Automatic detection and maintenance method and system for water conservancy and hydropower equipment

Abstract

The invention provides an automatic detection and maintenance method for water conservancy and hydropower equipment, which is applied to an inspection robot and comprises the following steps: acquiring real-time data acquired by the inspection robot; performing feature extraction on the collected data; simulating and constructing a physical environment model according to the extracted feature data, and rendering the physical environment model into an image or data for decision analysis; adjusting the inspection robot to an optimal view angle by sensing a Q function to perform data acquisition; updating the constructed physical environment model according to the data obtained from the optimal view angle; determining a basic strategy based on the updated physical environment model, and formulating or updating an inspection and maintenance strategy in combination with a residual strategy; and the inspection robot executes a corresponding strategy. According to the invention, the accuracy of monitoring and maintenance can be improved, the adaptability of a monitoring system is enhanced, the data acquisition efficiency is improved, and real-time optimization of decisions is realized.

Description

Technical Field

[0001] The present invention relates to the field of automatic monitoring and maintenance of robotics, artificial intelligence and water conservancy and hydropower industries, and in particular to an automatic detection and maintenance method and system for water conservancy and hydropower equipment based on residual strategy and perception Q function. Background Art

[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 and accidents. Traditional monitoring methods rely on regular manual inspections, which are not only time-consuming and labor-intensive, but also often fail to achieve real-time monitoring and rapid response. Although existing automated monitoring systems have improved the frequency and efficiency of monitoring, they are usually inflexible and difficult to adapt to complex and changing environmental conditions, especially when encountering non-standard situations or new monitoring scenarios, their accuracy and adaptability are often greatly reduced.

[0003] The deficiencies 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 the application of monitoring and maintenance strategies that are not accurate or timely enough. 2. Static selection of monitoring perspective: Traditional monitoring systems often use fixed or preset monitoring perspectives and lack the ability to dynamically optimize perspectives based on actual monitoring data, which limits the effectiveness of data collection and the comprehensiveness of monitoring. 3. Differences between simulation and actual environment: Existing simulation technologies are difficult to accurately simulate the complex physical and environmental conditions of the real world, resulting in the formulation of strategies that are less effective when actually implemented. 4. Insufficient adaptability and accuracy of decisions: Existing automatic monitoring systems often rely on static data models in the decision-making process and lack adaptive adjustment strategies to cope with real-time changes in the environment. Summary of the invention

[0004] In view of the problems existing in the prior art, a method and system for automatic detection and maintenance of water conservancy and hydropower equipment is provided. Through real-time model updating and automatic perspective selection, the accuracy of monitoring data is not only improved, but also the adaptability and robustness of the system are enhanced by dynamically adjusting the monitoring strategy to adapt to environmental changes. In addition, the introduction of the residual strategy effectively bridges the difference between the simulation and the real environment, improving the accuracy of decision-making and the effect of implementation.

[0005] The first aspect of the present invention provides an automatic detection and maintenance method for water conservancy and hydropower equipment, which is applied to an inspection robot and comprises:

[0006] Obtain real-time data collected by inspection robots;

[0007] Extract features from the collected data;

[0008] The physical environment model is constructed based on the extracted feature data and rendered into images or data for decision analysis;

[0009] Adjust the inspection robot to the best viewing angle for data collection through the perception Q function;

[0010] Update the constructed physical environment model based on data acquired from the best viewing angle;

[0011] Determine the basic strategy based on the updated physical environment model, and formulate or update the inspection and maintenance strategy in combination with the residual strategy;

[0012] The inspection robot executes the corresponding strategy.

[0013] As a preferred solution, it also includes monitoring and collecting execution results when the inspection robot executes the corresponding strategy, which is used for updating subsequent inspections and maintenance strategies.

[0014] As a preferred solution, the physical environment model is constructed based on the extracted feature data and rendered into an image or data for decision analysis, specifically including:

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

[0016] Simulation solution: Use numerical methods to solve partial differential equations in the physical environment model to obtain the physical state under different parameters;

[0017] Rendering process: Render the results of numerical simulation 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 the gradient descent method to update the physical parameters to minimize the loss function.

