Urban water dynamic collaborative optimization system and method based on multi-source data intelligent fusion

Abstract

The invention discloses an urban water dynamic collaborative optimization system and method based on multi-source data intelligent fusion, and belongs to the technical field of urban water resource management. The technical problems that in existing urban water system management, errors of state judgment and trend analysis are large, and the accuracy of decision basis cannot be guaranteed are solved. According to the technical scheme, the method comprises the steps of collecting multi-source urban water system data, completing intelligent fusion of preprocessing and deep learning, recognizing and optimizing opportunity points through collaborative analysis, intelligently matching an optimal optimization algorithm, constructing a collaborative optimization model to generate a regulation and control strategy, completing dynamic regulation and control in combination with real-time feedback, updating the model and a knowledge base through self-learning iteration, and achieving the regulation and control of the urban water system. And multi-target dynamic collaborative optimization of the urban water system is realized.

Description

Technical Field

[0001] This invention belongs to the field of urban water resource management technology, specifically relating to an urban water dynamic collaborative optimization system and method based on intelligent fusion of multi-source data. Background Technology

[0002] Urban water resources are one of the essential basic resources for urban development and survival. However, due to the continuous acceleration of urbanization and rapid population growth, urban water resources are facing increasingly serious pressure and challenges. The urban water system mainly includes urban rivers, water supply networks and drainage networks. The planning and construction of the urban water system should be adapted to social development. Therefore, how to manage the urban water system has become a key research topic.

[0003] Currently, in the process of urban water system management and optimization, there is a common problem of scattered and highly heterogeneous data sources. When conducting urban water dynamic monitoring and control, relying on data provided by single-type sensors and isolated systems makes it impossible to integrate and verify the consistency and reliability of multi-source data in real time. When the data itself contains noise, missing data, or contradictions, it will cause large errors in state judgment and trend analysis, and cannot guarantee the accuracy of decision-making basis.

[0004] In view of this, the present invention is hereby proposed. Summary of the Invention

[0005] To address the aforementioned technical problems in existing technologies, this invention provides a dynamic collaborative optimization system and method for urban water systems based on intelligent fusion of multi-source data. This system solves the problem of large errors in state judgment and trend analysis in urban water system management, which makes it impossible to guarantee the accuracy of decision-making.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] The first aspect is a collaborative optimization method for urban water dynamics based on intelligent fusion of multi-source data, including: S1. Collect multi-source urban water system data, including water resources data, water environment data and water disaster data, and generate multi-source water dynamic raw dataset; S2. Perform data preprocessing and intelligent fusion processing on the original dataset of multi-source water dynamics to generate multi-source water dynamics fusion data; S3. Based on the multi-source water dynamic fusion data, perform urban water dynamic collaborative analysis and processing to identify key parameters and collaborative relationships of the water system and generate water dynamic collaborative analysis results. S4. Based on the water dynamics collaborative analysis results, perform optimization algorithm type matching processing, select the optimal collaborative optimization algorithm based on the multi-source data characteristics, and generate target collaborative optimization algorithm type data. S5. Construct a dynamic collaborative optimization model for urban water, and perform dynamic optimization calculations on the water system by combining the target collaborative optimization algorithm type data to generate a dynamic collaborative optimization strategy for water. S6. Execute the water dynamics collaborative optimization strategy to carry out real-time regulation and feedback adjustment of the urban water system and generate the final water dynamics optimization result. S7. Based on the final water dynamic optimization results, perform long-term optimization and self-learning processing to update and iterate the optimization model and knowledge base.

[0008] Furthermore, the collection of multi-source urban water system data specifically includes: S11. Collect real-time water resource data, including reservoir water level, pipeline flow and user water consumption, through an Internet of Things sensor network deployed in the urban water system, and generate a dynamic water resource dataset. S12. Collect water quality parameter data, including pH value, turbidity, and chemical oxygen demand, through water environment monitoring equipment, and generate a dynamic water environment dataset by combining it with meteorological data. S13. Collect water disaster data, including water level at waterlogging points, rainfall intensity and soil moisture, through flood monitoring sensors and historical disaster databases, and generate a dynamic dataset of water disasters; S14. The water resources dynamic dataset, water environment dynamic dataset, and water disaster dynamic dataset are initially integrated, redundant and conflicting data are removed, and a multi-source water dynamic raw dataset is generated.

[0009] Furthermore, the data preprocessing and intelligent fusion processing specifically include: S21. Perform data quality assessment on the original dataset of multi-source water dynamics, calculate data integrity indicators and consistency indicators, and generate a data quality report; S22. Based on the data quality report, perform data cleaning processing, use interpolation algorithms to fill in missing values, and use filtering algorithms to smooth noisy data; S23. The intelligent fusion algorithm is used to fuse the cleaned multi-source data. The intelligent fusion algorithm uses a convolutional neural network for feature extraction and an attention mechanism for weighted fusion to generate multi-source water dynamic fusion data. S24. Standardize the multi-source water dynamic fusion data, unify the data scale and units, and form a standardized dataset with a unified data interface.

[0010] Furthermore, the application of the intelligent fusion algorithm in step S23 includes the following steps: S231. Extract features from the cleaned multi-source data, use a temporal convolutional neural network to process the dynamic water resources data, and generate a spatiotemporal feature sequence. S232. Apply a multi-head attention mechanism to perform weighted fusion of spatiotemporal feature sequences, calculate the feature importance scores of different data sources, and dynamically adjust the fusion weights. S233. The weighted feature sequences are reduced in dimensionality and integrated through a fully connected layer to output standardized multi-source water dynamic fusion data. S234. Perform consistency verification on the fused data, use cross-validation to check the consistency error between the fused results and the original data source, and generate the final fused data report.

