Data analysis method and system in construction engineering project life cycle management

Abstract

The invention relates to the technical field of construction engineering data analysis, in particular to a data analysis method and system in construction engineering project life cycle management. The method comprises the following steps: performing building design simulation modeling processing on building design parameters to generate building design simulation modeling data; analyzing construction project demand structured data according to the building design simulation modeling data and the construction project demand data; analyzing and optimizing construction project resource scheduling constraint data according to the construction project demand structured data and the construction project resource data; performing disturbance simulation of construction project construction event simulation based on the optimized construction project resource scheduling constraint data and the building design simulation modeling data, and generating disturbance construction event simulation data; and performing construction event risk feature analysis based on the disturbance construction event analogue simulation data to generate construction event risk feature data. According to the invention, intelligent construction engineering project life cycle management is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of construction engineering data analysis, and in particular to a data analysis method and system in the life cycle management of a construction engineering project. Background Art

[0002] In the life cycle management of construction projects, the introduction of data analysis technology is an important means to deal with complexity and uncertainty. Due to the large amount of data and the complexity of data at different stages, it is difficult to achieve efficient processing and deep mining of these data in the life cycle management of construction projects. Through data analysis technology, the data of the entire life cycle of construction projects can be integrated and analyzed for quantification and dynamic monitoring, thereby reducing the risk of life cycle management of construction projects. However, the existing data analysis methods in the life cycle management of construction projects have poor data collaborative analysis effects. The data collection and analysis of each stage of the project life cycle have failed to achieve efficient interconnection, resulting in insufficient accuracy in matching demand and resources, and the inability to optimize the scheduling plan in a timely manner; secondly, the resource scheduling method is usually based on experience, lacking comprehensive consideration and constraint optimization of changes in multi-stage resource demand, which easily leads to conflicts and inefficiencies in resource allocation; and the construction event simulation analysis method has limited ability to dynamically adjust construction disturbance events, and cannot effectively identify potential risks and give reasonable response measures, affecting the overall progress and quality control of the project. Summary of the invention

[0003] Based on this, the present invention provides a data analysis method and system in the life cycle management of a construction project to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a data analysis method in the life cycle management of a construction project includes the following steps: Step S1: collecting construction project configuration data through a construction project information system interface to generate construction project configuration data, wherein the construction project configuration data includes construction project demand data, construction project resource data, and building design parameters; performing building design simulation modeling processing on the building design parameters through building information modeling technology to generate building design simulation modeling data; performing construction project demand process analysis based on the building design simulation modeling data and the construction project demand data to generate construction project demand process data; Step S2: Performing a structured analysis of construction project demand according to the construction project demand process data to generate structured data of construction project demand; Step S3: Analyze the demand and resource matching relationship of the construction project according to the structured data of the construction project demand and the construction project resource data, and generate the demand-resource matching relationship data of the construction project; analyze the priority adjustment of the cross-stage resource demand scheduling according to the structured data of the construction project demand, and generate the cross-stage resource demand scheduling priority adjustment data; analyze and process the optimized construction project resource scheduling constraints based on the cross-stage resource demand scheduling priority adjustment data and the construction project demand-resource matching relationship data, and generate the optimized construction project resource scheduling constraint data; Step S4: Based on the optimized construction project resource scheduling constraint data and the architectural design simulation modeling data, disturbance simulation processing of construction project construction event simulation is performed to generate disturbance construction event simulation data; by optimizing the construction project resource scheduling constraint data, the disturbance construction event simulation data is dynamically optimized and adjusted to generate optimized and adjusted construction event simulation data; based on the optimized and adjusted construction event simulation data, construction event risk feature analysis is performed to generate construction event risk feature data; the optimized and adjusted construction event simulation data and the construction event risk feature data are transmitted to the terminal to execute the construction project life cycle management feedback processing.

[0005] Further, step S1 includes the following steps: Step S11: collecting construction project configuration data through the construction project information system interface to generate construction project configuration data; Step S12: Performing architectural design simulation modeling processing on the architectural design parameters through building information modeling technology to generate architectural design simulation modeling data; Step S13: performing demand analysis processing on the construction project demand data to generate construction project demand analysis data; Step S14: Mapping the construction project demand analysis data to the construction project demand process through the architectural design simulation modeling data to generate construction project demand process data.

[0006] Further, step S2 includes the following steps: Step S21: performing construction project demand process dependency analysis according to the construction project demand process data to generate construction project demand dependency data; Step S22: Based on the construction project demand dependency data, the construction project demand process data is subjected to construction project demand structured mapping processing to generate construction project demand structured data.

[0007] Further, step S21 includes the following steps: Step S211: performing demand process time series analysis according to the construction project demand process data to generate construction project demand process time series data; Step S212: performing demand process type analysis on the construction project demand process data to generate construction project demand process type data; Step S213: performing demand process stage division processing on the construction project demand process data to generate construction project demand process stage data; Step S214: Based on the construction project demand process type data and the construction project demand process stage data, the construction project demand process dependency analysis is performed on the construction project demand process time series data to generate construction project demand dependency data.

[0008] Further, step S3 includes the following steps: Step S31: Analyze the construction project demand and resource matching relationship based on the construction project demand structured data and the construction project resource data to generate construction project demand-resource matching relationship data; Step S32: Establish a construction project demand-resource scheduling relationship matrix through the construction project demand-resource matching relationship data, perform construction project resource scheduling constraint analysis based on the construction project demand-resource scheduling relationship matrix, and generate construction project resource scheduling constraint data; Step S33: Perform cross-stage resource demand scheduling analysis based on the construction project demand structured data to generate cross-stage resource demand scheduling data; Step S34: performing priority adjustment analysis of cross-stage resource demand scheduling based on the cross-stage resource demand scheduling data to generate cross-stage resource demand scheduling priority adjustment data; Step S35: Optimizing the construction project resource scheduling constraint data by adjusting the priority of resource demand scheduling across stages to generate optimized construction project resource scheduling constraint data.

[0009] Further, step S34 includes the following steps: Step S341: performing cross-stage resource demand conflict analysis according to the cross-stage resource demand scheduling data to generate cross-stage resource demand conflict data; Step S342: performing priority adjustment analysis of resource demand scheduling at each stage on the cross-stage resource demand scheduling data based on the preset resource allocation priority decision, and generating priority adjustment data of resource demand scheduling at each stage; Step S343: performing cross-stage resource demand weight analysis according to the cross-stage resource demand conflict data to generate cross-stage resource demand weight data; Step S344: performing priority adjustment analysis of inter-stage conflicting resource demands according to the inter-stage resource demand weight data, and generating inter-stage conflicting resource demand priority adjustment data; Step S345: performing priority adjustment analysis of cross-stage resource demand scheduling based on each stage resource demand scheduling priority adjustment data and cross-stage conflicting resource demand scheduling priority adjustment data to generate cross-stage resource demand scheduling priority adjustment data.

[0010] Further, step S4 includes the following steps: Step S41: Mapping the optimized construction project resource scheduling constraint data to the architectural design simulation modeling data to perform construction project construction event simulation processing to generate construction project construction event simulation data; Step S42: using Monte Carlo simulation technology and preset construction simulation disturbance parameters to perform random disturbance construction event simulation processing on the construction event simulation data of the construction project to generate disturbance construction event simulation data; Step S43: performing risk factor analysis of construction event simulation according to the disturbance construction event simulation data to generate a construction event simulation risk factor; Step S44: performing dynamic optimization and adjustment processing of construction event simulation on the disturbed construction event simulation data by optimizing the construction project resource scheduling constraint data, and generating optimized and adjusted construction event simulation data; Step S45: dynamically updating the construction event simulation risk factor according to the optimized and adjusted construction event simulation data to generate an updated construction event simulation risk factor; Step S46: performing risk characteristic analysis of construction events according to the updated construction event simulation risk factors to generate risk characteristic data of construction events; Step S47: Transmitting the optimized and adjusted construction event simulation data and the construction event risk characteristic data to the terminal to execute the construction project life cycle management feedback processing.

[0011] Further, step S43 includes the following steps: Step S431: Establishing an event tree model for construction risk assessment based on construction project construction event simulation data to generate a construction risk assessment event tree model; Step S432: transmitting the disturbance construction event simulation data to the construction risk assessment event tree model to extract the risk factors of the construction event simulation and generate the construction event simulation risk factors.

[0012] Further, step S431 includes the following steps: According to the construction event simulation data of the construction project, the logical path encapsulation processing of the construction event is carried out to generate the construction event logical path encapsulation data; the construction logical path event tree structure is designed through the construction event logical path encapsulation data; based on the preset construction event risk indicators, the construction logical path event tree structure is empowered with the construction risk assessment relationship of the event tree to generate a construction risk assessment event tree model.

