An automatic detection and maintenance method and system for water conservancy and hydropower equipment

Abstract

This invention provides an automatic detection and maintenance method for water conservancy and hydropower equipment, applied to an inspection robot. The method includes: acquiring real-time data collected by the inspection robot; extracting features from the collected data; simulating and constructing a physical environment model based on the extracted feature data, and rendering it into images or data for decision analysis; adjusting the inspection robot to the optimal viewing angle for data acquisition using a perceptual Q-function; updating the constructed physical environment model based on the data acquired from the optimal viewing angle; determining a basic strategy based on the updated physical environment model, and formulating or updating inspection and maintenance strategies in conjunction with residual strategies; and the inspection robot executing the corresponding strategies. This invention can improve the accuracy of monitoring and maintenance, enhance the adaptability of the monitoring system, improve data acquisition efficiency, and achieve real-time optimization of decision-making.

Description

Technical Field

[0001] This invention relates to the fields of robotics, artificial intelligence, and automatic monitoring and maintenance in the water conservancy and hydropower industry, and particularly to an automatic detection and maintenance method and system for water conservancy and hydropower equipment based on residual strategy and perception Q function. Background Technology

[0002] In the water conservancy and hydropower industry, monitoring and maintaining the health of water conservancy facilities is a key task to ensure the sustainable use of water resources and prevent disasters. Traditional monitoring methods rely on periodic manual inspections, which are not only time-consuming and labor-intensive but also often fail to achieve real-time monitoring and rapid response. While existing automated monitoring systems have improved the frequency and efficiency of monitoring, they generally lack flexibility and struggle to adapt to complex and changing environmental conditions. In particular, their accuracy and adaptability are often significantly compromised when encountering non-standard situations or new monitoring scenarios.

[0003] The shortcomings of existing technologies mainly include: 1. Insufficient real-time data processing: Existing technologies often fail to reflect environmental changes in a timely manner when processing real-time data, especially in complex and dynamic environments (such as water conservancy and hydropower facilities), resulting in inaccurate or untimely application of monitoring and maintenance strategies. 2. Static selection of monitoring perspectives: Traditional monitoring systems often use fixed or preset monitoring perspectives, lacking the ability to dynamically optimize perspectives based on actual monitoring data, which limits the effectiveness of data acquisition and the comprehensiveness of monitoring. 3. Differences between simulation and actual environment: Existing simulation technologies struggle to accurately simulate the complex physical and environmental conditions of the real world, leading to reduced effectiveness of formulated strategies during actual implementation. 4. Insufficient adaptability and accuracy of decision-making: Existing automatic monitoring systems often rely on static data models during decision-making, lacking adaptive adjustment strategies to cope with real-time environmental changes. Summary of the Invention

[0004] To address the problems existing in current technologies, this paper provides an automatic detection and maintenance method and system for water conservancy and hydropower equipment. Through real-time model updates and automatic perspective selection, it not only improves the accuracy of monitoring data but also enhances the system's adaptability and robustness by dynamically adjusting monitoring strategies to adapt to environmental changes. Furthermore, the introduction of residual strategies effectively bridges the differences between simulation and real-world environments, improving the accuracy of decision-making and the effectiveness of implementation.

[0005] The first aspect of this invention proposes an automatic inspection and maintenance method for water conservancy and hydropower equipment, applied to an inspection robot, comprising:

[0006] Acquire real-time data collected by the inspection robot;

[0007] Feature extraction is performed on the collected data;

[0008] Based on the extracted feature data, a physical environment model is simulated and constructed, and then rendered into images or data for decision analysis.

[0009] The inspection robot is adjusted to the optimal viewing angle for data collection using the sensing Q function;

[0010] The physical environment model is updated based on data obtained from the best perspective;

[0011] The basic strategy is determined based on the updated physical environment model, and the inspection and maintenance strategy is formulated or updated in combination with the residual strategy.

[0012] The inspection robot executes the corresponding strategy.

[0013] As a preferred option, the system also includes monitoring and collecting the execution results when the inspection robot performs the corresponding strategy, which can be used to update the subsequent inspection and maintenance strategies.

