Deep sea deduction scene air route path intelligent planning system based on optimal control theory

Abstract

The invention discloses a deep sea deduction scene course path intelligent planning system based on an optimal control theory, and relates to the technical field of course path planning. The data management platform is in communication connection with an environment perception modeling module, a constraint condition definition module, a global path planning module, a simulation verification module, a control execution module and a feedback adjustment module. By receiving and fusing deep sea environment data and equipment state data in real time, position deviation, speed deviation and course deviation in the sailing process can be accurately detected and analyzed, and based on the optimal control theory, the system can quickly formulate an adjustment strategy to ensure that equipment is always kept in a safe sailing range, so that the sailing safety is improved. The real-time feedback and adjustment mechanism greatly improves the safety and reliability of navigation, effectively avoids the risk of colliding with obstacles or entering a dangerous area, and provides a solid guarantee for deep sea operation.

Description

Technical Field

[0001] The present invention relates to the technical field of route path planning, and in particular to an intelligent route path planning system for deep sea simulation scenarios based on optimal control theory. Background Art

[0002] The deep sea is an area with a complex environment and extremely harsh conditions, and it has a high degree of uncertainty. In deep-sea exploration and operations, especially in the application of autonomous vehicles such as unmanned submersibles and underwater robots, it is often necessary to carry out a large number of simulations and deductions in advance to predict possible risks and obstacles in navigation and ensure that the mission can be completed smoothly. Optimal control theory uses mathematical models and optimization algorithms to provide the system with the optimal control scheme under specific constraints. The use of optimal control theory can ensure the safety and efficiency of navigation under different sea conditions through simulation and deduction of the marine environment, vehicle behavior, mission objectives, etc.

[0003] In the existing technology, there are various obstacles on the routes of deep-sea operation equipment, such as seabed obstacles, other aircraft, floating ice, and shipwrecks, which can easily interfere with route planning. Therefore, how to plan the most suitable route in deep-sea simulation scenarios under complex constraints in combination with optimal control theory is a problem we need to solve. To this end, an intelligent route path planning system for deep-sea simulation scenarios based on optimal control theory is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent route planning system for deep-sea simulation scenarios based on optimal control theory to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: A deep-sea simulation scenario route path intelligent planning system based on optimal control theory includes a data management platform, which is communicatively connected with an environment perception modeling module, a constraint definition module, a global path planning module, a simulation verification module, a control execution module, and a feedback adjustment module; The environment perception modeling module is used to collect and perceive deep-sea environment data and construct a deep-sea three-dimensional environment model, wherein the deep-sea environment data includes obstacle data and ocean dynamic change data; The constraint definition module is used to define static constraints, dynamic constraints and performance constraints in the deep-sea environment, wherein the static constraints are used to define static obstacles in the deep-sea environment, the dynamic constraints are used to define dynamic obstacles in the deep-sea environment, and the performance constraints are used to define performance indicators for route planning; The global path planning module plans the optimal route from the starting point to the end point based on the optimal control theory and the defined constraints. During the planning process, the path is continuously optimized to meet the performance constraints and avoid all static and dynamic obstacles; The simulation verification module is used to simulate and test the optimal route path planned based on the optimal control theory in a simulation environment, check whether the path meets the constraint conditions and avoids all obstacles, and then evaluate the performance of the planned route path to ensure the optimization of the path planning; The control execution module is used to convert the latest optimal route path into control instructions, send them to the execution system of the deep-sea operation equipment, and adjust the navigation parameters of the deep-sea operation equipment in real time based on the optimal control theory to ensure that the equipment navigates according to the planned path; The feedback adjustment module is used to receive the latest deep-sea environmental data and make feedback adjustments to the route path in combination with the control execution results of the deep-sea operation equipment. A further improvement of the technical solution of the present invention is that the environment perception modeling module specifically includes: Using deep-sea exploration equipment and deep-sea sensor networks, collect and sense deep-sea environmental data including obstacle data and ocean dynamic change data, where obstacle data includes seabed topography data, seabed obstacle data and dynamic obstacle data, and ocean dynamic change data includes ocean flow field data, water temperature data, salinity data and ocean acoustic data; Preprocess the acquired raw deep-sea environmental data, remove noise data through data cleaning, repair or fill in missing data, and integrate different types of data using a Bayesian fusion multi-sensor data fusion algorithm to obtain comprehensive environmental information; Based on the pre-processed obstacle data, a geometric model of the obstacle is created in the 3D modeling software. A regular grid model is used to represent the seabed terrain and obstacles. The model is textured and textures reflecting the characteristics of marine vegetation and rocks are added to enhance the realism of the model. Using the pre-processed ocean dynamic change data, a 3D field model representing the distribution of physical and chemical parameters of seawater temperature, salinity, and velocity is created in 3D modeling software. Voxel grid technology is used to represent the characteristics of ocean dynamic changes. The model is rendered and illuminated to simulate the reflection and refraction characteristics of seawater to light and enhance the sense of immersion. The 3D obstacle model is fused with the 3D model of ocean dynamic changes to form a complete 3D deep-sea environment model. The level of detail (LOD) technology is used to automatically adjust the model's detail level according to the viewing distance, thereby optimizing the fused 3D environment model.

