Hydraulic engineering construction management integrated system based on multi-source data analysis

Abstract

The invention relates to the technical field of water conservancy project construction management systems, in particular to a water conservancy project construction management integrated system based on multi-source data analysis, and the system comprises a data collection module which is used for obtaining multi-source data related to a project; the data processing module is used for preprocessing and integrating the multi-source data; the progress analysis module is used for performing project progress analysis based on the preprocessed and integrated data; the resource optimization module is used for carrying out resource configuration optimization based on a project progress analysis result; and the management decision module is used for generating a management decision suggestion based on a resource configuration optimization result, and the system can comprehensively and accurately reflect the actual situation of a project through comprehensive utilization of multi-source data, and provides a solid data basis for management decision. The comprehensive data support enables managers to be able to operate, and decision errors caused by insufficient information in traditional management are effectively avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy project construction management systems, and more specifically, to a comprehensive water conservancy project construction management system based on multi-source data analysis. Background Art

[0002] Water conservancy projects are an important part of national infrastructure construction and are of great significance for flood prevention and disaster reduction, water resource utilization, and ecological environment protection. With the rapid development of my country's water conservancy industry, the number of water conservancy project construction projects has increased, the scale has continued to expand, and the construction process has become more and more complicated. This has put forward higher requirements for the construction and management of water conservancy projects.

[0003] Traditional water conservancy project construction management methods mainly rely on manual experience and simple information technology. This management method often seems to be inadequate when facing large and complex water conservancy projects. First, data collection is often scattered and incomplete, and it is difficult to fully reflect the actual situation of the project. Secondly, data processing and analysis mostly remain at the statistical level, lacking in-depth mining and prediction capabilities. Furthermore, resource allocation often relies on experience judgment, which makes it difficult to achieve precise optimization. Finally, the decision support system is relatively simple and cannot provide managers with comprehensive and scientific decision-making suggestions.

[0004] In recent years, with the development of information technology, some advanced management systems have begun to be applied to the management of water conservancy project construction. These systems have improved management efficiency to a certain extent, but there are still some obvious shortcomings. For example, most systems are still independently operated information islands, making it difficult to fully share and comprehensively utilize data. In addition, although these systems can perform some simple data analysis, they lack the ability to deeply understand and accurately predict the progress of complex projects. In terms of resource optimization, existing systems often use traditional methods such as linear programming, which makes it difficult to handle the nonlinear and dynamically changing resource requirements commonly found in water conservancy projects. In addition, the decision support function of existing systems is relatively weak, making it difficult to provide managers with comprehensive and reliable decision-making recommendations.

[0005] In view of the above problems, there is an urgent need for a comprehensive water conservancy project construction management system that can fully integrate multi-source data, deeply analyze project progress, accurately optimize resource allocation, and provide intelligent decision support. The present invention is proposed in response to this demand. Summary of the invention

[0006] The present invention provides a comprehensive water conservancy project construction management system based on multi-source data analysis, which effectively solves the above technical problems through innovative system architecture and advanced algorithms. The system realizes intelligent management of the entire process from data collection, processing, analysis to decision support, significantly improving the efficiency and quality of water conservancy project construction management.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0008] A comprehensive water conservancy project construction management system based on multi-source data analysis, including:

[0009] Data acquisition module, used to obtain multi-source data related to the project;

[0010] A data processing module, used for preprocessing and integrating the multi-source data;

[0011] A progress analysis module, used for performing engineering progress analysis based on the preprocessed and integrated data;

[0012] A resource optimization module, used for optimizing resource allocation based on the results of the project progress analysis;

[0013] as well as

[0014] A management decision module is used to generate management decision suggestions based on the result of the resource configuration optimization.

[0015] Preferably, the progress analysis module comprises:

[0016] A progress data matrix construction unit is used to construct a project progress data matrix;

[0017] A topological progress network construction unit, used to construct a topological progress network based on the engineering progress data matrix;

[0018] A spectral clustering unit, used for performing spectral clustering analysis on the topological progress network;

[0019] as well as

[0020] A trend prediction unit is used to predict the progress trend based on the result of the spectral clustering analysis.

