Large-scale water delivery pipe network safe operation evaluation method based on multi-parameter comprehensive analysis

Abstract

The invention discloses a large-scale water delivery pipe network safe operation evaluation method based on multi-parameter comprehensive analysis. According to the invention, through multi-parameter comprehensive analysis, the safe operation state, including water quality, structure, operation, environment and the like, of the water delivery pipe network can be comprehensively evaluated. Through analysis and visualization of a model output result, a decision maker and related departments can more intuitively understand the safety condition of the pipe network, and a risk early warning system is established according to an evaluation result, so that early recognition and early warning of potential risks are realized, and the risk of accidents is effectively reduced. A neural network machine learning algorithm is adopted to automatically learn weights, and the importance of each index is determined. The method gets rid of the subjectivity of determining the weight depending on expert experience in a traditional evaluation method, so that the evaluation result is more objective and accurate. Through the data-driven evaluation model, the safe operation state of the water delivery pipe network can be reflected more comprehensively, and powerful data support is provided for decision makers.

Description

Technical Field

[0001] The present invention belongs to the technical field of water pipeline network safety assessment, and specifically is a large-scale water pipeline network safety operation assessment method based on multi-parameter comprehensive analysis. Background Art

[0002] With the acceleration of urbanization, large-scale water pipeline networks play an increasingly important role in urban water supply systems. The safe operation of water pipeline networks is directly related to the safety and stability of urban water supply, and plays a vital role in ensuring the domestic water supply of residents, industrial production water and urban greening. However, the safe operation of water pipeline networks faces many challenges, such as water pollution, structural aging, and operational failures. In addition, the maintenance and operation costs of water pipeline networks are high. Once a failure or accident occurs, it will not only cause direct economic losses, but also affect the normal water supply of the city, and may even cause public safety issues. Therefore, effective safety operation assessment and monitoring of water pipeline networks is of great significance to ensure the stable operation of urban water supply systems.

[0003] However, the current safety operation assessment method of water pipeline networks mainly relies on expert experience and determines the weight of each indicator through subjective judgment. This method has certain subjectivity and limitations, and it is difficult to fully reflect the safety status of water pipeline networks. At the same time, existing methods often lack the ability to identify and warn of potential risks early, resulting in a high risk of accidents. Summary of the invention

[0004] The purpose of the present invention is to provide a method for evaluating the safe operation of a large-scale water supply network by comprehensive multi-parameter analysis in order to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention is as follows: a method for evaluating the safe operation of a large-scale water supply network by comprehensive analysis of multiple parameters, the method comprising the following steps:

[0006] S1: First determine the evaluation indicators, then collect data on the above indicators through on-site monitoring, historical archives, reports from relevant departments, etc.;

[0007] S2: Remove outliers, fill missing values, and unify data formats through data cleaning;

[0008] S3: Then through data standardization: standardize the indicator data of different dimensions to make them comparable;

[0009] S4: Use neural network machine learning algorithm to learn weights and determine the weights of each indicator;

[0010] S5: Construct a comprehensive evaluation model and use the weighted comprehensive evaluation method to build the evaluation model;

[0011] S6: Use the collected and preprocessed data to train the neural network model; during the training process, the neural network will learn the complex relationship between the input indicators and the target output, and automatically adjust the weights, dividing the data set into a training set, a validation set, and a test set; the training set is used to train the model, the validation set is used to adjust the model parameters and prevent overfitting, and the test set is used to finally evaluate the generalization ability of the model;

[0012] S7: Evaluate the performance of the model on the test set using evaluation metrics including mean square error (MSE), coefficient of determination (R 2 ) or classification accuracy; Analyze the difference between the model prediction results and the actual results, identify possible deviations or wrong predictions, so as to further optimize the model;

[0013] S8: Interpret the weights of the neural network output and analyze the contribution of each indicator to the comprehensive score, so as to understand which factors are most critical to the safe operation of the pipeline network; classify the safe operation status of the pipeline network into "safe", "needs attention" and "high risk" categories based on the comprehensive score output by the model; visualize the evaluation results through dashboards, heat maps or trend maps, so that decision makers and relevant departments can understand the evaluation results more intuitively;

[0014] S9: Based on the assessment results, establish a risk warning system to identify and warn of potential risks at an early stage;

[0015] S10: Continuously monitor the pipeline network and regularly collect new data to track changes in the pipeline network status; regularly retrain and update the model to adapt to the aging of the pipeline network, technological updates, or changes in the external environment to ensure the accuracy and timeliness of the evaluation model.

