EPC engineering information management system supporting bidirectional matching of business strategy and technical path

Abstract

The invention relates to the technical field of EPC engineering information management, in particular to an EPC engineering information management system supporting bidirectional matching of a business strategy and a technical path. The data acquisition and integration unit is used for acquiring multi-source heterogeneous data from the whole project implementation process; constructing a resource optimization allocation model based on the preprocessed business side data, and generating an actually executable resource allocation scheme; based on the resource configuration scheme and the model constraint condition, combining the technical side data and the external environment data to generate a technical path candidate set according with the business constraint condition; and based on the technical path candidate set and the business side data, constructing a bidirectional coupling optimization model, and performing multi-objective optimization solution to obtain an implementation scheme with an optimal score. A resource allocation scheme is mapped to each construction node, and a technical path candidate set conforming to business constraints is generated in combination with a BIM model and external environment data, so that accurate scheduling of materials, equipment and manpower and construction period prediction are realized.

Description

Technical Field

[0001] This invention relates to the field of EPC project information management technology, and more specifically, to an EPC project information management system that supports bidirectional matching of business strategies and technical paths. Background Technology

[0002] EPC (Engineering, Procurement, and Construction) turnkey contracting is a currently accepted international method for organizing and implementing engineering projects. Its core advantage lies in integrating design, procurement, and construction to achieve overall optimized control over project costs, schedules, and quality. However, in actual project management, business management and technical management dimensions are often independent of each other, creating "data silos" and "decision-making fragmentation," leading to low project management efficiency and difficulty in achieving expected goals.

[0003] Currently available engineering information management systems (such as traditional ERP, project management systems, or BIM management platforms) mostly focus on single-dimensional information recording and process approval. For example, they may focus on financial management or construction progress tracking. Moreover, during project execution, when faced with changes (such as design changes, price fluctuations, and abnormal weather), they cannot generate and select implementation plans that perform well in multiple objectives such as cost, schedule, risk, and efficiency based on real-time data. This results in poor project management flexibility and weak anti-interference capabilities. Therefore, this paper proposes to design an EPC engineering information management system that supports the two-way matching of business strategies and technical paths. Summary of the Invention

[0004] The purpose of this invention is to provide an EPC project information management system that supports the two-way matching of business strategies and technical paths, in order to solve the problems mentioned in the background art, which are that during the project execution process, when faced with changes (such as design changes, price fluctuations, and abnormal weather), it is impossible to generate and filter out implementation plans that perform well in multiple objectives such as cost, schedule, risk, and efficiency based on real-time data, resulting in poor project management flexibility and weak anti-interference ability.

[0005] To achieve the above objectives, the present invention aims to provide an EPC project information management system that supports the bidirectional matching of business strategies and technical paths, comprising:

[0006] A data acquisition and integration unit is used to collect multi-source heterogeneous data from the entire project implementation process and to preprocess the multi-source heterogeneous data.

[0007] Among them, multi-source heterogeneous data includes business-side data, technology-side data, and external environment data;

[0008] The business strategy unit constructs a resource optimization allocation model based on preprocessed business-side data and generates a practically executable resource allocation scheme. ;

[0009] The resource optimization allocation model includes an objective function and model constraints, and introduces contract period constraints. With risk coefficient This is used to apply constraints and filter candidate solutions generated by the resource allocation model.

[0010] Technology path unit, the technology path unit is based on resource allocation scheme Based on model constraints and combined with technical and external environmental data, the required materials, equipment, and labor for each stage are determined using the BIM model, generating a candidate set of technical paths that meet business constraints. ;

[0011] A bidirectional matching unit, the bidirectional matching unit being based on a candidate set of technical paths. By combining business-side data, a two-way coupled optimization model is constructed, and a non-dominated sorting genetic algorithm is used to solve multi-objective optimization problems, ultimately obtaining the optimal implementation scheme. .

[0012] As a further improvement to this technical solution, the data acquisition and integration unit includes a multi-source heterogeneous data acquisition module and a data preprocessing module;

[0013] Among them, the multi-source heterogeneous data acquisition module is used to collect multi-source heterogeneous data throughout the entire process of engineering construction, and the data preprocessing module preprocesses the collected multi-source heterogeneous data to perform noise smoothing and dimensionality unification on the collected data.

[0014] The business-side data includes at least project funding budget costs. Financing interest rate Contractual constraints on construction period Risk coefficient ;

[0015] The technical data includes at least the construction progress. Material strength parameters Construction procedures Mechanical equipment utilization rate Energy consumption data ;

[0016] The external environmental data includes climate temperature. Precipitation .

[0017] As a further improvement to this technical solution, the business strategy unit includes a resource allocation module and a cost constraint module;

[0018] The resource allocation module is used to receive preprocessed business-side data and construct an objective function based on the business-side data. The resource optimization allocation model is used to allocate business-side resources and output resource allocation candidate vectors.

[0019] The cost constraint module introduces model constraints based on preprocessed business-side data to the resource optimization allocation model, which is used to apply constraint filtering to the candidate solutions generated by the resource allocation model.

[0020] The resource allocation module and the cost constraint module perform collaborative calculations through a solver to obtain an optimized resource allocation scheme under model constraints. .

[0021] As a further improvement to this technical solution, a resource optimization allocation model is constructed based on the aforementioned business-side data. The specific steps involved are as follows:

[0022] The preprocessed business-side data is normalized to generate an input vector. ;

[0023] The project construction process is divided into: The project is divided into several phases, and various resources in the project are categorized into different types. kind;

[0024] Obtain the planned allocation of each type of resource at each stage. and actual usage Calculate each type of resource Different construction stages utilization efficiency ;

[0025] Based on utilization efficiency The utilization efficiency of each stage is weighted and calculated to obtain the first stage. Reference values ​​for the comprehensive utilization efficiency of similar resources at all stages of the entire project construction process ;

[0026] Based on the Class resources in the The allocation of resources for each construction phase is defined by the resource allocation vector. , resource allocation vector As a decision variable to be optimized;

[0027] Based on resource allocation vector , construct the first Comprehensive utilization efficiency function of class resources ;

[0028] Based on the Comprehensive utilization efficiency function of class resources and the Capital costs corresponding to similar resources And introduce risk coefficient The objective function is constructed to maximize resource utilization efficiency.

[0029] As a further improvement to this technical solution, the model constraints include capital cost constraints, contract period constraints, and risk constraints;

[0030] Among them, the cost of capital constraint is used to limit the total capital expenditure to not exceeding the project's budgeted cost. ;

[0031] Contract period constraints are used to limit the total construction period of the entire project to no more than the upper limit of the period stipulated in the contract.

