CN116976700A - Construction progress and cost prediction method, device, equipment and storage medium - Google Patents
Construction progress and cost prediction method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of data analysis, and discloses a construction progress and cost prediction method, a device, equipment and a storage medium, wherein the method comprises the following steps: acquiring historical three-dimensional model data of a building of the same type as a target building and standard specifications of the industry; training the GPT model based on the historical three-dimensional model data and the standard specification of the industry to obtain a BIM-GPT model; acquiring the current construction progress and cost of a target building, and inputting the current construction progress and cost into a BIM-GPT model to obtain three-dimensional model data reconstructed by the BIM-GPT model; and inputting the reconstructed three-dimensional model data into a pre-trained progress cost prediction model to obtain the construction progress and cost prediction result of the target building. According to the invention, automatic data processing is realized, the BIM-GPT model is used for reconstructing three-dimensional model data, manual operation calculation is omitted, and accuracy and prediction efficiency of a prediction result are improved through a progress cost prediction model.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a construction progress and cost prediction method, a construction progress and cost prediction device, construction equipment and a storage medium.
Background
Along with the acceleration of the urban process, the number and difficulty of building projects are gradually increased, and the method has important significance in large-scale engineering project management, construction quality control, cost control and progress control. The construction progress is monitored, managed and predicted, the overall construction level of the construction project can be effectively guaranteed, and the construction period delay is effectively avoided. The construction cost is monitored, controlled and predicted, and the progress management scheme can be conveniently and pertinently adjusted to adapt to market and industry changes.
In the prior art, progress analysis and cost analysis are mostly carried out by means of graphs, simulations and the like. However, the traditional construction management means have the problems of more manual intervention, difficult data acquisition, low analysis speed and the like. Therefore, the accuracy of the prediction results of the construction progress and the cost is low.
Disclosure of Invention
In view of the above, the present invention provides a construction progress and cost prediction method, apparatus, device and storage medium, so as to solve the problem of lower accuracy of the prediction result of the conventional prediction means.
In a first aspect, the present invention provides a construction progress and cost prediction method, the method comprising:
the method comprises the steps of obtaining historical three-dimensional model data of a building of the same type as a target building and standard specifications of industries, wherein the standard specifications comprise: construction progress and cost of various types of buildings;
training the GPT model based on historical three-dimensional model data and the standard specification of the industry to obtain a BIM-GPT model, wherein the BIM-GPT model is used for reconstructing three-dimensional model data according to construction progress and cost;
acquiring the current construction progress and cost of a target building, and inputting the current construction progress and cost into a BIM-GPT model to obtain three-dimensional model data reconstructed by the BIM-GPT model;
and inputting the three-dimensional model data reconstructed by the BIM-GPT model into a pre-trained progress cost prediction model to obtain the construction progress and cost prediction result of the target building.
According to the invention, the GPT model is trained according to the historical three-dimensional model data and the industry standard specification to realize automatic processing of the data, the BIM-GPT model is used for reconstructing the three-dimensional model data according to the construction progress and the cost, the manual operation and calculation process is omitted, the progress and the cost are predicted through the progress cost prediction model, so that the data is automatically processed and analyzed, and the accuracy and the prediction efficiency of a prediction result are improved.
In an alternative embodiment, before training the GPT model based on the historical three-dimensional model data and the industry standard specification, the method further comprises:
and carrying out data cleaning, formatting and encoding on the historical three-dimensional model data and the standard specifications of the industries to which the historical three-dimensional model data belong.
The invention can reduce the processing amount of the data and improve the reliability of the data by preprocessing the data so as to process and analyze the data, thereby improving the efficiency of data analysis.
In an alternative embodiment, the method further comprises:
acquiring actual construction progress and cost data;
and comparing and analyzing the actual construction progress and cost data and the construction progress and cost output by the progress cost analysis model to obtain a comparison analysis result.
According to the invention, the actual construction progress and cost data and the construction progress and cost output by the progress cost analysis model are compared and analyzed, so that the deviation of the result output by the model is corrected, and the error caused by the overlarge deviation of the result output by the model and the actual data is avoided.
