Auv data collection method, device, medium and equipment based on trajectory optimization

Abstract

Embodiments of the present application provide an AUV data collection method, device, medium and equipment based on trajectory optimization. The method comprises: periodically sending an RTR signal outward during the process of advancing according to a predetermined trajectory; if an RTS signal fed back by any underwater sensor node is received within a first predetermined time period after the RTR signal is sent, the motion state is changed to a hovering state; target data collection positions of the target AUV are determined according to node coordinate information in the received at least one RTS signal and the amount of data to be collected; the identification information and scheduling time of each underwater sensor node feeding back the RTS signal are determined, an ORDER data packet is generated and broadcasted; after moving to the target data collection position, data packets fed back by each underwater sensor node according to the scheduling time in the ORDER data packet are received, and after the receiving is completed, the advancing according to the predetermined trajectory is continued. The technical solution of the embodiments of the present application can reduce energy loss and delay during data collection, and ensure the data collection effect.

Description

Technical Field

[0001] This application relates to the field of underwater acoustic sensor network technology, and more specifically, to an AUV data collection method, apparatus, medium, and device based on trajectory optimization. Background Technology

[0002] Autonomous Underwater Vehicles (AUVs) are a product of advanced marine technology and have become key equipment in the marine field. The numerous characteristics of AUVs enable them to be tightly coupled with Underwater Acoustic Sensor Networks (UASNs), making them an indispensable component for establishing highly intelligent, unmanned, autonomous, and multifunctional marine information networks, greatly expanding the functional boundaries of UASNs. Current technical solutions for AUV-based data collection primarily rely on underwater node clustering and AUV path planning. However, these methods largely depend on prior knowledge of the underwater network before the AUV's deployment, which is not only difficult to implement but also increases energy consumption and latency during data collection, affecting the data collection effectiveness. Summary of the Invention

[0003] The embodiments of this application provide an AUV data collection method, apparatus, medium, and device based on trajectory optimization, which can at least reduce energy loss and delay during data collection and ensure data collection effectiveness.

[0004] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0005] According to one aspect of the embodiments of this application, an AUV data collection method based on trajectory optimization is provided, which is applied to a target AUV;

[0006] The method includes:

[0007] During the process of moving along a predetermined trajectory, RTR signals are periodically sent out, which are used to wake up the underwater sensor nodes;

[0008] If an RTS signal is received from any underwater sensor node within a first predetermined time after the RTR signal is sent, the state changes from motion to hovering. The RTS signal includes the underwater sensor node's identification information, node coordinate information, amount of data to be collected, remaining storage capacity, and remaining energy.

[0009] Based on the node coordinate information in at least one RTS signal received in the hovering state and the amount of data to be collected, the target data collection location that maximizes the data collection profit function corresponding to the target AUV is determined. The data collection profit function is determined based on the energy consumption cost of the target AUV, the energy consumption revenue of the underwater sensor node, and the delay revenue of data reception.

[0010] Based on the target data collection location and the remaining storage capacity and remaining energy in the at least one RTS signal, determine the access priority and scheduling time of each underwater sensor node that feeds back the RTS signal;

[0011] Based on the identification information of each underwater sensor node in the feedback RTS signal and the scheduling time, the corresponding ORDER data packet is generated and broadcast.

[0012] After moving to the target data collection location, it receives data packets fed back by each underwater sensor node according to the scheduling time in the ORDER data packet, and continues to move forward according to the predetermined trajectory after receiving the data.

[0013] According to one aspect of the embodiments of this application, an AUV data collection device based on trajectory optimization is provided, which is applied to a target AUV;

[0014] The device includes:

[0015] The first broadcast module is used to periodically send RTR signals outward while moving along a predetermined trajectory. The RTR signals are used to wake up the underwater sensor nodes.

[0016] The receiving module is configured to change from a motion state to a hovering state if it receives an RTS signal fed back by any underwater sensor node within a first predetermined time after sending the RTR signal. The RTS signal includes the underwater sensor node's identification information, node coordinate information, amount of data to be collected, remaining storage capacity, and remaining energy.

[0017] The first determining module is used to determine the target data collection location that maximizes the data collection profit function corresponding to the target AUV based on the node coordinate information in at least one RTS signal received in the hovering state and the amount of data to be collected. The data collection profit function is determined based on the energy consumption cost of the target AUV, the energy consumption revenue of the underwater sensor node, and the delay revenue of data reception.