[0019] As a preferred solution, the method of adjusting the inspection robot to the optimal viewing angle for data collection by perceiving the Q function specifically includes:

[0020] Perceptual Q function evaluation: For each possible perspective, the corresponding perceptual Q function value is calculated to evaluate the amount of information at each perspective;

[0021] Determine the best viewing angle: The viewing angle that maximizes the perceived Q function value is taken as the best 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 solution, the formulation or updating of the inspection and maintenance strategy based on the updated physical environment model specifically includes:

[0024] The basic policy is trained based on the physical state data at the best viewpoint in the physical environment model. During the training process, the basic policy attempts to maximize the expected return.

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

[0026] As a preferred solution, when the inspection robot executes the corresponding strategy and the execution result does not meet the expectation, the formulated strategy is updated by adjusting the residual strategy and then sent to the inspection robot for execution.

[0027] The second aspect of the present invention proposes an automatic detection and maintenance system for water conservancy and hydropower equipment, which is implemented based on a patrol robot and includes:

[0028] Environmental perception and data acquisition module, used to obtain raw data from various sensors installed on the robot;

[0029] Data processing and feature extraction module, used to extract features from various sensor raw data;

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

[0031] The perception Q function and perspective selection module analyzes the amount of information under different data collection perspectives based on the perception Q function and selects the best perspective for monitoring;

[0032] The decision-making module is used to determine the basic strategy based on the physical environment model and the data monitored at the best viewing angle, 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 strategies provided by the decision module, and to feed back the data generated during the execution to the decision module in real time.

[0034] As a preferred solution, the working process of the physical simulation rendering module includes:

[0035] A parametric model that defines the physical scenario, including all relevant physical parameters and initial conditions;

[0036] Use numerical methods to solve 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 through computer graphics technology. During the rendering process, the physical parameters are updated by the gradient descent method to minimize the loss function, thereby reducing the difference between the rendered image and the actual monitored image.

[0038] As a preferred solution, the working process of the perception Q function and the view selection module includes:

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

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

[0041] Control the robot to adjust the position of the camera and sensors according to the best viewing angle to perform monitoring tasks.

[0042] As a preferred solution, when formulating or updating the inspection and maintenance strategy, the decision module trains the basic strategy based on the simulation of the physical environment model, and then trains the residual strategy based on the difference between the actual execution result and the expected result to adjust the basic strategy to complete the formulation or update of the final strategy.

[0043] Compared with the prior art, the beneficial effects of adopting the above technical solution are:

[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 the adaptability of the system: The introduction of the residual strategy enables the system to better adapt to environmental changes, reduce the difference between simulation and reality, and ensure that the actual implementation of decisions is more accurate.

[0046] 3. Improve data collection efficiency: The dynamic perspective selection mechanism can ensure that data is collected from the most critical perspective, reduce the collection of invalid data, and improve overall monitoring efficiency.

[0047] 4. Realize 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. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0049] Figure 2 Schematic diagram of the physical environment model of an embodiment of the present invention.

[0050] Figure 3 The diagram is a schematic diagram of strategy formulation in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar modules or modules with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be construed as limitations on the present application. On the contrary, the embodiments of the present application include all changes, modifications and equivalents that fall within the spirit and connotation of the appended claims.

[0052] Example 1

[0053] In view of the problems in existing solutions such as limited data processing capabilities, inability to update monitoring strategies in real time to cope with environmental changes, fixed automatic monitoring perspectives, inability to optimize data collection angles according to task requirements, and lack of effective strategies to bridge the differences between simulation and real environments, which affect the reliability of monitoring data and the effectiveness of maintenance decisions, the embodiment of the present invention proposes an automatic detection and maintenance method for water conservancy and hydropower equipment, which is applied to inspection robots. By integrating differentiable physical simulation and rendering technology, the present invention realizes real-time model updates and automatic perspective selection, significantly improving the accuracy of monitoring data and the efficiency of maintenance tasks. In particular, the technology reduces redundancy in data collection by automatically selecting the optimal monitoring perspective, and adapts to environmental changes through real-time model updates to ensure that monitoring and maintenance strategies are always effective. In addition, the present invention adopts a residual strategy to learn to bridge the behavioral differences between simulation and real environments, thereby enhancing the system's adaptability to new environments and its ability to continuously learn. Please refer to Figure 1 , the specific plan is as follows:

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

[0055] Data is used as the basis for the final strategy formulation. In this embodiment, data about water conservancy and hydropower facilities are collected mainly through sensors (such as cameras, temperature sensors, etc.) mounted on the inspection robot. When the inspection robot is inspecting, it works continuously and can collect various physical and environmental parameters of 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. The preprocessing may include filtering, denoising, etc., and the key features may include crack recognition in the image, temperature anomalies, etc.

