Engineering Optimization Design Method Driven by Big Data

Abstract

The present invention provides a big data driven engineering optimization design method, which relates to the field of data processing technology. The method includes obtaining historical big data of engineering designs of the same type, extracting design driving data, and forming historical engineering design driving data; obtaining design data information of a target engineering design, and performing design optimization adjustment analysis in combination with the historical engineering design driving data to form engineering design optimization adjustment data; collecting real-time engineering design data of the target engineering design, and performing engineering design plan deviation optimization adjustment analysis in combination with the historical engineering design driving data to form engineering design plan optimization adjustment data. The method reasonably tracks and compares the planned information of the engineering design using historical big data to optimize and adjust it, so as to reasonably ensure the quality and efficiency of the engineering design in real time and efficiently.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an engineering optimization design method driven by big data. Background Art

[0002] In the field of construction, engineering design is an important part of the development of construction projects and directly determines the quality of the projects. At present, most of the design of engineering projects is still carried out in a traditional way, from preliminary exploration to plan setting to subsequent engineering preparation. On the one hand, it takes a lot of resources to ensure the rationality and efficiency of engineering design. On the other hand, if the formed engineering design needs to be adjusted and optimized, it will take more resources to achieve it, especially in the optimization and adjustment of the design plan. This aspect is more prominent.

[0003] With the advancement of technology, engineering design has gradually begun to realize informatization, and the efficiency and quality of engineering design have been continuously improved under the support of big data, making engineering design operations more efficient and accurate. However, there is no reasonable and efficient form for how to optimize engineering design based on big data, especially in the planning of engineering design.

[0004] Therefore, it is an urgent problem to design a big data-driven engineering optimization design method, which uses historical big data to reasonably track, compare and optimize the planning information of engineering design, so as to reasonably ensure the quality and efficiency of engineering design in real time and efficiently. Summary of the invention

[0005] The purpose of the present invention is to provide a big data driven engineering optimization design method, by obtaining the historical big data of the same type of engineering design to extract the driving data, form the design optimization determination data for the target engineering design, the driving data is derived from the same type of engineering design, so it has strong reference and rationality. On the one hand, the optimization and adjustment of the engineering design is analyzed and adjusted from the rationality of the initial design information of the target engineering design, ensuring that the preliminary generated engineering design data, especially the design plan, has strong feasibility and reasonable planning. On the other hand, the implementation process of the target engineering design is effectively tracked and monitored, so as to make reasonable correction and optimization adjustments for the plan deviation, and ensure that the target engineering design can be adjusted and optimized in time when the plan deviation occurs, so as to ensure that the target engineering design can be smoothly implemented and reasonably completed according to the plan. Based on big data, the design optimization drive of the target engineering design is fully utilized, and the rationality and implementation stability of the target engineering design are effectively guaranteed, so that the target engineering design can be completed with quality and efficiency.

[0006] In a first aspect, the present invention provides a big data-driven engineering design optimization method, comprising obtaining historical big data of engineering designs of the same type, and extracting design driving data to form historical engineering design driving data; obtaining design data information of a target engineering design, and performing design optimization adjustment analysis in combination with the historical engineering design driving data to form engineering design optimization adjustment data; collecting real-time engineering design data of the target engineering design, and performing engineering design plan deviation optimization adjustment analysis in combination with the historical engineering design driving data to form engineering design plan optimization adjustment data.

[0007] In the present invention, the method extracts the driving data by acquiring the historical big data of the same type of engineering design, and forms the design optimization determination data for the target engineering design. The driving data is derived from the same type of engineering design, so it has strong reference and rationality. On the one hand, the optimization and adjustment of the engineering design is analyzed and adjusted from the rationality of the initial design information of the target engineering design, so as to ensure that the preliminary generated engineering design data, especially the design plan, has strong feasibility and reasonable planning. On the other hand, the implementation process of the target engineering design is effectively tracked and monitored, so as to make reasonable correction and optimization adjustments for the deviation from the plan, so as to ensure that the target engineering design can be adjusted and optimized in time when the plan deviation occurs, so as to ensure that the target engineering design can be smoothly implemented and reasonably completed according to the plan. The design optimization drive of the target engineering design based on big data gives full play to the characteristics of big data, effectively ensures the rationality of the target engineering design and the stability of implementation, so that the target engineering design can be completed with quality and efficiency.

[0008] As a possible implementation method, historical big data of engineering designs of the same type is obtained, and design driving data is extracted to form historical engineering design driving data, including: extracting design project parameter information based on historical big data of engineering designs of the same type to form a necessary design project set; extracting engineering design plan information based on historical big data of engineering designs of the same type to form engineering design plan reference information; extracting design project correction information of engineering designs based on historical big data of engineering designs of the same type to form design project correction measures data.

[0009] In the present invention, the extraction of driving data for the historical big data of the same type of engineering design is mainly aimed at three aspects. On the one hand, it is considered that the same type of engineering design has a high degree of synergy in design projects. Therefore, for most design projects, it is necessary to use these necessary design projects as driving data to verify whether the target engineering design has a fully completed design project. The second aspect is that the plan of the engineering design is an important component, and the rationality of the guarantee plan is the basic guarantee for completing the engineering design as required. Therefore, it is necessary to use the historical plan information as the driving information for optimizing the target engineering design. The third aspect is that in any engineering design, there will be deviations from the plan. Correction has become an important way to adjust the plan to ensure that the engineering design is generally implemented as required. Therefore, it is necessary to use the correction information as the driving data for optimizing the target engineering design when the plan deviation occurs.

