Intelligent decision support method for engineering project risk assessment

Abstract

The invention relates to the technical field of intelligent decision-making, in particular to an intelligent decision-making support method for engineering project risk assessment, which comprises the steps of data collection and integration, collecting climate data, geographic data and historical disaster records related to the natural disaster risk of the region; constructing a risk prediction model based on natural disasters: constructing the natural disaster prediction model based on the collected climate data, geographic data and historical disaster records; predicting extra cost: predicting natural disasters by using the natural disaster prediction model, analyzing project progress influence and resource allocation influence according to a prediction result, and calculating the extra cost based on the project progress influence and the resource allocation influence; emergency plan making and optimization: automatically generating an emergency plan according to extra cost prediction; according to the method, a powerful basis is provided for project planning and decision making, and the accuracy and foresight of risk assessment are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent decision-making technology, and in particular to an intelligent decision-making support method for engineering project risk assessment. Background Art

[0002] With global climate change and frequent natural disasters, the risks faced by engineering projects are becoming more and more complex, especially during the construction and operation of projects, which are often affected by sudden natural disasters. Natural disasters such as typhoons, floods, earthquakes, etc., not only cause serious interference to the progress of the project, but may also lead to waste of resources, rising costs, and even endanger the normal progress and completion of the project. Therefore, how to effectively evaluate the natural disaster risks that the project may face, and formulate corresponding emergency plans and risk response strategies based on the evaluation results, has become an important task in current engineering project management.

[0003] At present, the risk assessment methods of most engineering projects mainly rely on manual judgment and empirical analysis, lacking scientific and systematic disaster prediction and risk assessment methods. In the existing technology, although there are some methods that can predict natural disasters, they are usually limited to the analysis of single factors or local data, and fail to fully integrate multi-dimensional information such as meteorological monitoring, geographic information system (GIS) and historical disaster data. In addition, many existing technologies lack a comprehensive assessment of the impact of disasters on project progress, resource allocation, and cost, and fail to effectively generate optimized emergency plans based on different risk levels. As a result, when disasters occur, project managers often find it difficult to make scientific decisions quickly, increasing the uncertainty and risk of the project. Summary of the invention

[0004] The invention provides an intelligent decision support method for engineering project risk assessment.

[0005] An intelligent decision support method for engineering project risk assessment, characterized by comprising the following steps:

[0006] S1, Data Collection and Integration: Collect climate data, geographic data and historical disaster records related to natural disaster risks in the project area through meteorological monitoring systems, geographic information systems and historical disaster data sources;

[0007] S2, build a risk prediction model based on natural disasters: build a natural disaster prediction model based on the collected climate data, geographical data and historical disaster records;

[0008] S3, predicting additional costs: using the natural disaster prediction model to predict natural disasters, and analyzing the impact on project schedule and resource allocation based on the prediction results, and calculating additional costs based on the impact on project schedule and resource allocation;

[0009] S4, emergency plan formulation: Based on the additional cost forecast, the risk level is divided and the emergency plan is automatically generated.

[0010] Optionally, the S1 includes:

[0011] S11, meteorological monitoring data collection: collect climate data related to the project area through the meteorological monitoring system, and obtain meteorological change information in real time;

[0012] S12, geographic information data collection: obtaining the geographic environment data of the region through the spatial collection and analysis function of the geographic information system, wherein the geographic data includes topography, river distribution and land use;

[0013] S13, historical disaster data collection: obtain data on natural disaster events that have occurred in the project area from the government public data of the project location, including typhoon paths, time and frequency of floods.

[0014] Optionally, S2 includes:

[0015] S21, data preprocessing: standardize the climate data, geographic data and historical disaster records collected in S1;

[0016] S22, build a natural disaster prediction model: Based on the pre-processed climate data, geographic data and historical disaster records, define the disaster risk factor, which is calculated as:

[0017] Among them, R(t) is the natural disaster risk value at time t, ρ g is the weight coefficient of disaster risk factor i, indicating the contribution of this factor to the overall risk, X g (t) represents the value of the disaster risk factor g at time t, and n represents the number of disaster risk factors;

[0018] S23, Disaster Probability Prediction: Based on the calculated disaster risk factors, the time series analysis method is used to predict the probability of future natural disasters using the weighted moving average method to construct a natural disaster prediction model. The natural disaster prediction model is expressed as:

[0019]

[0020] Among them, P(t) is the probability of natural disasters occurring at time t, α j is the weighting coefficient, which indicates the influence of the risk assessment at the past time point tj on the current probability of disaster occurrence, and m is the observation period of historical data, i.e., the past m time periods.

