Meteorological engineering project management risk assessment method

Abstract

The invention relates to the technical field of meteorological engineering, and discloses a meteorological engineering project management risk assessment method, which comprises the following steps of: firstly, collecting historical risk event data of a meteorological engineering project and related meteorological conditions, engineering parameters and management measures, and performing data cleaning and integration to form a standardized risk data set; and based on the data set, constructing a risk prediction model, and predicting types and probabilities of risks possibly encountered by the project in the future. Meanwhile, a risk influence assessment system is established in combination with historical data, and the risk is comprehensively assessed from three dimensions of risk severity, occurrence possibility and controllability. The risk prediction and the assessment system are combined, the risk comprehensive influence value is calculated, then the risks are sorted and graded, and coping strategies are made for high-risk and medium-risk links. And finally, the risk assessment result and strategy are presented in a visual report form, comprehensive risk assessment support is provided for project management personnel, and the risk prevention and control capability of meteorological engineering project management is effectively improved.

Description

Technical Field

[0001] The invention relates to the technical field of meteorological engineering, and in particular to a meteorological engineering project management risk assessment method. Background Art

[0002] Risk assessment is a crucial task in the management of meteorological engineering projects. Meteorological engineering projects, such as the construction of meteorological observation stations, the installation of meteorological radars, the development and implementation of meteorological forecasting systems, etc., are often affected by a variety of factors, especially the complex and changeable meteorological conditions, which makes the project face many uncertainties during its implementation. Traditional risk assessment methods mostly rely on the experience and judgment of project managers and simple statistics of historical data, which makes it difficult to comprehensively and accurately predict the risks that the project may encounter in the future.

[0003] Meteorological conditions, such as temperature, humidity, wind speed, wind direction, precipitation and air pressure, have direct or indirect impacts on the construction progress, quality, safety and cost of engineering projects. For example, extreme weather conditions may lead to construction delays, equipment damage or casualties, thereby increasing project costs and reducing project success rates. At the same time, engineering parameters such as project scale, construction difficulty, technical complexity, construction period and budget, as well as management measures such as quality control, safety management, progress monitoring and resource allocation, are also important factors affecting project risks.

[0004] However, existing risk assessment methods often ignore the interactions and dynamic changes between these factors, resulting in a lack of accuracy and timeliness in risk assessment results. In addition, there is also a lack of a systematic assessment system for the impact, probability of occurrence, and controllability of risk events, which makes project managers lack scientific basis when formulating risk response strategies. Summary of the invention

[0005] The purpose of the present invention is to provide a meteorological engineering project management risk assessment method to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a meteorological engineering project management risk assessment method, the method comprising: Step 1: Collect various risk event data of meteorological engineering projects in the historical implementation process, as well as meteorological conditions, engineering parameters and management measures during project execution; the risk event data include risk type, occurrence time, impact degree and duration; the meteorological conditions include temperature, humidity, wind speed, wind direction, precipitation and air pressure; the engineering parameters include project scale, construction difficulty, technical complexity, construction period and budget; the management measures include quality control, safety management, progress monitoring and resource allocation; Step 2: Clean, integrate and format the data collected in step 1 to form a standardized risk data set; Step 3: Based on the standardized risk data set, a risk prediction model for meteorological engineering projects is constructed. The model combines meteorological conditions, engineering parameters and management measures to predict the types of risks and their probabilities that the project may encounter in the future; Step 4: Obtain real-time meteorological conditions, engineering progress and management status data of the current meteorological engineering project; Step 5: Input the data obtained in step 4 into the risk prediction model, and receive the predicted risk type and probability output by the model; Step 6: Based on historical risk event data, establish a risk impact assessment system that includes three dimensions: risk severity, likelihood of occurrence, and controllability; Step 7: Combine the risk type and probability predicted in step 5 with the risk impact assessment system established in step 6 to calculate the comprehensive impact value of each risk; Step 8: Sort and grade the risks based on the calculated comprehensive risk impact value, and determine the project links with high, medium and low risks; Step 9: Develop risk response strategies and plans for high-risk and medium-risk project links, including risk avoidance, risk mitigation, risk transfer and risk acceptance; Step 10: Present the risk assessment results and response strategies in the form of a visual report and send it to project managers and relevant decision makers.

