Building construction progress supervision system and method based on big data
Through a construction progress supervision system based on big data, the optimal construction sequence path is generated, which solves the problem of inefficient supervision of traditional construction progress, and achieves accurate planning of construction progress and improves efficiency.
Patent Information
- Application Number
- CN202411492621.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-24
- Publication Date
- 2025-02-14
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Traditional construction progress supervision relies on manual inspection and manual recording, and the project cannot be accurately planned, resulting in inefficient construction efficiency and progress risks.
A construction progress supervision system based on big data is adopted to generate first- and second-level construction goals, obtain the linear relationship equations between the number of workers and the construction materials and progress, establish the optimal construction sequence path, generate the construction progress plan and implement it.
Accurate planning of the project with existing initial construction resources has been achieved, construction efficiency has been improved, and progress risks have been reduced.
Smart Images
Figure CN119443614A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of construction progress analysis, and in particular to a system and method for monitoring construction progress based on big data. Background Art
[0002] With the acceleration of urbanization, the scale of the construction industry is becoming larger and larger, and the complexity and diversity of engineering projects are also increasing. Therefore, the supervision of construction progress has become an important link to ensure the timely completion of projects and control costs. In recent years, with the rapid development of information technology, the application of big data technology has gradually penetrated into the construction industry. By collecting and analyzing massive amounts of data, construction units can better predict the progress of projects.
[0003] Traditional construction progress supervision mainly relies on manual inspection and manual recording, and cannot accurately plan the project based on the existing initial construction resources, which makes the construction efficiency low and prone to errors, resulting in the inability to grasp the construction dynamics and potential progress risks in real time. Therefore, how to improve the construction efficiency of the project while accurately planning the project with the existing initial construction resources is a difficulty of the existing technology. To this end, a construction progress supervision system and method based on big data is provided. Summary of the invention
[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a construction progress monitoring system and method based on big data.
[0005] In order to achieve the above object, the present invention provides the following technical solutions: A construction progress supervision method based on big data includes the following steps: Step S1: Generate multiple first-level construction targets according to a preset construction plan, and then obtain several historical construction records containing the first-level construction targets through the Internet; Step S2: Segment a number of primary construction targets from the historical building construction records, and again segment a number of secondary construction targets from each primary construction target, and then aggregate the secondary construction targets of the same type to obtain the number of workers corresponding to each secondary construction target and the linear relationship equation between the construction materials and the construction progress; Step S3, establishing a number of secondary construction nodes according to the number of secondary construction target types, and then taking each secondary construction node as the starting construction node in turn, and selecting other secondary construction nodes as the final construction nodes in turn, and obtaining the primary optimal construction sequence path and the secondary optimal construction sequence path corresponding to each primary construction target under different initial construction conditions according to the number of workers corresponding to each secondary construction target and the linear relationship equation between construction materials and construction progress; Step S4: Obtain the initial construction conditions and construction plan of the target building according to the construction plan, and then match the corresponding first-level optimal construction sequence path and second-level optimal construction sequence path to generate a construction schedule and execute it.
[0006] Furthermore, the building construction plan includes a construction structure diagram, a building construction plan, and estimated construction personnel information; The construction structure drawing includes n building structure names and building structure image data corresponding to each building structure name, where n is a natural number greater than 0; The construction plan consists of n construction phases, one for each building structure name, and the construction phases include the corresponding building structure name, the associated building construction phase name, the construction steps, and the functions and numbers of construction personnel required for the construction steps; The estimated construction personnel information includes the construction functions of each construction personnel; The historical building construction records include the building structure name, the historical construction records corresponding to the construction of each building structure, and the historical structure construction sequence; The historical construction record is composed of a number of historical construction stages, wherein a historical construction stage is composed of a number of historical construction steps and a historical construction sequence; The historical construction steps include the functions and number of construction personnel, image data of construction results, names of construction steps, amount of construction materials used, and construction duration; The historical construction sequence records the execution order and execution start and end time of each historical construction step.
[0007] Furthermore, the process of dividing the primary construction target and the secondary construction target includes: Extract all historical construction steps from each historical construction record and record them as historical first-level construction targets, classify them according to the building structure names corresponding to each historical first-level construction target, and record the historical construction steps in each historical first-level construction target as historical second-level construction targets; According to the construction step names corresponding to each historical secondary construction target, the same type of historical primary construction targets with the same historical secondary construction targets are secondary classified.
