Emergency communication methods, systems, electronic devices, and storage media based on diving systems

Abstract

This application provides an emergency communication method and system based on a diving system, applied in the field of signal processing technology. The method includes: obtaining an emergency communication signal transmitted by a diving bell, the emergency communication signal including at least: an underwater acoustic positioning signal, an environmental signal, and an optical signal; determining the estimated position of the diving bell based on a particle algorithm and the emergency communication signal; wherein the environmental signal is used to adjust the particle distribution, the optical signal is used to constrain the particle distribution range, and the underwater acoustic positioning signal is used to adjust the particle weights.

Description

Technical Field

[0001] This application relates to the field of information processing technology, and in particular to an emergency communication method and system based on a diving system. Background Technology

[0002] In the field of marine engineering, saturation diving is one of the important working methods for deep-sea operations. During saturation diving operations, the diving bell and the surface mother ship maintain a communication connection via an umbilical cord. However, due to the complexity of the marine environment, the umbilical cord connection between the diving bell and the mother ship may be interrupted due to unforeseen factors such as mechanical damage or cable entanglement. To ensure the safety of the personnel, when the umbilical cord connection is interrupted, the diving bell will activate an emergency communication system so that the mother ship can locate the diving bell and launch a timely rescue operation.

[0003] Currently, emergency communication for diving bells mainly relies on underwater acoustic signals transmitted by underwater acoustic positioning devices. However, because underwater acoustic signals are easily interfered with by environmental factors such as water flow, temperature, and salinity, the mother ship has difficulty accurately estimating the actual location of the diving bell, which seriously affects the efficiency and safety of emergency rescue. Summary of the Invention

[0004] In view of this, this application provides an emergency communication method and system based on a diving system.

[0005] According to the first aspect of this application, an emergency communication method based on a diving system is provided, comprising:

[0006] The emergency communication signal transmitted by the diving bell is obtained, and the emergency communication signal includes at least: underwater acoustic positioning signal, environmental signal and optical signal;

[0007] The estimated location of the diving bell is determined based on the particle algorithm and the emergency communication signal.

[0008] The environmental signal is used to adjust the distribution of particles, the optical signal is used to constrain the distribution range of particles, and the underwater acoustic positioning signal is used to adjust the weight of particles.

[0009] According to an embodiment of this application, determining the estimated location of the diving bell based on the particle algorithm and the emergency communication signal includes:

[0010] The first particle is obtained. The first particle is a Gaussian distribution state space sampling point randomly generated based on the multidimensional state vector and the initial covariance matrix with the last known position of the diving bell as the reference center. It is used to characterize the initial state distribution of the diving bell. The initial covariance matrix is ​​a symmetric positive definite matrix that characterizes the degree of uncertainty of the initial state of the diving bell in multiple dimensions.

[0011] The motion model corresponding to the first particle is modified based on the environmental signal to obtain the second particle. The motion model is a discrete-time linear equation describing the evolution of the particle state over time, which is used to predict the change of the motion state of the diving bell within a given time step. The motion model includes a state transition matrix, control input, and process noise. The state transition matrix is ​​determined by the environmental signal. The control input characterizes the influence of water flow on particle motion. The noise term characterizes the uncertainty of the motion model.

[0012] The likelihood probability of the second particle is calculated based on the underwater acoustic positioning signal to obtain the weight of the second particle. The likelihood probability of the second particle represents the probability value of the degree of matching between the predicted state of the second particle and the actual prediction result corresponding to the underwater acoustic positioning signal.

[0013] The distribution range of the second particle is determined based on the optical signal;

[0014] The third particle is obtained by removing the second particle that deviates from the distribution range and has a weight less than the weight threshold;

[0015] The estimated position of the diving bell is determined based on the third particle.

[0016] According to an embodiment of this application, the method further includes:

[0017] The Kalman gain of the emergency communication signal is calculated to correct the estimated position, thereby obtaining the predicted position of the diving bell. The Kalman gain characterizes the correction factor matrix calculated based on the uncertainty of the estimated position using the particle algorithm and the measurement noise of the emergency communication signal.

[0018] According to an embodiment of this application, calculating the Kalman gain of the emergency communication signal to correct the estimated position and obtain the predicted position of the diving bell includes:

[0019] Based on the estimated position of the diving bell, a state vector of the diving bell is defined, which represents the three-dimensional position and velocity components of the diving bell.

[0020] The first covariance matrix of the diving bell is predicted based on the motion model and estimated position.

[0021] Calculate the Kalman gain based on the emergency communication signal;

[0022] The first covariance matrix is ​​adjusted by the Kalman gain;

[0023] The estimated position is corrected based on the second covariance matrix to obtain the predicted position of the diving bell.

[0024] According to an embodiment of this application, the method further includes:

[0025] In the case of multiple diving bells, the propagation delay, signal amplitude, and signal phase of each emergency communication signal are determined based on the center frequency, transmission time slot, and pseudo-random code carried by each emergency communication signal.

[0026] Based on the propagation delay, signal amplitude, and signal phase of each emergency communication signal, the diving bell corresponding to the issuance of each emergency communication signal is determined.

[0027] According to an embodiment of this application, the emergency communication system of the diving bell includes an underwater acoustic communicator, an emergency positioning device, and a strobe light. Obtaining the emergency communication signal sent by the diving bell includes:

[0028] The environmental signal transmitted by the underwater acoustic communication device is obtained. The environmental signal includes water flow velocity, underwater acoustic propagation speed and environmental parameters. The water flow velocity and underwater acoustic propagation speed are determined based on the environmental parameters, which include at least one of water temperature, salinity and pressure.

[0029] Obtain the underwater acoustic positioning signal sent by the emergency positioning device, wherein the underwater acoustic positioning signal includes at least one of the distance from the diving bell to the mother ship, the azimuth angle, and the pitch angle;

[0030] The optical signal transmitted by the strobe light is obtained, and the optical signal is used to calculate the angle of the strobe light relative to the mother ship.

[0031] According to an embodiment of this application, after obtaining the emergency communication signal sent by the diving bell, the method further includes:

[0032] The clock drift error is obtained, which characterizes the relative time difference between the mother ship's reference clock and the diving bell's local clock when recording the same time period.

[0033] The environmental signal is linearly interpolated based on the clock drift error.

[0034] The underwater acoustic positioning signal is subjected to cubic spline interpolation based on the clock drift error.

