Building engineering data information intelligent management platform
By designing an intelligent management platform for construction engineering data information, the problem that construction engineering data in the existing technology cannot directly reflect the quality of project completion is solved, and more efficient information management and more intuitive reflection of engineering situations are achieved.
Patent Information
- Application Number
- CN202411681935.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-22
- Publication Date
- 2025-03-11
AI Technical Summary
In the prior art, construction project data cannot be directly used to determine the completion quality of the project, resulting in poor intelligent information management and weak adaptability, and cannot directly or intuitively reflect the construction project situation.
An intelligent management platform for construction engineering data information is designed, including engineering data acquisition and formulation system, engineering feature extraction system, engineering traceability analysis system and engineering visual display system. The platform determines the engineering data acquisition frequency of each engineering link, extracts reflective features, compares the differences between actual values and standard values, judges the completion quality, and conducts traceability analysis and visual display of the causes of low quality.
It improves the effectiveness and adaptability of intelligent information management, can reflect the construction project situation more directly or intuitively, facilitate real-time monitoring, and improves the reliability of project quality assessment.
Smart Images
Figure CN119599604A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of construction engineering data processing, and in particular to an intelligent management platform for construction engineering data information. Background Art
[0002] Due to the increasing demand for high efficiency, environmental protection and safety in the construction industry, as the urbanization process accelerates, problems such as building energy consumption, safety management and operational efficiency are becoming increasingly prominent. Intelligent construction management introduces modern information technology, communication technology, automatic control technology, computer technology, etc. to achieve real-time collection, processing and analysis of various information on the construction site, improve construction efficiency, reduce costs and improve building quality. At the same time, national policy support, market demand drive and technological innovation drive have jointly made intelligent construction management a new trend in the development of the industry. The widespread application of new generation information technologies such as big data technology, cloud computing and the Internet of Things in the field of construction engineering has provided a solid technical foundation for the implementation of intelligent management solutions.
[0003] In the existing technology, the completion quality of the project cannot be determined directly through the construction project data. This completion quality involves three factors: progress, quality and risk. This leads to poor information intelligent management effects and weak adaptability, and cannot directly or intuitively reflect the construction project situation.
[0004] Therefore, how to improve the effectiveness and adaptability of intelligent information management is a technical problem that needs to be solved. Summary of the invention
[0005] The purpose of the present invention is to solve the problems of poor intelligent management effect and weak adaptability of construction engineering information in the prior art, and to propose an intelligent management platform for construction engineering data information, which includes an engineering data collection and formulation system, an engineering feature extraction system, an engineering tracing analysis system and an engineering visualization display system.
[0006] The engineering data collection and formulation system is used to obtain the engineering management link process according to the target engineering project plan mapping, determine the engineering data collection frequency of each engineering link in the engineering management link process, collect the engineering data generated in the target project according to the engineering data collection frequency, and formulate standard values under multiple monitoring dimensions of each engineering link;
[0007] The engineering feature extraction system is used to preprocess engineering data, extract reflection features from the preprocessed engineering data, and comprehensively reflect the features to calculate actual values under multiple monitoring dimensions;
[0008] The engineering traceability analysis system is used to compare the difference between the actual value and the standard value under multiple monitoring dimensions of each engineering link to determine the completion quality, and judge whether it is in a low-quality engineering state by the completion quality. When the target project is in a low-quality engineering state, the engineering link is traced and analyzed to obtain the reasons for the low quality of the project;
[0009] The engineering visualization display system is used to visualize the engineering management process and mark the completion quality and the reasons for low engineering quality on the engineering links corresponding to the engineering management process, so as to intelligently display the construction engineering data information and assist construction management;
[0010] Among them, multiple monitoring dimensions include project progress, project quality and project risks.
[0011] In some embodiments of the present application, the engineering data collection and formulation system includes a data collection module and a standard formulation module.