[0016] In a preferred embodiment, in step S1, the evaluation indicators include:

[0017] Water quality indicators: pH value, turbidity, dissolved oxygen, chemical oxygen demand, biological oxygen demand, total nitrogen, total phosphorus;

[0018] Structural indicators: pipeline material, pipeline age, pipeline diameter, pipeline wall thickness, and interface form;

[0019] Operation indicators: flow rate, pressure, flow velocity, leakage rate, equipment failure rate;

[0020] Environmental indicators: geological conditions, climatic conditions, and surrounding pollution sources.

[0021] In a preferred embodiment, in step S2, the specific process of data cleaning is as follows:

[0022] Identify missing values: First, check whether there are missing values ​​in the water pipeline network dataset, including completely missing records and missing fields;

[0023] Handling missing values: For missing values, choose to fill or delete;

[0024] Identify outliers: Identify outliers in water distribution network data through statistical analysis;

[0025] Processing outliers: Process outliers and replace them with values ​​within a reasonable range, or delete these records directly;

[0026] Remove duplicate values: Check and remove duplicate records in the data set to ensure the uniqueness of each record;

[0027] Data consistency check: ensure the consistency of water pipe network data in terms of format, unit, and coding, including unified date format and conversion of dimensions;

[0028] Data type conversion: Convert data into the appropriate type, including converting categorical variables into factor types and converting date fields into date formats.

[0029] In a preferred embodiment, in step S3, the water pipe network data is converted into a form with zero mean and unit variance; the specific steps are as follows:

[0030] S1. Calculate the mean and standard deviation: For each feature, calculate its mean (Mean) and standard deviation (Std);

[0031] S2. Apply transformation formula: For each eigenvalue x in the water pipe network dataset, use the following formula for transformation:

[0032]

[0033] Among them, X std is the transformed value;

[0034] S3. Transform the water pipeline network dataset: Apply the above transformation to each feature of the entire water pipeline network dataset; after Z-score standardization, the mean of each feature is 0 and the standard deviation is 1, and the distribution of the water pipeline network dataset will be similar to the standard normal distribution.

[0035] In a preferred embodiment, the step S4 specifically includes:

[0036] Step 4-1: Data preparation:

[0037] Input data X: an m×n matrix, where m is the number of samples and n is the number of indicators;

[0038] Target data Y: an m×1 vector containing the target score or classification of each sample;

[0039] Step 4-2: Neural network model construction, building a neural network model that maps input data to target data;

[0040] Input layer: has n neurons, corresponding to n indicators;

[0041] Hidden layer: one or more hidden layers, each with a number of neurons;

[0042] Output layer: one neuron, output comprehensive score

[0043] Step 4-3: Model training, use the back propagation algorithm to train the neural network, and use the gradient descent algorithm to update the weights;

[0044] Step 4-4: Weight learning. During the training process, the neural network learns the weights from the input layer to the hidden layer and from the hidden layer to the output layer. The weight W represents the weight matrix connecting the neurons. The bias b represents the bias term of each neuron.

[0045] Step 4-5: Calculate weights. In a neural network, the weights from the input layer to the hidden layer can be regarded as the learning result of the indicator weights.

[0046] Step 4-6: Model validation and testing, evaluate model performance on the validation set and test set to ensure that weight learning is effective;

[0047] Step 4-7: Explain the results, explain the weights of the neural network output, and convert them into indicator weights; where the indicator weight W i Indicates the importance of each indicator through W in Definitely worth it.

[0048] In a preferred embodiment, in step 4-3, the difference between the model prediction value and the actual value is measured by the loss function in the process of training the neural network using the back propagation algorithm, and the calculation formula is: Among them, Y i is the actual value, Y^ i is the predicted value.

