A water conservancy configuration system configuration component adaptive adjustment system

Abstract

This invention discloses an adaptive adjustment system for configuration components of a water conservancy configuration system, belonging to the interdisciplinary field of water conservancy informatization and artificial intelligence. It includes: a time-series-aware clustering module, which divides the operating states of the water conservancy configuration system into different operating condition clusters; a deep reinforcement learning decision-making module, which models the water conservancy configuration system as a Markov decision process, designs a multi-objective reward function, and uses a proximal policy optimization algorithm and a deep Q-network algorithm to learn the optimal adjustment strategy; a configuration component adaptive adjustment module, which parses the optimal adjustment strategy and converts it into control signals, coordinating the adjustment of related components' operating parameters when adjusting the operating parameters of individual components; and a digital twin simulation interaction module, which pre-simulates the adjustment results in a digital twin and triggers the deep reinforcement learning decision-making module to re-optimize the optimal adjustment strategy based on the pre-simulated adjustment results. This system can improve the response speed, adjustment accuracy, and operational safety of the adaptive adjustment of configuration components.

Description

Technical Field

[0001] This invention relates to an adaptive adjustment system for configuration components of a water conservancy configuration system, belonging to the interdisciplinary field of water conservancy informatization and artificial intelligence. Background Technology

[0002] The water conservancy configuration system is the core carrier of automated monitoring of water conservancy projects. Its configuration components, such as gate control, flow regulation, and data acquisition, need to cope with nonlinear fluctuations in hydrological time series, sudden changes in operating conditions, and multi-objective constraints. The water conservancy configuration system collects key data such as water level, flow rate, pressure, water quality, turbidity, residual chlorine, equipment operating status, and environmental parameters around the clock through devices such as sensors, programmable logic controllers, and smart instruments, and uploads them to the monitoring platform through communication methods such as 4G / 5G, GPRS, Wi-Fi, or fiber optics.

[0003] However, traditional configuration systems rely on fixed configurations and manual parameter tuning. Hydrological data has time axis offsets and long-term dependencies. Traditional clustering is difficult to adapt to dynamic distributions, resulting in component response lag. When operating conditions change, component parameters cannot be iterated in real time, which easily leads to over-adjustment or under-adjustment. It is difficult to balance the benefits of multiple objectives. The component linkage optimization of large-scale water conservancy systems suffers from the curse of dimensionality. Traditional optimization algorithms are difficult to meet the millisecond-level response requirements.

[0004] In existing technologies, digital twin water conservancy scheduling relies heavily on mechanism models and offline optimization, lacking the ability to dynamically mine time-series data and make online adaptive decisions. Although single deep reinforcement learning can achieve sequential decision-making, it is insufficient in clustering and fusing high-dimensional time-series features and is prone to getting trapped in local optima. Summary of the Invention

[0005] The purpose of this invention is to provide an adaptive adjustment system for configuration components in a water conservancy configuration system, which can improve the response speed, adjustment accuracy, and operational safety of adaptive adjustment of configuration components.

[0006] To achieve the above objectives, the present invention provides the following technical solution: An adaptive adjustment system for configuration components of a water conservancy configuration system, comprising: The time-aware clustering module is used to acquire time-series data of the water conservancy configuration system, use the dynamic time warping algorithm to measure the sequence similarity of the time-series data, use the gated recurrent unit neural network to extract the periodic features of the time-series data, and perform online clustering based on sequence similarity and periodic features to divide the operating status of the water conservancy configuration system into different operating condition clusters and generate operating condition cluster feature vectors. The deep reinforcement learning decision module is used to model the water conservancy configuration system as a Markov decision process. It takes the operating state and working condition cluster feature vector of the water conservancy configuration system as the state input, the action space of the configuration components as the action space, designs a multi-objective reward function, and uses the proximal policy optimization algorithm and deep Q network algorithm to learn the optimal adjustment strategy for determining the optimal action of the configuration components. The configuration component adaptive adjustment module is used to parse the optimal adjustment strategy and convert it into control signals. The operating parameters of the configuration component are dynamically adjusted through the control signals. A component-level and system-level linkage adaptation mechanism is built. When adjusting the operating parameters of a single component, the operating parameters of related components are adjusted in tandem. The actual operating status data of the adjusted configuration component is fed back to the deep reinforcement learning decision module. The digital twin simulation interaction module is used to construct a digital twin. The adjustment results are simulated in the digital twin using control signals as adjustment instructions. When the simulated adjustment results deviate from the expected adjustment target or violate the constraints, optimization suggestions are generated and fed back to the deep reinforcement learning decision module, which triggers the deep reinforcement learning decision module to re-optimize the optimal adjustment strategy. The multi-objective constraint management module is used to issue an alarm signal when a control signal is detected to violate the constraint conditions, block the execution of the control signal, and trigger the deep reinforcement learning decision module to re-optimize the optimal adjustment strategy.