[0032] Risk constraints are used to limit the overall risk indicators introduced by the resource allocation scheme to not exceed a preset risk threshold.

[0033] As a further improvement to this technical solution, the technical path unit includes a construction resource mapping module and a technical path generation and optimization module;

[0034] Among them, the construction resource mapping module uses the BIM model to map resource allocation schemes. Mapping to construction nodes at each construction stage, outputting a set of candidate technical path solutions. It is used to determine the materials, equipment and labor required for each stage, and at the same time calculates the expected construction efficiency and projected construction period for each stage.

[0035] The technology path generation and optimization module, based on the model constraints of the candidate technology path solution set and the business strategy unit, optimizes the candidate technology path solution set. The process involves filtering and sorting to generate a candidate set of technology paths that meet business constraints. .

[0036] As a further improvement to this technical solution, the BIM model incorporates resource allocation schemes. Mapping to construction nodes at each construction stage and outputting a set of candidate technical path solutions involves the following specific steps:

[0037] The project construction process is divided into: Each stage yields a set of nodes for that stage. , where each node There are several components, and the material requirement for each component is... ;

[0038] Then the resource allocation scheme The first in Class resources in the stage Allocation amount The required amount of materials for nodes is The actual material usage at the node was calculated. And by analyzing the actual material usage of the node sub-units. Summing yields the total actual material usage for each node. ;

[0039] Material usage at all nodes within the phase Summing gives the total material usage for each stage. ;

[0040] Based on the Class resources in the stage Allocation amount Calculate the number of devices required for each stage. ;

[0041] Based on node construction procedures And introduce the construction phase. workload This yields the labor demand for each type of job. ;

[0042] Phase The manual requirements of all nodes within the process are aggregated to obtain the stage. Total internal human resource utilization demand ;

[0043] Based on construction stage Total internal material usage Quantity of equipment Labor demand Construct a nonlinear regression model to output the construction phase. The internal construction efficiency is expected, and this is achieved by incorporating climate temperature. and precipitation The optimized nonlinear regression model was used to calculate the optimized expected construction efficiency. ;

[0044] Based on the expected construction efficiency and stages Internal workload To obtain the project duration forecast ;

[0045] The material usage, equipment scheduling, labor requirements, expected construction efficiency, and projected construction period at each stage are mapped to generate a set of candidate technical path solutions. .

[0046] As a further improvement to this technical solution, the set of candidate technical path solutions is... The specific steps involved in filtering and sorting to generate a candidate set of technology paths that meet business constraints are as follows:

[0047] Based on the set of candidate technology paths And model constraints, for the set of candidate technical path solutions Constraints are determined one by one, and the model constraints include at least capital cost constraints, contract period constraints, and risk constraints.

[0048] Candidate solutions that do not meet any of the above constraints will be eliminated.

[0049] For candidate technology path solutions that meet the constraints, a set of candidate technology paths that satisfy the business constraints is generated based on the Pareto optimal ranking algorithm. .

[0050] As a further improvement to this technical solution, the bidirectional matching unit includes a conflict detection module, a bidirectional coupling optimization module, and a multi-ordering decision module;

[0051] The conflict detection module is based on a candidate set of technical paths. Furthermore, it introduces constraints on capital costs, contract periods, and risks, and performs contract period conflict detection, capital conflict detection, and risk conflict detection on each candidate solution, outputting a set of feasible solutions that meet all constraints. ;

[0052] The bidirectional coupling optimization module is based on a set of feasible solutions. A bidirectional coupled multi-objective optimization model is constructed, and the set of feasible solutions is solved using a non-dominated sorting genetic algorithm to generate a Pareto front solution set. ;

[0053] Among them, the business-side optimization objectives include minimizing total cost and minimizing risk level, while the technology-side optimization objectives include minimizing construction period and maximizing construction efficiency;

[0054] The multi-ranking decision module is used to process the Pareto front solution set. The candidate solutions are weighted and ranked to determine the optimal implementation. .

[0055] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0056] 1. In this EPC project information management system that supports two-way matching of business strategies and technical paths, a resource optimization allocation model is constructed based on preprocessed business data. This model includes an objective function and multiple constraints (funding, cycle, risk), and outputs the optimal resource allocation scheme to ensure that resource allocation maximizes utilization efficiency within the controllable range of budget, schedule, and risk.

[0057] Meanwhile, by mapping resource allocation schemes to each construction node and combining BIM models with external environmental data, a set of technical path candidates that meet business constraints is generated, enabling precise scheduling of materials, equipment, and labor, as well as project schedule prediction.

[0058] 2. In this EPC project information management system that supports bidirectional matching of business strategies and technical paths, a bidirectional coupled optimization model is constructed based on the non-dominated sorting genetic algorithm (NSGA-II). The model performs multi-objective collaborative optimization (cost, risk, schedule, efficiency) on the candidate set of technical paths, and finally outputs the Pareto front optimal solution. The final implementation plan is determined by weighted sorting, thereby achieving a balance between business objectives and technical objectives. Attached Figure Description

[0059] Figure 1 This is a flowchart illustrating the overall process of the present invention.

[0060] The meanings of the labels in the diagram are as follows:

[0061] 1. Data acquisition and integration unit;

[0062] 2. Business Strategy Unit; 21. Resource Allocation Module; 22. Cost Constraint Module;

[0063] 3. Technology Path Unit; 31. Construction Resource Mapping Module; 32. Technology Path Generation and Optimization Module;

[0064] 4. Bidirectional matching unit; 41. Conflict detection module; 42. Bidirectional coupling optimization module; 43. Multi-sorting decision module. Detailed Implementation

[0065] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0066] Please see Figure 1As shown, an EPC project information management system that supports bidirectional matching of business strategies and technical paths is provided. The system includes a data acquisition and integration unit 1, which is used to collect multi-source heterogeneous data from the entire project implementation process and preprocess the multi-source heterogeneous data.

[0067] Among them, multi-source heterogeneous data includes business-side data, technology-side data, and external environment data;

[0068] Furthermore, the data acquisition and integration unit 1 includes a multi-source heterogeneous data acquisition module and a data preprocessing module;

[0069] Among them, the multi-source heterogeneous data acquisition module is used to collect multi-source heterogeneous data throughout the entire process of engineering construction, and the data preprocessing module preprocesses the collected multi-source heterogeneous data to perform noise smoothing and dimensionality unification on the collected data.

[0070] The business-side data includes at least project funding budget costs. Financing interest rate Contractual constraints on construction period Risk coefficient ;

[0071] The technical data includes at least the construction progress. Material strength parameters Construction procedures Mechanical equipment utilization rate Energy consumption data ;

[0072] The external environmental data includes climate temperature. Precipitation .