[0021] Preferably, the progress data matrix construction unit constructs the project progress data matrix by the following steps:

[0022] Obtain the planned completion time, actual completion percentage and weight information of the project nodes;

[0023] Construct the progress data matrix M, where:

[0024]

[0025] Among them, t i1 is the planned completion time of the i-th engineering node, p i1 is the actual completion percentage of the i-th project node, w i1 is the weight of the i-th engineering node; the progress deviation vector D is calculated based on the progress data matrix M:

[0026]

[0027] Among them, t current is the current time.

[0028] Preferably, the topology progress network construction unit constructs the topology progress network by the following steps:

[0029] Construct an adjacency matrix A based on the progress deviation vector D:

[0030]

[0031] in, and They represent the i-th and j-th elements of the progress deviation vector respectively.

[0032] Preferably, the spectral clustering unit performs spectral clustering analysis by the following steps:

[0033] Based on the adjacency matrix A, the Laplacian matrix L is calculated: L=DA,

[0034] Where D is the degree matrix, and D ii =∑ j A ij ;

[0035] Calculate the eigenvalues ​​and eigenvectors of the Laplace matrix L;

[0036] Select the eigenvectors corresponding to the smallest k non-zero eigenvalues ​​to form the matrix U;

[0037] Perform k-means clustering on the rows of the matrix U to obtain clusters C = {C1, C2, ..., C k}.

[0038] Preferably, the trend prediction unit performs progress trend prediction through the following steps:

[0039] For each progress cluster c i , using the modified Gompertz function for prediction:

[0040]

[0041] Among them, a i ,b i ,c i is the Gompertz function parameter, d i ,ω i ,φ i is the periodic adjustment parameter, t is the time variable; the parameter is determined by minimizing the following objective function:

[0042]

[0043] Among them, λ is the regularization parameter.

[0044] Preferably, the resource optimization module performs resource configuration optimization through the following steps:

[0045] Define the decision variable x ij , represents the proportion of resource j allocated to schedule cluster i;

[0046] Construct the objective function:

[0047]

[0048] Among them, w i is the weight of schedule cluster i, r j is the efficiency coefficient of the resource, T is the target completion time, P i (T) is the predicted completion degree of schedule cluster i at time T;

[0049]

[0050] The optimization problem is solved using the interior point method to obtain the optimal resource allocation solution.

[0051] Preferably, the data acquisition module comprises:

[0052] Personnel construction information collection unit, used to collect personnel construction information including daily work completion quantity, construction work process, safety inspection records, construction progress data, attendance data and personnel flow data;

[0053] Engineering design data acquisition unit, used to collect various data reports in the engineering design process;

[0054] Engineering data collection unit, used to collect project documents during the construction process;

[0055] Engineering supervision data collection unit, used to collect supervision documents during the supervision process;

[0056] as well as

[0057] The engineering inspection data collection unit is used to collect inspection files during the inspection process.

[0058] Preferably, the data processing module comprises:

[0059] A data cleaning unit, used for performing error detection and correction on the multi-source data;

[0060] Data standardization unit, used to convert data from different sources into a unified format and standard;

[0061] Data integration unit, which is used to integrate the cleaned and standardized data into a unified database;

[0062] as well as

[0063] The data quality assessment unit is used to perform quality assessment on the processed data to ensure the reliability and consistency of the data.

[0064] Preferably, the management decision module includes:

[0065] A decision rule library for storing predefined decision rules and strategies;

[0066] A decision generating unit, configured to generate a management decision suggestion based on the result of the resource configuration optimization and the decision rule base;

[0067] A decision evaluation unit, used to evaluate the feasibility and potential impact of the generated decision recommendations;

[0068] as well as

[0069] The decision display unit is used to present decision suggestions in a visual way to facilitate managers' understanding and implementation.

[0070] Compared with the prior art, the beneficial effects of the present invention are embodied in the following aspects:

[0071] Specifically, the system of the present invention has significant technical advantages and beneficial effects in many aspects. First, at the macro level, the system realizes the digitalization and intelligence of the entire process of water conservancy project construction management. Through the comprehensive use of multi-source data, the system can comprehensively and accurately reflect the actual situation of the project and provide a solid data foundation for management decisions. This comprehensive data support enables managers to make strategic plans and effectively avoid decision-making errors caused by insufficient information in traditional management.

[0072] Secondly, from the perspective of system architecture, the present invention adopts a modular design, and each functional module is relatively independent and closely coordinated. This design not only ensures the flexibility and scalability of the system, but also achieves a seamless connection between data and functions. For example, the close cooperation between the data acquisition module and the data processing module ensures the data quality of subsequent analysis; and the coordination between the progress analysis module and the resource optimization module achieves a dynamic balance between project progress and resource allocation.

