AUV energy efficiency optimization method and system based on SOC state

Abstract

The invention provides an SOC state-based AUV energy efficiency optimization method and system, and the method comprises the steps: obtaining a state parameter of a to-be-optimized AUV, obtaining a planning path of the to-be-optimized AUV according to the state parameter, and obtaining the power output of the AUV according to the SOC state and the planning path; according to the ocean topographic data of the position of the AUV to be optimized, establishing a three-dimensional seabed model according to the ocean topographic data, loading a preset ocean current velocity field model to the three-dimensional seabed model, and obtaining a corresponding underwater environment constraint; constructing an attraction potential field and a repulsion potential field in the three-dimensional seabed model through an artificial potential field method APF, obtaining an improved three-dimensional seabed model, and obtaining a reference path of the to-be-optimized AUV in the improved three-dimensional seabed model through an improved locust algorithm GOA; and performing energy efficiency optimization according to the state parameter, the SOC state, the underwater environment constraint and the reference path.

Description

Technical Field

[0001] The present application belongs to the field of artificial intelligence, and in particular, relates to a method and system for optimizing the energy efficiency of an AUV based on the SOC state. Background Art

[0002] As an advanced underwater detection and operation tool, autonomous underwater vehicles (AUVs) have been widely used in the fields of marine science, resource exploration, and military applications in recent years. The efficient autonomy and flexible maneuverability of AUVs enable them to perform a variety of tasks in complex underwater environments, such as marine mapping, ecological environment monitoring, and seabed geological exploration. With the development of technology, the application scope of AUVs continues to expand, and their importance in scientific research and engineering practice has become increasingly prominent.

[0003] However, the energy consumption of AUV has always been one of the bottlenecks restricting its widespread application. The increase in the size, weight and sensor functions of AUV during its development and design has limited the durability and driving endurance of AUV to a certain extent. Therefore, further research on how to effectively reduce the energy consumption of AUV and improve its endurance and mission execution efficiency is also one of its important research directions. Summary of the invention

[0004] The main purpose of the embodiments of the present invention is to provide a method, system, electronic device and storage medium for battery SOC prediction, which can enable the AUV to achieve a balance between planning the path and battery consumption according to the SOC state of the battery, thereby ensuring the navigation time of the AUV.

[0005] In a first aspect, a method for optimizing AUV energy efficiency based on SOC state is provided, the method comprising:

[0006] The state parameters of the AUV to be optimized are obtained through a preset AUV six-degree-of-freedom dynamic model and an AUV six-degree-of-freedom kinematic model, and a planned path of the AUV to be optimized is obtained according to the state parameters. The SOC state of the battery of the AUV to be optimized is predicted by an ampere-hour integration method, and the power output of the AUV is obtained according to the SOC state and the planned path. The state parameters include: the attitude, speed and force characteristics of the AUV to be optimized;

[0007] According to the ocean topography data of the location of the AUV to be optimized, a three-dimensional seabed model is established according to the ocean topography data, and a preset ocean current velocity field model is loaded into the three-dimensional seabed model to obtain corresponding underwater environmental constraints;

[0008] An attractive potential field and a repulsive potential field are constructed in the three-dimensional seabed model by using an artificial potential field method APF to obtain an improved three-dimensional seabed model, and a reference path of the AUV to be optimized is obtained in the improved three-dimensional seabed model by using an improved locust algorithm GOA;

[0009] Energy efficiency optimization is performed according to the state parameters, SOC state, underwater environmental constraints and reference path.

[0010] In a possible implementation, the AUV six-degree-of-freedom kinematic model is:

[0011]

[0012] Among them, (ξ,η,ζ) and is the three-dimensional position vector and three-dimensional attitude angle vector of the body coordinate system relative to the earth coordinate system, (u, v, w) and (p, q, r) are the three-dimensional linear velocity and three-dimensional angular velocity of the AUV.