[0007] Preferably, in step 2, the data preprocessing method includes: For missing values, linear interpolation algorithm is used to fill them; For outliers, the threshold judgment method is used to regard data points that exceed the preset threshold range as outliers and replace them with the mean of their neighboring points; Using the Z-score standardization method, each feature value is subtracted from its mean and divided by its standard deviation, so that the processed data conforms to the standard normal distribution. The formula for standardization is: ,in, is the original data, is the mean of the original data, is the standard deviation of the original data, is the standardized data.

[0008] Preferably, a long short-term memory network (LSTM) algorithm is used to construct a risk prediction model.

[0009] Preferably, the step of constructing a risk prediction model includes: Step 3.1: Use a standardized risk dataset as model input, which includes relevant historical data, environmental conditions, project-specific parameters, and time-dependent characteristics within a time window; The standardized risk dataset is randomly divided into a training set and a test set. The training set is used exclusively for the model training process to learn the relationship between data features and prediction targets; the test set is used for subsequent model evaluation to verify the generalization ability of the model. Step 3.2: Design the LSTM network structure, which includes at least one LSTM hidden layer and specify the appropriate number of units for each LSTM hidden layer; select ReLU as the activation function and apply it to the LSTM unit output; Step 3.3: Using the training set data, the LSTM model parameters are iteratively optimized through the back propagation algorithm and gradient descent strategy; during the training process, the model weights are continuously adjusted to minimize the error between the predicted value and the true value; Step 3.4: Use the test set data to evaluate the performance of the trained LSTM model. By comparing the predicted results with the actual situation, the prediction accuracy and stability of the model are evaluated. According to the evaluation feedback, the hyperparameters of the model are adjusted, including the number of network layers, the number of hidden layer units, the type of activation function, and the learning rate, until the predetermined prediction accuracy standard is reached.

[0010] Preferably, during the training process, the mean square error MSE algorithm is used as the loss function to evaluate the difference between the model prediction value and the actual measurement value. The MSE value is defined as the mean of the sum of the squares of the difference between the predicted value and the actual value. The smaller the MSE value, the higher the prediction accuracy of the model. The specific algorithm is: Where n is the number of data points, is the parameter value predicted by the model, is the actual measured parameter value.

[0011] Preferably, in step 6, the specific steps of establishing a risk impact assessment system include: Step 6.1: Organize a team of experts to identify and classify the types of risks that may be encountered in meteorological engineering projects; Step 6.2: For each risk type, evaluate it from three dimensions: risk severity, likelihood of occurrence, and controllability, and determine the evaluation criteria and level of each dimension; Step 6.3: Establish a risk impact assessment matrix based on the assessment criteria and levels. Each element in the matrix represents the comprehensive impact value of a risk type. Step 6.4: Verify and revise the risk impact assessment matrix.

[0012] Preferably, in step 7, the method for calculating the comprehensive risk impact value includes: Step 7.1: Use the weighted average method to assign weights to each dimension based on the importance of risk severity, likelihood of occurrence, and controllability; Step 7.2: Convert the evaluation level of each dimension into a corresponding numerical value and multiply it by the corresponding weight; Step 7.3: Add the weighted values ​​of each dimension to obtain the comprehensive impact value of the risk.

[0013] Preferably, in step 8, the risk ranking and grading criteria include: Step 8.1: Set the threshold of the comprehensive impact value and classify the risk into three levels: high risk, medium risk and low risk; Step 8.2: Sort the risks in descending order according to the size of the comprehensive impact value to form a risk priority list.

[0014] Preferably, in step 9, the step of formulating risk response strategies and plans includes: Step 9.1: Analyze the causes and influencing factors of each risk type and determine feasible response strategies; Step 9.2: Select appropriate response strategies based on the severity and controllability of the risk, including risk avoidance, risk mitigation, risk transfer or risk acceptance; Step 9.3: Develop detailed response plans for high and medium risks, including early warning mechanisms, emergency measures, resource allocation, and allocation of responsibilities; Step 9.4: Review and revise the developed response strategies and plans.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention constructs a comprehensive risk assessment data set by collecting various risk event data during the historical implementation of meteorological engineering projects, as well as meteorological conditions, engineering parameters and management measures during project execution. The risk prediction model constructed based on this data set can comprehensively consider multiple influencing factors and accurately predict the risk types and probabilities that the project may encounter in the future, thereby greatly improving the comprehensiveness and accuracy of risk assessment.