[0014] As a preferred embodiment, the step of simulating and constructing a physical environment model based on the extracted feature data and rendering it into images or data for decision analysis specifically includes:

[0015] Establish a physical environment model: Define the parametric model of the physical scene, including all relevant physical parameters and initial conditions;

[0016] Simulation solution: The partial differential equations in the physical environment model are solved using numerical methods to obtain the physical state under different parameters;

[0017] Rendering process: The results of numerical simulation are rendered into images using computer graphics technology;

[0018] Optimization: Define the loss function as the difference between the rendered image and the actual monitored image, and use gradient descent to update the physical parameters to minimize the loss function.

[0019] As a preferred embodiment, the step of adjusting the inspection robot to the optimal viewing angle for data collection using a sensing Q-function specifically includes:

[0020] Perceptual Q-function evaluation: For each possible viewpoint, calculate the corresponding perceptual Q-function value to evaluate the amount of information per viewpoint;

[0021] Determine the optimal viewing angle: The viewing angle that maximizes the perceived Q-function value is taken as the optimal viewing angle;

[0022] Actual monitoring: The inspection robot adjusts the position of its camera and sensors according to the best viewing angle to perform monitoring tasks and collect data.

[0023] As a preferred embodiment, the formulation or updating of inspection and maintenance strategies based on the updated physical environment model specifically includes:

[0024] In the physical environment model, a basic policy is trained based on physical state data from the best perspective. During the training process, the basic policy attempts to maximize the expected return.

[0025] In a real-world environment, based on the difference between the actual execution results and the expected results, the residual policy is trained to adjust the output of the basic policy, thus obtaining the final policy.

[0026] As a preferred option, if the inspection robot fails to achieve the expected results after executing the corresponding strategy, the strategy is updated by adjusting the residual strategy before being sent back to the inspection robot for execution.

[0027] The second aspect of this invention proposes an automatic inspection and maintenance system for water conservancy and hydropower equipment, implemented based on an inspection robot, comprising:

[0028] The environmental perception and data acquisition module is used to acquire raw data from various sensors mounted on the robot.

[0029] The data processing and feature extraction module is used to extract features from the acquired raw data from various sensors.

[0030] The physics simulation and rendering module is used to simulate and build a physical environment model based on the extracted feature data, simulate possible physical scenarios, and render them into images or data for decision analysis.

[0031] The perceptual Q-function and viewpoint selection module analyzes the amount of information under different data acquisition views based on the perceptual Q-function and selects the best viewpoint for monitoring.

[0032] The decision-making module is used to determine the basic strategy based on the physical environment model and the monitoring data from the best perspective, and to formulate or update the inspection and maintenance strategy in combination with the residual strategy.

[0033] The execution module is used to perform specific physical operations according to the strategy provided by the decision module, and to feed back the data generated during the execution process to the decision module in real time.

[0034] As a preferred embodiment, the operation of the physical simulation rendering module includes:

[0035] Define the parametric model of the physical scene, including all relevant physical parameters and initial conditions;

[0036] Numerical methods are used to solve the partial differential equations in the physical environment model to obtain the physical state under different parameters;

[0037] The numerical simulation results are converted into images using computer graphics technology. During the rendering process, physical parameters are updated using gradient descent to minimize the loss function, thereby reducing the difference between the rendered image and the actual monitored image.

[0038] As a preferred embodiment, the operation of the perception Q-function and the viewpoint selection module includes:

[0039] For each possible viewpoint, calculate the perceptual Q-function;

[0040] Determine the optimal viewing angle that maximizes the perceptual Q-function;

[0041] The robot is controlled to adjust the positions of the cameras and sensors according to the optimal viewing angle to perform monitoring tasks.

[0042] As a preferred embodiment, when formulating or updating inspection and maintenance strategies, the decision-making module trains a basic strategy based on a simulated physical environment model, and then trains a residual strategy based on the difference between the actual execution results and the expected results to adjust the basic strategy, thereby completing the formulation or update of the final strategy.

[0043] Compared with existing technologies, the beneficial effects of adopting the above technical solution are as follows:

[0044] 1. Improve the accuracy of monitoring and maintenance: Through real-time updated simulation and dynamic perspective selection, key problem areas can be monitored more accurately, improving the accuracy of problem detection.