[0010] As a possible implementation method, design project parameter information is extracted based on historical big data of engineering designs of the same type to form a necessary design project set, including: obtaining design project plan information of different engineering designs of the same type, and performing semantic-based design project extraction to form an engineering design project set of different engineering designs; performing the following necessary design project extraction based on repeatability on the engineering design project set of different engineering designs: setting a necessary repetition number threshold m, if there are no less than m engineering design project sets containing the same design project, then marking the corresponding design project as a necessary design project; and gathering all necessary design projects to form a necessary design project set.

[0011] In the present invention, for obtaining necessary design items, it is considered that necessary design items will inevitably appear repeatedly in the same type of engineering design, so the design items can be obtained by combining the same type of different engineering design data in the historical data to perform a repeatability analysis. The threshold of the necessary number of repetitions can be determined based on big data analysis or set according to actual conditions to ensure that the necessary design items can be accurately and comprehensively obtained.

[0012] As a possible implementation method, engineering design plan information is extracted based on the historical big data of engineering designs of the same type to form engineering design plan reference information, including: obtaining design project plan information of different engineering designs of the same type, and extracting effective labor efficiency, effective unit-shift efficiency, effective engineering quantity and effective completion time for each design project in the design project plan information to form design project historical reference data of the corresponding design project; extracting design project historical reference data of design projects of the same type, analyzing and processing effective completion information, and establishing a design project effective completion reference formula for design projects of the corresponding type: Among them, α represents the engineering quantity contribution coefficient, Meff Indicates effective labor efficiency, n represents the manual number used in the design project. is the average work efficiency of the worker numbered n, It represents the total amount of work done by the worker numbered n in the design project. represents the total man-hours consumed by the laborer numbered n in the design project, β1 represents the labor contribution coefficient, C eff Indicates effective work efficiency. k represents the machine number scheduled in the design project, represents the average working efficiency of the machine numbered k, It represents the total amount of work done by the machine numbered k in the design project. It represents the total man-hours consumed by the machine numbered k in the design project, β2 represents the machine contribution coefficient, β0 represents the project adjustment constant, and T0 represents the effective completion time of the design project.

[0013] In the present invention, for the acquisition of the planned reference information of the engineering design, it can be understood that the design projects are arranged in a certain order in the entire design plan of the engineering design, and only by implementing the design projects in sequence according to the design plan can the entire engineering design be successfully completed. Therefore, the planned reference information of the engineering design focuses on the information of the design project, and the information of the design project should be reflected in the design plan, mainly including the engineering quantity information, mechanical unit information, completion information and manual information that affect the completion of the design project. These information are interconnected, such as completing the design project on schedule, and certain manpower and mechanical equipment are required to process the determined engineering quantity. Of course, the relationship between these information is basically constant under the same design project, so the relationship data between these information can be established, and then used as an important reference for the planning evaluation and optimization adjustment analysis of the later target design project. Considering that there are certain personalized differences under big data, the relationship information between these information established also needs to be adaptively adjusted to obtain reasonable and accurate results.

[0014] As a possible implementation method, the design project historical reference data of the same type of design projects are extracted, and the effective completion information is analyzed and processed to establish the design project effective completion reference formula of the corresponding type of design projects, including: for the design project historical reference data of the same type of design projects, four design project historical reference data are randomly collected as a group to form different analysis and processing groups; for each analysis and processing group, the effective labor efficiency, effective unit-shift efficiency, effective engineering quantity and effective completion time are grouped to obtain the engineering quantity contribution coefficient E i , Group artificial contribution coefficient F i, Grouping mechanical contribution coefficient G i and the grouping item adjustment constant H i Determination, i represents the number of different analysis and processing groups; according to the grouping engineering quantity contribution coefficient E of different analysis and processing groups i , determine the engineering quantity contribution coefficient α, where, According to the grouping artificial contribution coefficient F of different analysis treatment groups i , determine the artificial contribution coefficient β1, where, According to the different analysis treatment groups, the mechanical contribution coefficient G i , determine the mechanical contribution coefficient β2, where, Adjust the constant H according to the grouping items of different analysis treatment groups i , determine the project adjustment constant β0, where,

[0015] In the present invention, the contribution coefficient and adjustment constant are the adjustment information given by the individual differences eliminated by big data when establishing the relationship between different information in the project plan. Considering that the grouping information obtained under big data does not have personalized differences, averaging can further reduce the impact of personalized differences and ensure the rationality and accuracy of the reference formula.

[0016] As a possible implementation method, design project correction information of engineering design is extracted based on the same type of engineering design history big data to form design project correction measure data, including: determining the correction measures corresponding to the same type of design projects in the engineering design history big data to form a design project correction measure set for different types of design projects; and aggregating the design project correction measure sets for different types of design projects to form design project correction measure data.

[0017] In the present invention, the correction measures are diverse. When deviations occur in the target engineering design and correction optimization and adjustment are required, they need to be analyzed and determined in combination with the actual situation. Therefore, when conducting big data analysis of the correction information, this application does not conduct further detailed analysis and extraction, but provides correction information on a large scale, so as to provide more ways and means for the subsequent selection of correction measures in combination with the actual situation, thereby ensuring that the correction optimization and adjustment can be implemented more efficiently and reasonably.