[0008] Furthermore, the process of obtaining the linear relationship equation corresponding to the secondary construction target includes: Establish a corresponding dynamic construction image model based on the construction structure image data of each historical secondary construction target, and divide the dynamic construction image model corresponding to the historical secondary construction target of the same classification into a number of local dynamic image blocks with the same volume; Then, the corresponding construction material usage is marked on each local dynamic image block according to the construction material usage in the historical secondary construction target, and the total model volume value of the dynamic construction image model corresponding to each historical secondary construction target is obtained, which is recorded as the total construction result volume; Based on the total construction result quantity, the number of construction personnel and the construction time of each historical secondary construction target, the construction progress change curve of each historical secondary construction target is obtained, and the construction progress change curve has an independent variable parameter, a fixed parameter and a result parameter built in, wherein the independent variable parameter is the time value, the fixed parameter is the number of construction personnel, and the result parameter is the construction result quantity; A three-dimensional coordinate system is established, and the construction progress change curves corresponding to the same classification of historical secondary construction targets are mapped onto the same three-dimensional coordinate system. Each construction progress change curve is divided into a number of three-dimensional space points, and a series of three-dimensional space points with the largest number of adjacent three-dimensional space points are selected from all three-dimensional space points, and recorded as space mass points under corresponding independent variable parameters and fixed parameters; Obtain the covariance of each three-dimensional space point to the corresponding spatial particle under each independent variable parameter and fixed parameter, establish the covariance matrix P corresponding to each historical secondary construction target, and then obtain the eigenvector of the covariance matrix P and the common particle of each spatial particle; Then, the linear relationship equation corresponding to the historical secondary construction target is established according to the eigenvectors of the covariance matrix and the common particles.
[0009] Furthermore, the process of acquiring the spatial particles includes: Set a spatial distance threshold, select all three-dimensional space points under the same independent variable parameter and fixed parameter, take each three-dimensional space point as the center coordinate point in turn, and calculate the spatial distance between the center coordinate point and other three-dimensional space points, and then determine whether the spatial distance between the center coordinate point and other three-dimensional space points is less than or equal to the spatial distance threshold; If it is less than or equal to, the three-dimensional space point is recorded as the associated three-dimensional space point corresponding to the center coordinate point, otherwise no operation is performed; When all the center coordinate points and other three-dimensional space points are judged, the three-dimensional space point with the most associated three-dimensional space points is selected as the space particle under the corresponding independent variable parameters and fixed parameters.
[0010] Furthermore, the process of establishing the secondary optimal construction sequence path includes: According to the number of historical secondary construction target types, several secondary construction nodes are established, and according to the historical structural construction sequence recorded by each historical primary construction target, directed connecting lines are set between the corresponding secondary construction nodes to obtain the corresponding secondary construction sequence path, and the functions, number of construction personnel and construction duration of each secondary construction node are marked; Merge the secondary construction sequence paths of the same classification of historical primary construction targets, obtain the names of the first and last executed construction steps of each historical structure construction sequence, and record the names of each construction step as the starting secondary construction node and the final secondary construction node; Pair each starting secondary construction node and the final secondary construction node with each other. If the construction step names corresponding to the starting secondary construction node and the final secondary construction node are the same, it is determined that the matching fails, otherwise it is determined that the matching succeeds; Taking each group of starting secondary construction nodes and final secondary construction nodes as the construction starting point and construction end point in turn, and then generating a number of secondary preparatory construction sequence paths of each group of starting secondary construction nodes and final secondary construction nodes according to the directed connection lines between each secondary construction node in the merged secondary construction sequence path; Setting a number of initial construction conditions for each secondary preparatory construction sequence path, wherein the initial construction conditions include the functions and number of construction personnel; Input each initial construction condition into the secondary preparatory construction sequence path in turn, obtain the estimated construction time of each secondary construction node according to the linear relationship equation of the historical secondary construction target, and then obtain the secondary optimal construction sequence path of each group of starting secondary construction nodes and the final secondary construction node under each initial construction condition according to the progressive relationship between each secondary construction node; Furthermore, the process of establishing the first-level optimal construction sequence path includes: According to the number of types of historical first-level construction targets, a number of first-level construction nodes are established, and according to the historical structure construction sequence recorded in each historical building construction record, the corresponding first-level construction nodes are connected to generate a first-level construction sequence path. Then, the second-level optimal construction sequence path generation process is adopted to generate the first-level optimal construction sequence path for each group of starting first-level construction nodes and the final first-level construction node under different initial construction conditions.
