A method, apparatus and device for visualizing fish movement simulation

Abstract

The fish motion visual simulation method provided in the application comprises the following steps: constructing a simulation system and configuring simulation parameters, the simulation system comprising a circular arc array base station transceiver model and a fish motion model composed of multiple array elements; simulating an actual motion track of the fish based on an initial position of the fish and the fish motion model; controlling each array element to transmit and receive signals according to a preset transmission and reception cycle, wherein each array element transmits a detection signal in a transmission state and receives a return signal in a receiving state; estimating a calculated position of the fish according to the detection signal and the return signal of each array element in each transmission and reception cycle; determining a track deviation of the fish according to a calculated motion track composed of multiple calculated positions and the actual motion track; and generating a visual report for reflecting a motion state of the fish based on the actual motion track, the calculated motion track and the track deviation. The fish motion visual simulation method, device and equipment provided in the application can provide accurate and reliable data for fishery management, acoustic detection and biological behavior research.

Description

Technical Field

[0001] This application relates to the field of motion simulation, and in particular to a method, apparatus and equipment for visualizing and simulating fish motion. Background Technology

[0002] In aquaculture, the patterns of fish movement are directly related to the management of the aquaculture environment, the optimization of feeding strategies, and the early warning and control of escape risks. Current technologies for studying fish behavior mainly rely on manual observation or video image-based detection methods. While these methods can capture some aspects of fish movement, their accuracy is often low in complex underwater environments, insufficient lighting, or when fish are densely packed. Furthermore, these methods are mostly two-dimensional and cannot accurately reflect the movement trajectories of fish in three-dimensional space.

[0003] Existing research also presents the results of the exercise in a relatively simple way, lacking intuitive visualization tools, and thus failing to provide accurate and reliable data for fisheries management, acoustic detection, and biological behavior research. Summary of the Invention

[0004] In view of this, this application provides a fish movement visualization simulation method, apparatus and equipment for visually displaying the movement status of fish, so as to provide accurate and reliable data for fisheries management, acoustic detection and biological behavior research.

[0005] Specifically, this application is implemented through the following technical solution:

[0006] The first aspect of this application provides a method for visualizing and simulating fish movement, the method comprising:

[0007] A simulation system is constructed, and simulation parameters are configured for the simulation system; the simulation system includes an array transceiver model and a fish motion model; the array transceiver model is a circular arc array composed of multiple array elements;

[0008] The fish's actual movement trajectory is simulated based on its initial position and the fish's motion model.

[0009] Each element in the control array transceiver model periodically transmits and receives signals according to a preset transmission and reception cycle; wherein, each element is in the transmission state for the preset duration of each transmission and reception cycle, transmitting a detection signal, and in the reception state for the remaining duration, receiving a return signal; the detection signal of each element consists of a sinusoidal modulated pulse encoded with a Gold sequence.

[0010] During each transmission and reception cycle, the fish's measured position is estimated based on the detection signals of each array element and the return signals of each array element.

[0011] The trajectory deviation of the fish is determined based on the calculated motion trajectory formed by multiple measured positions and the actual motion trajectory.

[0012] Based on the actual movement trajectory, the calculated movement trajectory, and the trajectory deviation, a visual report reflecting the fish's movement status is generated and displayed to the user.

[0013] A second aspect of this application provides a fish movement visualization simulation device, the device comprising a simulation module, a control module, a calculation module, a determination module, and a display module; wherein...

[0014] The simulation module is used to construct a simulation system and configure simulation parameters for the simulation system; the simulation system includes an array transceiver model and a fish motion model; the array transceiver model is a circular arc array composed of multiple array elements;

[0015] The simulation module is used to simulate the actual movement trajectory of the fish based on the fish's initial position and the fish's motion model.

[0016] The control module is used to control each array element in the base transceiver model to periodically transmit and receive signals according to a preset transmission and reception cycle; wherein, each array element is in the transmission state for a preset duration of each transmission and reception cycle, transmitting a detection signal, and in the reception state for the remaining duration, receiving a return signal; the detection signal of each array element consists of a sinusoidal modulated pulse encoded with Gold sequence.

[0017] The calculation module is used to estimate the fish's position based on the detection signals and return signals of each array element during each transmission and reception cycle.

[0018] The determining module is used to determine the trajectory deviation of the fish based on the calculated motion trajectory formed by multiple calculated positions and the actual motion trajectory;

[0019] The display module is used to generate a visual report reflecting the fish's movement state based on the actual movement trajectory, the calculated movement trajectory, and the trajectory deviation, and to display the visual report to the user.

[0020] A third aspect of this application provides a fish motion visualization simulation device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of any of the methods provided in the first aspect of this application.

[0021] The fish motion visualization simulation method, apparatus, and equipment provided in this application simulate and calculate the fish's motion trajectory by constructing an array transceiver model and a fish motion model. First, the actual motion trajectory of the fish is generated based on its initial position and motion model, serving as a reference for subsequent comparative analysis. Then, through an array arrangement in the form of a circular arc array, the array elements are controlled to periodically transmit and receive sinusoidal modulated pulses encoded by GOLD sequences. The cross-correlation between the detected and returned signals is used to estimate the fish's calculated position at each moment. Furthermore, by comparing the calculated trajectory formed by multiple calculated positions with the simulated actual trajectory, not only can the trajectory deviation during the fish's motion be obtained, but a visual report reflecting the fish's motion state can also be generated. This method can comprehensively reflect the fish's motion state, intuitively show the difference between the calculated results and the actual situation, and help verify the accuracy and reliability of the simulation system, improving the analytical efficiency and visualization effect in fish motion research and related applications. Attached Figure Description

[0022] Figure 1 A flowchart of an embodiment of the fish motion visualization simulation method provided in this application;

[0023] Figure 2 This is a schematic diagram of an arc array shown in an exemplary embodiment of this application;

[0024] Figure 3 A timing diagram of each array element in each transmit / receive cycle, as shown in an exemplary embodiment of this application;

[0025] Figure 4 This is a schematic diagram of a trajectory comparison map shown in an exemplary embodiment of this application;

[0026] Figure 5 This is a schematic diagram illustrating a fish movement trajectory tracking diagram as an exemplary embodiment of this application;

[0027] Figure 6 This is a schematic diagram of a trajectory deviation map shown in an exemplary embodiment of this application;

[0028] Figure 7 This is a schematic diagram illustrating a velocity variation curve as shown in an exemplary embodiment of this application;

[0029] Figure 8 This is a schematic diagram illustrating a change in the direction of motion, as shown in an exemplary embodiment of this application.