[0006] A further improvement of the technical solution of the present invention is that the constraint condition definition module specifically includes: Based on the acquired deep-sea environment data and the constructed deep-sea three-dimensional environment model, the static constraints, dynamic constraints and performance constraints in the deep-sea environment are defined, wherein the static constraints are to define the static obstacles in the deep-sea environment, the dynamic constraints are to define the dynamic obstacles in the deep-sea environment, and the performance constraints are to define the performance indicators of the route planning; For the definition of static constraints, extract static obstacle data from the environment perception modeling module, classify the obstacles and mark their positions, sizes and shapes, define boundaries for each static obstacle to ensure that the static obstacle area is avoided during path planning, and set a safety distance based on the type and size of the obstacle to ensure that the navigation equipment maintains a safe distance from the obstacle. Then, store the static constraints as a data structure for use by the global path planning module; For the definition of dynamic constraints, the dynamic obstacle data is extracted from the environment perception modeling module, the dynamic obstacles are classified and their motion characteristics are annotated, and their future positions and paths are predicted based on their motion trajectories. Dynamic constraint areas are defined for dynamic obstacles to ensure that dynamic obstacle areas are avoided during path planning, and dynamic constraints are updated in real time to reflect the latest status of dynamic obstacles. For the definition of performance constraints, extract performance indicators including navigation time, energy consumption and communication bandwidth, define the maximum allowable navigation time from the starting point to the end point, define the maximum allowable energy consumption during navigation, define the communication bandwidth limit between the navigation equipment and the control center, and convert the performance indicators into specific values, and then store the performance constraints as a data structure for use by the global path planning module; Integrate performance constraints with static and dynamic constraints to form a complete set of constraints, detect conflicts between constraints, and resolve conflicts based on priority settings, then output the defined set of constraints to the global path planning module for use in path planning.

[0007] A further improvement of the technical solution of the present invention is that the global path planning module specifically includes: Read static constraints, dynamic constraints and performance constraints, and determine the starting and ending positions of navigation equipment. Based on the deep-sea 3D environment model provided by the environment perception modeling module, understand the obstacle distribution and ocean dynamic information; On the premise of satisfying static constraints, a heuristic search algorithm is used to generate a preliminary path from the starting point to the end point to avoid major static obstacles. Based on dynamic constraints, the position and movement trend of dynamic obstacles are monitored in real time to adjust the path to avoid dynamic obstacles. On the basis of satisfying static and dynamic constraints, the path performance is optimized by adjusting path nodes and changing the speed to shorten the navigation time, reduce energy consumption and ensure sufficient communication bandwidth; Check whether the planned path meets all constraints. If the path does not meet the constraints, repeat the path planning and adjust the parameters to continuously iterate and optimize the path until the optimal route path that meets all constraints is found.

[0008] A further improvement of the technical solution of the present invention is that the simulation verification module specifically includes: Based on the deep-sea 3D environment model, a virtual deep-sea environment is created in the simulation software (Gazebo), including the seabed terrain, obstacles, and dynamic changes of the ocean. The physical and dynamic models of the navigation equipment are imported into the simulation environment to ensure that their behavior is consistent with the real equipment. Simulation parameters including time step and simulation speed are set according to actual needs. Import the optimal route path generated by the global path planning module into the simulation environment as the reference trajectory of the navigation equipment, and visualize the path in the simulation environment for observation and analysis; Start the navigation equipment in the simulation environment, navigate according to the planned path, and monitor the status of the navigation equipment in real time, including position, speed and attitude; In the simulation environment, the optimal route path is run to check the constraints and whether the path satisfies the static and dynamic constraints. If the path violates any constraint, the simulation verification module will record the violation and feed it back to the global path planning module for re-planning. According to the simulation results, the optimal route path is evaluated in terms of navigation time, energy consumption, and communication bandwidth utilization, the path performance evaluation index is calculated, and the evaluation results are compared with the performance constraints to check whether the path meets the performance requirements; Analyze the data generated during the simulation test to identify potential problems and optimization space in path planning. Generate a simulation report based on the analysis results to summarize the performance and existing problems of path planning. Then, based on the simulation results and analysis report, make path optimization suggestions to the global path planning module, including adjusting path nodes and changing the speed. According to the optimization suggestions, the global path planning module re-plans the path and submits the new path to the simulation verification module for re-testing. The simulation test process is repeated, and the path is continuously optimized iteratively until the optimal path that meets all constraints is found.

[0009] A further improvement of the technical solution of the present invention is that the calculation process of the path performance evaluation index is: Combined with the defined performance constraints, determine the theoretical maximum values ​​of the maximum allowable flight time, maximum allowable energy consumption and communication bandwidth; Run the optimal route in the simulation environment, record the actual navigation time of the navigation equipment from the starting point to the end point, repeat the simulation test N times, and obtain multiple navigation time data; The energy consumption of the navigation equipment in each simulation is calculated and recorded through the dynamic model in the simulation software, the communication data volume between the navigation equipment and the control center is monitored, and the communication bandwidth utilization rate is calculated, that is, the ratio of the actual communication bandwidth to the theoretical maximum value of the communication bandwidth; For each simulation, the normalized value of the flight time, the normalized value of the energy consumption, and the normalized value of the communication bandwidth utilization are calculated; The normalized value of the flight time, the normalized value of the energy consumption, and the normalized value of the communication bandwidth utilization are added together to calculate the comprehensive performance index of each simulation; The comprehensive performance index of each simulation is integrated, and the average value of the sum of squares of the comprehensive performance index is calculated to obtain the average performance index of all simulations. The root mean square of all simulation average performance indexes is analyzed, and then the inverse of the root mean square is taken and multiplied by 100 to calculate the path performance evaluation index. Analyze the path performance evaluation index. If the path performance evaluation index is close to 100, it means that the path performance is excellent and meets all performance constraints. If the path performance evaluation index is low, it means that the path performance is poor and needs to be optimized.