[0073] In this embodiment, the project funding budget cost Data collected from the financial management platform, including material procurement costs, labor costs, and equipment rental fees;

[0074] financing interest rate Data collected from banks or financing platforms;

[0075] Construction progress Data collected from the on-site construction monitoring system, including the completion rate of work processes and deviations from planned progress;

[0076] Material strength parameters Quality assessment parameters were collected from materials laboratories and suppliers.

[0077] Energy consumption data This includes material prices, labor costs, equipment rental costs, material consumption, and energy consumption;

[0078] External environmental data is collected from the meteorological monitoring system;

[0079] Among them, construction progress Energy consumption data Noise smoothing is achieved using an adaptive Kalman filter algorithm;

[0080] Project funding budget cost Financing interest rate Contractual constraints on construction period Risk coefficient The Z-score standardization algorithm is used to unify the units of measurement;

[0081] Material strength parameters Construction sequence Mechanical equipment utilization rate The Min-Max normalization algorithm is used for numerical mapping;

[0082] Climate temperature With precipitation A sliding window averaging and normalization process is used to eliminate short-term fluctuations.

[0083] This EPC project information management system, which supports two-way matching of business strategies and technical paths, also includes a business strategy unit 2. Business strategy unit 2 constructs a resource optimization allocation model based on preprocessed business-side data, generating a practically executable resource configuration plan. ;

[0084] The resource optimization allocation model includes an objective function and model constraints, and introduces contract period constraints. With risk coefficient This is used to apply constraints and filter candidate solutions generated by the resource allocation model.

[0085] The optimized resource allocation scheme The model constraint results are output to the bidirectional matching unit and coupled with the results of the technical path unit.

[0086] In this embodiment, the business strategy unit 2 includes a resource allocation module 21 and a cost constraint module 22;

[0087] The resource allocation module 21 is used to receive preprocessed business side data, construct an objective function based on the business side data, and use the resource optimization allocation model to optimally allocate business side resources, output resource allocation candidate vectors, and ensure that resources are reasonably scheduled between various processes or stages of the project.

[0088] Cost constraint module 22, based on preprocessed business-side data, introduces model constraints to the resource optimization allocation model (project funding budget cost). The costs (including material costs, labor costs, and equipment usage costs) are used to apply constraints and filters to the candidate solutions generated by the resource allocation model. This ensures that the resource optimization allocation model does not exceed the project budget boundary when performing scheduling optimization, thereby achieving the economic rationality and cost controllability of resource scheduling while ensuring construction progress and quality objectives.

[0089] The resource allocation module 21 and the cost constraint module 22 perform collaborative calculations through a solver. The solver employs linear programming, integer programming, or heuristic search algorithms to obtain an optimized optimal resource allocation scheme under model constraints. ;

[0090]

[0091] In the formula, This indicates that in the optimal resource allocation scheme, the th Class resources in the Optimal allocation quantity for each construction stage; This represents a resource category index, where resource categories include materials, labor, machinery and equipment, etc. Indicates the total number of resource categories; This indicates the construction phase index, which includes civil engineering, equipment installation, material handling, assembly, etc. This indicates the total number of construction phases; This represents the optimized resource allocation scheme, obtained by the solver under constraints of capital cost, contract period, and risk.

[0092] In this embodiment, the cost constraint module 22 determines the feasibility of candidate resource allocation schemes based on model constraints, namely:

[0093] The resource allocation vector obtained from the solution The input is sent to the cost constraint module 22, which then verifies the candidate solutions.

[0094] If the funding cost, contract period, or risk constraints are not met, adjustment suggestions are generated and fed back to the resource allocation module 21 for iterative optimization, so that the optimization process forms a closed loop in the three dimensions of funding, period, and risk.

[0095] The specific steps involved in constructing the resource optimization allocation model based on the aforementioned business-side data are as follows:

[0096] The preprocessed business-side data is normalized to generate an input vector. In the formula, Indicates the first A normalized business-side data feature The total dimensions representing the characteristics of business-side data (business-side data should at least include project funding budget costs (including material costs, labor costs, equipment usage costs, financing interest rates, contract cycle constraints, and risk coefficients). Let represent the normalized business-side input vector, and the input vector represent... Reference value for calculating comprehensive utilization efficiency ;

[0097] The project construction process is divided into: Phase , Indicates the first The construction phases include civil engineering, equipment installation, material handling, and assembly.

[0098] The project's resources are divided into kind , Indicates the first Resources include materials, labor, and machinery.

[0099] Obtain the planned allocation of each type of resource at each stage. and actual usage (Before calculation, the above data needs to be preprocessed, including normalization, outlier removal, and unit standardization, to ensure consistency in subsequent calculations.) Calculate each type of resource. Different construction stages utilization efficiency ;

[0100]

[0101] In the formula, Indicates the first Class resources in the The actual usage of each construction phase (valid data after normalization and outlier removal). Indicates the first Class resources in the The planned allocation for each construction phase, that is, the estimated resource demand according to the construction plan;

[0102] Based on utilization efficiency The utilization efficiency of each stage is weighted and calculated to obtain the first stage. Reference values ​​for the comprehensive utilization efficiency of similar resources at all stages of the entire project construction process ;

[0103]

[0104] In the formula, Indicates the first Reference values ​​for the comprehensive utilization efficiency of similar resources at all stages of the entire project construction process; Indicates the first Class resources in the Utilization efficiency of each construction phase; Indicates the construction stage The weighting coefficient is used to represent the relative importance of this stage in the entire construction process. It is set according to the sensitivity of the construction stage to cost, schedule or risk. For example, the civil engineering stage (capital-intensive) has a larger weight, while the material handling stage has a smaller weight. ; This indicates the total number of construction phases, including civil engineering, equipment installation, material handling, assembly, etc.

[0105] Define resource allocation vector Let be the decision variable to be optimized, where Indicates the first Class resources in the The allocation of each construction phase (decision variables before optimization). This represents the set of resource allocation vectors, representing all resource configuration schemes to be optimized.