[0049] In a preferred embodiment, in step 4-3, when the gradient descent algorithm is used to update the weights of the neural network, the weights and biases of the network need to be initialized first; then, an iterative training process is entered, and each step includes forward propagation to calculate the output of the network, and then the loss function is calculated to evaluate the difference between the output and the actual value; next is back propagation, which calculates the gradient of the loss function with respect to each weight, that is, the contribution of each weight to the loss function; finally, based on the calculated gradient, each weight is updated using the gradient descent formula, and this update is carried out in the direction of reducing the loss function; this process is repeated until the network weights converge to a set of values ​​so that the loss function reaches a local minimum, thereby completing the weight update; in this process, the learning rate determines the step size of the weight update, and needs to be reasonably selected to ensure that the algorithm can converge effectively.

[0050] In a preferred embodiment, in step S6, the model training and verification stops when the performance of the verification set no longer improves to prevent overfitting. The specific method flow includes:

[0051] Divide the dataset into training set, validation set and test set;

[0052] Initialize model parameters and hyperparameters;

[0053] Train the model using the training set data and monitor the performance of the validation set;

[0054] Adjust hyperparameters until the best model configuration is found;

[0055] Apply early stopping to stop training when the performance on the validation set no longer improves.

[0056] In a preferred embodiment, in step S7, for classification problems, a confusion matrix is ​​used to evaluate the classification performance of the model; for binary classification problems, a receiver operating characteristic (ROC) curve can be used to evaluate the performance of the model;

[0057] The confusion matrix is ​​a 2×2 table that shows the performance of a classification model on a test dataset; it compares the model’s predictions with the actual results to calculate true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN).

[0058] The ROC curve is a graphical tool used to evaluate the performance of a binary classification model; it plots the True Positive Rate (TPR) against the False Positive Rate (FPR), where TPR is the recall rate and FPR is the proportion of false positives.

[0059] In a preferred embodiment, in step S8, a sensitivity analysis report is generated based on the results of feature importance assessment and model stability test; the report should include a list of key features, their impact on model predictions, and the sensitivity of the model to changes in these features; the SHAP model interpretability tool is used to understand the logic behind the model predictions and identify which features have the greatest impact on model predictions.

[0060] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0061] 1. In the present invention, through multi-parameter comprehensive analysis, the safe operation status of the water supply network can be comprehensively evaluated, including water quality, structure, operation and environment. Through the analysis and visualization of the model output results, decision makers and relevant departments can more intuitively understand the safety status of the network, and establish a risk warning system based on the evaluation results to achieve early identification and early warning of potential risks, thereby effectively reducing the risk of accidents. A neural network machine learning algorithm is used to automatically learn weights and determine the importance of each indicator. This method gets rid of the subjectivity of the traditional evaluation method that relies on expert experience to determine weights, making the evaluation results more objective and accurate. Through a data-driven evaluation model, the safe operation status of the water supply network can be more comprehensively reflected, and strong data support can be provided for decision makers.

[0062] 2. In the present invention, a data-driven way of thinking is adopted. A large amount of data is collected through on-site monitoring, historical archives and reports from relevant departments, and the importance of each indicator is determined by automatically learning weights using a neural network machine learning algorithm. This method can objectively reflect the degree of influence of each indicator on the safe operation of the water supply network, and help decision makers understand which factors are most critical to the safe operation of the network, so as to formulate optimization decisions and maintenance strategies in a targeted manner. At the same time, based on the evaluation results, a risk warning system is established to identify and warn potential risks that may arise early. In addition, the method also emphasizes the continuous monitoring of the network and model updating to ensure the accuracy and timeliness of the evaluation model. This innovation enables the evaluation method to not only provide the current safety status, but also predict future risks, providing decision makers with a more comprehensive basis for decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0065] Reference Figure 1 ,

[0066] A multi-parameter comprehensive analysis method for evaluating the safe operation of a large-scale water supply network comprises the following steps:

[0067] S1: First determine the evaluation indicators, then collect data on the above indicators through on-site monitoring, historical archives, reports from relevant departments, etc.;

[0068] S2: Remove outliers, fill missing values, and unify data formats through data cleaning;

[0069] S3: Then through data standardization: standardize the indicator data of different dimensions to make them comparable;

[0070] S4: Use neural network machine learning algorithm to learn weights and determine the weights of each indicator;

[0071] S5: Construct a comprehensive evaluation model and use the weighted comprehensive evaluation method to build the evaluation model;