[0013] This specification provides a data analysis system in the life cycle management of a construction project, which is used to execute the data analysis method in the life cycle management of a construction project as described above. The data analysis system in the life cycle management of a construction project includes: The construction project configuration data management module is used to collect construction project configuration data through the construction project information system interface to generate construction project configuration data, wherein the construction project configuration data includes construction project demand data, construction project resource data and building design parameters; to perform building design simulation modeling processing on the building design parameters through the building information modeling technology to generate building design simulation modeling data; to perform construction project demand process analysis based on the building design simulation modeling data and the construction project demand data to generate construction project demand process data; A construction engineering demand structured processing module is used to perform a construction engineering demand structured analysis based on the construction engineering demand process data and generate construction engineering demand structured data; The construction project resource scheduling constraint analysis module is used to analyze the demand and resource matching relationship of the construction project based on the construction project demand structured data and the construction project resource data, and generate the construction project demand-resource matching relationship data; perform priority adjustment analysis of cross-stage resource demand scheduling based on the construction project demand structured data, and generate cross-stage resource demand scheduling priority adjustment data; optimize the construction project resource scheduling constraint analysis and processing based on the cross-stage resource demand scheduling priority adjustment data and the construction project demand-resource matching relationship data, and generate optimized construction project resource scheduling constraint data; The construction project life cycle management module is used to perform disturbance simulation processing of construction project construction event simulation based on optimized construction project resource scheduling constraint data and architectural design simulation modeling data, and generate disturbance construction event simulation data; dynamically optimize and adjust the disturbance construction event simulation data by optimizing the construction project resource scheduling constraint data, and generate optimized and adjusted construction event simulation data; perform construction event risk feature analysis based on the optimized and adjusted construction event simulation data, and generate construction event risk feature data; transmit the optimized and adjusted construction event simulation data and construction event risk feature data to the terminal to perform construction project life cycle management feedback processing.

[0014] The beneficial effect of the present application is that, through the configuration data collection of the construction project information system interface and the application of building information modeling technology, various types of data in the construction project, including demand data, resource data and architectural design parameters, can be fully and accurately acquired and processed. This data collection and processing method avoids the situation of scattered and incomplete data in the traditional management model, and ensures data integration and information transparency of the whole process of the project. Through the architectural design simulation modeling processing, the design parameters can be dynamically simulated and optimized, and the architectural design simulation modeling data can be generated, providing accurate basic data support for the subsequent engineering demand process analysis. In addition, by integrating the architectural design simulation modeling data with the construction project demand data, the demand process can be deeply analyzed, and the construction project demand process data can be generated, further laying a solid data foundation for the subsequent structured analysis of demand data and resource scheduling optimization. By performing structured analysis on the construction project demand process data, complex engineering requirements can be effectively converted into clear and operable data forms, which is convenient for subsequent resource scheduling and optimization. Through the construction project demand process dependency analysis, the interdependence between different links in the demand data can be revealed, providing a comprehensive reference for subsequent demand mapping and scheduling. This analysis method can avoid the situation where demand relationships are ignored or improperly handled in traditional management methods, thereby reducing scheduling conflicts caused by insufficient data dependency processing. Based on the construction project demand dependency data, the demand data is structured and mapped, providing accurate data support for resource matching and optimal scheduling. It effectively improves the operability and analyzability of demand data, provides a more scientific basis for resource scheduling optimization, and thus improves the execution efficiency of engineering projects and the rationality of resource allocation. The analysis of the structured data and resource data of construction project demand effectively solves the problem of inaccuracy in the process of demand and resource matching. Through the analysis of demand and resource matching relationships, the generated demand-resource matching relationship data can clearly reflect the correlation between resources and demand, and significantly improve the rationality of resource allocation. The demand-resource scheduling relationship matrix is ​​constructed and the resource scheduling constraint optimization analysis is performed to comprehensively evaluate the conflicts and restrictions in the resource allocation process, providing a scientific basis for optimizing resource scheduling. The cross-stage resource demand scheduling priority adjustment analysis fully considers the dynamic changes in resource demand and the priority allocation problem in multiple stages. By combining the cross-stage resource demand conflict analysis and weight analysis, it effectively alleviates the conflicts in the resource scheduling process and improves the overall coordination and flexibility of the scheduling scheme. Based on the resource scheduling processing of optimization constraints, the generated optimized construction project resource scheduling constraint data provides a guarantee for the resource utilization efficiency in the subsequent construction stage. This optimization processing method greatly enhances the intelligent level of resource scheduling and improves the overall execution efficiency and resource utilization of the project.Combining the optimization of construction project resource scheduling constraint data with architectural design simulation modeling data, the disturbance simulation and dynamic optimization adjustment of construction events are realized, providing a flexible response mechanism for emergencies that occur during the construction process. Using Monte Carlo simulation technology to perform random disturbance analysis on construction events can not only simulate various scenarios encountered during the construction process more realistically, but also improve the accuracy and adaptability of simulation through dynamic optimization and adjustment methods. The combination of risk factor analysis and dynamic update processing can continuously track and adjust the changes in risk characteristics during the construction process. The generated risk characteristic data provides specific management suggestions for the implementation of construction projects for risk control throughout the construction process. By constructing a logical path event tree model of construction events and enabling construction risk assessment relationships, the risk transmission path can be intuitively displayed, key risk points can be identified, and risk prediction and response capabilities can be improved. By transmitting the optimized and adjusted construction simulation data and construction event risk characteristic data to the terminal device, the closed-loop processing from simulation optimization to actual feedback is completed. This closed-loop mechanism provides all-round dynamic support and feedback guarantee for the life cycle management of construction projects, significantly improving the safety and controllability of the project.

[0015] Therefore, the data analysis method in the life cycle management of the construction project of the present invention comprehensively collects and integrates the construction project configuration data of the construction project life cycle, including demand data, resource data and architectural design parameters, by introducing the construction project information system interface and building information modeling technology, and realizes the efficient interconnection and intercommunication of data at each stage of the project life cycle, thereby significantly improving the accuracy of demand and resource matching and the optimization ability of the scheduling scheme. Secondly, in terms of resource scheduling, by combining the structured data and resource data of construction project demand, adopting the demand-resource matching relationship analysis and the priority adjustment analysis of cross-stage resource demand scheduling, the priority adjustment analysis method of cross-stage resource demand scheduling is designed, and the dynamic changes of multi-stage resource demand and its priority adjustment are fully considered, which overcomes the shortcomings of the traditional resource scheduling method that relies on experience, and effectively avoids the conflict and inefficiency of resource allocation. Finally, in the construction stage, by optimizing the combination of construction project resource scheduling constraint data and architectural design simulation modeling data, using disturbance simulation and dynamic optimization adjustment processing, the accuracy and coping ability of construction event simulation simulation are effectively improved. In particular, the use of technologies such as Monte Carlo simulation can dynamically adjust construction disturbance events, identify potential risks in a timely manner, and analyze construction risk characteristics based on optimized adjustment data, providing the project with a more comprehensive risk assessment and response strategy, thereby improving the project's risk resistance and ensuring effective control of project progress and quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of the steps of a data analysis method in the life cycle management of a construction project according to the present invention; Figure 2 for Figure 1 Detailed implementation steps of step S3 in FIG. Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0017] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0018] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0019] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0020] To achieve this, please refer to Figures 1 to 3 The present invention provides a data analysis method for life cycle management of a construction project. In the embodiments of the present invention, please refer to Figure 1 As shown, it is a schematic diagram of the steps of a data analysis method in the life cycle management of a construction project according to the present invention. The data analysis method in the life cycle management of a construction project comprises the following steps: Step S1: collecting construction project configuration data through a construction project information system interface to generate construction project configuration data, wherein the construction project configuration data includes construction project demand data, construction project resource data, and building design parameters; performing building design simulation modeling processing on the building design parameters through building information modeling technology to generate building design simulation modeling data; performing construction project demand process analysis based on the building design simulation modeling data and the construction project demand data to generate construction project demand process data; In the embodiment of the present invention, various types of construction project configuration data are collected through the construction project information system interface. Specifically, first, the system interacts with the on-site construction management platform, resource scheduling system and design platform to automatically obtain information such as demand data, resource data and architectural design parameters of the construction project. These data include equipment requirements, material usage plans, construction personnel arrangements, schedule plans and technical parameters of architectural design drawings. The collection process adopts a standardized data format for transmission through the data interface to ensure real-time update and accuracy of the data. All collected data will be backed up in the data storage system to provide data support for subsequent analysis and decision-making. Building information modeling (BIM) technology is used to simulate and model the collected architectural design parameters, and architectural design data such as design drawings, structural layout, and facility equipment are input into the BIM platform, and the three-dimensional space of the building is modeled using BIM software. During the modeling process, the system automatically verifies and optimizes various design parameters to ensure the feasibility of the design and compliance with building specifications. The modeling process not only includes the modeling of the external form of the building, but also covers the modeling of facilities such as the internal structure, pipeline system, and air conditioning system. During the simulation process, the system will automatically make design changes and optimize calculations according to the design requirements, and output architectural design simulation data, including but not limited to the structural performance, functional zoning, material requirements and other information of the building. The key to this step is to use BIM technology to convert the design data into a three-dimensional model that can be simulated and analyzed, providing an accurate basis for the subsequent demand process analysis. The construction project demand data is processed for demand analysis, and the construction project demand data collected from the system interface is extracted, including key factors such as project scale, resource requirements, and time node requirements. Then, through the demand analysis model, such as the BERT model based on natural language processing, the demand analysis is performed to refine these data into demand descriptions for each specific link. The demand analysis process uses an algorithm model to classify and calculate different types of data to ensure the structuring of demand data, facilitate subsequent matching and optimization, ensure the comprehensive deconstruction of the original demand data, and provide an accurate demand basis for resource scheduling and construction progress. The construction project demand analysis data is mapped to the demand process using the architectural design simulation modeling data, and the construction project demand analysis data is matched with the architectural design simulation modeling data to confirm the corresponding relationship between the demand data and each parameter in the design model to ensure the consistency between demand and design. Then, based on the design simulation model, the demand data is mapped, and the specific construction requirements (such as materials, personnel, equipment, etc.) are linked with the design models of each stage in the construction process to generate the corresponding demand process data. After the mapping is completed, the system will output the construction project demand process data and provide it for subsequent data structured analysis and resource scheduling optimization. Through the precise mapping of the demand process, the close coordination between design and demand is ensured, providing guarantee for the execution of the entire project.

