CN118469312B - Civil construction cost data analysis system - Google Patents
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Abstract
The invention relates to the technical field of construction cost data analysis, in particular to a civil construction cost data analysis system which comprises an engineering quantity list structure analysis module, a cost correlation network construction module, a material cost fluctuation analysis module, a project overflow risk simulation module, a cost control strategy formulation module, a budget optimization decision module, a construction scheme evaluation module and a risk management and relief module. According to the invention, the prediction capability of the cost fluctuation trend is effectively enhanced by analyzing the material cost fluctuation and the market data, the decision accuracy in the uncertain market is promoted, the project manager can identify the potential cost hyperbranched at an early stage by simulating the price overflow risk in the project, the strategy is timely adjusted, the project is helped to grasp the key cost control point, the pertinence and the efficiency of the resource use are improved, the optimal budget allocation scheme is accurately captured, and the dual targets of the cost control and the project efficiency are effectively combined.
Description
Technical Field
The invention relates to the technical field of construction cost data analysis, in particular to a civil construction cost data analysis system.
Background
The technical field of construction cost data analysis is focused on predicting and controlling the cost of construction projects by utilizing data processing and analysis technology. Elements of data science, architecture and economics are fused in an effort to provide accurate cost estimates by analyzing historical data, market trends and project characteristics. Techniques in this field typically include, but are not limited to, data collection (e.g., engineering inventory, material costs, labor costs, etc.), data processing (cleaning, sorting, integration), and advanced data analysis (using statistical models, machine learning algorithms, etc.), with the aim of improving budgeting accuracy, optimizing resource allocation, reducing risk, and improving overall project management efficiency.
Wherein the civil construction cost data analysis system is a computer system specifically designed for analyzing and predicting the cost of the civil construction project. The main purpose is to improve the accuracy and reliability of the construction cost budget of civil engineering projects. By integrating and analyzing data from different sources (such as material costs, labor, project schedules, and historical data for previous projects), a more accurate cost estimate can be provided. The method aims to help project managers optimize resource allocation through data-driven decision support, reduce waste and effectively control cost in the whole project life cycle.
The prior art shows obvious limitations in practical applications. Especially in dynamic market environments, it is poorly adapted to material cost fluctuations and project risks. Existing systems often rely on historical data and empirical estimates, lack of response mechanisms to rapidly changing market conditions, which often lead to inaccuracy in budgeting, increasing the economic risk of the project. For example, in the absence of effective risk assessment and management tools, projects may be under-reacted to sudden market changes, resulting in cost outages and progress delays. The lack of flexibility in resource allocation and budget optimization often fails to accurately match project requirements, resulting in wasted or insufficient resources, thereby affecting the economic benefit and execution efficiency of the project.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a data analysis system for civil construction cost.
In order to achieve the above purpose, the present invention adopts the following technical scheme: a civil construction cost data analysis system comprising:
The engineering quantity list structure analysis module performs dependency sequencing on engineering tasks based on an engineering quantity list, converts the engineering tasks and material requirements into nodes, converts work dependency relations into edges, analyzes key paths and nodes and generates a list structure map;
the cost association network construction module converts project elements in the engineering quantity list into network nodes based on the list structure map, converts the cost association relationship into network edges, and identifies key nodes and cost flow modes in the network to generate a cost association map;
The material cost fluctuation analysis module quantitatively analyzes the fluctuation of the material cost based on market data and historical cost information, predicts and analyzes the cost fluctuation trend, identifies potential risk points and fluctuation modes, and generates a material cost fluctuation analysis result;
The project overflow risk simulation module is used for performing simulation analysis on overflow risks in projects based on project parameters and market conditions, simulating overflow scenes by combining potential influences of project cost and time, and generating overflow risk prediction results;
The cost control strategy making module analyzes key cost control points and potential risk factors in the project based on the cost association graph and the overflow price risk prediction result, and makes preventive measures and countermeasures according to the analysis result to generate a cost control strategy;
the budget optimization decision module optimizes project budget based on the cost control strategy, captures an optimal budget allocation scheme, and combines project targets including cost minimization and efficiency maximization to generate an optimization decision scheme;
The construction scheme evaluation module comprehensively evaluates the differential construction scheme based on the optimized decision scheme and the engineering quantity list, and the comprehensive performance of the scheme is evaluated by comparing the cost, time and quality standards of the scheme, so as to generate a construction scheme evaluation result;
And the risk management and relieving module quantitatively analyzes and manages potential risks in the project based on the material cost fluctuation analysis result and the construction scheme evaluation result, and establishes a risk relieving and managing strategy by simulating the influence of different risk factors on the project to generate a risk management scheme.
As a further scheme of the invention, the inventory structure map comprises a dependency sequence of project tasks and a hierarchy relation of work items, the cost association map specifically refers to cost dependency and circulation paths among project elements, the material cost fluctuation analysis result comprises a predicted price fluctuation range and a potential risk interval, the overflow price risk prediction result specifically refers to potential strategy selection of a benefit associator and the result thereof, the cost control strategy comprises key cost control points and cost management measures, the optimization decision scheme specifically refers to adjusted budget allocation and cost benefit ratio analysis, the construction scheme evaluation result specifically refers to cost benefit comparison of each scheme and recommended construction method, and the risk management scheme comprises identification of potential risk points, relief strategies and risk monitoring measures.