[0106] Based on resource allocation vector , construct the first Comprehensive utilization efficiency function of class resources ;

[0107]

[0108] In the formula, Indicates the first The comprehensive utilization efficiency function of a resource type throughout the entire construction cycle is used to measure the overall utilization level of that resource type at all stages. Specifically, when the... Comprehensive utilization efficiency function of class resources A higher level indicates that such resources are allocated reasonably and utilized fully throughout the project lifecycle. Comprehensive utilization efficiency function of class resources A low value indicates a waste of resources or insufficient allocation, requiring adjustment. ;

[0109] Based on the Comprehensive utilization efficiency function of class resources and the Capital costs corresponding to similar resources And introduce risk coefficient To construct an objective function that maximizes resource utilization efficiency;

[0110]

[0111] In the formula, The objective function value represents the value of the current resource allocation vector. The overall optimization benefits; This indicates that among all feasible resource allocation schemes, the objective function is... Maximize to obtain the optimal solution Optimal solution It includes the optimal allocation results for all resource categories and all construction stages; Indicates the first The capital costs of such resources include material costs, labor costs, equipment usage fees, etc. This represents the risk penalty coefficient, used to adjust the weight of risk in the objective function; This represents the overall risk coefficient, used to quantify the current resource allocation plan. The construction risks introduced are obtained by weighted aggregation of multiple key risk factors (including at least schedule risk, cost risk, safety risk, quality risk and supply chain risk).

[0112] In this embodiment, the risk coefficient is included in the objective function of the resource optimization allocation model. It is introduced as a risk penalty term to quantitatively assess the overall risk level of resource allocation schemes and to reduce the comprehensive optimization value of high-risk schemes, thereby achieving a balance between resource utilization efficiency, capital economy and construction risk controllability in the model optimization process.

[0113] Furthermore, the model constraints include capital cost constraints, contract period constraints, and risk constraints;

[0114] Among them, the cost of capital constraint is used to limit the total capital expenditure to not exceeding the project's budgeted cost. ;

[0115] In this embodiment, the capital cost constraint is specifically as follows:

[0116]

[0117] In the formula, This indicates the project's budgeted costs, including material costs, labor costs, equipment rental costs, etc. This represents a resource category index, where resource categories include materials, labor, machinery and equipment, etc. Indicates the total number of resource categories; This indicates the construction phase index, which includes civil engineering, equipment installation, material handling, assembly, etc. This indicates the total number of construction phases; This represents the total expenditure across all resource categories and all construction phases, taking into account the construction phases. Corresponding financing interest rate The actual funding needs afterward Indicates resource allocation scheme The Middle Class resources in the stage The amount allocated;

[0118] Contract period constraints are used to limit the total construction period of the entire project to no more than the upper limit of the period stipulated in the contract.

[0119] In this embodiment, the contract period constraint is specifically as follows:

[0120]

[0121] In the formula, Indicating in the resource allocation scheme Next, the Construction period (construction duration) for each construction phase; This indicates the upper limit of the period stipulated in the contract, i.e., the contractually binding construction period;

[0122] Risk constraints are used to limit the overall risk indicators introduced by the resource allocation scheme from not exceeding a preset risk threshold.

[0123] In this embodiment, the risk constraints are specifically as follows:

[0124]

[0125] In the formula, Indicating in the resource allocation scheme The overall risk index value below; This indicates the preset risk tolerance threshold, which is the maximum tolerable risk level stipulated in the business or contract.

[0126] The EPC project information management system, which supports two-way matching of business strategies and technology paths, also includes a technology path unit 3, which is based on resource allocation schemes. Based on model constraints and combined with technical and external environmental data, the required materials, equipment, and labor for each stage are determined using the BIM model, generating a candidate set of technical paths that meet business constraints. ;

[0127] In this embodiment, the technology path unit 3 includes a construction resource mapping module 31 and a technology path generation and optimization module 32;

[0128] Among them, the construction resource mapping module 31 uses the BIM model to map resource allocation schemes. Mapping to construction nodes at each construction stage, outputting a set of candidate technical path solutions. It is used to determine the materials, equipment and labor required for each stage, and at the same time calculates the expected construction efficiency and projected construction period for each stage.

[0129] In this embodiment, the BIM model includes information on each construction node of the project, node components (length, area, volume, coordinate position), material type and quantity, construction sequence, equipment requirements and labor input information (type of work and workload).

[0130] The BIM model is used to map the resource allocation scheme to each construction node, and calculate the material usage plan, equipment scheduling and labor input of each stage by combining the node attributes, thereby generating stage construction efficiency and schedule prediction.

[0131] The technology path generation and optimization module 32, based on the candidate technology path solution set and the model constraints of the business strategy unit 2, optimizes the candidate technology path solution set. The process involves filtering and sorting to generate a candidate set of technology paths that meet business constraints. .

[0132] The BIM model includes resource allocation schemes. Mapping to construction nodes at each construction stage and outputting a set of candidate technical path solutions involves the following specific steps:

[0133] The project construction process is divided into: Each stage yields a set of nodes for that stage. In the formula, Indicates the first The total number of nodes included in each construction phase. Indicates the first The first of the construction phases Nodes ( ), where each node There are several components, and the material requirement for each component is... ;

[0134] Then the resource allocation scheme The first in Class resources in the stage Allocation amount The required amount of materials for nodes is The actual material usage at the node was calculated. And by analyzing the actual material usage of the node sub-units. Summing yields the total actual material usage for each node. ;

[0135]

[0136] In the formula, Represents a node The actual amount of materials used; Represents a node The material requirements;

[0137] The material usage of all nodes within the stage for:

[0138]

[0139] Material usage at all nodes within the phase Summing up yields the total material usage for each stage. ;

[0140]

[0141] In the formula, Indicates traversing nodes All components within are used to calculate the total material quantity of the nodes; Represents a node The total amount of materials used within the node The amount of material used for all internal components is summed up. Indicates the first Total material usage for each construction phase; Indicates from the first Node set of each construction stage In the middle, each node is extracted in turn. ; This indicates the node index, representing the construction phase. A construction node within;

[0142] Based on the Class resources in the stage Allocation amount Calculate the number of devices required for each stage. This is used to implement device scheduling mapping;

[0143] Based on node construction procedures And introduce the construction phase. workload This yields the labor demand for each type of job. , used to implement artificial resource mapping;

[0144] Phase The manual requirements of all nodes within the process are aggregated to obtain the stage. Total internal human resource utilization demand ;

[0145] In this embodiment, the number of devices required for each stage In the formula, This indicates rounding up to the nearest integer, ensuring the number of devices is an integer. Indicates the first The efficiency coefficient of a type of equipment (i.e., the amount of work that each piece of equipment can complete per hour). Indicates the first The first phase of construction required Number of devices of this type (rounded to the nearest integer);

[0146] Among them, the labor demand for each type of job for:

[0147]

[0148] In the formula, Represents a node The demand for manpower within the country; Represents a node The corresponding set of construction procedures; Represents a node During the construction process The proportion of tasks in the total; Indicate process The unit task labor requirement coefficient; Indicates the index of the construction process of the node; Indicates the construction stage The workload;

[0149] stage Total internal human resource utilization demand for:

[0150]

[0151] In the formula, Indicates the first Total labor demand during each construction phase;

[0152] Based on construction stage Total internal material usage Quantity of equipment Labor demand Construct a nonlinear regression model to output the construction phase. The internal construction efficiency is expected, and this is achieved by incorporating climate temperature. and precipitation The optimized nonlinear regression model was used to calculate the optimized expected construction efficiency. ;

[0153] Based on the expected construction efficiency and stages Internal workload To obtain the project duration forecast ;

[0154] In this embodiment, the specific steps involved in constructing a nonlinear regression model based on machine learning algorithms are as follows:

[0155] Total material usage Quantity of equipment Labor demand As input;

[0156] At the same time, the climate temperature and precipitation As an additional factor of influence;

[0157] Construct the vector of factors affecting construction efficiency All resource inputs and environmental conditions are mapped to a unified feature space (the above features are mapped to a feature space with unified dimensions, which can be processed by normalization or standardization).