[0045] 2. Enhance system adaptability: The introduction of residual strategies enables the system to better adapt to environmental changes, reduce the difference between simulation and reality, and ensure that the actual execution of decisions is more accurate.

[0046] 3. Improve data collection efficiency: The dynamic perspective selection mechanism ensures that data is collected from the most critical perspectives, reduces the collection of invalid data, and improves overall monitoring efficiency.

[0047] 4. Real-time optimization of decision-making: With the help of real-time data and differentiable rendering technology, the decision-making module can quickly respond to environmental changes, adjust monitoring and maintenance strategies in real time, and improve the system's response speed and operational efficiency. Attached Figure Description

[0048] Figure 1 This is a flowchart of the automatic detection and maintenance method for water conservancy and hydropower equipment proposed in an embodiment of the present invention.

[0049] Figure 2 This is a schematic diagram of the physical environment model of an embodiment of the present invention.

[0050] Figure 3 This is a schematic diagram illustrating strategy formulation in an embodiment of the present invention. Detailed Implementation

[0051] The embodiments of this application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar modules or modules 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 this application, and should not be construed as limiting this application. Rather, the embodiments of this application include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.

[0052] Example 1

[0053] To address the limitations of existing solutions, such as limited data processing capabilities, inability to update monitoring strategies in real time to adapt to environmental changes, fixed automatic monitoring perspectives that cannot optimize data acquisition angles according to task needs, and a lack of effective strategies to bridge the differences between simulation and real environments, thus affecting the reliability of monitoring data and the effectiveness of maintenance decisions, this invention proposes an automatic detection and maintenance method for water conservancy and hydropower equipment, applied to an inspection robot. By integrating differentiable physical simulation and rendering technologies, this invention achieves real-time model updates and automatic perspective selection, significantly improving the accuracy of monitoring data and the efficiency of maintenance tasks. Specifically, this technology reduces redundancy in data acquisition by automatically selecting the optimal monitoring perspective, and adapts to environmental changes through real-time model updates, ensuring the continued effectiveness of monitoring and maintenance strategies. Furthermore, this invention employs a residual strategy to learn and bridge the behavioral differences between simulation and real environments, enhancing the system's adaptability to new environments and its continuous learning ability. Please refer to [reference needed]. Figure 1 The specific plan is as follows:

[0054] Step 1: Obtain real-time data collected by the inspection robot.

[0055] Data serves as the basis for final strategy formulation. In this embodiment, data on water conservancy and hydropower facilities is mainly collected through sensors (such as cameras and temperature sensors) mounted on the inspection robot. The inspection robot operates continuously during its inspections, collecting various physical and environmental parameters of the water conservancy and hydropower facilities.

[0056] Step 2: Extract features from the collected data.

[0057] In this embodiment, the collected data is preprocessed to extract key features, which are used in subsequent simulation and decision-making processes. Preprocessing may include filtering, noise reduction, etc.; key features may include crack identification and temperature anomalies in the image.

[0058] Step 3: Simulate and construct a physical environment model based on the extracted feature data, and render it into images or data for decision analysis.

[0059] In this embodiment, please refer to Figure 2This method utilizes differentiable physical simulation and rendering techniques to construct a physical environment model based on extracted features, simulating possible physical scenarios and rendering them into images or data suitable for analysis. Ensuring the model closely approximates the real world is crucial, contributing to the accuracy of subsequent decision-making. This embodiment introduces differential physical simulation and rendering techniques, enabling the physical simulation process to be optimized through backpropagation, allowing for real-time adjustment of model parameters to more realistically simulate the physical environment. This method allows the system to dynamically adjust and optimize the simulation model based on actual monitoring data, thereby improving the accuracy of decision-making and monitoring.

[0060] Furthermore, this embodiment also provides a specific model building process, including:

[0061] (1) Establish a physical environment model: Define the parameter model of the physical scene, including all relevant physical parameters and initial conditions; where u(t,x;θ) is defined as the physical state at time t and position x, and θ represents all relevant physical parameters.