In an alternative embodiment, the method further comprises:
acquiring influence factor data of construction progress and cost, wherein the influence factors comprise material supply and construction quality;
establishing a deviation analysis model based on the influence factor data of the construction progress and the cost;
and inputting the comparison analysis result into a deviation analysis model to obtain construction progress and cost deviation analysis results.
According to the invention, the construction progress and cost deviation are analyzed through the deviation analysis model, the deviation is found in time, so that constructors or management staff can conveniently adjust the construction scheme in time, and the economic loss and the manpower loss caused by the deviation of the construction progress and the cost are reduced.
In an alternative embodiment, the method further comprises:
and inputting the construction progress and the cost deviation analysis result into a preset optimization model to obtain an optimization scheme.
According to the invention, an optimization scheme is generated according to the construction progress and the cost deviation analysis result through the optimization model, so that a feasible implementation scheme is formed, and meanwhile, a constructor or a manager can conveniently adjust according to the optimization scheme in time.
In an alternative embodiment, inputting the construction progress and the cost deviation analysis result into a preset optimization model to obtain an optimization scheme, including:
the method comprises the steps of taking the minimum construction cost as a target, and taking construction limitation as a condition, establishing a preset optimization model;
and solving the preset optimization model by using a solver to obtain an optimization scheme.
According to the invention, the optimization model is built, the minimum construction cost is taken as a target, the construction limitation is taken as a condition, the construction cost is reduced as much as possible on the premise of meeting the construction requirement, and the optimization scheme is obtained by solving, so that the operator can conveniently adjust the construction according to the optimization scheme.
In an alternative embodiment, the method further comprises:
and acquiring the current construction progress in real time, feeding back the current construction progress to the BIM-GPT model, and retraining the model.
According to the invention, the model is retrained according to the real-time data to update the model, so that the prediction accuracy of the model is improved, and the accuracy of the model output result is further improved.
In a second aspect, the present invention provides a construction progress and cost prediction apparatus, the apparatus comprising:
the acquisition module is used for acquiring historical three-dimensional model data of a building of the same type as the target building and standard specifications of the industry, wherein the standard specifications comprise: construction progress and cost of various types of buildings;
the first obtaining module is used for training the GPT model based on the historical three-dimensional model data and the standard specification of the industry to obtain a BIM-GPT model, wherein the BIM-GPT model is used for reconstructing the three-dimensional model data according to the construction progress and the cost;
the second obtaining module is used for obtaining the current construction progress and cost of the target building, inputting the current construction progress and cost into the BIM-GPT model and obtaining three-dimensional model data reconstructed by the BIM-GPT model;
and the third obtaining module is used for inputting the three-dimensional model data reconstructed by the BIM-GPT model into a pre-trained progress cost prediction model to obtain the construction progress and cost prediction result of the target building.
In a third aspect, the present invention provides a computer device comprising: the system comprises a memory and a processor, wherein the memory and the processor are in communication connection, the memory stores computer instructions, and the processor executes the computer instructions so as to execute the construction progress and cost prediction method of the first aspect or any implementation mode corresponding to the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon computer instructions for causing a computer to execute the construction progress and cost prediction method of the first aspect or any one of the embodiments corresponding thereto.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a construction progress and cost prediction method according to an embodiment of the present invention;
FIG. 2 is a flow chart of another construction progress and cost prediction method according to an embodiment of the present invention;
FIG. 3 is a flow chart of yet another construction progress and cost prediction method according to an embodiment of the present invention;
FIG. 4 is a flow chart of yet another construction progress and cost prediction method according to an embodiment of the present invention;
FIG. 5 is a block diagram of a construction progress and cost prediction apparatus according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the related art, the construction progress and cost are predicted based on means such as charts or simulation, and generally manually modeling and analyzing are adopted, so that complex factors during construction and association relations before each task cannot be comprehensively considered, and the change of the construction progress and cost is difficult to accurately predict. Moreover, manual modeling and analysis requires a lot of manpower and time and is prone to errors. For various and complex problems in the modeling process, it is often only possible to simplify the process. Meanwhile, the data analysis capability is weak, only a single influencing factor can be processed, and deep mining and comprehensive analysis on a large number of factors cannot be performed.