[0011] Furthermore, the process of generating the construction schedule includes: Generate corresponding initial construction conditions according to the estimated construction personnel information in the construction plan of the target building, and then match the relevant first-level optimal construction sequence path according to the construction plan and the initial construction conditions, and then input the initial construction conditions into the second-level optimal construction sequence path associated with each first-level construction node in the matched first-level optimal construction sequence path, and then select the second-level optimal construction sequence path with the shortest application time for each first-level construction node; The selected secondary optimal construction sequence paths are integrated and the execution priority is set to obtain a construction schedule. A construction period is set for each secondary optimal construction sequence path in the construction schedule. Then, each secondary optimal construction sequence path is executed and real-time construction result data of the corresponding construction position is collected. The real-time construction result data is input into the corresponding optimal construction schedule path. Then, it is determined whether there is any abnormality in the current construction. According to the determination result, the corresponding position of the target building is repaired until the execution of all secondary optimal construction sequence paths is completed.
[0012] A construction progress monitoring system based on big data, including a historical data collection module, a historical data analysis module, a construction path generation module and a construction plan module; The historical data acquisition module is used to generate multiple first-level construction targets according to a preset building construction plan, and then obtain several historical building construction records containing the first-level construction targets through the Internet; The historical data analysis module is used to generate corresponding primary construction targets and secondary construction targets based on historical building construction records, and then obtain the number of workers corresponding to each secondary construction target and the linear relationship equation between construction materials and construction progress; The construction path generation module is used to obtain the first-level optimal construction sequence path and the second-level optimal construction sequence path corresponding to each first-level construction target under different initial construction conditions according to the linear relationship equation corresponding to each second-level construction target; The construction plan module is used to obtain the initial construction conditions and construction plan of the target building according to the construction plan, and then match the corresponding first-level optimal construction sequence path and second-level optimal construction sequence path to generate a construction progress plan.
[0013] Compared with the prior art, the present invention has the following beneficial effects: The present invention obtains the linear relationship equation between the number of workers corresponding to each secondary construction target and the construction materials and the construction progress, and obtains the first-level optimal construction sequence path and the second-level optimal construction sequence path corresponding to each first-level construction target under different initial construction conditions. The initial construction conditions and the construction plan of the target building are obtained according to the construction plan, and then the corresponding first-level optimal construction sequence path and the second-level optimal construction sequence path are matched to generate a construction progress plan and execute it, thereby achieving accurate planning of the project with existing initial construction resources while improving the construction efficiency of the project. BRIEF DESCRIPTION OF THE DRAWINGS
[0014] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention.
[0015] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION
[0016] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention. Example
[0017] like Figure 1 As shown, a construction progress supervision method based on big data includes the following steps: Step S1: Generate multiple first-level construction targets according to a preset construction plan, and then obtain several historical construction records containing the first-level construction targets through the Internet; Step S2: Segment a number of primary construction targets from the historical building construction records, and again segment a number of secondary construction targets from each primary construction target, and then aggregate the secondary construction targets of the same type to obtain the number of workers corresponding to each secondary construction target and the linear relationship equation between the construction materials and the construction progress; Step S3, establishing a number of secondary construction nodes according to the number of secondary construction target types, and then taking each secondary construction node as the starting construction node in turn, and selecting other secondary construction nodes as the final construction nodes in turn, and obtaining the primary optimal construction sequence path and the secondary optimal construction sequence path corresponding to each primary construction target under different initial construction conditions according to the number of workers corresponding to each secondary construction target and the linear relationship equation between construction materials and construction progress; Step S4: Obtain the initial construction conditions and construction plan of the target building according to the construction plan, and then match the corresponding first-level optimal construction sequence path and second-level optimal construction sequence path to generate a construction schedule and execute it. Example