[0062] (2) Simulation Solution: The partial differential equations in the physical environment model are solved using numerical methods to obtain the physical state under different parameters. In this embodiment, numerical methods such as the finite difference method and the finite element method can be used to solve u(t,x;θ).

[0063] (3) Rendering process: The results of the numerical simulation are rendered into images using computer graphics technology. Here, Isim(θ) represents the image rendered based on the parameter θ, which is obtained by rendering the simulation result u(t,x;θ).

[0064] (4) Optimization: The loss function is defined as the difference between the rendered image and the actual monitored image, and gradient descent is used to update the physical parameters to minimize the loss function. The optimization process is as follows:

[0065] Define the loss function L(θ) as the difference between the rendered image and the actual monitored image Ireal, L(θ) = ||Isim(θ) - Ireal||². Use gradient descent to update the parameter θ to minimize the loss function. The update formula is: Where α is the learning rate.

[0066] Step 4: Adjust the inspection robot to the optimal viewing angle using the Q-function to collect data.

[0067] In this embodiment, the value of monitoring data obtained from different perspectives is evaluated by using the perceptual Q function, and the monitoring perspective that provides the most key information is dynamically selected, which ensures the optimization of data collection and increases the quality and relevance of monitoring data.

[0068] Specifically, the process of selecting the optimal viewpoint using the perceptual Q-function is as follows:

[0069] (1) Perceptual Q function evaluation: For each possible viewpoint, calculate the corresponding perceptual Q function value to evaluate the information content of each viewpoint.

[0070] For each possible viewpoint v, Q(sv,a) is computed, where sv is the state observed from viewpoint v and a is the set of possible actions. The perceptual Q function Q(sv,a) can be used to evaluate the information content of each viewpoint by predicting the expected reward after taking action a.

[0071] (2) Determine the best perspective: The perspective that maximizes the value of the perception Q function is taken as the best perspective.

[0072] In this embodiment, the viewpoint v that maximizes the perceptual Q function Q(sv,a) is selected as the optimal viewpoint. This means that observing and operating the environment from this viewpoint yields the greatest expected results. The viewpoint selection formula is expressed as: v*=argmaxvQ(sv,a).

[0073] (3) Actual monitoring: The inspection robot adjusts the position of its camera and sensors according to the best viewing angle v* to perform monitoring tasks and collect data.

[0074] Step 5: Update the constructed physical environment model based on the data obtained from the best perspective. By updating the physical environment model, we can ensure that the model can reflect the latest environmental conditions.

[0075] Step 6: Determine the basic strategy based on the updated physical environment model, and formulate or update the inspection and maintenance strategy in conjunction with the residual strategy.

[0076] In this embodiment, decisions are primarily made based on the updated physical environment model to formulate update inspection and maintenance strategies. Since the physical environment model is a constructed virtual model, it may differ from the real environment. Therefore, a residual strategy is introduced in conjunction with the basic strategy based on the physical environment model to compensate for execution errors caused by the differences between the model and the actual environment, reducing the discrepancy between the simulation and the real world, and improving the adaptability and accuracy of the strategy in the real environment.

[0077] For details, please refer to Figure 3 The process of formulating the strategy is as follows:

[0078] (1) Model Training: In this embodiment, a basic policy π is trained in the physical environment model based on the physical state data monitored from the best perspective. base During the training of the basic policy, it needs to be validated in the physical environment model, and the policy attempts to maximize the expected return.

[0079] (2) Residual learning: In a real-world environment, a residual policy π is trained based on the difference between the actual execution result and the expected result. res To adjust the output of the basic strategy.

[0080] (3) Strategy Generation: The final generated strategy is a t =π base (s t )+π res (s t ), where s t Given the current state, the residual strategy is represented as π. res =argmin π Ε[(a * -(π base (s)+π(s))) 2 ], where a * It is the optimal action, π base π(s) is the action generated by the base policy, and π(s) is the action adjustment generated by the residual policy. Base policy π base (s): This is a pre-trained policy in a simulated environment, used to handle most expected operational scenarios. The base policy is typically developed under controlled conditions to achieve optimal performance under ideal circumstances. The residual policy π... res When the base policy is applied to the real world, performance may degrade due to various unforeseen factors. The residual policy, in practical applications, fine-tunes the output of the base policy based on real-time data to compensate for this discrepancy.