[0030] Figure 9 This is a schematic diagram illustrating the direction angle of movement of a fish at a certain moment, as shown in an exemplary embodiment of this application.

[0031] Figure 10 This is a schematic diagram illustrating a detection signal as shown in an exemplary embodiment of this application;

[0032] Figure 11 This is a schematic diagram illustrating the detection signal and return signal as an exemplary embodiment of this application;

[0033] Figure 12 A schematic diagram illustrating a cross-correlation curve for an exemplary embodiment of this application;

[0034] Figure 13 This is a schematic diagram of the structure of Embodiment 1 of the fish motion visualization simulation device provided in this application;

[0035] Figure 14 This application provides an exemplary embodiment illustrating a fish motion visualization simulation device. Detailed Implementation

[0036] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0037] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used herein are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0038] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0039] The following specific embodiments are given to illustrate the technical solution of this application in detail.

[0040] Figure 1 This is a flowchart of an embodiment of the fish motion visualization simulation method provided in this application. Please refer to... Figure 1 The method provided in this embodiment may include:

[0041] S101. Construct a simulation system and configure simulation parameters for the simulation system; the simulation system includes an array transceiver model and a fish motion model; the array transceiver model is a circular arc array composed of multiple array elements.

[0042] Specifically, the simulation system can run on a computer terminal or a distributed simulation platform, providing functions such as fish trajectory generation, signal transmission and reception, cross-correlation processing, and visualization. This simulation system is implemented in software and can operate independently or interface with external data acquisition equipment or underwater sensor systems.

[0043] It should be noted that the array transceiver model is a circular arc array composed of multiple array elements. The circular arc array can provide a wider coverage angle in the horizontal direction, while also creating a certain spatial interval between the array elements, which is beneficial for improving the accuracy of three-dimensional positioning and enhancing the resolution of the echo signal. Furthermore, compared to linear or regular rectangular arrays, the circular arc array can better reduce blind spots when tracking moving targets, improving the ability to simultaneously monitor multiple targets.

[0044] Figure 2 This is a schematic diagram of an arc array shown in an exemplary embodiment of this application. Please refer to... Figure 2 , Figure 2 Figure (A) is a top view of the circular arc array in the horizontal direction. Figure 2 Figure (B) is a cross-sectional view of the circular arc array in the vertical direction. The array comprises nine array elements. These nine elements are distributed in an arc shape in the horizontal direction and in a triangular wave-like undulating distribution in the vertical direction. The arc-shaped distribution in the horizontal direction provides a wider coverage angle, creating a certain spatial interval between the array elements, thereby improving the three-dimensional positioning accuracy in the horizontal direction, reducing blind spots, and enhancing the ability to monitor multiple targets simultaneously. The triangular wave-like undulating distribution in the vertical direction, with its staggered vertical undulations, improves the spatial resolution of the array elements in the vertical direction, increases the accuracy of measuring the vertical position of fish, reduces mutual interference between array elements, enhances target distinguishability, and optimizes the overall array coverage, enabling the system to more comprehensively and accurately monitor the movement trajectory of fish schools in the water space.

[0045] Understandably, the array transceiver model is used to simulate the transmission and reception of sound wave signals, detect fish, and calculate the fish's position based on the detection results.

[0046] Furthermore, the fish motion model is used to characterize the movement patterns of fish in the simulation scenario. In one possible implementation, the fish motion model is based on a random swim model, which can generate the three-dimensional position coordinates of the fish to approximate the movement characteristics of a school of fish in actual water.

[0047] Furthermore, the simulation parameters configured for the simulation system are configured according to actual needs, and this embodiment does not limit this. In one possible implementation, the simulation parameters may include acoustic parameters, array parameters, signal parameters, and monitoring parameters, the specific contents and meanings of which are shown in Table 1:

[0048] Table 1 Simulation System Parameters

[0049]

[0050] S102. Simulate the actual movement trajectory of the fish based on its initial position and the fish motion model.

[0051] Specifically, the initial position of the fish is set by the user according to actual needs; in this embodiment, it is not limited. In practice, the user can set the initial position of the fish in the simulation environment. The fish motion model is used to characterize the movement patterns of the fish in the simulation scene. The fish motion model can be implemented based on random swimming, circular motion, undulating motion, or other algorithms that conform to the actual behavioral characteristics of fish schools. Optionally, in one possible implementation, it is assumed that the fish performs circular motion in the horizontal direction and undulating motion in the z-direction; the fish motion model can be expressed as:

[0052] ;

[0053] ;

[0054] ;

[0055] Where (x, y, z) represents the actual position of the fish, , , ) indicates the center of the preset active area; radius indicates the radius of the preset active area.

[0056] Based on the fish's initial position and the above-mentioned fish motion model, the fish's real-time actual position can be calculated, resulting in an actual motion trajectory composed of multiple actual positions.

[0057] It should be noted that the generated actual motion trajectory will serve as a reference for the subsequent transmission and reception of signals by the array transceiver model, supporting the fish's position calculation, trajectory deviation analysis, and motion visualization.

[0058] S103. Control each element in the array transceiver model to periodically transmit and receive signals according to a preset transmission and reception cycle; wherein, each element is in the transmission state for the preset duration of each transmission and reception cycle, transmitting a detection signal, and in the reception state for the remaining duration, receiving a return signal; the detection signal of each element consists of a sinusoidal modulated pulse encoded with Gold sequence.

