Construction method of project quality evaluation model

Abstract

The invention relates to the technical field of overseas engineering project management, in particular to an engineering quality assessment model construction method, which comprises the following steps: acquiring contract files, policies and regulations and exchange rate data information of an overseas engineering project, sorting and analyzing the acquired contract files, policies and regulations and exchange rate data information, extracting information required by risk assessment indexes, and establishing a risk assessment model; constructing an evaluation index system of contract risk, policy risk and exchange rate risk according to the extracted contract file, policy and regulation and exchange rate data information; according to the overseas engineering project risk assessment method, the overseas engineering project risk assessment algorithm can comprehensively consider the influence of contract risks, policy risks and exchange rate risks on the project, the accuracy and comprehensiveness of risk assessment are improved, through a risk early warning mechanism, the risks can be found and dealt with in time, the project risk loss is reduced, meanwhile, the algorithm has universality and expandability, and the risk assessment accuracy is improved. The method can be suitable for different types of overseas engineering projects.

Description

Technical Field

[0001] The present invention relates to the technical field of overseas engineering project management, and in particular to a method for constructing an engineering quality assessment model. Background Art

[0002] The construction method of engineering quality assessment model is a comprehensive technical process, which involves determining the assessment index system, building a multi-dimensional assessment method, and implementing a real-time monitoring and dynamic assessment mechanism. This method uses scientific data analysis and expert systems to quantitatively evaluate the quality of the project to ensure that the project meets the established quality standards and requirements;

[0003] With the development of economic globalization, more and more companies are involved in overseas engineering projects. However, overseas engineering projects face many risks, such as contract risk, policy risk and exchange rate risk. These risks may have a significant impact on the progress, cost and quality of the project, and even lead to project failure. Therefore, a method for constructing an engineering quality assessment model is proposed to address the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a method for constructing an engineering quality assessment model to solve the problem that overseas engineering projects are faced with many risks, such as contract risk, policy risk and exchange rate risk, etc. These risks may have a significant impact on the progress, cost and quality of the project, and even lead to project failure.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for constructing a project quality assessment model, comprising: obtaining contract documents, policies and regulations, and exchange rate data information of overseas engineering projects, sorting and analyzing the obtained contract documents, policies and regulations, and exchange rate data information, and extracting information required for risk assessment indicators, and constructing an assessment indicator system for contract risk, policy risk, and exchange rate risk based on the extracted contract documents, policies and regulations, and exchange rate data information;

[0007] Contract risk is recorded as C, policy risk is recorded as P, and exchange rate risk is recorded as R. C, P, and R are assigned a weight, which are wci, wpi, and wri respectively, where i represents the risk factors of different contracts, policies, or exchange rates. C, P, and R are scored according to the actual situation, and the score range is 0-10, where 0 represents extremely low risk and 10 represents extremely high risk;

[0008] Based on the risk assessment algorithm, the contract risk score, policy risk score and exchange rate risk score are calculated.

[0009] As a further optimized content of the present invention, the weighted score calculation formulas of C, P and R are respectively:

[0010]

[0011] In the formula, sc i 、sp i and sr i are the risk scores corresponding to C, P, and R.

[0012] As a further optimized content of the present invention, the calculation formula of the comprehensive risk score is:

[0013] S=α·C+β·P+γ·R

[0014] Where S is the comprehensive risk score, α, β and γ are the weight coefficients of contract, policy and exchange rate risks, and α+β+γ=1.

[0015] As further optimized content of the present invention, the contract risk indicators include the clarity of contract terms, the difficulty of change management, and the rationality of payment terms;

[0016] The policy risk indicators include political stability, frequency of policy changes, and intensity of industry supervision;

[0017] The exchange rate risk indicators include the exchange rate fluctuation range and frequency as well as the stability of the currencies involved in the project.

[0018] As a further optimization of the present invention, the comprehensive risk score S is divided into three levels: low risk, medium risk and high risk, each level corresponds to a different risk warning threshold, wherein the low risk threshold is applicable to a stable external environment, and the project management team does not take additional measures; the medium risk threshold is applicable to situations where potential risks change, and the project management team should take preventive measures; the high risk threshold is applicable to situations where there are obvious risks, and urgent intervention measures are required.

[0019] As a further optimization of the present invention, the algorithm sends a warning to the project management team when the risk warning threshold is triggered, prompting them to take corresponding countermeasures.

