Underwater robot cooperative control method and device, electronic equipment and storage medium

Abstract

The invention provides an underwater robot cooperative control method which comprises the steps that current step facility node detection data collected by at least one first target underwater robot executing an underwater facility detection task are acquired, and each first target underwater robot corresponds to one current step facility node detection data; determining at least one second target underwater robot executing the underwater facility detection task in the next step based on the facility node detection data in the current step; and according to an execution instruction of the underwater facility detection task in the next step, issuing the execution instruction to the at least one second target underwater robot. Through intelligent task allocation and data analysis, the efficiency, accuracy and flexibility of underwater facility detection can be remarkably improved, meanwhile, energy consumption and cost are reduced, and an efficient and reliable solution is provided for underwater facility detection.

Description

Technical Field

[0001] The present invention relates to the field of underwater inspection technology, and in particular to an underwater robot collaborative control method, device, electronic equipment and storage medium. Background Art

[0002] In underwater scenes such as the ocean, in order to detect the status of pipelines (such as oil pipelines, communication cables, communication optical cables) or other facilities on the seabed, a robot is usually deployed to operate on the seabed. The robot obtains the status of the facility through sensors such as cameras and sonars, and transmits it to the ground control console in real time; the ground control console gives the robot another instruction based on human judgment based on the information fed back by the robot, instructing the robot to perform another operation. Due to the vastness of the ocean and the huge size of seabed facilities, this method will inevitably lead to a long detection time and rely too much on manual operation, which ultimately leads to low detection efficiency and low accuracy of detection results. Summary of the invention

[0003] The embodiment of the present invention provides an underwater robot cooperative control method, which aims to improve the detection efficiency and accuracy of underwater facilities. Through intelligent task allocation and data analysis, the efficiency, accuracy and flexibility of underwater facility detection can be significantly improved, while reducing energy consumption and costs, providing an efficient and reliable solution for underwater facility detection.

[0004] In a first aspect, an embodiment of the present invention provides an underwater robot collaborative control method, the underwater robot collaborative control method is used for an underwater robot data analysis platform, the underwater robot data analysis platform is communicatively connected with a plurality of underwater robots, and the plurality of underwater robots are communicatively connected with each other, the method comprising the following steps:

[0005] Acquire current step facility node detection data collected by at least one first target underwater robot that performs an underwater facility detection task, each of the first target underwater robots corresponds to one current step facility node detection data;

[0006] Based on the current step facility node detection data, determining at least one second target underwater robot to perform the underwater facility detection task in the next step;

[0007] According to the underwater facility detection task, the execution instruction is sent to the at least one second target underwater robot in the next step.

[0008] Optionally, the current step facility node detection data includes current step detection data of the current step facility node and current step environment detection data of the current step facility node, and the step of determining at least one second target underwater robot for performing the underwater facility detection task in the next step based on the current step facility node detection data includes:

[0009] Based on the current step facility node detection data, determining whether the current step facility node has a fault;

[0010] If there is a fault in the current step facility node, at least one second target underwater robot is determined to perform the underwater facility detection task in the next step based on the fault type of the fault and the environmental detection data of the current step facility node. The next step of performing the underwater facility detection task is the fault handling task of the current step facility node.

[0011] Optionally, the current step detection data includes image data of the current step facility node and sonar data of the current step facility node, and the step of determining whether the current step facility node has a fault based on the current step facility node detection data includes:

[0012] Performing image feature extraction processing on the image data to obtain image features of the current step facility node;

[0013] and, performing sonar feature extraction processing on the sonar data to obtain the sonar features of the current facility node;

[0014] Fusing the image feature with the sonar feature to obtain a fused feature;

[0015] Performing fault classification processing on the fusion features to obtain a fault classification result;

[0016] Based on the fault classification result, determine whether there is a fault in the current facility node.

[0017] Optionally, the step of determining at least one second target underwater robot for performing the underwater facility detection task in the next step based on the fault type of the fault and the current step facility node environment detection data includes:

[0018] Combining the fault type of the fault and the current step facility node environment detection data to obtain a target combination;

[0019] Based on a preset corresponding relationship between the combination and the number of underwater robots, determining the number of target underwater robots corresponding to the target combination;

[0020] Based on the number of target underwater robots, at least one second target underwater robot for performing the underwater facility detection task in the next step is determined within a preset range of the current step facility node.

[0021] Optionally, after the step of determining whether the current step facility node has a fault based on the current step facility node detection data, the method further includes:

[0022] If there is no fault in the current step facility node, obtain the first historical environment detection data of the current step facility node, and obtain the second historical environment detection data of the next step facility node;

[0023] Determining environmental prediction data of the next facility node based on the first historical environmental detection data, the current step environmental detection data, and the second historical environmental detection data;

[0024] Based on the environmental prediction data, at least one second target underwater robot is determined to perform the underwater facility detection task in the next step, and the underwater facility detection task is performed in the next step as the next facility node detection task.