[0021] Optionally, the S3 includes:

[0022] S31, Natural Disaster Prediction: Based on the constructed natural disaster prediction model, potential natural disasters in the project area are predicted according to the collected climate data, geographical data and historical disaster records, and time series prediction results of the probability of occurrence, disaster intensity and impact range of natural disasters are generated;

[0023] S32, project impact simulation: based on the natural disaster prediction results, a multi-dimensional impact simulation model of additional costs caused by project progress and resource allocation is established, and the multi-dimensional impact simulation model includes progress impact simulation and resource allocation impact simulation.

[0024] Optionally, S31 includes: based on the constructed natural disaster prediction model, according to the collected climate data, geographical data and historical disaster records, predicting potential natural disasters in the project area, and generating a time series of the probability of occurrence of natural disasters P(t), disaster intensity I(t) and impact range A(t).

[0025] Optionally, in S32, based on the result of the natural disaster prediction model, Monte Carlo simulation is used to simulate the impact on the project schedule, and the simulation of the impact on the project schedule is expressed as:

[0026]

[0027] Among them, ΔT is the project progress delay time, w i is the weight coefficient, which indicates the influence of different disaster events on the progress delay. i ) is the time t i The probability of disaster occurrence at a certain time, I(t i ) is the time t i The disaster intensity at the moment, A(t i ) is the time t i The disaster impact range at the time, k is the number of time points considered in the simulation.

[0028] Optionally, in S32, based on the result of the natural disaster model prediction model, the resource allocation impact simulation is expressed as:

[0029]

[0030] Among them, ΔR(t) is the change in resource demand of the project at time t, β i is the coefficient of the impact of disaster events on resource demand, P(t i ) is the time t i The probability of disaster occurrence at a certain time, I(t i ) is the time t i The disaster intensity at the moment, R iis the resource demand value associated with disaster event i, and k is the number of time points considered in the simulation.

[0031] Optionally, in S32, based on the natural disaster prediction, combined with the results of the schedule impact simulation and the resource allocation impact simulation, the project cost change caused by the natural disaster is estimated, which is expressed as:

[0032]

[0033] Among them, ΔC(t) is the cost change of the project at time t, γ i is the impact coefficient of disaster events on cost increase, ΔR(t i ) is time t i The resource demand change at the time, δ is the unit time cost increase coefficient caused by schedule delay, and ΔT(t) is the total time of project schedule delay.

[0034] Optionally, the S4 includes:

[0035] S41, Risk level determination: Based on the additional cost forecast results, the project is divided into different risk levels, including low risk, medium risk and high risk;

[0036] S42, automatic generation of emergency plans: According to the risk levels, a corresponding emergency plan is automatically generated for each risk, and the emergency plans include low-risk plans, medium-risk plans and high-risk plans.

[0037] Beneficial effects of the present invention:

[0038] The present invention provides an intelligent decision support method for project risk assessment, which realizes a comprehensive and accurate natural disaster risk assessment by integrating meteorological monitoring systems, geographic information systems and historical disaster data sources. Through the natural disaster prediction model constructed based on these multi-dimensional data, the types of natural disasters that may be encountered in the project area, the probability of occurrence and the degree of impact of the disaster can be effectively predicted, providing a strong basis for project planning and decision-making, and significantly improving the accuracy and foresight of risk assessment.