[0016] The present invention uses real-time meteorological conditions, project progress and management status data to dynamically input the risk prediction model, thus achieving real-time update of risk assessment. This enables project managers to timely grasp the changes in project risks and provide timely and effective decision support for risk response.

[0017] Based on historical risk event data, the present invention establishes a risk impact assessment system including three dimensions: risk severity, probability of occurrence, and controllability. This system provides a scientific basis for the quantitative assessment of risk events, enables project managers to more accurately judge the impact of risks, and provides strong support for the formulation of risk response strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A step diagram of a meteorological engineering project management risk assessment method according to the present invention; Figure 2 A flow chart for building a risk prediction model for meteorological engineering projects; Figure 3 Flowchart for developing risk response strategies and plans. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] See also Figure 1-3 The present invention provides a technical solution: a meteorological engineering project management risk assessment method, the method comprising: Step 1: Data Collection Collect data on various risk events that occurred during the historical implementation of meteorological engineering projects. These data include but are not limited to risk types (such as construction delays, equipment failures, safety accidents, etc.), time of occurrence, degree of impact on the project (such as economic losses, number of days of construction delay, etc.), and duration of risk events. At the same time, collect data on meteorological conditions during project execution, covering key meteorological elements such as temperature, humidity, wind speed, wind direction, precipitation, and air pressure. In addition, it is necessary to collect engineering parameters such as the scale of the project, construction difficulty, technical complexity, scheduled construction period and budget, as well as management measures such as quality control measures, safety management regulations, progress monitoring methods, and resource allocation strategies taken during project execution.

[0021] Step 2: Data Preprocessing The raw data collected in step 1 is cleaned to remove duplicate, erroneous or incomplete data records. Subsequently, the cleaned data is integrated and formatted to ensure data consistency and readability. Finally, a standardized risk data set is formed to provide a reliable data foundation for the subsequent risk prediction model construction.

[0022] Step 3: Model building Based on the standardized risk data set, a meteorological engineering project risk prediction model is constructed. The model uses advanced machine learning algorithms, such as long short-term memory network (LSTM) or support vector machine (SVM), etc., combined with multi-dimensional features such as meteorological conditions, engineering parameters and management measures, to train the model to predict the types of risks that the project may encounter in the future and their probability of occurrence. During the model construction process, the model parameters need to be optimized and adjusted to improve the accuracy and stability of the prediction.

[0023] Step 4: Real-time data acquisition Through meteorological monitoring equipment, project management information systems and other channels, real-time meteorological condition data, project progress information and management status data of current meteorological engineering projects are obtained. These data will be used as inputs to the risk prediction model to predict project risks in real time.

[0024] Step 5: Risk prediction Input the real-time data obtained in step 4 into the established risk prediction model, and the model will output the predicted risk type and the corresponding probability of occurrence. These prediction results will provide an important basis for subsequent risk assessment.

[0025] Step 6: Establish a risk impact assessment system Based on historical risk event data, a risk impact assessment system is established. The system includes three dimensions: risk severity (i.e., the potential loss or impact of risk events on the project), likelihood of occurrence (i.e., the probability of risk events occurring in the future), and controllability (i.e., the project manager's ability to control or respond to risk events). By quantifying these three dimensions, the comprehensive impact of risk events can be more comprehensively assessed.

[0026] Step 7: Calculate the comprehensive risk impact value Combine the risk type and probability predicted in step 5 with the risk impact assessment system established in step 6 to calculate the comprehensive impact value of each risk. The comprehensive impact value can be obtained by weighted summation or other appropriate mathematical methods to reflect the comprehensive effects of the severity, likelihood and controllability of the risk event.