[0013] In another possible implementation, the six-degree-of-freedom dynamics model of the AUV is:

[0014]

[0015] Where M is the inertia matrix, and M∈R 6×6 ; C(ν) is the Coriolis centripetal force matrix, and C(ν)∈R 6×6 ; D(ν) is the fluid damping matrix, and D(ν)∈R 6×6 ; g(η) is the column vector of the restoring force (torque) generated by gravity and buoyancy, g(η)∈R 6 ×1 ; τ is the force (torque) vector generated by the actuator, τ∈R 6×1 ; τ E is the environmental disturbance force (torque) vector, τ E ∈R 6×1 .

[0016] In another possible implementation, the ampere-hour integration method model is:

[0017]

[0018] Among them, SOC(t) is the SOC value of the battery at the moment, SOC(t0) is the SOC value of the battery at the moment t0, C n is the nominal capacity of the battery, and I(t) is the battery current at time t.

[0019] In another possible implementation, the observation equation is: k =H·SOC k +∈, H is the measurement matrix, ∈ is the observation noise.

[0020] In another possible implementation, the three-dimensional seabed model is:

[0021]

[0022] Among them, x and y represent the horizontal and vertical position components of the ocean current, exp[] is the simplified expression of the exponential term, n represents the total number of peaks, (x i ,y i ) represents the center coordinate of the i-th peak; h i is the terrain parameter, controlling the height; x si and si are the attenuation and control slope of the i-th peak along the x-axis and y-axis respectively.

[0023] In another possible implementation, the underwater environment constraint is:

[0024] z(t)>z(x,y)+d

[0025] Among them, z(t) is the depth of the underwater robot at a certain moment in path planning, and d represents the set minimum distance.

[0026] The ocean current velocity field model is:

[0027]

[0028] Among them, r is the position state variable, e is the exponential constant, x and y represent the horizontal and vertical position components of the ocean current, x0 and y0 are the horizontal and vertical position constants of the ocean current, V x (r), V y (r), V z (r) are the velocity components of the ocean current in the horizontal, vertical and vertical directions, respectively; λ, ζ and r0 are the coordinates of the eddy intensity, eddy radius and eddy center position, respectively; if λ is greater than 0, the eddy current is counterclockwise; if λ is less than 0, the eddy current is clockwise.

[0029] In a second aspect, a system for optimizing AUV energy efficiency based on SOC state is provided, the system comprising:

[0030] A power output acquisition module, used to acquire state parameters of the AUV to be optimized through a preset AUV six-degree-of-freedom dynamic model and an AUV six-degree-of-freedom kinematic model, acquire a planned path of the AUV to be optimized according to the state parameters, predict the SOC state of the battery of the AUV to be optimized through an ampere-hour integration method, and acquire the power output of the AUV according to the SOC state and the planned path, wherein the state parameters include: the attitude, speed and force characteristics of the AUV to be optimized;

[0031] An underwater environment constraint acquisition module is used to establish a three-dimensional seabed model according to the ocean terrain data of the location of the AUV to be optimized, load a preset ocean current velocity field model into the three-dimensional seabed model, and obtain corresponding underwater environment constraints;

[0032] A reference path acquisition module is used to construct an attractive potential field and a repulsive potential field in the three-dimensional seabed model by using an artificial potential field method APF to obtain an improved three-dimensional seabed model, and to obtain a reference path of the AUV to be optimized in the improved three-dimensional seabed model by using an improved locust algorithm GOA;

[0033] The energy efficiency optimization module is used to optimize the energy efficiency according to the state parameters, SOC state, underwater environmental constraints and reference path.

[0034] In a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for optimizing the energy efficiency of an AUV based on the SOC state as provided in the first aspect is implemented.

[0035] In a fourth aspect, a non-transitory computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for optimizing the energy efficiency of an AUV based on the SOC state as provided in the first aspect is implemented.

[0036] The beneficial effect of the present application is that the SOC of electrical equipment in a low-temperature, high-pressure deep-sea environment can be accurately predicted. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in describing the embodiments of the present application are briefly introduced below.