[0081] Step 7: The inspection robot executes the corresponding strategy.

[0082] After a specific inspection and maintenance strategy is formulated, the inspection robot can execute actions according to the strategy, with the actual actions being performed by a. t =π base (s t )+π res (s t This decision is made to ensure the accuracy and effectiveness of the operation.

[0083] It should be further explained that during the execution of the inspection robot's strategy, the execution effect is continuously monitored and feedback is provided for decision-making updates and optimizations. In this embodiment, this is mainly achieved by determining whether the execution effect meets expectations. If it does not meet expectations, the residual strategy is adjusted, the established strategy is updated, and then the new strategy is issued to the inspection robot for execution. By integrating the latest simulation results and monitoring data, the decision is updated in real time to ensure that the strategy continuously adapts to environmental changes.

[0084] This invention integrates differentiable physical simulation and rendering technologies to achieve real-time model updates and automatic perspective selection, significantly improving the accuracy of monitoring data and the efficiency of maintenance tasks. Specifically, by automatically selecting the optimal monitoring perspective, redundancy in data acquisition is reduced, while real-time model updates adapt to environmental changes, ensuring the effectiveness of monitoring and maintenance strategies. Furthermore, this invention employs a residual strategy to learn and bridge the behavioral differences between the simulated and real environments, enhancing the system's adaptability to new environments and its continuous learning capabilities. This invention is applicable to applications in the water conservancy and hydropower industry requiring high-precision monitoring and real-time strategy updates, such as water pipe inspection, dam safety monitoring, and river maintenance. It significantly improves the performance of automatic monitoring and maintenance systems for water conservancy and hydropower facilities, which is of great significance for improving the operational safety and efficiency of these facilities.

[0085] Example 2

[0086] This embodiment provides an automatic inspection and maintenance system for water conservancy and hydropower equipment, based on an inspection robot. Utilizing a sensing Q-function and residual strategy technology, it automatically collects and processes monitoring data, evaluates the information value from different monitoring perspectives, and selects the optimal perspective for data acquisition. Then, through differentiable physical simulation and rendering technology, it updates its operational model in real time to adapt to environmental changes and adjusts monitoring strategies accordingly. The system maps actions in the simulation environment model to actions in the real physical world, bridging the differences between the simulation and real environments using residual strategies to optimize the implementation of execution strategies, ensuring efficient and accurate execution of monitoring and maintenance tasks. This system can adjust monitoring and maintenance strategies in real time and optimize operational execution, ensuring that its application in water conservancy and hydropower facilities can effectively prevent and solve problems, thereby improving safety and reliability. Specifically:

[0087] The automatic detection and maintenance system for water conservancy and hydropower equipment includes:

[0088] The environmental perception and data acquisition module is used to acquire raw data from various sensors (such as cameras, temperature sensors, and pressure sensors) mounted on the robot, such as images, temperature readings, and pressure readings. This module is used to collect environmental data about water conservancy facilities and provide the system with real-time information needed for decision-making.

[0089] The data processing and feature extraction module is used to extract features from the raw data acquired from various sensors. This module is used to process and analyze the raw data to extract key features and state information that are useful for decision-making.

[0090] The physics simulation and rendering module is used to simulate and construct a physical environment model based on the extracted feature data, simulate possible physical scenarios, and render them into images or data for decision analysis. This module is used to provide the system with a simulation environment that can reflect real-world physical phenomena, including dynamically simulating physical processes (such as water flow, pressure changes, etc.) and converting these simulations into analyzable images or data, enabling the system to make high-quality decision predictions.

[0091] The perceptual Q-function and viewpoint selection module analyzes the amount of information under different data acquisition views based on the perceptual Q-function and selects the optimal viewpoint for monitoring. This module automatically selects the viewpoint providing the most effective information based on the perceptual Q-function analysis of the information content under different data acquisition views. For example, when visually inspecting the inside of a pipe, it ensures that the camera is focused on the most critical parts.