[0027] Step 8: Risk ranking and grading According to the calculated comprehensive risk impact value, risks are ranked and graded. Risks can be divided into three levels: high risk, medium risk and low risk according to actual needs, or more risk levels can be set according to specific circumstances. Risk ranking and grading help project managers give priority to and deal with high-risk links.

[0028] Step 9: Develop a risk response strategy Develop corresponding risk response strategies and plans for high-risk and medium-risk project links. Risk response strategies can include risk avoidance (such as adjusting construction plans to avoid bad weather), risk mitigation (such as strengthening quality control to reduce equipment failures), risk transfer (such as transferring part of the risk to the insurance company through insurance) and risk acceptance (i.e., for risks that cannot be avoided or mitigated, be prepared to respond and accept their possible consequences).

[0029] Step 10: Visualize and report risk assessment results Finally, the risk assessment results and response strategies are presented in the form of a visual report. The report should include risk prediction results, comprehensive risk impact values, risk ranking and classification, and corresponding risk response strategies and plans. The report should be presented to project managers and relevant decision makers in a clear and intuitive way so that they can quickly understand the project risk status and make decisions. At the same time, the report should also be updated regularly to reflect the dynamic changes in project risks.

[0030] The present invention will be further described below in conjunction with Examples 1 to 3: Embodiment 1: In the data preprocessing stage, the following processing is performed on the collected original risk data set: Missing value processing: For missing values ​​in the data set, linear interpolation algorithm is used to fill them. Specifically, for missing values ​​in time series data, linear interpolation is performed using the values ​​of the two valid data points before and after it to estimate the missing values. For example, if the temperature data of a certain day is missing, and the temperature of the previous day and the next day is 20℃ and 22℃ respectively, the temperature of that day is estimated to be 21℃ through linear interpolation.

[0031] Outlier processing: Use the threshold judgment method to identify and process outliers. First, set a reasonable threshold range based on historical data and professional knowledge. Then, traverse the data set and treat data points that exceed the threshold range as outliers. For outliers, replace them with the mean of their neighboring points. For example, if the wind speed data on a certain day is abnormally high and exceeds the set threshold, replace the data point with the mean of the wind speed data of the days before and after it.

[0032] Data standardization: Use the Z-score standardization method to standardize the data set. For each feature value in the data set, subtract its mean and divide it by its standard deviation so that the processed data conforms to the standard normal distribution. The formula for standardization is: in, is the original data, is the mean of the original data, is the standard deviation of the original data, is the standardized data. Through standardization, the dimensional differences between different features can be eliminated and the prediction performance of the model can be improved.

[0033] The long short-term memory network (LSTM) algorithm is used to build a meteorological engineering project risk prediction model. The specific steps are as follows: Step 3.1: Data preparation A preprocessed standardized risk dataset is used as model input. The feature dataset includes relevant historical data within the time window (such as meteorological conditions and engineering progress over the past period of time), environmental conditions (such as current meteorological conditions), project-specific parameters (such as project scale, construction difficulty, etc.), and time-related features (such as season, weekdays / weekends, etc.).

[0034] The standardized risk dataset is randomly divided into a training set and a test set. The training set accounts for the majority of the dataset (e.g., 80%) and is dedicated to the model training process to learn the association between data features and prediction targets. The test set accounts for a small portion of the dataset (e.g., 20%) and is used for subsequent model evaluation to verify the generalization ability of the model.

[0035] Step 3.2: Design LSTM network structure Design an LSTM network structure that contains at least one LSTM hidden layer. Depending on the complexity of the dataset and the requirements of the prediction task, the number of LSTM hidden layers and the number of units in each layer can be adjusted. For example, you can set up a network with two LSTM hidden layers, each containing 128 LSTM units.

[0036] Select ReLU (Rectified Linear Unit) as the activation function and apply it to the output of the LSTM unit. The ReLU function has nonlinear characteristics that can help the model capture complex patterns in the data.

[0037] Step 3.3: Model training Using the training set data, the LSTM model parameters are iteratively optimized through the back propagation algorithm and gradient descent strategy. During the training process, the model weights are continuously adjusted to minimize the error between the predicted value and the true value. The mean squared error (MSE) or cross-entropy loss (Cross-Entropy Loss) can be used as a loss function to measure the prediction error.