[0035] Another aspect of this application provides an emergency communication system based on a diving system, comprising:

[0036] Another aspect of this application provides an electronic device comprising:

[0037] One or more processors;

[0038] Memory, used to store one or more programs.

[0039] When the one or more programs are executed by the one or more processors, the one or more processors implement the method described above.

[0040] Another aspect of this application provides a computer-readable storage medium storing computer-executable instructions that, when executed, are used to implement the method described above.

[0041] By implementing the embodiments of this application, an emergency communication signal including underwater acoustic positioning signal, environmental signal and optical signal is obtained, and these signals are fused based on the particle algorithm. The environmental signal is used to adjust the particle distribution to adapt to the complex underwater environment, the optical signal is used to constrain the particle distribution range to eliminate abnormal estimation, and the underwater acoustic positioning signal is used to adjust the particle weight to quantify the location confidence. In this way, the estimated position of the diving bell takes into account the influence of environmental factors and improves the positioning accuracy through the complementarity of multi-source information.

[0042] Compared with existing technologies, this application no longer relies solely on a single underwater acoustic signal that is susceptible to environmental interference for positioning. Instead, it fully utilizes the guiding role of environmental signals on particle motion characteristics and the constraining role of optical signals on particle distribution. By organically fusing multi-source information through a particle algorithm, it effectively overcomes the problem of inaccurate positioning caused by environmental interference of underwater acoustic signals and improves the estimation accuracy of the diving bell position by the mother ship during emergency communication.

[0043] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0044] The above and other objects, features and advantages of this application will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0045] Figure 1 A flowchart illustrating an emergency communication method based on a diving system provided in this application is shown schematically.

[0046] Figure 2 This schematic diagram illustrates a structural block diagram of an emergency communication system based on a diving system provided in this application.

[0047] Figure 3 A schematic block diagram of an electronic device provided in this application is shown. Detailed Implementation

[0048] The embodiments of this application will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of this application. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of this application for ease of explanation. However, it will be apparent that one or more embodiments may be implemented without these specific details. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0049] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0050] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0051] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0052] Figure 1 The flowchart illustrating an emergency communication method based on a diving system according to an embodiment of this application is shown schematically.

[0053] like Figure 1 As shown, the method includes steps S101 to S102.

[0054] Step S101: Obtain the emergency communication signal sent by the diving bell. The emergency communication signal includes at least: underwater acoustic positioning signal, environmental signal and optical signal.

[0055] The diving bell refers to a sealed underwater operating chamber, resembling an inverted bell shape. It primarily consists of a steel pressure shell, observation windows, entrance and exit doors, an environmental control system, and various control equipment. In this embodiment, it can be understood as a saturation diving platform with independent emergency communication capabilities. This platform is equipped with an underwater acoustic communication unit, an emergency positioning device, and strobe lights, among other emergency communication subsystems. The underwater acoustic communication unit includes an internal and external unit, while the emergency positioning device and strobe lights are mounted on a top support bracket on the outside of the bell. The diving bell provides divers with a safe and reliable underwater survival environment during deep-sea saturation diving operations. It provides air, power, and communication functions via an umbilical cable connection to the surface mother ship. In the event of an unexpected interruption of umbilical communication, it can autonomously activate the emergency communication system to maintain emergency contact with the mother ship and provide accurate location information for search and rescue operations.

[0056] Emergency communication signals refer to a set of multi-source heterogeneous signals automatically sent by the emergency communication system of the diving bell when umbilical communication between the diving bell and the mother ship is interrupted. In the embodiments of this application, it can be understood as a signal set composed of underwater acoustic positioning signals, environmental signals, and optical signals. The underwater acoustic positioning signal is sent by the bell unit of the emergency positioning device to determine the distance and azimuth angle from the diving bell to the mother ship; the environmental signal is collected and sent by the bell unit of the underwater acoustic communicator, containing key parameters affecting underwater acoustic propagation such as water temperature, salinity, and pressure; the optical signal is emitted by a strobe light in a low-light environment to assist the mother ship in visual search and rescue. This set of emergency communication signals is used to provide the mother ship with precise location information of the diving bell in emergency situations such as umbilical communication interruption, realizing the reliability of underwater emergency communication and the accuracy of positioning. At the same time, through the complementarity of multi-source data, the interference caused by the complex marine environment to a single signal can be effectively overcome.

[0057] Based on the above embodiments, as an optional embodiment, the emergency communication system of the diving bell includes an underwater acoustic communication device, an emergency positioning device, and a strobe light.

[0058] Specifically, the emergency communication system adopts a distributed architecture design, comprising two main parts: the diving bell end and the mother ship end. The diving bell end mainly consists of an internal unit of the underwater acoustic communication unit mounted on the wall inside the bell, an external unit of the underwater acoustic communication unit fixed to the external support frame, an upper unit of the emergency positioning device mounted on the top support frame, and an upper unit of the strobe light. The mother ship end includes a surface unit of the underwater acoustic communication unit located in the control room, a surface transducer deployed in the water, and a surface unit of the emergency positioning device located in the control room. This distributed architecture design ensures that the system retains independent signal transmission and reception capabilities even when umbilical communication is interrupted.

[0059] When umbilical communication is interrupted, the internal unit of the underwater acoustic communication unit first collects environmental parameters from inside the diving bell. The internal unit monitors key parameters such as water temperature, salinity, and pressure in real time using built-in sensors, and transmits these environmental signals to the surface via the external unit. The surface transducer on the mother ship receives these signals and transmits them to the surface unit for processing, thereby obtaining real-time environmental data affecting underwater acoustic propagation.

[0060] Simultaneously, the bell unit of the emergency positioning device periodically transmits underwater acoustic positioning signals. These signals are received by the surface transducer and transmitted to the surface unit of the emergency positioning device. By analyzing the signal propagation delay and angle of arrival, and combining this with the previously obtained underwater acoustic propagation speed, the surface unit can calculate the distance and bearing information of the diving bell relative to the mother ship. This continuous positioning signal transmission mechanism ensures that the mother ship can monitor the diving bell's position changes in real time.

[0061] In low-light conditions, the strobe light mounted on the top bracket outside the clock automatically activates, emitting regular optical signals. These signals are received by optical detectors on the mother ship, and by analyzing the angle of arrival and intensity of the light signals, supplementary location references are provided for underwater acoustic positioning. Although the optical signals from the strobe light have limited propagation distance, they can provide intuitive visual guidance during close-range search and rescue operations.