[0018] This embodiment is a further limitation of Embodiment 1, and Step S1 is implemented by the following process: Set up a cloud management platform, and then the staff can scan the construction plan, convert it into electronic data and upload it to the cloud management platform; The construction plan includes a construction structure drawing, a construction plan, and estimated construction personnel information; The construction structure drawing includes n building structure names and building structure image data corresponding to each building structure name, where n is a natural number greater than 0; The construction plan consists of n construction phases, one for each building structure name, and the construction phases include the corresponding building structure name, the associated building construction phase name, the construction steps, and the functions and numbers of construction personnel required for the construction steps; The estimated construction personnel information includes the construction functions of each construction personnel, such as bricklaying, surveying, painting, etc.; The cloud management platform records the building construction phase corresponding to each building structure name in the building construction plan as a primary construction target, and obtains historical building construction records containing the building structure names included in one or more building construction plans through the Internet; The historical building construction records include the building structure name, the historical construction records corresponding to the construction of each building structure, and the historical structure construction sequence; The historical construction record is composed of a number of historical construction stages, wherein a historical construction stage is composed of a number of historical construction steps and a historical construction sequence; The historical construction steps include the functions and number of construction personnel, image data of construction results, names of construction steps, amount of construction materials used, and construction duration; The historical construction sequence records the execution order and execution start and end time of each historical construction step. Example
[0019] This embodiment is a further limitation of Embodiment 1, and Step S2 is implemented by the following process: Extract all historical construction steps from each historical construction record and record them as historical first-level construction targets, classify them according to the building structure names corresponding to each historical first-level construction target, and record the historical construction steps in each historical first-level construction target as historical second-level construction targets; According to the names of the construction steps corresponding to each historical secondary construction target, the same type of historical primary construction targets with the same historical secondary construction targets are secondary classified; Then, a corresponding dynamic construction image model is established according to the construction structure image data of each historical secondary construction target, and the dynamic construction image model changes dynamically with time; The dynamic construction image model corresponding to the historical secondary construction target of the same classification is divided into a number of local dynamic image blocks with the same volume. Since the dynamic construction image model changes dynamically with time, each local dynamic image block also disappears or appears with time. Then, the corresponding construction material usage is marked on each local dynamic image block according to the construction material usage in the historical secondary construction target, and the total model volume value of the dynamic construction image model corresponding to each historical secondary construction target is obtained, which is recorded as the total construction result volume; Based on the total construction results, number of construction personnel and construction time of each historical secondary construction target, the construction progress change curve of each historical secondary construction target is obtained; The construction process change curve has built-in independent variable parameters, fixed parameters and result parameters, wherein the independent variable parameter is the time value, the fixed parameter is the number of construction personnel, and the result parameter is the construction result quantity; A three-dimensional coordinate system is established, and the construction progress change curves corresponding to the same classification of historical secondary construction targets are mapped onto the same three-dimensional coordinate system. Each construction progress change curve is divided into a number of three-dimensional space points, and the independent variable parameters and fixed parameters are used as independent variables. A series of three-dimensional space points with the largest number of adjacent three-dimensional space points are selected from all three-dimensional space points, and recorded as space mass points under the corresponding independent variable parameters and fixed parameters; The process of obtaining the spatial particle includes: setting a spatial distance threshold, selecting all three-dimensional spatial points under the same independent variable parameter and fixed parameter, taking each three-dimensional spatial point as the central coordinate point in turn, and calculating the spatial distance between the central coordinate point and other three-dimensional spatial points, and then judging whether the spatial distance between the central coordinate point and other three-dimensional spatial points is less than or equal to the spatial distance threshold; If it is less than or equal to, the three-dimensional space point is recorded as the associated three-dimensional space point corresponding to the center coordinate point, otherwise no operation is performed; When all the center coordinate points and other three-dimensional space points have been judged, the three-dimensional space point with the most associated three-dimensional space points is selected as the space mass point under the corresponding independent variable parameters and fixed parameters; Obtain the covariance of each 3D space point to the corresponding space particle under each independent variable parameter and fixed parameter, establish the covariance matrix P corresponding to each historical secondary construction target, and then obtain the eigenvector (d x , d y , d z ), and the common mass points of each space mass point, where the common mass point is represented by ,in , , ,in Respectively represent the average value of the coordinates of each space particle on each coordinate axis, num represents the total number of space particles, and x, y, z represent the coordinate values on each coordinate axis; Then according to the eigenvector (d x , d y , d z) and the common mass points to establish a linear relationship equation corresponding to the historical secondary construction target, where the linear relationship equation is expressed as: . Example