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

[0059] In this embodiment, please refer to Figure 2, using differentiable physical simulation and rendering technology, a physical environment model is constructed based on the extracted features, possible physical scenes are simulated, and rendered into images or data that can be used for analysis. This ensures that the model is as close to the real world as possible, which helps to make subsequent decisions with accuracy. This embodiment introduces differential physical simulation and rendering technology so that the physical simulation process can be optimized through back propagation, and the model parameters can be adjusted in real time to simulate the physical environment more realistically. The 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 a parametric 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: numerical methods are used to solve the partial differential equations in the physical environment model to obtain the physical state under different parameters. In this embodiment, numerical methods such as finite difference method and finite element method can be used to solve u(t, x; θ).

[0063] (3) Rendering process: The results of numerical simulation are rendered into images using computer graphics technology. Here, Isim(θ) represents the image rendered according to the parameter θ, which is rendered by 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 the gradient descent method 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 monitoring image Ireal, L(θ) = ||Isim(θ)-Ireal||2, and use the gradient descent method to update the parameter θ to minimize the loss function. The update formula is: Where α is the learning rate.

[0066] Step 4: Use the perception Q function to adjust the inspection robot to the optimal viewing angle for data collection.

[0067] In this embodiment, the value of monitoring data obtained from different perspectives is evaluated by perceiving the Q function, and the monitoring perspective that can best provide key information is dynamically selected, thereby ensuring optimization of data collection and increasing the quality and relevance of the monitoring data.

[0068] Specifically, the process of selecting the best viewing angle using the perceptual Q function is as follows:

[0069] (1) Perceptual Q function evaluation: For each possible perspective, the corresponding perceptual Q function value is calculated to evaluate the amount of information at each perspective.

[0070] For each possible view v, Q(sv,a) is calculated, where sv is the state observed from view v and a is the set of possible actions. The perceptual Q function Q(sv,a) can evaluate the amount of information in each view by predicting the expected return after taking action a.

[0071] (2) Determine the optimal viewing angle: The viewing angle that maximizes the perceived Q function value is taken as the optimal viewing angle.

[0072] In this embodiment, the viewing angle v that maximizes the perceptual Q function Q(sv,a) is selected as the optimal viewing angle, which means that the maximum expected effect can be obtained by observing and operating the environment from this viewing angle. The viewing angle selection formula is expressed as: v*=argmaxvQ(sv,a).

[0073] (3) Actual monitoring: The inspection robot adjusts the position of its camera and sensor according to the optimal 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 viewing angle. By updating the physical environment model, it can be ensured that the model can reflect the latest environmental status.

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

[0076] In this embodiment, decisions are mainly made based on the updated physical environment model to formulate updated inspection and maintenance strategies. Since the physical environment model is a constructed virtual model, it may be different from the real environment. Therefore, a residual strategy is introduced to combine with the basic strategy based on the physical environment model to compensate for the execution error caused by the difference between the model and the actual environment, reduce the difference between the simulation and the real world, and improve the adaptability and accuracy of the strategy in the actual environment.

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

[0078] (1) Model training: In this embodiment, a basic strategy π is trained in the physical environment model based on the physical state data under the best viewing angle monitoring. base ,During the training of the basic policy, it is necessary to verify it in the physical environment model, and the policy attempts to maximize the expected reward.

[0079] (2) Residual learning: In a real environment, a residual strategy π is trained based on the difference between the actual execution results and the expected results. 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 is the current state, and the residual strategy is represented by π res =argmin π Ε[(a * -(π base (s)+π(s))) 2 ], where a * is the optimal action, π base (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 policy pre-trained in a simulated environment to handle most of the expected operation scenarios. The base policy is usually developed under controlled conditions and aims to achieve optimal performance under ideal conditions. The residual policy π res : When the base policy is applied in the real world, the performance may degrade due to various unforeseen factors. The residual policy fine-tunes the output of the base policy based on real-time data in real applications to compensate for this difference.

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

[0082] After formulating specific inspection and maintenance strategies, the inspection robot can execute actions according to the strategies. The actual actions are performed by a t =π base (s t )+π res (s t ) decisions to ensure the accuracy and effectiveness of the operation.