As a further aspect of the present invention, the material cost fluctuation analysis module includes:
The fluctuation mode identification submodule adopts an information entropy theory to count the occurrence frequency of cost items based on market data and historical cost information, calculates information entropy, and identifies a statistical mode of material cost fluctuation by analyzing time sequence changes of the information entropy to generate a fluctuation mode analysis result;
The cost trend analysis sub-module is used for carrying out model fitting and analysis by adopting an autoregressive moving average model based on the fluctuation mode analysis result, inputting historical cost data, calculating model parameters, predicting future cost trend and generating a cost trend prediction result;
And the fluctuation risk assessment sub-module is used for assessing risk by using a normal distribution probability calculation formula based on the cost trend prediction result, calculating the mean value and standard deviation of cost prediction, determining the probability of exceeding expected fluctuation, analyzing risk points and intervals and generating a material cost fluctuation analysis result.
As a further aspect of the present invention, the project overflow risk simulation module includes:
The strategy simulation sub-module defines a strategy set of a stakeholder by utilizing Nash equilibrium theory based on project parameters and market conditions, creates a game model, sets strategy options and corresponding utility values for participants, calculates expected utility under each strategy combination, simulates strategy selection and results, and generates strategy simulation analysis results;
The game analysis submodule builds and executes game tree analysis based on the strategy simulation analysis result, defines decision options and potential results for each node of the tree, adds decision points and decision paths, analyzes the influence of each path, calculates the total cost and the completion time under the difference decision combination, and generates a game analysis result;
And the risk prediction submodule carries out comprehensive prediction of the risk of the overflow price based on the game analysis result, evaluates the possibility and potential influence of the overflow price under the combination of the difference strategies by combining the strategy selection of the stakeholders and the game tree analysis result, predicts the risk situation of the overflow price of the project and generates a risk prediction result of the overflow price.
As a further scheme of the invention, the Nash equalization theory is as follows:
；
The expected utility of each participant is calculated, wherein, For participantsIs used in the present invention,For participantsIs used in the method of the present invention,To remove participantsIn addition to the policy combinations of the other participants,For policy combinationThe probability of the occurrence of this is,When the strategies are combined intoWhen the participantIs used in the field of the present invention,As the weight coefficient of the light-emitting diode,For participantsThe risk assessment of the policy is performed,For the synergistic effect of the other participant policies,Combining policies for market environmentsIs a function of (a) and (b).
As a further aspect of the present invention, the cost control policy formulation module includes:
The control point identification submodule carries out key cost control point identification based on the cost association graph and the premium risk prediction result, analyzes the risk degree and control urgency of the difference cost nodes and generates a control point identification result;
The effect prediction submodule carries out sensitivity analysis based on the control point identification result, inputs cost data of the control point and a potential adjustment scheme, calculates the influence degree of the adjustment scheme on the total project cost, predicts the effect of the difference control strategy and generates an effect prediction analysis result;
And the strategy making submodule synthesizes various scenes of the project based on the effect prediction analysis result, including cost limitation, time frame and resource availability, and plans cost control measures under the difference scene, including budget adjustment and resource redistribution, by combining the control point analysis and the effect prediction result to generate a cost control strategy.
As a further aspect of the present invention, the budget optimization decision module includes:
The cost benefit analysis sub-module solves the optimization problem by adopting a linear programming algorithm based on the cost control strategy and combining project data, sets cost coefficients and resource limitations as parameters to calculate, and generates a cost benefit evaluation record;
The budget adjustment submodule processes the budget allocation problem based on the cost-benefit evaluation record, sets budget limit and resource allocation as parameters, adjusts the budget allocation to achieve optimal resource utilization, and generates a budget adjustment scheme;
the decision optimization submodule carries out random sampling simulation on decision variables based on the budget adjustment scheme, optimizes the effectiveness of the decision by simulating the cost and efficiency result under the differential budget scheme, and generates an optimized decision scheme.
As a further aspect of the present invention, the linear programming algorithm is as follows:
；
The adjusted total cost is calculated, wherein, As a total cost of the product,Is the firstThe base cost coefficient of each activity is set,Is the firstThe coefficient of variation cost of the individual activities,Is the firstThe varying factor of the individual activities is used,Is the firstThe decision variables of the individual activities are chosen,Is the firstThe weight of the individual resources is determined,Is the firstGreen index of individual resources.
Compared with the prior art, the invention has the advantages and positive effects that:
According to the invention, the prediction capability of the cost fluctuation trend is effectively enhanced by analyzing the material cost fluctuation and market data, the decision accuracy in the uncertain market is promoted, the project manager can identify the cost potential hyperbranched at an early stage by simulating the price overflow risk in the project, the strategy is timely adjusted, the project is helped to grasp the key cost control point, the pertinence and the efficiency of resource use are improved, the optimal budget allocation scheme is accurately captured, the dual targets of cost control and project efficiency are effectively combined, the comprehensiveness of different construction schemes is evaluated, the optimal configuration of cost, time and quality is ensured, and the overall stability of the project is improved by managing and buffering the potential risk.