[0020] As a further optimized content of the present invention, wherein: the risk assessment algorithm further includes an analysis of historical risk data to automatically optimize the weight coefficient of the risk assessment indicator.

[0021] As further optimized content of the present invention, among others: the risk assessment algorithm integrates artificial intelligence technology, trains project data through a machine learning algorithm, optimizes weight parameters and improves the accuracy of risk assessment; among others, the machine learning algorithm uses regression analysis, support vector machines or neural network models to dynamically adjust the weights of various risk factors based on historical project data, thereby enhancing the adaptability and predictive ability of the algorithm.

[0022] As further optimized content of the present invention, the algorithm further includes a dynamic monitoring module for continuously collecting real-time data of overseas projects and dynamically adjusting the scoring criteria according to data changes to ensure the real-time and accuracy of the evaluation results.

[0023] As further optimized content of the present invention, the algorithm provides a user feedback mechanism, allowing the project management team to provide feedback on the evaluation results, and incorporate the feedback information into model training to continuously optimize the prediction ability and risk identification accuracy of the evaluation algorithm.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] 1. In the present invention, the risk assessment algorithm for overseas engineering projects can comprehensively consider the impact of contract risk, policy risk and exchange rate risk on the project, improve the accuracy and comprehensiveness of risk assessment, and through the risk early warning mechanism, can timely discover and respond to risks and reduce project risk losses. At the same time, the algorithm is universal and scalable and can be applied to different types of overseas engineering projects;

[0026] 2. In the present invention, the risk assessment algorithm improves the risk identification and response efficiency of overseas engineering projects through a graded early warning mechanism, historical data optimization and dynamic monitoring functions. The integration of artificial intelligence technology enables the algorithm to adaptively adjust various risk weights and provide more accurate assessment results. At the same time, the user feedback mechanism and real-time data collection ensure the practicality and real-time nature of the assessment, enabling the project management team to quickly take countermeasures in different risk scenarios, reduce the possibility of project failure and optimize management decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 The present invention is a flowchart of a method for constructing an engineering quality assessment model. DETAILED DESCRIPTION

[0028] Example 1

[0029] See also Figure 1 , the present invention provides a technical solution:

[0030] A method for building a project quality assessment model, data collection:

[0031] Collect relevant data on overseas engineering projects, including contract documents, policies and regulations, and exchange rate data;

[0032] Organize and analyze data to extract information needed for risk assessment indicators;

[0033] Risk indicator scoring: Score each risk indicator according to the scoring criteria:

[0034] Contract risk (C): Define a series of contract-related risk factors, such as the clarity of contract terms, the ease of change management, and the reasonableness of payment terms, and assign a weight w to each factor. ci (i represents different contract risk factors), and each factor is scored according to the actual situation ci (The score range is set from 0-10, with 0 indicating very low risk and 10 indicating very high risk);

[0035] Policy risk (P): Consider the political stability of the target country, the frequency of policy changes, and the intensity of industry supervision, and also assign a weight w to each factor pi , and score pi ;

[0036] Exchange rate risk (R): Analyze the amplitude and frequency of exchange rate fluctuations and the stability of the currencies involved in the project, and set the weight w ri , and score the exchange rate risk ri ;

[0037] Risk score calculation:

[0038] Based on the risk assessment algorithm, the contract risk score, policy risk score and exchange rate risk score are calculated.

[0039] Single risk calculation:

[0040] Contract Risk Score:

[0041] Policy risk score:

[0042] Exchange Rate Risk Score:

[0043] Comprehensive risk assessment:

[0044] The formula for calculating the comprehensive risk score is:

[0045] S=α·C+β·P+γ·R

[0046] Where S is the comprehensive risk score, α, β and γ are the weight coefficients of contract, policy and exchange rate risks, and α+β+γ=1. The weight coefficient is adjusted according to the specific circumstances of the project and the degree of attention the company pays to different risks.