[0007] Furthermore, the time-aware clustering module includes: The data preprocessing and feature extraction unit is used to preprocess time series data and extract features including trends, cycles, volatility and outliers from the preprocessed time series data to form sample feature vectors. The dynamic clustering analysis unit is used to calculate the similarity distance between time series data segments corresponding to sample feature vectors using the dynamic time warping algorithm. It uses a time window weighting mechanism with a time decay factor to perform time weighting on the similarity distance, and performs dynamic clustering on the sample feature vectors based on the weighted similarity distance to generate the initial working condition cluster division. The clustering result evaluation and iteration unit is used to evaluate the clustering quality of the initial working condition cluster division, obtain the clustering quality evaluation result, and dynamically adjust the cluster center of the working condition cluster according to the clustering quality evaluation result, redistribute the clustering affiliation of the sample feature vectors, and repeat the iteration until the iteration termination condition is reached to obtain the final working condition cluster division. The decision knowledge transmission unit is used to transform the final working condition cluster division into working condition cluster feature vectors that characterize the operating features of the working condition clusters, and transmit them as decision knowledge to the deep reinforcement learning decision module.

[0008] Further preprocessing includes cleaning, alignment, and normalization.

[0009] Furthermore, the deep reinforcement learning decision-making module includes: The Markov decision process modeling unit is used to model a hydraulic configuration system as a Markov decision process, including: defining the state space. This includes the operating status and condition cluster feature vectors of the water conservancy configuration system; defining the action space. This includes configuring the actions of components; defining state transition probabilities. It is determined by hydrodynamics, equipment response characteristics, and random disturbances; among them, Indicates the current state. Indicates the next state. Indicates the current action; defines the discount factor. ; The multi-objective reward function unit is used to design multi-objective reward functions and to calculate the instantaneous reward value for each state transition using the multi-objective reward function. The multi-objective reward function is: ; in, Indicates in Execute Later transferred to Instant reward value, Indicates the first Individual goals Execute Later transferred to Instant rewards Indicates the first The weight of each sub-goal Indicates the number of sub-targets; During training, the deep reinforcement learning decision module uses the immediate reward value of each state transition to guide the proximal policy optimization algorithm and the deep Q-network algorithm to iterate the policy, learn the optimal adjustment policy that maximizes the cumulative discount reward, and train a policy model to achieve the optimal adjustment policy.

[0010] Furthermore, the operational status of the water conservancy configuration system includes reservoir water level, flow rate, water quality, equipment operating status, and energy storage level; the actions of the configuration components include adjusting the gate opening, starting and stopping water pumps, switching water supply modes, and flood discharge decisions.

[0011] Furthermore, the sub-objectives include flood control safety, ecological flow guarantee, water supply efficiency, and energy consumption control.

[0012] Furthermore, the configuration component adaptive adjustment module includes: The strategy parsing unit is used to parse the optimal adjustment strategy into a description of the target action of the configuration component; The solution space construction and candidate solution screening unit is used to describe the target action based on the configuration component. According to the operating status of the water conservancy configuration system, it constructs a parameter adjustment solution space consisting of the adjustment values ​​of the operating parameters of the configuration component, and selects candidate parameter adjustment schemes that meet the safe operating parameter range of the component from the parameter adjustment solution space. The future state assessment unit is used to predict the future state of the hydraulic configuration system after the implementation of the candidate parameter adjustment scheme using a prediction model, and to assess the future state to obtain the future state assessment result. The control signal generation unit is used to select the candidate parameter adjustment scheme that makes the future state optimal by comparison and screening based on the future state evaluation results, and convert the optimal parameter adjustment scheme into a control signal that the configuration component can recognize. The security verification unit is used to perform security verification on the control signals and transmit the control signals that have passed the security verification to the configuration components for execution. The comparison and verification unit is used to compare and verify the actual operating status data of the configuration components after the execution of the control signal with the future state of the water conservancy configuration system corresponding to the optimal parameter adjustment scheme, and to calculate the degree of achievement of the target using a multi-objective reward function. The online learning and strategy update unit is used to trigger the online learning of the strategy model in the deep reinforcement learning decision module based on the goal achievement evaluation, and to update the parameters of the strategy model using the actual operating status data of the configuration components after the execution of control signals. The anomaly monitoring and safety protection unit is used to monitor the actual operating parameters of the configuration components. When the reconstruction error of the actual operating parameters of the configuration components exceeds the error threshold, safety protection measures are triggered.