[0072] S6: Use the collected and preprocessed data to train the neural network model. During the training process, the neural network will learn the complex relationship between the input indicators and the target output, and automatically adjust the weights, dividing the data set into training set, validation set and test set. The training set is used to train the model, the validation set is used to adjust the model parameters and prevent overfitting, and the test set is used to finally evaluate the generalization ability of the model;

[0073] S7: Evaluate the performance of the model on the test set using evaluation metrics including mean square error (MSE), coefficient of determination (R 2 ) or classification accuracy. Analyze the difference between the model prediction results and the actual results, identify possible deviations or wrong predictions, and further optimize the model;

[0074] S8: Interpret the weights of the neural network output and analyze the contribution of each indicator to the comprehensive score, so as to understand which factors are most critical to the safe operation of the pipeline network; classify the safe operation status of the pipeline network into "safe", "needs attention" and "high risk" categories based on the comprehensive score output by the model; visualize the evaluation results through dashboards, heat maps or trend maps, so that decision makers and relevant departments can understand the evaluation results more intuitively;

[0075] S9: Based on the assessment results, establish a risk warning system to identify and warn of potential risks at an early stage;

[0076] S10: Continuously monitor the pipeline network and regularly collect new data to track changes in the pipeline network status. Regularly retrain and update the model to adapt to the aging of the pipeline network, technological updates, or changes in the external environment to ensure the accuracy and timeliness of the evaluation model.

[0077] In step S1, the evaluation indicators include:

[0078] Water quality indicators: pH value, turbidity, dissolved oxygen, chemical oxygen demand, biological oxygen demand, total nitrogen, total phosphorus;

[0079] Structural indicators: pipeline material, pipeline age, pipeline diameter, pipeline wall thickness, and interface form;

[0080] Operation indicators: flow rate, pressure, flow velocity, leakage rate, equipment failure rate;

[0081] Environmental indicators: geological conditions, climatic conditions, and surrounding pollution sources.

[0082] In step S2, the specific process of data cleaning is as follows:

[0083] Identify missing values: First, check whether there are missing values ​​in the water pipeline network dataset, including completely missing records and missing fields;

[0084] Handling missing values: For missing values, choose to fill or delete;

[0085] Identify outliers: Identify outliers in water distribution network data through statistical analysis;

[0086] Processing outliers: Process outliers and replace them with values ​​within a reasonable range, or delete these records directly;

[0087] Remove duplicate values: Check and remove duplicate records in the data set to ensure the uniqueness of each record;

[0088] Data consistency check: ensure the consistency of water pipe network data in terms of format, unit, and coding, including unified date format and conversion of dimensions;

[0089] Data type conversion: Convert data into the appropriate type, including converting categorical variables into factor types and converting date fields into date formats.

[0090] In step S3, the water pipe network data is converted into a form with zero mean and unit variance; the specific steps are as follows:

[0091] S1. Calculate the mean and standard deviation: For each feature, calculate its mean (Mean) and standard deviation (Std);

[0092] S2. Apply transformation formula: For each eigenvalue x in the water pipe network dataset, use the following formula for transformation:

[0093]

[0094] Among them, X std is the transformed value;

[0095] S3. Transform the water pipeline network dataset: Apply the above transformation to each feature of the entire water pipeline network dataset; after Z-score standardization, the mean of each feature is 0 and the standard deviation is 1, and the distribution of the water pipeline network dataset will be similar to the standard normal distribution.

[0096] Step S4 specifically includes:

[0097] Step 4-1: Data preparation:

[0098] Input data X: an m×n matrix, where m is the number of samples and n is the number of indicators.

[0099] Target data Y: an m×1 vector containing the target score or classification for each sample.

[0100] Step 4-2: Neural network model construction, build a neural network model that maps input data to target data.

[0101] Input layer: has n neurons, corresponding to n indicators.

[0102] Hidden layer: One or more hidden layers, each with a number of neurons.

[0103] Output layer: one neuron, output comprehensive score

[0104] Step 4-3: Model training, use the back propagation algorithm to train the neural network, and use the gradient descent algorithm to update the weights;

[0105] Step 4-4: Weight learning. During the training process, the neural network learns the weights from the input layer to the hidden layer and from the hidden layer to the output layer. The weight W represents the weight matrix connecting the neurons. The bias b represents the bias term of each neuron.