[0018] As a possible implementation method, the design data information of the target engineering design is obtained, and the design optimization and adjustment analysis is performed in combination with the historical engineering design driving data to form the engineering design optimization and adjustment data, including: extracting the target design plan information of the target engineering design, determining all the target design projects, and forming a target design project set; according to the necessary design project set, the target design project set is subjected to the following optimization and adjustment analysis: if the necessary design project set is included in the target design project set, then the design project completion information is formed; if the necessary design project set is not included in the target design project set, then the design project necessity missing information is formed, and the remainder after the necessary design project set intersects with the target design project set is output.

[0019] In the present invention, the design optimization and adjustment analysis first determines the necessary design items. It can be understood that for the target engineering design, if its design items are complete and reasonable, then the target design items of the target engineering design should include all the necessary design items. Therefore, when performing the optimization and adjustment analysis, the analysis and determination can be performed efficiently by analyzing the inclusion relationship, so that big data can present a more direct and efficient integrity optimization and adjustment analysis result on the necessity of the design project.

[0020] As a possible implementation method, the design data information of the target engineering design is obtained, and the design optimization adjustment analysis is performed in combination with the historical engineering design driving data to form the engineering design optimization adjustment data, including: according to the effective completion reference formula of the design project of different types of design projects, the expected completion time of each target design project in the target design project set is determined. Where x represents the sequence number of different target design projects determined according to the target design plan information of the target engineering design; the planned completion time of each target design project is determined according to the target design plan information of the target engineering design. Design the expected completion time of the project according to different goals and planned completion time Perform the following optimization and adjustment analysis: For each target design project: If Then the plan is formed; if The corresponding target design project is marked as an unexpected design project, and the following analysis and judgment are performed: Then the overall plan normal information is formed; if This will generate a warning message about the total plan delay and output the planned completion time of all design projects that exceed expectations.

[0021] In the present invention, for the optimization and adjustment analysis of the target engineering design in the design plan, two aspects are mainly considered. On the one hand, whether the planned completion time of each target design project is reasonable. It can affect the smooth progress of the design plan. On the other hand, it is considered that the existence of a single target design project only affects the planned implementation of the adjacent target design project, but does not affect the overall design plan. Therefore, it is also necessary to continue to analyze whether the overall plan is overdue when judging the situation that a single target design project is overdue. In this way, the optimization and adjustment analysis driven by big data can be realized from both a single target design project and the overall design project plan, so as to effectively ensure the smooth implementation of the target engineering design in multiple ways.

[0022] As a possible implementation method, real-time engineering design data of the target engineering design is collected, and combined with historical engineering design driving data, the engineering design plan deviation optimization adjustment analysis is performed to form engineering design plan optimization adjustment data, including: real-time tracking of the target design items of the target engineering design, and the following analysis and judgment: If Then the plan execution is normal information, where 1<y<x; if According to the order of the target design projects in the target design plan information, a set of design project corrective measures for the subsequent y-1 target design projects excluding the current target design project is obtained, and the corrective measures data are extracted and analyzed to form optimized and adjusted corrective measures data.

[0023] In the present invention, the optimization and adjustment analysis of the deviation from the plan in the target engineering design usually occurs in real time during the implementation process. Therefore, the correction can only be performed on the part of the target design project that has not been carried out. Therefore, when conducting the big data-driven optimization analysis for the correction, it is also implemented by considering the correction adjustment of the subsequent target design project. Of course, the scope of consideration of the correction optimization and adjustment analysis should not be limited to adjacent target design projects, but should be based on the plan of the target engineering design, and the overall optimization and adjustment analysis should be carried out to ensure the rationality and effectiveness of the implementation of the correction process.

[0024] As a possible implementation method, according to the order of the target design projects in the target design plan information, a set of design project corrective measures for the next y-1 target design projects excluding the current target design project is obtained, and the corrective measures data are extracted and analyzed to form optimized and adjusted corrective measures data, including: estimating the time consumption of different corrective measures corresponding to each target design project in the next y-1 target design projects to form the expected correction time consumption of each corrective measure corresponding to the target design project; determining the allowable consumption time margin according to the planned completion time and expected completion time of the target design project; determining the corrective measures in each target design project whose expected correction time consumption does not exceed the corresponding allowable consumption time margin, and marking them as reasonable corrective measures corresponding to the target design project; estimating the cost of all reasonable corrective measures corresponding to each target design project to form the expected cost corresponding to the reasonable corrective measures; sorting the different reasonable corrective measures in the target design project in order of expected cost from small to large, and extracting the first three reasonable corrective measures to form a reference corrective measures group corresponding to the target design project; and combining all reference corrective measures groups to form optimized and adjusted corrective measures data.

[0025] In the present invention, of course, for the deviation correction optimization and adjustment analysis, there are various deviation correction measures, but the deviation correction measures that meet the current deviation situation also need to be screened based on the allowable time consumption of the target design project to ensure the rationality and real-time nature of the deviation correction measures taken. Of course, not all the deviation correction measures screened out need to be adopted together to complete the deviation correction. Some or a single deviation correction measure can complete it. Therefore, in order to ensure the good effect of the deviation correction measures, selection and guidance from the cost point of view can effectively reduce the cost of the deviation correction on the one hand, and on the other hand, it can also ensure the rationality of the deviation correction.