Drawings
FIG. 1 is a system flow diagram of the present invention;
FIG. 2 is a schematic diagram of a system framework of the present invention;
FIG. 3 is a flow chart of the engineering quantity list structure analysis module of the present invention;
FIG. 4 is a flow chart of a cost-associated network building block of the present invention;
FIG. 5 is a flow chart of a material cost fluctuation analysis module of the present invention;
FIG. 6 is a flow chart of the project overflow risk simulation module of the present invention;
FIG. 7 is a flow chart of a cost control strategy formulation module of the present invention;
FIG. 8 is a flow chart of a budget optimization decision module in accordance with the present invention;
FIG. 9 is a flow chart of a construction plan evaluation module of the present invention;
FIG. 10 is a flow chart of a risk management and mitigation module of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the description of the present invention, it should be understood that the terms "length", "" width "," "upper", "" lower "," "front", "" rear "," "left", "" right "," "vertical", "" horizontal "," "top", "" bottom "," "inner", "" outer "andthe like indicate orientations or positional relationships based on the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "" plurality "means two or more, unless specifically defined otherwise.
Embodiment one: referring to fig. 1, a civil construction cost data analysis system includes:
The engineering quantity list structure analysis module performs dependency sequencing on engineering tasks based on an engineering quantity list, converts the engineering tasks and material requirements into nodes, converts work dependency relations into edges, analyzes key paths and nodes and generates a list structure map;
The cost association network construction module converts project elements in the engineering quantity list into network nodes based on the list structure map, converts the cost association relationship into network edges, identifies key nodes and cost flow modes in the network, and generates a cost association map;
The material cost fluctuation analysis module quantitatively analyzes the fluctuation of the material cost based on market data and historical cost information, predicts and analyzes the cost fluctuation trend, identifies potential risk points and fluctuation modes, and generates a material cost fluctuation analysis result;
The project overflow risk simulation module is used for performing simulation analysis on overflow risks in projects based on project parameters and market conditions, simulating overflow scenes by combining potential influences of project cost and time, and generating overflow risk prediction results;
the cost control strategy making module analyzes key cost control points and potential risk factors in the project based on the cost association graph and the overflow price risk prediction result, and makes preventive measures and response schemes according to the analysis result to generate a cost control strategy;
the budget optimization decision module optimizes project budget based on a cost control strategy, captures an optimal budget allocation scheme, and combines project targets including cost minimization and efficiency maximization to generate an optimization decision scheme;
the construction scheme evaluation module comprehensively evaluates the differential construction scheme based on the optimization decision scheme and the engineering quantity list, and the comprehensive performance of the scheme is evaluated by comparing the cost, time and quality standards of the scheme to generate a construction scheme evaluation result;
The risk management and relieving module quantitatively analyzes and manages potential risks in the project based on the material cost fluctuation analysis result and the construction scheme evaluation result, and creates a risk relieving and managing strategy by simulating the influence of different risk factors on the project to generate a risk management scheme.
The inventory structure map comprises a dependency sequence of project tasks and a hierarchy relation of work items, the cost association map specifically refers to cost dependency and circulation paths among project elements, the material cost fluctuation analysis result comprises a predicted price fluctuation range and a potential risk interval, the overflow price risk prediction result specifically refers to potential strategy selection of a stakeholder and the result thereof, the cost control strategy comprises key cost control points and cost management measures, the optimization decision scheme specifically refers to budget allocation and cost benefit ratio analysis after adjustment, the construction scheme evaluation result specifically refers to cost benefit comparison and recommended construction methods of each scheme, and the risk management scheme comprises identification of potential risk points, alleviation strategies and risk monitoring measures.
Referring to fig. 2 and 3, the engineering quantity list structure analysis module:
The node analysis submodule carries out node analysis based on the engineering quantity list, reads and processes engineering quantity list data, creates a directed graph, takes engineering tasks and material demands as nodes of the graph, determines node attributes including task names, required materials and predicted construction periods, carries out node classification, and generates a node classification graph;
Each task in the engineering quantity list and related materials and construction period data thereof are firstly extracted, and each task is uniquely marked by an identifier. Then, the node properties are set according to the required materials and the expected construction period. For example, for a specific task "building foundation", if the material requirements include "rebar" and "concrete", the predicted period is 10 days, the node is labeled { "task name": "building foundation", "material": [ "reinforced bar", "concrete" ], construction period ":10}. Further, the nodes are classified according to the material types and construction periods in the node attributes, such as short-term tasks, material-intensive tasks and the like, and different graphic identifiers and colors are set for each class so as to distinguish in subsequent visualizations, the node attributes including task names, required materials and predicted construction periods are determined, node classification is performed, and a node classification diagram is generated.
The dependency analysis submodule carries out dependency analysis based on the node classification diagram, determines the dependency between nodes according to the task sequence in the engineering quantity list, distributes corresponding edges for each node, carries out visual representation and generates a dependency diagram;
First, analyzing the sequence of each task in the engineering quantity list, and determining the dependency relationship among the nodes according to the sequence. For example, if wall construction is only possible after the building foundation is completed, a dependency edge is established between the "building foundation" node and the "wall construction" node. Each dependency is represented by a directed edge, the direction of which is directed to the subsequent task of the dependency. Then, the relationships are visualized through a graphical tool, the arrow is used for representing the dependence direction, different types of dependence such as time dependence, material dependence and the like are identified by assisting different colors, corresponding edges are allocated to each node for visualization, and a dependency relationship graph is generated.