[0073] At the algorithm level, the present invention adopts a number of innovative technologies. The spectral clustering algorithm and improved Gompertz function used in the progress analysis module can deeply explore the inherent laws of project progress and realize accurate analysis and prediction of complex project progress. The nonlinear programming method adopted in the resource optimization module solves the problem of nonlinear and dynamic changes in resource demand in water conservancy projects. The comprehensive application of these algorithms not only improves the analysis accuracy of the system, but also enhances the system's adaptability to complex situations.

[0074] From the perspective of effect, the system of the present invention achieves the superposition and coordination of multiple effects. For example, accurate progress analysis not only directly improves the efficiency of project management, but also provides a reliable basis for resource optimization, thereby achieving the improvement of resource utilization efficiency. At the same time, optimized resource allocation in turn promotes the improvement of project progress, forming a virtuous circle. The superposition of such multiple effects is ultimately reflected in the significant improvement of the overall project management level.

[0075] In practical applications, the system of the present invention also shows strong adaptability and scalability. Although the system is designed for water conservancy projects, its core concept and technical framework are universal and can be easily extended to other types of large-scale engineering project management. This wide applicability greatly increases the practical value of the present invention.

[0076] In general, the integrated water conservancy project construction management system based on multi-source data analysis of the present invention effectively solves the problems of data fragmentation, shallow analysis, insufficient optimization, weak decision support, etc. existing in traditional water conservancy project management through innovative system design and advanced algorithm application. It can not only significantly improve the management efficiency and quality of water conservancy projects, but also is expected to promote the development of the entire project management field towards digitalization and intelligence, which has important theoretical significance and practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is a flowchart of the overall system of the present invention.

[0078] Figure 2 It is a logic block diagram of the data acquisition module of the present invention.

[0079] Figure 3 It is a logic block diagram of the data processing module of the present invention.

[0080] Figure 4 It is a logic block diagram of the progress analysis module of the present invention.

[0081] Figure 5 It is a logic block diagram of the resource optimization module of the present invention.

[0082] Figure 6It is a logic block diagram of the management decision module of the present invention. DETAILED DESCRIPTION

[0083] like Figure 1-6 As shown, the present invention relates to a comprehensive water conservancy project construction management system based on multi-source data analysis, and in particular to an intelligent management system that can comprehensively analyze and optimize the water conservancy project construction process.

[0084] The present invention provides a comprehensive water conservancy project construction management system based on multi-source data analysis, which includes the following modules: data acquisition module 1, data processing module 2, progress analysis module 3, resource optimization module 4, and management decision module 5. These modules work together to achieve intelligent management of the entire process of water conservancy project construction.

[0085] Specifically, the data acquisition module 1 is used to obtain multi-source data related to water conservancy project construction. These data may come from multiple channels, including but not limited to on-site construction data, design documents, supervision reports, etc. The design purpose of the data acquisition module 1 is to ensure that the system can obtain comprehensive and accurate project information and provide a reliable data basis for subsequent analysis and decision-making.

[0086] Data processing module 2 is responsible for preprocessing and integrating the multi-source data obtained by data acquisition module 1. In the process of water conservancy project construction, data from different sources often have problems such as inconsistent formats and different precision. The role of data processing module 2 is to convert these heterogeneous data into a unified format and perform necessary cleaning and verification to ensure the quality and availability of the data.

[0087] The progress analysis module 3 is one of the core modules of the present invention, which performs project progress analysis based on processed and integrated data. This module uses an innovative algorithm to evaluate the actual progress of the project and predict future progress trends. This is crucial for managers of water conservancy projects because it can help them discover potential progress problems in a timely manner and take corresponding measures.

[0088] Resource optimization module 4 optimizes the allocation of project resources based on the analysis results of progress analysis module 3. In the construction of water conservancy projects, the rational allocation of human, material and financial resources is the key to ensure the smooth progress of the project. Resource optimization module 4 provides managers with the best resource allocation plan through advanced optimization algorithms.

[0089] Finally, the management decision module 5 comprehensively considers the results of schedule analysis and resource optimization and generates decision suggestions for managers. These suggestions may involve multiple aspects such as project schedule adjustment, resource reallocation, and risk management, aiming to help managers make scientific and reasonable decisions.