[0059] Figure 3 The timing diagram for each array element in each transmit / receive cycle is shown as an exemplary embodiment of this application. Please refer to... Figure 3 During each transmit / receive cycle, each array element goes through two stages: transmit and receive, as shown in the diagram: transmit state and receive state.

[0060] Specifically, within each transmit / receive cycle, the array element is in transmit mode for a preset transmission duration, transmitting detection signals; during the remaining duration, the array element switches to receive mode to receive return signals reflected back from targets in the water (such as fish). This periodic operation mode ensures that the system can continuously track and monitor targets in the time domain.

[0061] Specifically, in one possible implementation, the detection signal employs sinusoidal modulated pulses encoded with a Gold sequence. As a pseudo-random sequence, the Gold sequence possesses excellent autocorrelation properties and low cross-correlation properties, enhancing the anti-interference performance and target resolution of signal detection in complex underwater noise environments. The sinusoidal modulated pulses provide a stable carrier frequency, enabling efficient signal propagation in water and making it suitable for cross-correlation detection. By combining these two methods, a detection signal that is both periodic and anti-interference can be generated, providing a reliable foundation for subsequent distance estimation and target localization.

[0062] Furthermore, in multi-element emission scenarios, this application preferably uses Gold sequences for encoding. Gold sequences are generated by XORing a pair of preferred M sequences, inheriting the good autocorrelation properties of M sequences and exhibiting superior performance in cross-correlation, making them particularly suitable for independent encoding scenarios involving multiple elements. Compared to a single M sequence, Gold sequences offer more encoding options, for… For the order Gold sequence, it can generate The Gold sequences are distinct, ensuring that each element receives an independent, non-interfering code even with a large number of array elements. Furthermore, the low cross-correlation between the Gold sequences significantly reduces mutual interference between different elements during signal transmission, thus improving signal separation and positioning accuracy. Additionally, the Gold sequences exhibit good balance, with approximately equal numbers of "0"s and "1"s. This characteristic facilitates signal detection and energy distribution equilibrium, further enhancing the system's stability and reliability in complex underwater environments.

[0063] Specifically, this embodiment uses a total of 9 array elements. Only a 5th-order Gold sequence is needed to select 9 from 33 available Gold sequences and assign them to each array element for independent encoding. This ensures sufficient mutual distinguishability and strong anti-interference performance. In terms of signal form, the transmitted detection signal can be represented as:

[0064]

[0065] in, It is a Gold sequence, and its values ​​are... ; It is a sinusoidal carrier frequency.

[0066] Understandably, theoretically, the first The return signal received by each array element can be represented as:

[0067]

[0068] in, It is the distance decay factor. It is the array element The actual distance to the fish It is the round-trip propagation delay of the signal, and the propagation delay of the signal in water. It is superimposed environmental noise.

[0069] Specifically, referring to Table 1, in this embodiment, the transmit / receive cycle is 2 seconds, with a transmission duration of 20 milliseconds and the remaining time used to receive the return signal. Furthermore, the carrier frequency of the detection signal is set to 25 kHz, and the sampling rate is 100 kHz to ensure the accuracy of signal acquisition and processing.

[0070] S104. During each transmission and reception cycle, the measured position of the fish is estimated based on the detection signals of each array element and the return signals of each array element.

[0071] Specifically, the distance information from the fish to each array element is determined by using the detection signals emitted by each array element and the returned signals received. Then, combined with the known distribution of the array elements in three-dimensional space, the measured position of the fish in the simulation scene is calculated based on the ranging results of multiple array elements.

[0072] Optionally, in one possible implementation, the specific implementation process of this step may include:

[0073] Step 1: For each array element, discretize the detection signal and return signal of the array element according to the same sampling frequency to obtain the first discretized sequence corresponding to the detection signal and the second discretized sequence corresponding to the return signal.

[0074] Specifically, for the continuous detection signals emitted by the array elements and the continuous return signals received by the array elements. According to a uniform sampling frequency Sampling is performed at (unit: Hz). The sampling time is... ,in Integer indices are used. The discretized probe signal (i.e., the first discretized sequence) and the discretized return signal (i.e., the second discretized sequence) can be represented as follows:

[0075] ;

[0076] ;

[0077] in, The number of discrete points contained in the first discretized sequence. The number of discrete points contained in the second discretized sequence.

[0078] As described above, the reception duration is longer than the transmission duration, and the number of discrete points contained in the second discretization sequence is much greater than the number of discrete points contained in the first discretization sequence.

[0079] Step 2: Determine the number of discrete points contained in the first discretized sequence, and determine the size of the sliding window based on the number; wherein the size of the sliding window is equal to the number.

[0080] Specifically, the first discretized sequence contains the following number of discrete points: Therefore, the size of the sliding window is set to... For example, in one possible implementation, the first discretization sequence contains 10 discrete points, and the size of the sliding window is set to 10.

[0081] Step 3: Using the first discrete point in the second discretized sequence as the starting point of the sliding window, calculate the cross-correlation value between the subsequence corresponding to the sliding window and the first discretized sequence, and move the sliding window according to a preset moving step size. Calculate the cross-correlation value between the subsequence corresponding to the sliding window and the first discretized sequence again to obtain the cross-correlation value corresponding to each discrete point in the second discretized sequence.

[0082] The sliding window will slide over the second discretized sequence, and each time it will truncate a length of... The subsequence is cross-correlated with the first discretized sequence to ensure that the time domain length of the comparison is consistent with the transmitted signal.

[0083] In specific implementation, in this step, the starting point of the sliding window is placed at the first discrete point of the second discretization sequence, and the truncation length is... The subsequence is cross-correlated with the first discretized sequence. The specific calculation formula is as follows:

[0084] ;

[0085] in, This is the first discretized sequence. This is the second discretized sequence; This is the starting index of the sliding window. The sliding window moves according to a preset step size. By moving sequentially, we obtain the cross-correlation values ​​corresponding to all possible positions in the second discretized sequence.