[0020] This embodiment is a further limitation of Embodiment 1, and step S3 is implemented by the following process: According to the number of historical secondary construction target types, several secondary construction nodes are established, and according to the historical structural construction sequence recorded by each historical primary construction target, directed connecting lines are set between the corresponding secondary construction nodes to obtain the corresponding secondary construction sequence path, and the functions, number of construction personnel and construction duration of each secondary construction node are marked; It should be noted that the arrows of the directed connection line indicate that the construction steps corresponding to the two secondary construction nodes can be executed progressively. If the arrows are bidirectional, it means that the corresponding construction steps can be executed simultaneously. Merge the secondary construction sequence paths of the same classification of historical primary construction targets, obtain the names of the first and last executed construction steps of each historical structure construction sequence, and record the names of each construction step as the starting secondary construction node and the final secondary construction node; Pair each starting secondary construction node and the final secondary construction node with each other. If the construction step names corresponding to the starting secondary construction node and the final secondary construction node are the same, it is determined that the matching fails, otherwise it is determined that the matching succeeds; Taking each group of starting secondary construction nodes and final secondary construction nodes as the construction starting point and construction end point in turn, and then generating a number of secondary preparatory construction sequence paths of each group of starting secondary construction nodes and final secondary construction nodes according to the directed connection lines between each secondary construction node in the merged secondary construction sequence path; Setting a number of initial construction conditions for each secondary preparatory construction sequence path, wherein the initial construction conditions include the functions and number of construction personnel; Input each initial construction condition into the secondary preparatory construction sequence path in turn, obtain the estimated construction time of each secondary construction node according to the linear relationship equation of the historical secondary construction target, and then obtain the secondary optimal construction sequence path of each group of starting secondary construction nodes and the final secondary construction node under each initial construction condition according to the progressive relationship between each secondary construction node; According to the number of types of historical first-level construction targets, a number of first-level construction nodes are established, and according to the historical structure construction sequence recorded in each historical building construction record, the corresponding first-level construction nodes are connected to generate a first-level construction sequence path. Then, the second-level optimal construction sequence path generation process is adopted to generate the first-level optimal construction sequence path for each group of starting first-level construction nodes and the final first-level construction node under different initial construction conditions. Example
[0021] This embodiment is a further limitation of Embodiment 1, and step S4 is implemented by the following process: Generate corresponding initial construction conditions according to the estimated construction personnel information in the construction plan of the target building, and then match the relevant first-level optimal construction sequence path according to the construction plan and the initial construction conditions, and then input the initial construction conditions into the second-level optimal construction sequence path associated with each first-level construction node in the matched first-level optimal construction sequence path, and then select the second-level optimal construction sequence path with the shortest application time for each first-level construction node; The selected secondary optimal construction sequence paths are integrated and the execution priority is set to obtain a construction schedule. A construction period is set for each secondary optimal construction sequence path in the construction schedule. Then, each secondary optimal construction sequence path is executed and real-time construction result data of the corresponding construction position is collected. The real-time construction result data is input into the corresponding optimal construction schedule path. Then, it is determined whether there is any abnormality in the current construction. According to the determination result, the corresponding position of the target building is repaired until the execution of all secondary optimal construction sequence paths is completed.
[0022] Furthermore, the present invention also discloses a construction progress monitoring system based on big data, including a historical data collection module, a historical data analysis module, a construction path generation module and a construction plan module; The historical data acquisition module is used to generate multiple first-level construction targets according to a preset building construction plan, and then obtain several historical building construction records containing the first-level construction targets through the Internet; The historical data analysis module is used to generate corresponding primary construction targets and secondary construction targets based on historical building construction records, and then obtain the number of workers corresponding to each secondary construction target and the linear relationship equation between construction materials and construction progress; The construction path generation module is used to obtain the first-level optimal construction sequence path and the second-level optimal construction sequence path corresponding to each first-level construction target under different initial construction conditions according to the linear relationship equation corresponding to each second-level construction target; The construction plan module is used to obtain the initial construction conditions and construction plan of the target building according to the construction plan, and then match the corresponding first-level optimal construction sequence path and second-level optimal construction sequence path to generate a construction progress plan.
[0023] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.