[0047] For example, for overseas engineering projects in EU countries, the following parameters were determined after evaluation:

[0048] Contract risk factors, weights and scores:

[0049] Clarity of contract terms (weight w c1=0.3, score s c1 =6)

[0050] Change management difficulty (weight w c2 =0.4, score s c2 =7)

[0051] Reasonableness of payment terms (weight w c3 =0.3, score s c3 =5)

[0052] Policy risk factors, weights and scores:

[0053] Political stability (weight wp1=0.4, score sp1=4)

[0054] Frequency of policy changes (weight wp2=0.3, score sp2=6)

[0055] Industry supervision intensity (weight wp3 = 0.3, score sp3 = 5)

[0056] Exchange rate risk factors, weights and scores:

[0057] Exchange rate fluctuation range (weight w r1 =0.5, score s r1 =7)

[0058] Frequency of exchange rate fluctuations (weight w r2 =0.5, score s r2 =6)

[0059] Assume that the contract risk weight α=0.4, the policy risk weight β=0.3, and the exchange rate risk weight γ=0.3.

[0060] First, calculate the individual risk scores:

[0061] Contract risk score: C = 0.3 × 6 + 0.4 × 7 + 0.3 × 5 = 6.1

[0062] Policy risk score: P = 0.4 × 4 + 0.3 × 6 + 0.3 × 5 = 4.9

[0063] Exchange rate risk score: R = 0.5 × 7 + 0.5 × 6 = 6.5

[0064] The composite risk score is then calculated:

[0065] S=0.4×6.1+0.3×4.9+0.3×6.5=5.82

[0066] According to the size of the comprehensive risk score, the risk level of overseas engineering projects is assessed and graded, and corresponding risk management measures are taken. The higher the score, the greater the risk, and more attention needs to be paid to risk management and the formulation of corresponding strategies;

[0067] Risk warning: Set risk warning levels based on comprehensive risk scores, such as low risk, medium risk, and high risk;

[0068] When the comprehensive risk score reaches or exceeds a certain warning level, a risk warning signal is issued to remind project managers to take appropriate risk response measures.

[0069] Example 2

[0070] See also Figure 1 , the present invention provides a technical solution:

[0071] A method for constructing an engineering quality assessment model, in which the comprehensive risk score S is divided into three levels: low risk, medium risk, and high risk. Each level corresponds to a different risk warning threshold. The low risk threshold is applicable to a stable external environment, and the project management team does not take additional measures; the medium risk threshold is applicable to situations where potential risks change, and the project management team should take preventive measures; the high risk threshold is applicable to situations where there are obvious risks, and urgent intervention measures are required. This grading system can help the project management team quickly identify the severity of the risk and take corresponding response measures according to different levels of risk warning thresholds. No additional measures are required to save costs when the risk is low, preventive measures can be formulated in advance when the risk is medium, and rapid intervention is required when the risk is high to prevent the project from being seriously affected, thereby optimizing the response efficiency of risk management;

[0072] When the risk warning threshold is triggered, the algorithm will issue a warning to the project management team, prompting them to take appropriate countermeasures. The automatic warning function ensures that the project management team can obtain risk information in a timely manner, avoiding management negligence that may be caused by information lag. The project management team can receive notifications when risks first appear, quickly analyze and take countermeasures, thereby improving the initiative to deal with risks and the safety of the project.

[0073] The risk assessment algorithm further includes the analysis of historical risk data to automatically optimize the weight coefficients of risk assessment indicators. By automatically optimizing the weight coefficients through the analysis of historical data, the assessment algorithm can be closer to the actual risk situation, improving the accuracy of risk assessment. At the same time, this automated weight adjustment can dynamically adapt to changes in different project environments over time and reduce human errors.

[0074] The risk assessment algorithm integrates artificial intelligence technology, and uses machine learning algorithms to train project data, optimize weight parameters and improve the accuracy of risk assessment. Among them, the machine learning algorithm uses regression analysis, support vector machines or neural network models to dynamically adjust the weights of various risk factors based on historical project data, thereby enhancing the adaptability and prediction ability of the algorithm. Artificial intelligence technology and machine learning algorithms can continuously optimize weight parameters using a large amount of project data, improve the prediction accuracy and adaptability of the assessment model, and through the application of multiple models, the algorithm can identify complex nonlinear relationships, making the assessment results more reliable, especially more flexible when facing a complex and changing risk environment;

[0075] The algorithm further includes a dynamic monitoring module, which is used to continuously collect real-time data of overseas projects and dynamically adjust the scoring criteria according to data changes to ensure the real-time and accuracy of the evaluation results. The dynamic monitoring module ensures the system's ability to respond to real-time changes in projects and can adjust risk assessment results in a timely manner according to environmental changes. The project management team can quickly obtain updated assessment reports when actual risks change, thereby more accurately controlling risk conditions and further reducing project risks;

[0076] The algorithm provides a user feedback mechanism that allows the project management team to provide feedback on the evaluation results and incorporate the feedback information into model training to continuously optimize the prediction ability and risk identification accuracy of the evaluation algorithm. The user feedback mechanism enables the project management team to feed back actual experience to the system to form a continuous improvement loop. By incorporating feedback into model training, the system's evaluation accuracy is continuously improved, and its adaptability to specific project risk identification is enhanced, making future evaluations more instructive and practical.