[0021] Step S2: Performing a structured analysis of construction project demand according to the construction project demand process data to generate structured data of construction project demand; In the embodiment of the present invention, the demand process dependency analysis is performed based on the construction project demand process data, and the data of each demand node is extracted from the construction project demand process data, including information such as material demand, resource demand, and equipment demand. Then, the system uses an algorithm based on graph theory to perform dependency analysis on these demands, and identifies the order of demands and their interdependence. During the dependency analysis process, the system automatically identifies and constructs a dependency network of the demand process, and maps the mutual influence relationship between each demand and other demand nodes. After the analysis is completed, the system generates construction project demand dependency data, identifies the dependency order and hierarchy between each demand node, forms a demand process dependency network, and supports subsequent data structured processing and resource scheduling optimization. Based on the construction project demand dependency data, the construction project demand process data is subjected to data structured mapping processing, and the hierarchical relationship and dependency sequence between each demand are obtained from the construction project demand dependency data. The system converts these dependencies into a multidimensional data structure, including dimensions such as demand type, resource allocation, and time node. Next, the system performs structured processing on the construction project demand process data based on these dependency data, maps it into a data format that meets the project management standard, and forms construction project demand structured data. This data format includes clear definitions of demand priority, timeliness, resource allocation and other information, and facilitates subsequent calculations and analysis. The generated structured data on construction project demand can not only clearly display the relationship between various demands in the project, but also provide accurate data information for subsequent scheduling optimization, risk analysis and other links.

[0022] Step S3: Analyze the demand and resource matching relationship of the construction project according to the structured data of the construction project demand and the construction project resource data, and generate the demand-resource matching relationship data of the construction project; analyze the priority adjustment of the cross-stage resource demand scheduling according to the structured data of the construction project demand, and generate the cross-stage resource demand scheduling priority adjustment data; analyze and process the optimized construction project resource scheduling constraints based on the cross-stage resource demand scheduling priority adjustment data and the construction project demand-resource matching relationship data, and generate the optimized construction project resource scheduling constraint data; In the embodiment of the present invention, the matching relationship analysis between demand and resource is performed according to the structured data of construction project demand and the resource data of construction project, and the detailed information of all demand items, including time nodes, demand quantity, type, etc., and the corresponding construction phase, task and progress requirements, are extracted from the structured data of construction project demand. The resource information extracted from the construction project resource data is obtained, such as resource types (materials, equipment, manpower, etc.), resource quantity, available time and allocation method, etc. Then, the system uses linear programming or optimization matching algorithm to perform matching analysis between demand and resource according to the factors such as type, quantity, time arrangement and so on of demand and resource, and finds the best matching scheme between demand item and resource item. The system calculates the resource matching degree of each demand by constructing a mathematical model between demand and resource, thereby generating construction project demand-resource matching relationship data. The demand-resource scheduling relationship matrix is ​​established through the construction project demand-resource matching relationship data. Based on the demand-resource matching relationship data, the system establishes a matrix, wherein the rows of the matrix represent each demand item, the columns represent various resource items, and the value of each matrix element represents the matching degree between a certain resource item and a certain demand item. For the matching relationship between each demand item and resource item, the system performs weighted processing based on factors such as demand priority, resource availability, and task urgency to obtain the scheduling relationship weight between each pair of demand and resource. Next, the system identifies the constraints in the resource scheduling process, such as the maximum available amount of resources, the minimum demand for tasks, and the urgency of time, based on the demand-resource scheduling relationship matrix and through resource scheduling constraint analysis. The system applies the constraint optimization algorithm to perform constraint analysis on resource scheduling and generates construction project resource scheduling constraint data, which provides the necessary constraints for subsequent scheduling optimization. According to the structured data of construction project demand, cross-stage resource demand scheduling analysis is performed to identify the resource demand situation of each construction stage, including materials, equipment, labor and other resources required for each stage, and associate it with the stage schedule, construction progress and other information. Analyze the intersection of resource demand between different stages, identify the cross-stage resource scheduling demand, predict the allocation of cross-stage resource demand through scheduling analysis, and generate cross-stage resource demand scheduling data, record the correlation, conflict points and resource scheduling scheme of resource demand between stages, and provide decision support for subsequent resource scheduling optimization. Based on the cross-stage resource demand scheduling data, the resource scheduling priority adjustment analysis is carried out. From the cross-stage resource demand scheduling data, each resource demand item is assigned a priority based on the priority of each stage, the urgency of the task, and the scarcity of resources. For example, in a certain construction stage, if the supply of a certain type of resource is very tight, the system will assign a higher priority to the resource demand item to ensure its timely scheduling.In the priority adjustment analysis, the resource requirements of each stage are prioritized according to the overall goals and schedule of the project through a weighted algorithm to generate cross-stage resource demand scheduling priority adjustment data. The construction project resource scheduling constraint data is optimized through the cross-stage resource demand scheduling priority adjustment data to obtain the cross-stage resource demand scheduling priority adjustment data and the construction project resource scheduling constraint data. Through the scheduling optimization algorithm, the priority adjustment information of the cross-stage resource requirements is combined with the resource scheduling constraints to dynamically optimize the resource scheduling plan. Through iterative optimization technology, the allocation of each resource is recalculated, and the order and allocation ratio of resource scheduling are adjusted to generate optimized construction project resource scheduling constraint data.

[0023] Step S4: Based on the optimized construction project resource scheduling constraint data and the architectural design simulation modeling data, disturbance simulation processing of construction project construction event simulation is performed to generate disturbance construction event simulation data; by optimizing the construction project resource scheduling constraint data, the disturbance construction event simulation data is dynamically optimized and adjusted to generate optimized and adjusted construction event simulation data; based on the optimized and adjusted construction event simulation data, construction event risk feature analysis is performed to generate construction event risk feature data; the optimized and adjusted construction event simulation data and the construction event risk feature data are transmitted to the terminal to execute the construction project life cycle management feedback processing.