[0038] Set appropriate hyperparameters such as learning rate, batch size, and number of training rounds (epochs). The learning rate controls the step size of the model parameter update, the batch size determines the number of samples used for each update, and the number of training rounds determines the number of times the entire training set is traversed. These hyperparameters need to be adjusted according to actual conditions to obtain the best model performance. During the training process, the mean square error MSE algorithm is used as the loss function to evaluate the difference between the model prediction value and the actual measurement value. The MSE value is defined as the mean of the sum of the squares of the difference between the predicted value and the actual value. The smaller the MSE value, the higher the prediction accuracy of the model. The specific algorithm is: Where n is the number of data points, is the parameter value predicted by the model, is the actual measured parameter value.

[0039] Step 3.4: Model evaluation and optimization Use the test set data to evaluate the performance of the trained LSTM model. By comparing the predicted results with the actual situation, the prediction accuracy and stability of the model can be evaluated. Indicators such as accuracy, recall, and F1 score can be used to evaluate the classification performance of the model, or indicators such as mean square error and mean absolute error can be used to evaluate the regression performance of the model.

[0040] According to the evaluation feedback, adjust the model's hyperparameters, including the number of network layers, the number of hidden layer units, the type of activation function, and the learning rate. Through continuous adjustment and optimization, the model reaches the predetermined prediction accuracy standard. For example, you can try to increase the number of LSTM hidden layers or adjust the number of units in each layer to improve the model's prediction ability; or try different activation function and learning rate combinations to find the best model configuration.

[0041] Embodiment 2: In order to comprehensively assess risks and formulate corresponding response strategies in meteorological engineering project management, the present invention proposes a method for establishing a risk impact assessment system and a method for calculating the comprehensive risk impact value. The following is a description of the detailed implementation of these methods: The steps to establish a risk impact assessment system include: Step 6.1: Organize a team of meteorological experts, project management personnel, and risk assessment experts to comprehensively identify and classify the types of risks that may be encountered in meteorological engineering projects. Through expert meetings, brainstorming, and literature review, identify the possible risks in the project, such as extreme weather events (such as heavy rain, strong winds, lightning, etc.), construction delays, equipment failures, etc. Then, classify these risks according to the nature and source of the risks, such as natural risks, technical risks, management risks, etc.

[0042] Step 6.2: For each risk type, evaluate it from three dimensions: risk severity, likelihood, and controllability. Develop detailed evaluation criteria and levels to quantify the risk. For example, risk severity can be divided into four levels: minor, moderate, severe, and catastrophic; likelihood can be divided into five levels: very low, low, moderate, high, and very high; and controllability can be divided into three levels: fully controllable, partially controllable, and uncontrollable. Assign a corresponding value or score to each level for subsequent calculations.

[0043] Step 6.3: Establish a risk impact assessment matrix based on the assessment criteria and levels. The rows of the matrix represent the risk types, and the columns represent the three dimensions of risk severity, likelihood of occurrence, and controllability. Each element in the matrix represents the assessment value or level of a risk type in a specific dimension. By combining the assessment values ​​of these three dimensions, the comprehensive impact value of each risk type can be obtained. In order to more intuitively represent the risk impact, the comprehensive impact value can be divided into different intervals and correspond to different risk levels, such as low risk, medium risk, high risk, etc.

[0044] Step 6.4: Verify and revise the established risk impact assessment matrix. Verify the rationality and accuracy of the assessment matrix through expert review, field investigation and data analysis. If unreasonable or inaccurate parts are found in the assessment matrix, adjustments and corrections should be made in a timely manner. At the same time, as the project progresses and the external environment changes, the risk impact assessment matrix also needs to be dynamically updated and adjusted.

[0045] The steps to calculate the comprehensive risk impact value include: Step 7.1: Calculate the comprehensive impact value of risk using the weighted average method. First, assign weights to each dimension based on the importance of risk severity, likelihood, and controllability. The weight assignment should be based on expert judgment and the actual situation of the project to ensure the rationality and objectivity of the weight. For example, for a meteorological engineering project, risk severity and likelihood may be more important, so a higher weight can be given.