[0062] Furthermore, step S101, obtaining the emergency communication signal sent by the diving bell, may further include the following steps:

[0063] Step S201: Obtain the environmental signal sent by the underwater acoustic communication device. The environmental signal includes water flow velocity, underwater acoustic propagation velocity, and environmental parameters. The water flow velocity and underwater acoustic propagation velocity are determined based on the environmental parameters, which include at least one of water temperature, salinity, and pressure.

[0064] Among them, the underwater acoustic communication device is used to monitor environmental parameters and obtain environmental signals, including but not limited to: water temperature T, salinity S, and pressure P;

[0065] Among them, the speed of sound propagation in water There is a nonlinear relationship between sound and three key environmental parameters: water temperature, salinity, and pressure. Changes in water temperature significantly affect the propagation characteristics of sound waves in a medium because temperature directly alters the elastic modulus and acoustic impedance of the water, causing the propagation speed of sound to exhibit a nonlinear variation with temperature. When the water temperature rises, the increased molecular thermal motion leads to enhanced elasticity of the medium, thereby increasing the propagation speed of sound; conversely, when the water temperature decreases, the opposite effect occurs.

[0066] The effects of salinity are primarily manifested in its alteration of seawater density and acoustic properties. Increased salinity increases seawater density and also affects the bonding state between water molecules; both factors influence the sound wave propagation process. Although the effect of salinity on sound speed is relatively small compared to temperature, it cannot be ignored in precise positioning applications, especially in areas with a distinct halocline.

[0067] Pressure parameters primarily reflect the influence of water depth on the speed of sound. As depth increases, the rise in hydrostatic pressure causes the water to be compressed, altering its density and elastic properties. Although the change in the speed of sound caused by this pressure effect is relatively slow, its cumulative effect in the deep-sea environment is significant and must be considered in the calculation of the speed of sound propagation.

[0068] For water flow velocity The temperature gradient, with its differences in horizontal and vertical distribution, induces thermal circulation, a significant driving force for near-surface water flow. When significant temperature stratification exists, heat exchange between different water layers leads to complex vertical circulation structures. The salinity gradient primarily affects flow field distribution by altering water density. In regions with a distinct halocline, salinity differences generate density flow, thus influencing the vertical mixing process. This salinity-driven flow is particularly influential in estuaries and the deep sea floor. The pressure gradient manifests as the motion characteristics of deep water. With depth changes, the pressure gradient force interacts with the temperature-salinity gradient force, jointly shaping the deep-sea circulation structure. Especially in topographically complex sea areas, the spatial distribution of the pressure gradient affects local flow field characteristics.

[0069] In one feasible implementation, an empirical formula method can be used. This involves directly calculating the underwater acoustic propagation speed by substituting measured water temperature, salinity, and pressure data into a pre-calibrated empirical formula for sound velocity. Simultaneously, the density flow equation is used to establish the correspondence between environmental parameter gradients and water flow velocity, enabling rapid estimation of the flow field. This method is computationally simple and suitable for scenarios with relatively slow environmental changes.

[0070] In another feasible implementation, numerical simulation can be used. By constructing a marine environment model that includes temperature, salinity, and pressure fields, the finite element or finite difference methods can be used to solve the sound wave propagation equation and fluid motion equation, thereby obtaining a more accurate sound velocity distribution and flow field structure. Although this method involves a large amount of computation, it can better reflect the spatial distribution characteristics of environmental parameters.

[0071] In another feasible implementation, a data-driven approach can be employed, utilizing historical observation data to establish a statistical relationship model between environmental parameters and sound speed and current velocity. The model is then trained using machine learning algorithms, enabling it to rapidly predict acoustic and flow field characteristics based on real-time monitored environmental parameters. This method is suitable for marine areas exhibiting periodic variations.

[0072] It should be noted that the choice of the above implementation method can be based on the specific application scenario requirements, such as computational efficiency requirements, accuracy requirements, and environmental complexity. This application does not impose specific limitations on this, and those skilled in the art can choose an appropriate implementation method according to actual needs.

[0073] Specifically, the internal unit of the underwater acoustic communication device is equipped with a variety of high-precision sensors for real-time monitoring of key environmental parameters. A temperature sensor continuously collects water temperature data; since the speed of underwater sound propagation has a non-linear relationship with water temperature, temperature changes directly affect the accuracy of sound speed calculations. A conductivity sensor measures seawater salinity; changes in salinity alter the acoustic properties of seawater, thus affecting the refractive index of sound waves in the medium. A pressure sensor acquires water pressure data; water pressure not only accurately calculates the operating depth of the diving bell but also reflects changes in its vertical position. Furthermore, a current meter installed on the external unit measures the surrounding water flow velocity.

[0074] Step S202: Obtain the underwater acoustic positioning signal sent by the emergency positioning device. The underwater acoustic positioning signal includes at least one of the following: distance from the diving bell to the mother ship, azimuth angle, and pitch angle.

[0075] Specifically, the positioning device is used to measure the distance and azimuth from the diving bell to the mother ship. When the surface unit receives the positioning signal from the bell unit, it calculates the propagation delay based on the signal's transmission and reception times. Combined with the real-time underwater acoustic propagation speed obtained from environmental signals, the straight-line distance from the diving bell to the mother ship can then be calculated. This time-delay-based ranging method is simple and reliable, but requires the system to have a precise time synchronization mechanism.

[0076] The azimuth angle is measured by analyzing the phase difference when the signal reaches the transducer array at the water surface. The surface unit calculates the azimuth angle of the diving bell relative to the mother ship by determining the relative positions of the diving bell and the mother ship on the horizontal plane. This calculation process requires accurately obtaining the horizontal coordinates of both objects and then using trigonometric functions to determine the azimuth angle.

[0077] Determining the pitch angle requires considering the depth difference between the diving bell and the mother ship. The surface unit calculates the pitch angle of the diving bell relative to the mother ship by analyzing the depth information of both. This angle information reflects the relative position of the diving bell in the vertical direction and plays an important supplementary role in all-around positioning. The depth of the mother ship is usually negligible, while the depth of the diving bell can be obtained by converting pressure parameters from environmental signals.