[0025] Optionally, the step of determining the environmental prediction data of the next facility node based on the first historical environmental detection data, the current step environmental detection data and the second historical environmental detection data includes:

[0026] Splicing the first historical environment detection data with the current step environment detection data to obtain spliced ​​data;

[0027] The spliced ​​data is used as auxiliary prediction data for the second historical environment detection data, and the environment prediction is performed on the second historical environment detection data through an environment prediction model to obtain the environment prediction data of the next facility node.

[0028] Optionally, the step of determining at least one second target underwater robot for performing the underwater facility detection task in the next step based on the environmental prediction data includes:

[0029] Determining the target number of underwater robots for the next facility node based on the environmental prediction data of the next facility node;

[0030] Based on the number of target underwater robots, at least one second target underwater robot for performing the underwater facility detection task in the next step is determined within a preset range of the next step facility node.

[0031] In a second aspect, an embodiment of the present invention further provides an underwater robot collaborative control device, the underwater robot collaborative control device is used for an underwater robot data analysis platform, the underwater robot data analysis platform is communicatively connected with a plurality of underwater robots, and the plurality of underwater robots are communicatively connected with each other, the underwater robot collaborative control device comprises:

[0032] An acquisition module, used to acquire current step facility node detection data collected by at least one first target underwater robot performing an underwater facility detection task, each first target underwater robot corresponds to one current step facility node detection data;

[0033] A processing module, configured to determine at least one second target underwater robot for performing the underwater facility detection task in the next step based on the current step facility node detection data;

[0034] The sending module is used to send the execution instruction of the next step according to the underwater facility detection task to the at least one second target underwater robot.

[0035] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the underwater robot collaborative control method provided in the embodiment of the invention are implemented.

[0036] In an embodiment of the present invention, the current step facility node detection data collected by at least one first target underwater robot performing an underwater facility detection task is obtained, and each first target underwater robot corresponds to one current step facility node detection data; based on the current step facility node detection data, at least one second target underwater robot performing the underwater facility detection task in the next step is determined; and according to the underwater facility detection task, the execution instruction of the next step is sent to the at least one second target underwater robot. Through intelligent task allocation and data analysis, the present invention can significantly improve the efficiency, accuracy and flexibility of underwater facility detection, while reducing energy consumption and costs, and provides an efficient and reliable solution for underwater facility detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 This is a flow chart of an underwater robot cooperative control system provided by an embodiment of the present invention.

[0039] Figure 2 is a flow chart of an underwater robot cooperative control method provided by an embodiment of the present invention;

[0040] Figure 3 It is a structural schematic diagram of an underwater robot cooperative control device provided by an embodiment of the present invention;

[0041] Figure 4 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0043] like Figure 1 As shown, Figure 1 It is an architecture diagram of an underwater robot cooperative control system provided by an embodiment of the present invention. The underwater robot cooperative control system includes a client (user interface), a robot data analysis platform and several underwater robots. The robot data analysis platform includes functional modules such as an external interface, a data model, a data algorithm, a data analysis, a data collection, a command generation, a command issuance, a command algorithm and a data storage. The robot data analysis platform communicates with the client and several underwater robots through an external interface. The data collection is used to obtain the current step facility node detection data collected by at least one first target underwater robot performing the underwater facility detection task, and each first target underwater robot corresponds to one current step facility node detection data. The data model, the data algorithm and the data analysis are used to determine at least one second target underwater robot for performing the underwater facility detection task in the next step based on the current step facility node detection data. The command algorithm, the command generation and the command issuance are used to send the at least one second target underwater robot under the execution command of the next step according to the underwater facility detection task. The data storage is used to store the detection data collected by the underwater robot and the task execution data.

[0044] The data analysis platform located in the cloud collects data from multiple underwater robots in real time. The data analysis platform analyzes the data according to the pre-set data model and algorithm, and generates target data corresponding to the facility detection; based on the target data, the data analysis platform generates the next step of instructions based on the instruction generation algorithm, and issues the instructions to the corresponding underwater robot. This process is fully automatic and does not require human intervention. It is divided into the following situations:

[0045] 1. The data analysis platform collects data from an underwater robot in real time, generates instructions based on the model and algorithm, and issues them to the underwater robot to perform the next step;

[0046] 2. The data analysis platform collects data from a certain underwater robot in real time, generates instructions based on the model and algorithm, and issues them to other robots (and the underwater robot) to work together on the next step;

[0047] 3. The data analysis platform collects data from several underwater robots in real time, generates instructions based on models and algorithms, and issues them to the underwater robots that reported the information and several other underwater robots to collaborate on the next step.

[0048] like Figure 2 As shown, Figure 2 The method flow chart of an underwater robot collaborative control method provided by an embodiment of the present invention. The underwater robot collaborative control method is used for an underwater robot data analysis platform, the underwater robot data analysis platform is connected to a plurality of underwater robots in communication, and the plurality of underwater robots are connected to each other in communication. The underwater robot collaborative control method comprises the following steps:

[0049] 101. Obtain current step facility node detection data collected by at least one first target underwater robot that performs an underwater facility detection task.

[0050] In an embodiment of the present invention, the underwater facility detection task is the detection task determined in the previous step, or is the detection task determined in the initial step. During the underwater facility detection process, the underwater facilities can be divided into several facility nodes for detection, and each facility node is detected by one or more underwater robots.