[0010] A further improvement of the technical solution of the present invention is that the control execution module specifically includes: Receive the latest optimal route from the global path planning module. The path information includes waypoint coordinates, speed requirements and navigation direction, and decompose the path into a series of discrete waypoints. Each waypoint contains location information (longitude, latitude, depth) and navigation parameters (speed, heading angle); Establish a kinematic model of deep-sea operation equipment, and calculate a series of control instructions, including propeller thrust, rudder angle adjustment, and buoyancy adjustment, based on the optimal route and the kinematic model of the deep-sea operation equipment, so as to enable the equipment to navigate along the planned path, and then send the generated control instructions to the execution system of the deep-sea operation equipment through the communication system; Monitor the status indicators of deep-sea operation equipment in real time through the sensor network, including position, speed, attitude and depth information, analyze the position deviation, speed deviation, attitude deviation and depth deviation of each data point in the monitoring period, and calculate the equipment status evaluation index in combination with the baseline value of each status indicator to determine whether the equipment is in an abnormal operating state, where the abnormal operating state includes position deviation, speed deviation, attitude abnormality and depth abnormality; According to the optimal route and real-time status data, the optimal control theory (PID control) is used to adjust the navigation parameters and calculate the control instructions of the current waypoint to ensure that the equipment navigates along the planned path. According to the real-time status of the equipment and environmental changes, the speed and heading angle are dynamically adjusted to adapt to the complex deep-sea environment. The control instructions of the current waypoint include position control, speed control, heading control and depth control. The control instructions are sent to the execution system of the deep-sea operation equipment through a communication link. After receiving the instructions, the execution system allocates thrust to the thrusters, adjusts the navigation attitude, and continuously monitors the equipment status and environmental information to form a closed-loop control loop. Once deviations or abnormal conditions are found, adjustments and corrections are made immediately to ensure that the equipment always remains on the planned path. Deviations or abnormal conditions include depth exceeding the limit, abnormal attitude, and communication interruption.

[0011] A further improvement of the technical solution of the present invention is that the expression of the equipment status evaluation index is: ; Where ES is the equipment status evaluation index, which is used to comprehensively evaluate the operating status of the equipment, M is the number of data points in the monitoring period, is the position deviation of the jth data point, is the reference value of position deviation, indicating the maximum allowable position deviation during normal operation. is the velocity deviation of the jth data point, It is the reference value of speed deviation, indicating the maximum allowable speed deviation during normal operation. is the posture deviation of the jth data point, is the reference value of attitude deviation, indicating the maximum allowable attitude deviation during normal operation. is the depth deviation of the j-th data point, It is the reference value of the depth deviation, indicating the maximum allowable depth deviation during normal operation. The value range of ES is [0,100]. When the device status fully meets the reference value, ES is close to 100, indicating that the device is in good operating condition.

[0012] A further improvement of the technical solution of the present invention is that the feedback adjustment module specifically includes: The feedback adjustment module receives the latest deep-sea environmental data from the environmental perception modeling module and the real-time status data of the deep-sea operating equipment from the control execution module, and then integrates the deep-sea environmental data and the equipment status data to generate a unified input data format for subsequent processing; By comparing the difference between the actual navigation trajectory and the planned path, detect whether there is a path deviation, analyze the path deviation, including path position deviation, path speed deviation and heading deviation, and calculate the deviation risk assessment coefficient based on the size and direction of the deviation, comprehensively consider the path position deviation, path speed deviation and heading deviation, evaluate its impact on the navigation safety of the equipment and the completion of the task, and determine whether the deviation has the risk of causing the equipment to enter a dangerous area or violate constraints; According to the deviation analysis results, formulate corresponding feedback adjustment strategies, including adjusting the heading angle, changing the speed and re-planning the local path, and make real-time adjustments to the route path according to the adjustment strategy to ensure that the deep-sea operation equipment can navigate safely and efficiently along the planned path; The adjusted path and related data are fed back to the global path planning module to provide a reference for subsequent path planning, and the path adjustment information is fed back to the environmental perception module to update the deep-sea three-dimensional environmental model.

[0013] A further improvement of the technical solution of the present invention is that the expression of the deviation risk assessment coefficient is: ; Where DR is the deviation risk assessment coefficient, which is used to comprehensively evaluate the impact of path deviation on equipment navigation safety and mission completion, M is the number of data points in the monitoring period, is the path position deviation of the jth data point, which represents the combination of horizontal position deviation and vertical position deviation. is the reference value of the path position deviation, indicating the maximum allowable position deviation during normal operation. is the speed deviation of the jth data point, indicating the difference between the current speed and the planned speed. It is the reference value of speed deviation, indicating the maximum allowable speed deviation during normal operation. is the heading deviation of the jth data point, indicating the difference between the current heading angle and the planned heading angle. It is the reference value of heading deviation, indicating the maximum allowable heading deviation during normal operation. The value range of DR is between 0 and 1. When the path deviation of the equipment completely meets the reference value, DR is close to 1, indicating that the equipment is in good operating condition and the risk is low.

[0014] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art: The present invention provides an intelligent route planning system for deep-sea simulation scenarios based on optimal control theory. By receiving and integrating deep-sea environmental data and equipment status data in real time, it can accurately detect and analyze position deviation, speed deviation and heading deviation during navigation. Based on optimal control theory, the system can quickly formulate adjustment strategies to ensure that the equipment always remains within a safe navigation range. This real-time feedback and adjustment mechanism greatly improves the safety and reliability of navigation, effectively avoids the risk of collision with obstacles or entering dangerous areas, and provides a solid guarantee for deep-sea operations.