[0013] Furthermore, the digital twin simulation interaction module includes: The data base layer is used to aggregate and manage basic geospatial data, engineering building information model or city information model data, real-time monitoring and sensing data, historical operation data, business management data and external shared data; The model platform layer, driven by the data base layer, provides professional water network models, intelligent identification models, visualization models, and simulation engines to simulate control signals and obtain simulation results. The knowledge platform layer is used to represent, extract, fuse, and reason about the simulation results using the knowledge engine, forming decision knowledge about the adjustment effect, and feeding the decision knowledge back to the deep reinforcement learning decision module to optimize the optimal adjustment strategy; The interactive application layer is used to transform the simulation process of the model platform layer and the decision knowledge of the knowledge platform layer into a user-oriented visual interface, providing a digital twin and user interaction interface for viewing the simulation process, adjusting simulation parameters, and assisting in decision-making.

[0014] Furthermore, the multi-objective constraint management module includes: The multimodal and standardization submodule is used to acquire multi-source heterogeneous data related to the actions of configuration components and perform standardization processing to obtain standardized multi-source heterogeneous data. The compliance determination submodule is used to compare the standardized multi-source heterogeneous data with the constraints to obtain the compliance verification results. The graded early warning and real-time intervention submodule is used to determine the risk level based on the compliance verification results and trigger corresponding early warning and intervention mechanisms based on the risk level. These mechanisms include: issuing an alarm signal when a control signal is detected to violate the constraints, blocking the execution of the control signal, and feeding the blocking information back to the deep reinforcement learning decision module to trigger the deep reinforcement learning decision module to re-optimize the optimal adjustment strategy; and recording the compliance verification date.

[0015] Furthermore, the constraints are a compliance rule base and intelligent algorithm model pre-set based on water conservancy project scheduling procedures, flood control standards, and ecological flow guarantee knowledge, including flood control level thresholds, ecological flow lower limits, and component operation safety parameter ranges.

[0016] Compared with the prior art, the beneficial effects of the present invention are: The adaptive adjustment system for configuration components in a water conservancy configuration system provided by this invention identifies operating conditions in real time through time-series sensing clustering, outputs the optimal control strategy through deep reinforcement learning, verifies the control effect through digital twin pre-simulation, and performs safety verification through multi-objective constraint management. This achieves real-time and accurate adjustment of configuration components, safe and compliant operation, and coordinated linkage of related components. It solves the problems of configuration components relying on fixed configurations and manual parameter adjustment in traditional water conservancy configuration systems, as well as the problems of response lag and inaccurate adjustment when facing dynamic hydrological conditions. It can reduce component failure rate, reduce manual intervention, and meet the needs for rapid, accurate, and safe adaptive adjustment of configuration components under extreme operating conditions. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the architecture of the adaptive adjustment system of the water conservancy configuration system configuration components provided in the embodiment of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be further described in detail below with reference to specific embodiments.

[0019] Embodiments of the present invention are described in detail below. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. Unless otherwise specified, embodiments of the present invention and the technical features thereof can be combined with each other.