[0011] Furthermore, the urban water dynamics collaborative analysis and processing in step S3 specifically includes: S31. Extract key parameters from the multi-source water dynamic fusion data, including peak water flow, water quality change trend, and disaster risk index; S32. Construct a collaborative relationship graph model of the urban water system, where nodes represent water system components and edges represent data flows, and analyze the dynamic interactions between components. S33. Use time series analysis algorithm to predict water dynamic changes, combine with collaborative relationship diagram to identify collaborative optimization opportunity points, and generate water dynamic collaborative analysis results; S34. Visualize the results of the water dynamic collaborative analysis and generate a collaborative heat map and optimization suggestion report.

[0012] Furthermore, the optimization algorithm type matching process in step S4 specifically includes: S41. Establish an optimization algorithm type library to store various standard collaborative optimization algorithms based on different optimization principles and their applicable scenario descriptions. The algorithm types include evolutionary algorithms, swarm intelligence algorithms, and machine learning algorithms. S42. Calculate the feature matching degree between the water dynamics collaborative analysis results and each algorithm in the optimization algorithm type library, and use the cosine similarity algorithm to perform matching evaluation; S43. Select the optimal algorithm based on the matching degree result, generate target collaborative optimization algorithm type data, and load the corresponding algorithm parameters; S44. Verify the applicability of the target collaborative optimization algorithm type data, and evaluate the convergence and stability of the algorithm under simulated water dynamic conditions through simulation tests.

[0013] Furthermore, step S5, which involves constructing a collaborative optimization model for urban water dynamics, specifically includes: S51. Initialize the collaborative optimization model according to the target collaborative optimization algorithm type data, and set the optimization objective function and constraints. The objective function includes minimizing water resource waste and maximizing water quality safety margin. S52. Input the multi-source water dynamic fusion data into the optimization model, perform iterative optimization calculations, and generate a preliminary optimization strategy; S53. Conduct a sensitivity analysis on the preliminary optimization strategy, analyze the degree of change in the strategy output index within the preset parameter disturbance range, and adjust the strategy parameters accordingly. S54. Generate a dynamic and coordinated optimization strategy for water, including specific control instructions, timetables, and expected performance indicators.

[0014] Furthermore, the execution of the water dynamic collaborative optimization strategy in step S6 specifically includes: S61. The water dynamic collaborative optimization strategy is parsed and converted into a series of executable control instructions; S62. Obtain real-time monitoring data reflecting the state of the water system, compare it with the preset expected state data in the water dynamic collaborative optimization strategy, and generate an execution deviation report. S63. Based on the execution deviation report, a PID control algorithm is used to calculate the correction amount of the control parameter according to the error value in the deviation report, and the control parameter is iteratively updated according to the correction amount. S64. Generate the final water dynamic optimization results, including the optimized system operating parameter set and resource consumption comparison dataset.

[0015] Furthermore, step S7 specifically includes: S71. Perform long-term trend analysis on the final water dynamics optimization results and use a machine learning model to predict future water dynamics changes. S72. Update and optimize the algorithm library and model parameters based on trend analysis results to achieve self-learning and adaptive optimization; S73. Generate a water dynamic optimization knowledge base, store historical optimization cases and best practices, and support subsequent optimization decisions.

[0016] Secondly, the urban water dynamic collaborative optimization system based on multi-source data intelligent fusion, applied to the aforementioned urban water dynamic collaborative optimization method based on multi-source data intelligent fusion, includes: The multi-source data acquisition module is used to collect multi-source urban water system data through IoT sensor network unit, meteorological monitoring equipment unit and user terminal interaction unit, and generate multi-source water dynamic raw dataset; The data intelligent fusion module is used to receive the original dataset of multi-source water dynamics, clean and standardize it through the data preprocessing unit, and extract and weight fusion it through the deep learning fusion unit to generate multi-source water dynamics fusion data. The water dynamics collaborative analysis module is used to receive the multi-source water dynamics fusion data, analyze the interaction relationship of water system components through the graph neural network analysis unit, and use the time series prediction unit to identify dynamic change trends and collaborative optimization points to generate water dynamics collaborative analysis results. The optimization algorithm matching module is used to receive the water dynamic collaborative analysis results, provide multiple algorithm types through the pre-stored optimization algorithm library unit, and calculate feature similarity through the intelligent matching unit to select the optimal algorithm and generate target collaborative optimization algorithm type data. The collaborative optimization execution module is used to receive the target collaborative optimization algorithm type data and the multi-source water dynamic fusion data, set the objective function and constraints through the multi-objective optimization model construction unit, and generate the water dynamic collaborative optimization strategy through the iterative optimization calculation unit. The feedback control module is used to receive the water dynamic collaborative optimization strategy, regulate the water system hardware through the strategy execution unit, and collect execution data using the real-time monitoring and feedback unit to dynamically adjust the strategy parameters and generate the final water dynamic optimization result.

[0017] The beneficial effects of this invention are as follows: (1) By collecting multi-source urban water system data and performing data preprocessing, a high-quality multi-source water dynamic raw dataset is constructed. At the same time, the multi-source data is weighted and fused and consistency is verified by the intelligent fusion algorithm based on deep learning. This can verify and ensure the consistency and reliability of the multi-source water dynamic fusion data in real time, overcome the noise and bias problems that may exist in a single data source, ensure the accuracy of the collaborative optimization decision basis, and further reduce the optimization error caused by data quality. (2) By constructing a collaborative relationship diagram model of urban water system based on multi-source water dynamic fusion data, the dynamic interaction relationship between various components of the water system is analyzed in real time and collaborative optimization opportunities are identified, so that the system can grasp the overall operating status; at the same time, by executing water dynamic collaborative optimization strategies and performing dynamic regulation and parameter iteration updates based on real-time feedback data, the system can coordinate and correct in real time when local conflicts occur or deviations from the expected goals occur in the operation of the water system, so as to ensure the overall coordination and optimal performance of the urban water system. (3) By establishing a library of optimization algorithm types containing multiple optimization principles and intelligently matching them with the results of water dynamic collaborative analysis, the optimal collaborative optimization algorithm is adaptively selected. At the same time, an urban water dynamic collaborative optimization model is constructed and a sensitivity analysis is conducted. By self-learning and updating the model parameters and updating and iterating the knowledge base based on the long-term optimization results, the system can adapt to the continuous changes in the external environment and load, realize multi-time scale and multi-objective phased adaptive optimization, reduce the risk of strategy failure, and further improve the resilience, efficiency and safety of the long-term operation of the urban water system. Attached Figure Description