[0024] In the embodiment of the present invention, the optimized construction project resource scheduling constraint data is mapped to the architectural design simulation modeling data to perform construction project construction event simulation processing. According to the optimized construction project resource scheduling constraint data, the resource scheduling constraint of each stage is obtained, including the availability of each resource, demand priority and other information. These scheduling constraint data are combined with the architectural design simulation modeling data to ensure that in the construction simulation process, the various parameters in the architectural design model meet the actual resource configuration restrictions, and the construction project construction event simulation data is generated. The data describes in detail the construction process simulation under the resource scheduling constraint conditions, including the time of resource use, the change of resource allocation, etc. The Monte Carlo simulation technology and the preset construction simulation disturbance parameters are used to perform random disturbance construction event simulation processing on the construction project construction event simulation data, and the construction tasks and resources that need to be disturbed are selected from the construction project construction event simulation data. The system uses the Monte Carlo simulation method to introduce disturbance parameters in the construction process, which are data sets of digital disturbance parameters designed with reference to actual construction application scenarios, to perform random disturbance simulation on construction events. The disturbance in each simulation process will follow a certain probability distribution, simulating the possibility of construction under different scenarios. Through multiple simulations, disturbance construction event simulation data is generated, including random disturbances in the construction process and changes in construction progress after the disturbance. According to the disturbance construction event simulation data, the risk factor analysis of the construction event simulation is carried out, and the generated disturbance construction event simulation data is analyzed to identify potential risk factors in the construction process. These risk factors include the impact of resource shortages, equipment failures and other factors on the construction progress. Through the analysis of multiple disturbance scenarios, the system evaluates the impact of each risk factor on the project progress, cost and quality, and assigns a risk value to each risk factor to indicate its potential impact on the construction event, and generates construction event simulation risk factor data. By optimizing the resource scheduling constraint data of the construction project, the disturbed construction event simulation data is dynamically optimized and adjusted for the construction event simulation. According to the optimized resource scheduling constraint data of the construction project, the disturbed construction event simulation data is adjusted to obtain the resource requirements and disturbance conditions of each construction task. According to the optimized resource scheduling constraint data of the construction project, the disturbed construction event simulation data is adjusted. By applying the scheduling optimization algorithm, it is ensured that under the disturbance conditions, the project progress can still be kept within an acceptable range. Through these optimization adjustments, the system generates optimized and adjusted construction event simulation data, which includes the construction progress and resource usage after resource reallocation. According to the optimized and adjusted construction event simulation data, the risk factors of the construction event simulation are dynamically updated, the optimized and adjusted construction event simulation data are obtained, and the risk factors therein are dynamically and intelligently updated.According to the optimized simulation data, the system identifies new risk factors or changed risk factors that appear after optimization, adjusts the weight of each risk factor in real time through the risk factor update model, and re-evaluates its impact on the construction progress, cost, etc., generates updated construction event simulation risk factor data, and reflects the latest risk assessment results. According to the updated construction event simulation risk factors, the risk characteristics of the construction event are analyzed to obtain the updated construction event simulation risk factor data, which contains the current state and predicted state of each risk factor. On this basis, the system extracts the main characteristics of the updated risk factors through risk analysis models, such as principal component analysis, so as to perform characteristic analysis on each risk factor and identify the risk factors that have the most significant impact on the construction process. The system uses a weighted analysis method to describe the risk characteristics in detail according to the weight and influence of the risk factors, and generates construction event risk characteristic data. This data provides a comprehensive risk analysis for project managers and helps decision makers take targeted risk response measures. The optimized construction event simulation data and risk characteristic data are transmitted to the project management terminal through the data interface. According to the received optimization data and risk data, the terminal updates the project management information in real time and adjusts the project execution plan. For example, the terminal will adjust the construction schedule, reallocate resources, modify the project budget, or adjust the risk response strategy based on the new construction progress and risk data. Ultimately, through this data transmission and feedback processing process, the system ensures the timeliness and accuracy of project management decisions, effectively responds to various risks in the project, and ensures that the project proceeds efficiently as planned.

[0025] Further, step S1 includes the following steps: Step S11: collecting construction project configuration data through the construction project information system interface to generate construction project configuration data; Step S12: Performing architectural design simulation modeling processing on the architectural design parameters through building information modeling technology to generate architectural design simulation modeling data; Step S13: performing demand analysis processing on the construction project demand data to generate construction project demand analysis data; Step S14: Mapping the construction project demand analysis data to the construction project demand process through the architectural design simulation modeling data to generate construction project demand process data.

[0026] In the embodiment of the present invention, various types of construction project configuration data are collected through the construction project information system interface. Specifically, first, the system interacts with the on-site construction management platform, resource scheduling system and design platform to automatically obtain information such as demand data, resource data and architectural design parameters of the construction project. These data include equipment requirements, material usage plans, construction personnel arrangements, schedules and technical parameters of architectural design drawings. The collection process uses a standardized data format to transmit through the data interface to ensure real-time update and accuracy of the data. All collected data will be backed up in the data storage system to provide data support for subsequent analysis and decision-making. The core technical feature of this step is to perform automated data collection through the information system interface to avoid manual intervention and improve the efficiency and accuracy of data collection. The collected architectural design parameters are simulated and modeled using building information modeling (BIM) technology, and architectural design data such as design drawings, structural layout, and facility equipment are input into the BIM platform, and the three-dimensional space of the building is modeled using BIM software. During the modeling process, the system automatically verifies and optimizes various design parameters to ensure the feasibility of the design and compliance with building specifications. The modeling process not only includes the modeling of the external form of the building, but also covers the modeling of facilities such as the internal structure, piping system, and air conditioning system. During the simulation process, the system will automatically make design changes and optimization calculations according to the design requirements, and output the building design simulation data, including but not limited to the structural performance, functional zoning, material requirements and other information of the building. The key to this step is to use BIM technology to convert the design data into a three-dimensional model that can be simulated and analyzed, providing an accurate basis for the subsequent demand process analysis. The construction project demand data is processed for demand analysis, and the construction project demand data collected from the system interface is extracted, including key factors such as project scale, resource requirements, and time node requirements. Then, through the demand analysis model, such as the BERT model based on natural language processing, the demand analysis is performed to refine these data into demand descriptions for each specific link. For example, the material requirements for the construction project can be further refined into the specific material types, quantities, and usage time of different construction stages; the demand for human resources can be refined into the types, quantities, and working hours of personnel required for each construction stage. The demand analysis process uses an algorithm model to classify and calculate different types of data to ensure the structuring of demand data, facilitate subsequent matching and optimization, ensure the comprehensive deconstruction of the original demand data, and provide an accurate demand basis for resource scheduling and construction progress. Use architectural design simulation modeling data to perform demand process mapping on construction project demand analysis data, match construction project demand analysis data with architectural design simulation modeling data, confirm the correspondence between demand data and various parameters in the design model, and ensure consistency between demand and design.Then, based on the design simulation model, the demand data is mapped to link specific building requirements (such as materials, personnel, equipment, etc.) with the design models of each stage in the construction process. For example, if a special material is required at a certain stage in the building design, the system will map the demand and usage period of the material to the construction schedule based on the demand analysis data to generate the corresponding demand process data. After the mapping is completed, the system will output the construction project demand process data for subsequent data structured analysis and resource scheduling optimization. This step ensures the close coordination between design and demand through accurate mapping of the demand process, providing guarantee for the execution of the entire project.

[0027] Step S21: performing construction project demand process dependency analysis according to the construction project demand process data to generate construction project demand dependency data; Step S22: Based on the construction project demand dependency data, the construction project demand process data is subjected to construction project demand structured mapping processing to generate construction project demand structured data.

[0028] In the embodiment of the present invention, the demand process dependency analysis is performed based on the construction project demand process data, and the data of each demand node is extracted from the construction project demand process data, including information such as material demand, resource demand, and equipment demand. Then, the system uses a graph theory-based algorithm to perform dependency analysis on these demands, and identifies the order of demands and their interdependent relationships. For example, if the demand for a certain building material is related to a specific construction stage, the demand data forms a dependency relationship with the task data of the construction stage; similarly, the scheduling demand of construction personnel affects each other with equipment demand and material supply. During the dependency analysis process, the system automatically identifies and constructs a dependency network of the demand process, and maps out the mutual influence relationship between each demand and other demand nodes. After the analysis is completed, the system generates construction project demand dependency data, identifies the dependency order and hierarchical structure between each demand node, forms a demand process dependency network, and supports subsequent data structured processing and resource scheduling optimization. Based on the construction project demand dependency data, the construction project demand process data is subjected to data structured mapping processing, and the hierarchical relationship and dependency sequence between each demand are obtained from the construction project demand dependency data. For example, resource requirements for some construction phases must be started after the previous phase is completed, while some resources are carried out in parallel. The system converts these dependencies into a multidimensional data structure, which includes dimensions such as demand type, resource allocation, and time nodes. Next, the system structures the construction project demand process data based on these dependency data, maps it into a data format that meets the project management standards, and forms the construction project demand structured data. This data format includes a clear definition of information such as the priority, timeliness, and resource allocation of the requirements, and facilitates subsequent calculations and analysis. For example, the system matches the resource requirements and time nodes of a certain stage to ensure that resources can be allocated to the correct construction tasks at the right time. Finally, the generated construction project demand structured data can not only clearly show the relationship between the various requirements in the project, but also provide accurate data information for subsequent scheduling optimization, risk analysis and other links.

[0029] Further, step S21 includes the following steps: Step S211: performing demand process time series analysis according to the construction project demand process data to generate construction project demand process time series data; Step S212: performing demand process type analysis on the construction project demand process data to generate construction project demand process type data; Step S213: performing demand process stage division processing on the construction project demand process data to generate construction project demand process stage data; Step S214: Based on the construction project demand process type data and the construction project demand process stage data, the construction project demand process dependency analysis is performed on the construction project demand process time series data to generate construction project demand dependency data.