[0038] Figure 1 A flowchart of a method for optimizing AUV energy efficiency based on SOC state provided by one embodiment of the present invention;

[0039] Figure 2 A structural diagram of a system for optimizing AUV energy efficiency based on SOC state provided by one embodiment of the present invention;

[0040] Figure 3 A schematic diagram of three-dimensional seabed topography provided by an embodiment of the present invention;

[0041] Figure 4 A schematic diagram of a three-dimensional seabed model provided by an embodiment of the present invention;

[0042] Figure 5 A top view of a three-dimensional seabed model provided by an embodiment of the present invention;

[0043] Figure 6 A schematic diagram of a three-dimensional seabed planning path and tracking control simulation provided by an embodiment of the present invention;

[0044] Figure 7 A schematic diagram of the physical structure of an electronic device provided by the present invention.

[0045] Specific implementation method

[0046] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar modules or modules with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be interpreted as limiting the present invention.

[0047] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, modules and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, modules, components and / or groups thereof. It should be understood that when we refer to a module as being "connected" or "coupled" to another module, it may be directly connected or coupled to the other modules, or there may be intermediate modules. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any modules and all combinations of one or more associated listed items.

[0048] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation method of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0049] The technical solution of the present application and how to solve the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0050] like Figure 1 FIG. 1 is a flow chart of a method for optimizing AUV energy efficiency based on SOC state provided by an embodiment of the present invention, the method comprising:

[0051] Step S101, obtaining state parameters of the AUV to be optimized through a preset AUV six-degree-of-freedom dynamic model and an AUV six-degree-of-freedom kinematic model, obtaining a planned path of the AUV to be optimized according to the state parameters, predicting the SOC state of the battery of the AUV to be optimized through an ampere-hour integration method, and obtaining the power output of the AUV according to the SOC state and the planned path, wherein the state parameters include: the attitude, speed and force characteristics of the AUV to be optimized;

[0052] Step S102, based on the ocean topography data of the location of the AUV to be optimized, a three-dimensional seabed model is established according to the ocean topography data, a preset ocean current velocity field model is loaded into the three-dimensional seabed model, and corresponding underwater environment constraints are obtained;

[0053] Step S103, constructing an attractive potential field and a repulsive potential field in the three-dimensional seabed model by using an artificial potential field method APF to obtain an improved three-dimensional seabed model, and obtaining a reference path of the AUV to be optimized in the improved three-dimensional seabed model by using an improved locust algorithm GOA;

[0054] Step S104, performing energy efficiency optimization according to the state parameters, SOC state, underwater environmental constraints and reference path.

[0055] In step S101, the remaining battery power can be reflected in real time through power output.

[0056] The AUV six-degree-of-freedom kinematic model is:

[0057]

[0058] Among them, (ξ,η,ζ) and is the three-dimensional position vector and three-dimensional attitude angle vector of the body coordinate system relative to the earth coordinate system, (u, v, w) and (p, q, r) are the three-dimensional linear velocity and three-dimensional angular velocity of the AUV.

[0059] The AUV six-degree-of-freedom dynamic model is:

[0060]

[0061] Where M is the inertia matrix, and M∈R 6×6 ; C(ν) is the Coriolis centripetal force matrix, and C(ν)∈R 6×6 ; D(ν) is the fluid damping matrix, and D(ν)∈R 6×6 ; g(η) is the column vector of the restoring force (torque) generated by gravity and buoyancy, g(η)∈R 6 ×1 ; τ is the force (torque) vector generated by the actuator, τ∈R 6×1 ; τE is the environmental disturbance force (torque) vector, τ E ∈R 6×1 .

[0062] The ampere-hour integration method model is:

[0063]

[0064] Among them, SOC(t) is the SOC value of the battery at the moment, SOC(t0) is the SOC value of the battery at the moment t0, C n is the nominal capacity of the battery, and I(t) is the battery current at time t.