[0026] The beneficial effects of the big data driven engineering optimization design method provided by the present invention are:

[0027] This method extracts driving data by acquiring historical big data of the same type of engineering design, and forms design optimization determination data for the target engineering design. Since the driving data comes from the same type of engineering design, it has strong reference and rationality. On the one hand, the optimization and adjustment of the engineering design is analyzed and adjusted from the rationality of the initial design information of the target engineering design, ensuring that the initially generated engineering design data, especially the design plan, has strong feasibility and reasonable planning. On the other hand, the implementation process of the target engineering design is effectively tracked and monitored, so as to make reasonable corrections and optimization adjustments for the deviations from the plan, and ensure that the target engineering design can be adjusted and optimized in time when the plan deviation occurs, so as to ensure that the target engineering design can be smoothly implemented and reasonably completed according to the plan. The design optimization drive of the target engineering design based on big data gives full play to the characteristics of big data, effectively ensures the rationality of the target engineering design and the stability of implementation, and completes the target engineering design with quality and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments of the present invention are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0029] Figure 1 A step diagram of a big data driven engineering optimization design method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present invention will be described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0031] In the field of construction, engineering design is an important part of the development of construction projects and directly determines the quality of the projects. At present, most of the design of engineering projects is still carried out in a traditional way, from preliminary exploration to plan setting to subsequent engineering preparation. On the one hand, it takes a lot of resources to ensure the rationality and efficiency of engineering design. On the other hand, if the formed engineering design needs to be adjusted and optimized, it will take more resources to achieve it, especially in the optimization and adjustment of the design plan. This aspect is more prominent.

[0032] With the advancement of technology, engineering design has gradually begun to realize informatization, and the efficiency and quality of engineering design have been continuously improved under the support of big data, making engineering design operations more efficient and accurate. However, there is no reasonable and efficient form for how to optimize engineering design based on big data, especially in the planning of engineering design.

[0033] refer to Figure 1 , the embodiment of the present invention provides a big data driven engineering optimization design method, which extracts driving data by acquiring historical big data of engineering designs of the same type, and forms design optimization determination data for the target engineering design. Since the driving data is derived from the same type of engineering designs, it has strong reference and rationality. On the one hand, the optimization and adjustment of the engineering design is analyzed and adjusted from the rationality of the initial design information of the target engineering design, ensuring that the initially generated engineering design data, especially the design plan, has strong feasibility and reasonable planning. On the other hand, the implementation process of the target engineering design is effectively tracked and monitored, so as to make reasonable correction and optimization adjustments for the plan deviation, and ensure that the target engineering design can be adjusted and optimized in time when the plan deviation occurs, so as to ensure that the target engineering design can be smoothly implemented and reasonably completed according to the plan. The design optimization drive of the target engineering design based on big data gives full play to the characteristics of big data, effectively ensures the rationality of the target engineering design and the stability of implementation, so that the target engineering design can be completed with quality and efficiency.

[0034] The big data-driven engineering optimization design method specifically includes the following steps:

[0035] S1: Obtain historical big data of the same type of engineering design, and extract design driving data to form historical engineering design driving data.

[0036] Obtain historical big data of engineering designs of the same type, and extract design driving data to form historical engineering design driving data, including: extracting design project parameter information based on historical big data of engineering designs of the same type to form a necessary design project set; extracting engineering design plan information based on historical big data of engineering designs of the same type to form engineering design plan reference information; extracting design project correction information of engineering designs based on historical big data of engineering designs of the same type to form design project correction measures data.

[0037] The extraction of driving data from historical big data of the same type of engineering design is mainly aimed at three aspects. On the one hand, considering that the same type of engineering design has a high degree of synergy in design projects, most of the design projects are necessary design projects of this type of engineering design. Therefore, these necessary design projects need to be used as driving data to verify whether the target engineering design has a fully completed design project. The second aspect is that the plan of engineering design is an important component, and the rationality of the guarantee plan is the basic guarantee for completing the engineering design as required. Therefore, the historical plan information needs to be used as the driving information for optimizing the target engineering design. The third aspect is that in any engineering design, there will be deviations from the plan. Correction has become an important way to adjust the plan to ensure that the engineering design is generally implemented as required. Therefore, the correction information needs to be used as the driving data for optimizing the target engineering design when the plan deviation occurs.

[0038] Among them, based on the historical big data of engineering designs of the same type, the design project parameter information is extracted to form a necessary design project set, including: obtaining the design project plan information of different engineering designs of the same type, and performing semantic-based design project extraction to form an engineering design project set of different engineering designs; performing the following necessary design project extraction based on repeatability on the engineering design project set of different engineering designs: setting a necessary repetition number threshold m, if there are no less than m engineering design project sets containing the same design project, the corresponding design project is marked as a necessary design project; and all necessary design projects are collected to form a necessary design project set.

[0039] For the acquisition of necessary design items, the necessary design items will inevitably appear repeatedly in the same type of engineering design. Therefore, the repetitive analysis of design items can be performed by combining the same type of different engineering design data in historical data to obtain them. The threshold of the necessary number of repetitions can be determined based on big data analysis or set according to actual conditions to ensure that the necessary design items can be accurately and comprehensively acquired.

