

[0020] The step 2 comprises:

[0021] Step 21: Obtain node features corresponding to each of the digital construction nodes respectively, perform feature deepening and highlighting processing on each of the digital construction nodes according to the node features, and establish a node model for each of the digital construction nodes;

[0022] Step 22: Arrange the node models using the node distribution relationship between the digital construction nodes to obtain a construction model for this construction, and determine the node implementation data range corresponding to each node model in the construction model;

[0023] Step 23: adjusting the model operation data corresponding to each node model according to the node implementation data range, and respectively running each adjusted node model in the construction model to obtain the operation data corresponding to each node model;

[0024] Step 24: According to the node implementation data range and the operation data corresponding to each digital construction node, the operation process of the corresponding digital construction node is constructed to obtain the simulation construction method corresponding to each construction method.

[0025] In one practicable manner,

[0026] The step 3 comprises:

[0027] Step 31: Identify key information for each of the simulated construction methods, obtain key construction points corresponding to each of the simulated construction methods, and count the key construction points to generate key supervision information for this construction;

[0028] Step 32: Divide each of the simulated construction methods into a number of construction steps, locate the key construction steps corresponding to the key construction points in the construction steps, and find the key construction foundations and key construction results corresponding to each of the key construction steps;

[0029] Step 33: derive the basic construction basis and basic construction results corresponding to each construction step in the corresponding simulated construction method according to the key construction basis and key construction results, and establish the construction execution method corresponding to each of the simulated construction methods according to the basic construction basis;

[0030] Step 34: construct a construction supervision process corresponding to each of the simulated construction methods according to the basic construction results, adjust the construction supervision process according to the key supervision information, and construct a construction supervision method corresponding to each of the simulated construction methods.

[0031] In one practicable manner,

[0032] The step 4 comprises:

[0033] Step 41: Evaluate the required construction time corresponding to each of the construction projects according to the construction execution mode corresponding to each of the simulated construction modes, and establish a construction timeline for each of the construction projects;

[0034] Step 42: Mapping the construction supervision method and construction execution method corresponding to each of the simulated construction methods to the same analysis domain, the plurality of supervision information contained in the construction supervision method and the execution information corresponding to each of the supervision information;

[0035] Step 43: locating the execution time period corresponding to each execution information in the construction timeline, and determining the supervision information corresponding to each specified time period in the construction timeline according to the locating result;

[0036] Step 44: Establish a construction plan for this construction based on the cross information of the supervision information and the execution information in the construction timeline.

[0037] In one practicable manner,

[0038] The step 21 comprises:

[0039] Step 211: respectively obtaining node features corresponding to each of the digital construction nodes, respectively performing copy and superposition training on each of the node features, obtaining feature change information corresponding to each superposition, and establishing a repetitive execution rule of the corresponding digital construction node according to the feature change information;

[0040] Step 212: feature stratification of the digital construction nodes according to the repeated execution rule, obtain feature values ​​corresponding to different node regions in the digital construction nodes, and perform reinforcement processing on the node regions to be reinforced whose feature values ​​are higher than the feature mean in the same digital construction node;

[0041] Step 213: Obtain node presentation data contained in each enhanced digital construction node, convert the node presentation data into model data, and construct a node model corresponding to each digital construction node.

[0042] In one practicable manner,

[0043] Also includes:

[0044] When the same simulated construction method includes two or more key construction points, the construction steps between two adjacent key construction points are derived and integrated to obtain the basic construction foundation and basic construction results corresponding to each construction step.

[0045] This embodiment provides a node deepening design system for digital construction based on deep learning, including:

[0046] The engineering analysis module is used to obtain a number of construction projects of this construction and the construction method corresponding to each of the construction projects, and construct corresponding digital construction nodes;

[0047] A modeling execution module is used to construct a construction model of this construction based on the digital construction nodes, and simulate the simulated construction method corresponding to each of the construction methods;

[0048] A deep analysis module, used to perform deep analysis on each of the simulated construction methods, and assign corresponding construction execution methods and construction supervision methods to the corresponding simulated construction methods according to the analysis results;

[0049] The scheme construction module is used to construct a construction scheme for this construction by using the construction execution method and the construction supervision method.

[0050] In one practicable manner,

[0051] Also includes:

[0052] The construction works include: foundation treatment works, earthwork works, drainage works, fire protection pipe network works, pavement works and perimeter works.