[0083] It should be noted that, in the process of executing the strategy, the inspection robot continuously monitors and provides feedback on the execution effect for updating and optimizing the decision. In this embodiment, it mainly determines whether the execution effect meets expectations. If it does not meet expectations, the residual strategy is adjusted, the formulated strategy is updated, and then sent to the inspection robot to execute the new strategy. By integrating the latest simulation results and monitoring data, the decision is updated in real time to ensure that the strategy continues to adapt to environmental changes.

[0084] The present invention realizes real-time model updating and automatic perspective selection by integrating differentiable physical simulation and rendering technology, which significantly improves the accuracy of monitoring data and the efficiency of maintenance tasks. In particular, by automatically selecting the best monitoring perspective, redundancy in data collection is reduced. At the same time, through real-time model updating, environmental changes are adapted to ensure that monitoring and maintenance strategies are always effective. In addition, the present invention adopts residual strategy learning to bridge the behavioral differences between simulation and real environment, which enhances the system's adaptability to new environments and continuous learning ability. The present invention is suitable for application scenarios in the water conservancy and hydropower industry that require 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 the automatic monitoring and maintenance system of water conservancy and hydropower facilities, which is of great significance to improving the operational safety and efficiency of the facilities.

[0085] Example 2

[0086] The present embodiment provides an automatic detection and maintenance system for water conservancy and hydropower equipment, which is implemented based on a patrol robot and uses perception Q functions and residual strategy-based technology to automatically collect and process monitoring data, evaluate the information value of different monitoring perspectives, and select the optimal perspective for data collection. Then, through differentiable physical simulation and rendering technology, its operation model is updated in real time to cope with environmental changes, and the monitoring strategy is adjusted according to these changes. The execution actions in the simulation environment model are mapped to the execution of actions in the real physical world, and the residual strategy is used to bridge the difference between the simulation and the real environment, optimize the implementation of the execution strategy, and ensure the efficient and accurate execution of monitoring and maintenance tasks. The system of the present invention can adjust the monitoring and maintenance strategies in real time, optimize the operation execution, and ensure that the 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 obtain the raw data of various sensors (such as cameras, temperature sensors, and pressure sensors) equipped 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 required for decision-making.

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

[0090] The physical simulation rendering module is used to simulate and build a physical environment model based on the extracted feature data, simulate possible physical scenes 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 dynamic simulation of physical processes (such as water flow, pressure changes, etc.) and converting these simulations into analyzable images or data forms, so that the system can make high-quality decision predictions.

[0091] The Perception Q Function and Viewpoint Selection Module analyzes the amount of information under different data collection perspectives based on the perception Q function and selects the best perspective for monitoring. This module automatically selects the perspective that provides the most effective information for monitoring based on the perception Q function. For example, when visually inspecting the inside of a pipeline, it can ensure that the camera focuses on the most critical part.

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

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

[0094] Specifically, the physical simulation rendering module is mainly used to introduce differentiable physical simulation and rendering technology, so that the physical simulation process can be optimized through back propagation to achieve real-time adjustment of model parameters to simulate the physical environment more realistically. The working process of the physical simulation rendering module is as follows:

[0095] (1) Establish a physical environment model: Define a parametric 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: numerical methods are used to solve the partial differential equations in the physical environment model to obtain the physical state under different parameters. In this embodiment, numerical methods such as finite difference method and finite element method can be used to solve u(t, x; θ).

[0097] (3) Rendering process: The results of numerical simulation are rendered into images using computer graphics technology. Here, Isim(θ) represents the image rendered according to the parameter θ, which is rendered by 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 the gradient descent method 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 monitoring image Ireal, L(θ) = ||Isim(θ)-Ireal||2, and use the gradient descent method to update the parameter θ to minimize the loss function. The update formula is: Where α is the learning rate.

[0100] The perception Q function and perspective selection module mainly uses the perception Q function to automatically evaluate and select the best monitoring perspective, and dynamically adjusts according to real-time data to obtain the most valuable information. In this embodiment, the working process of the perception Q function and perspective selection module is as follows:

[0101] (1) Perceptual Q function evaluation: For each possible perspective, the corresponding perceptual Q function value is calculated to evaluate the amount of information at each perspective.

[0102] For each possible view v, Q(sv,a) is calculated, where sv is the state observed from view v and a is the set of possible actions. The perceptual Q function Q(sv,a) can evaluate the amount of information in each view by predicting the expected return after taking action a.

[0103] (2) Determine the optimal viewing angle: The viewing angle that maximizes the perceived Q function value is taken as the optimal viewing angle.