[0020] This invention provides an adaptive adjustment system for configuration components of a hydraulic configuration system, such as... Figure 1 As shown, the system includes: The time-aware clustering module is used to acquire time-series data of the water conservancy configuration system, use the dynamic time warping algorithm to measure the sequence similarity of the time-series data, use the gated recurrent unit neural network to extract the periodic features of the time-series data, and perform online clustering based on sequence similarity and periodic features to divide the operating status of the water conservancy configuration system into different operating condition clusters and generate operating condition cluster feature vectors. The deep reinforcement learning decision module is used to model the water conservancy configuration system as a Markov decision process. It takes the operating state and working condition cluster feature vector of the water conservancy configuration system as the state input, the action space of the configuration components as the action space, designs a multi-objective reward function, and uses the proximal policy optimization algorithm and deep Q network algorithm to learn the optimal adjustment strategy for determining the optimal action of the configuration components. The configuration component adaptive adjustment module is used to parse the optimal adjustment strategy and convert it into control signals. The operating parameters of the configuration component are dynamically adjusted through the control signals. A component-level and system-level linkage adaptation mechanism is built. When adjusting the operating parameters of a single component, the operating parameters of related components are adjusted in tandem. The actual operating status data of the adjusted configuration component is fed back to the deep reinforcement learning decision module. The digital twin simulation interaction module is used to construct a digital twin. The adjustment results are simulated in the digital twin using control signals as adjustment instructions. When the simulated adjustment results deviate from the expected adjustment target or violate the constraints, optimization suggestions are generated and fed back to the deep reinforcement learning decision module, which triggers the deep reinforcement learning decision module to re-optimize the optimal adjustment strategy. The multi-objective constraint management module is used to issue an alarm signal when a control signal is detected to violate the constraint conditions, block the execution of the control signal, and trigger the deep reinforcement learning decision module to re-optimize the optimal adjustment strategy.

[0021] The adaptive adjustment system for configuration components in a water conservancy configuration system provided in this invention achieves real-time response, precise execution, and safe and compliant operation of configuration component adjustments through closed-loop collaboration of time-series sensing clustering for real-time identification of operating conditions, deep reinforcement learning for outputting optimal control strategies, digital twin pre-simulation verification, and multi-objective constraint management for safety verification. It also enables coordinated linkage of related components to avoid action conflicts, reducing scheduling response time from minutes to seconds, lowering component failure rates, and minimizing manual intervention. This meets the demand for rapid, accurate, and safe adaptive adjustment of configuration components under extreme operating conditions. It solves the problems of traditional water conservancy configuration systems, such as reliance on fixed configurations and manual parameter tuning, delayed response and inaccurate adjustments when facing dynamic hydrological conditions, and a lack of real-time linkage optimization and safety compliance verification capabilities for configuration components under multi-objective constraints.

[0022] In one possible embodiment, the time-aware clustering module includes: The data preprocessing and feature extraction unit is used to preprocess time series data. The preprocessing includes cleaning, alignment and standardization. It also extracts features including trends, cycles, volatility and outliers from the preprocessed time series data to form sample feature vectors. The dynamic clustering analysis unit is used to calculate the similarity distance between time series data segments corresponding to sample feature vectors using the dynamic time warping algorithm. It uses a time window weighting mechanism with a time decay factor to time-weight the similarity distance, so that recent time series data can be given higher weight. Based on the weighted similarity distance, the sample feature vectors are dynamically clustered to generate the initial working condition cluster division. The clustering result evaluation and iteration unit is used to evaluate the clustering quality of the initial working condition cluster division, obtain the clustering quality evaluation result, and dynamically adjust the cluster center of the working condition cluster according to the clustering quality evaluation result, redistribute the clustering affiliation of the sample feature vectors, and repeat the iteration until the iteration termination condition is reached to obtain the final working condition cluster division. The decision knowledge transmission unit is used to transform the final working condition cluster division into working condition cluster feature vectors that characterize the operating features of the working condition clusters, and transmit them as decision knowledge to the deep reinforcement learning decision module.

[0023] In this embodiment, the formula for calculating the similarity distance is: ; in, Represents the test sequence With reference sequence The similar distance between them Represents the test sequence The sequence points With reference sequence The Local distance between sequence points Indicates the alignment path.

[0024] The weighting function for the time window weighting mechanism is: ; in, Indicates similarity distance in The weight corresponding to each time point This represents the attenuation factor, with a value range of [0.85, 0.95], determined according to the specific scenario. Indicates the size of the time window. Corresponding to the earliest data, This corresponds to the most recent data.

[0025] The weighted distance from the sample feature vector to the cluster center is calculated based on the weighting function: ; in, Indicates in Time-of-flight sample feature vector To the cluster center The weighted distance, Represents the sample feature vector To the cluster center The Euclidean distance.

[0026] In this embodiment, the clustering response time of the time-aware clustering module is less than or equal to 300ms.