[0053] The beneficial effects that can be achieved by the present invention are as follows: in order to achieve the purpose of reasonably arranging the construction progress, digital construction nodes are established before construction according to the construction projects to be carried out in this construction and their corresponding construction methods, thereby constructing a construction model of this construction, in which the simulated construction method corresponding to each construction method can be determined, and the corresponding construction execution method and construction supervision method are allocated to them through in-depth analysis of the simulated construction methods, thereby constructing a construction plan for this construction, and through the above steps, a feasible construction plan can be established before construction, and during the construction process, the construction results can be predicted in advance according to the construction plan, thereby reasonably arranging the construction time, construction personnel, etc. of different construction projects, which can not only ensure the progress of the construction period, but also avoid rework and ensure the construction quality.

[0054] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0055] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0057] Figure 1 A schematic diagram of the workflow of a node deepening design method for digital construction based on deep learning in an embodiment of the present invention;

[0058] Figure 2 Schematic diagram of the composition of a node deepening design system for digital construction based on deep learning in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0060] Example 1

[0061] This embodiment provides a node in-depth design method for digital construction based on deep learning, such as Figure 1 As shown, including:

[0062] Step 1: Obtain several construction projects of this construction and the construction methods corresponding to each of the construction projects, and construct corresponding digital construction nodes;

[0063] Step 2: constructing a construction model of this construction based on the digital construction nodes, and simulating the simulated construction method corresponding to each of the construction methods;

[0064] Step 3: Conduct an in-depth analysis on each of the simulated construction methods, and assign corresponding construction execution methods and construction supervision methods to the corresponding simulated construction methods according to the analysis results;

[0065] Step 4: Use the construction execution method and the construction supervision method to construct a construction plan for this construction.

[0066] In this example, each construction project may correspond to one or more construction methods, and different construction methods may be combined to form a new construction method;

[0067] In this example, one construction project corresponds to one digital construction node, and the digital construction node represents the way of simulating the construction project using digital technology;

[0068] In this example, one simulation construction method corresponds to one construction execution method and one construction supervision method;

[0069] In this example, the construction plan refers to the plan for completing this construction work.

[0070] The working principle and beneficial effects of the above technical solution: In order to achieve the purpose of reasonably arranging the construction progress, digital construction nodes are established before construction according to the construction projects to be carried out and their corresponding construction methods, thereby constructing a construction model of this construction. In the construction model, the simulated construction method corresponding to each construction method can be determined, and the corresponding construction execution method and construction supervision method are assigned to them through in-depth analysis of the simulated construction method, thereby constructing a construction plan for this construction. Through the above steps, a feasible construction plan can be established before construction. During the construction process, the construction results can be predicted in advance according to the construction plan, so as to reasonably arrange the construction time, construction personnel, etc. of different construction projects, which can not only ensure the progress of the construction period, but also avoid rework and ensure the construction quality.

[0071] Example 2

[0072] On the basis of Example 1, the node deepening design method of digital construction based on deep learning further includes:

[0073] The construction works include: foundation treatment works, earthwork works, drainage works, fire protection pipe network works, pavement works and perimeter works.

[0074] Example 3

[0075] On the basis of Example 1, the node deepening design method of digital construction based on deep learning, the step 1 comprises:

[0076] Step 11: construct multiple sets of construction data for this construction according to the construction information uploaded by the user, establish multiple construction projects for this construction according to the construction data, and establish a sequence between the construction projects according to the data connection relationship between different construction data;

[0077] Step 12: Analyze each of the construction projects to obtain the construction specifications corresponding to each of the construction projects, and construct a construction switching method between different construction projects based on the sequence;

[0078] Step 13: Decompose each of the construction switching modes into a plurality of switching actions, obtain the action correlation degree between each of the switching actions and the corresponding construction specifications, integrate each of the switching actions into the corresponding construction specifications according to the action correlation degree, and generate the construction mode corresponding to each of the construction projects;

[0079] Step 14: Simulate each of the construction methods in a preset simulation space to obtain digital construction information corresponding to each of the construction projects, and use the digital construction information to construct digital construction nodes for each of the construction projects.