[0106] Step 4-5: Calculate weights. In a neural network, the weights from the input layer to the hidden layer can be regarded as the learning result of the indicator weights.

[0107] Step 4-6: Model validation and testing, evaluate the model performance on the validation set and test set to ensure that the weight learning is effective.

[0108] Step 4-7: Explain the results, explain the weights of the neural network output, and convert them into indicator weights. The indicator weight W i Indicates the importance of each indicator through W in It's definitely worth it.

[0109] In step 4-3, the difference between the model prediction value and the actual value is measured by the loss function in the process of training the neural network using the back propagation algorithm. The calculation formula is: Among them, Y i is the actual value, Y^ i is the predicted value.

[0110] In step 4-3, when using the gradient descent algorithm to update the weights of the neural network, the network weights and biases need to be initialized first. Next, the iterative training process begins, where each step includes forward propagation to calculate the output of the network, and then calculates the loss function to evaluate the difference between the output and the actual value. Next is backpropagation, which calculates the gradient of the loss function with respect to each weight, that is, the contribution of each weight to the loss function. Finally, based on the calculated gradient, the gradient descent formula is used to update each weight, and this update is carried out in the direction of reducing the loss function. This process is repeated until the network weights converge to a set of values ​​that make the loss function reach a local minimum, thereby completing the weight update. In this process, the learning rate determines the step size of the weight update, and needs to be selected reasonably to ensure that the algorithm can converge effectively.

[0111] In step S6, the model training and verification stops when the performance of the verification set no longer improves to prevent overfitting. The specific method flow includes:

[0112] Divide the dataset into training set, validation set and test set.

[0113] Initialize model parameters and hyperparameters.

[0114] Train the model using the training set data and monitor the performance on the validation set.

[0115] Tune hyperparameters until you find the best model configuration.

[0116] Apply early stopping to stop training when the performance on the validation set no longer improves.

[0117] In step S7, for classification problems, a confusion matrix is ​​used to evaluate the classification performance of the model. For binary classification problems, a receiver operating characteristic (ROC) curve can be used to evaluate the performance of the model.

[0118] The confusion matrix is ​​a 2×2 table that shows the performance of a classification model on a test dataset. It compares the model's predictions with the actual results to calculate true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN);

[0119] The ROC curve is a graphical tool used to evaluate the performance of a binary classification model. It plots the True Positive Rate (TPR) against the False Positive Rate (FPR), where TPR is the recall rate and FPR is the proportion of false positives.

[0120] In step S8, a sensitivity analysis report is generated based on the results of feature importance assessment and model stability test. The report should include a list of key features, their impact on model predictions, and the sensitivity of the model to changes in these features. Use the SHAP model interpretability tool to understand the logic behind model predictions and identify which features have the greatest impact on model predictions.

[0121] In the present invention, through multi-parameter comprehensive analysis, the safe operation status of the water supply network can be comprehensively evaluated, including water quality, structure, operation and environment. Through the analysis and visualization of the model output results, decision makers and relevant departments can more intuitively understand the safety status of the network, and establish a risk warning system based on the evaluation results to achieve early identification and early warning of potential risks, thereby effectively reducing the risk of accidents. A neural network machine learning algorithm is used to automatically learn weights and determine the importance of each indicator. This method gets rid of the subjectivity of the traditional evaluation method that relies on expert experience to determine weights, making the evaluation results more objective and accurate. Through a data-driven evaluation model, the safe operation status of the water supply network can be more comprehensively reflected, and strong data support can be provided for decision makers.

[0122] In the present invention, a data-driven way of thinking is adopted. A large amount of data is collected through on-site monitoring, historical archives and reports from relevant departments, and the neural network machine learning algorithm is used to automatically learn the weights to determine the importance of each indicator. This method can objectively reflect the degree of influence of each indicator on the safe operation of the water supply pipeline network, and help decision makers understand which factors are most critical to the safe operation of the pipeline network, so as to formulate optimization decisions and maintenance strategies in a targeted manner. At the same time, based on the evaluation results, a risk warning system is established to identify and warn potential risks that may arise early. In addition, the method also emphasizes the continuous monitoring of the pipeline network and the updating of the model to ensure the accuracy and timeliness of the evaluation model. This innovation enables the evaluation method to not only provide the current safety status, but also predict future risks, providing decision makers with a more comprehensive basis for decision-making.