The map construction submodule carries out map construction based on the dependency graph, calculates the degree of ingress and egress of each node in the graph, identifies a key path and key nodes, optimizes the integrity and accuracy of the map, and generates a list structure map;
The calculation of the in-degree and the out-degree is performed according to the number of edges connected to the node, for example, the in-degree of the node "wall construction" is 1 (from "building foundation"), and the out-degree is 2 (pointing to "roof installation" and "exterior wall painting"). In this way, critical nodes and critical paths in the project are identified, such as delaying "wall construction" which may affect the progress of the entire project. And (3) optimizing the integrity and accuracy of the map by adjusting the processing sequence and resource allocation of the key nodes, and generating a list structure map.
Referring to fig. 2 and 4, the cost-associated network construction module includes:
The network node constructing module constructs a complex network model based on the list structure map, defines project elements as nodes, sets node attributes including project names and cost estimation, defines cost association relations among the nodes as edges, designates the attributes of the edges including association degrees, and generates a network structure model;
The network node construction module constructs a complex network model based on the inventory structure map, defines project elements as nodes, and sets node attributes including project names and cost estimation. First, each project element such as "construction stage" or "material procurement" is set as a node in the network, and each node is assigned an attribute such as the project name "construction stage", and the cost estimate is set as the preliminary estimate C1. Next, a cost association relationship between nodes is defined, the relationship being regarded as edges in the network, each edge representing a direct cost impact between two project elements. The attributes of the edges include a degree of association, which is formulated Representation of whereinAndIs a cost estimate of connected nodes indicating an average correlation of costs between the two nodes, and generates a network structure model.
The cost flow analysis submodule is used for carrying out cost flow analysis by applying a maximum flow minimum cut algorithm based on a network structure model, determining a cost flow path and a key channel in a network, capturing a transmission mode and key flow nodes of cost in the network and generating a cost flow analysis result;
the cost flow analysis sub-module applies a maximum flow minimum cut algorithm to conduct cost flow analysis based on the network structure model, and calculates the possible maximum cost flow from the source node to the sink in the network. By the formula Representation of whereinAndThe method comprises the steps of respectively determining total output and input costs of nodes, identifying a key channel in a network, namely a path with the largest influence on total cost flow, and key flow nodes, namely nodes with the largest influence on cost flow, mainly searching nodes and edges which can influence cost maximization transfer, determining the cost flow path and the key channel in the network, capturing the transfer mode of cost in the network and the key flow nodes, and generating a cost flow analysis result.
The correlation diagram generation submodule analyzes key nodes and cost flow paths in the network based on the cost flow analysis result, and performs visualization processing to generate a cost correlation diagram;
the correlation diagram generation submodule analyzes key nodes and cost flow paths in the network based on the cost flow analysis result and performs visualization processing. Including drawing a graphical representation of the cost flow, highlighting key nodes and paths. The importance of dividing into streams of size and path with color and line thickness, e.g., a path with a larger flow uses thicker lines and warm tone representations to help more intuitively understand the distribution and impact of costs throughout the project, generating a cost correlation graph.
Referring to fig. 2 and 5, the material cost fluctuation analysis module includes:
The fluctuation mode identification submodule adopts an information entropy theory to count the occurrence frequency of cost items based on market data and historical cost information, calculates information entropy, and identifies a statistical mode of material cost fluctuation by analyzing time sequence changes of the information entropy to generate a fluctuation mode analysis result;
the fluctuation mode identification submodule calculates information entropy by adopting an information entropy theory and counting the occurrence frequency of cost items based on market data and historical cost information. First, the module collects and collates market data with historical cost data, counts the frequency of occurrence of various cost items, such as specific materials or services WhereinRepresenting a particular cost term. Next, an information entropy formula is usedWhereinInformation of the cost term is calculated to evaluate uncertainty and complexity of the cost. And analyzing the time sequence change of the information entropy by comparing the information entropy change in the continuous time period, thereby identifying the statistical mode of the material cost fluctuation and generating a fluctuation mode analysis result.
The cost trend analysis sub-module is used for carrying out model fitting and analysis by adopting an autoregressive moving average model based on the fluctuation mode analysis result, inputting historical cost data, calculating model parameters, predicting future cost trend and generating a cost trend prediction result;
Based on the analysis result of the fluctuation mode, the cost trend analysis submodule adopts an autoregressive moving average model (ARMA) to carry out model fitting and analysis, inputs historical cost data, and firstly determines the model order AndFor exampleAnd. The model uses a combination of an autoregressive portion and a moving average portion to fit the historical data and predict future cost trends. The formula isWhereinRepresenting the observed value at time point t, t representing the time series point,Is a constant term that is used to determine the degree of freedom,Is an error term of the error term,AndThe parameters of autoregressive and moving average, respectively. Parameters are estimated through a least square method and the like to provide a best-fit cost trend, and a cost trend prediction result is generated.
The fluctuation risk assessment submodule is used for assessing risks by using a normal distribution probability calculation formula based on cost trend prediction results, calculating the average value and standard deviation of cost prediction, determining the probability exceeding expected fluctuation, analyzing risk points and intervals and generating a material cost fluctuation analysis result;
The fluctuation risk assessment sub-module assesses risk by using a normal distribution probability calculation formula based on the cost trend prediction result. In the calculation process, firstly, the average value of the cost predicted value is determined And standard deviation. Parameters are obtained from the output of the cost trend prediction model. Using a normal distribution probability density functionCalculating a specific cost valueIs a probability of occurrence of (a). Further, by setting a threshold valueTo determine the probability of exceeding the expected fluctuation, e.g. the probability of exceeding a threshold value isWhereinThe method is a cumulative distribution function of normal distribution, helps to identify risk points and risk intervals in the cost trend, and generates a material cost fluctuation analysis result.