[0051] The detection target of the above-mentioned underwater facility detection task is to detect whether there is a fault. The above-mentioned first target underwater robot refers to a robot that performs the underwater facility detection task in the current step facility node. It can detect multiple underwater facility nodes at the same time. At the same time, each facility node is detected by at least one first target underwater robot.

[0052] The detection data collected by each first target underwater robot can be obtained in real time or periodically, and each first target underwater robot corresponds to one current step facility node detection data.

[0053] 102. Based on the current step facility node detection data, determine at least one second target underwater robot that performs the underwater facility detection task in the next step.

[0054] In an embodiment of the present invention, the data analysis platform processes the current step facility node detection data according to a preset data model, data algorithm and data analysis to obtain a fault detection result corresponding to the current step facility node, and determines at least one second target underwater robot to perform the underwater facility detection task in the next step based on the fault detection result corresponding to the current step facility node.

[0055] The second target underwater robot that performs the underwater facility detection task in the next step may be one or more.

[0056] 103. Send the underwater facility detection task to the at least one second target underwater robot under the next execution instruction.

[0057] In the embodiment of the present invention, after obtaining the fault detection result corresponding to the current facility node, it is determined that the next underwater facility detection task is the fault handling task of the current facility node or the detection task of the next facility node.

[0058] The instruction generation process for the next underwater facility detection task is performed through the instruction algorithm and instruction generation, the robot instruction corresponding to the next underwater facility detection task is obtained, and the robot instruction is sent to the corresponding second target underwater robot.

[0059] The above-mentioned second target underwater robot refers to the underwater robot required to perform the next step of underwater facility inspection task.

[0060] In an embodiment of the present invention, the current step facility node detection data collected by at least one first target underwater robot performing an underwater facility detection task is obtained, and each first target underwater robot corresponds to one current step facility node detection data; based on the current step facility node detection data, at least one second target underwater robot performing the underwater facility detection task in the next step is determined; and according to the underwater facility detection task, the execution instruction of the next step is sent to the at least one second target underwater robot. Through intelligent task allocation and data analysis, the present invention can significantly improve the efficiency, accuracy and flexibility of underwater facility detection, while reducing energy consumption and costs, and provides an efficient and reliable solution for underwater facility detection.

[0061] It should be noted that the underwater robot collaborative control method provided by the embodiment of the present invention can be applied to computers, edge terminals, servers, cloud platforms and other devices that can perform the underwater robot collaborative control method.

[0062] It is understandable that in the specific implementation of this application, underwater data, robot data, image data, sonar data, environmental data and other related data are involved. When the embodiments in this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of relevant data, as well as the training, deployment and use of models, need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0063] Optionally, the current step facility node detection data includes current step detection data of the current step facility node and current step environment detection data of the current step facility node, and the step of determining at least one second target underwater robot for performing the underwater facility detection task in the next step based on the current step facility node detection data includes:

[0064] Based on the current step facility node detection data, determining whether the current step facility node has a fault;

[0065] If there is a fault in the current step facility node, at least one second target underwater robot is determined to perform the underwater facility detection task in the next step based on the fault type of the fault and the environmental detection data of the current step facility node. The next step of performing the underwater facility detection task is the fault handling task of the current step facility node.

[0066] In the embodiment of the present invention, the current detection data may represent the status and performance of the facility node itself, and may include the structural integrity, operating status, possible damage or wear of the facility, etc.

[0067] The current step environmental detection data can be the environmental conditions around the facility node, such as water temperature, water quality, water flow rate, surrounding biological activities, etc. The current step environmental detection data is crucial for evaluating the working environment of the facility and its possible impacts.

[0068] In the process of determining the next underwater robot to perform the inspection task, based on the inspection data of the current step facility node (including the current step inspection data and the current step environment inspection data), the system will determine whether the facility node has a fault. It can be compared with the preset safety or performance threshold, and the abnormal pattern of the data can be analyzed by algorithm. If it is determined that the current step facility node has a fault, the system will further analyze the type of fault (for example, whether it is structural damage, functional failure, or other types of problems).

[0069] At the same time, the system will consider the environmental detection data of the current facility node, because some specific fault types may be more serious or require emergency treatment under certain environmental conditions, such as leakage and fracture.

[0070] Based on the type of fault and environmental conditions, the system will determine the next underwater facility inspection task to be performed, that is, the fault handling task of the current facility node. Fault handling tasks may include further detailed inspection, emergency repair, environmental cleanup, etc.

[0071] The system will select at least one second target underwater robot to perform the task based on the nature and urgency of the task, as well as the capabilities and locations of each underwater robot. The selection process may involve comprehensive consideration of multiple factors such as the underwater robot's professional capabilities, endurance, and distance from the fault node.

[0072] The embodiment of the present invention is a dynamic, real-time decision-making process that ensures the safety and continuous operation of underwater facilities through accurate data collection, efficient data analysis and intelligent task allocation algorithms.