[0158] Based on historical sample data, a machine learning algorithm (i.e., a nonlinear regression model) is used to analyze the vector of factors affecting construction efficiency. By performing a fitting operation, the construction efficiency can be obtained. :

[0159]

[0160] In the formula, This represents a nonlinear regression model based on machine learning algorithms, used to map construction efficiency influencing factors to expected construction efficiency. The machine learning algorithms include, but are not limited to, Support Vector Regression (SVR), Random Forest Regression (RF), Gradient Boosting Decision Tree (GBDT), Multilayer Perceptron (MLP), or Long Short-Term Memory Network (LSTM). For model parameters (such as weights, biases, tree node splitting parameters, etc.), minimize the loss function. Trained;

[0161] Among them, the loss function for:

[0162]

[0163] In the formula, This represents the loss function value, used to measure the error between the predicted construction efficiency and the historical actual construction efficiency, and is used to train the nonlinear regression model. This represents the first prediction obtained based on a nonlinear regression model. Expected construction efficiency for each construction phase; Indicates the first Historical construction efficiency for each construction phase (historical sample data provided, calculated based on the actual amount of work completed and the actual time spent in each construction phase);

[0164] Specifically, historical samples and historical construction efficiency Used for training nonlinear regression models By minimizing the loss function To update model parameters ;

[0165] For tree-based models (such as RF and GBDT), model parameters are trained using split gain or residual fitting methods.

[0166] Specifically, in this embodiment, the machine learning algorithm preferably employs Support Vector Regression (SVR):

[0167] Support Vector Regression (SVR) Model Parameters This mainly includes the weights and bias terms of the support vectors. In addition, the present invention preferably uses a Gaussian kernel as the kernel function, and the regularization parameter is set between 5 and 15, preferably 10; the insensitive loss parameter is set between 0.01 and 0.2, preferably 0.1.

[0168] Using the preprocessed training dataset (construction efficiency influencing factor vector) ) and corresponding tags (historical construction efficiency) ), and historical data (historical construction efficiency influencing factor vector) Corresponding historical construction efficiency The support vector regression (SVR) model was trained by randomly dividing the dataset into training and test sets in a 7:3 ratio and then using a combination of grid search and cross-validation to find the optimal parameter combination.

[0169] In this embodiment, the material usage, equipment scheduling and labor requirements, expected construction efficiency and projected construction period at each stage are mapped and a set of candidate technical path solutions is generated. ;

[0170]

[0171] In the formula, Represents a set of candidate technology path solutions; Indicates the first Each of the candidate technology paths is 100. It represents a complete sequence of construction phase plans, consisting of resource usage and construction efficiency information for each phase, including material usage, equipment demand, labor demand, construction efficiency, and schedule prediction, which is used for subsequent construction plan optimization and scheduling decisions. This indicates the total number of construction phases in the project. Indicates the index of the project's construction phase; This represents the total number of candidate technology path solutions. This represents the index of candidate technology path schemes.

[0172] In this embodiment, the set of candidate technical path solutions... The specific steps involved in filtering and sorting to generate a candidate set of technology paths that meet business constraints are as follows:

[0173] Based on the set of candidate technology paths And model constraints, for the set of candidate technical path solutions Constraints are determined one by one, and the model constraints include at least capital cost constraints, contract period constraints, and risk constraints.

[0174] Candidate solutions that do not meet any of the above constraints will be eliminated.

[0175] For candidate technology path solutions that meet the constraints, a set of candidate technology paths that satisfy the business constraints is generated based on the Pareto optimal ranking algorithm. .

[0176] In this embodiment, a candidate set of technology paths that meet business constraints is generated based on the Pareto optimal ranking algorithm. The specific steps involved are as follows:

[0177] Set of candidate technology paths Each of the schemes Define its multi-objective performance vector:

[0178]

[0179] in:

[0180] This indicates the candidate technical path scheme. Total project duration target (unit: days or hours);

[0181] This indicates the candidate technical path scheme. Total cost;

[0182] This indicates the candidate technical path scheme. The overall risk index (dimensionless and normalized to) In the formula, Indicates the total number of risk factors (e.g.) (This refers to risks such as schedule risk, cost risk, safety risk, quality risk, and supply chain risk). Indicates the risk factor index; Indicates the first The weights of each risk factor (determined based on historical project data and expert experience). Normalization ; Indicate candidate technology path schemes In the The risk measure on each risk factor is normalized and mapped to... ;

[0183] This indicates the candidate technical path scheme. Each stage The ratio of construction efficiency to the overall cost of this stage represents the construction efficiency (dimensionless) brought about by unit cost input.

[0184] In the formula, Indicate candidate technology path schemes The multi-objective performance vector is used for subsequent Pareto optimal ranking, serving as a multi-dimensional metric for judging the merits of different candidate technical path solutions; Indicates the index of the project's construction phase; Indicates the total number of construction phases in the project; Indicates the first The capital cost corresponding to this type of resource; Indication and construction phase The corresponding cost of capital or financing interest rate (dimensionless). Indicates the construction stage The overall cost (reflecting the construction phase) The total input cost (in yuan) is the sum of material costs, labor costs, equipment rental costs, the value of material consumption, and the value of energy consumption. Indicating in candidate technology path schemes Next construction phase The expected construction efficiency; This indicates the candidate technology path scheme. The calculated risk value.