[0086] It should be noted that the specific value of the preset step size is set according to actual needs, and is not limited in this embodiment. The following explanation uses a preset step size of 1 as an example.

[0087] Step 4: Find the maximum value of the cross-correlation value corresponding to each discrete point in the second discretization sequence, and estimate the measured distance of the array element from the fish based on the location of the maximum value.

[0088] Referring to the preceding description, it can be understood that through step 3, the cross-correlation value corresponding to each discrete point can be calculated. In this step, firstly, the maximum value among multiple cross-correlation values ​​is found, and further, the measured distance of the array element from the fish is estimated based on the location of the maximum value.

[0089] In a specific implementation, optionally, in one possible implementation, estimating the measured distance between the array element and the fish based on the location of the maximum value includes:

[0090] (1) Determine the time delay based on the location and the sampling frequency.

[0091] Specifically, in cross-correlation sequences Find the position corresponding to the maximum value in the middle:

[0092]

[0093] in, The index corresponding to the maximum value among the cross-correlation values. The maximum cross-correlation value is the index corresponding to the maximum cross-correlation value, which reflects the time delay between the transmitted and returned signals. In practical implementation, this can be determined based on the sampling rate. The mathematical expression for calculating latency is as follows:

[0094]

[0095] in, The total delay of the return signal propagation, in seconds.

[0096] (2) Determine the measured distance based on the time delay and the preset sound wave propagation speed.

[0097] Specifically, combining the speed of sound in water... The calculated distance between the fish and the array element can be obtained, and its mathematical expression is as follows:

[0098]

[0099] Where d is the measured distance between the fish and the array element.

[0100] It should be noted that, following the above method, the measured distance from the fish to each array element can be calculated, denoted as . , Take numbers 1 to 9.

[0101] S305. Determine the calculated position of the fish based on the position of each array element in the array transceiver model, the calculated distance of each array element from the fish, and the preset range of movement of the fish.

[0102] In a specific implementation, one possible approach to this step may include:

[0103] (1) Construct an objective function; wherein the objective function optimizes the estimation of the fish position by minimizing the difference between the actual distance between the fish position and the position of each array element and the calculated distance between each array element and the fish.

[0104] It should be noted that the positions of each array element in the array transceiver model are set according to actual needs, and are not limited in this embodiment. For example, in one embodiment, the position of each element in the array transceiver model is... The position of each element is Furthermore, following the above steps, the calculated distance between each array element and the fish is... .

[0105] In this step, the objective function is constructed by combining the ranging results of multiple array elements as follows:

[0106] ;

[0107] in, The measurement location of the fish to be measured. For the fish to reach the formation The actual distance For the calculation of the first The calculated distance between each element and the fish. The total number of array elements is denoted as . This objective function achieves the fusion of distance measurement information from multiple array elements by minimizing the sum of squared residuals between the fish's position and the distances to each array element, thereby obtaining the optimal measured position of the fish.

[0108] (2) Based on the preset range of fish movement and the measured distance of each array element fish, the target function is solved iteratively by a specified optimization algorithm to obtain the measured position of the fish.

[0109] Furthermore, during optimization, a preset fish movement range can be used as a constraint to prevent solutions from exceeding the reasonable water area. The objective function is iteratively solved using a specified optimization algorithm. This optimization algorithm can be a general nonlinear least squares solution method. In this embodiment, an iterative nonlinear least squares algorithm (such as the Levenberg-Marquardt method) is selected, which has the advantage of being suitable for handling highly nonlinear and multidimensional estimation problems and can effectively converge to the global optimum.

[0110] S105. Determine the trajectory deviation of the fish based on the calculated motion trajectory formed by multiple calculated positions and the actual motion trajectory.

[0111] Specifically, the measured positions of the fish in each harvesting cycle are obtained through the above steps. The measured positions from multiple sending and receiving cycles constitute the fish's measured movement trajectory. Meanwhile, the actual motion trajectory was obtained using the fish motion model. ,in This represents the fish's actual position at the corresponding time point. Based on the measured trajectory and the actual trajectory, the trajectory deviation of the fish at each time point can be calculated. The specific formula is as follows:

[0112]

[0113] in, In the first Trajectory deviation at each point in time.

[0114] Furthermore, the trajectory deviations at each time point are summarized to form an overall trajectory deviation index. For example, the average deviation or maximum deviation can be calculated. Through this step, the system can quantify the deviation between the measured trajectory and the actual motion trajectory, providing a basis for subsequent simulation accuracy evaluation, model optimization, or fish-driving strategy adjustment. It can also serve as the core data foundation for visualization and performance analysis.

[0115] S106. Generate a visualization report reflecting the fish's movement state based on the actual movement trajectory, the calculated movement trajectory, and the trajectory deviation, and display the visualization report to the user.

[0116] Specifically, based on the actual movement trajectory Calculate the trajectory of motion In addition to trajectory deviation, the system can generate visual reports reflecting the fish's movement status. Through visualization, users can intuitively understand the fish's movement status, measurement accuracy, and potential deviation distribution, which helps adjust simulation model parameters, optimize measurement algorithms, or develop more reasonable monitoring and fish control strategies. Furthermore, the visualization results can be exported as report files for long-term monitoring and comparative analysis.

[0117] In a specific implementation, in one possible approach, generating a visual report reflecting the fish's movement state based on the actual movement trajectory, the calculated movement trajectory, and the trajectory deviation includes:

[0118] (1) Generate a trajectory comparison diagram based on the actual motion trajectory and the calculated motion trajectory; the trajectory comparison diagram is used to simultaneously display the actual motion trajectory and the calculated motion trajectory in the same coordinate system.

[0119] (2) Generate a fish motion trajectory tracking diagram based on the actual motion trajectory, the calculated motion trajectory, and the position of each array element in the array transceiver model; the fish motion trajectory tracking diagram is used to simultaneously display the position, actual motion trajectory, and calculated motion trajectory of each array element in the same coordinate system.