[0065] In step S102, the three-dimensional seabed model is:

[0066]

[0067] Among them, x and y represent the horizontal and vertical position components of the ocean current, exp[] is the simplified expression of the exponential term, n represents the total number of peaks, (x i ,y i ) represents the center coordinate of the i-th peak; h i is the terrain parameter, controlling the height; x si and si are the attenuation and control slope of the i-th peak along the x-axis and y-axis respectively.

[0068] The underwater environmental constraints are:

[0069] z(t)>z(x,y)+d

[0070] Among them, z(t) is the depth of the underwater robot at a certain moment in path planning, and d represents the set minimum distance.

[0071] The ocean current velocity field model is:

[0072]

[0073] Among them, r is the position state variable, e is the exponential constant, x and y represent the horizontal and vertical position components of the ocean current, x0 and y0 are the horizontal and vertical position constants of the ocean current, V x (r), V y (r), V z (r) are the velocity components of the ocean current in the horizontal, vertical and vertical directions, respectively; λ, ζ and r0 are the coordinates of the eddy intensity, eddy radius and eddy center position, respectively; if λ is greater than 0, the eddy current is counterclockwise; if λ is less than 0, the eddy current is clockwise.

[0074] In step S103, the improved locust algorithm GOA simulates the behavior of locust colonies searching for food sources and finds the optimal path from the starting point to the target through iteration. The locust algorithm can explore different paths and select a better path based on the objective function of the path. The artificial potential field is mainly used to generate a virtual force field that guides locusts to find a better SOC, and provides the locust algorithm with directional information of the local path. By setting the attraction and repulsion, the artificial potential field provides directional guidance for each locust individual, so that it approaches the target or avoids obstacles along the direction where the SOC is minimized and the path is relatively smooth and short.

[0075] The improved locust algorithm is:

[0076]

[0077] Among them, c is the decreasing coefficient, which determines the size of the comfort zone, repulsion zone and attraction zone; N is the number of locusts; ub d and lb d They are social power function s(r)=fe -r / l -e -r Upper and lower bounds in d-dimensional space; and represents the position information of the i-th locust and the j-th locust; d ij =(x j -x i ) / d ij represents the unit vector pointing from the i-th locust to the j-th locust; is the best solution for the locust position in d-dimensional space so far. At the same time, the influence of gravity is not considered in the formula, and it is assumed that the wind direction always points to the optimal solution.

[0078] The attractive potential field and the repulsive potential field are:

[0079]

[0080] Among them, U att (x), U rep (x) is the field strength of the gravitational field and repulsive field at the location of the underwater robot, ζ is the basic attraction coefficient, η is the repulsive potential gain coefficient, k and m are SOC influencing factors, SOC ref is the reference path of SOC decreasing state, x is the position state quantity, x obs is the position coordinate of the repulsion point, x goal is the target position coordinate, d safe For a safe distance.

[0081] In step S104, the energy efficiency optimization is performed according to the state parameters, SOC state, underwater environmental constraints and reference path, including:

[0082] Trajectory tracking control and SOC state optimization are performed through model predictive control (MPC).

[0083] The cost function of the MPC is:

[0084]

[0085] Where J is the controller cost function, Indicates the current position x k With reference path position x ref The error term, Q is the tracking accuracy weight matrix, which is used to minimize the tracking deviation; represents the control input (i.e., the thruster power output); R is the power consumption weight matrix, and the parameter μ is used to adjust the weight of the SOC state in the control target.