[0080] In this example, the construction data refers to the data created before the current construction process and used to represent the data generated during the construction process;

[0081] In this example, different construction projects need to be connected to ensure the normal progress of the project;

[0082] In this example, a construction switching mode may correspond to one or more switching actions;

[0083] In this example, the higher the action association degree, the higher the connection degree between the switching action and the construction specification;

[0084] In this example, the preset simulation space represents a three-dimensional space used for modeling analysis;

[0085] In this example, the construction digital information refers to information generated during the simulation of the construction project.

[0086] The working principle and beneficial effects of the above technical solution: In order to ensure the efficiency of this construction, multiple groups of construction data are constructed according to the construction information uploaded by the user before the construction, so as to establish multiple construction projects and establish the sequence between different construction projects, so as to construct the construction switching method between the construction specifications of different construction projects according to the sequence, and construct the construction method corresponding to each construction project through action allocation, and construct the construction digital information corresponding to each construction project in the preset simulation space through simulation, so as to establish a digital construction node for it. In this way, not only the construction specifications can be preliminarily screened, but also different construction projects can be arranged in a certain order according to the actual situation, thereby improving the efficiency of subsequent construction.

[0087] Example 4

[0088] On the basis of Example 1, the node deepening design method of digital construction based on deep learning, the step 2 comprises:

[0089] Step 21: Obtain node features corresponding to each of the digital construction nodes respectively, perform feature deepening and highlighting processing on each of the digital construction nodes according to the node features, and establish a node model for each of the digital construction nodes;

[0090] Step 22: Arrange the node models using the node distribution relationship between the digital construction nodes to obtain a construction model for this construction, and determine the node implementation data range corresponding to each node model in the construction model;

[0091] Step 23: adjusting the model operation data corresponding to each node model according to the node implementation data range, and respectively running each adjusted node model in the construction model to obtain the operation data corresponding to each node model;

[0092] Step 24: According to the node implementation data range and the operation data corresponding to each digital construction node, the operation process of the corresponding digital construction node is constructed to obtain the simulation construction method corresponding to each construction method.

[0093] In this example, each digital construction node corresponds to a node feature and a node model, and the node feature represents the features presented by the construction method in the digital construction node;

[0094] In this example, the node distribution relationship represents the composition relationship of multiple digital construction nodes;

[0095] In this example, the node implementation data range represents the data presented by the node model;

[0096] In this example, the model operation data indicates the operation range of the node model.

[0097] The working principle and beneficial effects of the above technical solution: In order to further analyze the specific details of each construction method, a node model is first constructed according to the node characteristics corresponding to each digital construction node, and the node model is arranged according to the distribution arrangement relationship of the digital construction nodes, generating a construction model that can reflect this construction. In the model, the node implementation data range of each node model is established, so that the data range is used to adjust the corresponding node model, and the operation data of each node model is determined through operation, so as to construct its corresponding operation process, and obtain the simulated construction method corresponding to each construction method. In this way, not only can the simulation process be completed, but also the corresponding data range can be adjusted during the simulation process, thereby improving the accuracy of the data.

[0098] Example 5

[0099] On the basis of Example 1, the node deepening design method for digital construction based on deep learning, the step 4 comprises:

[0100] Step 31: Identify key information for each of the simulated construction methods, obtain key construction points corresponding to each of the simulated construction methods, and count the key construction points to generate key supervision information for this construction;

[0101] Step 32: Divide each of the simulated construction methods into a number of construction steps, locate the key construction steps corresponding to the key construction points in the construction steps, and find the key construction foundations and key construction results corresponding to each of the key construction steps;

[0102] Step 33: derive the basic construction basis and basic construction results corresponding to each construction step in the corresponding simulated construction method according to the key construction basis and key construction results, and establish the construction execution method corresponding to each of the simulated construction methods according to the basic construction basis;

[0103] Step 34: construct a construction supervision process corresponding to each of the simulated construction methods according to the basic construction results, adjust the construction supervision process according to the key supervision information, and construct a construction supervision method corresponding to each of the simulated construction methods.

[0104] In this instance, key information identification represents the process of extracting key information contained in the simulated construction method;

[0105] In this example, the construction step represents a complete construction action from the simulated construction house;

[0106] In this example, the key construction basis represents the beginning of executing the key construction steps, and the key construction results represent the results obtained after executing the key construction steps.