[0018] The second determining module is used to determine the access priority and scheduling time of each underwater sensor node that feeds back the RTS signal based on the target data collection location and the remaining storage capacity and remaining energy in the at least one RTS signal.

[0019] The second broadcast module is used to generate and broadcast the corresponding ORDER data packet based on the identification information of each underwater sensor node that feeds back the RTS signal and the scheduling time.

[0020] The processing module is used to receive data packets fed back by each underwater sensor node according to the scheduling time in the ORDER data packet after moving to the target data collection location, and to continue moving forward according to the predetermined trajectory after receiving the data packets.

[0021] According to one aspect of the embodiments of this application, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the trajectory-optimized AUV data collection method as described in the above embodiments.

[0022] According to one aspect of the embodiments of this application, an electronic device is provided, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the trajectory-optimized AUV data collection method as described in the above embodiments.

[0023] According to one aspect of the embodiments of this application, a computer program product or computer program is provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the trajectory-optimized AUV data collection method provided in the above embodiments.

[0024] In some embodiments of this application, as the target AUV moves along a predetermined trajectory, it periodically transmits RTR signals to wake up underwater sensor nodes. If an RTS signal is received from any underwater sensor node within a first predetermined time after the RTR signal is transmitted, the target AUV transitions from a moving state to a hovering state. The RTS signal includes the underwater sensor node's identification information, node coordinate information, the amount of data to be collected, remaining storage capacity, and remaining energy. Then, based on the node coordinate information and the amount of data to be collected from at least one RTS signal received in the hovering state, a target data collection position that maximizes the data collection profit function for the target AUV is determined. The data collection profit function is determined based on the target AUV's energy consumption cost, the underwater sensor node's energy consumption benefit, and the data reception delay benefit. Based on the target data collection location and the remaining storage capacity and energy in at least one RTS signal, the access priority and scheduling time of each underwater sensor node that feeds back the RTS signal are determined. Then, based on the identification information of each underwater sensor node feeding back the RTS signal and the scheduling time, a corresponding ORDER data packet is generated and broadcast. After the target AUV moves to the target data collection location, it receives data packets fed back by each underwater sensor node according to the scheduling time in the ORDER data packet, and continues to move along a predetermined trajectory after receiving the packets. Therefore, the target AUV can determine the corresponding target data collection location after launch based on the actual access status of each underwater sensor node, and continue to move along a predetermined trajectory after data collection is completed, thereby reducing energy loss and delay during data collection and ensuring data collection effectiveness.

[0025] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0026] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0027] Figure 1 A schematic flowchart of a trajectory-optimized AUV data collection method according to an embodiment of this application is shown;

[0028] Figure 2 A block diagram of a trajectory-optimized AUV data collection apparatus according to an embodiment of this application is shown;

[0029] Figure 3 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0030] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0031] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0032] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0033] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0034] Figure 1 A schematic flowchart of a trajectory-optimized AUV data collection method according to an embodiment of this application is shown. This method can be applied to a target AUV that can communicate with multiple underwater sensor nodes to collect data stored by each underwater sensor node.

[0035] In a practical application scenario, an underwater acoustic sensor network can include a target AUV and multiple underwater sensor nodes randomly distributed underwater. The target AUV, acting as a data collection device, is launched from the surface sink node and navigates within a plane at a fixed maximum diving depth. Therefore, the ordinate of the target AUV in a three-dimensional Cartesian coordinate system is a constant value H. The underwater sensor nodes, acting as data providing devices, are distributed on the seabed, and their ordinates are all 0. In this application scenario, the target AUV has no prior information about the specific locations and data distribution of the underwater sensor nodes, and similarly, the underwater sensor nodes have no prior information about the target AUV's trajectory, consistent with actual underwater data collection conditions. The underwater sensor nodes can determine whether to activate the signal listening mode in real time based on the remaining proportion of their own data storage space. When the ratio between the remaining storage space and the initial storage space is less than q, the underwater sensor activates the listening mode and enters the QUIET-LISTEN state; otherwise, it remains in the IDLE state, only collecting and storing sensor data. (The value of q can be determined according to the actual situation. When the sensor data packet generation rate is high, the value of q should be larger; conversely, the value of q should be smaller.)