In accordance with an embodiment of the present invention, there is provided an embodiment of a construction progress and cost prediction method, it being noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
In this embodiment, a construction progress and cost prediction method is provided, which may be used for a mobile terminal, and fig. 1 is a flowchart of a construction progress and cost prediction method according to an embodiment of the present invention, as shown in fig. 1, where the flowchart includes the following steps:
step S101, historical three-dimensional model data of a building of the same type as a target building and standard specifications of industries of the building are obtained, wherein the standard specifications comprise: construction progress and cost of various types of buildings.
In the embodiment of the invention, the types of the building mainly comprise civil houses, public buildings and the like, the three-dimensional model of the building is a BIM model, the standard specification of the industry is the data of construction progress, cost budget and the like of each type of the industry, and the historical three-dimensional model data and the standard specification are integrated.
For example, when the target building is a pumped storage power station, a great number of three-dimensional models and detailed design diagrams of corresponding facilities such as a machine room, a factory building, a power transmission substation, a main transformer and the like related to the hydropower station are obtained, so that the analysis of the target building is facilitated.
Specifically, the three-dimensional model data comprises information such as the number of components, a construction sequence, a process duration and the like, the construction progress comprises information such as time nodes, process progress, completed work and the like, and the cost comprises information such as various engineering quantities, unit price, cost constitution and the like.
Step S102, training the GPT model based on the historical three-dimensional model data and the standard specifications of the industry to obtain a BIM-GPT model, wherein the BIM-GPT model is used for reconstructing the three-dimensional model data according to the construction progress and the cost.
In the embodiment of the invention, the collected historical three-dimensional model data and the standard specifications of the industry are used for training and modeling the GPT model to obtain the BIM-GPT model, so that the trained BIM-GPT model can automatically reconstruct BIM model data according to the externally input construction progress and cost.
Specifically, when the GPT model is trained, the component information in the BIM model and the information such as the sequence, the node, the weight and the like in the GPT model can be correspondingly trained, so that the trained BIM-GPT model can also identify the association and influence relation among different construction sequences and working procedures and the dependence and priority among different construction sequences.
And step S103, acquiring the current construction progress and cost of the target building, and inputting the current construction progress and cost into the BIM-GPT model to obtain three-dimensional model data reconstructed by the BIM-GPT model.
In the embodiment of the invention, the current construction progress of the target building can be extracted or calculated from the site construction report, and the cost of the target building can be extracted or calculated from the construction budget file. And automatically reconstructing BIM model data according to the current construction progress and cost by the trained BIM-GPT model.
And step S104, inputting the three-dimensional model data reconstructed by the BIM-GPT model into a pre-trained progress cost prediction model to obtain the construction progress and cost prediction result of the target building.
In the embodiment of the invention, construction progress and cost data are synthesized, and a comprehensive progress cost prediction model is established, wherein the progress cost prediction model is used for predicting and analyzing construction progress and cost.
Specifically, the construction progress and the cost prediction result can be presented in a visual mode, so that both a construction party and a homeowner can directly know the progress condition of the project, and the construction party can make timely adjustment for different problems.
According to the construction progress and cost prediction method, the GPT model is trained according to the historical three-dimensional model data and the industry standard specifications to realize automatic data processing, the three-dimensional model data is reconstructed according to the construction progress and cost through the BIM-GPT model, the manual operation and calculation process is omitted, the progress and cost are predicted through the progress cost prediction model, automatic processing analysis of the data is realized, and the accuracy and the prediction efficiency of a prediction result are improved.
In this embodiment, a construction progress and cost prediction method is provided, which may be used in the above mobile terminal, such as a mobile phone, a tablet pc, etc., and fig. 2 is a flowchart of a construction progress and cost prediction method according to an embodiment of the present invention, as shown in fig. 2, where the flowchart includes the following steps:
step S201, historical three-dimensional model data of a building of the same type as a target building and standard specifications of the industry are obtained. Please refer to step S101 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S202, data cleaning, formatting and encoding are carried out on the historical three-dimensional model data and the standard specifications of the industry.
In the embodiment of the invention, the data is cleaned, that is, the repeated value, the missing value, the abnormal value and the like in the data are checked, for example, the repeated value is deleted, the missing value is replaced or deleted, and the abnormal value is subjected to data discretization. The data is formatted, and the information such as the component name, the component number and the like in the BIM model, the construction progress and the corresponding information in the cost are subjected to format matching. And carrying out unified numbering and coding treatment on the data so as to count the data.