[0073] Optionally, the current step detection data includes image data of the current step facility node and sonar data of the current step facility node, and the step of determining whether the current step facility node has a fault based on the current step facility node detection data includes:

[0074] Performing image feature extraction processing on the image data to obtain image features of the current step facility node;

[0075] and, performing sonar feature extraction processing on the sonar data to obtain the sonar features of the current facility node;

[0076] Fusing the image feature with the sonar feature to obtain a fused feature;

[0077] Performing fault classification processing on the fusion features to obtain a fault classification result;

[0078] Based on the fault classification result, determine whether there is a fault in the current facility node.

[0079] In an embodiment of the present invention, image data can be collected and uploaded through a camera or other image acquisition device of the underwater robot, so that the underwater robot data analysis platform obtains image data of the current step facility node.

[0080] Sonar data can be collected by the underwater robot's sonar equipment and uploaded to the underwater robot data analysis platform, and the underwater robot data analysis platform obtains the sonar data of the current step facility node.

[0081] In the process of image feature extraction, the collected image data can be processed to extract key features in the image. The extracted image features can include geometric shapes such as points, lines, and surfaces, colors, textures, etc.

[0082] Image feature extraction methods may involve computer vision and image processing techniques, such as SIFT, SURF and other algorithms, which are used to identify and describe key points in images.

[0083] In the sonar feature extraction process, the sonar data can be processed to extract features in the sonar signal. The sonar features may include the strength, frequency, duration, etc. of the signal.

[0084] Sonar feature extraction may use signal processing techniques, such as short-time energy, short-time average amplitude, etc., to analyze the dynamic characteristics of sonar signals.

[0085] The extracted image features and sonar features are fused. The purpose of fusion is to comprehensively utilize two different types of data information to improve the accuracy and reliability of fault detection.

[0086] Feature fusion can adopt data fusion algorithms, such as weighted average, decision fusion, etc., to ensure the complementarity and consistency of the two features during the fusion process.

[0087] Use machine learning or deep learning algorithms to classify the fused features. These algorithms may include support vector machines (SVM), neural networks, etc.

[0088] The trained model is used to classify the fused features into fault categories, determine whether there is a fault in the current facility node, and identify the type of fault.

[0089] Based on the fault classification results, determine whether the current step facility node has a fault. If the classification results indicate that there is a fault, then further corresponding fault handling measures can be taken.

[0090] The above fault detection process can accurately conduct comprehensive detection and fault classification of the current facility nodes by comprehensively using image processing and sonar signal processing technology.

[0091] Optionally, the step of determining at least one second target underwater robot for performing the underwater facility detection task in the next step based on the fault type of the fault and the current step facility node environment detection data includes:

[0092] Combining the fault type of the fault and the current step facility node environment detection data to obtain a target combination;

[0093] Based on a preset corresponding relationship between the combination and the number of underwater robots, determining the number of target underwater robots corresponding to the target combination;

[0094] Based on the number of target underwater robots, at least one second target underwater robot for performing the underwater facility detection task in the next step is determined within a preset range of the current step facility node.

[0095] In the embodiment of the present invention, the fault type of the fault and the environmental detection data of the current facility node can be combined to form a target combination. These combinations comprehensively consider the fault type and environmental factors, providing a comprehensive information basis for subsequent decision-making.

[0096] Perform a correspondence query, and query the number of underwater robots corresponding to the target combination according to the preset correspondence. The above correspondence can be established based on historical experience, simulation experiments or expert judgment, aiming to ensure that the appropriate number of underwater robots can be allocated to efficiently complete the task for different types of faults and specific environmental conditions.

[0097] After determining the number of underwater robots required, the system will search for suitable underwater robots within the preset range of the current facility node. The preset range can be set based on multiple factors such as communication distance, robot endurance, and mission urgency.

[0098] Specifically, the signal coverage range can be considered: the communication capability of underwater robots is limited, especially in underwater environments, where signal transmission may be affected by multiple factors such as water flow, salinity, temperature, etc. Therefore, the preset range should ensure that the underwater robot can maintain stable communication with the data analysis platform or other underwater robots within the range.

[0099] Consider relay station settings: If the detection area is large, you can consider setting up underwater communication relay stations to extend the communication distance, allowing the underwater robot to perform tasks in a wider area.

[0100] Consider the battery life of the underwater robot: The battery life of the underwater robot is limited, and the preset range should take into account the underwater robot's ability to travel back and forth between the mission point and the charging station or mother ship to avoid being unable to complete the mission due to exhaustion of power.

[0101] Consider the energy optimization of underwater robots: the actual working time of underwater robots can be extended by optimizing the movement path of underwater robots, reducing unnecessary energy consumption, and so on, thereby expanding their effective working range.

[0102] Consider the response time of underwater robots: In emergency situations, such as leakage or damage to facilities, underwater robots are required to arrive at the scene quickly for assessment and disposal. At this time, the preset scope should focus more on the ability of underwater robots to quickly reach the accident site.

[0103] Consider the mission priority of the underwater robot: Depending on the urgency and importance of the mission, the preset range can be adjusted. For high-priority missions, the range may need to be narrowed to ensure that the underwater robot can respond quickly; for low-priority missions, the range can be appropriately expanded for more comprehensive detection.

[0104] Consider the water flow speed of the current facility node: The water flow speed will affect the movement speed and direction control of the underwater robot. When setting the preset range, it is necessary to consider the impact of the water flow on the movement of the underwater robot to ensure that the underwater robot can reach the specified location within the scheduled time.