[0086] In the embodiment of the present invention, the state parameters of the AUV to be optimized are obtained through the preset AUV six-degree-of-freedom dynamic model and the AUV six-degree-of-freedom kinematic model, the planned path of the AUV to be optimized is obtained according to the state parameters, the SOC state of the battery of the AUV to be optimized is predicted by the ampere-hour integration method, and the power output of the AUV is obtained according to the SOC state and the planned path. The state parameters include: the attitude, speed and force characteristics of the AUV to be optimized; according to the ocean terrain data of the location of the AUV to be optimized, a three-dimensional seabed model is established according to the ocean terrain data, and a preset ocean current velocity field model is loaded into the three-dimensional seabed model to obtain the corresponding underwater environmental constraints; the artificial potential field method APF is used to construct an attractive potential field and a repulsive potential field in the three-dimensional seabed model to obtain an improved three-dimensional seabed model, and the reference path of the AUV to be optimized is obtained in the improved three-dimensional seabed model by the improved locust algorithm GOA; energy efficiency optimization is performed according to the state parameters, SOC state, underwater environmental constraints and reference path. The AUV achieves a balance between the planned path and battery consumption according to the SOC state of the battery, thereby ensuring the navigation time of the AUV.

[0087] like Figure 2 FIG. 1 is a structural diagram of a system for optimizing AUV energy efficiency based on SOC state provided by an embodiment of the present invention, wherein the system comprises:

[0088] The power output acquisition module 201 is used to acquire the state parameters of the AUV to be optimized through the preset AUV six-degree-of-freedom dynamic model and the AUV six-degree-of-freedom kinematic model, acquire the planned path of the AUV to be optimized according to the state parameters, predict the SOC state of the battery of the AUV to be optimized through the ampere-hour integration method, and acquire the power output of the AUV according to the SOC state and the planned path, wherein the state parameters include: the attitude, speed and force characteristics of the AUV to be optimized;

[0089] The underwater environment constraint acquisition module 202 is used to establish a three-dimensional seabed model according to the ocean terrain data of the location of the AUV to be optimized, load a preset ocean current velocity field model into the three-dimensional seabed model, and obtain corresponding underwater environment constraints;

[0090] A reference path acquisition module 203 is used to construct an attractive potential field and a repulsive potential field in the three-dimensional seabed model by using an artificial potential field method APF to obtain an improved three-dimensional seabed model, and to obtain a reference path of the AUV to be optimized in the improved three-dimensional seabed model by using an improved locust algorithm GOA;

[0091] The energy efficiency optimization module 204 is used to optimize the energy efficiency according to the state parameters, SOC state, underwater environmental constraints and reference path.

[0092] like Figure 3 Shown is a schematic diagram of three-dimensional seabed topography provided by an embodiment of the present invention.

[0093] like Figure 4 Shown is a schematic diagram of a three-dimensional seabed model provided by an embodiment of the present invention.

[0094] like Figure 5 Shown is a top view of a three-dimensional seabed model provided by an embodiment of the present invention.

[0095] like Figure 6 Shown is a schematic diagram of a three-dimensional seabed planning path and tracking control simulation provided by an embodiment of the present invention.

[0096] Figure 7 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 7As shown, the electronic device may include: a processor (processor) 701, a communication interface (Communications Interface) 702, a memory (memory) 703 and a communication bus 704, wherein the processor, the communication interface and the memory communicate with each other through the communication bus. The processor can call the logic instructions in the memory to execute the method for optimizing the energy efficiency of the AUV based on the SOC state, the method comprising: obtaining the state parameters of the AUV to be optimized through a preset AUV six-degree-of-freedom dynamic model and an AUV six-degree-of-freedom kinematic model, obtaining the planned path of the AUV to be optimized according to the state parameters, predicting the SOC state of the battery of the AUV to be optimized through the ampere-hour integration method, and obtaining the power output of the AUV according to the SOC state and the planned path, wherein the state parameters include: the attitude, speed and force characteristics of the AUV to be optimized; according to the ocean terrain data of the location of the AUV to be optimized, a three-dimensional seabed model is established according to the ocean terrain data, a preset ocean current velocity field model is loaded into the three-dimensional seabed model, and corresponding underwater environmental constraints are obtained; an attractive potential field and a repulsive potential field are constructed in the three-dimensional seabed model through an artificial potential field method APF to obtain an improved three-dimensional seabed model, and a reference path of the AUV to be optimized is obtained in the improved three-dimensional seabed model through an improved locust algorithm GOA; and energy efficiency optimization is performed according to the state parameters, the SOC state, the underwater environmental constraints and the reference path.