[0078] For example, calculating underwater acoustic ranging: ;

[0079] In the formula, This indicates the time difference between the mother ship sending a signal and receiving the echo. Indicates the speed of sound propagation in water;

[0080] Calculate the azimuth angle: ;

[0081] In the formula, ( , ) represents the horizontal coordinate of the diving bell, ( , () represents the horizontal coordinates of the mother ship;

[0082] Calculate the pitch angle: ;

[0083] In the formula, ( , () indicates the depth of the diving bell and mother ship.

[0084] Step S203: Obtain the optical signal sent by the strobe light. The optical signal is used to calculate the angle of the strobe light relative to the mother ship.

[0085] Specifically, the strobe light provides optical positioning signals in low-light environments. The mothership receives the strobe signals via an optical detector and calculates their direction information. The strobe light uses a high-brightness LED light source and flashes periodically at a preset frequency. Its flashing pattern is optimized to ensure signal recognizability while avoiding interference with ambient light. The optical detector on the mothership uses a high-sensitivity photoelectric detection array capable of capturing weak underwater light signals. By analyzing the projection position of the received light signal onto the detection array, the detector calculates the angle of the strobe light relative to the mothership.

[0086] However, underwater optical signals exhibit significant attenuation characteristics. The intensity of the optical signal decreases rapidly with increasing propagation distance, showing an inverse square law attenuation. The system assesses the availability of optical positioning by calculating the optical signal intensity in real time.

[0087] Furthermore, based on the initial luminous intensity of the strobe light and the distance from the diving bell to the mother ship obtained through underwater acoustic ranging, the intensity of the light signal reaching the optical detector is calculated. When the calculated light signal intensity is lower than the detector's sensitivity threshold, the system automatically determines that the optical signal is unusable, and the positioning system will then rely primarily on the underwater acoustic signal.

[0088] Calculate the optical detection angle: ;

[0089] In the formula, Indicates the angle of the strobe light;

[0090] Calculate the optical signal intensity, where the optical signal intensity decreases with the square of the distance: ;

[0091] In the formula, This indicates the initial light intensity of the strobe light. This represents the distance from the diving bell to the mother ship obtained through underwater acoustic ranging; if the light signal strength... If the signal is below the detection threshold, the mother ship may be unable to detect the strobe signal, at which point the optical signal becomes unusable.

[0092] Finally, the mother ship integrates data from the underwater acoustic communication device, emergency positioning device, and strobe lights to form a standard signal vector for emergency communication signals: [ , , , T, S, P ].

[0093] By employing the embodiments of this application, three complementary emergency communication subsystems—an underwater acoustic communication unit, an emergency positioning device, and a strobe light—are configured on the diving bell, enabling the coordinated operation of multi-source heterogeneous signals. Specifically, the underwater acoustic communication unit collects and transmits environmental signals such as water temperature, salinity, and pressure in real time, providing key parameters for underwater acoustic propagation modeling; the underwater acoustic positioning signal transmitted by the emergency positioning device is used to calculate the distance and azimuth between the diving bell and the mother ship; and the strobe light provides optical positioning signals in low-light environments, offering intuitive visual guidance for close-range search and rescue. This integrated use of multi-source signals effectively overcomes the limitations of single signal sources being susceptible to environmental interference.

[0094] Based on the above embodiments, as an optional embodiment, after receiving the emergency communication signal sent by the diving bell, the following steps may also be included:

[0095] Step S301: Obtain the clock drift error.

[0096] Clock drift error refers to the timing deviation between different clock systems caused by factors such as hardware circuit characteristics, changes in ambient temperature, and equipment aging. In this embodiment, it can be understood as the relative time difference between the mother ship reference clock and the diving bell's local clock when recording the same time period. This difference can be quantified by comparing the timestamps recorded by the two clock systems at a specific point in time. Time stamps are used to calibrate and synchronize multi-source heterogeneous data such as environmental signals and underwater acoustic positioning signals from the diving bell, ensuring that various signals can be fused and processed under a unified time reference system, thereby improving the positioning accuracy of the emergency communication system.

[0097] For example, let the mother ship's reference clock be... The local clock of the diving bell is , are their timestamps at time t, selecting two time points. and Perform drift rate calculation:

[0098] ;

[0099] The corrected time is: .

[0100] Step S302: Perform linear interpolation on the environmental signal based on the clock drift error.

[0101] Due to clock drift, directly using environmental signals (such as temperature, salinity, and pressure) may lead to time axis misalignment during signal processing. However, the physical quantities reflected by environmental signals, such as water temperature, salinity, and pressure, typically exhibit stable and gradual changes over a short period. This pattern of change allows for time synchronization of environmental signals through linear interpolation.

[0102] Specifically, after obtaining the clock drift error, the time interval requiring interpolation is first determined. Environmental signal observations from two adjacent time points within this interval are selected. Based on the assumption of a linear relationship between these two known discrete sampling points, the environmental signal interpolation at any given time is obtained by calculating a time scaling factor. The interpolation process essentially maps the environmental signal from the diving bell's local time axis to a unified time axis after clock drift correction, thus giving the environmental signal a standardized time reference.

[0103] Furthermore, considering potential signal abrupt changes in practical applications, the system evaluates the effectiveness of linear interpolation by comparing the rate of change of environmental signals at adjacent sampling points. When the rate of change exceeds a preset threshold, it indicates that the environmental parameters within the current time period may have changed drastically. In this case, the system shortens the interpolation time interval and increases the sampling point density to improve interpolation accuracy. This adaptive interpolation strategy effectively addresses the nonlinear characteristics of environmental signals while ensuring computational efficiency.

[0104] For example, suppose at two adjacent time points and Observed environmental signals: and ;

[0105] For any given time, the interpolation is calculated as follows:

[0106] ;

[0107] Step S303: Perform cubic spline interpolation on the underwater acoustic positioning signal based on the clock drift error.

[0108] Compared to the stable variation of environmental signals, underwater acoustic positioning signals are easily affected by underwater multipath effects, changes in seawater sound velocity gradients, and environmental noise during propagation, resulting in significant fluctuations and discontinuities in the signal. This signal characteristic makes it difficult for simple linear interpolation methods to accurately describe the variation patterns of underwater acoustic positioning signals. Therefore, a cubic spline interpolation method with higher-order continuity is needed to address the time synchronization problem of underwater acoustic positioning signals.