[0027] In one possible embodiment, the deep reinforcement learning decision module includes: The Markov decision process modeling unit is used to model a hydraulic configuration system as a Markov decision process, including: defining the state space. This includes the operating status and condition cluster feature vectors of the water conservancy configuration system. The operating status of the water conservancy configuration system includes reservoir water level, flow rate, water quality, equipment operating status, and energy storage level; defining the action space. This includes the actions of configuration components, such as adjusting gate opening, starting and stopping water pumps, switching water supply modes, and flood discharge decisions; defining state transition probabilities. It is determined by hydrodynamics, equipment response characteristics, and random disturbances; among them, Indicates the current state. Indicates the next state. Indicates the current action; defines the discount factor. The value should be close to 1 to reflect the long-term nature of water resource management; The multi-objective reward function unit is used to design multi-objective reward functions and to calculate the instantaneous reward value for each state transition using these functions.

[0028] In this embodiment, the multi-objective reward function is: ; in, Indicates in Execute Later transferred to Instant reward value, Indicates the first Individual goals Execute Later transferred to The immediate rewards include sub-goals such as flood control safety, ecological flow assurance, water supply efficiency, and energy consumption control. Indicates the first The weights of each sub-objective are determined through expert scoring or optimization algorithms to reflect the priority of the objectives. Indicates the number of sub-targets.

[0029] In this embodiment, the deep reinforcement learning decision module uses the immediate reward value of each state transition to guide the proximal policy optimization algorithm and the deep Q-network algorithm to iterate the policy during training, learning the optimal adjustment policy that maximizes the cumulative discount reward, and training a policy model to achieve the optimal adjustment policy. During the decision-making process, the deep reinforcement learning decision module uses the operating state and condition cluster feature vectors of the hydraulic configuration system as specific inputs to the state space, and outputs the optimal action of the configuration components through the policy model.

[0030] In one possible embodiment, the configuration component adaptive adjustment module includes: The strategy parsing unit is used to parse the optimal adjustment strategy into a description of the target action of the configuration component; The solution space construction and candidate solution screening unit is used to describe the target action based on the configuration component. According to the operating status of the water conservancy configuration system, it constructs a parameter adjustment solution space consisting of the adjustment values ​​of the operating parameters of the configuration component, and selects candidate parameter adjustment schemes that meet the safe operating parameter range of the component from the parameter adjustment solution space. The future state assessment unit is used to predict the future state of the hydraulic configuration system after the implementation of the candidate parameter adjustment scheme using a prediction model, and to assess the future state to obtain the future state assessment result. The control signal generation unit is used to select the candidate parameter adjustment scheme that makes the future state optimal by comparison and screening based on the future state evaluation results, and convert the optimal parameter adjustment scheme into a control signal that the configuration component can recognize. The security verification unit is used to perform security verification on the control signals and transmit the control signals that have passed the security verification to the configuration components for execution. The comparison and verification unit is used to compare and verify the actual operating status data of the configuration components after the execution of the control signal with the future state of the water conservancy configuration system corresponding to the optimal parameter adjustment scheme, and to calculate the degree of achievement of the target using a multi-objective reward function. The online learning and strategy update unit is used to trigger the online learning of the strategy model in the deep reinforcement learning decision module based on the goal achievement evaluation, and to update the parameters of the strategy model using the actual operating status data of the configuration components after the execution of control signals. The anomaly monitoring and safety protection unit is used to monitor the actual operating parameters of the configuration components. When the reconstruction error of the actual operating parameters of the configuration components exceeds the error threshold, safety protection measures are triggered.

[0031] In this embodiment, the control accuracy of the adaptive control module of the configuration component is -5% to 5%. The control accuracy refers to the relative deviation between the actual operating parameters of the configuration component after the control signal is executed and the target parameters determined by the optimal parameter adjustment scheme.

[0032] In one possible embodiment, the digital twin simulation interaction module includes: The data base layer is used to aggregate and manage basic geospatial data, engineering building information model or city information model data, real-time monitoring and sensing data, historical operation data, business management data and external shared data, providing data support for the model platform layer. The model platform layer, driven by the data base layer, provides professional water network models, intelligent identification models, visualization models, and simulation engines to simulate control signals and obtain simulation results. The knowledge platform layer is used to represent, extract, fuse, and reason about the simulation results using the knowledge engine, forming decision knowledge about the adjustment effect, and feeding the decision knowledge back to the deep reinforcement learning decision module to optimize the optimal adjustment strategy; The interactive application layer is used to transform the simulation process of the model platform layer and the decision knowledge of the knowledge platform layer into a user-oriented visual interface, providing a digital twin and user interaction interface for viewing the simulation process, adjusting simulation parameters, and assisting in decision-making.