[0012] The data collection module is used to determine the engineering data collection frequency of each engineering link, and collect the engineering data generated in the target project according to the engineering data collection frequency, which is specifically:
[0013] Determine the resource requirements and risk profiles of each project link in the project management process, and quantify the resource requirements and risk profiles through the resource requirements and risk profiles to obtain the resource consumption and risk profiles of each project link;
[0014] Define the first key engineering link in the engineering management process according to the target engineering project plan, identify the second key engineering link other than the first key engineering link based on resource consumption and risk, record the engineering links other than the first key engineering link and the second key engineering link as other engineering links, calculate the relevance of other engineering links with the first key engineering link and the second key engineering link respectively, obtain the overall relevance of other engineering links, and select some other engineering links as the third key engineering link based on the overall relevance of other engineering links;
[0015] The engineering links other than the first key engineering link, the second key engineering link, and the third key engineering link are recorded as common engineering links, and the criticality of each engineering link is determined according to the resource consumption and risk of the first key engineering link, the second key engineering link, the third key engineering link, and the common engineering link;
[0016] Collect project data similar to the target project plan in previous construction projects, and use these project data to determine the frequency and magnitude of changes in each project link, and integrate the frequency and magnitude of changes to determine the degree of change in each project link;
[0017] The frequency of engineering data collection for each engineering link is set according to the criticality and variability of each engineering link.
[0018] In some embodiments of the present application, the standard setting module is used to set standard values under multiple monitoring dimensions of each engineering link, which are specifically:
[0019] Analyze the engineering content of each engineering link to generate monitoring weights of the three dimensions of engineering progress, engineering quality and engineering risk. Map the criticality of each engineering link to obtain the link monitoring attention intensity. According to the link monitoring attention intensity and the monitoring weights of the three dimensions, generate the initial standard values of the three dimensions of engineering progress, engineering quality and engineering risk. Based on the degree of change of each engineering link, adjust the initial standard values of the three dimensions of engineering progress, engineering quality and engineering risk to obtain the standard values under the three monitoring dimensions.
[0020] In some embodiments of the present application, the engineering feature extraction system includes a data processing module and a feature synthesis module.
[0021] The data processing module is used to preprocess the engineering data and extract the reflection features from the preprocessed engineering data, which is specifically:
[0022] Preprocessing includes data cleaning, data denoising and data normalization. Data cleaning includes missing value processing, outlier processing and duplicate value processing. Data denoising includes filtering processing or data transformation.
[0023] Feature extraction is performed on the preprocessed engineering data to obtain reflection features, which include one or more of time series features, statistical features, frequency domain features, time-frequency features, and correlation features.
[0024] In some embodiments of the present application, the feature integration module is used to comprehensively reflect the features to calculate the actual values under multiple monitoring dimensions, which are specifically:
[0025] The actual values of the three monitoring dimensions of project progress, project quality and project risk are calculated respectively by comprehensively considering one or more of time series characteristics, statistical characteristics, frequency domain characteristics, time-frequency characteristics and correlation characteristics.
[0026] In some embodiments of the present application, the engineering traceability analysis system includes a completion quality assessment module, which is used to compare the difference between the actual value and the standard value under multiple monitoring dimensions of each engineering link to determine the completion quality, which is specifically:
[0027] Compare the differences between the actual values and standard values under multiple monitoring dimensions of each engineering link to determine the difference of each monitoring dimension. The difference of each monitoring dimension is positive or negative.
[0028] Integrate the positive or negative differences of each monitoring dimension to evaluate the completion quality of the project link;
[0029]
[0030] Among them, Q i is the completion quality of the i-th engineering link, 3 represents the three monitoring dimensions of engineering progress, engineering quality and engineering risk, α i , j is the influence weight of the jth monitoring dimension in the i-th engineering link, W i , j is the positive or negative difference of the jth monitoring dimension in the i-th engineering link, max(|α i , j W i , j |) is the one with the largest absolute value of positive or negative difference among the three monitoring dimensions, min(|α i , j W i , j |) is the one with the smallest absolute value of positive difference or negative difference among the three monitoring dimensions, k i is the preset constant for the i-th engineering link.
[0031] In some embodiments of the present application, the engineering traceability analysis system further includes a completion quality judgment module, which is used to judge whether the engineering is in a low-quality engineering state by the completion quality, which is specifically:
[0032] Compare the relationship between the completion quality of each engineering link and the preset completion quality threshold. If the completion quality exceeds the completion quality threshold, the engineering link is not in a low-quality engineering state.
[0033] Otherwise, this engineering link is in a low-quality engineering state.