[0105] Consider the complexity of the terrain of the current facility node: The underwater terrain may be complex and changeable, such as obstacles such as reefs and sunken ships. The preset range should avoid these dangerous areas to ensure the safety of the robot.

[0106] Within the preset range, the system can comprehensively consider factors such as the underwater robot's status (such as power level, maintenance status), performance (such as operating capability, sensor accuracy) and location, and select at least one second target underwater robot that is most suitable for performing the next underwater facility inspection task.

[0107] The present invention can dynamically determine the number of underwater robots required and the underwater robots to perform specific tasks according to actual conditions, thereby ensuring the efficient and accurate execution of underwater facility inspection tasks. It not only improves the flexibility and efficiency of task execution, but also enables quick and intelligent decision-making in complex and changeable underwater environments.

[0108] Optionally, after the step of determining whether the current step facility node has a fault based on the current step facility node detection data, the method further includes:

[0109] If there is no fault in the current step facility node, obtain the first historical environment detection data of the current step facility node, and obtain the second historical environment detection data of the next step facility node;

[0110] Determining environmental prediction data of the next facility node based on the first historical environmental detection data, the current step environmental detection data, and the second historical environmental detection data;

[0111] Based on the environmental prediction data, at least one second target underwater robot is determined to perform the underwater facility detection task in the next step, and the underwater facility detection task is performed in the next step as the next facility node detection task.

[0112] In the embodiment of the present invention, if there is no fault in the current facility node, it means that there is no need to process the current facility node, and the next facility node needs to be detected.

[0113] The first historical environmental detection data of the current step facility node is obtained. The first historical environmental detection data may include environmental parameters such as water temperature, salinity, turbidity, and pH value of the node before. The first historical environmental detection data can assist in understanding the environmental change trend of the current step facility node.

[0114] At the same time, the second historical environmental detection data of the next facility node is also obtained. The second historical environmental detection data can be used to predict the environmental condition of the next node.

[0115] Combining the first historical environmental detection data, the current step environmental detection data and the second historical environmental detection data, the environmental conditions of the next facility node are predicted through data analysis and prediction models (such as time series analysis, machine learning models, etc.).

[0116] The prediction results may include multiple environmental parameters, such as water quality, water flow velocity, water temperature, etc. The above environmental prediction data can be used as a basis for selecting an underwater robot suitable for performing the next detection task.

[0117] Based on the predicted environmental data, the system can evaluate which type of underwater robot is best suited to work in the predicted environmental conditions. For example, if the next node is predicted to have turbulent water flow, it may be necessary to select an underwater robot with stronger stability and power system.

[0118] Specifically, the underwater robot’s endurance, sensor configuration, and matching degree with predicted environmental conditions can be evaluated. For example, if it is predicted that there may be a certain pollutant at the next node, it would be more appropriate to choose an underwater robot equipped with a corresponding pollutant detection sensor.

[0119] The system determines at least one second target underwater robot to perform the detection task of the next facility node based on the environmental prediction data and the performance and characteristics of the underwater robot.

[0120] The embodiments of the present invention ensure that when performing underwater facility inspections, intelligent decisions can be made based on real-time and historical data, and appropriate underwater robots can be selected to cope with different environmental conditions, thereby improving inspection efficiency and accuracy.

[0121] Optionally, the step of determining the environmental prediction data of the next facility node based on the first historical environmental detection data, the current step environmental detection data and the second historical environmental detection data includes:

[0122] Splicing the first historical environment detection data with the current step environment detection data to obtain spliced ​​data;

[0123] The spliced ​​data is used as auxiliary prediction data for the second historical environment detection data, and the environment prediction is performed on the second historical environment detection data through an environment prediction model to obtain the environment prediction data of the next facility node.

[0124] In the embodiment of the present invention, the first historical environment detection data can be spliced ​​with the current step environment detection data, that is, the two data segments can be merged into a longer data sequence. In this way, the historical data and the latest data can be combined to more comprehensively reflect the trend and law of environmental changes.

[0125] The spliced ​​data is used as auxiliary prediction data for the second historical environment detection data. The spliced ​​data can be used to enhance or assist the analysis and prediction of the second historical environment detection data.

[0126] The second historical environmental detection data is subjected to environmental prediction by using an environmental prediction model. The environmental prediction model can infer future environmental conditions based on historical data. The environmental prediction model can be in the form of time series analysis, machine learning algorithms, etc., and can predict future environmental data based on the changing patterns of historical data.

[0127] In the application process of the environmental prediction model, the spliced ​​data serves as auxiliary prediction data, providing the environmental prediction model with richer information, which helps the model to more accurately predict the environmental status of the next facility node.

[0128] Through the analysis and calculation of the environmental prediction model, the environmental prediction data of the next facility node is obtained. The environmental prediction data can include various environmental parameters, such as water temperature, water quality, flow rate, etc., which are used to guide subsequent underwater facility inspection tasks.