[0109] Specifically, after obtaining the clock drift error, a set of discrete underwater acoustic positioning signal sampling points are first selected on the corrected time axis. Based on these known data points, a piecewise cubic polynomial function is constructed, ensuring not only the continuity of function values ​​between adjacent segments but also the continuity of the first and second derivatives. By setting natural boundary conditions and solving the tridiagonal matrix equations, the coefficients of each cubic polynomial segment can be obtained, thereby achieving high-precision interpolation calculation of the underwater acoustic positioning signal at any given time.

[0110] Furthermore, considering the potential for abrupt changes or outliers in the underwater acoustic positioning signal, the system identifies these feature points by calculating the signal change rate between adjacent sampling points. When a potentially abnormal region is detected, the density of control points in that region is automatically increased, allowing the interpolation curve to better fit the local features of the signal. Simultaneously, by adjusting boundary conditions and node positions, the smoothness of the interpolation curve can be controlled, achieving an optimal balance between interpolation accuracy and curve smoothness.

[0111] In existing technologies, diving bell emergency communication systems often neglect the drift problem between different clock systems when processing multi-source signals. This clock drift causes misalignment between environmental signals and underwater acoustic positioning signals on the time axis. Especially during long-term operation, the accumulated time error can significantly affect the accuracy of signal fusion. Furthermore, existing technologies typically use a uniform interpolation method to process all types of signals, without considering the physical characteristics and variation patterns of different signals, resulting in poor signal reconstruction quality and affecting the accuracy of subsequent positioning calculations.

[0112] By employing the embodiments of this application, the synchronization problem between the reference clock on the mother ship and the local clock of the diving bell is solved by calculating the clock drift error. This drift error calculation method based on timestamp comparison can track and compensate for the relative deviation between the two clock systems in real time, ensuring that signal data from different sources can be processed under a unified time reference.

[0113] More importantly, this application employs differentiated interpolation strategies based on the physical characteristics of different signals. For environmental signals exhibiting stable and gradual changes, a linear interpolation method with lower computational cost is used, ensuring both interpolation accuracy and processing efficiency. For underwater acoustic positioning signals susceptible to multipath effects and noise interference, a cubic spline interpolation method with high-order derivative continuity is employed. By constructing a piecewise cubic polynomial function, not only can the signal's changing trend be accurately reconstructed, but interference from random fluctuations can also be effectively suppressed.

[0114] Step S102: Determine the estimated location of the diving bell based on the particle algorithm and emergency communication signals; wherein, environmental signals are used to adjust the distribution of particles, optical signals are used to constrain the distribution range of particles, and underwater acoustic positioning signals are used to adjust the weight of particles.

[0115] After obtaining the time-synchronized emergency communication signal, a reasonable algorithm strategy is needed to determine the estimated position of the diving bell. Considering the complexity of the underwater environment, a single positioning algorithm is insufficient to cope with the influence of uncertainties such as ocean current disturbances and changes in sound velocity profiles. Therefore, this application adopts a multi-source signal fusion method based on particle algorithm to estimate the position state of the diving bell through the evolution of a large number of random sampling points.

[0116] Environmental signals are used to adjust the distribution of particles. Environmental parameters such as water temperature, salinity, and pressure collected by the underwater acoustic communication device can correct the state transition model of the particle swarm. In this embodiment, it can be understood as constructing an underwater acoustic environment model based on real-time environmental parameters, and using this model to update the motion state equations of the particles, so that the evolution process of the particles can accurately reflect the possible motion characteristics of the diving bell in the current hydrological environment. This provides more accurate prior state information in the particle prediction stage, overcomes the prediction bias caused by traditional particle algorithms relying solely on fixed motion models, and improves the system's adaptability to complex underwater environmental changes.

[0117] In this process, optical signals are used to constrain the distribution range of particles. Optical position signals emitted by a strobe lamp are used to limit and filter the spatial distribution of the particle swarm. In this embodiment, it can be understood that a reliable search space range is determined based on the angle of the strobe lamp relative to the mother ship received by the optical detector. This range is then used as the boundary condition for the effective distribution area of ​​the particle swarm, eliminating abnormal particles that significantly deviate from their true positions and preventing the particle swarm from spreading into unreasonable spatial regions.

[0118] In this embodiment, the underwater acoustic positioning signal is used to adjust the particle weights. The likelihood probability of each particle is calculated using the underwater acoustic signal sent by the emergency positioning device, and the particle weights are dynamically updated based on this probability. In this application embodiment, it can be understood that by comparing the difference between the predicted particle position and the measurement results of the underwater acoustic positioning signal, the confidence level of the assumed position corresponding to each particle is calculated, and this confidence level is used as the basis for updating the particle weights. This is used to determine the contribution of each particle to the final position estimate during the observation update stage, thereby achieving the optimal estimation of the diving bell's position.

[0119] Specifically, the initial particle swarm is first initialized based on the last known position of the diving bell. These particles represent the possible positions of the diving bell, and their spatial distribution reflects the uncertainty of the position estimation. Subsequently, the system uses environmental signals collected by an underwater acoustic communicator to correct the particle motion model. Since the environmental signals contain key parameters such as water temperature, salinity, and pressure, which directly affect the underwater sound wave propagation characteristics and the diving bell's motion state, the particle motion equations can be adjusted based on these real-time environmental parameters, making the particle evolution process more consistent with actual physical laws.

[0120] During the particle prediction phase, the system uses the received underwater acoustic positioning signals as observations and updates the particle weights by calculating the likelihood probability between each particle's position and the actual observed value. A larger weight indicates that the position hypothesis represented by that particle is more likely to be close to the actual position of the diving bell. This weight update mechanism based on underwater acoustic positioning signals effectively utilizes distance and azimuth information, improving the accuracy of position estimation.

[0121] Considering the potential for multipath effects and noise interference during underwater acoustic signal propagation, this application introduces optical signals as a constraint. Although the optical signals emitted by the strobe lamp have a limited propagation distance, they can provide relatively reliable directional information within their effective range. The system utilizes this characteristic, using the optical signal detection range as a spatial constraint on particle distribution, promptly eliminating abnormal particles that deviate from this range, thereby preventing particle swarms from dispersing to unreasonable locations.

[0122] Finally, the system calculates the estimated position of the diving bell based on the weighted average of the remaining particles. This position estimation method based on particle swarm statistics considers both the weighted contribution of each particle and preserves the uncertainty information of the position distribution, providing a reliable basis for subsequent trajectory prediction and search and rescue planning.