[0034] In some embodiments of the present application, the engineering traceability analysis system further includes a cause tracing module, which is further used to, when the target engineering is in a low-quality engineering state, perform a cause tracing analysis on the engineering links to obtain the cause of the low quality of the engineering, which is specifically:
[0035] Quality problems are identified by one or more of time series characteristics, statistical characteristics, frequency domain characteristics, time-frequency characteristics, and correlation characteristics, and causal reasoning is performed on the identified quality problems to further identify the causes of low engineering quality.
[0036] In some embodiments of the present application, the engineering visualization display system is used to visualize the engineering management process and mark the completion quality and the reasons for low engineering quality on the engineering process corresponding to the engineering link of the engineering management process, which is specifically:
[0037] Use visualization tools to visualize the process of engineering management, display the completion quality, actual value and standard value of each monitoring dimension on the flowchart in different colors or icons, annotate the reasons for low quality of the project on the flowchart, and provide interactive functions.
[0038] Compared with the prior art, the present invention has the following beneficial effects:
[0039] 1. Determine the frequency of engineering data collection for each engineering link, analyze the criticality and variability of each engineering link to set the data collection frequency, ensure the reliability of data collection, and effectively reduce the generation of redundant data. Formulate standard values under multiple monitoring dimensions for each engineering link, set the standard value of each monitoring dimension under each engineering link according to the bias of the monitoring dimension of each engineering link, and use this as the subsequent evaluation standard to improve the reliability of the quality assessment of project completion.
[0040] 2. Compare the differences between the actual values and standard values under multiple monitoring dimensions of each project link to determine the completion quality, conduct causal analysis on the project links, and mark the completion quality and the reasons for low project quality on the corresponding project links in the project management process for visual display. This improves the effect and adaptability of intelligent information management, can directly or intuitively reflect the situation of construction projects, and facilitates personnel to monitor construction projects in real time. BRIEF DESCRIPTION OF THE DRAWINGS
[0041] Figure 1 This is one of the structural schematic diagrams of an intelligent management platform for construction engineering data information proposed by the present invention;
[0042] Figure 2 This is one of the structural schematic diagrams of an intelligent management platform for construction engineering data information proposed by the present invention. DETAILED DESCRIPTION
[0043] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.
[0044] Reference Figure 1 , an intelligent management platform for construction engineering data information, which includes an engineering data collection and formulation system, an engineering feature extraction system, an engineering traceability analysis system and an engineering visualization display system,
[0045] The engineering data collection and formulation system is used to obtain the engineering management link process according to the target engineering project plan mapping. In the engineering management link process, the engineering data collection frequency of each engineering link is determined, the engineering data generated in the target project is collected according to the engineering data collection frequency, and standard values under multiple monitoring dimensions of each engineering link are formulated.
[0046] In this embodiment, the target engineering project plan includes information such as time nodes, key tasks, resource allocation, etc. The situations of different engineering links are different, and the required data collection frequency is also different. The collection frequency is designed in a targeted manner, which can improve the validity of the data and reduce data redundancy.
[0047] The details of each system are as follows Figure 2 shown.
[0048] In some embodiments of the present application, the engineering data collection and formulation system includes a data collection module and a standard formulation module.
[0049] The data collection module is used to determine the engineering data collection frequency of each engineering link, and collect the engineering data generated in the target project according to the engineering data collection frequency, which is specifically:
[0050] Determine the resource requirements and risk profiles of each project link in the project management process, and quantify the resource requirements and risk profiles through the resource requirements and risk profiles to obtain the resource consumption and risk profiles of each project link;
[0051] Define the first key engineering link in the engineering management process according to the target engineering project plan, identify the second key engineering link other than the first key engineering link based on resource consumption and risk, record the engineering links other than the first key engineering link and the second key engineering link as other engineering links, calculate the relevance of other engineering links with the first key engineering link and the second key engineering link respectively, obtain the overall relevance of other engineering links, and select some other engineering links as the third key engineering link based on the overall relevance of other engineering links;
[0052] The engineering links other than the first key engineering link, the second key engineering link, and the third key engineering link are recorded as common engineering links, and the criticality of each engineering link is determined according to the resource consumption and risk of the first key engineering link, the second key engineering link, the third key engineering link, and the common engineering link;
[0053] Collect project data similar to the target project plan in previous construction projects, and use these project data to determine the frequency and magnitude of changes in each project link, and integrate the frequency and magnitude of changes to determine the degree of change in each project link;
[0054] The frequency of engineering data collection for each engineering link is set according to the criticality and variability of each engineering link.