[0040] Based on the historical big data of engineering designs of the same type, the engineering design plan information is extracted to form the engineering design plan reference information, including: obtaining the design project plan information of different engineering designs of the same type, and extracting the effective labor efficiency, effective unit-shift efficiency, effective engineering quantity and effective completion time for each design project in the design project plan information to form the design project historical reference data of the corresponding design project; extracting the design project historical reference data of the same type of design projects, analyzing and processing the effective completion information, and establishing the design project effective completion reference formula of the corresponding type of design project: Among them, α represents the engineering quantity contribution coefficient, M eff Indicates effective labor efficiency, n represents the manual number used in the design project. is the average work efficiency of the worker numbered n, It represents the total amount of work done by the worker numbered n in the design project. represents the total man-hours consumed by the laborer numbered n in the design project, β1 represents the labor contribution coefficient, C eff Indicates effective work efficiency. k represents the machine number scheduled in the design project, represents the average working efficiency of the machine numbered k, It represents the total amount of work done by the machine numbered k in the design project. It represents the total man-hours consumed by the machine numbered k in the design project, β2 represents the machine contribution coefficient, β0 represents the project adjustment constant, and T0 represents the effective completion time of the design project.

[0041] For the acquisition of planned reference information for engineering design, it can be understood that in the entire design plan of engineering design, the design projects are arranged in a certain order. Only by implementing the design projects in sequence according to the design plan can the entire engineering design be successfully completed. Therefore, the planned reference information for engineering design focuses on the information of the design project. The information of the design project should be reflected in the design plan, mainly including the engineering quantity information, mechanical unit information, completion information and labor information that affect the completion of the design project. These information are interrelated. For example, to complete the design project on schedule, a certain amount of manpower and mechanical equipment are required to process the determined engineering quantity. Of course, the relationship between these information is basically constant under the same design project, so the relationship data about this information can be established, which can be used as an important reference for planning evaluation and optimization adjustment analysis of the later target design project. Considering that there are certain personalized differences under big data, the relationship information between these information also needs to be adaptively adjusted to obtain reasonable and accurate results.

[0042] Extract the historical reference data of design projects of the same type, analyze and process the effective completion information, and establish the effective completion reference formula of design projects of the corresponding type, including: for the historical reference data of design projects of the same type, randomly collect four historical reference data of design projects as a group to form different analysis and processing groups; for each analysis and processing group, group the effective labor efficiency, effective unit efficiency, effective engineering quantity and effective completion time to obtain the engineering quantity contribution coefficient E i , Group artificial contribution coefficient F i , Grouping mechanical contribution coefficient G i and the grouping item adjustment constant Hi Determination, i represents the number of different analysis and processing groups; according to the grouping engineering quantity contribution coefficient E of different analysis and processing groups i , determine the engineering quantity contribution coefficient α, where, According to the grouping artificial contribution coefficient F of different analysis treatment groups i , determine the artificial contribution coefficient β1, where, According to the different analysis treatment groups, the mechanical contribution coefficient G i , determine the mechanical contribution coefficient β2, where, Adjust the constant H according to the grouping items of different analysis treatment groups i , determine the project adjustment constant β0, where,

[0043] The contribution coefficient and adjustment constant are the adjustment information given when establishing the relationship between different information in the project plan, relative to the individual differences eliminated by big data. Considering that the grouping information obtained under big data does not have individual differences, averaging can further reduce the impact of individual differences and ensure the rationality and accuracy of the reference.

[0044] Based on the historical big data of engineering designs of the same type, design project correction information of engineering designs is extracted to form design project correction measures data, including: determining the correction measures corresponding to the same type of design projects in the historical big data of engineering designs to form a set of design project correction measures for different types of design projects; and combining the set of design project correction measures for different types of design projects to form design project correction measures data.

[0045] There are various corrective measures. When deviations occur in the target engineering design and corrective optimization and adjustment are required, they need to be analyzed and determined in combination with the actual situation. Therefore, when conducting big data analysis of the corrective information, this application does not conduct further detailed analysis and extraction, but instead provides corrective information on a large scale, so as to provide more ways and means for the subsequent selection of corrective measures in combination with the actual situation, and ensure that the correction optimization and adjustment can be implemented more efficiently and reasonably.

[0046] S2: Obtain design data information of the target engineering design, and perform design optimization and adjustment analysis in combination with historical engineering design driving data to form engineering design optimization and adjustment data.

[0047] The design data information of the target engineering design is obtained, and the design optimization and adjustment analysis is performed in combination with the historical engineering design driving data to form the engineering design optimization and adjustment data, including: extracting the target design plan information of the target engineering design, determining all the target design projects, and forming a target design project set; according to the necessary design project set, the target design project set is subjected to the following optimization and adjustment analysis: if the necessary design project set is included in the target design project set, then the design project completion information is formed; if the necessary design project set is not included in the target design project set, then the design project necessity missing information is formed, and the remainder after the necessary design project set intersects with the target design project set is output.

[0048] The design optimization and adjustment analysis first determines the necessary design items. It is understandable that for the target engineering design, if its design items are complete and reasonable, then the target design items of the target engineering design should include all the necessary design items. Therefore, when performing the optimization and adjustment analysis, the analysis of the inclusion relationship can be performed efficiently, so that big data can present a more direct and efficient integrity optimization and adjustment analysis result on the necessity of the design project.