[0123] Based on the above embodiments, as an optional embodiment, step S102, which determines the estimated location of the diving bell based on the particle algorithm and emergency communication signals, may further include the following steps:

[0124] Step S401: Obtain the first particle. The first particle is a Gaussian distribution state space sampling point randomly generated based on the multidimensional state vector and the initial covariance matrix, with the last known position of the diving bell as the reference center. It is used to characterize the initial state distribution of the diving bell. The initial covariance matrix is ​​a symmetric positive definite matrix that characterizes the degree of uncertainty of the initial state of the diving bell in multiple dimensions.

[0125] Here, the first particle refers to a set of state-space sampling points generated during the initialization phase of the particle algorithm. In this embodiment, it can be understood as a set of state-space sampling points randomly generated based on the multidimensional state vector and the initial covariance matrix, taking the last known position of the diving bell as the reference center, and conforming to a Gaussian distribution. These points are used to characterize the initial state distribution of the diving bell and provide initial conditions for subsequent state estimation and trajectory prediction.

[0126] The initial covariance matrix refers to a symmetric positive definite matrix that describes the degree of uncertainty in each dimension of the initial state of the diving bell. In this embodiment, it can be understood as a sixth-order square matrix, where the diagonal elements represent the variances of the position and velocity components, and the off-diagonal elements represent the correlations between different state components. This matrix is ​​used to control the distribution range and shape of the first particle in the state space, ensuring that the initial particle swarm can reasonably cover the possible state space of the diving bell.

[0127] Specifically, each first particle contains complete six-dimensional state information, with three dimensions describing its spatial position coordinates and the other three dimensions describing its corresponding velocity components. The system generates multiple state hypotheses by randomly sampling around the last known position.

[0128] During the sampling process, the initial covariance matrix controls the dispersion of particles in each dimension. A larger covariance value indicates higher uncertainty in that dimension, which will result in a more dispersed particle distribution.

[0129] The above-mentioned sampling mechanism based on probability distribution ensures that the initial particle swarm can reasonably cover the possible state space of the diving bell, while avoiding the waste of computational resources caused by blind uniform sampling.

[0130] For example, let the state vector of the diving bell be:

[0131] ;

[0132] In the formula, ( ) indicates the position of the diving bell in three-dimensional space, ( () indicates the velocity component of the diving bell;

[0133] The first particle set is initialized as follows:

[0134] ;

[0135] In the formula, Indicates the last known location of the diving bell. Let represent the initial covariance matrix.

[0136] Step S402: Based on the environmental signal, the motion model corresponding to the first particle is modified to obtain the second particle. The motion model is a discrete-time linear equation describing the evolution of the particle state over time, which is used to predict the change of the diving bell's motion state within a given time step. The motion model includes a state transition matrix, control input, and process noise. The state transition matrix is ​​determined by the environmental signal, the control input characterizes the influence of water flow on the particle motion, and the noise term characterizes the uncertainty of the motion model.

[0137] Due to the complex and variable nature of the underwater environment, simple linear motion models are insufficient to accurately describe the actual motion of the diving bell. Therefore, it is necessary to dynamically correct the particle motion model using real-time acquired environmental signals to better reflect the impact of the current hydrological environment on the diving bell's motion.

[0138] The motion model corresponding to the first particle refers to the discrete-time linear system equation describing the evolution of the particle's state over time. In the embodiments of this application, it can be understood as a state prediction equation composed of three parts: a state transition matrix, control input, and process noise. This equation maps the six-dimensional state vector of the first particle to the next moment and is used to predict the change in the motion state of the diving bell within a given time step.

[0139] The core of this motion model is the state transition matrix, which characterizes the kinematic relationship between position and velocity components. Its structure reflects the basic characteristics of uniform motion, and the time step parameter determines the time scale of state prediction. The control input term includes three-dimensional water flow velocity components, used to characterize the influence of environmental forces on particle motion. The process noise term describes the uncertainty of the model prediction through its covariance matrix. This noise term considers both the randomness of environmental disturbances and the errors introduced by model simplification.

[0140] Furthermore, the second particle refers to the set of state space sampling points after correction by environmental signals. In the embodiments of this application, it can be understood as a new generation of particle swarm obtained by integrating the influence of environmental signals into the state transition matrix and predicting and updating the first particle, which is used to more accurately characterize the possible motion state of the diving bell in the current hydrological environment.

[0141] Specifically, the system first collects environmental parameters such as water temperature, salinity, and pressure, which directly affect the underwater acoustic propagation speed and the water flow field distribution. Based on these environmental signals, the system calculates the three-dimensional water flow velocity components, which are then added as external control inputs to the particle's state transition matrix.

[0142] The state transition matrix control input term incorporates the influence of water flow on particle motion, enabling the model to reflect the effects of environmental forces. Simultaneously, the system introduces a process noise term to characterize model uncertainty; the covariance matrix of the process noise is dynamically adjusted based on changes in environmental parameters to adapt to disturbance characteristics under different hydrological conditions.

[0143] For example, let the component of the water flow velocity be:

[0144] =( , , );

[0145] The state transition matrix based on the motion model can be represented as:

[0146] ;

[0147] The state transition matrix is ​​expressed as:

[0148] ;

[0149] In the formula, Indicates the time step;

[0150] Control input:

[0151] ;

[0152] Process noise: ;

[0153] In the formula, The covariance matrix represents the process noise.

[0154] Step S403: Calculate the likelihood probability of the second particle based on the underwater acoustic positioning signal to obtain the weight of the second particle. The likelihood probability of the second particle represents the probability value of the degree of matching between the predicted state of the second particle and the actual prediction result corresponding to the underwater acoustic positioning signal.

[0155] During particle filtering localization, the reliability of the second particle needs to be evaluated using the underwater acoustic positioning signal to determine the contribution of each particle to the final state estimate. Since the underwater acoustic positioning signal provides distance measurement information from the mother ship to the diving bell, the system can calculate the particle weight by comparing the difference between the predicted distance of the particle and the actual measured distance.

[0156] Here, the likelihood probability of the second particle refers to the probability value characterizing the degree of match between the predicted state of the second particle and the actual measurement result. In the embodiments of this application, it can be understood as a numerical measure based on the Gaussian likelihood function, reflecting the magnitude of the difference between the distance from the predicted position of the second particle to the mother ship and the underwater acoustic ranging information, used to evaluate the credibility of the state prediction of each particle.