[0120] (3) Generate a trajectory deviation map based on the trajectory deviation.

[0121] (4) Integrate the trajectory comparison diagram, the fish movement trajectory tracking diagram, and the trajectory deviation diagram to generate the visualization report.

[0122] specific, Figure 4 This is a schematic diagram of a trajectory comparison chart shown in an exemplary embodiment of this application. Please refer to... Figure 4 , Figure 4 This diagram illustrates the comparison between the actual and calculated motion trajectories in the same coordinate system. It includes the overall path shape of both trajectories, as well as the markers for the actual and calculated start and end points. This diagram clearly shows the differences between the calculated and actual trajectories, intuitively reflecting the accuracy of the positioning method in spatial path reconstruction. This visualization not only helps in the quantitative analysis of trajectory deviations but also provides an intuitive reference for subsequent optimization of fish motion models or array parameters, thereby improving the system's verifiability and interpretability.

[0123] Figure 5 This is a schematic diagram illustrating a fish movement trajectory tracking diagram as shown in an exemplary embodiment of this application. Please refer to... Figure 5 , Figure 5 This visualization displays the positional distribution of each element in the array transceiver model, the actual and calculated movement trajectories of the fish, and its actual and calculated positions at different time points, along with velocity indicators. Through this 3D tracking diagram, users can intuitively understand the relationship between the array distribution and the fish's movement in a spatial dimension, clearly comparing the deviations between the calculated and actual trajectories. This visualization not only helps analyze the accuracy of the positioning algorithm in 3D space but also provides an intuitive reference for further optimizing array arrangement and signal processing methods, thereby improving the overall system's reliability and applicability.

[0124] Figure 6 This is a schematic diagram of a trajectory deviation map shown in an exemplary embodiment of this application. Please refer to... Figure 6 This graph visually illustrates the fluctuation trend of trajectory error at different time points, reflecting the changing pattern of the deviation between the calculated and actual positions. This visualization helps analyze the stability of the positioning algorithm's accuracy during dynamic processes, providing a reference for subsequent model optimization and parameter adjustment.

[0125] Furthermore, in a preferred implementation, after estimating the fish's measured position based on the detection signals and return signals of each array element, the method further includes:

[0126] (1) Based on the estimated position of the fish at the current moment and the estimated position of the fish at the previous moment, determine the X-direction velocity component of the fish in the X direction, the Y-direction velocity component of the fish in the Y direction, and the Z-direction velocity component of the fish in the Z direction at the current moment.

[0127] Specifically, the fish's current position is The fish's position at the previous moment was The time interval between the two measurements is Then the fish are direction, direction and The X-axis velocity components, Y-axis velocity components, and Z-axis velocity components are as follows:

[0128]

[0129]

[0130]

[0131] in, , , They are fish in direction, direction and The X-axis velocity component, Y-axis velocity component, and Z-axis velocity component in the direction of motion. It is the sampling time interval.

[0132] (2) Calculate the resultant velocity of the fish at the current moment based on the X-axis velocity component, the Y-axis velocity component and the Z-axis velocity component.

[0133] Specifically, the fish's resultant velocity is calculated based on its X-axis, Y-axis, and Z-axis velocity components. Specifically, the resultant velocity is obtained using the Euclidean distance formula:

[0134]

[0135] in, It is the fish's current velocity in three-dimensional space.

[0136] (3) Determine the horizontal angle of the fish in the horizontal direction at the current moment based on the X-axis velocity component and the Y-axis velocity component.

[0137] Specifically, the horizontal direction angle of the fish in the XY plane is calculated using the following formula:

[0138]

[0139] in, It is the horizontal angle of the fish in the horizontal direction. , These are the X-axis velocity component and the Y-axis velocity component, respectively.

[0140] (4) Calculate the pitch angle of the fish in the vertical direction at the current moment based on the X-axis velocity component, the Y-axis velocity component and the Z-axis velocity component.

[0141] Specifically, based on the velocity components in the three directions, the fish's pitch angle is calculated to represent its vertical motion. Calculated using the following formula:

[0142]

[0143] in, It is the fish's pitch angle in the vertical direction. It is the vertical velocity, pitch angle It reflects the fish's tendency to move up and down.

[0144] (5) Generate the velocity change curve of the fish based on the resultant velocity of the fish at each moment, and generate the change diagram of the fish's motion direction based on the horizontal direction angle of the fish in the horizontal direction and the pitch angle of the fish in the vertical direction at each moment.

[0145] Specifically, the calculated resultant velocity values ​​at each moment are used to generate a velocity change curve for the fish. This curve visually displays the trend of the fish's velocity over time. Furthermore, the azimuth angle at each moment is... and pitch angle By combining the generated map of the fish's movement direction changes, the changes in the fish's movement direction at different points in time can be displayed in a three-dimensional or two-dimensional coordinate system.

[0146] Specifically, Figure 7 This is a schematic diagram of a velocity variation curve shown in an exemplary embodiment of this application. Please refer to... Figure 7The graph shows time on the horizontal axis and the fish's instantaneous velocity on the vertical axis. The curve depicts the fish's velocity changes at different points in time. This graph allows users to intuitively understand the fish's acceleration, deceleration, and changes in its motion.

[0147] Figure 8 This is a schematic diagram illustrating the change in motion direction of an exemplary embodiment of this application. Please refer to... Figure 8 The figure illustrates the changes in the fish's horizontal and vertical pitch angles over time, with the horizontal axis representing time and the vertical axes representing the horizontal and pitch angles, respectively. This figure allows users to visually observe the changing patterns of the fish's movement direction at different points in time, understanding its dynamic behavior in both horizontal and vertical dimensions. It provides a visual basis for analyzing fish movement patterns, calculating trajectories, and verifying the accuracy of positioning algorithms.