[0030] In the embodiment of the present invention, the demand process time series analysis is performed according to the construction project demand process data, and all demand information related to the project progress, including construction materials, labor, equipment, etc., is extracted from the construction project demand process data, and arranged in chronological order. Based on the time series analysis method of project management, the system uses the timestamp and the time node of the demand to mark each demand data in time series. For example, the system will mark that the demand time of a certain building material is the first week of construction, and the labor demand is phased, such as analyzing the demand of each stage of the construction project. According to the time nodes of these demands, the system generates the construction project demand process time series data by establishing a time series model, and sorts and optimizes it for subsequent resource allocation and scheduling optimization. The construction project demand process data performs demand process type analysis. First, according to the content in the construction project demand process data, the types of various demands are identified, such as equipment demand, material demand, labor demand, etc. Each type of demand is classified and identified, for example, all demands involving building materials are classified into the "material demand" category, and labor demand is classified into the "human resource demand" category. The demand process type analysis determines the nature and characteristics of each demand by identifying the essence of the demand item. The system classifies the construction project demand process according to the demand type data, providing a clear classification basis for subsequent dependency analysis and data structured mapping. The generated construction project demand process type data clearly identifies different types of demand information with their corresponding construction tasks and resource allocation, which is helpful for further processing and optimization of the demand. The construction project demand process data is divided into demand process stages. According to the characteristics of the construction project demand data, the system divides the demand process of the entire project into multiple stages. First, the system divides the project into multiple construction stages according to the construction plan, project schedule and the task content of each stage. For example, the system divides the project into preparation stage, construction stage, decoration stage, etc. Then, according to the specific tasks of each stage, the system divides the demand into corresponding stages, and matches the materials, equipment, manpower and other requirements required for each stage with the corresponding construction stage. For example, a large amount of support frame materials are required in one stage, while the configuration of electrical installation equipment is required in another stage. According to this stage division, the system will generate construction project demand process stage data, which specifically lists the resource requirements, time nodes and resource allocation information corresponding to each stage. Based on the construction project demand process type data and construction project demand process stage data, the demand process dependency analysis is performed on the construction project demand process time series data. First, the system combines the demand process type data with the demand process stage data to form a full picture of the demand process, and analyzes the dependency relationship between the demands at each stage according to the time series arrangement of the demands.The system analyzes the order of demands and identifies direct or indirect dependencies between demand types and demand stages. For example, the demand for material supply must be completed before the demand for equipment, or the human resource demand at a certain construction stage is closely related to the time when materials arrive. By applying graph theory algorithms or logistic regression models, the system analyzes the dependencies between demands and generates construction project demand dependency data, which accurately shows the relationship structure between different demands and provides reliable data support for subsequent resource scheduling, priority management and other links.

[0031] Further, as an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S3 in the embodiment, step S3 includes the following steps: Step S31: Analyze the construction project demand and resource matching relationship based on the construction project demand structured data and the construction project resource data to generate construction project demand-resource matching relationship data; In the embodiment of the present invention, the matching relationship analysis between demand and resources is performed based on the construction project demand structured data and the construction project resource data. First, the system extracts detailed information of all demand items from the construction project demand structured data, including time nodes, demand quantities, types, etc., and their corresponding construction stages, tasks and progress requirements. At the same time, the system obtains resource information extracted from the construction project resource data, such as resource types (materials, equipment, manpower, etc.), resource quantities, available time and allocation methods, etc. Then, the system uses linear programming or optimization matching algorithms to perform matching analysis between demand and resources based on factors such as the type, quantity, and time arrangement of demand and resources, and finds the best matching solution between demand items and resource items. The system calculates the resource matching degree of each demand by constructing a mathematical model between demand and resources, thereby generating construction project demand-resource matching relationship data. The data includes the matching relationship between demand items and resource items, such as the matching of material A and equipment B, the correspondence between labor demand and construction progress, etc., to provide a basis for subsequent resource scheduling.

[0032] Step S32: Establish a construction project demand-resource scheduling relationship matrix through the construction project demand-resource matching relationship data, perform construction project resource scheduling constraint analysis based on the construction project demand-resource scheduling relationship matrix, and generate construction project resource scheduling constraint data; In an embodiment of the present invention, a demand-resource scheduling relationship matrix is ​​established through construction project demand-resource matching relationship data. First, based on the demand-resource matching relationship data, the system establishes a matrix, in which the rows of the matrix represent various demand items, the columns represent various resource items, and the value of each matrix element represents the degree of matching between a certain resource item and a certain demand item. For the matching relationship between each demand item and resource item, the system performs weighted processing according to factors such as the priority of the demand, the availability of resources, the urgency of the task, etc., to obtain the scheduling relationship weight between each pair of demand and resource. Next, the system identifies the constraints in the resource scheduling process, such as the maximum available amount of resources, the minimum demand for tasks, the urgency of time, etc., based on the demand-resource scheduling relationship matrix through resource scheduling constraint analysis. The system applies a constraint optimization algorithm to perform constraint analysis on resource scheduling and generates construction project resource scheduling constraint data, which provides necessary constraints for subsequent scheduling optimization.

[0033] Step S33: Perform cross-stage resource demand scheduling analysis based on the construction project demand structured data to generate cross-stage resource demand scheduling data; In an embodiment of the present invention, a cross-stage resource demand scheduling analysis is performed based on the structured data of construction project demand, and the resource demand situation of each construction stage is identified, including the materials, equipment, labor and other resources required for each stage, and it is associated with the stage schedule, construction progress and other information. The intersection of resource demand between different stages is analyzed to identify the cross-stage resource scheduling requirements. For example, if a large amount of equipment is required in a certain stage, and the same equipment is also required in another stage, the system will consider the conflicting demand for the equipment in the two stages. Through scheduling analysis, the system predicts the allocation of cross-stage resource demand, generates cross-stage resource demand scheduling data, records the correlation, conflict points and resource scheduling schemes between the resource demands of each stage, and provides decision support for subsequent resource scheduling optimization.

[0034] Step S34: performing priority adjustment analysis of cross-stage resource demand scheduling based on the cross-stage resource demand scheduling data to generate cross-stage resource demand scheduling priority adjustment data; In an embodiment of the present invention, a resource scheduling priority adjustment analysis is performed based on cross-stage resource demand scheduling data, and a priority is assigned to each resource demand item based on factors such as the priority of each stage, the urgency of the task, and the scarcity of resources. For example, in a certain construction stage, if the supply of a certain type of resource is very tight, the system will assign a higher priority to the resource demand item to ensure its timely scheduling. In the priority adjustment analysis, the resource requirements of each stage are prioritized according to the overall goals and schedule of the project through a weighted algorithm. Through this priority adjustment analysis, cross-stage resource demand scheduling priority adjustment data is generated. This data reflects how to adjust the scheduling priority of resources according to the importance of resource requirements between different stages to ensure the rationality and efficiency of resource allocation.

[0035] Step S35: Optimizing the construction project resource scheduling constraint data by adjusting the priority of resource demand scheduling across stages to generate optimized construction project resource scheduling constraint data.

[0036] In an embodiment of the present invention, the resource scheduling constraint data of the construction project is optimized by adjusting the priority of the resource demand scheduling across stages, and the priority of the resource demand scheduling across stages and the resource scheduling constraint data of the construction project are obtained. The priority adjustment information of the resource demand across stages is combined with the constraint of the resource scheduling through the scheduling optimization algorithm, and the resource scheduling scheme is dynamically optimized. Through the iterative optimization technology, the allocation of each resource is recalculated, and the order and allocation ratio of the resource scheduling are adjusted. For example, in some stages, the demand for resources is adjusted to a higher priority, thereby affecting the resource allocation in other stages. Based on these optimization adjustments, the system generates optimized construction project resource scheduling constraint data to ensure that the allocation of resources is more reasonable, the project can proceed smoothly as planned, and minimize resource conflicts, delays and other problems.

[0037] Further, step S34 includes the following steps: Step S341: performing cross-stage resource demand conflict analysis according to the cross-stage resource demand scheduling data to generate cross-stage resource demand conflict data; Step S342: performing priority adjustment analysis of resource demand scheduling at each stage on the cross-stage resource demand scheduling data based on the preset resource allocation priority decision, and generating priority adjustment data of resource demand scheduling at each stage; Step S343: performing cross-stage resource demand weight analysis according to the cross-stage resource demand conflict data to generate cross-stage resource demand weight data; Step S344: performing priority adjustment analysis of inter-stage conflicting resource demands according to the inter-stage resource demand weight data, and generating inter-stage conflicting resource demand priority adjustment data; Step S345: performing priority adjustment analysis of cross-stage resource demand scheduling based on each stage resource demand scheduling priority adjustment data and cross-stage conflicting resource demand scheduling priority adjustment data to generate cross-stage resource demand scheduling priority adjustment data.