[0049] Obtain the design data information of the target engineering design, and conduct design optimization and adjustment analysis in combination with historical engineering design driving data to form engineering design optimization and adjustment data, including: determining the expected completion time of each target design project in the target design project set based on the effective completion reference formula of design projects of different types of design projects Where x represents the sequence number of different target design projects determined according to the target design plan information of the target engineering design; the planned completion time of each target design project is determined according to the target design plan information of the target engineering design. Design the expected completion time of the project according to different goals and planned completion time Perform the following optimization and adjustment analysis: For each target design project: If Then the plan is formed; if The corresponding target design project is marked as an unexpected design project, and the following analysis and judgment are performed: Then the overall plan normal information is formed; if This will generate a warning message about the total plan delay and output the planned completion time of all design projects that exceed expectations.

[0050] For the optimization and adjustment analysis of the design plan of the target engineering design, two aspects are mainly considered. On the one hand, whether the planned completion time of each target design project is reasonable. It can affect the smooth progress of the design plan. On the other hand, it is considered that the existence of a single target design project only affects the planned implementation of the adjacent target design project, but does not affect the overall design plan. Therefore, it is also necessary to continue to analyze whether the overall plan is overdue when judging the situation that a single target design project is overdue. In this way, big data-driven optimization and adjustment analysis can be achieved from both a single target design project and the overall design project plan, so as to effectively ensure the smooth implementation of the target engineering design in multiple ways.

[0051] S3: Collect real-time engineering design data of the target engineering design, and combine it with historical engineering design driving data to conduct engineering design plan deviation optimization adjustment analysis to form engineering design plan optimization adjustment data.

[0052] Collect the real-time engineering design data of the target engineering design, and combine it with the historical engineering design driving data to conduct engineering design plan deviation optimization adjustment analysis to form engineering design plan optimization adjustment data, including: real-time tracking of the target design items of the target engineering design, and the following analysis and judgment: If Then the plan execution is normal information, where 1<y<x; if According to the order of the target design projects in the target design plan information, a set of design project corrective measures for the subsequent y-1 target design projects excluding the current target design project is obtained, and the corrective measures data are extracted and analyzed to form optimized and adjusted corrective measures data.

[0053] The optimization and adjustment analysis for plan deviations in target engineering design usually occurs in real time during the implementation process. Therefore, corrections can only be made to the part of the target design project that has not been carried out. Therefore, when conducting big data-driven optimization analysis for corrections, it is also implemented by considering the correction adjustments of the subsequent target design projects. Of course, the scope of correction optimization and adjustment analysis should not be limited to adjacent target design projects. It should be based on the plan of the target engineering design and conduct optimization and adjustment analysis on the whole to ensure the rationality and effectiveness of the implementation of the correction process.

[0054] According to the order of the target design projects in the target design plan information, a set of design project corrective measures for the next y-1 target design projects excluding the current target design project is obtained, and the corrective measures data are extracted and analyzed to form optimized and adjusted corrective measures data, including: estimating the time consumption of different corrective measures corresponding to each target design project in the next y-1 target design projects to form the expected correction time consumption of each corrective measure corresponding to the target design project; determining the allowable consumption time margin according to the planned completion time and expected completion time of the target design project; determining the corrective measures in each target design project whose expected correction time consumption does not exceed the corresponding allowable consumption time margin, and marking them as reasonable corrective measures corresponding to the target design project; estimating the cost of all reasonable corrective measures corresponding to each target design project to form the expected cost corresponding to the reasonable corrective measures; sorting the different reasonable corrective measures in the target design project in order of expected cost from small to large, and extracting the first three reasonable corrective measures to form a reference corrective measures group corresponding to the target design project; and combining all reference corrective measures groups to form optimized and adjusted corrective measures data.

[0055] Of course, for the correction optimization and adjustment analysis, there are various correction measures, but the correction measures that meet the current deviation situation also need to be screened based on the allowable time consumption of the target design project to ensure the rationality and real-time nature of the correction measures taken. Of course, not all the corrective measures screened out need to be adopted together to complete the correction. Some or a single corrective measure can complete it. Therefore, in order to ensure the good effect of the corrective measures, selection and guidance from the cost perspective can effectively reduce the cost of correction on the one hand, and on the other hand, ensure the rationality of the correction.

[0056] In summary, the engineering optimization design method driven by big data provided by the embodiment of the present invention has the following beneficial effects:

[0057] This method extracts driving data by acquiring historical big data of the same type of engineering design, and forms design optimization determination data for the target engineering design. Since the driving data comes from the same type of engineering design, it has strong reference and rationality. On the one hand, the optimization and adjustment of the engineering design is analyzed and adjusted from the rationality of the initial design information of the target engineering design, ensuring that the initially generated engineering design data, especially the design plan, has strong feasibility and reasonable planning. On the other hand, the implementation process of the target engineering design is effectively tracked and monitored, so as to make reasonable corrections and optimization adjustments for the deviations from the plan, and ensure that the target engineering design can be adjusted and optimized in time when the plan deviation occurs, so as to ensure that the target engineering design can be smoothly implemented and reasonably completed according to the plan. The design optimization drive of the target engineering design based on big data gives full play to the characteristics of big data, effectively ensures the rationality of the target engineering design and the stability of implementation, and completes the target engineering design with quality and efficiency.