[0077] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method and core ideas of the present invention. The above is only a preferred implementation of the present invention. It should be pointed out that due to the limitations of textual expression and the objective existence of infinite specific structures, ordinary technicians in this technical field can make several improvements, modifications or changes without departing from the principles of the present invention, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, should be regarded as the protection scope of the present invention.

Claims

1. A method for constructing a project quality assessment model, characterized in that: include: Obtain contract documents, policies and regulations, and exchange rate data information of overseas engineering projects, organize and analyze the obtained contract documents, policies and regulations, and exchange rate data information, and extract the information required for risk assessment indicators, and build an assessment indicator system for contract risk, policy risk, and exchange rate risk based on the extracted contract documents, policies and regulations, and exchange rate data information; Contract risk is recorded as C, policy risk is recorded as P, and exchange rate risk is recorded as R. C, P, and R are assigned a weight, which are wci, wpi, and wri respectively, where i represents the risk factors of different contracts, policies, or exchange rates. C, P, and R are scored according to the actual situation, and the score range is 0-10, where 0 represents extremely low risk and 10 represents extremely high risk; Based on the risk assessment algorithm, the contract risk score, policy risk score and exchange rate risk score are calculated.

2. The method for constructing a project quality assessment model according to claim 1, characterized in that: The weighted score calculation formulas of C, P, and R are: In the formula, sc i 、sp i and sr i are the risk scores corresponding to C, P, and R.

3. The method for constructing a project quality assessment model according to claim 1, characterized in that: The calculation formula of the comprehensive risk score is: S=α·C+β·P+y·R Where S is the comprehensive risk score, α, β and γ are the weight coefficients of contract, policy and exchange rate risks, and α+β+γ=1.

4. The method for constructing a project quality assessment model according to claim 1, characterized in that: The contract risk indicators include the clarity of contract terms, the difficulty of change management, and the rationality of payment terms; The policy risk indicators include political stability, frequency of policy changes, and intensity of industry supervision; The exchange rate risk indicators include the exchange rate fluctuation range and frequency as well as the stability of the currencies involved in the project.

5. The method for constructing a project quality assessment model according to claim 1, characterized in that: The comprehensive risk score S is divided into three levels: low risk, medium risk and high risk. Each level corresponds to a different risk warning threshold. The low risk threshold is suitable for a stable external environment, and the project management team does not take additional measures. The medium risk threshold applies to situations where potential risks change and the project management team should take preventive measures; The high risk threshold applies to situations where there is a clear risk and urgent intervention is required.

6. The method for constructing a project quality assessment model according to claim 1, characterized in that: The algorithm sends an early warning to the project management team when the risk early warning threshold is triggered, prompting them to take corresponding countermeasures.

7. The method for constructing a project quality assessment model according to claim 1, characterized in that: The risk assessment algorithm further includes an analysis of historical risk data to automatically optimize the weight coefficients of risk assessment indicators.

8. The method for constructing a project quality assessment model according to claim 1, characterized in that: The risk assessment algorithm integrates artificial intelligence technology, trains project data through machine learning algorithms, optimizes weight parameters and improves the accuracy of risk assessment; among them, the machine learning algorithm uses regression analysis, support vector machines or neural network models to dynamically adjust the weights of various risk factors based on historical project data, thereby enhancing the adaptability and predictive ability of the algorithm.

9. The method for constructing a project quality assessment model according to claim 1, characterized in that: The algorithm further includes a dynamic monitoring module for continuously collecting real-time data of overseas projects and dynamically adjusting the scoring criteria based on data changes to ensure the timeliness and accuracy of the evaluation results.

10. The method for constructing a project quality assessment model according to claim 1, characterized in that: The algorithm provides a user feedback mechanism that allows the project management team to provide feedback on the assessment results and incorporate the feedback information into model training to continuously optimize the predictive ability and risk identification accuracy of the assessment algorithm.

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