[0046] Step 7.2: Convert the assessment level of each dimension into a corresponding numerical value. This can be achieved by mapping the level to a specific numerical range, such as mapping the four levels of risk severity to 1, 2, 3, and 4, and mapping the five levels of likelihood to 1, 2, 3, 4, and 5, etc. Then, multiply these numerical values ​​with the corresponding weights to obtain the weighted value of each dimension.

[0047] Step 7.3: Add the weighted values ​​of each dimension to get the comprehensive impact value of the risk. This value can be used to compare the relative importance of different risk types and serve as a basis for formulating risk response strategies. For example, for risk types with higher comprehensive impact values, response measures should be taken to reduce the risk first; for risk types with lower comprehensive impact values, monitoring and observation strategies can be adopted.

[0048] Take the risk of "construction delay caused by heavy rain" in a meteorological engineering project as an example. First, organize an expert team to identify and classify the risk and determine that it is a natural risk. Then, evaluate it from three dimensions: risk severity, probability of occurrence, and controllability. Assume that the evaluation results are: risk severity is "serious" (corresponding to a value of 3), probability of occurrence is "high" (corresponding to a value of 4), and controllability is "partially controllable" (corresponding to a value of 2). Next, according to expert judgment and the actual situation of the project, assign weights to these three dimensions, assuming they are 0.5, 0.3, and 0.2 respectively. Then, convert the evaluation level into a numerical value and multiply it by the weight to obtain the weighted value of each dimension: the weighted value of risk severity is 30.5=1.5, the weighted value of probability of occurrence is 40.3=1.2, and the weighted value of controllability is 2*0.2=0.4. Finally, add these weighted values ​​to get the comprehensive impact value: 1.5+1.2+0.4=3.1. According to the preset risk level classification standards, the comprehensive impact value of this risk belongs to the high risk level, so priority response measures need to be taken to reduce the risk.

[0049] Embodiment 3: In step 8, the risk ranking and grading criteria include: In order to effectively manage the identified risks, we first need to classify them according to their comprehensive impact values. Through expert discussion and historical data analysis, we set thresholds for comprehensive impact values ​​and classify risks into three levels: high risk, medium risk, and low risk. For example, we can set risks with a comprehensive impact value greater than or equal to 7 as high risk, risks between 4 and 6.99 as medium risk, and risks less than 4 as low risk. Such classification helps the project team quickly identify risks that need to be focused on and prioritized.

[0050] On the basis of risk classification, risks are sorted in descending order according to the comprehensive impact value of each risk to form a risk priority list. This list clearly shows the relative importance of all risks in the project and provides a basis for the project team to formulate risk response strategies. When sorting, multiple dimensions such as risk severity, probability of occurrence and controllability can be considered comprehensively to ensure the rationality and accuracy of the sorting results.

[0051] Suppose that five risks are identified in a meteorological engineering project, namely construction delays caused by heavy rain, equipment failure, funding shortage, policy changes, and technical difficulties. Through the risk impact assessment system, the comprehensive impact value of each risk is calculated, which is 8.5 (construction delays caused by heavy rain), 6.0 (equipment failure), 4.5 (funding shortage), 3.0 (policy changes), and 2.0 (technical difficulties). According to the set threshold, these risks can be divided into high risk (construction delays caused by heavy rain), medium risk (equipment failure, funding shortage), and low risk (policy changes, technical difficulties). Then, according to the size of the comprehensive impact value, these risks are sorted in descending order to form a risk priority list so that the project team can formulate response strategies in order of priority.

[0052] Steps in developing risk response strategies and plans include: Step 9.1: For each risk type, conduct an in-depth analysis of its causes and influencing factors, including risk sources, triggering conditions, transmission pathways, etc. Through detailed analysis, the nature and characteristics of the risk can be more accurately understood, providing a basis for formulating effective response strategies.

[0053] Step 9.2: Choose appropriate response strategies based on the severity and controllability of the risk. For risks with high risk and low controllability, you can adopt risk avoidance strategies, such as adjusting the project plan, changing the construction plan, etc.; for risks with high risk but high controllability, you can adopt risk mitigation strategies, such as strengthening safety protection, improving equipment reliability, etc.; for risks with medium or low risk and high controllability, you can consider risk transfer strategies, such as purchasing insurance, subcontracting to professional teams, etc.; for risks with low risk and controllability, you can choose a risk acceptance strategy, that is, do not take special measures, but need to maintain monitoring and prepare response plans.