[0033] In one possible embodiment, the multi-objective constraint management module includes: The multimodal and standardization submodule is used to acquire multi-source heterogeneous data related to the actions of configuration components and perform standardization processing to obtain standardized multi-source heterogeneous data, providing factual basis for compliance judgment. The compliance determination submodule is used to compare the standardized multi-source heterogeneous data with the constraints to obtain the compliance verification results. The graded early warning and real-time intervention submodule is used to determine the risk level based on the compliance verification results and trigger corresponding early warning and intervention mechanisms based on the risk level. This includes: issuing an alarm signal when a control signal is detected to violate the constraints, blocking the execution of the control signal, and feeding the blocking information back to the deep reinforcement learning decision module to trigger the deep reinforcement learning decision module to re-optimize the optimal adjustment strategy; at the same time, recording the compliance verification date to provide data support for subsequent scheduling review and strategy optimization.

[0034] In this embodiment, the multi-source heterogeneous data is collected through hardware units deployed on-site.

[0035] In one possible embodiment, the constraints are a compliance rule base and intelligent algorithm model pre-set based on water conservancy project scheduling procedures, flood control standards, and ecological flow guarantee knowledge, including flood control level thresholds, ecological flow lower limits, and component operation safety parameter ranges.

[0036] This invention provides an adaptive adjustment method for configuration components of a water conservancy configuration system, which specifically includes the following steps: Step 1: Data Acquisition and Preprocessing: Collect real-time time-series data of the water conservancy configuration system, including water level, inflow, outflow, rainfall forecast, downstream discharge capacity and equipment status, perform outlier cleaning, normalization and time-series alignment, and generate a standardized time-series dataset for time-series-aware clustering modeling in Step 2. Step 2: Time-aware clustering modeling: Step 2.1: The dynamic time warping algorithm is used to calculate the similarity distance between different time series in the time series data to eliminate the nonlinear offset of the time axis, and a time window weighting mechanism is adopted; the weighted distance from the sample feature vector to the cluster center is calculated; the weighted distance is used to determine the cluster affiliation of each sample feature vector in the subsequent online K-means clustering. Step 2.2: Combine the trend, periodic and abrupt change features of the time series data extracted by the gated recurrent unit neural network, and map the original high-dimensional time series data to the low-dimensional embedding space; the low-dimensional embedding space integrates the similarity measured by the dynamic time warping algorithm and the long-period features extracted by the gated recurrent unit neural network; Step 2.3: Perform online K-means clustering on time series data based on low-dimensional embedding space, use the weighted distance in Step 2.1 to determine the clustering affiliation of each sample feature vector, divide the working condition clusters, and output the cluster labels and working condition cluster feature vectors as the state input for deep reinforcement learning in Step 3; Step 3: Deep reinforcement learning strategy construction: In the digital twin simulation environment, the water conservancy configuration system is modeled as a Markov decision process; during the training process, the multi-objective reward function is used to calculate the instantaneous reward value of each state transition, guide the proximal policy optimization algorithm and the deep Q-network algorithm to perform policy iteration, train to obtain the policy model until the reward function converges, and after training is completed, it is deployed to the production environment and supports online fine-tuning. Step 4: Configure components for adaptive adjustment: Step 4.1: Acquire time-series data in real time, and match the current operating condition cluster using time-series-aware clustering as described in Step 2; Step 4.2: The deep reinforcement learning decision module outputs configuration component adjustment instructions based on the feature vector of the current working condition cluster and the current operating state of the hydraulic configuration system through the strategy model trained in Step 3. Step 4.3: The configuration component executes the configuration component adjustment command, while the digital twin simulation module previews the adjustment effect. The multi-objective constraint management module verifies whether the configuration component adjustment command violates the preset constraint conditions. If the constraint conditions are not met, the command execution is blocked and the violation information is fed back to the deep reinforcement learning decision module, triggering policy re-optimization and forming a closed-loop adjustment. Step 5: Online Iteration and Self-Optimization: Continuously collect the actual operating status data of each configuration component after executing the adjustment command, update the time series clustering model using the actual operating status data, and dynamically adjust the policy model in the deep reinforcement learning decision module; when the operating conditions change over a long period of time, the model retraining is automatically triggered to ensure the long-term adaptability of the system.

[0037] Those skilled in the art will understand that embodiments of the present invention can be provided as systems, system products, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0038] This invention is described with reference to flowchart illustrations and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1A system that specifies functions in one or more boxes.