[0090] Below, the present invention will provide a more detailed description of each module of the system.

[0091] First, regarding the data acquisition module 1, it includes multiple submodules for collecting different types of data. For example, the personnel construction information acquisition unit 11 is used to collect data such as the number of completed jobs per day, the construction job process, and safety inspection records. These data reflect the daily progress and safety status of the project. The engineering design data acquisition unit 12 is responsible for collecting data such as design drawings and technical specifications, which provide a basis for the overall planning and quality control of the project.

[0092] Secondly, the data processing module 2 includes a data cleaning unit 21, a data standardization unit 22, etc. The data cleaning unit 21 is mainly used to deal with problems such as abnormal values ​​and missing values ​​in the original data. For example, for construction progress data, if the progress data of a certain day is abnormally high or low, the data cleaning unit 21 will correct it according to historical data and preset rules. The data standardization unit 22 is responsible for converting data from different sources and different formats into a unified format. This is crucial for subsequent data analysis, because only data with a unified format can be effectively compared and analyzed.

[0093] The progress analysis module 3 is the core of the present invention, which includes a progress data matrix construction unit 31, a topological progress network construction unit 32, a spectral clustering unit 33 and a trend prediction unit 34. The progress data matrix construction unit 31 first constructs a project progress data matrix M. Each row of this matrix represents a project node, which contains the planned completion time, actual completion percentage and weight information of the node. For example, for a large dam project, the matrix M may be as follows:

[0094]

[0095] In this example, the first row represents a project node that is scheduled to be completed within 30 days, is currently 80% complete, and has a weight of 0.2.

[0096] In water conservancy project management, the Progress Data Matrix (PDM) is an important tool for recording and analyzing the planned completion time, actual completion percentage, and weight information of each project node. These data form the basis of progress analysis and are crucial for evaluating project progress and identifying potential problems.

[0097] Based on this matrix, the progress data matrix construction unit 31 calculates a progress deviation vector D. This vector reflects the difference between the actual progress and the planned progress of each engineering node. The calculation formula is as follows:

[0098]

[0099] Among them, t i1 is the planned completion time of the i-th engineering node, p i1 is the actual completion percentage, w i1 is the weight, t current This formula takes into account the remaining workload, time urgency and importance of project nodes, and can fully reflect the actual situation of project progress.

[0100] By introducing weights and remaining time, the progress deviation vector can not only reflect the completion status of the nodes, but also highlight the impact of key nodes, allowing managers to more accurately identify areas that need special attention. As the project progresses, the progress deviation vector will be continuously updated, helping managers to grasp the overall progress of the project in real time and take corrective measures in a timely manner.

[0101] Next, the topological progress network construction unit 32 constructs a topological progress network based on the progress deviation vector D. This network is represented by an adjacency matrix A, where the calculation formula of the matrix elements is:

[0102]

[0103] in, and They represent the i-th and j-th elements of the progress deviation vector respectively.

[0104] The design concept of this formula is that nodes with similar schedule deviations should be more closely connected. For example, if two engineering nodes have large schedule deviations, they may face similar problems and should be managed together.

[0105] The spectral clustering unit 33 performs spectral clustering analysis on the topological network. It first calculates the Laplacian matrix L = DA, where D is the degree matrix (diagonal matrix, D ii =∑ j A ij ). Then, it calculates the eigenvalues ​​and eigenvectors of L, selects the eigenvectors corresponding to the smallest k non-zero eigenvalues, and forms the matrix U. Finally, k-means clustering is performed on the rows of U to obtain the progress clusters C = {C1, C2, ..., C k This method can effectively identify groups of engineering nodes with similar progress characteristics and provide a basis for subsequent management decisions.

[0106] Topological Progress Network (TPN) is a network model based on the relationship between nodes, which is used to capture the dependencies and chain effects in project progress. Spectral Clustering is a clustering method based on graph theory, which can effectively identify groups of nodes with similar progress characteristics.

[0107] Through spectral clustering, nodes with similar schedule characteristics can be grouped into the same cluster, which is convenient for centralized management and resource allocation. The topological schedule network can capture the dependencies between nodes, help managers predict and respond to possible chain delays, and improve the controllability of the overall project.