[0018] Figure 1 A flowchart of a method for collaborative optimization of urban water dynamics based on intelligent fusion of multi-source data provided in an embodiment of the present invention; Figure 2This is an architecture diagram of an urban water dynamic collaborative optimization system based on intelligent fusion of multi-source data, provided in an embodiment of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0020] It should be noted that, unless otherwise specifically stated, the relative arrangement and numerical expressions of the components and steps described in these embodiments should not be construed as limiting the scope of the invention.

[0021] The following description of exemplary embodiments is merely illustrative and is not intended to limit the invention or its application or use in any way. Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail herein, but where applicable, such techniques, methods, and apparatus should be considered part of this specification.

[0022] Example 1 See Figure 1 , Figure 1 This is a flowchart of the urban water dynamic collaborative optimization method based on intelligent fusion of multi-source data proposed in this invention. Specific steps may include: S1. Collect multi-source urban water system data, including water resource data, water environment data, and water disaster data, and generate a multi-source water dynamic raw dataset; specific steps include: S11. Collect real-time water resource data, including reservoir water level, pipeline flow and user water consumption, through an Internet of Things sensor network deployed in the urban water system, and generate a dynamic water resource dataset. S12. Collect water quality parameter data, including pH value, turbidity, and chemical oxygen demand, through water environment monitoring equipment, and generate a dynamic water environment dataset by combining it with meteorological data. S13. Collect water disaster data, including water level at waterlogging points, rainfall intensity and soil moisture, through flood monitoring sensors and historical disaster databases, and generate a dynamic dataset of water disasters; S14. Initially integrate the dynamic datasets of water resources, water environment, and water disasters, remove redundant and conflicting data, and generate a multi-source dynamic raw dataset.

[0023] S2. Perform data preprocessing and intelligent fusion processing on the original multi-source water dynamics dataset to generate multi-source water dynamics fused data; specific steps include: S21. Conduct data quality assessment on the original dataset of multi-source water dynamics, calculate data integrity and consistency indicators, and generate a data quality report. S22. Based on the data quality report, perform data cleaning, use interpolation algorithms to fill in missing values, and use filtering algorithms to smooth noisy data. The process of filling in missing values ​​using an interpolation algorithm includes the following steps: S221. Identify missing data points in the original dataset of multi-source water dynamics and record their timestamps and spatial location labels; S222. Based on timestamps and spatial location tags, retrieve available valid data points within a preset time window and spatial neighborhood; S223. Using a linear interpolation algorithm, the imputation value for missing data points is calculated based on the values ​​of valid data points. The interpolation calculation process is defined by the following formula:

[0024] in, This represents the fill value at the missing point. Indicates the location of the missing point. and These represent the positions of adjacent valid data points. and Data values ​​collected at the location; S224. Write the filled values ​​into the original dataset of multi-source water dynamics to generate a complete preprocessed dataset. Smoothing noisy data using filtering algorithms includes the following steps: S225. Perform a fast Fourier transform on the preprocessed dataset to convert the time-series data to the frequency domain and generate a frequency domain data sequence. S226. In the frequency domain data sequence, identify and filter out frequency components with amplitudes higher than a preset threshold, and the frequency components are determined to be noise. S227. Perform an inverse Fourier transform on the filtered frequency domain data sequence to reconstruct it into time domain data, generating a smoothed and denoised dataset. The time domain reconstruction process is defined by the following formula:

[0025] in, This represents the reconstructed time-domain signal. This represents the filtered frequency domain signal. Represents frequency variables. Indicates a point in time. The imaginary unit; S23. Apply intelligent fusion algorithm to fuse the cleaned multi-source data. The intelligent fusion algorithm uses convolutional neural network for feature extraction and applies attention mechanism for weighted fusion to generate multi-source water dynamic fusion data. The application of the intelligent fusion algorithm in S23 includes the following steps: S231. Extract features from the cleaned multi-source data, and use a temporal convolutional neural network to process the dynamic water resource data to generate a spatiotemporal feature sequence, including the following steps: The cleaned water resource dynamic data is reorganized according to the time series to construct a two-dimensional input matrix with time dimension and feature dimension; The dilated convolutional layer of a temporal convolutional neural network is used to extract features from the input matrix. The dilated convolutional operation captures temporal features at different time scales. The calculation formula for the dilated convolutional operation is as follows:

[0026] in, Indicates at a point in time The convolution output, Indicates the first The weights of each convolutional kernel, Indicates the input sequence at time point The value, It is the expansion factor. The kernel size is [size]. Indicates a point in time; A non-linear activation function is applied to the convolutional output to enhance its feature representation capability. The calculation formula is as follows:

[0027] in, This represents the feature output after activation. This indicates a modified linear unit activation function. For bias terms, Indicates a point in time; Residual connections are used to fuse features from different levels, avoiding the gradient vanishing problem in deep networks. The calculation formula for residual connections is as follows:

[0028] in, Indicates the first The input features of the layer Indicates the first The nonlinear transformation output of the layer, Indicates the first The output features of the layer Indicates layer index variable; The extracted temporal features are concatenated according to the time step to generate a spatiotemporal feature sequence containing temporal dependencies and spatial features; S232. Apply a multi-head attention mechanism to perform weighted fusion of spatiotemporal feature sequences, calculate the feature importance scores of different data sources, and dynamically adjust the fusion weights, including the following steps: The spatiotemporal feature sequence is multiplied by the trainable query weight matrix, key weight matrix, and value weight matrix respectively to generate the corresponding query vector, key vector, and value vector. The query vector, key vector, and value vector are divided into multiple heads, and attention weights are calculated independently within each head. Within each head, the attention weights are summed with their corresponding value vectors to generate the output vector for each head. The core calculation process is defined by the following set of formulas:

[0029]

[0030] in, Let be the attention function. This represents the input spatiotemporal feature sequence matrix. To query the vector matrix, The key vector matrix, It is a value vector matrix. , , These are the trainable query, key, and value weight matrices, respectively. Let be the dimension of the key vector. It is a normalized exponential function; The output vectors of all heads are concatenated and then passed through a linear projection layer to generate a weighted fusion feature sequence. S233. The weighted feature sequences are reduced in dimensionality and integrated through a fully connected layer to output standardized multi-source water dynamic fusion data, including the following steps: The weighted feature sequence is used as input data and is prepared to be fed into the fully connected layer for processing. In a fully connected layer, each feature vector in the input feature sequence undergoes a linear transformation with a trainable weight matrix, and the output value is adjusted by a bias term. The formula for calculating this linear transformation is as follows:

[0031] in, This represents the output vector after the linear transformation. This represents the input feature vector. For a trainable weight matrix, It is the bias vector; The output of the linear transformation is processed by a nonlinear activation function to enhance the expressive power of the model. By adjusting the number of output nodes in the fully connected layer, high-dimensional feature sequences can be mapped to a preset target dimension, thus achieving feature dimensionality reduction. The reduced feature sequences are spliced ​​and integrated along the feature dimension to form a unified feature representation, and then standardized to generate the final standardized multi-source water dynamic fusion data. S234. Perform consistency verification on the fused data, using cross-validation to check the consistency error between the fused results and the original data source, and generate a final fused data report. The specific steps include: The original data source is divided into a training set and a validation set. The fusion process from S231 to S233 is performed using the training set data to generate preliminary fusion results. The preliminary fusion results are compared on the validation set, and the root mean square error (RMSE) between the results and the corresponding data in the validation set is calculated. This RMSE serves as a quantitative indicator of the consistency error. The calculation of the RMSE is defined by the following formula:

[0032] in, This represents the root mean square error. This represents the total number of data points in the validation set. Indicates the first The initial fusion result value of each data point Indicates the corresponding number in the verification set One original data value; Repeat the above division and verification process multiple times, calculate the average value of the consistency error, and when the average value is lower than the preset threshold, it is determined that the fusion result is consistent with the original data source. Based on the average consistency error and the judgment results, a final fused data report is generated, which includes error statistics, consistency conclusions, and data quality ratings.

[0033] S24. Standardize the dynamic fusion data of multi-source water, unify the data scale and units, and form a standardized dataset with a unified data interface.

[0034] S3. Based on the multi-source water dynamic fusion data, perform urban water dynamic collaborative analysis and processing to identify key parameters and collaborative relationships of the water system and generate water dynamic collaborative analysis results. Step S3, the collaborative analysis and processing of urban water dynamics, includes the following steps: S31. Extract key parameters from multi-source water dynamic fusion data, including peak water flow, water quality change trend, and disaster risk index; S32. Construct a collaborative relationship graph model of the urban water system, where nodes represent water system components and edges represent data flows, and analyze the dynamic interactions between components. S33. Predict water dynamic changes using time-series analysis algorithms, identify collaborative optimization opportunities by combining collaborative relationship diagrams, and generate water dynamic collaborative analysis results, including the following steps: S331. Perform stationarity tests on key parameters in multi-source water dynamic fusion data, and perform difference processing on non-stationary sequences until the sequences are stationary. S332. Identify the autoregressive order and moving average order of a time series model based on the autocorrelation function and partial autocorrelation function. S333. The maximum likelihood estimation method is used to fit the parameters of the ARIMA model, and this model is used to make multi-step predictions of key parameters. The mathematical expression of the model is as follows:

[0035] in, Indicates time Key parameter values, For lag operators, , , These are the order of autoregression, the order of differencing, and the order of moving average, respectively. These are the autoregressive coefficients. The moving average coefficient is... It is a white noise sequence; S334. Combine the prediction results with the collaborative relationship graph model to identify collaborative optimization opportunities at future time points; S34. Visualize the results of the water dynamic collaborative analysis and generate a collaborative heat map and optimization suggestion report.

[0036] S4. Based on the water dynamics collaborative analysis results, perform optimization algorithm type matching processing, select the optimal collaborative optimization algorithm based on the multi-source data characteristics, and generate target collaborative optimization algorithm type data. Step S4, which involves optimizing the algorithm type matching process, includes the following steps: S41. Establish an optimization algorithm type library to store various standard collaborative optimization algorithms based on different optimization principles and their applicable scenario descriptions. The algorithm types include evolutionary algorithms, swarm intelligence algorithms, and machine learning algorithms. The specific construction process includes the following steps: S411. Collect historical optimization case data, extract successful collaborative optimization algorithm instances and their corresponding water system scenario characteristics, and form a basic algorithm set. S412. Based on the similarity of algorithm principles, the algorithms in the basic algorithm set are divided into three categories: evolutionary algorithms, swarm intelligence algorithms, and machine learning algorithms, and an independent storage partition is established for each category. S413. For each algorithm in the library, define its key characteristic parameters, including algorithm convergence speed, applicable problem size, parameter sensitivity, and optimal solution quality preference. S414. Based on historical application results, generate a structured description of applicable scenarios for each algorithm, clarifying the most suitable water dynamic data type, system scale, and optimization target type for it; S415. Construct an algorithm feature index, associate and store algorithm feature parameters with applicable scenario descriptions to form a library of optimization algorithm types that can be quickly retrieved; S42. Calculate the feature matching degree between the water dynamics collaborative analysis results and each algorithm in the optimization algorithm type library, and use the cosine similarity algorithm for matching evaluation, including the following steps: S421. Vectorize the results of the water dynamics collaborative analysis to generate the first feature vector; S422. Vectorize the applicable scenario descriptions of each algorithm in the optimization algorithm type library to generate a second feature vector set; S423. Calculate the cosine value of each vector in the first and second eigenvector sets. The cosine value is calculated using the following formula:

[0037] in, The cosine similarity value is... The angle between the vectors, and These represent the first eigenvector and the second eigenvector, respectively. For vectors The One portion, For vectors The One portion, Let be the dimension of the vector; S424. Sort the algorithms according to the size of the cosine value, and determine the algorithm with the largest cosine value as the best algorithm with the highest matching degree. S43. Select the optimal algorithm based on the matching degree result, generate target collaborative optimization algorithm type data, and load the corresponding algorithm parameters; S44. Verify the applicability of the target collaborative optimization algorithm to the data type, and evaluate the convergence and stability of the algorithm under simulated water dynamic conditions through simulation tests, including the following steps: S441. Construct a simulated test environment, generate a representative test dataset based on historical water dynamic data, and set simulated water dynamic condition parameters consistent with the real scenario. S442. Run the selected objective co-optimization algorithm in a simulated test environment and continuously record the changes in the value of the objective function during the algorithm iteration process; S443. Based on the recorded changes in the objective function value, calculate the number of iterations and computation time required for the algorithm to reach a preset accuracy threshold. This serves as a convergence evaluation index, and the convergence condition is defined by the following formula:

[0038] in, Indicates the first The value of the objective function at the next iteration. Indicates the first The value of the objective function at the next iteration. The preset accuracy threshold, Index for iteration count; S444. Run the algorithm independently multiple times under the same simulation test conditions, and calculate the variance coefficient of the final optimization result for each run. This variance coefficient is used as a stability evaluation index and is defined by the following formula:

[0039] in, Represents the variance coefficient. This represents the standard deviation of the final optimization result for each run. This represents the average of the final optimization results from each run. S445. Conduct a comprehensive analysis of convergence and stability evaluation indicators, generate an algorithm applicability verification report, and clearly indicate whether the algorithm meets the performance standards required for practical applications.

[0040] S5. Construct a dynamic collaborative optimization model for urban water, and perform dynamic optimization calculations on the water system by combining the target collaborative optimization algorithm type data to generate a dynamic collaborative optimization strategy for water. Step S5 involves constructing a collaborative optimization model for urban water dynamics, which includes the following steps: S51. Initialize the collaborative optimization model based on the target collaborative optimization algorithm type data, and set the optimization objective function and constraints. The objective function includes minimizing water resource waste and maximizing water quality safety margin. S52. Input the dynamic fusion data of multi-source water into the optimization model, perform iterative optimization calculations, and generate a preliminary optimization strategy; S53. Perform sensitivity analysis on the preliminary optimization strategy to analyze the degree of change in the strategy output index within the preset parameter perturbation range, and adjust the strategy parameters, including the following steps: S531. Identify the key control parameters in the preliminary optimization strategy and set a preset relative disturbance range for each parameter based on its baseline value; S532. Using a single-parameter perturbation method, each key control parameter is perturbed sequentially within its perturbation range at a preset step size, while keeping other parameters constant. The changes in the strategy output index are calculated using the following formula:

[0041] in, Indicates when the first When key control parameters are disturbed, the strategy output index The change relative to the baseline value Indicates the first The output index value of the strategy after parameter perturbation This represents the initial strategy output metric value when all parameters are at the baseline value. S533. For each disturbed parameter, calculate the sensitivity index of the strategy output index relative to the change of that parameter. The calculation formula is as follows:

[0042] in, For the first Sensitivity index of each parameter For the first The disturbance of each parameter, The baseline values ​​for each parameter; S534. Based on the calculated sensitivity index All key control parameters are sorted, and the high-sensitivity parameters with the highest sensitivity ranking are identified. S535. Based on the sensitivity analysis results, the baseline values ​​of the identified high-sensitivity parameters are fine-tuned. The specific fine-tuning process includes the following steps: Based on the sensitivity index obtained in step S534, select the top N key control parameters to determine the set of high sensitivity parameters to be fine-tuned. For each parameter in the set of high-sensitivity parameters, based on its sensitivity index The sign and magnitude determine the direction and amplitude of the fine-tuning, where the fine-tuning amount... The calculation formula is as follows:

[0043] in, For the first The fine-tuning amount of each parameter, These are the global fine-tuning coefficients. For symbolic functions, It is a non-linear adjustment factor; Based on the calculated fine-tuning amount Generate a new value for this parameter after fine-tuning. :

[0044] in, For the first New values ​​for each parameter; New values ​​for all fine-tuned parameters Update the initial optimization strategy, replace the original parameter baseline values, and generate an optimization strategy parameter set with enhanced robustness; S54. Generate a dynamic and coordinated optimization strategy for water, including specific control instructions, timetables, and expected performance indicators.