[0185] For any two schemes If the following conditions are met:

[0186]

[0187] And there is at least one target. ,make ;

[0188] at the same time Then it is called a scheme Domination Plan , recorded as ;

[0189] In the set If a solution exists It is not dominated by any other scheme, that is:

[0190]

[0191] The solution is then determined to be Pareto optimal and included in the Pareto front set:

[0192]

[0193] In the formula, Indicates the index of the objective function. This represents the objective that needs to be minimized. This means that the condition holds true for all objective functions (in this case, time, cost, and risk). Representation scheme The unit cost construction efficiency (the higher the better, the goal is to maximize it). Representation scheme The unit cost of construction efficiency; Indicates relative to the scheme Any other candidate solution; Symbols indicating dominance relationships, if , then it represents the scheme Non-inferior on all objectives to be minimized And outperforms in at least one objective And in terms of efficiency targets, it should not be lower than ; The Pareto front set represents the set of all candidate solutions that are not dominated by other solutions, and is the set of optimal solutions that the decision-maker can ultimately choose from.

[0194] Using a non-dominated sorting method, all schemes are divided into several levels:

[0195] First layer ;

[0196] delete After considering the proposed solution, the dominance relationships are recalculated in the remaining set to obtain the second level. ;

[0197] This process continues until all schemes have been layered;

[0198] Within the same undominated layer, to avoid solution concentration and degradation, the crowding distance of each scheme is calculated:

[0199]

[0200] In the formula, Representation scheme The crowding distance is used to measure the relative sparseness of the distribution of solutions within the same non-dominated layer. The greater the crowding, the fewer solutions are around the solution, and the more preferred it is. Indicates the index of the objective function. ; Representation scheme In the The values ​​of the objective function; and , They represent the objective functions respectively. The maximum and minimum values ​​(used for normalization to ensure that the objective functions are comparable under different dimensions); , The objective functions are respectively The adjacent solutions before and after the sorting are the previous and next solutions, respectively, used to calculate the neighborhood spacing and measure the distribution density of the solutions.

[0201] Within the same layer, solutions with larger crowding distances are preferred to ensure a balanced distribution of the Pareto solution set;

[0202] Finally, the Pareto optimal solution set that meets the business constraints is obtained:

[0203]

[0204] In the formula, This represents the optimal set of candidate technology paths after non-dominated sorting and crowding screening, which meets the business constraints. Indicates the first The Pareto optimal solution Denotes the number of Pareto optimal solutions, and satisfies ,in The total number of candidate technology paths;

[0205] This will be output as a candidate set of technical paths that meet business constraints.

[0206] The EPC project information management system, which supports two-way matching of business strategies and technology paths, also includes a two-way matching unit 4, which is based on a candidate set of technology paths. By combining business-side data, a two-way coupled optimization model is constructed, and the non-dominated sorting genetic algorithm NSGA-II is used for multi-objective optimization to obtain the optimal implementation scheme. .

[0207] The bidirectional matching unit 4 includes a conflict detection module 41, a bidirectional coupling optimization module 42, and a multi-ranking decision module 43.

[0208] Among them, the conflict detection module 41 is based on the technology path candidate set. Furthermore, it introduces constraints on capital costs, contract periods, and risks, and performs contract period conflict detection, capital conflict detection, and risk conflict detection on each candidate solution, outputting a set of feasible solutions that meet all constraints. ;

[0209] In this embodiment, the conflict detection module 41 further includes a periodic conflict detection submodule, a funding conflict detection submodule, and a risk conflict detection submodule;

[0210] Among them, the periodic conflict detection submodule is used to analyze the candidate set of technical paths. Construction period of each implementation plan Contractual constraints on project duration To make a comparison, if Then it is determined to be a periodic conflict and is eliminated, where, Representation scheme During the construction phase Construction period (unit: days or hours). Indicates the contractually binding construction period (unit: days or hours);

[0211] The funding conflict detection submodule is based on the budget funding requirements of each candidate solution. Project funding budget cost provided by the business side To make a comparison, when Mark as a funding conflict and remove. Representation scheme During the construction phase Budgetary funding requirements (unit: yuan). Project funding budget cost (unit: yuan);

[0212] The risk conflict detection submodule is used to calculate the risk level of each candidate solution. and the risk tolerance threshold set by the business side. To make a comparison, when The event is marked as a risk conflict and removed, among which and All were normalized and mapped to ;

[0213] By detecting conflicts, a set of feasible solutions that meet both constraints is obtained. :

[0214]

[0215] In the formula, This represents the set of feasible solutions, indicating all candidate technical path solutions selected in the conflict detection module 41 that satisfy the constraints of capital cost, contract period, and risk. Each of the schemes It should include at least the following parameters: resource allocation parameters (material usage, equipment scheduling plan and labor demand), time and efficiency parameters (phased construction period forecast, phased construction efficiency and total construction period), and economic and risk parameters (total cost, financing cost and comprehensive risk indicators). This indicates a candidate technology path solution that belongs to the original candidate technology path set. Elements in; This represents the candidate set of technical paths obtained through non-dominated sorting and crowding distance filtering;

[0216] Bidirectional coupling optimization module 42 is based on a set of feasible solutions A bidirectional coupled multi-objective optimization model is constructed, and the set of feasible solutions is solved using the non-dominated sorting genetic algorithm (NSGA-II) to generate the Pareto front solution set. ;

[0217] In this embodiment, before starting the bidirectional coupling optimization module 42 to perform multi-objective optimization, a solution space preprocessing mechanism is introduced to preprocess the feasible solution set. Preliminary screening using heuristic rules based on prior knowledge eliminates obviously inferior solutions, thereby reducing the initial search space of the Non-Dominated Sorting Genetic Algorithm (NSGA-II) and lowering computational complexity. These heuristic rules based on prior knowledge include, but are not limited to:

[0218] If the construction efficiency of a certain plan is lower than the lower quartile of the efficiency of similar historical projects, it will be eliminated.

[0219] If the total cost of a plan exceeds a certain safety threshold in the budget (such as 90%), it is considered a high-risk plan and is eliminated.

[0220] If the total duration of a certain plan exceeds a certain buffer percentage of the contract duration (such as 110%), it is considered an infeasible plan.

[0221] If the predicted construction efficiency of a certain scheme under extreme environmental conditions (such as heavy rain or high temperature) is lower than the historical lowest value, it will be eliminated.