[0129] When there is no fault in the current facility node, in the process of planning the detection task of the next facility node, the historical environmental detection data of the current facility node and the next facility node and the current environmental detection data of the current facility node can be fully utilized. Through data splicing and model prediction, an accurate prediction is provided for the environmental status of the next facility node.

[0130] Optionally, the step of determining at least one second target underwater robot for performing the underwater facility detection task in the next step based on the environmental prediction data includes:

[0131] Determining the target number of underwater robots for the next facility node based on the environmental prediction data of the next facility node;

[0132] Based on the number of target underwater robots, at least one second target underwater robot for performing the underwater facility detection task in the next step is determined within a preset range of the next step facility node.

[0133] In an embodiment of the present invention, the environmental prediction data of the next facility node may include water quality, water temperature, water flow velocity, visibility, etc., which are all factors that need to be considered when selecting a suitable underwater robot.

[0134] Based on the analysis results of the environmental prediction data, the number of underwater robots required to carry out the inspection task at the next facility node is evaluated. For example, if the predicted environmental conditions are complex or harsh, more underwater robots can be added to ensure the successful completion of the task.

[0135] Based on the evaluation results, the required number of target underwater robots is determined. The target number of underwater robots can ensure the smooth progress of the inspection mission while taking into account factors such as cost, efficiency and safety.

[0136] Clarify the preset range of the next facility node. The preset range may be set based on multiple factors such as communication distance, robot endurance, mission requirements, etc., to ensure that the underwater robot can effectively perform detection tasks within this range.

[0137] Within the preset range, based on the number of target underwater robots, select the second target underwater robot suitable for the next underwater facility inspection task. Factors such as the performance, function, adaptability of the underwater robot and its ability to coordinate with other underwater robots may be considered during the screening.

[0138] From the screened underwater robots, at least one second target underwater robot is finally determined to perform the detection task. The data of the second target underwater robot can ensure that the robot can adapt to the predicted environmental conditions and efficiently complete the detection work.

[0139] The embodiments of the present invention can improve the accuracy and efficiency of underwater facility inspection while ensuring the safety and cost-effectiveness of the mission by deeply analyzing environmental prediction data, evaluating and determining the required number of underwater robots, and selecting the underwater robot most suitable for performing the inspection task within a preset range.

[0140] like Figure 3 As shown, an embodiment of the present invention provides an underwater robot collaborative control device, the underwater robot collaborative control device is used for an underwater robot data analysis platform, the underwater robot data analysis platform is communicatively connected with a plurality of underwater robots, and the plurality of underwater robots are communicatively connected with each other, and the underwater robot collaborative control device includes:

[0141] An acquisition module 201 is used to acquire current step facility node detection data collected by at least one first target underwater robot performing an underwater facility detection task, and each first target underwater robot corresponds to one current step facility node detection data;

[0142] A processing module 202 is used to determine at least one second target underwater robot to perform the underwater facility detection task in the next step based on the current step facility node detection data;

[0143] The sending module 203 is used to send the execution instruction of the next step according to the underwater facility detection task to the at least one second target underwater robot.

[0144] Optionally, the current step facility node detection data includes current step detection data of the current step facility node and current step environment detection data of the current step facility node, and the processing module 202 is further used to:

[0145] Based on the current step facility node detection data, determining whether the current step facility node has a fault;

[0146] If there is a fault in the current step facility node, at least one second target underwater robot is determined to perform the underwater facility detection task in the next step based on the fault type of the fault and the environmental detection data of the current step facility node. The next step of performing the underwater facility detection task is the fault handling task of the current step facility node.

[0147] Optionally, the current step detection data includes image data of the current step facility node and sonar data of the current step facility node, and the processing module 202 is further used to:

[0148] Performing image feature extraction processing on the image data to obtain image features of the current step facility node;

[0149] and, performing sonar feature extraction processing on the sonar data to obtain the sonar features of the current facility node;

[0150] Fusing the image feature with the sonar feature to obtain a fused feature;

[0151] Performing fault classification processing on the fusion features to obtain a fault classification result;

[0152] Based on the fault classification result, determine whether there is a fault in the current facility node.

[0153] Optionally, the processing module 202 is further used for:

[0154] Combining the fault type of the fault and the current step facility node environment detection data to obtain a target combination;

[0155] Based on a preset corresponding relationship between the combination and the number of underwater robots, determining the number of target underwater robots corresponding to the target combination;

[0156] Based on the number of target underwater robots, at least one second target underwater robot for performing the underwater facility detection task in the next step is determined within a preset range of the current step facility node.

[0157] Optionally, the processing module 202 is further used for:

[0158] If there is no fault in the current step facility node, obtain the first historical environment detection data of the current step facility node, and obtain the second historical environment detection data of the next step facility node;

[0159] Determining environmental prediction data of the next facility node based on the first historical environmental detection data, the current step environmental detection data, and the second historical environmental detection data;

[0160] Based on the environmental prediction data, at least one second target underwater robot is determined to perform the underwater facility detection task in the next step, and the underwater facility detection task is performed in the next step as the next facility node detection task.

[0161] Optionally, the processing module 202 is further used for:

[0162] Splicing the first historical environment detection data with the current step environment detection data to obtain spliced ​​data;

[0163] The spliced ​​data is used as auxiliary prediction data for the second historical environment detection data, and the environment prediction is performed on the second historical environment detection data through an environment prediction model to obtain the environment prediction data of the next facility node.