[0015] The present invention provides an intelligent route path planning system for deep-sea simulation scenarios based on optimal control theory. The route path and navigation parameters can be adjusted in real time according to the deep-sea environment and equipment status data. With the optimal control theory as a guide, it can ensure that the equipment always maintains the best state during navigation. By optimizing the heading angle, speed and path planning, the system can significantly reduce navigation time and energy consumption and improve navigation efficiency. At the same time, the system can also intelligently allocate propeller thrust and adjust buoyancy according to mission requirements and equipment performance to further reduce energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0017] Figure 1 It is a schematic diagram of the system function modules of the present invention; Figure 2 Schematic diagram of the workflow of the control execution module of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Embodiment 1, as Figure 1 As shown, the present invention provides a deep-sea simulation scenario route path intelligent planning system based on optimal control theory, including a data management platform, the data management platform is communicatively connected with an environment perception modeling module, a constraint condition definition module, a global path planning module, a simulation verification module, a control execution module and a feedback adjustment module; The environment perception modeling module is used to collect and perceive deep-sea environment data and build a deep-sea three-dimensional environment model. The deep-sea environment data includes obstacle data and ocean dynamic change data. Deep-sea detection equipment and deep-sea sensor networks are used to collect and perceive deep-sea environment data including obstacle data and ocean dynamic change data. Obstacle data includes seabed terrain data, seabed obstacle data and dynamic obstacle data. Multi-beam sonar (MBES) is used to obtain elevation data of seabed terrain, including seabed mountains, canyons, trenches, etc. Synthetic aperture sonar ( SAS) detects obstacles on the seabed, including shipwrecks, rocks, artificial structures, etc., monitors dynamic obstacles through underwater sensor networks, including other vehicles, floating ice, marine life, etc., and obtains ocean dynamic change data, including ocean flow field data, water temperature data, salinity data and ocean acoustic data, and measures ocean flow speed and direction through buoys, submersibles or AUV-mounted current meters to obtain flow field information, and measures water temperature, salinity and depth profile data through CTD (temperature, salinity and depth meter) to understand the physical characteristics of the ocean, and uses acoustic sensors to monitor the acoustic environment in the ocean, including Noise level and sound speed profile. Preprocess the acquired raw deep-sea environment data, remove noise data through data cleaning, repair or fill missing data, and integrate different types of data using Bayesian fusion multi-sensor data fusion algorithm to obtain comprehensive environmental information. Create a geometric model of the obstacle in the 3D modeling software based on the preprocessed obstacle data. Use a regular grid model to represent the seabed terrain and obstacles, and texture map the model. Add textures that reflect the characteristics of marine vegetation and rocks to enhance the realism of the model. Use the preprocessed ocean dynamic change data to create a 3D field model representing the distribution of physical and chemical parameters of seawater temperature, salinity, and flow velocity in the 3D modeling software. Use voxel grid technology to represent the characteristics of ocean dynamic changes. Render and illuminate the model to simulate the reflection and refraction characteristics of seawater to light and enhance the sense of immersion. Fuse the obstacle 3D model with the ocean dynamic change 3D model to form a complete deep-sea 3D environment model. Use the level of detail (LOD) technology to automatically adjust the model's detail level according to the viewing distance, and then optimize the fused 3D environment model. The constraint definition module is used to define static constraints, dynamic constraints and performance constraints in the deep-sea environment, wherein the static constraints are used to define static obstacles in the deep-sea environment, the dynamic constraints are used to define dynamic obstacles in the deep-sea environment, and the performance constraints are used to define the performance indicators of route planning. Based on the acquired deep-sea environment data and the constructed deep-sea three-dimensional environment model, the static constraints, dynamic constraints and performance constraints in the deep-sea environment are defined, wherein the static constraints are used to define static obstacles in the deep-sea environment, the dynamic constraints are used to define dynamic obstacles in the deep-sea environment, and the performance constraints are used to define the performance indicators of route planning. For the definition of static constraints, the static obstacle data is extracted from the environment perception modeling module, the obstacles are classified and their positions, sizes and shapes are annotated, the boundaries are defined for each static obstacle, to ensure that the static obstacle area is avoided during path planning, and the safety distance is set according to the type and size of the obstacle to ensure that the navigation equipment maintains a safe distance from the obstacle, and then the static constraints are stored as a data structure for global routing. The path planning module uses, for the definition of dynamic constraints, extracts dynamic obstacle data from the environment perception modeling module, classifies dynamic obstacles and labels their motion characteristics, predicts their future positions and paths based on their motion trajectories, and defines dynamic constraint areas for dynamic obstacles to ensure that dynamic obstacle areas are avoided during path planning. Dynamic constraints are updated in real time to reflect the latest status of dynamic obstacles. For the definition of performance constraints, extracts performance indicators including navigation time, energy consumption and communication bandwidth, defines the maximum allowable navigation time from the starting point to the end point, defines the maximum allowable energy consumption during navigation, defines the communication bandwidth limit between the navigation equipment and the control center, and converts the performance indicators into specific values, and then stores the performance constraints as a data structure for use by the global path planning module, integrates the performance constraints with the static and dynamic constraints to form a complete set of constraints, detects conflicts between constraints, and resolves conflicts according to priority settings, and then outputs the defined set of constraints to the global path planning module for path planning; The global path planning module plans the optimal route from the starting point to the end point based on the optimal control theory and the defined constraints. During the planning process, the path is continuously optimized to meet the performance constraints and avoid all static and dynamic obstacles. The static constraints, dynamic constraints and performance constraints are read, and the starting and ending positions of the navigation equipment are determined. Based on the deep-sea three-dimensional environmental model provided by the environmental perception modeling module, the obstacle distribution and ocean dynamic information are understood. On the premise of meeting the static constraints, a heuristic search algorithm is used to generate a preliminary path from the starting point to the end point to avoid major static