[0108] The trend prediction unit 34 uses the modified Gompertz function to perform trend prediction on each progress cluster. The prediction function is as follows:

[0109]

[0110] Among them, a i ,b i ,c i is the Gompertz function parameter, d i ,ω i ,φ i is the periodic adjustment parameter, and t is the time variable; the advantage of this function is that it can not only simulate the overall trend of the project progress (through the Gompertz function part), but also reflect the periodic fluctuations (through the sine function part). This is especially important for water conservancy projects, because the progress of water conservancy projects is often affected by seasonal factors.

[0111] To determine these parameters, the trend prediction unit 34 minimizes the following objective function:

[0112]

[0113] Wherein, λ is a regularization parameter used to prevent overfitting. In this way, the trend prediction unit 34 can generate an accurate progress prediction model for each progress cluster.

[0114] Trend forecasting is a method of predicting future progress changes by modeling historical progress data. The progress of water conservancy projects is often affected by many factors, such as seasonal changes, weather conditions, etc., so a forecasting model that can simulate the overall trend and reflect periodic fluctuations is needed.

[0115] By combining the Gompertz function and the sine wave, the overall trend and periodic fluctuation of the project progress can be simulated more accurately, providing reliable forecast results. The forecast results can help managers plan resource allocation and adjust construction plans in advance to avoid losses caused by progress delays.

[0116] The advantage of this progress analysis method is that it not only considers the progress of a single project node, but also the relationship between nodes and the overall trend. This is especially important for complex system engineering such as water conservancy projects, because the various parts of water conservancy projects are often closely related, and the delay of one part may cause a chain reaction on the entire project.

[0117] Through the above detailed description, it can be seen how the integrated water conservancy project construction management system based on multi-source data analysis of the present invention provides comprehensive and accurate progress analysis results for water conservancy project managers through innovative data processing and analysis methods. This can not only help managers to discover and solve problems in a timely manner, but also provide a reliable basis for resource optimization and decision-making, thereby significantly improving the management efficiency and quality of water conservancy projects.

[0118] Next, the present invention will explain in detail the resource optimization module 4. The resource optimization module 4 is a key component of the system of the present invention, which intelligently configures and optimizes various resources of the water conservancy project based on the output results of the progress analysis module 3.

[0119] Resource optimization refers to the rational allocation of human, material and financial resources to maximize project benefits under limited resource conditions. Resource optimization of water conservancy projects is a complex multi-objective optimization problem that requires balancing multiple conflicting objectives such as cost, time and quality. The objective function of resource optimization module 4 aims to maximize the resource input-output ratio while considering the phenomenon of diminishing marginal returns, giving priority to areas lagging behind in progress, and focusing on key project nodes.

[0120] In the preferred embodiment of the present invention, the resource optimization module 4 adopts an innovative optimization algorithm. The algorithm first defines the decision variable x ij , represents the proportion of resource j allocated to schedule cluster i. The resources here can be various forms of engineering resources such as manpower, equipment, and materials. The schedule cluster is the engineering node group obtained by the schedule analysis module 3 through spectral clustering.

[0121] Based on these decision variables, the resource optimization module 4 constructs the following objective function:

[0122]

[0123] Among them, w i is the weight of schedule cluster i, r jis the efficiency coefficient of the resource, T is the target completion time, P i (T) is the predicted completion degree of schedule cluster i at time T;

[0124] The design concept of this objective function is multifaceted. First, through the logarithmic function log(1+r j ·x ij ), which reflects the nonlinear relationship between resource input and output, which is consistent with the phenomenon of diminishing marginal benefits commonly seen in actual engineering. Secondly, multiply by (1-P i (T)) ensures that resources are allocated to the progress clusters with lower completion rates first, which helps balance the progress of the entire project. i The introduction of ensures that important engineering nodes can receive sufficient resource support.

[0125] In order to make the optimization results more in line with the actual situation, the system of the present invention also sets two key constraints:

[0126]

[0127] The optimization problem is solved using the interior point method to obtain the optimal resource allocation solution.

[0128] The first constraint ensures that each resource is fully allocated to avoid wasting resources. The second constraint ensures that the allocation ratio is always non-negative, which is in line with the actual situation.

[0129] Through the optimization algorithm, we can maximize the utilization of resources under limited resource conditions and ensure the smooth progress of the project. Reasonable allocation of resources not only improves work efficiency, but also promotes the improvement of project quality and reduces unnecessary rework and delays.