[0039] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0040] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0041] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A water conservancy configuration system configuration component adaptive adjustment system, characterized in that, include: The time-aware clustering module is used to acquire time-series data of the water conservancy configuration system, use the dynamic time warping algorithm to measure the sequence similarity of the time-series data, use the gated recurrent unit neural network to extract the periodic features of the time-series data, and perform online clustering based on sequence similarity and periodic features to divide the operating status of the water conservancy configuration system into different operating condition clusters and generate operating condition cluster feature vectors. The deep reinforcement learning decision module is used to model the water conservancy configuration system as a Markov decision process. It takes the operating state and working condition cluster feature vector of the water conservancy configuration system as the state input, the action space of the configuration components as the action space, designs a multi-objective reward function, and uses the proximal policy optimization algorithm and deep Q network algorithm to learn the optimal adjustment strategy for determining the optimal action of the configuration components. The configuration component adaptive adjustment module is used to parse the optimal adjustment strategy and convert it into control signals. The operating parameters of the configuration component are dynamically adjusted through the control signals. A component-level and system-level linkage adaptation mechanism is built. When adjusting the operating parameters of a single component, the operating parameters of related components are adjusted in tandem. The actual operating status data of the adjusted configuration component is fed back to the deep reinforcement learning decision module. The digital twin simulation interaction module is used to construct a digital twin. The adjustment results are simulated in the digital twin using control signals as adjustment instructions. When the simulated adjustment results deviate from the expected adjustment target or violate the constraints, optimization suggestions are generated and fed back to the deep reinforcement learning decision module, which triggers the deep reinforcement learning decision module to re-optimize the optimal adjustment strategy. The multi-objective constraint management module is used to issue an alarm signal when a control signal is detected to violate the constraint conditions, block the execution of the control signal, and trigger the deep reinforcement learning decision module to re-optimize the optimal adjustment strategy.

2. The water management system configuration component adaptive adjustment system of claim 1, wherein, The time-aware clustering module includes: The data preprocessing and feature extraction unit is used to preprocess time series data and extract features including trends, cycles, volatility and outliers from the preprocessed time series data to form sample feature vectors. The dynamic clustering analysis unit is used to calculate the similarity distance between time series data segments corresponding to sample feature vectors using the dynamic time warping algorithm. It uses a time window weighting mechanism with a time decay factor to perform time weighting on the similarity distance, and performs dynamic clustering on the sample feature vectors based on the weighted similarity distance to generate the initial working condition cluster division. The clustering result evaluation and iteration unit is used to evaluate the clustering quality of the initial working condition cluster division, obtain the clustering quality evaluation result, and dynamically adjust the cluster center of the working condition cluster according to the clustering quality evaluation result, redistribute the clustering affiliation of the sample feature vectors, and repeat the iteration until the iteration termination condition is reached to obtain the final working condition cluster division. The decision knowledge transmission unit is used to transform the final working condition cluster division into working condition cluster feature vectors that characterize the operating features of the working condition clusters, and transmit them as decision knowledge to the deep reinforcement learning decision module.

3. The water management system configuration component adaptive adjustment system of claim 2, wherein, Preprocessing includes cleaning, alignment, and normalization.

4. The water management system configuration component adaptive adjustment system of claim 1, wherein, The deep reinforcement learning decision-making module includes: A Markov decision process modeling unit is configured to model the water configuration system as a Markov decision process, comprising: defining a state space , including the operating state of the water configuration system and the working condition cluster feature vector; defining an action space , including the action of the configuration component; defining the state transition probability , determined by hydrology dynamics, equipment response characteristics and random disturbance; wherein, represents the current state, represents the next state, represents the current action; defining the discount factor ; The multi-objective reward function unit is used to design multi-objective reward functions and to calculate the instantaneous reward value for each state transition using the multi-objective reward function. The multi-objective reward function is: ； wherein, indicates that the instant reward value is transferred to is executed is transferred to indicates that the instant reward value is transferred to is executed is transferred to is executed is transferred to indicates the weight of the th sub-goal, indicates the number of sub-goals;​ During training, the deep reinforcement learning decision module uses the immediate reward value of each state transition to guide the proximal policy optimization algorithm and the deep Q-network algorithm to iterate the policy, learn the optimal adjustment policy that maximizes the cumulative discount reward, and train a policy model to achieve the optimal adjustment policy.