[0054] Step 9.3: Develop a detailed response plan for high and medium risks. The plan should include an early warning mechanism, clarifying the triggering conditions and early warning process of risk warnings; emergency measures, that is, emergency measures to be taken after the risk occurs; resource allocation, including the preparation and allocation of human, material, financial and other resources; and the allocation of responsible persons, clarifying the responsible persons and responsibilities of each link. By developing a detailed plan, you can ensure that you can respond quickly and effectively when risks occur.

[0055] Step 9.4: Review and revise the prepared response strategies and plans. Organize an expert team to review the plans, check their rationality and feasibility, and put forward suggestions for improvement. At the same time, according to the progress of the project and changes in the external environment, revise the plans in a timely manner to ensure that they always meet the actual needs of the project. Through continuous review and revision, risk response strategies and plans can be continuously improved, and the risk management level of the project can be improved.

[0056] Taking the risk of construction delay caused by heavy rain as an example, by analyzing its causes and influencing factors, we can determine coping strategies such as strengthening meteorological monitoring and formulating construction plan adjustment plans in advance. Since this risk is high-risk and has low controllability, risk avoidance strategies can be selected, such as adjusting the construction plan to avoid key construction during the heavy rain season. At the same time, a detailed response plan should be formulated, including establishing a meteorological early warning mechanism, preparing emergency materials and equipment, and clarifying the responsible persons. In the plan, it can be stipulated that when the weather forecast shows that there will be heavy rain in the next few days, the early warning mechanism will be activated immediately to notify the project team to prepare for the response; if the heavy rain makes it impossible to continue the construction, the construction plan will be adjusted according to the predetermined plan to ensure the safe and smooth progress of the project. By formulating and implementing such response strategies and plans, the impact of heavy rain on construction can be effectively reduced and the smooth progress of the project can be guaranteed.

[0057] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0058] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A meteorological engineering project management risk assessment method, characterized in that: The method comprises: Step 1: Collect various risk event data of meteorological engineering projects in the historical implementation process, as well as meteorological conditions, engineering parameters and management measures during project execution; the risk event data include risk type, occurrence time, impact degree and duration; the meteorological conditions include temperature, humidity, wind speed, wind direction, precipitation and air pressure; the engineering parameters include project scale, construction difficulty, technical complexity, construction period and budget; the management measures include quality control, safety management, progress monitoring and resource allocation; Step 2: Clean, integrate and format the data collected in step 1 to form a standardized risk data set; Step 3: Based on the standardized risk data set, a risk prediction model for meteorological engineering projects is constructed. The model combines meteorological conditions, engineering parameters and management measures to predict the types of risks and their probabilities that the project may encounter in the future; Step 4: Obtain real-time meteorological conditions, engineering progress and management status data of the current meteorological engineering project; Step 5: Input the data obtained in step 4 into the risk prediction model, and receive the predicted risk type and probability output by the model; Step 6: Based on historical risk event data, establish a risk impact assessment system that includes three dimensions: risk severity, likelihood of occurrence, and controllability; Step 7: Combine the risk type and probability predicted in step 5 with the risk impact assessment system established in step 6 to calculate the comprehensive impact value of each risk; Step 8: Sort and grade the risks based on the calculated comprehensive risk impact value, and determine the project links with high, medium and low risks; Step 9: Develop risk response strategies and plans for high-risk and medium-risk project links, including risk avoidance, risk mitigation, risk transfer and risk acceptance; Step 10: Present the risk assessment results and response strategies in the form of a visual report and send it to project managers and relevant decision makers.

2. A meteorological engineering project management risk assessment method according to claim 1, characterized in that: In step 2, the data preprocessing method includes: For missing values, linear interpolation algorithm is used to fill them; For outliers, the threshold judgment method is used to regard data points that exceed the preset threshold range as outliers and replace them with the mean of their neighboring points; Using the Z-score standardization method, each feature value is subtracted from its mean and divided by its standard deviation, so that the processed data conforms to the standard normal distribution. The formula for standardization is: ,in, is the original data, is the mean of the original data, is the standard deviation of the original data, is the standardized data.