[0038] In an embodiment of the present invention, a cross-stage resource demand conflict analysis is performed based on the cross-stage resource demand scheduling data, which includes the resources required for each construction stage and the resource scheduling time for each stage. The resource requirements of each stage are compared to check whether there is a time overlap or excessive demand for the same resource in different stages. For example, in construction stage A and construction stage B, the same type of equipment is also required for construction, and the system will identify the resource conflict between the two stages. The system performs conflict analysis based on factors such as resource demand time, demand quantity, and resource availability. By using a conflict detection algorithm, the system generates cross-stage resource demand conflict data, which records the resource conflict between different stages, such as dual demand for equipment, repeated allocation of materials, etc., and indicates the severity of the conflict, providing a basis for subsequent resource optimization scheduling. Based on the preset resource allocation priority decision, the system performs priority adjustment analysis of resource demand scheduling in each stage on the cross-stage resource demand scheduling data, and sorts the resource requirements of each stage according to the preset priority rules, such as setting the priority of each stage based on factors such as the urgency of the project schedule, the availability of resources, and the overall goals of the project. During the priority adjustment process, the system places the urgent demand for resources at a high priority, ensuring that these resources can be allocated first and postpone the resource demand of the low-priority stage. For example, if a construction task at a certain stage requires higher priority resource support due to time pressure, while other stages can postpone the use of this resource, the system will adjust the order of resource allocation. Through priority adjustment, the priority adjustment data of resource demand scheduling for each stage is generated, which reflects the scheduling priority of resource demand at each stage to guide subsequent resource allocation and scheduling decisions. The cross-stage resource demand weight analysis is performed based on the cross-stage resource demand conflict data, and the cross-stage resource demand conflict data is analyzed to evaluate the impact of each conflicting resource. For example, if a resource demand conflict causes a delay in the critical path, then the conflict will have a higher weight; conversely, if the resource conflict only affects the non-critical path, then its weight is lower. The system assigns different weight values ​​to each conflict based on the impact, urgency, and project schedule requirements of each resource demand conflict. This process is completed through a weighted algorithm, which considers factors such as resource type, demand, time window, etc. Finally, the cross-stage resource demand weight data is generated, which indicates the priority of each resource demand conflict and the corresponding resource allocation weight, providing data support for subsequent priority adjustment and resource optimization. The priority adjustment analysis of cross-stage conflicting resource demands is carried out based on the cross-stage resource demand weight data, which indicates the impact weight of each resource demand conflict. The system will sort the cross-stage resource demand conflicts through the priority adjustment algorithm to ensure that conflicts with higher weights can be resolved first.For example, for key resource demand conflicts that affect the overall progress of the project, the system will prioritize resolving these conflicts through resource scheduling, while other minor conflicts can be handled in subsequent adjustments. During the adjustment process, the system will also adjust the timing of resource allocation according to the actual progress requirements of the project and the availability of resources to ensure priority allocation of resources. This process generates cross-stage conflict resource demand priority adjustment data, which describes how to prioritize and adjust resource demand conflicts in each stage according to the impact weight of the conflict. Priority adjustment analysis of cross-stage resource demand scheduling is performed through the priority adjustment data of resource demand scheduling for each stage and the priority adjustment data of cross-stage conflict resource demand, and the priority adjustment data of resource demand scheduling for each stage is combined with the priority adjustment data of cross-stage conflict resource demand. By comprehensively considering the priorities of resource demands within and across stages, the system applies a comprehensive priority adjustment algorithm to reorder all resource demands. In the analysis, the system prioritizes high-priority resource demands, and adjusts them according to the dependencies between stages and the availability of resources to ensure that each resource can be reasonably allocated between different stages. For example, if the resource demand conflict in a certain stage is more serious and has a high priority, the system will give priority to adjusting the resource scheduling of other stages to resolve the conflict, ensure the smooth progress of the overall project, and generate adjusted cross-stage resource demand scheduling priority adjustment data to provide a basis for subsequent resource scheduling and optimization, ensuring the optimization of project resource allocation.

[0039] Further, as an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S4 in FIG. 1 , in this embodiment, step S4 includes the following steps: Step S41: Mapping the optimized construction project resource scheduling constraint data to the architectural design simulation modeling data to perform construction project construction event simulation processing to generate construction project construction event simulation data; In an embodiment of the present invention, the optimized construction project resource scheduling constraint data is mapped to the architectural design simulation modeling data to perform construction project construction event simulation processing. According to the optimized construction project resource scheduling constraint data, the resource scheduling constraint situation of each stage is obtained, including the availability of each resource, demand priority and other information, and these scheduling constraint data are combined with the architectural design simulation modeling data to ensure that in the construction simulation process, the various parameters in the architectural design model meet the actual resource configuration restrictions. For example, in the architectural design model, the operating time of a certain equipment is affected by resource scheduling. The system will adjust the equipment's usage time period so that it does not conflict with other construction tasks, ensuring that the allocation of each resource meets the scheduling constraints. Finally, the construction project construction event simulation data is generated, which describes in detail the construction process simulation under the resource scheduling constraint conditions, including the time of resource use, changes in resource allocation, etc.

[0040] Step S42: using Monte Carlo simulation technology and preset construction simulation disturbance parameters to perform random disturbance construction event simulation processing on the construction event simulation data of the construction project to generate disturbance construction event simulation data; In the embodiment of the present invention, the Monte Carlo simulation technology and the preset construction simulation disturbance parameters are used to perform random disturbance simulation processing on the construction event simulation data of the construction project, and the construction tasks and resources that need to be disturbed are selected from the construction event simulation data of the construction project. The system uses the Monte Carlo simulation method to introduce disturbance parameters (such as construction delays, resource unavailability, weather changes, etc.) in the construction process. The parameters are data sets of digital disturbance parameters designed with reference to actual construction application scenarios, and the construction events are randomly disturbed. Simulation. The disturbance in each simulation process will follow a certain probability distribution to simulate the possibility of construction in different scenarios. For example, if equipment failure or insufficient supply of raw materials occurs during the construction process, the impact of these events on the construction progress is simulated by setting relevant disturbance parameters. Through multiple simulations, disturbance construction event simulation data is generated, which includes random disturbance conditions in the construction process and changes in the construction progress after the disturbance.

[0041] Step S43: performing risk factor analysis of construction event simulation according to the disturbance construction event simulation data to generate a construction event simulation risk factor; In an embodiment of the present invention, risk factor analysis of construction event simulation is performed based on disturbance construction event simulation data, and the generated disturbance construction event simulation data is analyzed to identify potential risk factors in the construction process. These risk factors include the impact of factors such as resource shortages and equipment failures on the construction progress. By analyzing multiple disturbance scenarios, the system evaluates the degree of impact of each risk factor on project progress, cost, and quality. For example, it is found that in some scenarios, equipment failures will cause significant delays in the construction progress, while material shortages have less impact. Based on this analysis, the system assigns a risk value to each risk factor, indicating its potential impact on the construction event. Finally, construction event simulation risk factor data is generated to provide data support for further risk management.

[0042] Step S44: performing dynamic optimization and adjustment processing of construction event simulation on the disturbed construction event simulation data by optimizing the construction project resource scheduling constraint data, and generating optimized and adjusted construction event simulation data; In an embodiment of the present invention, the disturbance construction event simulation data is dynamically optimized and adjusted by optimizing the construction project resource scheduling constraint data to perform construction event simulation simulation. The disturbance construction event simulation data is simulated according to the optimized construction project resource scheduling constraint data to obtain the resource requirements and disturbance conditions of each construction task. Then, the system adjusts the disturbance construction event simulation data according to the optimized construction project resource scheduling constraint data. For example, in the simulation, if some resources are unavailable due to disturbance, the system will dynamically adjust the use time of these resources or deploy other resources to reduce the impact on the construction progress. This process ensures that the project progress can still be kept within an acceptable range under disturbance conditions by applying a scheduling optimization algorithm. Through these optimization adjustments, the system generates optimized and adjusted construction event simulation data, which includes the construction progress and resource usage after resource reallocation.

[0043] Step S45: dynamically updating the construction event simulation risk factor according to the optimized and adjusted construction event simulation data to generate an updated construction event simulation risk factor; In an embodiment of the present invention, the risk factors of construction event simulation are dynamically updated according to the optimized and adjusted construction event simulation data, the optimized and adjusted construction event simulation data are obtained, and the risk factors therein are dynamically and intelligently updated. Based on the optimized and adjusted simulation data, the system identifies new risk factors or changed risk factors that appear after the optimization. For example, if the impact of equipment failure on the construction progress is successfully reduced through resource adjustment, the system will reduce the risk value of equipment failure accordingly. For other risk factors that still have a greater impact, their risk values ​​will be increased. The system uses the risk factor update model to adjust the weight of each risk factor in real time, and re-evaluate its impact on the construction progress, cost, etc., to generate and update the construction event simulation risk factor data to reflect the latest risk assessment results.