[0157] The likelihood probability is calculated based on the Gaussian noise assumption in the measurement model, substituting the difference between the predicted and actual measured distances of the particle into the Gaussian probability density function. The standard deviation parameter of this function is determined by the ranging error and reflects the accuracy characteristics of the measurement system. When the difference between the predicted and measured distances is small, the corresponding likelihood probability is close to its maximum value, indicating that the predicted state of the particle is in high agreement with the actual observation; when the difference increases, the likelihood probability decays exponentially, reflecting a significant decrease in the reliability of the prediction results.

[0158] Specifically, the system first acquires ranging information from underwater acoustic positioning signals, which includes the influence of measurement noise. The measurement noise follows a Gaussian distribution with a mean of zero, and its standard deviation reflects the ranging accuracy. For each second particle, the system calculates the distance from its predicted position to the mother ship and compares this predicted distance with the actual measured distance. Using a Gaussian likelihood function, the system converts the distance difference into a particle weight value. When the predicted distance is close to the measured distance, the likelihood function gives a larger weight value, indicating that the particle has high reliability; conversely, when the distance difference is large, the weight value decays rapidly, indicating that the particle's prediction may deviate from the actual state. To ensure the effectiveness of the weights, the system normalizes the weights of all particles, making their sum equal to one.

[0159] For example, underwater acoustic positioning signals provide ranging information. The distance from the mother ship to the diving bell is:

[0160] ;

[0161] In the formula, It is measurement noise, which follows a Gaussian distribution: ;

[0162] In the formula, This represents the standard deviation of the distance measurement error;

[0163] Particle weight calculation:

[0164] ;

[0165] like near ,but Approaching the maximum value (high particle credibility), if ,but Rapid decay (low particle reliability).

[0166] Normalized particle weights:

[0167] ;

[0168] In the formula, N represents the total number of particles.

[0169] Step S404: Determine the distribution range of the second particle based on the optical signal.

[0170] Specifically, the optical signal emitted by the strobe lamp in low-light conditions is received by the optical detector on the mother ship, allowing the acquisition of the angle observation value of the diving bell relative to the mother ship. This angle observation value contains a certain amount of measurement noise, which can usually be described by a Gaussian distribution, and its standard deviation reflects the accuracy characteristics of the angle measurement. The system calculates the angle value of the predicted position of each second particle relative to the mother ship and compares this predicted angle with the observation angle provided by the optical signal. When the difference between the two exceeds a preset angle threshold, it indicates that the position assumption represented by the particle deviates significantly from the actual optical observation, and it needs to be marked as an invalid particle.

[0171] Furthermore, considering the limited propagation distance of underwater optical signals, the system also needs to assess the availability of optical constraints based on the light signal intensity. When the light signal intensity is below the detector sensitivity threshold, angle observation may not be reliable enough. In this case, the system will relax the judgment criteria for angle constraints accordingly to avoid excessive rejection of valid particles. This adaptive constraint mechanism based on signal quality ensures that particle swarm divergence can be effectively suppressed when the optical signal is reliable, while avoiding excessive restriction on particle distribution when the optical signal is unavailable.

[0172] The optical signal (strobe light) provides the angle observation of the diving bell relative to the mother ship, represented as:

[0173] ;

[0174] In the formula, The measurement noise is represented by a Gaussian distribution: , This represents the standard deviation of the angle measurement error.

[0175] For the i-th particle, calculate its angle:

[0176] .

[0177] Step S405: Remove the second particle that deviates from the distribution range and has a weight less than the weight threshold to obtain the third particle.

[0178] Specifically, the system first checks whether the predicted angle of each second particle satisfies the spatial distribution constraints given by the optical signal. By comparing the deviation between the predicted angle and the actual observed angle, it determines whether the particle is within the allowable angular error range. Particles that deviate from this range, even if their weights may be high, need to be discarded because this indicates that the position assumption represented by the particle does not match the actual optical observation. Simultaneously, the system also checks whether the normalized weight of each particle exceeds a preset weight threshold. A weight that is too low indicates a significant difference between the particle's predicted state and the underwater acoustic positioning signal observation; such particles contribute very little to the final position estimation, and continuing to retain low-quality particles may affect positioning accuracy.

[0179] Set the allowable angle error range :

[0180] like If so, then the particle is removed.

[0181] Elimination weight below the threshold Particles:

[0182] like If so, then the particle is removed.

[0183] Step S406: Determine the estimated position of the diving bell based on the third particle.

[0184] Specifically, the system uses a weighted average method to process the state information of the third particles. Each third particle carries complete six-dimensional state information, including three-dimensional spatial position coordinates and corresponding velocity components. Considering the differences in reliability among different particles, the system uses the previously calculated normalized weights as weighting coefficients to perform a weighted summation of the state vectors of all third particles to obtain the estimated position of the diving bell.

[0185] In one feasible implementation, to assess the uncertainty of the position estimate, the system also calculates a weighted covariance matrix of the third particle distribution. This matrix describes the dispersion of the particle swarm across various dimensions, and its diagonal elements reflect the variance of the position and velocity estimates, which can be used to quantify the reliability of the positioning results. A smaller covariance indicates better particle swarm aggregation and higher reliability of the position estimate; conversely, a larger covariance indicates greater uncertainty in the estimation results, potentially requiring the collection of more observation data to improve positioning accuracy.

[0186] By employing the embodiments of this application and fusing environmental signals, underwater acoustic positioning signals, and optical signals within the particle algorithm framework, this application solves the technical problems of existing technologies that rely solely on a single signal source for diving bell positioning, which is easily affected by environmental interference and suffers from unstable positioning accuracy. Specifically, this application adopts a method of initializing the first particle based on the last known position, avoiding the waste of computational resources caused by blindly scattering points in traditional methods; and obtains the second particle by correcting the motion model of the first particle using environmental signals, overcoming the limitation of existing technologies that use fixed motion models and cannot adapt to complex hydrological environments.

[0187] Specifically, this application uses underwater acoustic positioning signals to calculate the likelihood probability of the second particle and update its weights, while simultaneously introducing optical signals to constrain the distribution range of the second particle, achieving complementary advantages from multiple sources. This particle selection mechanism based on multi-source signals effectively solves the problem of particle divergence caused by observation noise in existing technologies by eliminating particles that deviate from the optical observation range and have weights below a threshold. Finally, the estimated position determined based on the third particle inherits the long-range ranging advantage of underwater acoustic positioning and integrates the precise angle information of optical signals, enabling the positioning results to maintain high accuracy and stability even in complex sea conditions.