[0104] In this embodiment, the viewing angle v that maximizes the perceptual Q function Q(sv,a) is selected as the optimal viewing angle, which means that the maximum expected effect can be obtained by observing and operating the environment from this viewing angle. The viewing angle selection formula is expressed as: v*=argmaxvQ(sv,a).

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

[0106] In this embodiment, when the decision module formulates or updates the inspection and maintenance strategy, it trains the basic strategy based on the simulation of the physical environment model, and then trains the residual strategy based on the difference between the actual execution result and the expected result to adjust the basic strategy, and completes the formulation or update of the final strategy. The process of formulating the strategy is as follows:

[0107] (1) Model training: In this embodiment, a basic strategy π is trained in the physical environment model based on the physical state data under the best viewing angle monitoring. base ,During the training of the basic policy, it is necessary to verify it in the physical environment model, and the policy attempts to maximize the expected reward.

[0108] (2) Residual learning: In a real environment, a residual strategy π is trained based on the difference between the actual execution results and the expected results. 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 is the current state, and the residual strategy is represented by π res =argmin π Ε[(a * -(π base (s)+π(s))) 2 ], where a * is the optimal action, π base (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 policy pre-trained in a simulated environment to handle most of the expected operation scenarios. The base policy is usually developed under controlled conditions and aims to achieve optimal performance under ideal conditions. The residual policy π res : When the basic strategy is applied to the real world, the performance may degrade due to various unforeseen factors. The residual strategy fine-tunes the output of the basic strategy according to real-time data in actual applications to make up for this difference and improve the adaptability and accuracy of the strategy in the actual environment.

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

[0111] The system proposed in the embodiment of the present invention integrates the perceptual Q function and the model-based residual strategy technology, which enables the system to evaluate the value of information while collecting data and automatically select the best monitoring perspective, significantly improving the efficiency of data collection and the accuracy of monitoring. At the same time, differentiable physical simulation and rendering technology is introduced to update the environmental model in real time to adapt to environmental changes, thereby dynamically adjusting the monitoring and maintenance strategies. Finally, the application of the residual strategy effectively solves the problem of the difference between simulation and real operation, and improves the adaptability and success rate of strategy execution.

[0112] To help understand the implementation scheme of the present invention, the water pipe monitoring task in water conservancy and hydropower facilities is used as an example. The system proposed by the present invention can be specifically applied as follows to ensure that the modules of the entire system can be efficiently linked to complete complex detection and maintenance tasks. The task objectives are: to detect cracks, blockages or other potential structural problems in water pipes. Ensure the normal operation of the water pipe system and the timely implementation 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, provide the system with necessary raw data, and enable the decision-making module to make accurate judgments based on the latest information.

[0114] For the data processing and feature extraction module: pre-process the collected data and extract key features, such as temperature anomalies, pressure changes, cracks in pipeline images, etc. This module converts raw data into a form that is easy to analyze and provides an accurate state description for decision-making.

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

[0116] For the perception Q function and view selection module: Based on the perception Q function, the information volume under different data collection perspectives is analyzed, and the perspective that provides the most effective information is automatically selected for monitoring, and the physical environment model is updated according to the data. This module optimizes the data collection process to ensure the quality and integrity of the monitoring data, especially when visually inspecting the inside of the pipeline, it can ensure that the camera focuses on the most critical part.

[0117] For the decision-making module: Generate a strategy for maintaining or inspecting the pipeline using the updated physical environment model and the collected data. This module will decide whether emergency repairs, routine maintenance, or continued observation are required. According to the simulation data obtained from the differentiable physics simulation and rendering modules, the inspection and maintenance strategy is formulated or updated to guide the execution module to perform specific operations to prevent or solve pipeline problems.

[0118] For the execution module: responsible for the actual execution of actions, such as the robot moving through the pipeline, using tools for maintenance or cleaning. Through this module, specific inspection or maintenance actions can be planned according to the strategy, such as adjusting the robot position, operating maintenance tools, etc.

[0119] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer readable medium, and the computer program includes a program code for executing the method shown in the flowchart.

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

[0121] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0122] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0123] As another aspect, the present application further provides a computer program product or a computer program, the computer program product or the computer program including computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the automatic detection and maintenance method for water conservancy and hydropower equipment described in the above embodiment.

[0124] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the automatic detection and maintenance method of water conservancy and hydropower equipment described in the above embodiment.

[0125] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0126] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation methods of the present application.