[0097] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0098] On the other hand, an embodiment of the present invention further provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, and when the program instructions are executed by a computer, the computer can execute the method for optimizing AUV energy efficiency based on the SOC state provided by the above-mentioned method embodiments, the method comprising: obtaining state parameters of the AUV to be optimized through a preset AUV six-degree-of-freedom dynamic model and an AUV six-degree-of-freedom kinematic model, obtaining a planned path of the AUV to be optimized according to the state parameters, predicting the SOC state of the battery of the AUV to be optimized through the ampere-hour integration method, and calculating the energy efficiency of the AUV based on the SOC state and the AUV energy efficiency. The power output of the AUV is obtained by planning a path, wherein the state parameters include: the attitude, speed and force characteristics of the AUV to be optimized; a three-dimensional seabed model is established according to the ocean terrain data of the location of the AUV to be optimized, and a preset ocean current velocity field model is loaded into the three-dimensional seabed model to obtain corresponding underwater environmental constraints; an attractive potential field and a repulsive potential field are constructed in the three-dimensional seabed model by an artificial potential field method APF to obtain an improved three-dimensional seabed model, and a reference path of the AUV to be optimized is obtained in the improved three-dimensional seabed model by an improved locust algorithm GOA; energy efficiency optimization is performed according to the state parameters, SOC state, underwater environmental constraints and reference path.

[0099] On the other hand, an embodiment of the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to execute the method for optimizing the energy efficiency of an AUV based on the SOC state provided in the above embodiments, the method comprising: obtaining state parameters of the AUV to be optimized through a preset AUV six-degree-of-freedom dynamic model and an AUV six-degree-of-freedom kinematic model, obtaining a planned path of the AUV to be optimized according to the state parameters, predicting the SOC state of the battery of the AUV to be optimized through an ampere-hour integration method, obtaining the power output of the AUV according to the SOC state and the planned path, and The state parameters include: the attitude, speed and force characteristics of the AUV to be optimized; according to the ocean terrain data of the location of the AUV to be optimized, a three-dimensional seabed model is established according to the ocean terrain data, and a preset ocean current velocity field model is loaded into the three-dimensional seabed model to obtain the corresponding underwater environmental constraints; an attractive potential field and a repulsive potential field are constructed in the three-dimensional seabed model by an artificial potential field method APF to obtain an improved three-dimensional seabed model, and a reference path of the AUV to be optimized is obtained in the improved three-dimensional seabed model by an improved locust algorithm GOA; energy efficiency optimization is performed according to the state parameters, SOC state, underwater environmental constraints and reference path.

[0100] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0101] The above is only a partial implementation of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for optimizing AUV energy efficiency based on SOC state, characterized in that: The method comprises: The state parameters of the AUV to be optimized are obtained through a preset AUV six-degree-of-freedom dynamic model and an AUV six-degree-of-freedom kinematic model, and a planned path of the AUV to be optimized is obtained according to the state parameters. The SOC state of the battery of the AUV to be optimized is predicted by an ampere-hour integration method, and the power output of the AUV is obtained according to the SOC state and the planned path. The state parameters include: the attitude, speed and force characteristics of the AUV to be optimized; According to the ocean topography data of the location of the AUV to be optimized, a three-dimensional seabed model is established according to the ocean topography data, and a preset ocean current velocity field model is loaded into the three-dimensional seabed model to obtain corresponding underwater environmental constraints; An attractive potential field and a repulsive potential field are constructed in the three-dimensional seabed model by using an artificial potential field method APF to obtain an improved three-dimensional seabed model, and a reference path of the AUV to be optimized is obtained in the improved three-dimensional seabed model by using an improved locust algorithm GOA; Energy efficiency optimization is performed according to the state parameters, SOC state, underwater environmental constraints and reference path.