And step S203, training the GPT model based on the historical three-dimensional model data and the standard specifications of the industry to obtain a BIM-GPT model, wherein the BIM-GPT model is used for reconstructing the three-dimensional model data according to the construction progress and the cost. Please refer to step S102 in the embodiment shown in fig. 1 in detail, which is not described herein.
And S204, acquiring the current construction progress and cost of the target building, and inputting the current construction progress and cost into the BIM-GPT model to obtain three-dimensional model data reconstructed by the BIM-GPT model. Please refer to step S103 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S205, three-dimensional model data reconstructed by the BIM-GPT model are input into a pre-trained progress cost prediction model, and construction progress and cost prediction results of the target building are obtained. Please refer to step S104 in the embodiment shown in fig. 1 in detail, which is not described herein.
The construction progress and cost prediction method provided by the embodiment preprocesses the data so as to process and analyze the data, reduce the processing amount of the data, improve the reliability of the data and further improve the efficiency of data analysis.
In this embodiment, a construction progress and cost prediction method is provided, which may be used in the above mobile terminal, such as a mobile phone, a tablet pc, etc., and fig. 3 is a flowchart of a construction progress and cost prediction method according to an embodiment of the present invention, as shown in fig. 3, where the flowchart includes the following steps:
step S301, historical three-dimensional model data of a building of the same type as a target building is obtained, and standard specifications of the industry are obtained. Please refer to step S101 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S302, training the GPT model based on the historical three-dimensional model data and the standard specifications of the industry to obtain a BIM-GPT model, wherein the BIM-GPT model is used for reconstructing the three-dimensional model data according to the construction progress and the cost. Please refer to step S102 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S303, the current construction progress and cost of the target building are obtained, and the current construction progress and cost are input into the BIM-GPT model to obtain three-dimensional model data reconstructed by the BIM-GPT model. Please refer to step S103 in the embodiment shown in fig. 1 in detail, which is not described herein.
And S304, inputting the three-dimensional model data reconstructed by the BIM-GPT model into a pre-trained progress cost prediction model to obtain the construction progress and cost prediction result of the target building. Please refer to step S104 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S305, acquiring actual construction progress and cost data.
And step S306, comparing and analyzing the actual construction progress and cost data and the construction progress and cost output by the progress cost analysis model to obtain a comparison analysis result.
In the embodiment of the invention, the actual construction progress is extracted from the site construction report, the actual cost data is extracted from the construction budget text, and the construction progress and cost output by the progress cost analysis model are obtained. The progress cost analysis model is trained in advance, so that the trained progress cost analysis model can output progress and cost according to construction data, material use data and other information.
Specifically, the progress cost analysis model can also output data such as a construction period, a resource requirement, a cost and the like, and the comparison analysis result also comprises indexes such as the construction period, the resource requirement, the cost and the like output by the model and actual data comparison analysis results.
According to the embodiment, the actual construction progress and cost data and the construction progress and cost output by the progress cost analysis model are compared and analyzed, so that deviation correction is carried out on the result output by the model, and errors caused by overlarge deviation between the model output result and the actual data are avoided.
In some alternative embodiments, the step S306 includes:
in step S3061, data of influencing factors including the construction progress and cost including the material supply and the construction quality are obtained.
And step S3062, establishing a deviation analysis model based on the influence factor data of the construction progress and the cost.
And step 3063, inputting the comparison analysis result into a deviation analysis model to obtain construction progress and cost deviation analysis results.
In the embodiment of the invention, the factors influencing the construction progress and the cost comprise factors such as material supply, construction quality, manpower resources and the like. For example, an untimely supply of material may lead to delays in the construction period, affecting the progress of the construction. Influencing factors also include influences between different processes, for example, construction sequence arrangement, overlap between processes, and the like can influence construction progress and cost.
Training the model based on the construction progress and cost influence factor data, and establishing a deviation analysis model to analyze the progress deviation and the cost deviation. For example, when the construction progress is later than the construction plan, the investment of manpower and material resources is small, or the construction sequence is wrong.