[0130] In practical applications, the resource optimization module 4 will use the interior point method to solve this optimization problem. The interior point method is an efficient nonlinear programming solution method, which is particularly suitable for dealing with optimization problems with inequality constraints. By solving this optimization problem, the system can obtain an optimal resource allocation plan, which can not only ensure the overall progress of the project, but also ensure that key nodes have sufficient resource support.

[0131] Preferably, the system of the present invention further comprises an important data acquisition module 1. This module is responsible for collecting various data during the construction of the water conservancy project and providing basic data support for the operation of the entire system.

[0132] Specifically, the data acquisition module 1 includes the following key units:

[0133] The personnel construction information collection unit 11 is used to collect personnel construction information including the number of completed works per day, construction work procedures, safety inspection records, construction progress data, attendance data and personnel flow data. These data can fully reflect the daily progress of the project and the utilization of human resources.

[0134] The engineering design data collection unit 12 is responsible for collecting various data reports in the engineering design process. These reports may include engineering drawings, technical specifications, design change records, etc., which are important bases for ensuring engineering quality.

[0135] The engineering data collection unit 13 mainly collects project documents during the construction process. These documents may include construction logs, material usage records, quality inspection reports, etc., which can provide detailed background information for subsequent analysis and decision-making.

[0136] The engineering supervision data collection unit 14 and the engineering inspection data collection unit 15 are responsible for collecting the supervision documents in the supervision process and the inspection documents in the inspection process respectively. These data are very important for ensuring the quality of the project and timely discovering and solving problems.

[0137] In one embodiment of the present invention, the data processing module 2 plays a role after the data acquisition module 1. The main task of this module is to pre-process and integrate the collected multi-source data to lay the foundation for subsequent analysis. The data processing module 2 includes the following key units:

[0138] The data cleaning unit 21 is responsible for error detection and correction of the original data. For example, it may check and correct the date format error, numerical anomaly and other issues. In large and complex projects such as water conservancy projects, data errors are difficult to avoid, so the role of this unit is particularly important.

[0139] The data standardization unit 22 is responsible for converting data from different sources and in different formats into a unified format and standard. This is crucial for subsequent data analysis, because only data with a unified format can be effectively compared and analyzed.

[0140] The data integration unit 23 integrates the cleaned and standardized data into a unified database. This process may involve operations such as merging and associating data, and ultimately forms a comprehensive data set that fully reflects the project status.

[0141] Finally, the data quality assessment unit 24 performs a quality assessment on the processed data to ensure the reliability and consistency of the data. This unit may use statistical methods to detect the completeness, accuracy and consistency of the data to provide quality assurance for subsequent analysis.

[0142] In the system of the present invention, the management decision module 5 is the final output end of the entire system. This module comprehensively considers the results of progress analysis and resource optimization, and provides scientific and reasonable decision suggestions for managers of water conservancy projects. Specifically, the management decision module 5 includes the following key units:

[0143] The decision rule library 51 is used to store predefined decision rules and strategies. These rules and strategies may come from industry standards, expert experience or summaries of historical projects, and provide a basic framework and basis for decision making.

[0144] The decision generation unit 52 generates specific management decision suggestions based on the results of resource allocation optimization and the rules in the decision rule library. These suggestions may involve multiple aspects such as project schedule adjustment, resource reallocation, and risk management.

[0145] The decision evaluation unit 53 is responsible for evaluating the feasibility and potential impact of the generated decision suggestions. It may consider multiple factors, such as cost, time, quality, safety, etc., to ensure that the generated decision suggestions are comprehensive and reliable.

[0146] Finally, the decision display unit 54 presents the decision suggestions in a visual way, which is convenient for managers to understand and implement. This may include various forms such as charts and reports, making complex decision information intuitive and easy to understand.

[0147] Through the above detailed description, it can be seen how the integrated water conservancy project construction management system based on multi-source data analysis of the present invention realizes intelligent management of the whole process from data collection, processing, analysis to decision support through multiple collaborative modules. This system can not only help managers to grasp the progress of the project in a timely manner, but also provide scientific decision support, significantly improving the management efficiency and quality of water conservancy projects.

[0148] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto; any person familiar with the art who, within the scope disclosed by the present invention, makes substitutions or changes based on the scheme and improved concepts of the present invention shall be covered within the protection scope of the present invention.