Referring to fig. 2 and 6, the project overflow risk simulation module includes:
The strategy simulation sub-module defines a strategy set of a stakeholder by utilizing Nash equilibrium theory based on project parameters and market conditions, creates a game model, sets strategy options and corresponding utility values for participants, calculates expected utility under each strategy combination, simulates strategy selection and results, and generates strategy simulation analysis results;
the Nash equilibrium theory is as follows:
；
The expected utility of each participant is calculated, wherein, For participantsIs used in the present invention,For participantsIs used in the method of the present invention,To remove participantsIn addition to the policy combinations of the other participants,For policy combinationThe probability of the occurrence of this is,When the strategies are combined intoWhen the participantIs used in the field of the present invention,As the weight coefficient of the light-emitting diode,For participantsThe risk assessment of the policy is performed,For the synergistic effect of the other participant policies,Combining policies for market environmentsIs a function of (a) and (b).
The execution process is as follows:
First, calculate the original expected utility formula part, accumulate the probabilities and corresponding utilities under each policy combination, then introduce risk assessment To adjust the strategyRisk exposure of (a), also, synergistic effectPolicy combination pair participant for evaluating other participantsIs, finally, the influence of the market environmentConsidering the effect of external market change on strategy combination, weight coefficientAnd determining through a linear regression model, wherein the independent variable is the actual effect of the past strategy, the dependent variable is a corresponding utility value, and obtaining the optimal value of each coefficient through regression analysis to ensure the proper influence of each part of the formula in the overall expected utility.
The game analysis submodule builds and executes game tree analysis based on the strategy simulation analysis result, defines decision options and potential results for each node of the tree, adds decision points and decision paths, analyzes the influence of each path, calculates the total cost and the completion time under the difference decision combination, and generates a game analysis result;
The game analysis sub-module builds and performs a game tree analysis based on the results of the policy simulation analysis, defining decision options and potential results for each node of the tree, each decision point representing a participant's policy selection, and each path representing a possible combination of policies. Next, cost and time impact analysis is performed on each path using AndCalculating total cost under differential decision combinationAnd completion timeWhereinAndRespectively are pathsIs added to the cost and time of (1),AndIs a relative weight, is helpful to reveal the economy and time efficiency of different decision paths, and generates game analysis results.
The risk prediction sub-module is used for comprehensively predicting the risk of the overflow price based on the game analysis result, and evaluating the possibility and potential influence of the overflow price under the combination of the difference strategies by combining the strategy selection of a stakeholder with the game tree analysis result, predicting the risk situation of the overflow price of the project and generating a risk prediction result of the overflow price;
And the risk prediction sub-module is used for comprehensively predicting the premium risk based on the game analysis result. And evaluating the possibility and potential influence of the occurrence of the overflow price under the differential strategy combination by combining the strategy selection of the stakeholder and the game tree analysis result. Using the formula WhereinIs the probability of a risk of a premium,Is a policy combinationThe probability of the occurrence of this is,Is the risk factor for the policy combination underflow price. By the method, the overflow price risk situations under different strategy combinations can be predicted, and the overflow price risk prediction result is generated.
Referring to fig. 2 and 7, the cost control policy making module includes:
the control point identification submodule carries out key cost control point identification based on the cost association graph and the overflow price risk prediction result, analyzes the risk degree and control urgency of the difference cost nodes and generates a control point identification result;
The control point identification submodule carries out key cost control point identification based on the cost association diagram and the overflow price risk prediction result. First, connectivity of each node in the cost correlation graph and a marker risk value in the premium risk prediction are evaluated. By analyzing the degree of risk of nodes, such as nodes whose probability exceeds a predetermined threshold, and controlling urgency, formulas are used Calculation of whereinThe risk score for a node is given,As the risk probability of a node's premium,And helping to identify the high-risk cost node needing to be controlled preferentially for the cost value of the node in the cost association graph, thereby allowing a targeted control measure to be formulated and generating a control point identification result.
The effect prediction sub-module performs sensitivity analysis based on the control point identification result, inputs cost data of the control point and a potential adjustment scheme, calculates the influence degree of the adjustment scheme on the total project cost, predicts the effect of the difference control strategy, and generates an effect prediction analysis result;
the effect prediction sub-module performs sensitivity analysis based on the control point identification result. Cost data and potential adjustment schemes for the control point are entered, for example, to reduce the cost of or to increase supervision of certain high risk nodes. Calculating the influence degree of the adjustment scheme on the total project cost by using a differential analysis method, wherein the formula is WhereinIs the amount of tapering of the overall cost,Is a nodeThe cost of the device is changed in proportion after adjustment,Is a nodeCost adjustment value of (2). By means of the analysis, the effects of different control strategies are predicted, the potential influence of the effects on the total project cost is evaluated, and an effect prediction analysis result is generated.
The strategy making sub-module synthesizes various scenes of the project based on the effect prediction analysis result, including cost limitation, time frame and resource availability, combines the control point analysis and the effect prediction result, and plans cost control measures under the difference scene, including budget adjustment and resource redistribution, so as to generate a cost control strategy;
the strategy making sub-module is used for predicting analysis results based on the effects and integrating various scenes of the project. And (3) planning cost control measures in a difference scene by considering cost limitation, a time frame and resource availability and combining control point analysis and effect prediction results. Policies include budget adjustment, resource reallocation, and optimization scheduling. For example, if the adjustment of a certain control point shows significant benefits to the cost and time frame, then consider increasing the allocation of resources for that point and reducing resources for other lower risk areas. And through comprehensive consideration of strategies, a more effective cost control scheme is formulated so as to adapt to different project execution scenes, and a cost control strategy is generated.