[0148] Furthermore, Figure 9 This is a schematic diagram illustrating the direction angle of a fish's movement at a certain moment, as shown in an exemplary embodiment of this application. Please refer to... Figure 9 This diagram is used to visually display the values ​​of the fish's horizontal angle in the horizontal direction and its pitch angle in the vertical direction at the current moment.

[0149] It should be noted that, Figure 4 , Figure 5 and Figure 9 The display can be linked, that is, when the fish is in the movement trajectory tracking map ( Figure 5 The trajectory comparison diagram is shown for each time the position is moved. Figure 4 ) and direction angle of motion ( Figure 9 All data are updated synchronously, reflecting the fish's current position, speed, direction, and measurement deviation. This linked display allows users to simultaneously observe the fish's real-time position, trajectory, and speed changes in space, providing a more intuitive understanding of the relationship between the fish's movement and the array measurements. This improves analysis accuracy and interactive experience, while also facilitating the discovery of measurement deviations, adjustment of array parameters, or optimization of the positioning algorithm, achieving integrated system visualization, analysis, and verification.

[0150] (6) Display the speed change curve and the motion direction change diagram to the user.

[0151] Specifically, the generated speed change curves and motion direction change graphs are displayed to users, supporting zooming in, zooming out, and rotation of the graphs. This allows users to intuitively analyze the fish's movement status and trends, providing visualized data support for subsequent fish behavior research, ecological monitoring, or aquaculture management.

[0152] The method provided in this embodiment, by combining an array transceiver model, a Gold sequence-encoded sinusoidal modulated pulse detection signal, and a fish motion model, achieves accurate monitoring of fish motion state and trajectory calculation, and has at least the following advantages:

[0153] (1) Improve the realism of simulation and reduce human intervention and error: By introducing the array transceiver model into the simulation system and using an arc array composed of multiple array elements for signal transmission and reception, the actual underwater acoustic detection environment can be simulated more realistically. This avoids the problem that traditional methods rely on simplified models and differ greatly from the actual detection situation. In addition, no manual measurement and judgment are required, avoiding human operation errors and improving the reliability and stability of monitoring.

[0154] (2) Improve the accuracy of positioning and trajectory calculation: By utilizing the spatial distribution of multi-element arrays and the characteristics of Gold sequence encoded signals, combined with the objective function minimization optimization algorithm, the fish's position and trajectory can be accurately calculated, effectively reducing measurement errors in complex underwater noise environments and ensuring the accuracy of monitoring data.

[0155] (3) Supports real-time visualization analysis and multi-graph linkage: By generating trajectory comparison charts, fish movement trajectory tracking charts, and velocity direction charts, multi-dimensional visualization of the fish school's movement status is achieved. When users observe changes in the fish school's movement trajectory, the three types of charts are updated in tandem, allowing users to more intuitively understand the fish's movement behavior and measurement deviations. This linked display not only enhances data interpretability but also facilitates the rapid detection of abnormal behavior, improves operational decision-making efficiency, and supports zooming in and out for refined analysis.

[0156] (4) Improve monitoring efficiency: The system can process data from multiple array elements simultaneously and quickly generate fish measurement position and trajectory deviation analysis reports, avoiding the time-consuming process of traditional manual analysis, and is suitable for real-time monitoring and long-term aquatic ecological management.

[0157] (5) Wide range of applications: The method of this application can provide a scientific basis for fisheries management, provide auxiliary verification means for underwater acoustic detection, and provide reliable data support for fish behavior research. It has strong practicality and promotion value.

[0158] The fish motion visualization simulation method provided in this application enables accurate monitoring and trajectory calculation of fish school movements in underwater environments through an array transceiver model and a fish motion model. First, a circular array composed of multiple array elements is set up, and each element is controlled to emit sinusoidal modulated pulse signals encoded with Gold sequences. Then, the reflected signals from the fish are efficiently collected in a complex underwater noise environment. Cross-correlation analysis is used to process the returned and transmitted signals, thereby accurately estimating the calculated distance between each array element and the fish. Next, combining the spatial position of each array element and the preset fish movement range, the three-dimensional calculated position of the fish is obtained through iterative calculation using an objective function minimization and optimization algorithm, constructing the calculated motion trajectory. Specifically, by comparing the actual motion trajectory with the calculated trajectory and generating a trajectory deviation analysis, the calculation accuracy can be intuitively evaluated. Furthermore, by generating a trajectory comparison diagram, a fish motion trajectory tracking diagram, and a velocity direction diagram, and achieving three-dimensional linked display, users can observe the real-time changes in the fish school's motion state from a three-dimensional perspective, and can zoom in or out, enhancing data interpretability and decision support capabilities. This method significantly improves the accuracy and reliability of fish movement monitoring, especially in dynamic waters and multi-fish environments, providing visualized real-time feedback and offering efficient and intuitive technical support for underwater aquaculture monitoring, scientific research experiments, and aquatic ecological management.

[0159] Optionally, in one possible implementation, the method further includes:

[0160] (1) For each transmit and receive cycle, generate a graphical representation of the detection signal of each array element based on the detection signal of each array element.

[0161] Figure 10 This is a schematic diagram illustrating a detection signal in an exemplary embodiment of this application. Please refer to... Figure 10 , Figure 10 Figure (A) is a schematic diagram of the complete detection signal. Figure 10 Figure (B) is a partial schematic diagram of the detection signal.

[0162] (2) Generate a graphical representation of the return signal of each array element based on the return signal of each array element.

[0163] Figure 11 This is a schematic diagram illustrating the detection signal and return signal of an exemplary embodiment of this application. Please refer to... Figure 11 , Figure 11 Figure (A) is a schematic diagram of the detection signal. Figure 11 Figure (B) shows a schematic diagram of the return signal received by the array element. During each transmit / receive cycle, each array element undergoes both transmission and reception phases. This periodic operation mode ensures that the system can continuously track and monitor the target in the time domain.