[0123] 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 term "comprises" or any other variant thereof is 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. In the absence of further restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0124] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-parameter comprehensive analysis method for evaluating the safe operation of a large-scale water supply network, characterized by: The method comprises the following steps: S1: First determine the evaluation indicators, then collect data on the above indicators through on-site monitoring, historical archives, and reports from relevant departments; S2: Remove outliers, fill missing values, and unify data formats through data cleaning; S3: Then through data standardization: standardize the indicator data of different dimensions to make them comparable; S4: Use neural network machine learning algorithm to learn weights and determine the weights of each indicator; S5: Construct a comprehensive evaluation model and use the weighted comprehensive evaluation method to build the evaluation model; S6: Use the collected and preprocessed data to train the neural network model; during the training process, the neural network will learn the complex relationship between the input indicators and the target output, and automatically adjust the weights, dividing the data set into a training set, a validation set, and a test set; the training set is used to train the model, the validation set is used to adjust the model parameters and prevent overfitting, and the test set is used to finally evaluate the generalization ability of the model; S7: Evaluate the performance of the model on the test set using evaluation metrics, including mean square error, coefficient of determination, or classification accuracy; analyze the difference between the model predictions and the actual results, and identify possible deviations or mispredictions to facilitate further optimization of the model; S8: Interpret the weights of the neural network output and analyze the contribution of each indicator to the comprehensive score, so as to understand which factors are most critical to the safe operation of the pipeline network; classify the safe operation status of the pipeline network into "safe", "need to pay attention" and "high risk" categories based on the comprehensive score output by the model; visualize the evaluation results through dashboards, heat maps or trend maps, so that decision makers and relevant departments can understand the evaluation results more intuitively; S9: Based on the assessment results, establish a risk warning system to identify and warn of potential risks at an early stage; S10: Continuously monitor the pipeline network and regularly collect new data to track changes in the pipeline network status; regularly retrain and update the model to adapt to the aging of the pipeline network, technological updates, or changes in the external environment to ensure the accuracy and timeliness of the evaluation model.

2. A method for evaluating the safe operation of a large-scale water supply network by comprehensive multi-parameter analysis as claimed in claim 1, characterized in that: In step S1, the evaluation indicators include: Water quality indicators: pH value, turbidity, dissolved oxygen, chemical oxygen demand, biological oxygen demand, total nitrogen, total phosphorus; Structural indicators: pipeline material, pipeline age, pipeline diameter, pipeline wall thickness, and interface form; Operation indicators: flow rate, pressure, flow velocity, leakage rate, equipment failure rate; Environmental indicators: geological conditions, climatic conditions, and surrounding pollution sources.

3. The method for evaluating the safe operation of a large-scale water supply network by comprehensive multi-parameter analysis according to claim 1, characterized in that: In step S2, the specific process of data cleaning is as follows: Identify missing values: First, check whether there are missing values ​​in the water pipeline network dataset, including completely missing records and missing fields; Handling missing values: For missing values, choose to fill or delete; Identify outliers: Identify outliers in water distribution network data through statistical analysis; Processing outliers: Process outliers and replace them with values ​​within a reasonable range, or delete these records directly; Remove duplicate values: Check and remove duplicate records in the data set to ensure the uniqueness of each record; Data consistency check: ensure the consistency of water pipe network data in terms of format, unit, and coding, including unified date format and conversion of dimensions; Data type conversion: Convert data into the appropriate type, including converting categorical variables into factor types and converting date fields into date formats.

4. A method for evaluating the safe operation of a large-scale water supply network by comprehensive multi-parameter analysis as claimed in claim 1, characterized in that: In step S3, the water pipe network data is converted into a form with zero mean and unit variance; the specific steps are as follows: S1. Calculate the mean and standard deviation: For each feature, calculate its mean and standard deviation; S2. Apply transformation formula: For each eigenvalue x in the water pipe network dataset, use the following formula for transformation: Among them, X strl is the transformed value; S3. Transform the water pipeline network dataset: Apply the above transformation to each feature of the entire water pipeline network dataset; after Z-score standardization, the mean of each feature is 0 and the standard deviation is 1, and the distribution of the water pipeline network dataset will be similar to the standard normal distribution.