Referring to fig. 2 and 8, the budget optimization decision module includes:
The cost benefit analysis submodule solves the optimization problem by adopting a linear programming algorithm based on a cost control strategy and combining project data, sets cost coefficients and resource limitations as parameters to calculate, and generates a cost benefit evaluation record;
Linear programming algorithm, according to the formula:
；
calculating the adjusted total cost, generating a cost-benefit assessment record, wherein, For the adjusted objective function, the total cost is represented,Is the firstThe base cost coefficient of each activity is set,Is the firstThe coefficient of variation cost of the individual activities,Is the firstA variable factor for each campaign, such as raw material price changes or market demand changes,Is the firstDecision variables for an activity, representing the size or level of the activityFor the weighting coefficients, their optimal values are determined by data analysis, for example by regression analysis of historical data,Is the firstThe weight of the individual resources, reflecting their extent of impact on the total cost,Is the firstThe green index of each resource reflects the environmental protection degree of the resource use.
The execution process is as follows:
first, the cost per activity is no longer fixed But takes into account that the cost may be subject to external factors such as price fluctuationsSo that a variation cost coefficient is increasedTo adjust the base cost factor, then, to cope with the environmental and sustainable development demands, the green index of the resource is introducedAnd corresponding weightsThe weight is set by evaluating the importance of the resource, and the weight coefficientThen the historical data analysis is adopted to ensure that the use of each resource and the environmental protection standard are properly considered so as to calculate the total cost after adjustmentIn the whole process, each parameter is ensured to accurately reflect the economic and environmental influences, so that the cost is optimized and the resource utilization efficiency is improved.
The budget adjustment submodule processes the budget allocation problem based on the cost-benefit evaluation record, sets budget limit and resource allocation as parameters, adjusts the budget allocation to achieve optimal resource utilization, and generates a budget adjustment scheme;
The budget adjustment submodule handles budget allocation issues based on the cost-benefit assessment records. Setting budget limit and resource allocation as parameters, and adjusting budget to achieve optimal utilization of resources. Weights for different resources are considered And green indexWhereinReflecting the extent to which the resource affects the overall cost,Reflecting the environmental protection degree of resource use. Through an optimization model minimizeWhereinRepresentation allocation to the firstAnd (3) adjusting the cost of each resource and budget allocation to realize double optimization of cost and environmental protection, and generating a budget adjustment scheme.
The decision optimization submodule carries out random sampling simulation on decision variables based on a budget adjustment scheme, optimizes the effectiveness of a decision by simulating the cost and efficiency result under a differential budget scheme, and generates an optimized decision scheme;
The decision optimization submodule carries out random sampling simulation on decision variables based on a budget adjustment scheme. By modeling cost and efficiency results under different budget scenarios, the effectiveness of the decision is assessed and optimized using the modeling results. In the simulation process, different decision variable combinations are randomly generated, the total cost and the expected benefit under each combination are calculated, and the cost-benefit ratio of the decision scheme is optimized by comparing the results, so that the optimized decision scheme is generated.
Referring to fig. 2 and 9, the construction plan evaluation module includes:
The scheme comparison sub-module performs cost benefit analysis based on the optimization decision scheme and the engineering quantity list, calculates the net present value of each scheme, performs data calculation and summarization, simulates the influence of different risk factors, calculates the risk probability distribution of the scheme, and generates a scheme comparison analysis result;
the scheme comparison sub-module performs a cost benefit analysis based on the optimization decision scheme and the engineering quantity list. First, the Net Present Value (NPV) for each scenario is calculated, using the formula: wherein Represents the firstAnnual cash flows (including costs and revenues),Is the discount rate of the product, the product is a discount rate,Is the total deadline. The economic value of each solution is determined by this method. Further, modeling the impact of differential risk factors such as market fluctuations, supply chain breaks, and using probability density functionsAnd calculating the risk probability distribution of the schemes, helping to determine the economic performance of each scheme under different risk situations, and generating a scheme comparison analysis result.
The scheme selection submodule carries out optimal construction scheme selection based on the scheme comparison analysis result, calculates weight and consistency ratio, defines an evaluation standard set and an evaluation grade, carries out fuzzy comprehensive evaluation, determines the scheme quality and generates an optimal construction scheme selection result;
the scheme selection submodule carries out optimal construction scheme selection based on the scheme comparison analysis result, calculates weight and consistency ratio of each scheme, and ensures logic consistency in the evaluation process. A fuzzy comprehensive evaluation method is used to define an evaluation standard set such as cost, time, quality, customer satisfaction and the like, and corresponding evaluation grades such as excellent, good, medium and poor. By constructing weight vectors Evaluation matrixAnd (3) performing comprehensive scoring calculation: wherein Representing the composite score for each regimen. And (5) evaluating the comprehensive performance of each scheme according to the scoring result, determining the advantages and disadvantages of the schemes, and generating an optimal construction scheme selection result.