obstacles. According to the dynamic constraints, the position and movement trend of dynamic obstacles are monitored in real time, and the path is adjusted to avoid dynamic obstacles. On the basis of meeting the static and dynamic constraints, the path is optimized by adjusting the path nodes and changing the speed to shorten the navigation time, reduce energy consumption and ensure the adequacy of the communication bandwidth. Check whether the planned path meets all the constraints. If the path does not meet the constraints, repeat the path planning, and adjust the parameters to continuously iterate and optimize the path until the optimal route path that meets all the constraints is found. The simulation verification module is used to simulate and test the optimal route path planned based on the optimal control theory in a simulation environment, check whether the path meets the constraints and avoids all obstacles, and then evaluate the performance of the planned route path to ensure the optimization of the path planning. Based on the deep-sea three-dimensional environment model, a virtual deep-sea environment is created in the simulation software (Gazebo), including the seabed terrain, obstacles and dynamic changes of the ocean, and the physical and dynamic models of the navigation equipment are imported into the simulation environment to ensure that its behavior is consistent with the real equipment. At the same time, the simulation parameters including the time step and simulation speed are set according to actual needs, and the optimal route path generated by the global path planning module is imported into the simulation environment as the reference trajectory of the navigation equipment. The path is visualized in the simulation environment for observation and analysis. The navigation equipment is started in the simulation environment and navigates according to the planned path. The status of the navigation equipment, including position, speed and attitude, is monitored in real time. In the simulation environment, the optimal route path is run, and constraint checks are performed to check whether the path meets static and dynamic constraints. If the path violates any constraint, the simulation verification module The violation will be recorded and fed back to the global path planning module for re-planning. For static constraint checks, check whether the navigation equipment avoids all static obstacles and maintains a safe distance. For dynamic constraint checks, check whether the navigation equipment avoids all dynamic obstacles and adjusts the path according to its movement trend. According to the simulation results, the optimal route path is evaluated for performance including navigation time, energy consumption, and communication bandwidth utilization. The path performance evaluation index is calculated, and the evaluation results are compared with the performance constraints to check whether the path meets the performance requirements. The data generated during the simulation test is analyzed to identify potential problems and optimization space in path planning. Based on the analysis results, a simulation report is generated to summarize the performance and existing problems of path planning. Then, based on the simulation results and analysis report, path optimization suggestions are made to the global path planning module, including adjusting path nodes and changing the speed. According to the optimization suggestions, the global path planning module re-plans the path and submits the new path to the simulation verification module for re-testing. The simulation test process is repeated to continuously iterate the optimization path until the optimal path that meets all constraints is found. The calculation process of the path performance evaluation index is: Combined with the defined performance constraints, determine the maximum allowable navigation time, the maximum allowable energy consumption and the theoretical maximum value of the communication bandwidth, run the optimal route in the simulation environment, record the actual navigation time of the navigation equipment from the starting point to the end point, repeat the simulation test N times, obtain multiple navigation time data, calculate and record the energy consumption of the navigation equipment in each simulation through the dynamic model in the simulation software, monitor the amount of communication data between the navigation equipment and the control center, calculate the communication bandwidth utilization, that is, the ratio of the actual communication bandwidth to the theoretical maximum value of the communication bandwidth, for each simulation, calculate the normalized value of the navigation time, the normalized value of the energy consumption and the normalized value of the communication bandwidth utilization, add the normalized value of the navigation time, the normalized value of the energy consumption and the normalized value of the communication bandwidth utilization, Calculate the comprehensive performance index of each simulation, combine the comprehensive performance index of each simulation, calculate the average of the sum of squares of the comprehensive performance index, obtain the average performance index of all simulations, and analyze the root mean square of all simulation average performance indicators, and then take the inverse of the root mean square and multiply it by 100 to calculate the path performance evaluation index, and analyze the path performance evaluation index. If the path performance evaluation index is close to 100, it means that the path performance is excellent and meets all performance constraints. If the path performance evaluation index is low, it means that the path performance is poor and needs to be optimized. If the navigation time is too long, consider optimizing the path nodes and reducing the path length. If the energy consumption is too high, consider adjusting the speed or optimizing the path to reduce energy consumption. If the communication bandwidth utilization is low, consider optimizing the path to reduce communication interference or adjust the communication strategy. The expression of path performance evaluation index is: ; Where PI is the path performance evaluation index, which is used to comprehensively evaluate the performance of the path, N is the total number of simulation tests, is the navigation time in the ith simulation, is the maximum permissible voyage time specified in the performance constraints, is the energy consumption in the ith simulation, is the maximum allowable energy consumption specified in the performance constraint, is the communication bandwidth utilization in the i-th simulation, is the theoretical maximum value of the communication bandwidth. The value range of PI is [0,100]. The closer PI is to 100, the better the path performance is. The closer PI is to 0, the worse the path performance is. When the path fully meets all performance constraints, PI is close to 100. When the path seriously violates the performance constraints, PI is close to 0. If the navigation time, energy consumption and communication bandwidth utilization of the path are close to the optimal value of the performance constraints, the PI value is high. If the navigation time or energy consumption of the path exceeds the allowable range, or the communication bandwidth utilization is low, the PI value will drop significantly. The control execution module is used to convert the latest optimal route path into control instructions and send them to the execution system of the deep-sea operation equipment. Based on the optimal control theory, the navigation parameters of the deep-sea operation equipment are adjusted in real time to ensure that the equipment navigates according to the planned path; The feedback adjustment module is used to receive the latest deep-sea environmental data and make feedback adjustments to the route path in combination with the control execution results of the deep-sea operation equipment.