2. The method according to claim 1, characterized in that The AUV six-degree-of-freedom kinematic model is: Among them, (ξ,η,ζ) and is the three-dimensional position vector and three-dimensional attitude angle vector of the body coordinate system relative to the earth coordinate system, (u, v, w) and (p, q, r) are the three-dimensional linear velocity and three-dimensional angular velocity of the AUV.

3. The method according to claim 1, characterized in that The six-degree-of-freedom dynamic model of AUV is: Where M is the inertia matrix, and M∈R 6×6 ; C(ν) is the Coriolis centripetal force matrix, and C(ν)∈R 6×6 ; D(ν) is the fluid damping matrix, and D(ν)∈R 6×6 ; g(η) is the column vector of the restoring force (torque) generated by gravity and buoyancy, g(η)∈R 6×1 ; τ is the force (torque) vector generated by the actuator, τ∈R 6×1 ; τ E is the environmental disturbance force (torque) vector, τ E ∈R 6×1 .

4. The method according to claim 1, characterized in that The ampere-hour integration method model is: Among them, SOC(t) is the SOC value of the battery at the moment, SOC(t0) is the SOC value of the battery at the moment t0, C n is the nominal capacity of the battery, and I(t) is the battery current at time t.

5. The method according to claim 1, characterized in that The observation equation is: V k =H·SOC k +∈, H is the measurement matrix, ∈ is the observation noise.

6. The method according to claim 1, characterized in that The three-dimensional seabed model is: Among them, x and y represent the horizontal and vertical position components of the ocean current, exp[] is the simplified expression of the exponential term, n represents the total number of peaks, (x i ,y i ) represents the center coordinate of the i-th peak; h i is the terrain parameter, controlling the height; x si and si are the attenuation and control slope of the i-th peak along the x-axis and y-axis respectively.

7. The method according to claim 1, characterized in that The underwater environmental constraints are: z(t)>z(x,y)+d Among them, z(t) is the depth of the underwater robot at a certain moment in path planning, and d represents the set minimum distance. The ocean current velocity field model is: Among them, r is the position state variable, e is the exponential constant, x and y represent the horizontal and vertical position components of the ocean current, x0 and y0 are the horizontal and vertical position constants of the ocean current, V x (r), V y (r), V z (r) are the velocity components of the ocean current in the horizontal, vertical and vertical directions, respectively; λ, ζ and r0 are the coordinates of the eddy intensity, eddy radius and eddy center position, respectively; if λ is greater than 0, the eddy is counterclockwise; if λ is less than 0, the eddy is clockwise.

8. A system for optimizing AUV energy efficiency based on SOC status, characterized in that: The system comprises: A power output acquisition module, used to acquire state parameters of the AUV to be optimized through a preset AUV six-degree-of-freedom dynamic model and an AUV six-degree-of-freedom kinematic model, acquire a planned path of the AUV to be optimized according to the state parameters, predict the SOC state of the battery of the AUV to be optimized through an ampere-hour integration method, and acquire the power output of the AUV according to the SOC state and the planned path, wherein the state parameters include: the attitude, speed and force characteristics of the AUV to be optimized; An underwater environment constraint acquisition module is used to establish a three-dimensional seabed model according to the ocean terrain data of the location of the AUV to be optimized, load a preset ocean current velocity field model into the three-dimensional seabed model, and obtain corresponding underwater environment constraints; A reference path acquisition module is used to construct an attractive potential field and a repulsive potential field in the three-dimensional seabed model by using an artificial potential field method APF to obtain an improved three-dimensional seabed model, and to obtain a reference path of the AUV to be optimized in the improved three-dimensional seabed model by using an improved locust algorithm GOA; The energy efficiency optimization module is used to optimize the energy efficiency according to the state parameters, SOC state, underwater environmental constraints and reference path.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for optimizing AUV energy efficiency based on SOC state according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for optimizing the energy efficiency of an AUV based on the SOC state according to any one of claims 1 to 5 is implemented.

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