[0055] In this embodiment, the degree of dependence of each link on resources is evaluated, including human, material, financial, etc. Links with high resource dependence are usually more important to the project. Risks are identified, evaluated and quantified for each link to determine its risk level. Links with high risk levels are usually more important to the project. The risk level can be determined based on the probability of problems occurring in the link and the degree of impact of the problems on the project goals. High-risk links require more attention and management.
[0056] In this embodiment, the first key engineering link is selected according to the project plan, and the second key engineering link is selected based on the resource consumption and risk. For example, the link with a large resource consumption or a large risk is often a more critical engineering link. The third key engineering link is screened based on the correlation between other links and the first key engineering link and the second key engineering link. The correlation between the links can be calculated by methods such as the Pearson correlation coefficient, the Spearman rank correlation coefficient, and the Kendall rank correlation coefficient. According to the calculated correlation coefficient value, the strength and direction of the correlation between the two engineering links are judged. The closer the correlation coefficient value is to 1 or -1, the stronger the correlation is; the closer it is to 0, the weaker the correlation is. The link with a stronger correlation is retained as the third key engineering link.
[0057] In this embodiment, historical data of similar projects in the past are collected and analyzed to understand the changing rules and trends of each link. Historical data can reflect the frequency and amplitude of link changes (quantify these situations), so as to determine the degree of change and describe the magnitude of the change in the link. The frequency of engineering data collection for each engineering link is set according to the criticality and degree of change of each engineering link. The more critical the link, the more frequent monitoring is often required. Similarly, the greater the degree of change, the more frequent monitoring is required. The two are combined to determine the frequency of engineering data collection, and a more suitable data collection frequency is set for each link to ensure the effectiveness of data collection.
[0058] In some embodiments of the present application, the standard setting module is used to set standard values under multiple monitoring dimensions of each engineering link, which are specifically:
[0059] Analyze the engineering content of each engineering link to generate monitoring weights of the three dimensions of engineering progress, engineering quality and engineering risk. Map the criticality of each engineering link to obtain the link monitoring attention intensity. According to the link monitoring attention intensity and the monitoring weights of the three dimensions, generate the initial standard values of the three dimensions of engineering progress, engineering quality and engineering risk. Based on the degree of change of each engineering link, adjust the initial standard values of the three dimensions of engineering progress, engineering quality and engineering risk to obtain the standard values under the three monitoring dimensions.
[0060] In this embodiment, project progress: measures the speed at which each link of the project is advanced as planned to ensure that the project is completed on time. Project quality: ensures that the results of each link of the project meet the predetermined quality standards and specifications. Project risk: identifies, evaluates and manages potential problems and uncertainties in each link of the project to reduce losses. The project content of each project link (the requirements of the target project plan) is analyzed to generate monitoring weights for the three dimensions of project progress, project quality and project risk, and quantitative methods such as the analytic hierarchy process (AHP) are used to determine the weight ratio. This method constructs a judgment matrix to compare the importance of different monitoring dimensions in key links, thereby deriving the weight ratio.
[0061] In this embodiment, the monitoring attention intensity of each link (the resources allocated to the link as a whole) is obtained by mapping the criticality of each engineering link. Different criticalities correspond to different resource amounts. Combined with the monitoring weights of the three dimensions, the initial standard value of each monitoring dimension is determined. Based on the degree of change of each engineering link, the initial standard values of the three dimensions of project progress, project quality and project risk are adjusted. Different degrees of change correspond to different correction coefficients (the corresponding relationship between different dimensions is different). The standard value is adjusted by using the correction coefficient * initial standard value to finally obtain an accurate standard value.
[0062] The engineering feature extraction system is used to preprocess engineering data, extract reflection features from the preprocessed engineering data, and comprehensively reflect the features to calculate actual values under multiple monitoring dimensions.
[0063] In this embodiment, the engineering data of the collected original data has a lot of noise or problems, and needs to be preprocessed. After the processing, features are extracted, and the actual values under multiple monitoring dimensions are determined based on the features.