[0036] Before the target AUV is launched to collect data, a predetermined trajectory is determined based on the shape of the data collection task area, with the principle of maximizing coverage of the task area using the shortest path. After launch, the target AUV moves along the predetermined trajectory, optimizing its trajectory and access protocol parameters based on information obtained from interactions with underwater sensor nodes to complete the collection of underwater sensor information. The entire process mainly includes underwater sensor node discovery, calculation of MAC access parameters, determination of the target data collection location, access and retreat time of each underwater sensor node, and hovering interval of the target AUV, before moving to the target data collection location to complete data collection.

[0037] like Figure 1 As shown, this trajectory optimization-based AUV data collection method includes at least steps S110 to S160, which are described in detail below:

[0038] In step S110, during the process of moving along a predetermined trajectory, an RTR signal is periodically sent out, which is used to wake up the underwater sensor node.

[0039] In this embodiment, before the underwater data collection mission begins, a predetermined trajectory for the target AUV can be set. This trajectory can be designed based on the characteristics of the mission area, achieving full coverage of the mission area via the shortest path while considering the AUV's mobility. After the underwater data collection mission begins, the target AUV can move along the predetermined trajectory, periodically sending RTR (Ready to Receive) signals to detect and wake up underwater sensor nodes within its communication range that are in the QUIET-LISTEN state. After broadcasting the RTR signal, the target AUV continues to move along the predetermined trajectory and enters the WF-RTS state, starting the WF-RTS timer. The first predetermined duration of the WF-RTS timer is 2τ. max +2T contr0l ,in, T represents the maximum propagation delay between the AUV and the underwater sensor node. control It refers to the transmission delay of the control packet.

[0040] The underwater sensor node stores its sensed information in its onboard memory. When the remaining storage capacity exceeds a certain percentage of the initial capacity, it indicates that the underwater sensor node has sufficient storage space and does not need to upload data. It continues to remain in IDLE state, only sensing information and ignoring all communication signals. When the remaining storage capacity falls below a predetermined threshold, the underwater sensor node initiates a communication listening mode, enters QUIET-LISTEN state, and waits for the arrival and wake-up of the target AUV before initiating data transmission. If the underwater sensor node receives a wake-up signal from the target AUV while in QUIET-LISTEN state, it replies with an RTS (Request to send) signal to request access.

[0041] In step S120, if an RTS signal is received from any underwater sensor node within a first predetermined time after the RTR signal is sent, the state changes from motion to hovering. The RTS signal includes the underwater sensor node's identification information, node coordinate information, amount of data to be collected, remaining storage capacity, and remaining energy.

[0042] In this embodiment, the underwater sensor node in the IDLE state will not receive the RTR signal. Upon receiving the RTR signal, the sensor node in the QUIET-LISTEN state adopts a non-backoff strategy to send an RTS signal to request access to the channel for data transmission. Simultaneously, the underwater sensor node transitions to the WF-ORDER state and starts a WF-ORDER timer for a duration of 2τ. max +2T control The RTS signal contains the underwater sensor node's serial number i (i.e., identification information) and node coordinate information (x, y, y). i,y i ,0) Data volume to be collected D i Remaining storage capacity L i and remaining energy E i .

[0043] If the target AUV receives an RTS signal within the first predetermined time period, it will change from a moving state to a hovering state upon receiving the first RTS signal, and record the coordinates (x0, y0, H) at this time.

[0044] If the target AUV does not receive an RTS signal within the first predetermined time period, it will continue to move along the predetermined trajectory.

[0045] In step S130, based on the node coordinate information in at least one RTS signal received in the hovering state and the amount of data to be collected, a target data collection location that maximizes the data collection profit function corresponding to the target AUV is determined. The data collection profit function is determined based on the energy consumption cost of the target AUV, the energy consumption revenue of the underwater sensor node, and the delay revenue of data reception.

[0046] In this embodiment, after the first predetermined duration, the target AUV can count the number of received RTS signals and parse each RTS signal to obtain relevant information about the corresponding underwater sensor node. Based on the node coordinates in each RTS signal and the amount of data to be collected, a target data collection location that maximizes the data collection profit function for the target AUV is determined. This data collection profit function is determined based on the energy consumption cost of the target AUV, the energy consumption benefit of the underwater sensor node, and the delay benefit of data reception. This allows for a comprehensive consideration of these three factors, reducing energy loss and delay during data collection and ensuring effective data collection.