[0020] Embodiment 2, as Figure 2 As shown, based on Example 1, the present invention provides a technical solution: preferably, the control execution module specifically includes: The latest optimal route is received from the global path planning module. The path information includes waypoint coordinates, speed requirements and navigation direction, and the path is decomposed into a series of discrete waypoints. Each waypoint contains location information (longitude, latitude, depth) and navigation parameters (speed, heading angle). The kinematic model of the deep-sea operation equipment is established, and a series of control instructions are calculated based on the optimal route and the kinematic model of the deep-sea operation equipment, including propeller thrust, rudder angle adjustment and buoyancy adjustment, so as to enable the equipment to navigate according to the planned path. The generated control instructions are then sent to the execution system of the deep-sea operation equipment through the communication system, and real-time monitoring is performed through the sensor network. The status indicators of deep-sea operation equipment are measured, including position, speed, attitude and depth information. The position deviation, speed deviation, attitude deviation and depth deviation of each data point in the monitoring period are analyzed. Combined with the baseline value of each status indicator, the equipment status evaluation index is calculated to determine whether the equipment is in an abnormal operating state. Among them, the abnormal operating state includes position deviation, speed deviation, attitude abnormality and depth abnormality. The position deviation is to check whether the equipment deviates from the planned path, the speed deviation is to check whether the equipment speed meets the planning requirements, the attitude abnormality is to check whether the equipment attitude exceeds the safe range, and the depth abnormality is to check whether the equipment depth exceeds the safe range. According to the optimal route, the equipment status evaluation index is calculated. The system uses the optimal control theory (PID control) to adjust the navigation parameters and calculate the control instructions of the current waypoint to ensure that the equipment navigates along the planned path. It also dynamically adjusts the speed and heading angle according to the real-time status of the equipment and environmental changes to adapt to the complex deep-sea environment. The control instructions of the current waypoint include position control, speed control, heading control and depth control. Position control is to use the PID controller to adjust the position of the equipment to make it close to the target waypoint. Speed ​​control is to adjust the thrust of the propeller according to the deviation between the target speed and the current speed. Heading control is to adjust the rudder angle according to the deviation between the target heading angle and the current heading angle. Depth control is to adjust the rudder angle according to the deviation between the target heading angle and the current heading angle. The system adjusts the buoyancy according to the deviation between the target depth and the current depth, and sends the control command to the execution system of the deep-sea operation equipment through the communication link. After receiving the command, the execution system allocates the thrust of the propeller, adjusts the navigation attitude, and continuously monitors the equipment status and environmental information to form a closed-loop control loop. Once a deviation or abnormality is found, it will be adjusted and corrected immediately to ensure that the equipment always stays on the planned path. Among them, the deviation or abnormality includes depth exceeding the limit, abnormal attitude and communication interruption. If the depth exceeds the limit, it is necessary to check whether the equipment depth exceeds the safe range. If the attitude is abnormal, it is necessary to check whether the equipment attitude exceeds the safe range. If the communication is interrupted, it is necessary to check whether the communication link is normal; The expression of equipment condition evaluation index is: ; Where ES is the equipment status evaluation index, which is used to comprehensively evaluate the operating status of the equipment, M is the number of data points in the monitoring period, is the position deviation of the jth data point, is the reference value of position deviation, indicating the maximum allowable position deviation during normal operation. is the velocity deviation of the jth data point, It is the reference value of speed deviation, indicating the maximum allowable speed deviation during normal operation. is the posture deviation of the jth data point, is the reference value of attitude deviation, indicating the maximum allowable attitude deviation during normal operation. is the depth deviation of the j-th data point, It is the reference value of the depth deviation, indicating the maximum allowable depth deviation during normal operation. The value range of ES is [0,100]. When the device status fully meets the reference value, ES is close to 100, indicating that the device is in good operating condition. When the device status seriously deviates from the reference value, ES is close to 0, indicating that the device is in abnormal operating condition. If the position, speed, attitude and depth deviations of the device are small and close to the reference value, the ES value is high. If a key parameter of the device has a large deviation, the ES value will drop significantly, indicating that the device may be in an abnormal operating state. The feedback adjustment module specifically includes: The feedback adjustment module receives the latest deep-sea environmental data from the environmental perception modeling module, and receives the real-time status data of the deep-sea operating equipment from the control execution module, and then integrates the deep-sea environmental data and the equipment status data to generate a unified input data format for subsequent processing. By comparing the difference between the actual navigation trajectory and the planned path, it detects whether there is a path deviation and analyzes the path deviation, including path position deviation, path speed deviation and heading deviation. According to the size and direction of the deviation, the deviation risk assessment coefficient is calculated by combining the path position deviation, path speed deviation and heading deviation to evaluate its impact on the navigation safety of the equipment and the completion of the task, and judge whether the deviation causes the equipment to enter a dangerous area or violate the regulations. The risk of anti-constraint conditions, where the path position deviation is the deviation between the actual navigation trajectory and the planned path, the path speed deviation is the deviation between the actual navigation speed and the planned speed, and the heading deviation is the deviation between the actual navigation heading angle and the planned heading angle. According to the deviation analysis results, formulate corresponding feedback adjustment strategies, including adjusting the heading angle, changing the speed, and replanning the local path, etc., and adjust the route path in real time according to the adjustment strategy to ensure that the deep-sea operating equipment can safely and efficiently navigate along the planned path. Feedback the adjusted path and related data to the global path planning module to provide reference for subsequent path planning, and feedback the path adjustment information to the environmental perception module to update the deep-sea three-dimensional environment model; The expression of the deviation risk assessment coefficient is: ; Where DR is the deviation risk assessment coefficient, which is used to comprehensively evaluate the impact of path deviation on equipment navigation safety and mission completion, M is the number of data points in the monitoring period, is the path position deviation of the jth data point, which represents the combination of horizontal position deviation and vertical position deviation. is the reference value of the path position deviation, indicating the maximum allowable position deviation during normal operation. is the speed deviation of the jth data point, indicating the difference between the current speed and the planned speed. It is the reference value of speed deviation, indicating the maximum allowable speed deviation during normal operation. is the heading deviation of the jth data point, indicating the difference between the current heading angle and the planned heading angle. It is the reference value of heading deviation, indicating the maximum allowable heading deviation during normal operation. The value range of DR is between 0 and 1. When the path deviation of the equipment is completely consistent with the reference value, DR is close to 1, indicating that the equipment is in good operating condition and the risk is low. When the path deviation of the equipment deviates seriously from the reference value, DR is close to 0, indicating that the equipment is in abnormal operating condition and the risk is high. If the position deviation, speed deviation and heading deviation of the equipment are all small, the DR value is high, indicating that the equipment is in good operating condition and the risk is low. If a key parameter of the equipment has a large deviation, the DR value will drop significantly, indicating that the equipment may be in a high-risk state.