[0064] In some embodiments of the present application, the engineering feature extraction system includes a data processing module and a feature synthesis module.
[0065] The data processing module is used to preprocess the engineering data and extract the reflection features from the preprocessed engineering data, which is specifically:
[0066] Preprocessing includes data cleaning, data denoising and data normalization. Data cleaning includes missing value processing, outlier processing and duplicate value processing. Data denoising includes filtering processing or data transformation.
[0067] Feature extraction is performed on the preprocessed engineering data to obtain reflection features, which include one or more of time series features, statistical features, frequency domain features, time-frequency features, and correlation features.
[0068] In this embodiment, missing value processing: check whether there are missing values in the data set, and adopt filling (such as mean filling, median filling, interpolation, etc.) or deletion strategies according to the actual situation. Outlier processing: identify and process outliers in the data, which may be caused by measurement errors, equipment failures, or data entry errors. Processing methods include replacement (such as replacement with median or mean), deletion, or special marking. Duplicate value processing: check and delete duplicate records in the data set to avoid interference with subsequent analysis. Filtering: For time series data containing noise, filtering algorithms (such as moving average filtering, Kalman filtering, etc.) can be used for smoothing to remove high-frequency noise. Data transformation: reduce or eliminate noise components in the data through data transformation (such as logarithmic transformation, differential transformation, etc.). Standardization: scale the data according to a certain ratio so that it falls into a small specific interval, usually between 0 and 1 or -1 to 1. Standardization can eliminate the dimensionality of the data and make different features comparable. Normalization: Scaling the data to a specific range (such as 0 to 1) to eliminate the absolute numerical differences in the data and make the data more concentrated and stable.
[0069] In this embodiment, time series characteristics: such as trend, periodicity, seasonality, autocorrelation, etc., these characteristics can reflect the change pattern of engineering data over time. Statistical characteristics: such as mean, variance, standard deviation, maximum value, minimum value, median, etc., these characteristics can describe the distribution and central tendency of data. Frequency domain characteristics: through frequency domain analysis methods such as Fourier transform, the spectral characteristics of data, such as frequency components, amplitude, etc., are extracted. These characteristics can reflect the periodicity and vibration characteristics of data. Time-frequency characteristics: combining the analysis methods of time domain and frequency domain, the time-frequency characteristics of data are extracted, such as wavelet transform coefficients, etc. These characteristics can simultaneously reflect the changes of data in time and frequency. Correlation characteristics: by calculating the correlation coefficient or covariance matrix between different variables, the correlation characteristics of data are extracted. These characteristics can reveal the correlation and degree of dependence between variables.
[0070] In some embodiments of the present application, the feature integration module is used to comprehensively reflect the features to calculate the actual values under multiple monitoring dimensions, which are specifically:
[0071] The actual values of the three monitoring dimensions of project progress, project quality and project risk are calculated respectively by comprehensively considering one or more of time series characteristics, statistical characteristics, frequency domain characteristics, time-frequency characteristics and correlation characteristics.
[0072] In this embodiment, these features are input into a preset calculation model to calculate the actual value of each monitoring dimension. For example, by establishing a probability model and statistical distribution, the characteristics, relationships and uncertainties of engineering quality data (such as pass rate, excellent rate, defect density, etc.) can be described and inferred, thereby calculating the actual value of the engineering quality.
[0073] The engineering traceability analysis system is used to compare the differences between actual values and standard values under multiple monitoring dimensions of each engineering link to determine the completion quality, and to judge whether the target project is in a low-quality engineering state by the completion quality. When the target project is in a low-quality engineering state, the engineering link is traced and analyzed to obtain the reasons for the low quality of the project.
[0074] In this embodiment, the completion quality is evaluated by comparing the difference between the actual value and the standard value. Here, the three dimensions of project progress, project quality and project risk are interrelated and influence each other. It may be unrealistic to require that all three dimensions meet the standards. Here, as long as the overall situation of the three meets the standards, the completion quality of this link is considered to be high.
[0075] In some embodiments of the present application, the engineering traceability analysis system includes a completion quality assessment module, which is used to compare the difference between the actual value and the standard value under multiple monitoring dimensions of each engineering link to determine the completion quality, which is specifically:
[0076] Compare the differences between the actual values and standard values under multiple monitoring dimensions of each engineering link to determine the difference of each monitoring dimension. The difference of each monitoring dimension is positive or negative.