According to the method, the construction progress and the cost deviation are analyzed through the deviation analysis model, the deviation is found timely, constructors or management staff can adjust a construction scheme timely, and economic loss and manpower loss caused by the deviation of the construction progress and the cost are reduced.
And step 3064, inputting the construction progress and the cost deviation analysis result into a preset optimization model to obtain an optimization scheme.
In the embodiment of the invention, the optimization scheme is determined according to the construction progress and the cost deviation analysis result so as to adjust the construction progress and the cost, for example, the construction period is shortened, the cost is reduced, the construction sequence is adjusted, the resource investment is increased and other adjustment measures are taken.
According to the embodiment, an optimization scheme is generated through the optimization model according to the construction progress and the cost deviation analysis result, so that a feasible implementation scheme is formed, and meanwhile, constructors or management staff can conveniently and timely adjust according to the optimization scheme.
In some alternative embodiments, step S3064 includes:
and step S30681, building a preset optimization model by taking the minimum construction cost as a target and taking the construction limit as a condition.
And step S30642, solving a preset optimization model by using a solver to obtain an optimization scheme.
In the embodiment of the invention, the preset optimization model is a linear programming model, the minimum construction cost is taken as a target, all construction limiting conditions are met at the same time, and the construction progress and cost can be optimized by utilizing methods such as mathematical programming and the like through the established optimization model. And solving the established preset optimization model, for example, adopting a solver to solve so as to obtain a construction progress and cost optimization scheme.
Specifically, a solver is utilized to solve, so that a plurality of optimization schemes are obtained, and a plurality of optimization schemes are provided for a worker to select. And evaluating and comparing different optimization schemes, and selecting an optimal scheme. By selecting an optimal solution, thresholds of various key nodes can be set, for example, whether the progress of a certain construction process reaches the expectations, whether the cost exceeds the budget, and the like. When the platform detects that a certain node exceeds a threshold value, an alarm notification is automatically sent to project management personnel, corresponding suggestions are provided, and the project management personnel can manually adjust the project of the construction progress and the cost on the platform, change the construction flow and the like.
And (3) carrying out optimization adjustment on the actual construction progress and cost according to an optimization scheme, for example, if the optimization scheme is to adjust the construction time of some projects, implementing the optimization scheme so as to achieve the optimal construction progress and cost. After the optimization scheme is adopted, the construction progress and cost are monitored in real time, and the control of the construction progress and cost is ensured.
According to the embodiment, the optimization model is built, the minimum construction cost is taken as a target, the construction limitation is taken as a condition, the construction cost is reduced as much as possible on the premise of meeting the construction requirement, and the optimization scheme is obtained by solving, so that a worker can conveniently adjust the construction according to the optimization scheme.
In this embodiment, a construction progress and cost prediction method is provided, which may be used in the above mobile terminal, such as a mobile phone, a tablet pc, etc., and fig. 4 is a flowchart of a construction progress and cost prediction method according to an embodiment of the present invention, as shown in fig. 4, where the flowchart includes the following steps:
step S401, historical three-dimensional model data of a building of the same type as a target building is obtained, and standard specifications of the industry are obtained. Please refer to step S101 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S402, training the GPT model based on the historical three-dimensional model data and the standard specifications of the industry to obtain a BIM-GPT model, wherein the BIM-GPT model is used for reconstructing the three-dimensional model data according to the construction progress and the cost. Please refer to step S102 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S403, the current construction progress and cost of the target building are obtained, and the current construction progress and cost are input into the BIM-GPT model to obtain three-dimensional model data reconstructed by the BIM-GPT model. Please refer to step S103 in the embodiment shown in fig. 1 in detail, which is not described herein.
And step S404, inputting the three-dimensional model data reconstructed by the BIM-GPT model into a pre-trained progress cost prediction model to obtain the construction progress and cost prediction result of the target building. Please refer to step S104 in the embodiment shown in fig. 1 in detail, which is not described herein.
And step S405, acquiring the current construction progress in real time, feeding back the current construction progress to the BIM-GPT model, and retraining the model.
In the embodiment of the invention, real-time construction progress data is fed back to the BIM-GPT model, and the model is retrained, so that the retrained BIM-GPT model can accurately predict the progress and cost of different construction stages and can also be updated according to historical data and real-time data.