[0044] Step S46: performing risk characteristic analysis of construction events according to the updated construction event simulation risk factors to generate risk characteristic data of construction events; In an embodiment of the present invention, a construction event risk feature analysis is performed based on the updated construction event simulation risk factor, and updated construction event simulation risk factor data is obtained, which includes the impact of the current state and predicted state of each risk factor. On this basis, the system extracts the main features of the updated risk factors through a risk analysis model, such as principal component analysis, so as to perform feature analysis on each risk factor and identify the risk factors that have the most significant impact on the construction process. The system uses a weighted analysis method to describe the risk features in detail based on the weight and influence of the risk factors. For example, if the weight of the risk factor for construction delay is greater than other factors, the system will mark it as a high-risk feature, prompting project managers to pay more attention to this aspect. In this way, the system generates construction event risk feature data, which provides project managers with a comprehensive risk analysis and helps decision makers take targeted risk response measures.

[0045] Step S47: Transmitting the optimized and adjusted construction event simulation data and the construction event risk characteristic data to the terminal to execute the construction project life cycle management feedback processing.

[0046] In an embodiment of the present invention, the construction event simulation data and risk characteristic data are optimized and adjusted and transmitted to the project management terminal through the data interface. The terminal updates the project management information in real time and adjusts the project execution plan based on the received optimization data and risk data. For example, the terminal will adjust the construction schedule, reallocate resources, modify the project budget, or adjust the risk response strategy based on the new construction progress and risk data. Ultimately, through this data transmission and feedback processing process, the system ensures the timeliness and accuracy of project management decisions, effectively responds to various risks that arise in the project, and ensures that the project is carried out efficiently as planned.

[0047] Further, step S43 includes the following steps: Step S431: Establishing an event tree model for construction risk assessment based on construction project construction event simulation data to generate a construction risk assessment event tree model; Step S432: transmitting the disturbance construction event simulation data to the construction risk assessment event tree model to extract the risk factors of the construction event simulation and generate the construction event simulation risk factors.

[0048] In the embodiment of the present invention, an event tree model for construction risk assessment is established based on construction project construction event simulation data. From obtaining disturbance construction event simulation data, it includes the numerical simulation status of various disturbance parameters in the construction process, including equipment failure, weather changes, material shortages and other potential risk factors. The system uses the event tree analysis method to establish a risk assessment model. The event tree model is unfolded layer by layer according to the order and impact of events. Each layer represents an event or state, and the logical relationship between events reflects the dependency of event occurrence. For example, the system analyzes whether equipment failure causes construction delays, thereby affecting the overall progress of the project. If equipment failure occurs, the model will further branch to explore the repair time and recovery progress after the failure, and calculate the distribution probability of risk events through the Bayesian algorithm to generate a construction risk assessment event tree model. Each node in the model represents a potential risk event, and the branch represents the distribution probability of various events (such as normal execution or the distribution probability of risk events). The disturbance construction event simulation data is transmitted to the construction risk assessment event tree model for risk factor extraction processing of construction event simulation, and the disturbance construction event simulation data is input into the established construction risk assessment event tree model. The data contains detailed information on various disturbances during the construction process, including disturbance factors such as equipment failure, material shortages, and construction progress delays. The system gradually extracts the risk factors of each event node based on the nodes and branches defined in the event tree model. These risk factors represent the probability of various events and their impact on the construction progress. For example, the system will calculate the probability of equipment failure, analyze the specific impact of equipment failure on the construction progress, and generate corresponding risk factors. Based on the structure of the event tree, the system can quantify the impact of each risk factor on the overall construction process, and finally generate construction event simulation risk factor data, which includes the possibility of each potential risk and its specific impact on the construction project, to help with subsequent risk management and the formulation of response strategies.

[0049] Further, step S431 includes the following steps: According to the construction event simulation data of the construction project, the logical path encapsulation processing of the construction event is carried out to generate the construction event logical path encapsulation data; the construction logical path event tree structure is designed through the construction event logical path encapsulation data; based on the preset construction event risk indicators, the construction logical path event tree structure is empowered with the construction risk assessment relationship of the event tree to generate a construction risk assessment event tree model.

[0050] In the embodiment of the present invention, the logical path encapsulation processing of the construction event is performed according to the construction project construction event simulation data, and the disturbance construction event simulation data is obtained, which describes the occurrence of various disturbance scenarios in the construction process and their impact on the construction progress and resource scheduling. According to these data, combined with the actual process of project construction, the system encapsulates the logical path of the construction event, maps each construction event with its related events, and connects each event with the subsequent events triggered by it by building an event flow network. In this process, the system encapsulates the impact path of the construction event according to the changes in the construction progress and resource status, and generates a set of encapsulation data containing all construction events and their associated paths. Each logical path represents a potential construction event process, each node in the path represents a construction event, and the connection between each node represents the causal relationship between the events. The construction logical path event tree structure is designed by encapsulating the construction event logical path data, the construction event logical path encapsulation data, and the system designs the event tree structure of the construction logical path. The system uses a decision tree to construct a tree structure based on the temporal relationship, mutual dependence and consequences of the construction events, wherein each event is a node of the tree, and the branches between the nodes represent the dependency relationship of the event occurrence. For example, if a construction event (such as equipment failure) occurs, the next level node in the tree structure describes the impact of the equipment failure (such as construction delays, additional resource consumption, etc.). The system continuously branches each construction event and its consequences according to the logical relationship, and calculates the probability of each branch through the Bayesian algorithm, and finally constructs a complete construction logic path event tree structure. This structure helps the system clearly display all potential events and their interdependencies in the construction process, forming a traceable risk event model. Based on the preset construction event risk indicators, the system enables the construction risk assessment relationship of the event tree for the construction logic path event tree structure. According to the designed construction logic path event tree structure, the system assigns the preset construction event risk indicators (such as the probability of accidents, the risk of resource consumption, the risk of project delay, etc.) to each event node in the tree structure. Specifically, the system assigns a risk value to each event node based on historical data, industry standards and project characteristics. For example, the probability of equipment failure is high, so the risk value of the node will be large, and the consequences caused by it will also be assigned a higher risk assessment value accordingly. In this way, the system can quantify the risk of each event node and add corresponding risk assessment relationships to each branch path in the event tree. The risk value of each path represents the overall risk of the path, helping project managers to make accurate response strategies when facing different construction risks. The construction risk assessment event tree model is generated through the construction logic path event tree structure. Based on the construction logic path event tree structure after empowerment processing, the system finally generates a construction risk assessment event tree model.This model not only includes all potential construction events, but also quantifies the risk assessment value of each event node, which is a comprehensive assessment of the weight of the construction risk event and the probability of the construction risk event. The total risk value on each path represents the overall risk of the construction plan corresponding to the path, which can provide a clear basis for construction risk assessment for project managers. This model helps the construction party to foresee the risks faced by the project, so as to prepare for the response in advance, optimize resource allocation, and reduce the probability of risk occurrence.

[0051] This specification provides a data analysis system in the life cycle management of a construction project, which is used to execute the data analysis method in the life cycle management of a construction project as described above. The data analysis system in the life cycle management of a construction project includes: The construction project configuration data management module is used to collect construction project configuration data through the construction project information system interface to generate construction project configuration data, wherein the construction project configuration data includes construction project demand data, construction project resource data and building design parameters; to perform building design simulation modeling processing on the building design parameters through the building information modeling technology to generate building design simulation modeling data; to perform construction project demand process analysis based on the building design simulation modeling data and the construction project demand data to generate construction project demand process data; A construction engineering demand structured processing module is used to perform a construction engineering demand structured analysis based on the construction engineering demand process data and generate construction engineering demand structured data; The construction project resource scheduling constraint analysis module is used to analyze the demand and resource matching relationship of the construction project based on the construction project demand structured data and the construction project resource data, and generate the construction project demand-resource matching relationship data; perform priority adjustment analysis of cross-stage resource demand scheduling based on the construction project demand structured data, and generate cross-stage resource demand scheduling priority adjustment data; optimize the construction project resource scheduling constraint analysis and processing based on the cross-stage resource demand scheduling priority adjustment data and the construction project demand-resource matching relationship data, and generate optimized construction project resource scheduling constraint data; The construction project life cycle management module is used to perform disturbance simulation processing of construction project construction event simulation based on optimized construction project resource scheduling constraint data and architectural design simulation modeling data, and generate disturbance construction event simulation data; dynamically optimize and adjust the disturbance construction event simulation data by optimizing the construction project resource scheduling constraint data, and generate optimized and adjusted construction event simulation data; perform construction event risk feature analysis based on the optimized and adjusted construction event simulation data, and generate construction event risk feature data; transmit the optimized and adjusted construction event simulation data and construction event risk feature data to the terminal to perform construction project life cycle management feedback processing.