[0077] Integrate the positive or negative differences of each monitoring dimension to evaluate the completion quality of the project link;
[0078]
[0079] Among them, Q i is the completion quality of the i-th engineering link, 3 represents the three monitoring dimensions of engineering progress, engineering quality and engineering risk, α i , j is the influence weight of the jth monitoring dimension in the i-th engineering link, W i , j is the positive or negative difference of the jth monitoring dimension in the i-th engineering link, max(|α i , j W i , j|) is the one with the largest absolute value of positive or negative difference among the three monitoring dimensions, min(|α i , j W i , j |) is the one with the smallest absolute value of positive difference or negative difference among the three monitoring dimensions, k i is the preset constant for the i-th engineering link.
[0080] In this embodiment, The average value of the maximum and minimum absolute values of the difference represents the correction of the comprehensive difference in the three monitoring dimensions of project progress, project quality and project risk.
[0081] In some embodiments of the present application, the engineering traceability analysis system further includes a completion quality judgment module, which is used to judge whether the engineering is in a low-quality engineering state by the completion quality, which is specifically:
[0082] Compare the relationship between the completion quality of each engineering link and the preset completion quality threshold. If the completion quality exceeds the completion quality threshold, the engineering link is not in a low-quality engineering state.
[0083] Otherwise, this engineering link is in a low-quality engineering state.
[0084] In some embodiments of the present application, the engineering traceability analysis system further includes a cause tracing module, which is further used to, when the target engineering is in a low-quality engineering state, perform a cause tracing analysis on the engineering links to obtain the cause of the low quality of the engineering, which is specifically:
[0085] Quality problems are identified by one or more of time series characteristics, statistical characteristics, frequency domain characteristics, time-frequency characteristics, and correlation characteristics, and causal reasoning is performed on the identified quality problems to further identify the causes of low engineering quality.
[0086] In this embodiment, these features are combined to identify quality problems. For example, the trend of data changes over time is analyzed, such as the rising trend of equipment failure rate, the fluctuation of product non-conforming rate, etc. Potential quality problems and risk points are identified through trend analysis. Based on the results of data analysis, the causal reasoning method is used to analyze the possible causes of quality problems. Causal reasoning can be performed using tools such as hypothesis testing, Bayesian networks, and cause-and-effect diagrams. On the basis of causal reasoning, the root causes of quality problems are further identified. Root cause identification needs to consider the combined effects of multiple factors, such as equipment failure, improper process parameter settings, raw material quality problems, operator errors, etc. Root cause analysis techniques, such as 5Why analysis and fault tree analysis, are used to deeply explore the root causes of quality problems.
[0087] The engineering visualization display system is used to visualize the engineering management process and mark the completion quality and the reasons for low engineering quality on the engineering links corresponding to the engineering management process, so as to intelligently display the construction engineering data information and assist construction management.
[0088] In some embodiments of the present application, the engineering visualization display system is used to visualize the engineering management process and mark the completion quality and the reasons for low engineering quality on the engineering process corresponding to the engineering link of the engineering management process, which is specifically:
[0089] Use visualization tools to visualize the process of engineering management, display the completion quality, actual value and standard value of each monitoring dimension on the flowchart in different colors or icons, annotate the reasons for low quality of the project on the flowchart, and provide interactive functions.
[0090] In this embodiment, according to project requirements and team preferences, select appropriate visualization tools, such as Gantt chart software (such as Microsoft Project, Smartsheet, etc.) and flowchart drawing tools (such as Visio, Lucidchart, etc.). Use the selected tools to display the engineering management links in a graphical manner. The Gantt chart can display the timeline, task allocation and progress of the project; the flowchart can clearly present the logical relationship and dependency relationship of each link. On the flowchart, the completion quality and value of each link are marked in the form of color, icon, etc. For example, green means qualified, yellow means warning, and red means unqualified. Display analysis results: In the visualization interface, the causal analysis results are displayed in an intuitive way. For example, you can click on the low-quality link on the flowchart to pop up a window or panel containing detailed analysis results. In order to facilitate managers to further understand and explore the problem, interactive functions can be provided, such as zooming in / out the view, viewing detailed data, exporting reports, etc.