[0039] The present invention can identify in advance the additional costs that may be faced by project schedule and resource allocation by quantitatively analyzing the predicted natural disaster risks. Based on this analysis, project managers can take effective risk response measures to reduce the negative impact of natural disasters on projects by optimizing resource scheduling and adjusting project schedules. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 is a flow chart of a method according to an embodiment of the present invention;

[0042] Figure 2 A data collection and integration diagram for an embodiment of the present invention;

[0043] Figure 3 The model construction flow chart of the embodiment of the present invention. DETAILED DESCRIPTION

[0044] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0045] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0046] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0047] like Figure 1-3 As shown, an intelligent decision support method for engineering project risk assessment is characterized by comprising the following steps:

[0048] S1, Data Collection and Integration: Collect climate data, geographic data and historical disaster records related to natural disaster risks in the project area through meteorological monitoring systems, geographic information systems (GIS) and historical disaster data sources;

[0049] S2, build a risk prediction model based on natural disasters: build a natural disaster prediction model based on the collected climate data, geographical data and historical disaster records;

[0050] S3, predict additional costs: use the natural disaster prediction model to predict natural disasters, and analyze the impact on project schedule and resource allocation based on the prediction results, and calculate additional costs based on the impact on project schedule and resource allocation;

[0051] S4, emergency plan formulation: Based on the additional cost forecast, the risk level is divided and the emergency plan is automatically generated.

[0052] S1 includes:

[0053] S11, meteorological monitoring data collection: collect climate data related to the project area through the meteorological monitoring system, and obtain meteorological change information in real time;

[0054] S12, Geographic information data collection: Obtain the geographic environment data of the region through the spatial collection and analysis function of the geographic information system. The geographic data includes topography, river distribution and land use;

[0055] S13, historical disaster data collection: obtain data on natural disaster events that have occurred in the project area from the government public data of the project location, including typhoon paths, time and frequency of floods.

[0056] S2 includes:

[0057] S21, data preprocessing: standardize the climate data, geographic data and historical disaster records collected in S1 to ensure the accuracy and consistency of the data;

[0058] S22, build a natural disaster prediction model: Based on the pre-processed climate data, geographic data and historical disaster records, define the disaster risk factor, which is calculated as:

[0059] Among them, R(t) is the natural disaster risk value at time t, ρ g is the weight coefficient of disaster risk factor i, indicating the contribution of this factor to the overall risk. The weight coefficient of disaster risk factor ρ g The value range is [0,1], X g(t) represents the value of the disaster risk factor g at time t, and n represents the number of disaster risk factors;

[0060] S23, Disaster Probability Prediction: Based on the calculated disaster risk factors, the time series analysis method is used to predict the probability of future natural disasters using the weighted moving average method to construct a natural disaster prediction model. The natural disaster prediction model is expressed as:

[0061]

[0062] Among them, P(t) is the probability of natural disasters occurring at time t, α j is a weighting coefficient, which indicates the influence of the risk assessment at the past time point tj on the current probability of disaster occurrence, and m is the observation period of historical data, that is, the past m time periods.

[0063] S3 includes:

[0064] S31, Natural Disaster Prediction: Based on the constructed natural disaster prediction model, potential natural disasters in the project area are predicted according to the collected climate data, geographical data and historical disaster records, and time series prediction results of the probability of occurrence, disaster intensity and impact range of natural disasters are generated;

[0065] S32, Project impact simulation: Based on the natural disaster prediction results, establish a multi-dimensional impact simulation model of the additional costs caused by project progress and resource allocation. The multi-dimensional impact simulation model includes progress impact simulation and resource allocation impact simulation.

[0066] S31 includes: based on the constructed natural disaster prediction model, according to the collected climate data, geographical data and historical disaster records, predicting the potential natural disasters in the project area, generating a time series of the probability of occurrence of natural disasters P(t), disaster intensity I(t) and impact range A(t).

[0067] In S32, based on the results of the natural disaster prediction model, Monte Carlo simulation is used to simulate the impact on the project schedule. The simulation of the impact on the project schedule is expressed as:

[0068]

[0069] Among them, ΔT is the project progress delay time, w i is the weight coefficient, w i The value range is [0,1], which indicates the impact of different disaster events on the progress delay. i ) is the time t i The probability of disaster occurrence at a certain time, I(t i) is the time t i The disaster intensity at the time (such as wind speed, precipitation), A(t i ) is the time t i is the disaster impact range at the time (e.g., the area of ​​the disaster-affected region), and k is the number of time points considered in the simulation.