[0107] The working principle and beneficial effects of the above technical solution: In order to better complete the construction, first construct the key supervision information given for this construction based on the key construction points contained in the simulated construction method, and further divide the simulated construction method into several construction steps, in which the key construction foundation and key construction results are located, and then the basic construction foundation and basic construction results corresponding to each basic construction step are determined by reverse deduction, so as to construct the construction execution method, and construct the construction supervision method in combination with the key supervision information already obtained, so as to lay the foundation for subsequent construction and supervision work.

[0108] Example 6

[0109] On the basis of Example 1, the node deepening design method for digital construction based on deep learning, the step 4 comprises:

[0110] Step 41: Evaluate the required construction time corresponding to each of the construction projects according to the construction execution mode corresponding to each of the simulated construction modes, and establish a construction timeline for each of the construction projects;

[0111] Step 42: Mapping the construction supervision method and construction execution method corresponding to each of the simulated construction methods to the same analysis domain, the plurality of supervision information contained in the construction supervision method and the execution information corresponding to each of the supervision information;

[0112] Step 43: locating the execution time period corresponding to each execution information in the construction timeline, and determining the supervision information corresponding to each specified time period in the construction timeline according to the locating result;

[0113] Step 44: Establish a construction plan for this construction based on the cross information of the supervision information and the execution information in the construction timeline.

[0114] In this example, the specified time period may be 24 hours.

[0115] The working principle and beneficial effects of the above technical solution are as follows: the construction timeline is established by evaluating the construction duration of the simulated construction method, and then the execution information and supervision information corresponding to different time periods are analyzed to construct the construction plan for this construction, so that supervision is achieved during the execution process, effectively eliminating hidden dangers from the root.

[0116] Example 7

[0117] On the basis of Example 4, the node deepening design method of digital construction based on deep learning, the step 21 includes:

[0118] Step 211: respectively obtaining node features corresponding to each of the digital construction nodes, respectively performing copy and superposition training on each of the node features, obtaining feature change information corresponding to each superposition, and establishing a repetitive execution rule of the corresponding digital construction node according to the feature change information;

[0119] Step 212: feature stratification of the digital construction nodes according to the repeated execution rule, obtain feature values ​​corresponding to different node regions in the digital construction nodes, and perform reinforcement processing on the node regions to be reinforced whose feature values ​​are higher than the feature mean in the same digital construction node;

[0120] Step 213: Obtain node presentation data contained in each enhanced digital construction node, convert the node presentation data into model data, and construct a node model corresponding to each digital construction node.

[0121] The working principle and beneficial effects of the above technical solution are as follows: In order to highlight the characteristics of digital construction nodes in the node model, the node characteristics are layered and enhanced to highlight their corresponding characteristics, and then the model data is established and the model is constructed to ensure the effectiveness of the model.

[0122] Example 8

[0123] On the basis of Example 5, the node deepening design method for digital construction based on deep learning further includes:

[0124] When the same simulated construction method includes two or more key construction points, the construction steps between two adjacent key construction points are derived and integrated to obtain the basic construction foundation and basic construction results corresponding to each construction step.

[0125] Example 9

[0126] This embodiment provides a node deepening design system for digital construction based on deep learning, such as Figure 2 As shown, including:

[0127] The engineering analysis module is used to obtain a number of construction projects of this construction and the construction method corresponding to each of the construction projects, and construct corresponding digital construction nodes;

[0128] A modeling execution module is used to construct a construction model of this construction based on the digital construction nodes, and simulate the simulated construction method corresponding to each of the construction methods;

[0129] A deep analysis module, used to perform deep analysis on each of the simulated construction methods, and assign corresponding construction execution methods and construction supervision methods to the corresponding simulated construction methods according to the analysis results;

[0130] The scheme construction module is used to construct a construction scheme for this construction by using the construction execution method and the construction supervision method.

[0131] In this example, each construction project may correspond to one or more construction methods, and different construction methods may be combined to form a new construction method;

[0132] In this example, one construction project corresponds to one digital construction node, and the digital construction node represents the way of simulating the construction project using digital technology;

[0133] In this example, one simulation construction method corresponds to one construction execution method and one construction supervision method;

[0134] In this example, the construction plan refers to the plan for completing this construction work.