According to the construction progress and cost prediction method provided by the embodiment, the model is retrained according to the real-time data to update the model, so that the prediction accuracy of the model is improved, and the accuracy of the model output result is further improved.
In this embodiment, a construction progress cost prediction apparatus is further provided, and the apparatus is used to implement the foregoing embodiments and preferred embodiments, and will not be described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
The present embodiment provides a construction progress cost prediction apparatus, as shown in fig. 5, including:
the first obtaining module 501 is configured to obtain historical three-dimensional model data of a building of the same type as the target building and standard specifications of industries to which the historical three-dimensional model data belongs, where the standard specifications include: construction progress and cost of various types of buildings.
The first obtaining module 502 is configured to train the GPT model based on the historical three-dimensional model data and the standard specifications of the industry to obtain a BIM-GPT model, where the BIM-GPT model is configured to reconstruct the three-dimensional model data according to the construction progress and the cost.
A second obtaining module 503, configured to obtain a current construction progress and cost of the target building, and input the current construction progress and cost into the BIM-GPT model to obtain three-dimensional model data reconstructed by the BIM-GPT model.
And a third obtaining module 504, configured to input the three-dimensional model data reconstructed by the BIM-GPT model into a pre-trained progress cost prediction model, so as to obtain a construction progress and cost prediction result of the target building.
In some alternative embodiments, the apparatus further comprises:
the processing module 505 is configured to perform data cleaning, formatting, and encoding on the historical three-dimensional model data and the standard specification of the industry.
In some alternative embodiments, the apparatus further comprises:
and a second obtaining module 506, configured to obtain data of influencing factors of the construction progress and the cost, where the influencing factors include material supply and construction quality.
And the establishing module 507 is used for establishing a deviation analysis model based on the influence factor data of the construction progress and the cost.
And a fourth obtaining module 508, configured to input the comparison analysis result into the deviation analysis model, and obtain a construction progress and cost deviation analysis result.
In some alternative embodiments, the apparatus further comprises:
and a fifth obtaining module 509, for inputting the construction progress and the cost deviation analysis result into a preset optimization model to obtain an optimization scheme.
In some alternative embodiments, the fifth deriving module 509 comprises:
the building unit is used for building a preset optimization model by taking the minimum construction cost as a target and taking the construction limit as a condition.
The obtaining unit is used for solving a preset optimization model by using a solver to obtain an optimization scheme.
In some alternative embodiments, the apparatus further comprises:
and the feedback module 510 is configured to acquire the current construction progress in real time, and feed back the current construction progress to the BIM-GPT model, and retrain the model.
Further functional descriptions of the above respective modules and units are the same as those of the above corresponding embodiments, and are not repeated here.
The construction progress cost prediction apparatus in this embodiment is presented in the form of functional units, where the units refer to ASIC (Application Specific Integrated Circuit ) circuits, processors and memories that execute one or more software or firmware programs, and/or other devices that can provide the above-described functions.
The embodiment of the invention also provides computer equipment, which is provided with the construction progress cost prediction device shown in the figure 5.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a computer device according to an alternative embodiment of the present invention, as shown in fig. 6, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are communicatively coupled to each other using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the computer device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In some alternative embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple computer devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 10 is illustrated in fig. 6.
The processor 10 may be a central processor, a network processor, or a combination thereof. The processor 10 may further include a hardware chip, among others. The hardware chip may be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general-purpose array logic, or any combination thereof.
Wherein the memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform the methods shown in implementing the above embodiments.
The memory 20 may include a storage program area that may store an operating system, at least one application program required for functions, and a storage data area; the storage data area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 20 may optionally include memory located remotely from processor 10, which may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk, or solid state disk; the memory 20 may also comprise a combination of the above types of memories.
The embodiments of the present invention also provide a computer readable storage medium, and the method according to the embodiments of the present invention described above may be implemented in hardware, firmware, or as a computer code which may be recorded on a storage medium, or as original stored in a remote storage medium or a non-transitory machine readable storage medium downloaded through a network and to be stored in a local storage medium, so that the method described herein may be stored on such software process on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, a solid state disk or the like; further, the storage medium may also comprise a combination of memories of the kind described above. It will be appreciated that a computer, processor, microprocessor controller or programmable hardware includes a storage element that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the methods illustrated by the above embodiments.
Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope of the invention as defined by the appended claims.
Claims (10)
1. A construction progress and cost prediction method, the method comprising:
acquiring historical three-dimensional model data of a building of the same type as a target building and standard specifications of the industry, wherein the standard specifications comprise: construction progress and cost of various types of buildings;
training a GPT model based on the historical three-dimensional model data and the standard specification of the industry to obtain a BIM-GPT model, wherein the BIM-GPT model is used for reconstructing three-dimensional model data according to construction progress and cost;
acquiring the current construction progress and cost of the target building, and inputting the current construction progress and cost into a BIM-GPT model to obtain three-dimensional model data reconstructed by the BIM-GPT model;
and inputting the three-dimensional model data reconstructed by the BIM-GPT model into a pre-trained progress cost prediction model to obtain the construction progress and cost prediction result of the target building.
2. The method of claim 1, wherein prior to the training of the GPT model based on the historical three-dimensional model data and the industry standard specification, the method further comprises:
and carrying out data cleaning, formatting and encoding on the historical three-dimensional model data and the standard specification of the industry.
3. The method according to claim 1, wherein the method further comprises:
acquiring actual construction progress and cost data;
and comparing and analyzing the actual construction progress and cost data and the construction progress and cost output by the progress cost analysis model to obtain a comparison analysis result.
4. A method according to claim 3, characterized in that the method further comprises:
acquiring influence factor data of construction progress and cost, wherein the influence factors comprise material supply and construction quality;
establishing a deviation analysis model based on the influence factor data of the construction progress and the cost;
and inputting the comparison analysis result into a deviation analysis model to obtain construction progress and cost deviation analysis results.
5. The method according to claim 4, wherein the method further comprises:
and inputting the construction progress and the cost deviation analysis result into a preset optimization model to obtain an optimization scheme.
6. The method according to claim 5, wherein the inputting the construction progress and cost deviation analysis result into a preset optimization model to obtain an optimization scheme includes:
the method comprises the steps of taking the minimum construction cost as a target, and taking construction limitation as a condition, establishing a preset optimization model;
and solving the preset optimization model by using a solver to obtain an optimization scheme.
7. The method according to claim 1, wherein the method further comprises:
and acquiring the current construction progress in real time, feeding back the current construction progress to a BIM-GPT model, and retraining the model.
8. A construction progress cost prediction apparatus, characterized by comprising:
the system comprises an acquisition module, a calculation module and a calculation module, wherein the acquisition module is used for acquiring historical three-dimensional model data of a building of the same type as a target building and standard specifications of the industry, and the standard specifications comprise: construction progress and cost of various types of buildings;
the first obtaining module is used for training the GPT model based on the historical three-dimensional model data and the standard specification of the industry to obtain a BIM-GPT model, and the BIM-GPT model is used for reconstructing three-dimensional model data according to construction progress and cost;
the second obtaining module is used for obtaining the current construction progress and cost of the target building, inputting the current construction progress and cost into the BIM-GPT model and obtaining three-dimensional model data reconstructed by the BIM-GPT model;
and the third obtaining module is used for inputting the three-dimensional model data reconstructed by the BIM-GPT model into a pre-trained progress cost prediction model to obtain the construction progress and cost prediction result of the target building.
9. A computer device, comprising:
a memory and a processor, the memory and the processor being communicatively connected to each other, the memory having stored therein computer instructions, the processor executing the construction progress and cost prediction method of any one of claims 1 to 7 by executing the computer instructions.
10. A computer-readable storage medium having stored thereon computer instructions for causing a computer to execute the construction progress and cost prediction method according to any one of claims 1 to 7.
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| CN117592949A (en) * | 2024-01-18 | 2024-02-23 | 一智科技(成都)有限公司 | Construction task management method, system and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117592949A (en) * | 2024-01-18 | 2024-02-23 | 一智科技(成都)有限公司 | Construction task management method, system and storage medium |
| CN117592949B (en) * | 2024-01-18 | 2024-06-11 | 一智科技(成都)有限公司 | Construction task management method, system and storage medium |
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