[0047] In one embodiment of this application, determining the target data collection location that maximizes the data collection profit function corresponding to the target AUV, based on node coordinate information in at least one RTS signal received in a hovering state and the amount of data to be collected, includes:

[0048] The target hovering range of the target AUV is determined based on the node coordinate information in at least one RTS signal received during hovering.

[0049] Based on the node coordinate information in the at least one RTS signal and the amount of data to be collected, determine the data collection profit function of the target AUV;

[0050] Select the coordinate point that maximizes the data collection profit function from the target hovering range as the target data collection location.

[0051] In this embodiment, it is assumed that the target AUV receives N RTS signals (N≥1). After decoding all RTS signals, the target AUV finds the maximum and minimum values ​​of the horizontal and vertical coordinates based on the coordinate information of the N nodes, using x... min x max y min y max Using vertices, construct a rectangular grid map to serve as the target hovering range for the target AUV.

[0052] Next, a data collection profit function is constructed to locate the target data collection location for the target AUV. First, the energy consumption cost of the target AUV is calculated according to the following formula:

[0053]

[0054] Among them, P n and P h These represent the propulsion and hovering power of the target AUV, respectively; v is the target AUV's speed; B is the bandwidth; and P... T It is the fixed transmission power of the node, N(f) c ) is the noise power, A(d) i ,f c ) represents the path loss of the underwater acoustic signal, d i It is the Euclidean distance: E o This is the energy consumption required for the target AUV to hover and collect energy at (x0, y0, H), expressed as follows:

[0055]

[0056] in,

[0057] Next, the energy consumption benefit of the underwater sensor node is calculated using the following formula:

[0058]

[0059] Then calculate the delay benefit of data reception using the following formula:

[0060]

[0061] Based on the aforementioned formula, the data collection profit function corresponding to the target AUV is:

[0062] W(x,y)=λΔE S +γΔT d -ηΔE AUV

[0063] Wherein, λ, γ, and η are the weights of the energy consumption revenue of the underwater sensor node, the delay revenue of data reception, and the energy consumption cost of the target AUV in the data collection profit function, respectively, and can be predetermined by those skilled in the art based on prior experience.

[0064] After determining the data collection profit function corresponding to the target AUV, the coordinate point that maximizes the data collection profit function can be selected from the target hovering range as the target data collection location.

[0065] In one embodiment, selecting the coordinate point from the target hovering range that maximizes the data collection profit function as the target data collection location includes:

[0066] Based on the Deep-Q Network reinforcement learning algorithm, the coordinates of the target hovering range are set as the action set, the data collection profit function is used as the reward function, and the coordinates that maximize the reward in the action set are determined as the target data collection location.

[0067] In this embodiment, to enable the target AUV to find the x and y coordinates that maximize the data collection profit function within the target hovering range, which are the x and y coordinates of the target AUV's target data collection position, that is...

[0068] To achieve this objective, this application employs a reinforcement learning algorithm based on Deep-Q Network. For the target AUV, its state is its position (x, y) at time t. t ,y t Considering the sparsity of the underwater network and the speed of the target AUV, the action set is set as the coordinates (x, y) of the target hovering range; the reward function is W(x, y), and the point of maximum profit is explored to obtain the coordinates (x, y) of the target data collection location. * ,y * H).

[0069] Please continue to refer to this. Figure 1 In step S140, based on the target data collection location and the remaining storage capacity and remaining energy in the at least one RTS signal, the access priority and scheduling time of each underwater sensor node that feeds back the RTS signal are determined.

[0070] In this embodiment, the access priority and scheduling time for each underwater sensor node can be determined based on the target data collection location and the remaining storage capacity and energy of each underwater sensor node that feeds back the RTS signal. It should be understood that the smaller the remaining storage capacity and energy of an underwater sensor node, the higher its access priority, and the earlier data collection should begin. Then, based on the access priority of each underwater sensor node and the corresponding amount of data to be collected, the data transmission time required for each underwater sensor node is determined, thereby determining the corresponding scheduling time.

[0071] In one embodiment of this application, the access priority and scheduling time of each underwater sensor node that feeds back the RTS signal are determined based on the target data collection location and the remaining storage capacity and remaining energy in the at least one RTS signal, including:

[0072] The urgency of each underwater sensor node is determined based on the remaining storage capacity and remaining energy in the at least one RTS signal.

[0073] The access priority of each underwater sensor node is determined according to the order of urgency from highest to lowest.