Claims
1. A construction progress supervision method based on big data, characterized in that: The following steps are involved: Step S1: Generate multiple first-level construction targets according to a preset construction plan, and then obtain several historical construction records containing the first-level construction targets through the Internet; Step S2: Segment a number of primary construction targets from the historical building construction records, and again segment a number of secondary construction targets from each primary construction target, and then aggregate the secondary construction targets of the same type to obtain the number of workers corresponding to each secondary construction target and the linear relationship equation between the construction materials and the construction progress; Step S3, establishing a number of secondary construction nodes according to the number of secondary construction target types, and then taking each secondary construction node as the starting construction node in turn, and selecting other secondary construction nodes as the final construction nodes in turn, and obtaining the primary optimal construction sequence path and the secondary optimal construction sequence path corresponding to each primary construction target under different initial construction conditions according to the number of workers corresponding to each secondary construction target and the linear relationship equation between construction materials and construction progress; Step S4: Obtain the initial construction conditions and construction plan of the target building according to the construction plan, and then match the corresponding first-level optimal construction sequence path and second-level optimal construction sequence path to generate a construction schedule and execute it.
2. A method for monitoring construction progress based on big data according to claim 1, characterized in that: The construction plan includes a construction structure drawing, a construction plan, and estimated construction personnel information; The construction structure drawing includes n building structure names and building structure image data corresponding to each building structure name, where n is a natural number greater than 0; The construction plan consists of n construction phases, one for each building structure name, and the construction phases include the corresponding building structure name, the associated building construction phase name, the construction steps, and the functions and numbers of construction personnel required for the construction steps; The historical building construction records include the building structure name, the historical construction records corresponding to the construction of each building structure, and the historical structure construction sequence; The historical construction record is composed of a number of historical construction stages, wherein a historical construction stage is composed of a number of historical construction steps and a historical construction sequence; The historical construction steps include the functions and number of construction personnel, construction result image data, construction step names, construction material usage, and construction duration.
3. A method for monitoring construction progress based on big data according to claim 2, characterized in that: The process of dividing the first-level construction target and the second-level construction target includes: Extract all historical construction steps from each historical construction record and record them as historical first-level construction targets, classify them according to the building structure names corresponding to each historical first-level construction target, and record the historical construction steps in each historical first-level construction target as historical second-level construction targets; According to the construction step names corresponding to each historical secondary construction target, the same type of historical primary construction targets with the same historical secondary construction targets are secondary classified.
4. A method for monitoring construction progress based on big data according to claim 3, characterized in that: The process of obtaining the linear relationship equation corresponding to the secondary construction target includes: Establish a corresponding dynamic construction image model based on the construction structure image data of each historical secondary construction target, and divide the dynamic construction image model corresponding to the historical secondary construction target of the same classification into a number of local dynamic image blocks; Then, the corresponding construction material usage is marked on each local dynamic image block according to the construction material usage in the historical secondary construction target, and the total model volume value of the dynamic construction image model corresponding to each historical secondary construction target is obtained, which is recorded as the total construction result volume; Based on the total construction result quantity, the number of construction personnel and the construction time of each historical secondary construction target, the construction progress change curve of each historical secondary construction target is obtained, and the construction progress change curve has an independent variable parameter, a fixed parameter and a result parameter built in, wherein the independent variable parameter is the time value, the fixed parameter is the number of construction personnel, and the result parameter is the construction result quantity; A three-dimensional coordinate system is established, and the construction progress change curves corresponding to the same classification of historical secondary construction targets are mapped onto the same three-dimensional coordinate system. Each construction progress change curve is divided into a number of three-dimensional space points, and a series of three-dimensional space points with the largest number of adjacent three-dimensional space points are selected from all three-dimensional space points, and recorded as space mass points under corresponding independent variable parameters and fixed parameters; Obtain the covariance of each three-dimensional space point to the corresponding spatial particle under each independent variable parameter and fixed parameter, establish the covariance matrix corresponding to each historical secondary construction target, and then obtain the eigenvector of the covariance matrix and the common particles of each spatial particle, and then establish the linear relationship equation corresponding to the historical secondary construction target based on the eigenvector of the covariance matrix and the common particles.