5. The adaptive adjustment system for configuration components of a water conservancy configuration system according to claim 4, characterized in that, The operational status of the water conservancy configuration system includes reservoir water level, flow rate, water quality, equipment operating status, and energy storage level; the actions of the configuration components include adjusting the gate opening, starting and stopping water pumps, switching water supply modes, and flood discharge decisions.

6. The adaptive adjustment system for configuration components of a water conservancy configuration system according to claim 4, characterized in that, The sub-objectives include flood control safety, ecological flow guarantee, water supply efficiency, and energy consumption control.

7. The adaptive adjustment system for configuration components of a water conservancy configuration system according to claim 1, characterized in that, The configuration component adaptive adjustment module includes: The strategy parsing unit is used to parse the optimal adjustment strategy into a description of the target action of the configuration component; The solution space construction and candidate solution screening unit is used to describe the target action based on the configuration component. According to the operating status of the water conservancy configuration system, it constructs a parameter adjustment solution space consisting of the adjustment values ​​of the operating parameters of the configuration component, and selects candidate parameter adjustment schemes that meet the safe operating parameter range of the component from the parameter adjustment solution space. The future state assessment unit is used to predict the future state of the hydraulic configuration system after the implementation of the candidate parameter adjustment scheme using a prediction model, and to assess the future state to obtain the future state assessment result. The control signal generation unit is used to select the candidate parameter adjustment scheme that makes the future state optimal by comparison and screening based on the future state evaluation results, and convert the optimal parameter adjustment scheme into a control signal that the configuration component can recognize. The security verification unit is used to perform security verification on the control signals and transmit the control signals that have passed the security verification to the configuration components for execution. The comparison and verification unit is used to compare and verify the actual operating status data of the configuration components after the execution of the control signal with the future state of the water conservancy configuration system corresponding to the optimal parameter adjustment scheme, and to calculate the degree of achievement of the target using a multi-objective reward function. The online learning and strategy update unit is used to trigger the online learning of the strategy model in the deep reinforcement learning decision module based on the goal achievement evaluation, and to update the parameters of the strategy model using the actual operating status data of the configuration components after the execution of control signals. The anomaly monitoring and safety protection unit is used to monitor the actual operating parameters of the configuration components. When the reconstruction error of the actual operating parameters of the configuration components exceeds the error threshold, safety protection measures are triggered.

8. The adaptive adjustment system for configuration components of a water conservancy configuration system according to claim 1, characterized in that, The digital twin simulation interaction module includes: The data base layer is used to aggregate and manage basic geospatial data, engineering building information model or city information model data, real-time monitoring and sensing data, historical operation data, business management data and external shared data; The model platform layer, driven by the data base layer, provides professional water network models, intelligent identification models, visualization models, and simulation engines to simulate control signals and obtain simulation results. The knowledge platform layer is used to represent, extract, fuse, and reason about the simulation results using the knowledge engine, forming decision knowledge about the adjustment effect, and feeding the decision knowledge back to the deep reinforcement learning decision module to optimize the optimal adjustment strategy; The interactive application layer is used to transform the simulation process of the model platform layer and the decision knowledge of the knowledge platform layer into a user-oriented visual interface, providing a digital twin and user interaction interface for viewing the simulation process, adjusting simulation parameters, and assisting in decision-making.

9. The adaptive adjustment system for configuration components of a water conservancy configuration system according to claim 1, characterized in that, The multi-objective constraint management module includes: The multimodal and standardization submodule is used to acquire multi-source heterogeneous data related to the actions of configuration components and perform standardization processing to obtain standardized multi-source heterogeneous data. The compliance determination submodule is used to compare the standardized multi-source heterogeneous data with the constraints to obtain the compliance verification results. The graded early warning and real-time intervention submodule is used to determine the risk level based on the compliance verification results and trigger corresponding early warning and intervention mechanisms based on the risk level. These mechanisms include: issuing an alarm signal when a control signal is detected to violate the constraints, blocking the execution of the control signal, and feeding the blocking information back to the deep reinforcement learning decision module to trigger the deep reinforcement learning decision module to re-optimize the optimal adjustment strategy; and recording the compliance verification date.

10. The adaptive adjustment system for configuration components of a water conservancy configuration system according to claim 1, characterized in that, The constraints are a compliance rule base and intelligent algorithm model pre-set based on water conservancy project scheduling procedures, flood control standards and ecological flow guarantee knowledge, including flood control level thresholds, ecological flow lower limits and component operation safety parameter ranges.

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