[0091] Among them, multiple monitoring dimensions include project progress, project quality and project risks.
[0092] Compared with the prior art, the present invention has the following beneficial effects:
[0093] 1. Determine the frequency of engineering data collection for each engineering link, analyze the criticality and variability of each engineering link to set the data collection frequency, ensure the reliability of data collection, and effectively reduce the generation of redundant data. Formulate standard values under multiple monitoring dimensions for each engineering link, set the standard value of each monitoring dimension under each engineering link according to the bias of the monitoring dimension of each engineering link, and use this as the subsequent evaluation standard to improve the reliability of the quality assessment of project completion.
[0094] 2. Compare the differences between the actual values and standard values under multiple monitoring dimensions of each project link to determine the completion quality, conduct causal analysis on the project links, and mark the completion quality and the reasons for low project quality on the corresponding project links in the project management process for visual display. This improves the effect and adaptability of intelligent information management, can directly or intuitively reflect the situation of construction projects, and facilitates personnel to monitor construction projects in real time.
[0095] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.
[0096] Those skilled in the art will appreciate that the accompanying drawings are merely schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily required for implementing the present invention.
[0097] Those skilled in the art will appreciate that the modules in the system in the implementation scenario can be distributed in the system of the implementation scenario according to the implementation scenario description, or can be changed accordingly and located in one or more systems different from the implementation scenario. The modules in the above implementation scenario can be combined into one module, or can be further split into multiple submodules.
[0098] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.
Claims
1. An intelligent management platform for construction engineering data information, characterized in that: Including engineering data collection and formulation system, engineering feature extraction system, engineering traceability analysis system and engineering visualization display system, The engineering data collection and formulation system is used to obtain the engineering management link process according to the target engineering project plan mapping, determine the engineering data collection frequency of each engineering link in the engineering management link process, collect the engineering data generated in the target project according to the engineering data collection frequency, and formulate standard values under multiple monitoring dimensions of each engineering link; The engineering feature extraction system is used to preprocess engineering data, extract reflection features from the preprocessed engineering data, and comprehensively reflect the features to calculate actual values under multiple monitoring dimensions; The engineering traceability analysis system is used to compare the difference between the actual value and the standard value under multiple monitoring dimensions of each engineering link to determine the completion quality, and judge whether it is in a low-quality engineering state by the completion quality. When the target project is in a low-quality engineering state, the engineering link is traced and analyzed to obtain the reasons for the low quality of the project; The engineering visualization display system is used to visualize the engineering management process and mark the completion quality and the reasons for low engineering quality on the engineering links corresponding to the engineering management process, so as to intelligently display the construction engineering data information and assist construction management; Among them, multiple monitoring dimensions include project progress, project quality and project risks.
2. The intelligent management platform for construction engineering data information according to claim 1 is characterized in that: The engineering data acquisition and formulation system includes a data acquisition module and a standard formulation module. The data collection module is used to determine the engineering data collection frequency of each engineering link, and collect the engineering data generated in the target project according to the engineering data collection frequency, which is specifically: Determine the resource requirements and risk profiles of each project link in the project management process, and quantify the resource requirements and risk profiles through the resource requirements and risk profiles to obtain the resource consumption and risk profiles of each project link; Define the first key engineering link in the engineering management process according to the target engineering project plan, identify the second key engineering link other than the first key engineering link based on resource consumption and risk, record the engineering links other than the first key engineering link and the second key engineering link as other engineering links, calculate the relevance of other engineering links with the first key engineering link and the second key engineering link respectively, obtain the overall relevance of other engineering links, and select some other engineering links as the third key engineering link based on the overall relevance of other engineering links; The engineering links other than the first key engineering link, the second key engineering link, and the third key engineering link are recorded as common engineering links, and the criticality of each engineering link is determined according to the resource consumption and risk of the first key engineering link, the second key engineering link, the third key engineering link, and the common engineering link; Collect project data similar to the target project plan in previous construction projects, and use these project data to determine the frequency and magnitude of changes in each project link, and integrate the frequency and magnitude of changes to determine the degree of change in each project link; The frequency of engineering data collection for each engineering link is set according to the criticality and variability of each engineering link.