[0092] The decision-making module is used to determine the basic strategy based on the physical environment model and the data monitored from the best perspective, and to formulate or update the inspection and maintenance strategy in combination with the residual strategy. This module mainly uses the current environment model and the collected data, combined with the execution status of the basic strategy in the environment model and the residual strategy, to generate specific maintenance or inspection strategies.

[0093] The execution module is used to perform specific physical operations according to the strategy provided by the decision module, and to feed back the data generated during the execution process to the decision module in real time.

[0094] Specifically, the physics simulation and rendering module is mainly used to introduce differentiable physics simulation and rendering technology, enabling the physics simulation process to be optimized through backpropagation, achieving real-time adjustment of model parameters to more realistically simulate the physical environment. The working process of this physics simulation and rendering module is as follows:

[0095] (1) Establish a physical environment model: Define the parameter model of the physical scene, including all relevant physical parameters and initial conditions; where u(t,x;θ) is defined as the physical state at time t and position x, and θ represents all relevant physical parameters.

[0096] (2) Simulation Solution: The partial differential equations in the physical environment model are solved using numerical methods to obtain the physical state under different parameters. In this embodiment, numerical methods such as the finite difference method and the finite element method can be used to solve u(t,x;θ).

[0097] (3) Rendering process: The results of the numerical simulation are rendered into images using computer graphics technology. Here, Isim(θ) represents the image rendered based on the parameter θ, which is obtained by rendering the simulation result u(t,x;θ).

[0098] (4) Optimization: The loss function is defined as the difference between the rendered image and the actual monitored image, and gradient descent is used to update the physical parameters to minimize the loss function. The optimization process is as follows:

[0099] Define the loss function L(θ) as the difference between the rendered image and the actual monitored image Ireal, L(θ) = ||Isim(θ) - Ireal||². Use gradient descent to update the parameter θ to minimize the loss function. The update formula is: Where α is the learning rate.

[0100] The perceptual Q-function and viewpoint selection module primarily utilizes the perceptual Q-function to automatically evaluate and select the optimal monitoring viewpoint, dynamically adjusting it based on real-time data to obtain the most valuable information. In this embodiment, the perceptual Q-function and viewpoint selection module operates as follows:

[0101] (1) Perceptual Q function evaluation: For each possible viewpoint, calculate the corresponding perceptual Q function value to evaluate the information content of each viewpoint.

[0102] For each possible viewpoint v, Q(sv,a) is computed, where sv is the state observed from viewpoint v and a is the set of possible actions. The perceptual Q function Q(sv,a) can be used to evaluate the information content of each viewpoint by predicting the expected reward after taking action a.

[0103] (2) Determine the best perspective: The perspective that maximizes the value of the perception Q function is taken as the best perspective.

[0104] In this embodiment, the viewpoint v that maximizes the perceptual Q function Q(sv,a) is selected as the optimal viewpoint. This means that observing and operating the environment from this viewpoint yields the greatest expected results. The viewpoint selection formula is expressed as: v*=argmaxvQ(sv,a).

[0105] (3) Actual monitoring: The inspection robot adjusts the position of its camera and sensors according to the best viewing angle v* to perform monitoring tasks and collect data.

[0106] In this embodiment, when formulating or updating inspection and maintenance strategies, the decision-making module trains a basic strategy based on a simulated physical environment model, and then trains a residual strategy based on the difference between the actual execution results and the expected results to adjust the basic strategy, thus completing the final strategy formulation or update. The strategy formulation process is as follows:

[0107] (1) Model Training: In this embodiment, a basic policy π is trained in the physical environment model based on the physical state data monitored from the best perspective. base During the training of the basic policy, it needs to be validated in the physical environment model, and the policy attempts to maximize the expected return.

[0108] (2) Residual learning: In a real-world environment, a residual policy π is trained based on the difference between the actual execution result and the expected result. res To adjust the output of the basic strategy.