[0058] In the embodiments of the present application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain information is called information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, the information to be indicated can be directly indicated, such as the information to be indicated itself or the index of the information to be indicated. The information to be indicated can also be indirectly indicated by indicating other information, wherein there is an association between the other information and the information to be indicated. It is also possible to indicate only a part of the information to be indicated, while the other parts of the information to be indicated are known or agreed in advance. For example, the indication of specific information can also be achieved by means of the arrangement order of each piece of information agreed in advance (for example, specified by the protocol), thereby reducing the indication overhead to a certain extent. At the same time, the common parts of each piece of information can also be identified and indicated uniformly to reduce the indication overhead caused by indicating the same information separately.

[0059] In addition, the specific indication method may also be various existing indication methods, such as but not limited to the above-mentioned indication methods and various combinations thereof. The specific details of the various indication methods can refer to the prior art and will not be repeated herein. As can be seen from the above, for example, when it is necessary to indicate multiple information of the same type, different indication methods may be used for different information. In the specific implementation process, the desired indication method can be selected according to specific needs. The embodiment of the present application does not limit the selected indication method. In this way, the indication method involved in the embodiment of the present application should be understood to cover various methods that can enable the party to be indicated to obtain the information to be indicated.

[0060] It should be understood that the information to be indicated can be sent as a whole, or divided into multiple sub-information and sent separately, and the sending period and / or sending time of these sub-information can be the same or different. The specific sending method is not limited in the embodiment of the present application. Among them, the sending period and / or sending time of these sub-information can be pre-defined, for example, pre-defined according to a protocol, or can be configured by the sending end device by sending configuration information to the receiving end device.

[0061] "Pre-definition" or "pre-configuration" can be implemented by pre-saving corresponding codes, tables or other methods that can be used to indicate relevant information in the device, and the embodiments of the present application do not limit the specific implementation method. Among them, "saving" can mean saving in one or more memories. The one or more memories can be set separately or integrated in an encoder or decoder, a processor, or a communication device. The one or more memories can also be partially set separately and partially integrated in a decoder, a processor, or a communication device. The type of memory can be any form of storage medium, which is not limited by the embodiments of the present application.

[0062] The "protocol" involved in the embodiments of the present application may refer to a protocol family in the communication field, a standard protocol with a similar protocol family frame structure, or a related protocol used in future communication systems, and the embodiments of the present application do not make specific limitations on this.

[0063] In the embodiments of the present application, descriptions such as "when...", "in the case of...", "if" and "if" all mean that the device will make corresponding processing under certain objective circumstances. It does not limit the time, nor does it require the device to have a judgment action when implementing it, nor does it mean that there are other limitations.

[0064] In the description of the embodiments of the present application, unless otherwise specified, " / " indicates that the objects associated before and after are in an "or" relationship, for example, A / B can represent A or B; "and / or" in the embodiments of the present application is only a description of the association relationship of the associated objects, indicating that there can be three relationships, for example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. In addition, in the description of the embodiments of the present application, unless otherwise specified, "multiple" refers to two or more than two. "At least one of the following" or its similar expression refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple. In addition, in order to facilitate the clear description of the technical solution of the embodiments of the present application, in the embodiments of the present application, the words "first" and "second" are used to distinguish the same or similar items with basically the same functions and effects. Those skilled in the art will appreciate that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit the difference. At the same time, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way for easy understanding.