5. The method for evaluating the safe operation of a large-scale water supply network by comprehensive multi-parameter analysis according to claim 1, characterized in that: The step S4 specifically includes: Step 4-1: Data preparation: Input data X: an m×n matrix, where m is the number of samples and n is the number of indicators; Target data Y: an m×1 vector containing the target score or classification of each sample; Step 4-2: Neural network model construction, building a neural network model that maps input data to target data; Input layer: has n neurons, corresponding to n indicators; Hidden layer: one or more hidden layers, each with a number of neurons; Output layer: one neuron, output comprehensive score Step 4-3: Model training, use the back propagation algorithm to train the neural network, and use the gradient descent algorithm to update the weights; Step 4-4: Weight learning. During the training process, the neural network learns the weights from the input layer to the hidden layer and from the hidden layer to the output layer. The weight W represents the weight matrix connecting the neurons. The bias b represents the bias term of each neuron. Step 4-5: Calculate weights. In a neural network, the weights from the input layer to the hidden layer are considered as the learning results of the indicator weights. Step 4-6: Model validation and testing, evaluate model performance on the validation set and test set to ensure that weight learning is effective; Step 4-7: Explain the results, explain the weights of the neural network output, and convert them into indicator weights; where the indicator weight W i Indicates the importance of each indicator through W in Definitely worth it.

6. A method for evaluating the safe operation of a large-scale water supply network by comprehensive multi-parameter analysis as claimed in claim 5, characterized in that: In step 4-3, the difference between the model prediction value and the actual value is measured by the loss function in the process of training the neural network using the back propagation algorithm. The calculation formula is: Among them, Y i is the actual value, Y^ i is the predicted value.

7. A method for evaluating the safe operation of a large-scale water supply network by comprehensive multi-parameter analysis as claimed in claim 6, characterized in that: In step 4-3, when the gradient descent algorithm is used to update the weights of the neural network, the weights and biases of the network need to be initialized first; then, an iterative training process is entered, and each step includes forward propagation to calculate the output of the network, and then the loss function is calculated to evaluate the difference between the output and the actual value; next is back propagation, which calculates the gradient of the loss function with respect to each weight, that is, the contribution of each weight to the loss function; finally, based on the calculated gradient, each weight is updated using the gradient descent formula, and this update is performed in the direction of reducing the loss function; this process is repeated until the network weights converge to a set of values ​​so that the loss function reaches a local minimum, thereby completing the weight update; in this process, the learning rate determines the step size of the weight update, and needs to be reasonably selected to ensure that the algorithm can converge effectively.

8. The method for evaluating the safe operation of a large-scale water supply network by comprehensive multi-parameter analysis as claimed in claim 1, characterized in that: In step S6, the model training and verification stops when the performance of the verification set no longer improves to prevent overfitting. The specific method flow includes: Divide the dataset into training set, validation set and test set; Initialize model parameters and hyperparameters; Train the model using the training set data and monitor the performance of the validation set; Adjust hyperparameters until you find the best model configuration; Apply early stopping to stop training when the performance on the validation set no longer improves.

9. A method for evaluating the safe operation of a large-scale water supply network by comprehensive multi-parameter analysis as claimed in claim 1, characterized in that: In step S7, for classification problems, a confusion matrix is ​​used to evaluate the classification performance of the model; for binary classification problems, a receiver operating characteristic curve is used to evaluate the performance of the model; The confusion matrix is ​​a 2×2 table that shows the performance of a classification model on a test dataset; it compares the model’s predictions with the actual results to count true positives, false positives, true negatives, and false negatives. The ROC curve is a graphical tool used to evaluate the performance of a binary classification model; it plots the True Positive Rate against the False Positive Rate, where TPR is the Recall Rate and FPR is the False Positive Rate.

10. A method for evaluating the safe operation of a large-scale water supply network by comprehensive multi-parameter analysis as claimed in claim 1, characterized in that: In step S8, a sensitivity analysis report is generated based on the results of the feature importance assessment and the model stability test; the report should include a list of key features, their impact on model prediction, and the sensitivity of the model to changes in these features; Use the SHAP model interpretability tool to understand the logic behind model predictions and identify which features have the greatest impact on model predictions.

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