The scheme selection submodule carries out optimal construction scheme selection based on the scheme comparison analysis result, calculates weight and consistency ratio of each scheme, and ensures logic consistency in the evaluation process. A fuzzy comprehensive evaluation method is used to define an evaluation standard set such as cost, time, quality, customer satisfaction and the like, and corresponding evaluation grades such as excellent, good, medium and poor. By constructing weight vectorsEvaluation matrixAnd (3) performing comprehensive scoring calculation: wherein Representing the composite score for each regimen. And (5) evaluating the comprehensive performance of each scheme according to the scoring result, determining the advantages and disadvantages of the schemes, and generating an optimal construction scheme selection result.
The scheme evaluation submodule carries out comprehensive evaluation of the scheme based on the optimal construction scheme selection result, defines key performance indexes including cost deviation, progress deviation and quality standard, tracks and evaluates the indexes, carries out data analysis and generates a construction scheme evaluation result;
the scheme evaluation submodule carries out comprehensive evaluation of the scheme based on the optimal construction scheme selection result. Key Performance Indicators (KPIs) are defined, including cost bias, progress bias, and quality criteria. Tracking and evaluating each index, e.g. by cost deviation And (5) calculating. And analyzing the performance of the project in the execution process by collecting related data, evaluating whether the project meets the preset project targets and standards, helping a project management team to identify potential problem areas, carrying out necessary adjustment, and generating a construction scheme evaluation result.
Referring to fig. 2 and 10, the risk management and mitigation module includes:
The risk identification submodule calculates statistical probability distribution of potential risks based on a material cost fluctuation analysis result and a construction scheme evaluation result, identifies and quantitatively analyzes key risk points of the projects, simulates project influence of different risk scenes and generates a risk identification list;
The risk identification submodule calculates the statistical probability distribution of the potential risk based on the material cost fluctuation analysis result and the construction scheme evaluation result. The fluctuation data associated with the material costs and the risk points identified in the construction plan, such as supply chain delays or price increases, are first summarized. By applying statistical analysis, such as a probability density function F (x) and a cumulative distribution function F (x), statistical probability distributions of risks are calculated, thereby identifying high probability and high impact risk points. Further, by simulating different risk scenarios, such as material shortage or construction period extension, the potential impact of the scenario on the whole project is analyzed to generate a risk identification list.
The relief measure preparation submodule classifies and rates risks based on the risk identification list, combines occurrence probability and influence degree of each risk, and establishes a targeted risk relief strategy by setting a threshold value and a standard, wherein the targeted risk relief strategy comprises cost control and time management, and generates a relief strategy plan;
the mitigation measure generation sub-module classifies and ranks risks based on the risk identification list, and analyzes according to the occurrence probability p and the influence degree i of each risk. Risks are ranked using a ranking system, such as low, medium, high. Based on these ratings and set thresholds, targeted risk mitigation strategies are formulated. Policies include cost control measures, such as purchasing policy optimization and price locking contracts, and time management measures, such as adjusting the schedule of construction and increasing the buffering time, to improve the adaptability and resilience of the project, reduce the negative impact of risk on the project, and generate a mitigation policy plan.
The risk monitoring submodule performs risk monitoring based on relief strategy planning, tracks key risk indexes, sets alarms and marks, periodically evaluates and updates a project risk database, adjusts a risk relief strategy and generates a risk management scheme;
The risk monitoring submodule performs risk monitoring based on the mitigation strategy planning, sets a tracking system of key risk indicators, such as a percentage of cost increase and delays of key progress milestones. Alarms and flags are set to trigger an alarm if the cost exceeds 10% of the budget or if the critical progress delay exceeds a predetermined time limit. Project risk databases are periodically assessed and updated to reflect new information and market changes. And through continuous monitoring and evaluation of the system, the risk relief strategy is timely adjusted, the effectiveness of the strategy and the successful execution of the project are ensured, and a risk management scheme is generated.
The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.
Claims (7)
1. A civil construction cost data analysis system, characterized in that: the system comprises:
The engineering quantity list structure analysis module performs dependency sequencing on engineering tasks based on an engineering quantity list, converts the engineering tasks and material requirements into nodes, converts work dependency relations into edges, analyzes key paths and nodes and generates a list structure map;
the cost association network construction module converts project elements in the engineering quantity list into network nodes based on the list structure map, converts the cost association relationship into network edges, and identifies key nodes and cost flow modes in the network to generate a cost association map;
The material cost fluctuation analysis module quantitatively analyzes the fluctuation of the material cost based on market data and historical cost information, predicts and analyzes the cost fluctuation trend, identifies potential risk points and fluctuation modes, and generates a material cost fluctuation analysis result;
The project overflow risk simulation module is used for performing simulation analysis on overflow risks in projects based on project parameters and market conditions, simulating overflow scenes by combining potential influences of project cost and time, and generating overflow risk prediction results;
The cost control strategy making module analyzes key cost control points and potential risk factors in the project based on the cost association graph and the overflow price risk prediction result, and makes preventive measures and countermeasures according to the analysis result to generate a cost control strategy;
the budget optimization decision module optimizes project budget based on the cost control strategy, captures an optimal budget allocation scheme, and combines project targets including cost minimization and efficiency maximization to generate an optimization decision scheme;
The construction scheme evaluation module comprehensively evaluates the differential construction scheme based on the optimized decision scheme and the engineering quantity list, and the comprehensive performance of the scheme is evaluated by comparing the cost, time and quality standards of the scheme, so as to generate a construction scheme evaluation result;
the risk management and relieving module quantitatively analyzes and manages potential risks in the project based on the material cost fluctuation analysis result and the construction scheme evaluation result, and establishes a risk relieving and managing strategy by simulating the influence of different risk factors on the project to generate a risk management scheme;
the cost control strategy formulation module comprises:
The control point identification submodule carries out key cost control point identification based on the cost association graph and the premium risk prediction result, analyzes the risk degree and control urgency of the difference cost nodes and generates a control point identification result;
The effect prediction submodule carries out sensitivity analysis based on the control point identification result, inputs cost data of the control point and a potential adjustment scheme, calculates the influence degree of the adjustment scheme on the total project cost, predicts the effect of the difference control strategy and generates an effect prediction analysis result;
And the strategy making submodule synthesizes various scenes of the project based on the effect prediction analysis result, including cost limitation, time frame and resource availability, and plans cost control measures under the difference scene, including budget adjustment and resource redistribution, by combining the control point analysis and the effect prediction result to generate a cost control strategy.