[0065] It should be understood that the processor in the embodiments of the present application may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0066] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0067] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0068] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0069] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0070] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0071] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0072] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0073] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0074] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0075] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0076] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0077] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. Big data driven engineering optimization design method, characterized by: include: Obtain historical big data of the same type of engineering design, and extract design-driven data to form historical engineering design-driven data; Acquire design data information of the target engineering design, and perform design optimization and adjustment analysis in combination with the historical engineering design driving data to form engineering design optimization and adjustment data; Collecting real-time engineering design data of the target engineering design, and combining it with the historical engineering design driving data, performing engineering design plan deviation optimization adjustment analysis to form engineering design plan optimization adjustment data; Obtain historical big data of the same type of engineering design, and extract design-driven data to form historical engineering design-driven data, including: Extracting design project parameter information based on the same type of engineering design history big data to form a necessary design project set; Extracting engineering design plan information based on the engineering design history big data of the same type to form engineering design plan reference information; Extracting design project correction information of engineering design based on the same type of engineering design history big data to form design project correction measures data; Based on the historical big data of engineering design of the same type, the design project parameter information is extracted to form a necessary design project set, including: Obtain design project plan information for different engineering designs of the same type, and perform semantic-based design project extraction to form an engineering design project set for different engineering designs; The following necessary design items are extracted based on repeatability from the engineering design project sets of different engineering designs: A necessary repetition threshold m is set, and if there are no less than m engineering design project sets containing the same design project, the corresponding design project is marked as a necessary design project; Gather all the necessary design items to form the necessary design item set; The design data information of the target engineering design is obtained, and design optimization adjustment analysis is performed in combination with the historical engineering design driving data to form engineering design optimization adjustment data, including: Extracting target design plan information of the target engineering design, determining all target design projects, and forming a target design project set; According to the necessary design project set, the target design project set is subjected to the following optimization adjustment analysis: If the necessary design project set is included in the target design project set, then design project completion information is generated; If the necessary design item set is not included in the target design item set, then forming design item necessity missing information, and outputting the remainder after the necessary design item set intersects with the target design item set; According to the same type of engineering design history big data, engineering design plan information is extracted to form engineering design plan reference information, including: Obtain design project plan information of different engineering designs of the same type, and extract effective labor efficiency, effective work efficiency, effective engineering quantity and effective completion time for each design project in the design project plan information to form design project historical reference data of the corresponding design project; The design project historical reference data of the design projects of the same type are extracted, and effective completion information is analyzed and processed to establish a design project effective completion reference formula for the design projects of the corresponding types: Among them, α represents the engineering quantity contribution coefficient, M eff Indicates effective labor efficiency, n represents the manual number used in the design project. is the average work efficiency of the worker numbered n, It represents the total amount of work done by the worker numbered n in the design project. represents the total man-hours consumed by the laborer numbered n in the design project, β1 represents the labor contribution coefficient, C eff Indicates effective work efficiency. k represents the machine number scheduled in the design project, represents the average working efficiency of the machine numbered k, It represents the total amount of work done by the machine numbered k in the design project. represents the total man-hours consumed by the machine numbered k in the design project, β2 represents the machine contribution coefficient, β0 represents the project adjustment constant, and T0 represents the effective completion time of the design project; Extracting the design project historical reference data of the design projects of the same type, analyzing and processing the effective completion information, and establishing a design project effective completion reference formula for the design projects of the corresponding type, including: For the design project historical reference data of the same type of design projects, four of the design project historical reference data are randomly grouped as one group to form different analysis and processing groups; For each of the analysis and processing groups, the project contribution coefficient E is grouped according to the effective labor efficiency, the effective unit-shift efficiency, the effective project quantity and the effective completion time. i , Group artificial contribution coefficient F i , Grouping mechanical contribution coefficient G i and the grouping item adjustment constant H i Determination of, i represents the number of different analysis and processing groups; According to the group engineering quantity contribution coefficient E of different analysis processing groups i , determine the engineering quantity contribution coefficient α, where, According to the grouping artificial contribution coefficient F of different analysis processing groups i , determine the artificial contribution coefficient β1, where, The grouping mechanical contribution coefficient G of different analysis processing groups i , determine the mechanical contribution coefficient β2, where, The constant H is adjusted according to the grouping items of different analysis and processing groups. i , determine the project adjustment constant β0, where, According to the same type of engineering design history big data, the design project correction information of the engineering design is extracted to form the design project correction measures data, including: Determine the corrective measures corresponding to the same type of design projects in the engineering design history big data to form a set of corrective measures for design projects of different types; Gather the design project corrective measures set of different types of design projects to form the design project corrective measures data; The design data information of the target engineering design is obtained, and design optimization adjustment analysis is performed in combination with the historical engineering design driving data to form engineering design optimization adjustment data, including: Determine the expected completion time of each target design project in the target design project set according to the effective completion reference formula of the design project of different types Wherein, x represents the sequence number of different target design projects determined according to the target design plan information of the target engineering design; Determine the planned completion time of each target design project based on the target design plan information of the target engineering design The expected completion time of the project according to different objectives and the planned completion time Perform the following optimization and adjustment analysis: For each of the stated goals design projects: like Then the normal information of the plan is formed; like The corresponding target design project is marked as an over-expected design project, and the following analysis and judgment are performed: like Then the normal information of the overall plan is formed; like Then, a warning message about the total plan overdue is generated, and the planned completion time of all the design projects that exceed expectations is output.

2. The big data driven engineering optimization design method according to claim 1, characterized in that: The collecting of the real-time engineering design data of the target engineering design and combining it with the historical engineering design driving data to perform engineering design plan deviation optimization adjustment analysis to form engineering design plan optimization adjustment data include: The target design items of the target engineering design are tracked in real time, and the following analysis and judgment are performed: like Then the plan execution is normal information, where 1<y<x; like Then, according to the order of the target design projects in the target design plan information, the design project corrective measure set of the last y-1 target design projects excluding the current target design project is obtained, and the corrective measure data is extracted and analyzed to form optimized and adjusted corrective measure data.

3. The big data driven engineering optimization design method according to claim 2, characterized in that: The step of obtaining the design project corrective measures set of the target design projects after y-1 target design projects excluding the current target design project according to the order of the target design projects in the target design plan information, extracting and analyzing the corrective measures data, and forming the optimized and adjusted corrective measures data includes: Estimating the time consumption of different corrective measures corresponding to each of the target design projects in the last y-1 target design projects, to form an expected correction time consumption of each corrective measure corresponding to the target design project; Determining an allowable consumption time margin according to the planned completion time and the expected completion time of the target design project; Determine the corrective measures in each target design project whose expected correction time does not exceed the corresponding allowable consumption time margin, and mark them as reasonable corrective measures corresponding to the target design project; Conducting cost estimation for all the reasonable corrective measures corresponding to each of the target design items to form expected costs corresponding to the reasonable corrective measures; According to the order of the expected cost from small to large, the different reasonable corrective measures in the target design project are sorted, and the first three reasonable corrective measures are extracted to form a reference corrective measure group corresponding to the target design project; All the reference deviation correction measures groups are collected to form the optimization and adjustment deviation correction measures data.

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