[0021] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A deep-sea simulation scenario route path intelligent planning system based on optimal control theory, including a data management platform, characterized by: The data management platform is communicatively connected with an environment perception modeling module, a constraint condition definition module, a global path planning module, a simulation verification module, a control execution module and a feedback adjustment module; The environment perception modeling module is used to collect and perceive deep-sea environment data and construct a deep-sea three-dimensional environment model, wherein the deep-sea environment data includes obstacle data and ocean dynamic change data; The constraint definition module is used to define static constraints, dynamic constraints and performance constraints in a deep-sea environment; The global path planning module plans the optimal route from the starting point to the end point based on the optimal control theory and the defined constraints; The simulation verification module is used to perform simulation tests on the optimal route path planned based on the optimal control theory in a simulation environment to check whether the path meets the constraint conditions, and then perform performance evaluation on the planned route path; The control execution module is used to convert the latest optimal route path into control instructions and send them to the execution system of the deep-sea operation equipment; The feedback adjustment module is used to receive the latest deep-sea environmental data and perform feedback adjustment on the route path in combination with the control execution results of the deep-sea operation equipment.

2. The deep sea simulation scenario route path intelligent planning system based on optimal control theory according to claim 1 is characterized by: The environment perception modeling module specifically includes: Using deep-sea exploration equipment and deep-sea sensor networks, collect and sense deep-sea environmental data including obstacle data and ocean dynamic change data, where obstacle data includes seabed topography data, seabed obstacle data and dynamic obstacle data, and ocean dynamic change data includes ocean flow field data, water temperature data, salinity data and ocean acoustic data; The original deep-sea environmental data is pre-processed to remove noise data through data cleaning, and different types of data are integrated using a Bayesian fusion multi-sensor data fusion algorithm to obtain comprehensive environmental information; Based on the preprocessed obstacle data, a geometric model of the obstacle is created in the 3D modeling software. A regular grid model is used to represent the seabed terrain and obstacles. The model is textured and textures that reflect the characteristics of marine vegetation and rocks are added. Using the pre-processed ocean dynamic change data, a three-dimensional field model representing the distribution of physical and chemical parameters of seawater temperature, salinity, and flow velocity is created in the three-dimensional modeling software. The voxel grid technology is used to represent the characteristics of the ocean dynamic change. The model is rendered and illuminated to simulate the reflection and refraction characteristics of seawater to light. The 3D obstacle model is fused with the 3D model of ocean dynamic changes to form a complete 3D deep-sea environment model. The level of detail technology is used to automatically adjust the model's detail level according to the viewing distance, and the fused 3D environment model is then optimized.

3. The deep sea simulation scenario route path intelligent planning system based on optimal control theory according to claim 2 is characterized by: The constraint definition module specifically includes: Based on the acquired deep-sea environment data and the constructed deep-sea three-dimensional environment model, the static constraints, dynamic constraints and performance constraints in the deep-sea environment are defined, wherein the static constraints are to define the static obstacles in the deep-sea environment, the dynamic constraints are to define the dynamic obstacles in the deep-sea environment, and the performance constraints are to define the performance indicators of the route planning; For the definition of static constraints, extract static obstacle data from the environment perception modeling module, classify obstacles and annotate their positions, sizes and shapes, define boundaries for each static obstacle to ensure that static obstacle areas are avoided during path planning, and set safety distances based on the type and size of obstacles. Then, store static constraints as a data structure for use by the global path planning module. For the definition of dynamic constraints, the dynamic obstacle data is extracted from the environment perception modeling module, the dynamic obstacles are classified and their motion characteristics are annotated, and their future positions and paths are predicted based on their motion trajectories. Dynamic constraint areas are defined for dynamic obstacles, and dynamic constraints are updated in real time to reflect the latest status of dynamic obstacles. For the definition of performance constraints, extract performance indicators including navigation time, energy consumption and communication bandwidth, define the maximum allowable navigation time from the starting point to the end point, define the maximum allowable energy consumption during navigation, define the communication bandwidth limit between the navigation equipment and the control center, and convert the performance indicators into specific values, and then store the performance constraints as a data structure for use by the global path planning module; Integrate performance constraints with static and dynamic constraints to form a complete set of constraints, detect conflicts between constraints, and resolve conflicts based on priority settings, then output the defined set of constraints to the global path planning module for use in path planning.

4. The deep sea simulation scenario route path intelligent planning system based on optimal control theory according to claim 3 is characterized by: The global path planning module specifically includes: Read static constraints, dynamic constraints and performance constraints, and determine the starting and ending positions of navigation equipment. Based on the deep-sea 3D environment model provided by the environment perception modeling module, understand the obstacle distribution and ocean dynamic information; On the premise of satisfying static constraints, a heuristic search algorithm is used to generate a preliminary path from the starting point to the end point to avoid major static obstacles. Based on dynamic constraints, the position and movement trend of dynamic obstacles are monitored in real time to adjust the path to avoid dynamic obstacles. On the basis of satisfying static and dynamic constraints, the performance of the path is optimized by adjusting the path nodes and changing the speed; Check whether the planned path meets all constraints. If the path does not meet the constraints, repeat the path planning and adjust the parameters to continuously iterate and optimize the path until the optimal route path that meets all constraints is found.

5. The deep sea simulation scenario route path intelligent planning system based on optimal control theory according to claim 4 is characterized by: The simulation verification module specifically includes: Based on the deep-sea 3D environment model, a virtual deep-sea environment is created in the simulation software, including the seabed terrain, obstacles and ocean dynamic changes, and the physical and dynamic models of the navigation equipment are imported into the simulation environment. Simulation parameters including time step and simulation speed are set according to actual needs; Import the optimal route path generated by the global path planning module into the simulation environment as a reference trajectory for the navigation equipment, and visualize the path in the simulation environment; Start the navigation equipment in the simulation environment, navigate according to the planned path, and monitor the status of the navigation equipment in real time, including position, speed and attitude; In the simulation environment, the optimal route path is run to check the constraints and whether the path satisfies the static and dynamic constraints. If the path violates any constraint, the simulation verification module will record the violation and feed it back to the global path planning module for re-planning. According to the simulation results, the optimal route path is evaluated in terms of navigation time, energy consumption, and communication bandwidth utilization, the path performance evaluation index is calculated, and the evaluation results are compared with the performance constraints to check whether the path meets the performance requirements; Analyze the data generated during the simulation test to identify potential problems and optimization space in path planning. Generate a simulation report based on the analysis results to summarize the performance and existing problems of path planning. Then, based on the simulation results and analysis report, make path optimization suggestions to the global path planning module, including adjusting path nodes and changing the speed. According to the optimization suggestions, the global path planning module re-plans the path and submits the new path to the simulation verification module for re-testing. The simulation test process is repeated, and the path is continuously optimized iteratively until the optimal path that meets all constraints is found.