[0074] Based on the target data collection location, the access priority of each underwater sensor node, and the node coordinate information, the scheduling time of each underwater sensor node is determined.

[0075] In this embodiment, the time required for each underwater sensor node to transmit data is first calculated according to the following formula:

[0076]

[0077] in,

[0078] To determine the data transmission order of each underwater sensor node, ω is defined. i The urgency of underwater sensor node i is satisfied as follows:

[0079]

[0080] Where E and L are the initial energy value and the maximum storage capacity value, and α and β are the weights of energy and capacity in the urgency assessment.

[0081] According to ω i The access priority of underwater sensor nodes is determined from largest to smallest. i δ i =rank p (Ω), δ i =1,2,…,N; where rank p() is a function that sorts the column vector Ω in descending order and outputs its index, where Ω = [ω1, ω2, ..., ω]. N ].

[0082] Therefore, based on the target data receiving location and access priority, the scheduling time of each underwater sensor node can be calculated using the following formula:

[0083]

[0084] in, ξ is the distance from the hovering point where the AUV receives the RTS to the hovering point where the data is collected, ξ is the protection interval for data transmission between the two nodes, and c is the speed of sound in water.

[0085] In step S150, based on the identification information of each underwater sensor node that feeds back the RTS signal and the scheduling time, the corresponding ORDER data packet is generated and broadcast.

[0086] In this embodiment, the identification information of each underwater sensor node and its corresponding scheduling time can be loaded into an ORDER data packet, and this ORDER data packet can be broadcast. After sending the ORDER data packet, the target AUV enters the WF-DATA state and starts the WF-DATA timer for a duration of [duration missing]. Waiting for data transmission from the underwater sensor nodes.

[0087] In step S160, after moving to the target data collection location, the underwater sensor nodes receive data packets fed back by each underwater sensor node according to the scheduling time in the ORDER data packet, and continue to move forward according to the predetermined trajectory after receiving the data packets.

[0088] In this embodiment, if an underwater sensor node in the WF-ORDER state receives an ORDER data packet before the WF-ORDER timer ends, it shuts down the timer and decodes the ORDER data packet. If it does not receive an ORDER data packet after the WF-ORDER timer ends, it indicates that there is a collision in the RTS signal it sent, and the target data collection location of the target AUV is outside the communication range of this underwater sensor node. The underwater sensor node then enters the QUIET-LISTEN state and waits for the next RTR wake-up.

[0089] Upon receiving an ORDER data packet, an underwater sensor node will exhibit two scenarios: 1. After decoding, if the ORDER data packet does not contain its own identification information (such as node ID), it indicates a collision occurred with its transmitted RTS signal. However, the target AUV's data collection location remains within the underwater sensor node's communication range. The underwater sensor node enters a QUIET-LISTEN state, waiting for the ACK packet from the target AUV to complete this round of data collection and wake it up. 2. After decoding, if the ORDER data packet contains its own identification information, it waits... Data packets will be sent after a set time.

[0090] Therefore, after completing the data collection, the target AUV continues to move along the predetermined trajectory to continue collecting data from other underwater sensor nodes.

[0091] In one embodiment of this application, before continuing along the predetermined trajectory after receiving data packets fed back by each underwater sensor node according to the ORDER data packet, the method further includes:

[0092] It broadcasts ACK data packets and receives data packets from underwater sensing nodes based on the ACK data packets.

[0093] In this embodiment, after the WF-DATA timer expires, the target AUV correctly receives all the agreed-upon data packets and broadcasts an ACK packet. Then, it enters the WF-RE-RTS state and starts the WF-RE-RTS timer for a duration of 2τ. max +2T control The purpose is to wait for the previous RTS collision, but for the underwater sensor nodes that are still within the target AUV's communication range in this round and were not within the target AUV's communication range in the previous round, but entered the AUV's communication range after the target AUV moved to the target data collection location, and then responded to the RTS to avoid data loss.

[0094] After the data packet is sent, underwater sensor node i enters the WF-ACK state and starts the WF-ACK timer for a duration of [duration missing]. When an underwater sensor node in the WF-ACK state receives an ACK packet before the timer expires, it indicates successful data transmission. The WF-ACK timer is then closed, and the node enters the IDLE state. It waits for λ≤q to return to the QUIET-LISTEN state and repeats the above steps. Upon receiving an ACK packet, the underwater sensor node in the QUIET-LISTEN state immediately replies with an RTS signal and starts the WF-ORDER timer for a duration of 2τ. max +2T control The data transmission steps described above will be performed and will not be repeated here.