[0070] In S32, based on the results of the natural disaster model prediction model, the resource allocation impact simulation is expressed as:

[0071]

[0072] Where ΔR(t) is the change in resource requirements of the project at time t (such as additional manpower or equipment requirements), β i is the coefficient of the impact of disaster events on resource demand, P(t i ) is the time t i The probability of disaster occurrence at a certain time, I(t i ) is the time t i The disaster intensity at the moment, R i is the resource demand value associated with disaster event i, k is the number of time points considered in the simulation, β i The value range of β is [0,1], i =0 means that the disaster type has no effect on resource demand, β i =1 means the disaster type has the greatest impact on resource demand, R i The value range of is [0,∞), depending on the specific resource demand, indicating that when a disaster occurs, there may be no additional resource demand (R i =0), or the demand value can be any positive number, depending on the specific project and disaster event.

[0073] In S32, based on the natural disaster prediction, combined with the results of schedule impact simulation and resource allocation impact simulation, the project cost change caused by natural disasters is estimated, expressed as:

[0074]

[0075] Among them, ΔC(t) is the cost change of the project at time t, γ i is the impact coefficient of disaster events on cost increase, with a value range of [0,∞), γ i = 0 means that disaster events will not add any additional costs, γ i >0 indicates that disaster events will increase additional project costs, usually γ i The value of is small, such as between 0 and 1, or greater than 1 in severe disasters, indicating that the disaster may cause a multiple increase in costs, ΔR(t i ) is time t iThe resource demand changes at each moment, δ is the unit time cost increase coefficient caused by schedule delay, and its value range is [0,∞). δ=0 means that schedule delay does not increase any additional cost, and δ>0 means that each unit time delay will result in additional costs. Usually, the value of δ varies according to the nature of the project. ΔT(t) is the total time of project schedule delay.

[0076] S4 includes:

[0077] S41, Risk level determination: Based on the additional cost forecast results, the project is divided into different risk levels, including low risk, medium risk and high risk;

[0078] Low risk: low additional costs and limited impact on project schedule and resources;

[0079] Medium risk: The additional cost is moderate, and the impact on the project is significant but still controllable;

[0080] High risk: The additional costs are high and may have a significant impact on the project, requiring urgent response;

[0081] S42, automatic generation of emergency plans: according to the risk levels, corresponding emergency plans are automatically generated for each risk, including low-risk plans, medium-risk plans and high-risk plans;

[0082] Low-risk plan: monitoring and early warning, preparing backup resources in advance;

[0083] Medium-risk plan: Strengthen resource dispatch, prepare response measures, and develop recovery plans;

[0084] High-risk plan: emergency response, launch of rapid dispatch mechanism, establishment of temporary management team, and emergency fund preparation.

[0085] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention; in order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, and those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0086] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An intelligent decision support method for engineering project risk assessment, characterized in that: The following steps are involved: S1, Data Collection and Integration: Collect climate data, geographic data and historical disaster records related to natural disaster risks in the project area through meteorological monitoring systems, geographic information systems and historical disaster data sources; S2, build a risk prediction model based on natural disasters: build a natural disaster prediction model based on the collected climate data, geographical data and historical disaster records; S3, predicting additional costs: using the natural disaster prediction model to predict natural disasters, and analyzing the impact on project schedule and resource allocation based on the prediction results, and calculating additional costs based on the impact on project schedule and resource allocation; S4, emergency plan formulation: Based on the additional cost forecast, the risk level is divided and the emergency plan is automatically generated.

2. The intelligent decision support method for engineering project risk assessment according to claim 1 is characterized in that: The S1 includes: S11, meteorological monitoring data collection: collect climate data related to the project area through the meteorological monitoring system, and obtain meteorological change information in real time; S12, geographic information data collection: obtaining the geographic environment data of the region through the spatial collection and analysis function of the geographic information system, wherein the geographic data includes topography, river distribution and land use; S13, historical disaster data collection: obtain data on natural disaster events that have occurred in the project area from the government public data of the project location, including typhoon paths, time and frequency of floods.