5. A method for monitoring construction progress based on big data according to claim 4, characterized in that: The process of acquiring the spatial particles includes: Set a spatial distance threshold, select all three-dimensional space points under the same independent variable parameter and fixed parameter, take each three-dimensional space point as the center coordinate point in turn, and calculate the spatial distance between the center coordinate point and other three-dimensional space points, and then determine whether the spatial distance between the center coordinate point and other three-dimensional space points is less than or equal to the spatial distance threshold; According to the judgment result, the three-dimensional space point is recorded as the associated three-dimensional space point of the corresponding center coordinate point. When all the center coordinate points and other three-dimensional space points are judged, the three-dimensional space point with the most associated three-dimensional space points is selected as the spatial particle under the corresponding independent variable parameters and fixed parameters.
6. A method for monitoring construction progress based on big data according to claim 4, characterized in that: The process of establishing the secondary optimal construction sequence path includes: According to the number of historical secondary construction target types, several secondary construction nodes are established, and according to the historical structural construction sequence recorded by each historical primary construction target, directed connecting lines are set between the corresponding secondary construction nodes to obtain the corresponding secondary construction sequence path, and the functions, number of construction personnel and construction duration of each secondary construction node are marked; Merge the secondary construction sequence paths of the same classification of historical primary construction targets, obtain the names of the first and last executed construction steps of each historical structure construction sequence, and then set a number of starting secondary construction nodes and final secondary construction nodes; Taking each group of starting secondary construction nodes and final secondary construction nodes as the construction starting point and construction end point in turn, and then generating a number of secondary preparatory construction sequence paths of each group of starting secondary construction nodes and final secondary construction nodes according to the directed connection lines between each secondary construction node in the merged secondary construction sequence path; Several initial construction conditions are set for each secondary preparatory construction sequence path, and each initial construction condition is input into the secondary preparatory construction sequence path in turn. According to the linear relationship equation of the historical secondary construction target, the estimated construction duration of each secondary construction node is obtained. Then, according to the progressive relationship between each secondary construction node, the secondary optimal construction sequence path of each group of starting secondary construction nodes and the final secondary construction node under each initial construction condition is obtained.
7. A method for monitoring construction progress based on big data according to claim 6, characterized in that: The process of establishing the first-level optimal construction sequence path includes: According to the number of types of historical first-level construction targets, a number of first-level construction nodes are established, and according to the historical structure construction sequence recorded in each historical building construction record, the corresponding first-level construction nodes are connected to generate a first-level construction sequence path. Then, the second-level optimal construction sequence path generation process is adopted to generate the first-level optimal construction sequence path for each group of starting first-level construction nodes and the final first-level construction node under different initial construction conditions.
8. The method for monitoring construction progress based on big data according to claim 7 is characterized in that: The generation process of the construction schedule includes: Generate corresponding initial construction conditions according to the estimated construction personnel information in the construction plan of the target building, and then match the relevant first-level optimal construction sequence path according to the construction plan and the initial construction conditions, and then input the initial construction conditions into the second-level optimal construction sequence path associated with each first-level construction node in the matched first-level optimal construction sequence path, and then select the second-level optimal construction sequence path with the shortest application time for each first-level construction node; The selected secondary optimal construction sequence paths are integrated and the execution priority is set to obtain a construction schedule. A construction period is set for each secondary optimal construction sequence path in the construction schedule. Then, each secondary optimal construction sequence path is executed and real-time construction result data of the corresponding construction position is collected. The real-time construction result data is input into the corresponding optimal construction schedule path. Then, it is determined whether there is any abnormality in the current construction. According to the determination result, the corresponding position of the target building is repaired until the execution of all secondary optimal construction sequence paths is completed.
9. A construction progress monitoring system based on big data, used to implement the construction progress monitoring method based on big data described in claims 1 to 8, characterized in that: It includes historical data collection module, historical data analysis module, construction path generation module and construction plan module; The historical data acquisition module is used to generate multiple first-level construction targets according to a preset building construction plan, and then obtain several historical building construction records containing the first-level construction targets through the Internet; The historical data analysis module is used to generate corresponding primary construction targets and secondary construction targets based on historical building construction records, and then obtain the number of workers corresponding to each secondary construction target and the linear relationship equation between construction materials and construction progress; The construction path generation module is used to obtain the first-level optimal construction sequence path and the second-level optimal construction sequence path corresponding to each first-level construction target under different initial construction conditions according to the linear relationship equation corresponding to each second-level construction target; The construction plan module is used to obtain the initial construction conditions and construction plan of the target building according to the construction plan, and then match the corresponding first-level optimal construction sequence path and second-level optimal construction sequence path to generate a construction progress plan.