[0164] (3) For each array element, based on the time delay corresponding to each discrete point in the second discretization sequence and the cross-correlation value corresponding to that discrete point, generate the cross-correlation curve between the cross-correlation value and the time delay, and obtain the cross-correlation curve corresponding to that array element.

[0165] Figure 12 A schematic diagram of the cross-correlation curve shown in an exemplary embodiment of this application is provided below. Figure 12 This diagram visually illustrates the correspondence between cross-correlation values ​​and time delay, improving the understanding and verification of the distance estimation process.

[0166] (4) For each array element, a visual report on the transmission and reception performance of the array element in the transmission and reception cycle is generated by integrating the graphical representation of the array element's detection signal, the graphical representation of the array element's return signal, and the cross-correlation curve corresponding to the array element.

[0167] In practice, the graphical representation of the array element's detection signal, the graphical representation of the array element's return signal, and the cross-correlation curve corresponding to the array element can be integrated together to generate a visualization report of the array element's transmission and reception performance in the corresponding transmission and reception cycle.

[0168] (5) Display the transmission and reception performance visualization report to the user.

[0169] As described above, by graphically representing the detection signals, return signals, and cross-correlation relationships of each array element, and visually displaying the fish's velocity change curves and movement direction change diagrams, users can intuitively grasp the changing patterns of the fish's trajectory, speed, and movement direction. They can also clearly understand the performance of each array element during transmission and reception. This multi-layered and intuitive visualization method not only enhances the interpretability and reliability of simulation results but also facilitates the detection of abnormal movements or signal deviations, improving research efficiency and the accuracy of data analysis. It has significant application value for fisheries management, underwater acoustic detection, and biological behavior research.

[0170] Corresponding to the aforementioned embodiment of a fish motion visualization simulation method, this application also provides an embodiment of a fish motion visualization simulation device.

[0171] Figure 13 This is a schematic diagram of the structure of Embodiment 1 of the fish motion visualization simulation device provided in this application. Please refer to... Figure 13 The device provided in this embodiment includes a simulation module 1301, a control module 1302, a calculation module 1303, a determination module 1304, and a display module 1305; wherein,

[0172] The simulation module 1301 is used to construct a simulation system and configure simulation parameters for the simulation system; the simulation system includes an array transceiver model and a fish motion model; the array transceiver model is an arc array composed of multiple array elements;

[0173] The simulation module 1301 is used to simulate the actual movement trajectory of the fish based on the initial position of the fish and the fish movement model.

[0174] The control module 1302 is used to control each array element in the array transceiver model to periodically transmit and receive signals according to a preset transmission and reception cycle; wherein, each array element is in the transmission state for a preset duration of each transmission and reception cycle, transmitting a detection signal, and is in the reception state for the remaining duration, receiving a return signal; the detection signal of each array element consists of a sinusoidal modulated pulse encoded with Gold sequence.

[0175] The calculation module 1303 is used to estimate the fish's position based on the detection signals of each array element and the return signals of each array element in each transmission and reception cycle.

[0176] The determining module 1304 is used to determine the trajectory deviation of the fish based on the calculated motion trajectory formed by multiple calculated positions and the actual motion trajectory.

[0177] The display module 1305 is used to generate a visual report reflecting the fish's movement state based on the actual movement trajectory, the calculated movement trajectory, and the trajectory deviation, and to display the visual report to the user.

[0178] The apparatus of this embodiment can be used to perform... Figure 1 The steps of the method embodiment shown are similar in principle and process, and will not be repeated here.

[0179] Figure 14 This is a schematic diagram illustrating a fish motion visualization simulation device, provided as an exemplary embodiment of this application. Please refer to... Figure 14 This application also provides a fish motion visualization simulation device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of any of the methods provided in the first aspect of this application.

[0180] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0181] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0182] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for visualizing and simulating fish movement, characterized in that, The method includes: A simulation system is constructed, and simulation parameters are configured for the simulation system; the simulation system includes an array transceiver model and a fish motion model; the array transceiver model is a circular arc array composed of multiple array elements; The fish's actual movement trajectory is simulated based on its initial position and the fish's motion model. Each element in the control array transceiver model periodically transmits and receives signals according to a preset transmission and reception cycle; wherein, each element is in the transmission state for the preset duration of each transmission and reception cycle, transmitting a detection signal, and in the reception state for the remaining duration, receiving a return signal; the detection signal of each element consists of a sinusoidal modulated pulse encoded with a Gold sequence. During each transmission and reception cycle, the fish's measured position is estimated based on the detection signals of each array element and the return signals of each array element. The trajectory deviation of the fish is determined based on the calculated motion trajectory formed by multiple measured positions and the actual motion trajectory. A visual report reflecting the fish's movement status is generated based on the actual movement trajectory, the calculated movement trajectory, and the trajectory deviation, and the visual report is displayed to the user. The method of estimating the fish's position based on the detection signals and return signals of each array element includes: For each array element, the detection signal and return signal of that array element are discretized at the same sampling frequency to obtain the first discretized sequence corresponding to the detection signal and the second discretized sequence corresponding to the return signal. The number of discrete points contained in the first discretized sequence is determined, and the size of the sliding window is determined based on the number; wherein the size of the sliding window is equal to the number. Using the first discrete point in the second discretized sequence as the starting point of the sliding window, the cross-correlation value between the subsequence corresponding to the sliding window and the first discretized sequence is calculated, and the sliding window is moved according to a preset moving step size. The cross-correlation value between the subsequence corresponding to the sliding window and the first discretized sequence is calculated again to obtain the cross-correlation value corresponding to each discrete point in the second discretized sequence. Find the maximum value of the cross-correlation value corresponding to each discrete point in the second discretized sequence, and estimate the measured distance of the array element from the fish based on the location of the maximum value. The calculated position of the fish is determined based on the position of each element in the array transceiver model, the calculated distance of each element from the fish, and the preset range of movement of the fish.