3. The intelligent decision support method for engineering project risk assessment according to claim 2 is characterized in that: The S2 includes: S21, data preprocessing: standardize the climate data, geographic data and historical disaster records collected in S1; S22, build a natural disaster prediction model: Based on the pre-processed climate data, geographic data and historical disaster records, define the disaster risk factor, which is calculated as: Among them, R(t) is the natural disaster risk value at time t, ρ g is the weight coefficient of disaster risk factor i, indicating the contribution of this factor to the overall risk, X g (t) represents the value of the disaster risk factor g at time t, and n represents the number of disaster risk factors; S23, Disaster Probability Prediction: Based on the calculated disaster risk factors, the time series analysis method is used to predict the probability of future natural disasters using the weighted moving average method to construct a natural disaster prediction model. The natural disaster prediction model is expressed as: Among them, P(t) is the probability of natural disasters occurring at time t, α j is the weighting coefficient, which indicates the influence of the risk assessment at the past time point tj on the current probability of disaster occurrence, and m is the observation period of historical data, i.e., the past m time periods.

4. The intelligent decision support method for engineering project risk assessment according to claim 3 is characterized in that: The S3 includes: S31, Natural Disaster Prediction: Based on the constructed natural disaster prediction model, potential natural disasters in the project area are predicted according to the collected climate data, geographical data and historical disaster records, and time series prediction results of the probability of occurrence, disaster intensity and impact range of natural disasters are generated; S32, project impact simulation: based on the natural disaster prediction results, a multi-dimensional impact simulation model of additional costs caused by project progress and resource allocation is established, and the multi-dimensional impact simulation model includes progress impact simulation and resource allocation impact simulation.

5. The intelligent decision support method for engineering project risk assessment according to claim 4 is characterized in that: The S31 includes: based on the constructed natural disaster prediction model, according to the collected climate data, geographical data and historical disaster records, predicting the potential natural disasters in the project area, generating a time series of the probability of occurrence of natural disasters P(t), disaster intensity I(t) and impact range A(t).

6. The intelligent decision support method for engineering project risk assessment according to claim 5 is characterized in that: In S32, based on the result of the natural disaster prediction model, Monte Carlo simulation is used to simulate the impact on the project schedule. The simulation of the impact on the project schedule is expressed as: Among them, ΔT is the project progress delay time, w i is the weight coefficient, which indicates the influence of different disaster events on the progress delay. i ) is the time t i The probability of disaster occurrence at a certain time, I(t i ) is the time t i The disaster intensity at the moment, A(t i ) is the time t i The disaster impact range at the time, k is the number of time points considered in the simulation.

7. The intelligent decision support method for engineering project risk assessment according to claim 5 is characterized in that: In S32, based on the result of the natural disaster model prediction model, the resource allocation impact simulation is expressed as: Among them, ΔR(t) is the change in resource demand of the project at time t, β i is the coefficient of the impact of disaster events on resource demand, P(t i ) is the time t i The probability of disaster occurrence at a certain time, I(t i ) is the time t i The disaster intensity at the moment, R i is the resource demand value associated with disaster event i, and k is the number of time points considered in the simulation.

8. The intelligent decision support method for engineering project risk assessment according to claim 6 is characterized in that: In S32, based on the natural disaster prediction, combined with the results of the schedule impact simulation and the resource allocation impact simulation, the project cost change caused by the natural disaster is estimated, which is expressed as: Among them, ΔC(t) is the cost change of the project at time t, γ i is the impact coefficient of disaster events on cost increase, ΔR(t i ) is time t i The resource demand change at the time, δ is the unit time cost increase coefficient caused by schedule delay, and ΔT(t) is the total time of project schedule delay.

9. The intelligent decision support method for engineering project risk assessment according to claim 1, characterized in that: The S4 includes: S41, Risk level determination: Based on the additional cost forecast results, the project is divided into different risk levels, including low risk, medium risk and high risk; S42, automatic generation of emergency plans: According to the risk levels, a corresponding emergency plan is automatically generated for each risk, and the emergency plans include low-risk plans, medium-risk plans and high-risk plans.

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