6. The deep sea simulation scenario route path intelligent planning system based on optimal control theory according to claim 5 is characterized by: The calculation process of the path performance evaluation index is: Combined with the defined performance constraints, determine the theoretical maximum values ​​of the maximum allowable flight time, maximum allowable energy consumption and communication bandwidth; Run the optimal route in the simulation environment, record the actual navigation time of the navigation equipment from the starting point to the end point, repeat the simulation test N times, and obtain multiple navigation time data; The energy consumption of the navigation equipment in each simulation is calculated and recorded through the dynamic model in the simulation software, the communication data volume between the navigation equipment and the control center is monitored, and the communication bandwidth utilization rate is calculated, that is, the ratio of the actual communication bandwidth to the theoretical maximum value of the communication bandwidth; For each simulation, the normalized value of the flight time, the normalized value of the energy consumption, and the normalized value of the communication bandwidth utilization are calculated; The normalized value of the flight time, the normalized value of the energy consumption, and the normalized value of the communication bandwidth utilization are added together to calculate the comprehensive performance index of each simulation; The comprehensive performance index of each simulation is integrated, and the average value of the sum of squares of the comprehensive performance index is calculated to obtain the average performance index of all simulations. The root mean square of all simulation average performance indexes is analyzed, and then the inverse of the root mean square is taken and multiplied by 100 to calculate the path performance evaluation index. Analyze the path performance evaluation index. If the path performance evaluation index is close to 100, it means that the path performance is excellent and meets all performance constraints. If the path performance evaluation index is low, it means that the path performance is poor and needs to be optimized.

7. The deep sea simulation scenario route path intelligent planning system based on optimal control theory according to claim 6 is characterized by: The control execution module specifically includes: Receive the latest optimal route from the global path planning module. The path information includes waypoint coordinates, speed requirements and navigation direction, and decompose the path into a series of discrete waypoints, each of which contains location information and navigation parameters; Establish a kinematic model of deep-sea operation equipment, and calculate a series of control instructions, including thrust of propeller, rudder angle adjustment and buoyancy adjustment, based on the optimal route path and the kinematic model of deep-sea operation equipment, and then send the generated control instructions to the execution system of the deep-sea operation equipment through the communication system; Monitor the status indicators of deep-sea operation equipment in real time through the sensor network, including position, speed, attitude and depth information, analyze the position deviation, speed deviation, attitude deviation and depth deviation of each data point in the monitoring period, and calculate the equipment status evaluation index in combination with the baseline value of each status indicator to determine whether the equipment is in an abnormal operating state, where the abnormal operating state includes position deviation, speed deviation, attitude abnormality and depth abnormality; According to the optimal route and real-time status data, the optimal control theory is used to adjust the navigation parameters, calculate the control instructions of the current waypoint, and dynamically adjust the speed and heading angle according to the real-time status of the equipment and environmental changes. The control instructions of the current waypoint include position control, speed control, heading control and depth control; The control instructions are sent to the execution system of the deep-sea operation equipment through a communication link. After receiving the instructions, the execution system allocates thrust to the thrusters, adjusts the navigation attitude, and continuously monitors the equipment status and environmental information to form a closed-loop control loop. Once deviations or abnormal conditions are found, adjustments and corrections are made immediately. Deviations or abnormal conditions include depth exceeding the limit, abnormal attitude, and communication interruption.

8. The deep sea simulation scenario route path intelligent planning system based on optimal control theory according to claim 7 is characterized by: The expression of the equipment status evaluation index is: ； Where ES is the equipment status evaluation index, M is the number of data points in the monitoring period, is the position deviation of the jth data point, is the reference value of position deviation, is the velocity deviation of the jth data point, is the reference value of speed deviation, is the posture deviation of the jth data point, is the baseline value of attitude deviation, is the depth deviation of the j-th data point, is the benchmark value of the depth deviation. The value range of ES is [0,100]. When the device status fully meets the benchmark value, ES is close to 100, indicating that the device is in good operating condition.

9. The deep sea simulation scenario route path intelligent planning system based on optimal control theory according to claim 8 is characterized by: The feedback adjustment module specifically includes: The feedback adjustment module receives the latest deep-sea environmental data from the environmental perception modeling module and receives the real-time status data of the deep-sea operating equipment from the control execution module, and then integrates the deep-sea environmental data and the equipment status data to generate a unified input data format; By comparing the difference between the actual navigation trajectory and the planned path, detect whether there is a path deviation, analyze the path deviation, including path position deviation, path speed deviation and heading deviation, and calculate the deviation risk assessment coefficient based on the size and direction of the deviation, comprehensively consider the path position deviation, path speed deviation and heading deviation, evaluate its impact on the navigation safety of the equipment and the completion of the task, and determine whether the deviation has the risk of causing the equipment to enter a dangerous area or violate constraints; According to the deviation analysis results, formulate corresponding feedback adjustment strategies, including adjusting the heading angle, changing the speed and replanning the local path, and make real-time adjustments to the route path according to the adjustment strategy; The adjusted path and related data are fed back to the global path planning module, and the path adjustment information is fed back to the environmental perception module to update the deep-sea three-dimensional environmental model.

10. The deep sea simulation scenario route path intelligent planning system based on optimal control theory according to claim 9 is characterized by: The expression of the deviation risk assessment coefficient is: ； Where DR is the deviation risk assessment coefficient, M is the number of data points in the monitoring period, is the path position deviation of the jth data point, is the reference value of the path position deviation, is the velocity deviation of the jth data point, is the reference value of speed deviation, is the heading deviation of the jth data point, It is the reference value of heading deviation. The value range of DR is between 0 and 1. When the path deviation of the equipment completely meets the reference value, DR is close to 1, indicating that the equipment is in good operating condition and the risk is low.

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