[0164] Optionally, the processing module 202 is further used for:

[0165] Determining the target number of underwater robots for the next facility node based on the environmental prediction data of the next facility node;

[0166] Based on the number of target underwater robots, at least one second target underwater robot for performing the underwater facility detection task in the next step is determined within a preset range of the next step facility node.

[0167] It should be noted that the underwater robot collaborative control device provided in the embodiment of the present invention can be applied to computers, edge terminals, servers, cloud platforms and other equipment that can perform underwater robot collaborative control methods.

[0168] The underwater robot cooperative control device provided in the embodiment of the present invention can implement each process implemented by the underwater robot cooperative control method in the above method embodiment, and can achieve the same beneficial effects. To avoid repetition, it will not be described here.

[0169] See also Figure 4 , Figure 4 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 4 As shown, it includes: a memory 302, a processor 301, and a computer program of an underwater robot cooperative control method stored in the memory 302 and executable on the processor 301, wherein:

[0170] The processor 301 is used to call the computer program stored in the memory 302 and execute the following steps:

[0171] Acquire current step facility node detection data collected by at least one first target underwater robot that performs an underwater facility detection task, each of the first target underwater robots corresponds to one current step facility node detection data;

[0172] Based on the current step facility node detection data, determining at least one second target underwater robot to perform the underwater facility detection task in the next step;

[0173] According to the underwater facility detection task, the execution instruction is sent to the at least one second target underwater robot in the next step.

[0174] Optionally, the current step facility node detection data includes current step detection data of the current step facility node and current step environment detection data of the current step facility node, and the step of determining at least one second target underwater robot for performing the underwater facility detection task in the next step based on the current step facility node detection data, performed by the processor 301, includes:

[0175] Based on the current step facility node detection data, determining whether the current step facility node has a fault;

[0176] If there is a fault in the current step facility node, at least one second target underwater robot is determined to perform the underwater facility detection task in the next step based on the fault type of the fault and the environmental detection data of the current step facility node. The next step of performing the underwater facility detection task is the fault handling task of the current step facility node.

[0177] Optionally, the current step detection data includes image data of the current step facility node and sonar data of the current step facility node, and the step of determining whether the current step facility node has a fault based on the current step facility node detection data performed by the processor 301 includes:

[0178] Performing image feature extraction processing on the image data to obtain image features of the current step facility node;

[0179] and, performing sonar feature extraction processing on the sonar data to obtain the sonar features of the current facility node;

[0180] Fusing the image feature with the sonar feature to obtain a fused feature;

[0181] Performing fault classification processing on the fusion features to obtain a fault classification result;

[0182] Based on the fault classification result, determine whether there is a fault in the current facility node.

[0183] Optionally, the step of determining at least one second target underwater robot for performing the underwater facility detection task in the next step based on the fault type of the fault and the current step facility node environment detection data performed by the processor 301 includes:

[0184] Combining the fault type of the fault and the current step facility node environment detection data to obtain a target combination;

[0185] Based on a preset corresponding relationship between the combination and the number of underwater robots, determining the number of target underwater robots corresponding to the target combination;

[0186] Based on the number of target underwater robots, at least one second target underwater robot for performing the underwater facility detection task in the next step is determined within a preset range of the current step facility node.

[0187] Optionally, after the step of determining whether the current step facility node has a fault based on the current step facility node detection data, the method executed by the processor 301 further includes:

[0188] If there is no fault in the current step facility node, obtain the first historical environment detection data of the current step facility node, and obtain the second historical environment detection data of the next step facility node;

[0189] Determining environmental prediction data of the next facility node based on the first historical environmental detection data, the current step environmental detection data, and the second historical environmental detection data;

[0190] Based on the environmental prediction data, at least one second target underwater robot is determined to perform the underwater facility detection task in the next step, and the underwater facility detection task is performed in the next step as the next facility node detection task.

[0191] Optionally, the step of determining the environmental prediction data of the next facility node based on the first historical environmental detection data, the current step environmental detection data, and the second historical environmental detection data, performed by the processor 301, includes:

[0192] Splicing the first historical environment detection data with the current step environment detection data to obtain spliced ​​data;

[0193] The spliced ​​data is used as auxiliary prediction data for the second historical environment detection data, and the environment prediction is performed on the second historical environment detection data through an environment prediction model to obtain the environment prediction data of the next facility node.

[0194] Optionally, the step of determining at least one second target underwater robot for performing the underwater facility detection task in the next step based on the environmental prediction data, performed by the processor 301, includes:

[0195] Determining the target number of underwater robots for the next facility node based on the environmental prediction data of the next facility node;

[0196] Based on the number of target underwater robots, at least one second target underwater robot for performing the underwater facility detection task in the next step is determined within a preset range of the next step facility node.

[0197] It should be noted that the electronic device provided in the embodiment of the present invention can be applied to computers, edge terminals, servers, cloud platforms and other devices that can perform underwater robot collaborative control methods, that is, the above-mentioned electronic devices can be computers, edge terminals, servers, cloud platforms and other devices.