[0045] S6. Execute the water dynamics collaborative optimization strategy to carry out real-time regulation and feedback adjustment of the urban water system and generate the final water dynamics optimization result. Step S6 involves implementing a water dynamic collaborative optimization strategy, which includes the following steps: S61. Parse the water dynamic collaborative optimization strategy and convert it into a series of executable control instructions; S62. Obtain real-time monitoring data reflecting the state of the water system, compare it with the expected state data preset in the water dynamic collaborative optimization strategy, and generate an execution deviation report. S63. Based on the execution deviation report, a PID control algorithm is used to calculate the correction amount of the control parameters according to the error value in the deviation report, and the control parameters are iteratively updated according to the correction amount, including the following steps: S631. Extract the system error value at the current sampling time from the execution deviation report, and at the same time read the stored error value and cumulative error value at the previous sampling time. S632. Calculate the control quantity according to the proportional, integral, and derivative terms of the PID control algorithm: the proportional term is proportional to the current error value, the integral term is proportional to the cumulative error value, and the derivative term is proportional to the error rate of change. S633. Add the outputs of the proportional, integral, and derivative terms to obtain the total correction amount of the control parameters required at the current moment. The calculation formula is as follows:

[0046] in, for Total correction at time, for Error value at time, for Error value at time, , , These are the preset proportional, integral, and differential coefficients, respectively. Indicates a point in time; S634. Perform output limit processing on the total correction amount to ensure that it does not exceed the allowable working range of the actuator; S635. Apply the correction amount after the restriction process to the corresponding water system control parameters to complete this iteration update, and store the current error value in the historical record for calculation at the next moment. S64. Generate the final water dynamic optimization results, including the optimized system operating parameter set and resource consumption comparison dataset.

[0047] S7. Based on the final water dynamics optimization results, perform long-term optimization and self-learning processing to update and iterate the optimization model and knowledge base; specifically including: S71. Conduct long-term trend analysis on the final water dynamics optimization results, and use machine learning models to predict future water dynamics changes, including the following steps: S711. Construct a training sample set, using the water dynamics optimization results of historical periods as features and the actual water dynamics changes of subsequent periods as labels; S712, Training a gradient boosting tree model, minimizing prediction error by iteratively constructing multiple decision trees, its first... The sample at the th The process of overlaying the predicted values ​​of the trees is as follows:

[0048] in, For the first Sample after the second iteration The predicted value, This is the predicted value from the previous iteration. For learning rate, For the sample eigenvectors, For the first A decision tree, Let the decision tree function space be... Index for iteration count; S713. Using the trained gradient boosting tree model, input the current final dynamic optimization result and output the predicted value of future water dynamic changes. S72. Update and optimize the algorithm library and model parameters based on trend analysis results to achieve self-learning and adaptive optimization; S73. Generate a water dynamic optimization knowledge base, store historical optimization cases and best practices, and support subsequent optimization decisions.

[0049] Example 2 See Figure 2 , Figure 2 This is an architecture diagram of the urban water dynamic collaborative optimization system based on intelligent fusion of multi-source data proposed in this invention, which may specifically include: M1, Multi-source data acquisition module, is used to collect multi-source urban water system data through IoT sensor network unit, meteorological monitoring equipment unit and user terminal interaction unit, and generate multi-source water dynamic raw dataset; M2, the data intelligent fusion module, is used to receive the original dataset of multi-source water dynamics, clean and standardize it through the data preprocessing unit, and extract and weight fusion it through the deep learning fusion unit to generate multi-source water dynamics fusion data. M3, the water dynamics collaborative analysis module, is used to receive multi-source water dynamics fusion data, analyze the interaction relationship of water system components through the graph neural network analysis unit, and use the time series prediction unit to identify dynamic change trends and collaborative optimization points to generate water dynamics collaborative analysis results. M4, the optimization algorithm matching module, is used to receive the results of water dynamic collaborative analysis, provide a variety of algorithm types through the pre-stored optimization algorithm library unit, and select the optimal algorithm by calculating feature similarity through the intelligent matching unit, and generate target collaborative optimization algorithm type data. M5, the collaborative optimization execution module, is used to receive target collaborative optimization algorithm type data and multi-source water dynamic fusion data, set objective functions and constraints through the multi-objective optimization model construction unit, and generate water dynamic collaborative optimization strategies through the iterative optimization calculation unit. M6, the feedback control module, is used to receive water dynamic collaborative optimization strategies, regulate water system hardware devices through the strategy execution unit, and collect execution data using the real-time monitoring and feedback unit to dynamically adjust strategy parameters and generate the final water dynamic optimization results.

[0050] The operation steps of the urban water dynamic collaborative optimization system and method based on multi-source data intelligent fusion are as follows: B1. Data Collection and Preliminary Integration of Multi-Source Urban Water Systems: First, a dynamic water resource dataset is generated by collecting real-time water resource data, including reservoir water levels, pipeline flow rates, and user water consumption, through an IoT sensor network deployed in the urban water system. Simultaneously, water quality parameter data, including pH, turbidity, and chemical oxygen demand, is collected using water environment monitoring equipment and combined with meteorological data to generate a dynamic water environment dataset. Furthermore, flood disaster data, including water levels at flood-prone areas, rainfall intensity, and soil moisture, is collected through flood monitoring sensors and a historical disaster database to generate a dynamic water disaster dataset. Finally, the aforementioned dynamic water resource dataset, dynamic water environment dataset, and dynamic water disaster dataset are preliminarily integrated, removing redundant and conflicting data to generate a high-quality, multi-source raw water dynamic dataset.

[0051] B2. Preprocessing and intelligent fusion of multi-source water dynamic data: Data quality was assessed on the original dataset of multi-source water dynamics, generating a data quality report. Based on this report, interpolation algorithms were used to fill in missing values, and filtering algorithms were used to smooth noisy data, completing data cleaning. Subsequently, an intelligent fusion algorithm was applied to fuse the cleaned multi-source data: a temporal convolutional neural network was used to extract features from the water resource dynamics data, generating a spatiotemporal feature sequence; a multi-head attention mechanism was applied to weighted fusion of this sequence, calculating the feature importance scores of different data sources and dynamically adjusting the fusion weights; the weighted feature sequence was then integrated through a fully connected layer for dimensionality reduction, outputting standardized multi-source water dynamics fused data. Finally, cross-validation was used to verify the consistency error between the fusion results and the original data sources, generating a final fused data report to ensure data reliability.