[0222] Furthermore, the bidirectional coupling optimization module 42 adopts a distributed parallel computing architecture (a parallel computing environment built on a cloud computing platform, implemented based on a master-worker model, and deployed on a containerized cloud computing platform (such as Kubernetes) to accelerate the execution of the non-dominated sorting genetic algorithm (NSGA-II); the initial population generation task is decomposed into multiple sub-tasks, which are generated in parallel by different computing nodes; the crossover and mutation operations of each individual or each pair of parents are performed independently and distributed to different computing nodes for parallel execution; a parallel non-dominated sorting algorithm is adopted (such as a parallel implementation based on fast non-dominated sorting, where the master node randomly and evenly divides the merged population (parents and offspring) into N sub-populations and distributes them to N worker nodes, each worker node performs fast non-dominated sorting in parallel on its local sub-population to obtain a local non-dominated hierarchy; the master node collects all local frontiers and passes them through a global...) The merge sorting process compares and merges frontiers from different nodes pairwise to obtain a global non-dominated sorting result. Individuals are assigned to different nodes for dominance relationship judgment and stratification. Crowding degree is calculated for each objective function and executed in parallel on different nodes. Finally, the results are aggregated (after determining the global non-dominated level, for individuals in the same frontier layer, the crowding degree calculation task for each objective function is assigned to different Worker nodes for parallel execution. After each node calculates the crowding degree for the specified objective function, it returns the result to the master node, which performs summation and aggregation to obtain the total crowding degree for each individual). This is used to parallelize the tasks of population initialization, crossover mutation, non-dominated sorting, and crowding degree calculation in the Non-Dominated Sorting Genetic Algorithm (NSGA-II), and deploy them on a cloud computing platform for collaborative computing. This compresses the optimization process, which originally took several hours or even days, to an acceptable time limit for engineering decisions (such as minutes or hours), meeting the needs of real-time decision-making on site.

[0223] Based on the aforementioned distributed parallel computing architecture and solution space preprocessing mechanism, the set of feasible solutions is processed using the Non-Dominated Sorting Genetic Algorithm (NSGA-II). Perform multi-objective optimization:

[0224]

[0225] In the formula, The Pareto front solution set is represented by the bidirectional coupled optimization module 42 based on the set of feasible solutions. Calculations show that if the scheme No plan Domination, then the plan To be the Pareto optimal solution, include ;

[0226] Among them, the business-side optimization objectives include minimizing total cost and minimizing risk level, while the technology-side optimization objectives include minimizing construction period and maximizing construction efficiency;

[0227] In this embodiment, a bidirectional coupled multi-objective optimization model is constructed that simultaneously minimizes (construction period, total cost, and risk level) and maximizes (construction efficiency):

[0228] Minimize total cost:

[0229]

[0230] Minimize risk level:

[0231]

[0232] Minimize construction period:

[0233]

[0234] Maximize construction efficiency:

[0235]

[0236] In the formula, Let be the total cost objective function, representing the total cost within the set of feasible solutions. Minimize total capital expenditure across all stages; Let be the overall risk objective function, representing the minimization of the sum of risk levels across all stages within the set of feasible solutions; Construction phase The risk level (dimensionless and normalized to) The result is obtained by weighting various risk factors (construction progress, construction cost, construction safety, construction quality, construction supply chain, etc.). Let be the objective function for the total project duration, representing the minimization of the total construction period (in days or hours) of all construction phases in the feasible solution. Let be the objective function for construction efficiency, representing the maximization of the total construction efficiency across all stages under feasible solutions; Indicates the construction stage Construction efficiency (usually obtained through machine learning prediction models);

[0237] The bidirectional coupled multi-objective optimization model is then:

[0238]

[0239] In the formula, Represents the set of feasible solutions One of the candidate solutions; the two-way coupled multi-objective optimization model is a multi-objective optimization that simultaneously considers the business side (minimizing total cost and minimizing risk) and the technical side (minimizing construction period and maximizing construction efficiency);

[0240] The multi-ranking decision module 43 is used to process the Pareto front solution set. The candidate solutions are weighted and ranked to determine the solution with the best score. .

[0241] In this embodiment, the multi-ranking decision module 43 is based on the weighted linear aggregation method and solves the Pareto front solution set. Introducing Business Weight Vector With technical weight vector The solution set is then weighted and sorted.

[0242]

[0243] in, ,and and All values ​​were assigned based on expert experience.

[0244] Finally, the multi-ranking decision module 43 outputs the optimal set of implementation schemes:

[0245]

[0246] In the formula, Indicate candidate solutions The weighted total score, obtained by weighting and aggregating multi-objective performance indicators through business weights and technical weights, is used for ranking decisions. This represents the business weight vector, indicating the relative importance of business objectives (total cost, risk level) in the overall score. The vector dimension is consistent with the number of business objectives. This represents the technology weight vector, indicating the relative importance of technical objectives (construction period, construction efficiency) in the overall score. The vector dimension is consistent with the number of technical objectives. Indicate candidate solutions The total construction period target value represents the construction time required to complete the plan; Indicate candidate solutions The total cost target value typically includes the combined costs of materials, labor, equipment, and energy consumption; Indicate candidate solutions The target value for risk level is determined by comprehensively considering risk factors such as schedule, cost, safety, quality, and supply chain, and then undergoing weighted normalization. Indicate candidate solutions The construction efficiency target value usually represents the construction efficiency per unit cost or per unit time. Indicates the vector transpose sign; This represents the optimal score output by the multi-ranking decision module 43, which is the candidate solution with the smallest weighted total score in the Pareto front solution set. This indicates the solution set from the Pareto front. The solution that minimizes the weighted total score is selected as the final implementation solution.

[0247] Specifically, the final implementation plan Specifically, these include resource allocation parameters (material usage, equipment scheduling plan, and manpower requirements), time and efficiency parameters (phased construction period forecast, phased construction efficiency, and total construction period), and economic and risk parameters (total cost, financing cost, and comprehensive risk indicators).

[0248] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. An EPC project information management system that supports bidirectional matching of business strategies and technical paths, characterized in that, include: The data acquisition and integration unit (1) is used to collect multi-source heterogeneous data from the entire project implementation process and to preprocess the multi-source heterogeneous data. Among them, multi-source heterogeneous data includes business-side data, technology-side data, and external environment data; Business strategy unit (2), which constructs a resource optimization allocation model based on preprocessed business-side data and generates a practically executable resource allocation scheme. ; The resource optimization allocation model includes an objective function and model constraints, and introduces contract period constraints. With risk coefficient This is used to apply constraints and filter candidate solutions generated by the resource allocation model. Technology path unit (3), the technology path unit (3) is based on resource allocation scheme Based on model constraints and combined with technical and external environmental data, the BIM model is used to determine the materials, equipment, and labor required for each stage, generating a candidate set of technical paths that meet business constraints. ; Bidirectional matching unit (4), the bidirectional matching unit (4) is based on the technology path candidate set By combining business-side data, a two-way coupled optimization model is constructed, and a non-dominated sorting genetic algorithm is used to solve multi-objective optimization problems, ultimately obtaining the optimal implementation scheme. .