[0109] (3) Strategy Generation: The final generated strategy is a t =π base (s t )+π res (s t ), where s t Given the current state, the residual strategy is represented as π. res =argmin π Ε[(a * -(π base (s)+π(s))) 2 ], where a * It is the optimal action, π base π(s) is the action generated by the base policy, and π(s) is the action adjustment generated by the residual policy. Base policy π base (s): This is a pre-trained policy in a simulated environment, used to handle most expected operational scenarios. The base policy is typically developed under controlled conditions to achieve optimal performance under ideal circumstances. The residual policy π... res When the base policy is applied to the real world, its performance may degrade due to various unforeseen factors. The residual policy, in practical applications, fine-tunes the output of the base policy based on real-time data to compensate for this discrepancy, thereby improving the policy's adaptability and accuracy in real-world environments.

[0110] In practical applications, the execution module is used to perform specific physical operations, such as moving, inspecting, and repairing, based on the strategies provided by the decision-making module. This module requires a highly precise control and feedback system to ensure the correctness of the operation.

[0111] The system proposed in this invention integrates a perceptual Q-function and a model-based residual strategy, enabling the system to evaluate the value of information while collecting data and automatically select the optimal monitoring perspective, significantly improving data acquisition efficiency and monitoring accuracy. Simultaneously, it introduces differentiable physical simulation and rendering technology to update the environmental model in real time to adapt to environmental changes, thereby dynamically adjusting monitoring and maintenance strategies. Finally, the application of the residual strategy effectively solves the discrepancy between simulation and real operation, improving the adaptability and success rate of strategy execution.

[0112] To aid in understanding the implementation of this invention, a water pipe monitoring task in water conservancy and hydropower facilities will be used as an example. The system proposed in this invention can be specifically applied as follows, ensuring that the modules of the entire system can work together efficiently to complete complex detection and maintenance tasks. The task objective is to detect cracks, blockages, or other potential structural problems in the water pipes, ensuring the normal operation of the water pipe system and the timely execution of maintenance work.

[0113] For the environmental perception and data acquisition module: collect real-time data about the water pipe system, including images, temperature, pressure and flow information, to provide the system with the necessary raw data so that the decision-making module can make accurate judgments based on the latest information.

[0114] For the data processing and feature extraction module: The collected data is preprocessed to extract key features, such as temperature anomalies, pressure changes, and cracks in pipe images. This module transforms the raw data into an easily analyzable format, providing an accurate state description for decision-making.

[0115] For the physics simulation rendering module: a physical environment model is built based on the extracted data and features.

[0116] For the perception Q-function and viewpoint selection module: Based on the perception Q-function analysis of the information content under different data acquisition views, it automatically selects the viewpoint that provides the most effective information for monitoring and updates the physical environment model based on the data. This module optimizes the data acquisition process, ensuring the quality and integrity of the monitoring data, especially when performing visual inspections inside pipelines, ensuring that the camera focuses on the most critical parts.

[0117] For the decision-making module: Utilizing the updated physical environment model and collected data, a strategy for maintaining or inspecting the pipeline is generated. This module determines whether emergency repairs, routine maintenance, or continued observation are necessary. Based on simulation data obtained from the differentiable physics simulation and rendering module, inspection and maintenance strategies are formulated or updated, guiding the execution module to perform specific operations, thereby preventing or resolving pipeline problems.

[0118] The execution module is responsible for the actual actions performed, such as moving the robot through pipes or using tools for maintenance or cleaning. This module can plan specific inspection or maintenance actions based on the strategy, such as adjusting the robot's position or operating maintenance tools.

[0119] In particular, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts.

[0120] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0121] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0122] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0123] In another aspect, this application also provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the automatic detection and maintenance method for water conservancy and hydropower equipment described in the above embodiments.

[0124] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to implement the automatic detection and maintenance method for water conservancy and hydropower equipment described in the above embodiments.

[0125] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0126] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of this application.