[0135] The working principle and beneficial effects of the above technical solution: In order to achieve the purpose of reasonably arranging the construction progress, digital construction nodes are established before construction according to the construction projects to be carried out and their corresponding construction methods, thereby constructing a construction model of this construction. In the construction model, the simulated construction method corresponding to each construction method can be determined, and the corresponding construction execution method and construction supervision method are assigned to them through in-depth analysis of the simulated construction method, thereby constructing a construction plan for this construction. Through the above steps, a feasible construction plan can be established before construction. During the construction process, the construction results can be predicted in advance according to the construction plan, so as to reasonably arrange the construction time, construction personnel, etc. of different construction projects, which can not only ensure the progress of the construction period, but also avoid rework and ensure the construction quality.

[0136] Example 10

[0137] On the basis of Example 9, the node deepening design method for digital construction based on deep learning further includes:

[0138] The construction works include: foundation treatment works, earthwork works, drainage works, fire protection pipe network works, pavement works and perimeter works.

[0139] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A node deepening design method for digital construction based on deep learning, characterized in that: include: Step 1: Obtain several construction projects of this construction and the construction methods corresponding to each of the construction projects, and construct corresponding digital construction nodes; Step 2: constructing a construction model of this construction based on the digital construction nodes, and simulating the simulated construction method corresponding to each of the construction methods; Step 3: Conduct an in-depth analysis on each of the simulated construction methods, and assign corresponding construction execution methods and construction supervision methods to the corresponding simulated construction methods according to the analysis results; Step 4: construct a construction plan for this construction using the construction execution method and the construction supervision method; The step 2 comprises: Step 21: Obtain node features corresponding to each of the digital construction nodes respectively, perform feature deepening and highlighting processing on each of the digital construction nodes according to the node features, and establish a node model for each of the digital construction nodes; Step 22: Arrange the node models using the node distribution relationship between the digital construction nodes to obtain a construction model for this construction, and determine the node implementation data range corresponding to each node model in the construction model; Step 23: adjusting the model operation data corresponding to each node model according to the node implementation data range, and respectively running each adjusted node model in the construction model to obtain the operation data corresponding to each node model; Step 24: constructing the operation process of the corresponding digital construction node according to the node implementation data range and the operation data corresponding to each digital construction node, and obtaining the simulation construction method corresponding to each construction method; The step 21 comprises: Step 211: respectively obtaining node features corresponding to each of the digital construction nodes, respectively performing copy and superposition training on each of the node features, obtaining feature change information corresponding to each superposition, and establishing a repetitive execution rule of the corresponding digital construction node according to the feature change information; Step 212: feature stratification of the digital construction nodes according to the repeated execution rule, obtain feature values ​​corresponding to different node regions in the digital construction nodes, and perform reinforcement processing on the node regions to be reinforced whose feature values ​​are higher than the feature mean in the same digital construction node; Step 213: Obtain node presentation data contained in each enhanced digital construction node, convert the node presentation data into model data, and construct a node model corresponding to each digital construction node.

2. The node deepening design method for digital construction based on deep learning according to claim 1, characterized in that: Also includes: The construction works include: foundation treatment works, earthwork works, drainage works, fire protection pipe network works, pavement works and perimeter works.

3. The node deepening design method for digital construction based on deep learning according to claim 1, characterized in that: The step 1 comprises: Step 11: construct multiple sets of construction data for this construction according to the construction information uploaded by the user, establish multiple construction projects for this construction according to the construction data, and establish a sequence between the construction projects according to the data connection relationship between different construction data; Step 12: Analyze each of the construction projects, obtain the construction specifications corresponding to each of the construction projects, and construct a construction switching method between different construction projects based on the sequence; Step 13: Decompose each of the construction switching modes into a plurality of switching actions, obtain the action correlation degree between each of the switching actions and the corresponding construction specifications, integrate each of the switching actions into the corresponding construction specifications according to the action correlation degree, and generate the construction mode corresponding to each of the construction projects; Step 14: Simulate each of the construction methods in a preset simulation space to obtain digital construction information corresponding to each of the construction projects, and use the digital construction information to construct digital construction nodes for each of the construction projects.