[0052] B3. Urban water dynamics collaborative analysis and optimization algorithm matching: Based on multi-source dynamic water data fusion, key parameters such as peak water flow, water quality change trends, and disaster risk index are extracted. A collaborative relationship graph model of the urban water system is constructed, with nodes representing water system components and edges representing data flows, analyzing the dynamic interaction relationships between components. Time-series analysis algorithms are used to predict water dynamic changes, and collaborative optimization opportunities are identified by combining the collaborative relationship graph to generate water dynamic collaborative analysis results. An optimization algorithm type library is established, storing various standard collaborative optimization algorithms, including evolutionary algorithms, swarm intelligence algorithms, and machine learning algorithms, along with their applicable scenario descriptions. The feature matching degree between the water dynamic collaborative analysis results and each algorithm in the algorithm library is calculated using the cosine similarity algorithm. The optimal algorithm is selected, target collaborative optimization algorithm type data is generated, and its convergence and stability are verified.

[0053] B4. Construction of Water Dynamics Collaborative Optimization Model and Strategy Generation: The collaborative optimization model is initialized based on the target collaborative optimization algorithm type data, setting the objective function and constraints as minimizing water resource waste and maximizing water quality safety margin. Multi-source water dynamic fusion data is input into the optimization model for iterative optimization calculations to generate a preliminary optimization strategy. Sensitivity analysis is performed on the preliminary strategy, and the sensitivity index of key control parameters is calculated using a single-parameter perturbation method. High-sensitivity parameters are identified and their baseline values ​​are fine-tuned to enhance the strategy's robustness. Finally, a water dynamic collaborative optimization strategy containing specific control instructions, timelines, and expected effect indicators is generated.

[0054] B5. Implementation and dynamic feedback adjustment of optimization strategies: The water dynamics collaborative optimization strategy is parsed into executable control commands, and an execution deviation report is generated by comparing real-time monitoring data with expected state data. Based on the deviation report, a PID control algorithm is used to calculate the correction amount of the control parameters according to the error value, and the parameters are iteratively updated to achieve dynamic feedback adjustment. The final water dynamics optimization result is generated, including the optimized system operating parameter set and resource consumption comparison dataset.

[0055] B6. Long-term self-learning and continuous system optimization: Long-term trend analysis is performed on the final water dynamics optimization results, and a gradient boosting tree model is used to predict future water dynamics changes. The optimization algorithm library and model parameters are updated based on the trend analysis results, enabling self-learning and adaptive optimization. A water dynamics optimization knowledge base is generated, storing historical optimization cases and best practices to support subsequent optimization decisions and ensure the system's continuous evolution capability.

[0056] The above specific embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for dynamic collaborative optimization of urban water resources based on intelligent fusion of multi-source data, characterized in that: include: S1. Collect multi-source urban water system data, including water resources data, water environment data and water disaster data, and generate multi-source water dynamic raw dataset; S2. Perform data preprocessing and intelligent fusion processing on the original dataset of multi-source water dynamics to generate multi-source water dynamics fusion data; S3. Based on the multi-source water dynamic fusion data, perform urban water dynamic collaborative analysis and processing to identify key parameters and collaborative relationships of the water system and generate water dynamic collaborative analysis results. S4. Based on the water dynamics collaborative analysis results, perform optimization algorithm type matching processing, select the optimal collaborative optimization algorithm based on the multi-source data characteristics, and generate target collaborative optimization algorithm type data. S5. Construct a dynamic collaborative optimization model for urban water, and perform dynamic optimization calculations on the water system by combining the target collaborative optimization algorithm type data to generate a dynamic collaborative optimization strategy for water. S6. Execute the water dynamics collaborative optimization strategy to carry out real-time regulation and feedback adjustment of the urban water system and generate the final water dynamics optimization result. S7. Based on the final water dynamic optimization results, perform long-term optimization and self-learning processing to update and iterate the optimization model and knowledge base.

2. The urban water dynamic collaborative optimization method based on intelligent fusion of multi-source data according to claim 1, characterized in that, The collection of multi-source urban water system data specifically includes: S11. Collect real-time water resource data, including reservoir water level, pipeline flow and user water consumption, through an Internet of Things sensor network deployed in the urban water system, and generate a dynamic water resource dataset. S12. Collect water quality parameter data, including pH value, turbidity, and chemical oxygen demand, through water environment monitoring equipment, and generate a dynamic water environment dataset by combining it with meteorological data. S13. Collect water disaster data, including water level at waterlogging points, rainfall intensity and soil moisture, through flood monitoring sensors and historical disaster databases, and generate a dynamic dataset of water disasters; S14. The water resources dynamic dataset, water environment dynamic dataset, and water disaster dynamic dataset are initially integrated, redundant and conflicting data are removed, and a multi-source water dynamic raw dataset is generated.

3. The urban water dynamic collaborative optimization method based on intelligent fusion of multi-source data according to claim 1, characterized in that, The data preprocessing and intelligent fusion processing specifically include: S21. Perform data quality assessment on the original dataset of multi-source water dynamics, calculate data integrity indicators and consistency indicators, and generate a data quality report; S22. Based on the data quality report, perform data cleaning processing, use interpolation algorithms to fill in missing values, and use filtering algorithms to smooth noisy data; S23. The intelligent fusion algorithm is used to fuse the cleaned multi-source data. The intelligent fusion algorithm uses a convolutional neural network for feature extraction and an attention mechanism for weighted fusion to generate multi-source water dynamic fusion data. S24. Standardize the multi-source water dynamic fusion data, unify the data scale and units, and form a standardized dataset with a unified data interface.