[0127] It should be noted that, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "set" and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances. The accompanying drawings in the embodiments are used to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0128] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. An automatic detection and maintenance method for water conservancy and hydropower equipment, characterized in that, Applications in inspection robots include: Acquire real-time data collected by the inspection robot; Feature extraction is performed on the collected data; Based on the extracted feature data, a physical environment model is simulated and constructed, and then rendered into images or data for decision analysis. The inspection robot is adjusted to the optimal viewing angle for data collection using the sensing Q function; The physical environment model is updated based on data obtained from the best perspective; The basic strategy is determined based on the updated physical environment model, and the inspection and maintenance strategy is formulated or updated in combination with the residual strategy. The inspection robot executes the corresponding strategy; The process of adjusting the inspection robot to the optimal viewing angle for data collection using the Q-function specifically includes: Perceptual Q-function evaluation: For each possible viewpoint, calculate the corresponding perceptual Q-function value to evaluate the amount of information per viewpoint; Determine the optimal viewing angle: The viewing angle that maximizes the perceived Q-function value is taken as the optimal viewing angle; Actual monitoring: The inspection robot adjusts the position of its camera and sensors according to the best viewing angle to perform monitoring tasks and collect data; The formulation or updating of inspection and maintenance strategies based on the updated physical environment model specifically includes: In the physical environment model, a basic policy is trained based on physical state data from the best perspective. During the training process, the basic policy attempts to maximize the expected return. In a real-world environment, based on the difference between the actual execution results and the expected results, the residual policy is trained to adjust the output of the basic policy and obtain the final policy. It also includes updating the established strategy by adjusting the residual strategy when the execution result of the inspection robot does not meet expectations after executing the corresponding strategy, and then re-issuing it to the inspection robot for execution.

2. The automatic detection and maintenance method for water conservancy and hydropower equipment according to claim 1, characterized in that, It also includes monitoring and collecting the execution results when the inspection robot executes the corresponding strategy, which is used for updating the subsequent inspection and maintenance strategy.

3. The automatic detection and maintenance method for water conservancy and hydropower equipment according to claim 1, characterized in that, The step of simulating and constructing a physical environment model based on the extracted feature data and rendering it into images or data for decision analysis specifically includes: Establish a physical environment model: Define the parametric model of the physical scene, including all relevant physical parameters and initial conditions; Simulation solution: The partial differential equations in the physical environment model are solved using numerical methods to obtain the physical state under different parameters; Rendering process: The results of numerical simulation are rendered into images using computer graphics technology; Optimization: Define the loss function as the difference between the rendered image and the actual monitored image, and use gradient descent to update the physical parameters to minimize the loss function.

4. An automatic detection and maintenance system for water conservancy and hydropower equipment, characterized in that, Implemented based on inspection robots, including: The environmental perception and data acquisition module is used to acquire raw data from various sensors mounted on the robot. The data processing and feature extraction module is used to extract features from the acquired raw data from various sensors. The physics simulation and rendering module is used to simulate and build a physical environment model based on the extracted feature data, simulate possible physical scenarios, and render them into images or data for decision analysis. The perceptual Q-function and viewpoint selection module analyzes the amount of information under different data acquisition views based on the perceptual Q-function and selects the best viewpoint for monitoring. The decision-making module is used to determine the basic strategy based on the physical environment model and the monitoring data from the best perspective, and to formulate or update the inspection and maintenance strategy in combination with the residual strategy. The execution module is used to perform specific physical operations according to the strategy provided by the decision module, and to feed back the data generated during the execution process to the decision module in real time. The working process of the physical simulation rendering module includes: Define the parametric model of the physical scene, including all relevant physical parameters and initial conditions; Numerical methods are used to solve the partial differential equations in the physical environment model to obtain the physical state under different parameters; The numerical simulation results are converted into images using computer graphics technology. During the rendering process, the physical parameters are updated using gradient descent to minimize the loss function, thereby reducing the difference between the rendered image and the actual monitored image. The working process of the perception Q-function and the view selection module includes: For each possible viewpoint, calculate the perceptual Q-function; Determine the optimal viewing angle that maximizes the perceptual Q-function; The robot is controlled to adjust the positions of cameras and sensors according to the optimal viewing angle to perform monitoring tasks; When formulating or updating inspection and maintenance strategies, the decision-making module trains the basic strategy based on a simulated physical environment model, and then trains the residual strategy based on the difference between the actual execution results and the expected results to adjust the basic strategy, thus completing the formulation or update of the final strategy.

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