[0198] The electronic device provided in the embodiment of the present invention can implement each process implemented by the underwater robot cooperative control method in the above method embodiment, and can achieve the same beneficial effects. To avoid repetition, it will not be described here.

[0199] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the various processes of the underwater robot collaborative control method provided by the embodiment of the present invention are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0200] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The computer-readable storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0201] The above disclosure is only the preferred embodiment of the present invention, which certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for cooperative control of an underwater robot, characterized in that: The underwater robot collaborative control method is used for an underwater robot data analysis platform, the underwater robot data analysis platform is communicatively connected with a plurality of underwater robots, and the plurality of underwater robots are communicatively connected with each other, and the method comprises the following steps: Acquire current step facility node detection data collected by at least one first target underwater robot that performs an underwater facility detection task, each of the first target underwater robots corresponds to one current step facility node detection data; Based on the current step facility node detection data, determining at least one second target underwater robot to perform the underwater facility detection task in the next step; According to the underwater facility detection task, the execution instruction of the next step is sent to the at least one second target underwater robot.

2. The underwater robot cooperative control method according to claim 1, characterized in that: The current step facility node detection data includes the current step detection data of the current step facility node and the current step environment detection data of the current step facility node. The step of determining at least one second target underwater robot for performing the underwater facility detection task in the next step based on the current step facility node detection data includes: Based on the current step facility node detection data, determining whether the current step facility node has a fault; If there is a fault in the current step facility node, based on the fault type of the fault and the environmental detection data of the current step facility node, at least one second target underwater robot is determined to perform the underwater facility detection task in the next step, and the next step of performing the underwater facility detection task is the fault handling task of the current step facility node.

3. The underwater robot cooperative control method according to claim 2, characterized in that: The current step detection data includes image data of the current step facility node and sonar data of the current step facility node. The step of determining whether the current step facility node has a fault based on the current step facility node detection data includes: Performing image feature extraction processing on the image data to obtain image features of the current step facility node; and, performing sonar feature extraction processing on the sonar data to obtain the sonar features of the current facility node; Fusing the image feature with the sonar feature to obtain a fused feature; Performing fault classification processing on the fusion features to obtain a fault classification result; Based on the fault classification result, determine whether there is a fault in the current facility node.

4. The underwater robot cooperative control method according to claim 3, characterized in that: The step of determining at least one second target underwater robot for performing the underwater facility detection task in the next step based on the fault type of the fault and the current step facility node environment detection data comprises: Combining the fault type of the fault and the current step facility node environment detection data to obtain a target combination; Based on a preset corresponding relationship between the combination and the number of underwater robots, determining the number of target underwater robots corresponding to the target combination; Based on the number of target underwater robots, at least one second target underwater robot for performing the underwater facility detection task in the next step is determined within a preset range of the current step facility node.

5. The underwater robot cooperative control method according to claim 2, characterized in that: After the step of determining whether the current step facility node has a fault based on the current step facility node detection data, the method further includes: If there is no fault in the current step facility node, obtaining the first historical environment detection data of the current step facility node, and obtaining the second historical environment detection data of the next facility node; Determining environmental prediction data of the next facility node based on the first historical environmental detection data, the current step environmental detection data, and the second historical environmental detection data; Based on the environmental prediction data, at least one second target underwater robot is determined to perform the underwater facility detection task in the next step, and the underwater facility detection task is performed in the next step as the next facility node detection task.

6. The underwater robot cooperative control method according to claim 5, characterized in that: The step of determining the environmental prediction data of the next facility node based on the first historical environmental detection data, the current step environmental detection data and the second historical environmental detection data comprises: Splicing the first historical environment detection data with the current step environment detection data to obtain spliced ​​data; The spliced ​​data is used as auxiliary prediction data for the second historical environment detection data, and the environment prediction is performed on the second historical environment detection data through an environment prediction model to obtain the environment prediction data of the next facility node.

7. The underwater robot cooperative control method according to claim 6, characterized in that: The step of determining at least one second target underwater robot to perform the underwater facility detection task in the next step based on the environmental prediction data comprises: Determining the target number of underwater robots for the next facility node based on the environmental prediction data of the next facility node; Based on the number of target underwater robots, at least one second target underwater robot for performing the underwater facility detection task in the next step is determined within a preset range of the next step facility node.

8. A cooperative control device for an underwater robot, characterized in that: The underwater robot collaborative control device is used for an underwater robot data analysis platform. The underwater robot data analysis platform is connected to a plurality of underwater robots in communication, and the plurality of underwater robots are connected to each other in communication. The underwater robot collaborative control device includes: An acquisition module, used to acquire current step facility node detection data collected by at least one first target underwater robot performing an underwater facility detection task, each first target underwater robot corresponds to one current step facility node detection data; A processing module, configured to determine at least one second target underwater robot for performing the underwater facility detection task in the next step based on the current step facility node detection data; The sending module is used to send the execution instruction of the next step according to the underwater facility detection task to the at least one second target underwater robot.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the underwater robot collaborative control method as claimed in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps in the underwater robot cooperative control method according to any one of claims 1 to 7 are implemented.

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