[0127] It should be noted that in the description of the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "setting" and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection, or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances; the 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 part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations.

[0128] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for automatic detection and maintenance of water conservancy and hydropower equipment, characterized in that: Applied to inspection robots, including: Obtain real-time data collected by inspection robots; Extract features from the collected data; The physical environment model is constructed based on the extracted feature data and rendered into images or data for decision analysis; Adjust the inspection robot to the best viewing angle for data collection through the perception Q function; Update the constructed physical environment model based on data acquired from the best viewing angle; Determine the basic strategy based on the updated physical environment model, and formulate or update the inspection and maintenance strategy in combination with the residual strategy; The inspection robot executes the corresponding strategy.

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

3. The automatic detection and maintenance method for water conservancy and hydropower equipment according to claim 1 is characterized in that: The method of simulating and constructing a physical environment model based on the extracted feature data and rendering it into an image or data for decision analysis specifically includes: Establish physical environment model: define the parametric model of the physical scene, including all relevant physical parameters and initial conditions; Simulation solution: Use numerical methods to solve partial differential equations in the physical environment model to obtain the physical state under different parameters; Rendering process: Render the results of numerical simulation 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 the gradient descent method to update the physical parameters to minimize the loss function.

4. The automatic detection and maintenance method for water conservancy and hydropower equipment according to claim 1 is characterized in that: The method of adjusting the inspection robot to the optimal viewing angle for data collection by perceiving the Q function specifically includes: Perceptual Q function evaluation: For each possible perspective, the corresponding perceptual Q function value is calculated to evaluate the amount of information at each perspective; Determine the best viewing angle: The viewing angle that maximizes the perceived Q function value is taken as the best 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.

5. The automatic detection and maintenance method for water conservancy and hydropower equipment according to claim 1 is characterized in that: The formulation or updating of the inspection and maintenance strategy based on the updated physical environment model specifically includes: The basic policy is trained based on the physical state data at the best viewpoint in the physical environment model. During the training process, the basic policy attempts to maximize the expected return. In a real environment, based on the difference between the actual execution results and the expected results, the residual strategy is trained to adjust the output of the basic strategy to obtain the final strategy.

6. The automatic detection and maintenance method for water conservancy and hydropower equipment according to claim 5 is characterized in that: It also includes that when the inspection robot executes the corresponding strategy and the execution result does not meet the expectation, the formulated strategy is updated by adjusting the residual strategy, and then sent to the inspection robot for execution.

7. An automatic detection and maintenance system for water conservancy and hydropower equipment, characterized in that: Based on the inspection robot, it includes: Environmental perception and data acquisition module, used to obtain raw data from various sensors installed on the robot; Data processing and feature extraction module, used to extract features from various sensor raw data; A physical simulation rendering module is used to simulate and build a physical environment model based on the extracted feature data, simulate possible physical scenes and render them into images or data for decision analysis; The perception Q function and perspective selection module analyzes the amount of information under different data collection perspectives based on the perception Q function and selects the best perspective for monitoring; The decision-making module is used to determine the basic strategy based on the physical environment model and the data monitored at the best viewing angle, 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 strategies provided by the decision module, and to feed back the data generated during the execution to the decision module in real time.

8. The automatic detection and maintenance system for water conservancy and hydropower equipment according to claim 7 is characterized in that: The working process of the physical simulation rendering module includes: A parametric model that defines the physical scenario, including all relevant physical parameters and initial conditions; Use numerical methods to solve partial differential equations in the physical environment model to obtain the physical state under different parameters; The numerical simulation results are converted into images through computer graphics technology. During the rendering process, the physical parameters are updated by the gradient descent method to minimize the loss function, thereby reducing the difference between the rendered image and the actual monitored image.

9. The automatic detection and maintenance system for water conservancy and hydropower equipment according to claim 7 is characterized in that: The working process of the perception Q function and view selection module includes: For each possible viewpoint, calculate the perceptual Q function; Determine the best viewing angle that maximizes the perceptual Q function; Control the robot to adjust the position of the camera and sensors according to the best viewing angle to perform monitoring tasks.

10. The automatic detection and maintenance system for water conservancy and hydropower equipment according to claim 7, characterized in that: When formulating or updating the inspection and maintenance strategy, the decision module trains the basic strategy based on the simulated physical environment model, and then trains the residual strategy based on the difference between the actual execution result and the expected result to adjust the basic strategy, thereby completing the formulation or update of the final strategy.

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