[0095] When the target AUV is in WF-RE-RTS state, if it receives a retransmitted RTS signal, it repeats the above signal transmission steps; if it does not receive an RTS signal, the target AUV navigates back to (x0, y0, H) and continues to move along the preset trajectory, broadcasting an RTR signal.

[0096] The following describes an apparatus embodiment of this application, which can be used to execute the trajectory-optimized AUV data collection method described in the above embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the trajectory-optimized AUV data collection method described above.

[0097] Figure 2 A block diagram of a trajectory-optimized AUV data collection apparatus according to an embodiment of this application is shown.

[0098] Reference Figure 2 As shown, an AUV data collection device based on trajectory optimization according to an embodiment of this application is applied to a target AUV;

[0099] The device includes:

[0100] The first broadcast module is used to periodically send RTR signals outward while moving along a predetermined trajectory. The RTR signals are used to wake up the underwater sensor nodes.

[0101] The receiving module is configured to change from a motion state to a hovering state if it receives an RTS signal fed back by any underwater sensor node within a first predetermined time after sending the RTR signal. The RTS signal includes the underwater sensor node's identification information, node coordinate information, amount of data to be collected, remaining storage capacity, and remaining energy.

[0102] The first determining module is used to determine the target data collection location that maximizes the data collection profit function corresponding to the target AUV based on the node coordinate information in at least one RTS signal received in the hovering state and the amount of data to be collected. The data collection profit function is determined based on the energy consumption cost of the target AUV, the energy consumption revenue of the underwater sensor node, and the delay revenue of data reception.

[0103] The second determining module is used to determine the access priority and scheduling time of each underwater sensor node that feeds back the RTS signal based on the target data collection location and the remaining storage capacity and remaining energy in the at least one RTS signal.

[0104] The second broadcast module is used to generate and broadcast the corresponding ORDER data packet based on the identification information of each underwater sensor node that feeds back the RTS signal and the scheduling time.

[0105] The processing module is used to receive data packets fed back by each underwater sensor node according to the scheduling time in the ORDER data packet after moving to the target data collection location, and to continue moving forward according to the predetermined trajectory after receiving the data packets.

[0106] In one embodiment of this application, the first determining module is used to:

[0107] The target hovering range of the target AUV is determined based on the node coordinate information in at least one RTS signal received during hovering.

[0108] Based on the node coordinate information in the at least one RTS signal and the amount of data to be collected, determine the data collection profit function of the target AUV;

[0109] Select the coordinate point that maximizes the data collection profit function from the target hovering range as the target data collection location.

[0110] In one embodiment of this application, after receiving the data packets fed back by each underwater sensor node according to the ORDER data packet, and before continuing to advance according to the predetermined trajectory, the processing module is further configured to:

[0111] It broadcasts ACK data packets and receives data packets from underwater sensing nodes based on the ACK data packets.

[0112] Figure 3 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown.

[0113] It should be noted that, Figure 3 The computer system of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0114] like Figure 3 As shown, the computer system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 302 or programs loaded from storage portion 308 into Random Access Memory (RAM) 303, such as performing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0115] The following components are connected to I / O interface 305: an input section 306 including a keyboard, mouse, etc.; an output section 307 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0116] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs various functions defined in the system of this application.

[0117] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0118] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0119] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0120] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

[0121] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0122] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of this application.

[0123] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0124] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for collecting AUV data based on trajectory optimization, characterized in that, Applied to target AUVs; The method includes: During the process of moving along a predetermined trajectory, RTR signals are periodically sent out, which are used to wake up the underwater sensor nodes; If an RTS signal is received from any underwater sensor node within a first predetermined time after the RTR signal is sent, the state changes from motion to hovering. The RTS signal includes the underwater sensor node's identification information, node coordinate information, amount of data to be collected, remaining storage capacity, and remaining energy. Based on the node coordinate information in at least one RTS signal received in the hovering state and the amount of data to be collected, the target data collection location that maximizes the data collection profit function corresponding to the target AUV is determined. The data collection profit function is determined based on the energy consumption cost of the target AUV, the energy consumption revenue of the underwater sensor node, and the delay revenue of data reception. Based on the target data collection location and the remaining storage capacity and remaining energy in the at least one RTS signal, determine the access priority and scheduling time of each underwater sensor node that feeds back the RTS signal; Based on the identification information of each underwater sensor node in the feedback RTS signal and the scheduling time, the corresponding ORDER data packet is generated and broadcast. After moving to the target data collection location, it receives data packets fed back by each underwater sensor node according to the scheduling time in the ORDER data packet, and continues to move forward according to the predetermined trajectory after receiving the data.