Claims

1. A comprehensive water conservancy project construction management system based on multi-source data analysis, characterized in that: include: Data acquisition module, used to obtain multi-source data related to the project; A data processing module, used for preprocessing and integrating the multi-source data; A progress analysis module, used for performing engineering progress analysis based on the preprocessed and integrated data; A resource optimization module, used for optimizing resource allocation based on the results of the project progress analysis; as well as A management decision module is used to generate management decision suggestions based on the result of the resource configuration optimization.

2. The system according to claim 1, characterized in that The progress analysis module includes: A progress data matrix construction unit is used to construct a project progress data matrix; A topological progress network construction unit, used to construct a topological progress network based on the engineering progress data matrix; A spectral clustering unit, used for performing spectral clustering analysis on the topological progress network; as well as A trend prediction unit is used to predict the progress trend based on the result of the spectral clustering analysis.

3. The system according to claim 2, characterized in that The progress data matrix construction unit constructs the project progress data matrix by the following steps: Obtain the planned completion time, actual completion percentage and weight information of the project nodes; Construct the progress data matrix M, where: Among them, t i1 is the planned completion time of the i-th engineering node, p i1 is the actual completion percentage of the i-th project node, w i1 is the weight of the i-th engineering node; the progress deviation vector D is calculated based on the progress data matrix M: Among them, t current is the current time.

4. The system according to claim 3, characterized in that The topology progress network construction unit constructs a topology progress network by the following steps: Construct an adjacency matrix A based on the progress deviation vector D: in, and They represent the i-th and j-th elements of the progress deviation vector respectively.

5. The system according to claim 4, characterized in that The spectral clustering unit performs spectral clustering analysis by the following steps: Based on the adjacency matrix A, the Laplacian matrix L is calculated: L = DA, where D is the degree matrix and D ii =∑ j A ij ; Calculate the eigenvalues ​​and eigenvectors of the Laplace matrix L; Select the eigenvectors corresponding to the smallest k non-zero eigenvalues ​​to form the matrix U; Perform k-means clustering on the rows of the matrix U to obtain clusters C = {C1, C2, ..., C k }.

6. The system according to claim 5, characterized in that The trend prediction unit performs progress trend prediction by the following steps: For each progress cluster c i , using the modified Gompertz function for prediction: Among them, a i ,b i ,c i is the Gompertz function parameter, d i ,ω i ,φ i is the periodic adjustment parameter, t is the time variable; the parameter is determined by minimizing the following objective function: Among them, λ is the regularization parameter.

7. The system according to claim 1, characterized in that The resource optimization module performs resource configuration optimization through the following steps: Define the decision variable x ij , represents the proportion of resource j allocated to schedule cluster i; Construct the objective function: Among them, w i is the weight of schedule cluster i, r j is the efficiency coefficient of the resource, T is the target completion time, P i (T) is the predicted completion degree of schedule cluster i at time T; The optimization problem is solved using the interior point method to obtain the optimal resource allocation solution.

8. The system according to claim 1, characterized in that The data acquisition module comprises: Personnel construction information collection unit, used to collect personnel construction information including daily work completion quantity, construction work process, safety inspection records, construction progress data, attendance data and personnel flow data; Engineering design data acquisition unit, used to collect various data reports in the engineering design process; Engineering data collection unit, used to collect project documents during the construction process; Engineering supervision data collection unit, used to collect supervision documents during the supervision process; as well as The engineering inspection data collection unit is used to collect inspection files during the inspection process.

9. The system according to claim 1, characterized in that The data processing module comprises: A data cleaning unit, used for performing error detection and correction on the multi-source data; Data standardization unit, used to convert data from different sources into a unified format and standard; Data integration unit, which is used to integrate the cleaned and standardized data into a unified database; as well as The data quality assessment unit is used to perform quality assessment on the processed data to ensure the reliability and consistency of the data.

10. The system according to claim 1, characterized in that The management decision module includes: A decision rule library for storing predefined decision rules and strategies; A decision generating unit, configured to generate a management decision suggestion based on the result of the resource configuration optimization and the decision rule base; A decision evaluation unit, used to evaluate the feasibility and potential impact of the generated decision recommendations; as well as The decision display unit is used to present decision suggestions in a visual way to facilitate managers' understanding and implementation.

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