4. The urban water dynamic collaborative optimization method based on intelligent fusion of multi-source data according to claim 3, characterized in that, Step S23, applying the intelligent fusion algorithm, includes the following steps: S231. Extract features from the cleaned multi-source data, use a temporal convolutional neural network to process the dynamic water resources data, and generate a spatiotemporal feature sequence. S232. Apply a multi-head attention mechanism to perform weighted fusion of spatiotemporal feature sequences, calculate the feature importance scores of different data sources, and dynamically adjust the fusion weights. S233. The weighted feature sequences are reduced in dimensionality and integrated through a fully connected layer to output standardized multi-source water dynamic fusion data. S234. Perform consistency verification on the fused data, use cross-validation to check the consistency error between the fused results and the original data source, and generate the final fused data report.

5. The urban water dynamic collaborative optimization method based on intelligent fusion of multi-source data according to claim 1, characterized in that, Step S3, the collaborative analysis and processing of urban water dynamics, specifically includes: S31. Extract key parameters from the multi-source water dynamic fusion data, including peak water flow, water quality change trend, and disaster risk index; S32. Construct a collaborative relationship graph model of the urban water system, where nodes represent water system components and edges represent data flows, and analyze the dynamic interactions between components. S33. Use time series analysis algorithm to predict water dynamic changes, combine with collaborative relationship diagram to identify collaborative optimization opportunity points, and generate water dynamic collaborative analysis results; S34. Visualize the results of the water dynamic collaborative analysis and generate a collaborative heat map and optimization suggestion report.

6. The urban water dynamic collaborative optimization method based on intelligent fusion of multi-source data according to claim 1, characterized in that, Step S4 specifically includes the following optimization algorithm type matching process: S41. Establish an optimization algorithm type library to store various standard collaborative optimization algorithms based on different optimization principles and their applicable scenario descriptions. The algorithm types include evolutionary algorithms, swarm intelligence algorithms, and machine learning algorithms. S42. Calculate the feature matching degree between the water dynamics collaborative analysis results and each algorithm in the optimization algorithm type library, and use the cosine similarity algorithm to perform matching evaluation; S43. Select the optimal algorithm based on the matching degree result, generate target collaborative optimization algorithm type data, and load the corresponding algorithm parameters; S44. Verify the applicability of the target collaborative optimization algorithm type data, and evaluate the convergence and stability of the algorithm under simulated water dynamic conditions through simulation tests.

7. The urban water dynamic collaborative optimization method based on intelligent fusion of multi-source data according to claim 1, characterized in that, Step S5, which involves constructing a collaborative optimization model for urban water dynamics, specifically includes: S51. Initialize the collaborative optimization model according to the target collaborative optimization algorithm type data, and set the optimization objective function and constraints. The objective function includes minimizing water resource waste and maximizing water quality safety margin. S52. Input the multi-source water dynamic fusion data into the optimization model, perform iterative optimization calculations, and generate a preliminary optimization strategy; S53. Conduct a sensitivity analysis on the preliminary optimization strategy, analyze the degree of change in the strategy output index within the preset parameter disturbance range, and adjust the strategy parameters accordingly. S54. Generate a dynamic and coordinated optimization strategy for water, including specific control instructions, timetables, and expected performance indicators.

8. The urban water dynamic collaborative optimization method based on intelligent fusion of multi-source data according to claim 1, characterized in that, The execution of the water dynamic collaborative optimization strategy in step S6 specifically includes: S61. The water dynamic collaborative optimization strategy is parsed and converted into a series of executable control instructions; S62. Obtain real-time monitoring data reflecting the state of the water system, compare it with the preset expected state data in the water dynamic collaborative optimization strategy, and generate an execution deviation report. S63. Based on the execution deviation report, a PID control algorithm is used to calculate the correction amount of the control parameter according to the error value in the deviation report, and the control parameter is iteratively updated according to the correction amount. S64. Generate the final water dynamic optimization results, including the optimized system operating parameter set and resource consumption comparison dataset.

9. The urban water dynamic collaborative optimization method based on intelligent fusion of multi-source data according to claim 1, characterized in that, Step S7 specifically includes: S71. Perform long-term trend analysis on the final water dynamics optimization results and use a machine learning model to predict future water dynamics changes. S72. Update and optimize the algorithm library and model parameters based on trend analysis results to achieve self-learning and adaptive optimization; S73. Generate a water dynamic optimization knowledge base, store historical optimization cases and best practices, and support subsequent optimization decisions.

10. A city water dynamic collaborative optimization system based on intelligent fusion of multi-source data, characterized in that, The method for coordinated optimization of urban water dynamics based on intelligent fusion of multi-source data, as described in any one of claims 1-9, includes: The multi-source data acquisition module is used to collect multi-source urban water system data through IoT sensor network unit, meteorological monitoring equipment unit and user terminal interaction unit, and generate multi-source water dynamic raw dataset; The data intelligent fusion module is used to receive the original dataset of multi-source water dynamics, clean and standardize it through the data preprocessing unit, and extract and weight fusion it through the deep learning fusion unit to generate multi-source water dynamics fusion data. The water dynamics collaborative analysis module is used to receive the multi-source water dynamics fusion data, analyze the interaction relationship of water system components through the graph neural network analysis unit, and use the time series prediction unit to identify dynamic change trends and collaborative optimization points to generate water dynamics collaborative analysis results. The optimization algorithm matching module is used to receive the water dynamic collaborative analysis results, provide multiple algorithm types through the pre-stored optimization algorithm library unit, and calculate feature similarity through the intelligent matching unit to select the optimal algorithm and generate target collaborative optimization algorithm type data. The collaborative optimization execution module is used to receive the target collaborative optimization algorithm type data and the multi-source water dynamic fusion data, set the objective function and constraints through the multi-objective optimization model construction unit, and generate the water dynamic collaborative optimization strategy through the iterative optimization calculation unit. The feedback control module is used to receive the water dynamic collaborative optimization strategy, regulate the water system hardware through the strategy execution unit, and collect execution data using the real-time monitoring and feedback unit to dynamically adjust the strategy parameters and generate the final water dynamic optimization result.

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