4. The node deepening design method for digital construction based on deep learning according to claim 1, characterized in that: The step 3 comprises: Step 31: Identify key information for each of the simulated construction methods, obtain key construction points corresponding to each of the simulated construction methods, and count the key construction points to generate key supervision information for this construction; Step 32: Divide each of the simulated construction methods into a number of construction steps, locate the key construction steps corresponding to the key construction points in the construction steps, and find the key construction foundations and key construction results corresponding to each of the key construction steps; Step 33: derive the basic construction basis and basic construction results corresponding to each construction step in the corresponding simulated construction method according to the key construction basis and key construction results, and establish the construction execution method corresponding to each of the simulated construction methods according to the basic construction basis; Step 34: construct a construction supervision process corresponding to each of the simulated construction methods according to the basic construction results, adjust the construction supervision process according to the key supervision information, and construct a construction supervision method corresponding to each of the simulated construction methods.

5. The node deepening design method for digital construction based on deep learning according to claim 1, characterized in that: The step 4 comprises: Step 41: Evaluate the required construction time corresponding to each of the construction projects according to the construction execution mode corresponding to each of the simulated construction modes, and establish a construction timeline for each of the construction projects; Step 42: Mapping the construction supervision method and construction execution method corresponding to each of the simulated construction methods to the same analysis domain, the plurality of supervision information contained in the construction supervision method and the execution information corresponding to each of the supervision information; Step 43: locating the execution time period corresponding to each execution information in the construction timeline, and determining the supervision information corresponding to each specified time period in the construction timeline according to the locating result; Step 44: Establish a construction plan for this construction based on the cross information of the supervision information and the execution information in the construction timeline.

6. The node deepening design method for digital construction based on deep learning as claimed in claim 4, characterized in that: Also includes: When the same simulated construction method includes two or more key construction points, the construction steps between two adjacent key construction points are derived and integrated to obtain the basic construction foundation and basic construction results corresponding to each construction step.

7. A node-deepening design system for digital construction based on deep learning, characterized in that: include: The engineering analysis module is used to obtain a number of construction projects of this construction and the construction method corresponding to each of the construction projects, and construct corresponding digital construction nodes; A modeling execution module is used to construct a construction model of this construction based on the digital construction nodes, and simulate the simulated construction method corresponding to each of the construction methods; A deep analysis module, used to perform deep analysis on each of the simulated construction methods, and assign corresponding construction execution methods and construction supervision methods to the corresponding simulated construction methods according to the analysis results; A scheme construction module, used to construct a construction scheme for this construction by using the construction execution method and the construction supervision method; The modeling execution module constructs a construction model of this construction based on the digital construction nodes, and simulates the process of the simulated construction method corresponding to each of the construction methods, including: Respectively obtain node features corresponding to each of the digital construction nodes, perform feature deepening and highlighting processing on each of the digital construction nodes according to the node features, and establish a node model for each of the digital construction nodes; Arranging the node models by using the node distribution relationship between the digital construction nodes to obtain a construction model for this construction, and determining the node implementation data range corresponding to each of the node models in the construction model; Adjust the model operation data corresponding to each node model according to the node implementation data range, and respectively operate each node model after adjustment in the construction model to obtain the operation data corresponding to each node model; According to the node implementation data range and the operation data corresponding to each digital construction node, the operation process of the corresponding digital construction node is constructed to obtain the simulation construction method corresponding to each construction method; The process of respectively obtaining node features corresponding to each of the digital construction nodes, performing feature deepening and highlighting processing on each of the digital construction nodes according to the node features, and establishing a node model for each of the digital construction nodes includes: Respectively obtain node features corresponding to each of the digital construction nodes, respectively perform copy and superposition training on each of the node features, obtain feature change information corresponding to each superposition, and establish a repetitive execution rule of the corresponding digital construction node according to the feature change information; According to the repeated execution rule, the digital construction nodes are characterized and layered to obtain characteristic values ​​corresponding to different node regions in the digital construction nodes, and the node regions to be strengthened whose characteristic values ​​are higher than the characteristic mean in the same digital construction node are strengthened; The node presentation data contained in each enhanced digital construction node is obtained, the node presentation data is converted into model data, and a node model corresponding to each digital construction node is constructed.

8. The node deepening design system for digital construction based on deep learning as claimed in claim 7, characterized in that: Also includes: The construction works include: foundation treatment works, earthwork works, drainage works, fire protection pipe network works, pavement works and perimeter works.

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