[0052] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0053] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A data analysis method for life cycle management of construction projects, characterized in that: The following steps are involved: Step S1: collecting construction project configuration data through a construction project information system interface to generate construction project configuration data, wherein the construction project configuration data includes construction project demand data, construction project resource data and building design parameters; Perform architectural design simulation modeling processing on architectural design parameters through building information modeling technology to generate architectural design simulation modeling data; Conduct construction engineering demand process analysis based on architectural design simulation modeling data and construction engineering demand data to generate construction engineering demand process data; Step S2: Performing a structured analysis of construction project demand according to the construction project demand process data to generate structured data of construction project demand; Step S3: Analyze the demand and resource matching relationship of the construction project according to the structured data of the construction project demand and the construction project resource data, and generate the construction project demand-resource matching relationship data; analyze the priority adjustment of the cross-stage resource demand scheduling according to the structured data of the construction project demand, and generate the cross-stage resource demand scheduling priority adjustment data; Based on the cross-stage resource demand scheduling priority adjustment data and the construction project demand-resource matching relationship data, the construction project resource scheduling constraint analysis and processing are optimized to generate the construction project resource scheduling constraint data; Step S4: Based on the optimized construction project resource scheduling constraint data and the architectural design simulation modeling data, disturbance simulation processing of construction project construction event simulation is performed to generate disturbance construction event simulation data; by optimizing the construction project resource scheduling constraint data, the disturbance construction event simulation data is dynamically optimized and adjusted for construction event simulation to generate optimized and adjusted construction event simulation data; Conduct risk characteristic analysis of construction events based on optimized and adjusted construction event simulation data to generate risk characteristic data of construction events; The optimized and adjusted construction event simulation data and the construction event risk characteristic data are transmitted to the terminal to execute the construction project life cycle management feedback processing.

2. The data analysis method in the construction project life cycle management according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: collecting construction project configuration data through the construction project information system interface to generate construction project configuration data; Step S12: Performing architectural design simulation modeling processing on the architectural design parameters through building information modeling technology to generate architectural design simulation modeling data; Step S13: performing demand analysis processing on the construction project demand data to generate construction project demand analysis data; Step S14: Mapping the construction project demand analysis data to the construction project demand process through the architectural design simulation modeling data to generate construction project demand process data.

3. The data analysis method in the life cycle management of a construction project according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: performing construction project demand process dependency analysis according to the construction project demand process data to generate construction project demand dependency data; Step S22: Based on the construction project demand dependency data, the construction project demand process data is subjected to construction project demand structured mapping processing to generate construction project demand structured data.

4. The data analysis method in the life cycle management of a construction project according to claim 3 is characterized in that: Step S21 includes the following steps: Step S211: performing demand process time series analysis according to the construction project demand process data to generate construction project demand process time series data; Step S212: performing demand process type analysis on the construction project demand process data to generate construction project demand process type data; Step S213: performing demand process stage division processing on the construction project demand process data to generate construction project demand process stage data; Step S214: Based on the construction project demand process type data and the construction project demand process stage data, the construction project demand process dependency analysis is performed on the construction project demand process time series data to generate construction project demand dependency data.

5. The data analysis method in the construction project life cycle management according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: Analyze the construction project demand and resource matching relationship based on the construction project demand structured data and the construction project resource data to generate construction project demand-resource matching relationship data; Step S32: Establish a construction project demand-resource scheduling relationship matrix through the construction project demand-resource matching relationship data, perform construction project resource scheduling constraint analysis based on the construction project demand-resource scheduling relationship matrix, and generate construction project resource scheduling constraint data; Step S33: Perform cross-stage resource demand scheduling analysis based on the construction project demand structured data to generate cross-stage resource demand scheduling data; Step S34: performing priority adjustment analysis of cross-stage resource demand scheduling based on the cross-stage resource demand scheduling data to generate cross-stage resource demand scheduling priority adjustment data; Step S35: Optimizing the construction project resource scheduling constraint data by adjusting the priority of resource demand scheduling across stages to generate optimized construction project resource scheduling constraint data.

6. The data analysis method in the construction project life cycle management according to claim 5 is characterized in that: Step S34 includes the following steps: Step S341: performing cross-stage resource demand conflict analysis according to the cross-stage resource demand scheduling data to generate cross-stage resource demand conflict data; Step S342: performing priority adjustment analysis of resource demand scheduling at each stage on the cross-stage resource demand scheduling data based on the preset resource allocation priority decision, and generating priority adjustment data of resource demand scheduling at each stage; Step S343: performing cross-stage resource demand weight analysis according to the cross-stage resource demand conflict data to generate cross-stage resource demand weight data; Step S344: performing priority adjustment analysis of inter-stage conflicting resource demands according to the inter-stage resource demand weight data, and generating inter-stage conflicting resource demand priority adjustment data; Step S345: performing priority adjustment analysis of cross-stage resource demand scheduling based on each stage resource demand scheduling priority adjustment data and cross-stage conflicting resource demand scheduling priority adjustment data to generate cross-stage resource demand scheduling priority adjustment data.

7. The data analysis method in the construction project life cycle management according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: Mapping the optimized construction project resource scheduling constraint data to the architectural design simulation modeling data to perform construction project construction event simulation processing to generate construction project construction event simulation data; Step S42: using Monte Carlo simulation technology and preset construction simulation disturbance parameters to perform random disturbance construction event simulation processing on the construction event simulation data of the construction project to generate disturbance construction event simulation data; Step S43: performing risk factor analysis of construction event simulation according to the disturbance construction event simulation data to generate a construction event simulation risk factor; Step S44: performing dynamic optimization and adjustment processing of construction event simulation on the disturbed construction event simulation data by optimizing the construction project resource scheduling constraint data, and generating optimized and adjusted construction event simulation data; Step S45: dynamically updating the construction event simulation risk factor according to the optimized and adjusted construction event simulation data to generate an updated construction event simulation risk factor; Step S46: performing risk characteristic analysis of construction events according to the updated construction event simulation risk factors to generate risk characteristic data of construction events; Step S47: Transmitting the optimized and adjusted construction event simulation data and the construction event risk characteristic data to the terminal to execute the construction project life cycle management feedback processing.

8. The data analysis method in the construction project life cycle management according to claim 7 is characterized in that: Step S43 includes the following steps: Step S431: Establishing an event tree model for construction risk assessment based on construction project construction event simulation data to generate a construction risk assessment event tree model; Step S432: transmitting the disturbance construction event simulation data to the construction risk assessment event tree model to extract the risk factors of the construction event simulation and generate the construction event simulation risk factors.

9. The data analysis method in the construction project life cycle management according to claim 8 is characterized in that: Step S431 includes the following steps: According to the construction event simulation data of the construction project, the logical path encapsulation processing of the construction event is carried out to generate the construction event logical path encapsulation data; the construction logical path event tree structure is designed through the construction event logical path encapsulation data; based on the preset construction event risk indicators, the construction logical path event tree structure is empowered with the construction risk assessment relationship of the event tree to generate a construction risk assessment event tree model.

10. A data analysis system for construction project life cycle management, characterized in that: Used to execute the data analysis method in the life cycle management of a construction project as claimed in claim 1, the data analysis system in the life cycle management of a construction project comprises: The construction project configuration data management module is used to collect construction project configuration data through the construction project information system interface to generate construction project configuration data, wherein the construction project configuration data includes construction project demand data, construction project resource data and building design parameters; to perform building design simulation modeling processing on the building design parameters through the building information modeling technology to generate building design simulation modeling data; to perform construction project demand process analysis based on the building design simulation modeling data and the construction project demand data to generate construction project demand process data; A construction engineering demand structured processing module is used to perform a construction engineering demand structured analysis based on the construction engineering demand process data and generate construction engineering demand structured data; The construction project resource scheduling constraint analysis module is used to analyze the demand and resource matching relationship of the construction project based on the construction project demand structured data and the construction project resource data, and generate the construction project demand-resource matching relationship data; perform priority adjustment analysis of cross-stage resource demand scheduling based on the construction project demand structured data, and generate cross-stage resource demand scheduling priority adjustment data; optimize the construction project resource scheduling constraint analysis and processing based on the cross-stage resource demand scheduling priority adjustment data and the construction project demand-resource matching relationship data, and generate optimized construction project resource scheduling constraint data; The construction project life cycle management module is used to perform disturbance simulation processing of construction project construction event simulation based on optimized construction project resource scheduling constraint data and architectural design simulation modeling data, and generate disturbance construction event simulation data; dynamically optimize and adjust the disturbance construction event simulation data by optimizing the construction project resource scheduling constraint data, and generate optimized and adjusted construction event simulation data; perform construction event risk feature analysis based on the optimized and adjusted construction event simulation data, and generate construction event risk feature data; transmit the optimized and adjusted construction event simulation data and construction event risk feature data to the terminal to perform construction project life cycle management feedback processing.

Images