2. The EPC project information management system supporting bidirectional matching of business strategies and technical paths according to claim 1, characterized in that: The data acquisition and integration unit (1) includes a multi-source heterogeneous data acquisition module and a data preprocessing module; Among them, the multi-source heterogeneous data acquisition module is used to collect multi-source heterogeneous data throughout the entire process of engineering construction, and the data preprocessing module preprocesses the collected multi-source heterogeneous data to perform noise smoothing and dimensionality unification on the collected data. The business-side data includes at least project funding budget costs. Financing interest rate Contractual constraints on construction period Risk coefficient ; The technical data includes at least the construction progress. Material strength parameters Construction procedures Mechanical equipment utilization rate Energy consumption data ; The external environmental data includes climate temperature. Precipitation .

3. The EPC project information management system supporting bidirectional matching of business strategies and technical paths according to claim 1, characterized in that: The business strategy unit (2) includes a resource allocation module (21) and a cost constraint module (22). The resource allocation module (21) is used to receive preprocessed business side data and construct an objective function based on the business side data. The resource optimization allocation model is used to allocate business side resources and output resource allocation candidate vectors. The cost constraint module (22) introduces model constraints for the resource optimization allocation model based on the preprocessed business side data, and applies constraint filtering to the candidate schemes generated by the resource allocation model. The resource allocation module (21) and the cost constraint module (22) perform collaborative calculations through a solver to obtain the optimized optimal resource allocation scheme under model constraints. .

4. The EPC project information management system supporting bidirectional matching of business strategies and technical paths according to claim 3, characterized in that: The specific steps involved in constructing a resource optimization allocation model based on the aforementioned business-side data are as follows: The preprocessed business-side data is normalized to generate an input vector. ; The project construction process is divided into: The project is divided into several phases, and various resources in the project are categorized into different types. kind; Obtain the planned allocation of each type of resource at each stage. and actual usage Calculate each type of resource Different construction stages utilization efficiency ; Based on utilization efficiency The utilization efficiency of each stage is weighted and calculated to obtain the first stage. Reference values ​​for the comprehensive utilization efficiency of similar resources at all stages of the entire project construction process ; Based on the Class resources in the The allocation of resources for each construction phase is defined by the resource allocation vector. , resource allocation vector As a decision variable to be optimized; Based on resource allocation vector , construct the first Comprehensive utilization efficiency function of class resources ; Based on the Comprehensive utilization efficiency function of class resources and the Capital costs corresponding to similar resources And introduce risk coefficient The objective function is constructed to maximize resource utilization efficiency.

5. The EPC project information management system supporting bidirectional matching of business strategies and technical paths according to claim 4, characterized in that: The model constraints include capital cost constraints, contract period constraints, and risk constraints. Among them, the cost of capital constraint is used to limit the total capital expenditure to not exceeding the project's budgeted cost. ; Contract period constraints are used to limit the total construction period of the entire project to no more than the upper limit of the period stipulated in the contract. Risk constraints are used to limit the overall risk indicators introduced by the resource allocation scheme to not exceed a preset risk threshold.

6. The EPC project information management system supporting bidirectional matching of business strategies and technical paths according to claim 1, characterized in that, The technical path unit (3) includes a construction resource mapping module (31) and a technical path generation and optimization module (32). Among them, the construction resource mapping module (31) uses the BIM model to map resource allocation schemes. Mapping to construction nodes at each construction stage, outputting a set of candidate technical path solutions. It is used to determine the materials, equipment and labor required for each stage, and at the same time calculates the expected construction efficiency and projected construction period for each stage. The technology path generation and optimization module (32) optimizes the candidate technology path scheme set based on the model constraints of the candidate technology path scheme set and the business strategy unit (2). The process involves filtering and sorting to generate a candidate set of technology paths that meet business constraints. .

7. The EPC project information management system supporting bidirectional matching of business strategies and technical paths according to claim 6, characterized in that: The BIM model will include resource allocation schemes. Mapping to construction nodes at each construction stage and outputting a set of candidate technical path solutions involves the following specific steps: The project construction process is divided into: Each stage yields a set of nodes for that stage. , where each node There are several components, and the material requirement for each component is... ; Then the resource allocation scheme The first in Class resources in the stage Allocation amount The required amount of materials for nodes is The actual material usage at the node was calculated. And by analyzing the actual material usage of the node sub-units. Summing yields the total actual material usage for each node. ; Material usage at all nodes within the phase Summing gives the total material usage for each stage. ; Based on the Class resources in the stage Allocation amount Calculate the number of devices required for each stage. ; Based on node construction procedures And introduce the construction phase. workload This yields the labor demand for each type of job. ; Phase The manual requirements of all nodes within the process are aggregated to obtain the stage. Total internal human resource utilization demand ; Based on construction stage Total internal material usage Quantity of equipment Labor demand Construct a nonlinear regression model to output the construction phase. The internal construction efficiency is expected, and this is achieved by incorporating climate temperature. and precipitation The optimized nonlinear regression model was used to calculate the optimized expected construction efficiency. ; Based on the expected construction efficiency and stages Internal workload To obtain the project duration forecast ; The material usage, equipment scheduling, labor requirements, expected construction efficiency, and projected construction period at each stage are mapped to generate a set of candidate technical path solutions. .

8. The EPC project information management system supporting bidirectional matching of business strategies and technical paths according to claim 7, characterized in that: For the set of candidate technology paths The specific steps involved in filtering and sorting to generate a candidate set of technology paths that meet business constraints are as follows: Based on the set of candidate technology paths And model constraints, for the set of candidate technical path solutions Constraints are determined one by one, and the model constraints include at least capital cost constraints, contract period constraints, and risk constraints. Candidate solutions that do not meet any of the above constraints will be eliminated. For candidate technology path solutions that meet the constraints, a set of candidate technology paths that satisfy the business constraints is generated based on the Pareto optimal ranking algorithm. .

9. The EPC project information management system supporting bidirectional matching of business strategies and technical paths according to claim 5, characterized in that: The bidirectional matching unit (4) includes a conflict detection module (41), a bidirectional coupling optimization module (42), and a multi-ranking decision module (43). The conflict detection module (41) is based on a candidate set of technical paths. Furthermore, it introduces constraints on capital costs, contract periods, and risks, and performs contract period conflict detection, capital conflict detection, and risk conflict detection on each candidate solution, outputting a set of feasible solutions that meet all constraints. ; The bidirectional coupling optimization module (42) is based on a set of feasible solutions. A bidirectional coupled multi-objective optimization model is constructed, and the set of feasible solutions is solved using a non-dominated sorting genetic algorithm to generate a Pareto front solution set. ; Among them, the business-side optimization objectives include minimizing total cost and minimizing risk level, while the technology-side optimization objectives include minimizing construction period and maximizing construction efficiency; The multi-ranking decision module (43) is used for the Pareto front solution set. The candidate solutions are weighted and ranked to determine the optimal implementation. .

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