3. The intelligent management platform for construction engineering data information according to claim 2 is characterized in that: The standard setting module is used to set standard values under multiple monitoring dimensions for each engineering link, which are specifically: Analyze the engineering content of each engineering link to generate monitoring weights of the three dimensions of engineering progress, engineering quality and engineering risk. Map the criticality of each engineering link to obtain the link monitoring attention intensity. According to the link monitoring attention intensity and the monitoring weights of the three dimensions, generate the initial standard values of the three dimensions of engineering progress, engineering quality and engineering risk. Based on the degree of change of each engineering link, adjust the initial standard values of the three dimensions of engineering progress, engineering quality and engineering risk to obtain the standard values under the three monitoring dimensions.
4. The intelligent management platform for construction engineering data information according to claim 1 is characterized in that: The engineering feature extraction system includes a data processing module and a feature synthesis module. The data processing module is used to preprocess the engineering data and extract the reflection features from the preprocessed engineering data, which is specifically: Preprocessing includes data cleaning, data denoising and data normalization. Data cleaning includes missing value processing, outlier processing and duplicate value processing. Data denoising includes filtering processing or data transformation. Feature extraction is performed on the preprocessed engineering data to obtain reflection features, which include one or more of time series features, statistical features, frequency domain features, time-frequency features, and correlation features.
5. The intelligent management platform for construction engineering data information according to claim 4 is characterized in that: The feature synthesis module is used to comprehensively reflect the features to calculate the actual values under multiple monitoring dimensions, which are specifically: The actual values of the three monitoring dimensions of project progress, project quality and project risk are calculated respectively by comprehensively considering one or more of time series characteristics, statistical characteristics, frequency domain characteristics, time-frequency characteristics and correlation characteristics.
6. The intelligent management platform for construction engineering data information according to claim 1 is characterized in that: The engineering traceability analysis system includes a completion quality assessment module, which is used to compare the difference between the actual value and the standard value under multiple monitoring dimensions of each engineering link to determine the completion quality, which is specifically: Compare the differences between the actual values and standard values under multiple monitoring dimensions of each engineering link to determine the difference of each monitoring dimension. The difference of each monitoring dimension is positive or negative. Integrate the positive or negative differences of each monitoring dimension to evaluate the completion quality of the project link; Among them, Q i is the completion quality of the i-th engineering link, 3 represents the three monitoring dimensions of engineering progress, engineering quality and engineering risk, α i , j is the influence weight of the jth monitoring dimension in the i-th engineering link, W i , j is the positive or negative difference of the jth monitoring dimension in the i-th engineering link, max(|α i , j W i , j |) is the one with the largest absolute value of positive or negative difference among the three monitoring dimensions, min(|α i , j W i , j |) is the one with the smallest absolute value of positive difference or negative difference among the three monitoring dimensions, k i is the preset constant for the ith engineering link.
7. The intelligent management platform for construction engineering data information according to claim 1 is characterized in that: The engineering traceability analysis system further includes a completion quality judgment module, which is used to judge whether the engineering is in a low-quality state by the completion quality, and is specifically: Compare the relationship between the completion quality of each engineering link and the preset completion quality threshold. If the completion quality exceeds the completion quality threshold, the engineering link is not in a low-quality engineering state. Otherwise, this engineering link is in a low-quality engineering state.
8. The intelligent management platform for construction engineering data information according to claim 5 is characterized in that: The engineering traceability analysis system further includes a cause tracing module, which is further used to, when the target engineering is in a low-quality engineering state, perform a cause tracing analysis on the engineering links to obtain the causes of the low quality of the engineering, which are specifically: Quality problems are identified by one or more of time series characteristics, statistical characteristics, frequency domain characteristics, time-frequency characteristics, and correlation characteristics, and causal reasoning is performed on the identified quality problems to further identify the causes of low engineering quality.
9. The intelligent management platform for construction engineering data information according to claim 1 is characterized in that: The engineering visualization display system is used to visualize the engineering management process and mark the completion quality and the reasons for low engineering quality on the engineering links corresponding to the engineering management process, which are specifically: Use visualization tools to visualize the process of engineering management, display the completion quality, actual value and standard value of each monitoring dimension on the flowchart in different colors or icons, annotate the reasons for low quality of the project on the flowchart, and provide interactive functions.