3. A meteorological engineering project management risk assessment method according to claim 1, characterized in that: The long short-term memory network (LSTM) algorithm is used to build a risk prediction model.

4. A meteorological engineering project management risk assessment method according to claim 3, characterized in that: The steps to build a risk prediction model include: Step 3.1: Use a standardized risk dataset as model input, which includes relevant historical data, environmental conditions, project-specific parameters, and time-dependent characteristics within a time window; The standardized risk dataset is randomly divided into a training set and a test set. The training set is used exclusively for the model training process to learn the relationship between data features and prediction targets; the test set is used for subsequent model evaluation to verify the generalization ability of the model. Step 3.2: Design the LSTM network structure, which includes at least one LSTM hidden layer and specify the appropriate number of units for each LSTM hidden layer; select ReLU as the activation function and apply it to the LSTM unit output; Step 3.3: Using the training set data, the LSTM model parameters are iteratively optimized through the back propagation algorithm and gradient descent strategy; during the training process, the model weights are continuously adjusted to minimize the error between the predicted value and the true value; Step 3.4: Use the test set data to evaluate the performance of the trained LSTM model. By comparing the predicted results with the actual situation, the prediction accuracy and stability of the model are evaluated. According to the evaluation feedback, the hyperparameters of the model are adjusted, including the number of network layers, the number of hidden layer units, the type of activation function, and the learning rate, until the predetermined prediction accuracy standard is reached.

5. A meteorological engineering project management risk assessment method according to claim 4, characterized in that: During the training process, the mean square error (MSE) algorithm is used as the loss function to evaluate the difference between the model prediction value and the actual measurement value. The MSE value is defined as the mean of the sum of the squares of the difference between the predicted value and the actual value. The smaller the MSE value, the higher the prediction accuracy of the model. The specific algorithm is: Where n is the number of data points, is the parameter value predicted by the model, is the actual measured parameter value.

6. A meteorological engineering project management risk assessment method according to claim 1, characterized in that: In step 6, the specific steps of establishing a risk impact assessment system include: Step 6.1: Organize a team of experts to identify and classify the types of risks that may be encountered in meteorological engineering projects; Step 6.2: For each risk type, evaluate it from three dimensions: risk severity, likelihood of occurrence, and controllability, and determine the evaluation criteria and level of each dimension; Step 6.3: Establish a risk impact assessment matrix based on the assessment criteria and levels. Each element in the matrix represents the comprehensive impact value of a risk type. Step 6.4: Verify and revise the risk impact assessment matrix.

7. A meteorological engineering project management risk assessment method according to claim 1, characterized in that: In step 7, the method for calculating the comprehensive risk impact value includes: Step 7.1: Use the weighted average method to assign weights to each dimension based on the importance of risk severity, likelihood of occurrence, and controllability; Step 7.2: Convert the evaluation level of each dimension into a corresponding numerical value and multiply it by the corresponding weight; Step 7.3: Add the weighted values ​​of each dimension to obtain the comprehensive impact value of the risk.

8. A meteorological engineering project management risk assessment method according to claim 1, characterized in that: In step 8, the risk ranking and grading criteria include: Step 8.1: Set the threshold of the comprehensive impact value and classify the risk into three levels: high risk, medium risk and low risk; Step 8.2: Sort the risks in descending order according to the size of the comprehensive impact value to form a risk priority list.

9. A meteorological engineering project management risk assessment method according to claim 1, characterized in that: In step 9, the steps of formulating risk response strategies and plans include: Step 9.1: Analyze the causes and influencing factors of each risk type and determine feasible response strategies; Step 9.2: Select appropriate response strategies based on the severity and controllability of the risk, including risk avoidance, risk mitigation, risk transfer or risk acceptance; Step 9.3: Develop detailed response plans for high and medium risks, including early warning mechanisms, emergency measures, resource allocation, and allocation of responsibilities; Step 9.4: Review and revise the developed response strategies and plans.

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