2. The method according to claim 1, characterized in that, After estimating the fish's measured position based on the detection signals and return signals of each array element, the method further includes: Based on the estimated position of the fish at the current moment and the estimated position of the fish at the previous moment, determine the X-axis velocity component of the fish in the X direction, the Y-axis velocity component of the fish in the Y direction, and the Z-axis velocity component of the fish in the Z direction at the current moment. Calculate the fish's combined velocity at the current moment based on the X-axis velocity component, the Y-axis velocity component, and the Z-axis velocity component; Based on the X-axis velocity component and the Y-axis velocity component, determine the horizontal angle of the fish in the horizontal direction at the current moment; Calculate the pitch angle of the fish in the vertical direction at the current moment based on the X-axis velocity component, the Y-axis velocity component, and the Z-axis velocity component. The fish's velocity change curve is generated based on the resultant velocity of the fish at each moment, and the fish's motion direction change diagram is generated based on the horizontal direction angle of the fish in the horizontal direction and the pitch angle of the fish in the vertical direction at each moment. The speed change curve and the motion direction change diagram are displayed to the user.

3. The method according to claim 1, characterized in that, The step of generating a visual report reflecting the fish's movement state based on the actual movement trajectory, the calculated movement trajectory, and the trajectory deviation includes: A trajectory comparison chart is generated based on the actual motion trajectory and the calculated motion trajectory; the trajectory comparison chart is used to simultaneously display the actual motion trajectory and the calculated motion trajectory in the same coordinate system; Based on the actual motion trajectory, the calculated motion trajectory, and the positions of each element in the array transceiver model, a fish motion trajectory tracking map is generated; the fish motion trajectory tracking map is used to simultaneously display the positions, actual motion trajectories, and calculated motion trajectories of each element in the same coordinate system. A trajectory deviation map is generated based on the trajectory deviation. The visualization report is generated by integrating the trajectory comparison map, the fish movement trajectory tracking map, and the trajectory deviation map.

4. The method according to claim 1, characterized in that, The step of determining the fish's calculated position based on the positions of each array element in the base transceiver model, the calculated distance of each array element from the fish, and the preset range of the fish's movement includes: Construct an objective function; wherein the objective function optimizes the estimation of the fish's position by minimizing the difference between the actual distance between the fish's position and the positions of each array element and the calculated distance between each array element and the fish; Based on the preset range of fish movement and the calculated distance of each element fish, the objective function is iteratively solved using a specified optimization algorithm to obtain the calculated position of the fish.

5. The method according to claim 1, characterized in that, The method further includes: For each transmit / receive cycle, a graphical representation of the detection signal of each array element is generated based on the detection signal of each array element. Based on the return signals of each array element, a graphical representation of the return signals of each array element is generated; For each array element, based on the time delay corresponding to each discrete point in the second discretization sequence and the cross-correlation value corresponding to that discrete point, a cross-correlation curve between the cross-correlation value and the time delay is generated, and the cross-correlation curve corresponding to that array element is obtained. For each array element, a visualization report of the transceiver performance of that array element in the corresponding transceiver cycle is generated by integrating the graphical representation of the array element's detection signal, the graphical representation of the array element's return signal, and the cross-correlation curve of the array element. The transmit and receive performance visualization report is then presented to the user.

6. The method according to claim 1, characterized in that, The step of estimating the distance between the array element and the fish based on the location of the maximum value includes: The time delay is determined based on the location and the sampling frequency; The measured distance is determined based on the time delay and the preset sound wave propagation speed.

7. The method according to claim 1, characterized in that, The circular arc array comprises nine array elements; the nine array elements are distributed in a circular arc shape in the horizontal direction and in a triangular wave-like undulating distribution in the vertical direction.

8. A fish movement visualization simulation device, characterized in that, The device includes a simulation module, a control module, a calculation module, a determination module, and a display module; wherein, The simulation module is used to construct a simulation system and configure simulation parameters for the simulation system; the simulation system includes an array transceiver model and a fish motion model; the array transceiver model is a circular arc array composed of multiple array elements; The simulation module is used to simulate the actual movement trajectory of the fish based on the fish's initial position and the fish's motion model. The control module is used to control each array element in the base transceiver model to periodically transmit and receive signals according to a preset transmission and reception cycle; wherein, each array element is in the transmission state for a preset duration of each transmission and reception cycle, transmitting a detection signal, and in the reception state for the remaining duration, receiving a return signal; the detection signal of each array element consists of a sinusoidal modulated pulse encoded with Gold sequence. The calculation module is used to estimate the fish's position based on the detection signals and return signals of each array element during each transmission and reception cycle. The determining module is used to determine the trajectory deviation of the fish based on the calculated motion trajectory formed by multiple calculated positions and the actual motion trajectory; The display module is used to generate a visual report reflecting the fish's movement state based on the actual movement trajectory, the calculated movement trajectory, and the trajectory deviation, and to display the visual report to the user. The method of estimating the fish's position based on the detection signals and return signals of each array element includes: For each array element, the detection signal and return signal of that array element are discretized at the same sampling frequency to obtain the first discretized sequence corresponding to the detection signal and the second discretized sequence corresponding to the return signal. The number of discrete points contained in the first discretized sequence is determined, and the size of the sliding window is determined based on the number; wherein the size of the sliding window is equal to the number. Using the first discrete point in the second discretized sequence as the starting point of the sliding window, the cross-correlation value between the subsequence corresponding to the sliding window and the first discretized sequence is calculated, and the sliding window is moved according to a preset moving step size. The cross-correlation value between the subsequence corresponding to the sliding window and the first discretized sequence is calculated again to obtain the cross-correlation value corresponding to each discrete point in the second discretized sequence. Find the maximum value of the cross-correlation value corresponding to each discrete point in the second discretized sequence, and estimate the measured distance of the array element from the fish based on the location of the maximum value. The calculated position of the fish is determined based on the position of each element in the array transceiver model, the calculated distance of each element from the fish, and the preset range of movement of the fish.

9. A fish movement visualization simulation device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the program, implements the steps of the method according to any one of claims 1-7.

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