2. The civil construction cost data analysis system according to claim 1, wherein: the inventory structure map comprises a dependency sequence of project tasks and a hierarchy relation of work items, the cost association map specifically refers to cost dependency and circulation paths among project elements, the material cost fluctuation analysis result comprises a predicted price fluctuation range and a potential risk interval, the overflow price risk prediction result specifically refers to potential strategy selection of a stakeholder and a result thereof, the cost control strategy comprises key cost control points and cost management measures, the optimization decision scheme specifically refers to adjusted budget allocation and cost benefit ratio analysis, the construction scheme evaluation result specifically refers to cost benefit comparison of each scheme and recommended construction methods, and the risk management scheme comprises identification of potential risk points, a release strategy and risk monitoring measures.
3. The civil construction cost data analysis system according to claim 1, wherein: the material cost fluctuation analysis module comprises:
The fluctuation mode identification submodule adopts an information entropy theory to count the occurrence frequency of cost items based on market data and historical cost information, calculates information entropy, and identifies a statistical mode of material cost fluctuation by analyzing time sequence changes of the information entropy to generate a fluctuation mode analysis result;
The cost trend analysis sub-module is used for carrying out model fitting and analysis by adopting an autoregressive moving average model based on the fluctuation mode analysis result, inputting historical cost data, calculating model parameters, predicting future cost trend and generating a cost trend prediction result;
And the fluctuation risk assessment sub-module is used for assessing risk by using a normal distribution probability calculation formula based on the cost trend prediction result, calculating the mean value and standard deviation of cost prediction, determining the probability of exceeding expected fluctuation, analyzing risk points and intervals and generating a material cost fluctuation analysis result.
4. The civil construction cost data analysis system according to claim 1, wherein: the project overflow price risk simulation module comprises:
The strategy simulation sub-module defines a strategy set of a stakeholder by utilizing Nash equilibrium theory based on project parameters and market conditions, creates a game model, sets strategy options and corresponding utility values for participants, calculates expected utility under each strategy combination, simulates strategy selection and results, and generates strategy simulation analysis results;
The game analysis submodule builds and executes game tree analysis based on the strategy simulation analysis result, defines decision options and potential results for each node of the tree, adds decision points and decision paths, analyzes the influence of each path, calculates the total cost and the completion time under the difference decision combination, and generates a game analysis result;
And the risk prediction submodule carries out comprehensive prediction of the risk of the overflow price based on the game analysis result, evaluates the possibility and potential influence of the overflow price under the combination of the difference strategies by combining the strategy selection of the stakeholders and the game tree analysis result, predicts the risk situation of the overflow price of the project and generates a risk prediction result of the overflow price.
5. The civil construction cost data analysis system according to claim 4, wherein: the Nash equilibrium theory is as follows:
；
The expected utility of each participant is calculated, wherein, For participantsIs used in the present invention,For participantsIs used in the method of the present invention,To remove participantsIn addition to the policy combinations of the other participants,For policy combinationThe probability of the occurrence of this is,When the strategies are combined intoWhen the participantIs used in the field of the present invention,As the weight coefficient of the light-emitting diode,For participantsThe risk assessment of the policy is performed,For the synergistic effect of the other participant policies,Combining policies for market environmentsIs a function of (a) and (b).
6. The civil construction cost data analysis system according to claim 1, wherein: the budget optimization decision module comprises:
The cost benefit analysis sub-module solves the optimization problem by adopting a linear programming algorithm based on the cost control strategy and combining project data, sets cost coefficients and resource limitations as parameters to calculate, and generates a cost benefit evaluation record;
The budget adjustment submodule processes the budget allocation problem based on the cost-benefit evaluation record, sets budget limit and resource allocation as parameters, adjusts the budget allocation to achieve optimal resource utilization, and generates a budget adjustment scheme;
the decision optimization submodule carries out random sampling simulation on decision variables based on the budget adjustment scheme, optimizes the effectiveness of the decision by simulating the cost and efficiency result under the differential budget scheme, and generates an optimized decision scheme.
7. The civil construction cost data analysis system according to claim 6, wherein: the linear programming algorithm is as follows:
；
The adjusted total cost is calculated, wherein, As a total cost of the product,Is the firstThe base cost coefficient of each activity is set,Is the firstThe coefficient of variation cost of the individual activities,Is the firstThe varying factor of the individual activities is used,Is the firstThe decision variables of the individual activities are chosen,Is the firstThe weight of the individual resources is determined,Is the firstGreen index of individual resources.
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