2. The method according to claim 1, characterized in that, Based on the node coordinate information in at least one RTS signal received during hovering and the amount of data to be collected, determine the target data collection location that maximizes the data collection profit function for the target AUV, including: The target hovering range of the target AUV is determined based on the node coordinate information in at least one RTS signal received during hovering. Based on the node coordinate information in the at least one RTS signal and the amount of data to be collected, determine the data collection profit function of the target AUV; Select the coordinate point that maximizes the data collection profit function from the target hovering range as the target data collection location.

3. The method according to claim 2, characterized in that, Selecting the coordinates that maximize the data collection profit function from the target hovering range as the target data collection location includes: Based on the Deep-Q Network reinforcement learning algorithm, the coordinates of the target hovering range are set as the action set, the data collection profit function is used as the reward function, and the coordinates that maximize the reward in the action set are determined as the target data collection location.

4. The method according to claim 1, characterized in that, After receiving data packets from each underwater sensor node based on the ORDER data packet, and before continuing along the predetermined trajectory, the method further includes: It broadcasts ACK data packets and receives data packets from underwater sensing nodes based on the ACK data packets.

5. The method according to any one of claims 1-4, characterized in that, If no RTS signal is received within the first predetermined time period, the target AUV continues to move along the predetermined trajectory. The first predetermined time period is determined by the maximum propagation delay between the AUV and the underwater sensor node and the transmission delay of the control packet.

6. The method according to any one of claims 1-4, characterized in that, Based on the target data collection location and the remaining storage capacity and remaining energy in the at least one RTS signal, determine the access priority and scheduling time of each underwater sensor node that feeds back the RTS signal, including: The urgency of each underwater sensor node is determined based on the remaining storage capacity and remaining energy in the at least one RTS signal. The access priority of each underwater sensor node is determined according to the order of urgency from highest to lowest. Based on the target data collection location, the access priority of each underwater sensor node, and the node coordinate information, the scheduling time of each underwater sensor node is determined.

7. An AUV data collection device based on trajectory optimization, characterized in that, Applied to target AUVs; The device includes: The first broadcast module is used to periodically send RTR signals outward while moving along a predetermined trajectory. The RTR signals are used to wake up the underwater sensor nodes. The receiving module is configured to change from a motion state to a hovering state if it receives an RTS signal fed back by any underwater sensor node within a first predetermined time after sending the RTR signal. The RTS signal includes the underwater sensor node's identification information, node coordinate information, amount of data to be collected, remaining storage capacity, and remaining energy. The first determining module is used to determine the target data collection location that maximizes the data collection profit function corresponding to the target AUV based on the node coordinate information in at least one RTS signal received in the hovering state and the amount of data to be collected. The data collection profit function is determined based on the energy consumption cost of the target AUV, the energy consumption revenue of the underwater sensor node, and the delay revenue of data reception. The second determining module is used to determine the access priority and scheduling time of each underwater sensor node that feeds back the RTS signal based on the target data collection location and the remaining storage capacity and remaining energy in the at least one RTS signal. The second broadcast module is used to generate and broadcast the corresponding ORDER data packet based on the identification information of each underwater sensor node that feeds back the RTS signal and the scheduling time. The processing module is used to receive data packets fed back by each underwater sensor node according to the scheduling time in the ORDER data packet after moving to the target data collection location, and to continue moving forward according to the predetermined trajectory after receiving the data packets.

8. The apparatus according to claim 7, characterized in that, The first determining module is used for: The target hovering range of the target AUV is determined based on the node coordinate information in at least one RTS signal received during hovering. Based on the node coordinate information in the at least one RTS signal and the amount of data to be collected, determine the data collection profit function of the target AUV; Select the coordinate point that maximizes the data collection profit function from the target hovering range as the target data collection location.

9. A computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the trajectory-optimized AUV data collection method as described in any one of claims 1 to 6.

10. An electronic device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the trajectory-optimized AUV data collection method as described in any one of claims 1 to 6.

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