[0188] Furthermore, the technical solution of this application enables the system to adaptively adjust the weight contributions of different signal sources through multi-level signal processing and particle optimization strategies. When the underwater acoustic signal is interfered with by multipath effects, the system relies more on optical observations for particle constraint; while when insufficient light makes the optical signal unreliable, the system mainly relies on underwater acoustic positioning signals and environmental parameters for position estimation. This dynamic balancing mechanism significantly improves the system's adaptability to various complex environments.

[0189] However, relying solely on the particle swarm algorithm may result in high-frequency jitter on short timescales. Therefore, this application introduces Kalman filtering for local smoothing, building upon the global estimation provided by the particle swarm algorithm. The system uses the weighted average state of the particle swarm as input to the Kalman filter, combined with a water flow correction term, to predict the short-term trajectory of the diving bell. The state is updated using underwater acoustic positioning signals, resulting in better system stability and noise resistance within the local timeframe.

[0190] Based on the above embodiments, as an optional embodiment, the above method may further include the following steps: calculating the Kalman gain of the emergency communication signal to correct the estimated position through the Kalman gain, thereby obtaining the predicted position of the diving bell.

[0191] In this context, the Kalman gain of the emergency communication signal refers to a dynamic feedback coefficient that establishes an optimal weighted relationship between the predicted and measured values. In this embodiment, it can be understood as a correction factor matrix calculated based on the uncertainty of the estimated position using the particle algorithm and the measurement noise of the emergency communication signal. This matrix adaptively adjusts the weight ratios of the predicted and observed values ​​according to their reliability, thereby obtaining optimal state correction when fusing the global estimation results of the particle algorithm with short-term observation data, thus improving the accuracy and stability of the diving bell position prediction.

[0192] Based on the above embodiments, as an optional embodiment, the above steps may further include the following processes:

[0193] Step S501: Based on the estimated position of the diving bell, define the state vector of the diving bell. The state vector represents the three-dimensional position and velocity components of the diving bell.

[0194] Specifically, the system defines a six-dimensional state vector based on the estimated position of the diving bell. This vector contains the position coordinates of the diving bell in three-dimensional space and the corresponding velocity components.

[0195] Step S502: Based on the motion model and estimated position, predict the first covariance matrix of the diving bell.

[0196] The first covariance matrix refers to a symmetric positive definite matrix that describes the degree of uncertainty in the predicted state of the diving bell. In this embodiment, it can be understood as a sixth-order square matrix, where the diagonal elements represent the variances of the predicted values ​​of each component of position and velocity, and the off-diagonal elements represent the correlations between different state components. This matrix is ​​used to quantify the uncertainties introduced during the state prediction process by factors such as motion model simplification, system noise, and environmental disturbances, providing necessary prior information for the Kalman filter to adjust its gain coefficients during the observation update phase.

[0197] To accurately predict the motion state of a diving bell, the system needs to assess the uncertainty of the state prediction. Based on the defined state vector, the system first uses a motion model to predict the state of the diving bell at the next moment. This prediction process considers the kinematic relationships described by the state transition matrix and the external forces represented by the control input matrix.

[0198] Based on this, the system transfers the covariance matrix from the previous time step through the state transition matrix and superimposes the influence of process noise to obtain the first covariance matrix characterizing the uncertainty of the predicted state. The diagonal elements of this matrix reflect the variance of the predicted values ​​of each component of position and velocity, while the off-diagonal elements describe the correlation between different state components.

[0199] For example, based on the motion model of a diving bell, predict the state at the next moment:

[0200] ;

[0201] In the formula, Represents the state transition matrix; Represents the control input matrix;

[0202] Predict the first covariance matrix:

[0203] ;

[0204] In the formula, Let the covariance matrix at the previous time step be denoted as . This represents the process noise covariance matrix.

[0205] Step S503: Calculate the Kalman gain based on the emergency communication signal.

[0206] Specifically, the system establishes a mapping relationship between observation vectors and state vectors through the observation matrix corresponding to emergency communication signals, enabling the system to process different types of measurement information in a unified state space. Based on the observation model, the system calculates the Kalman gain, which serves as a dynamic weighting coefficient. Its magnitude depends on the prediction uncertainty represented by the first covariance matrix and the observation reliability described by the measurement noise covariance matrix. When prediction uncertainty is high but observation reliability is relatively high, the Kalman gain is large, and the system will place more trust in the observation data; conversely, when prediction is relatively accurate but observation noise is high, the Kalman gain is small, and the system will rely more on the prediction results. This gain mechanism, dynamically adjusted based on prediction uncertainty and observation reliability, allows the system to establish an optimal weighting relationship between predicted and observed values, thereby improving the accuracy and robustness of state estimation.

[0207] For example, the observation matrix corresponding to emergency communication signals is represented as follows: [ , , , , ];

[0208] Its corresponding observation model is expressed as: ;

[0209] in, Represents the observation matrix;

[0210] Calculate the Kalman gain: ;

[0211] In the formula, This represents the measurement noise covariance matrix.

[0212] Step S504: Adjust the first covariance matrix using Kalman gain.

[0213] After obtaining the Kalman gain, the system needs to update the first covariance matrix, which characterizes the uncertainty of the state, to reflect the improvement of the state estimation accuracy by the observation data.

[0214] Specifically, the system constructs an update factor by taking the difference between the product of the identity matrix, the observation matrix, and the Kalman gain, and then multiplies this factor by the first covariance matrix to obtain the corrected second covariance matrix.

[0215] The covariance-based update mechanism described above considers the impact of observation information on the uncertainty of state estimation. The magnitude of the update factor depends on the Kalman gain and the characteristics of the observation matrix. When the system obtains reliable observation data, the updated second covariance matrix typically has fewer diagonal elements than the first covariance matrix, indicating that the introduction of observation data reduces the uncertainty of state estimation.

[0216] For example, the updated covariance matrix is ​​represented as follows:

[0217] ;

[0218] In the formula, Represents the identity matrix. This represents the update factor.

[0219] Step S505: Correct the estimated position based on the second covariance matrix to obtain the predicted position of the diving bell.

[0220] Specifically, the system uses Kalman gain to weight the difference (measurement error) between the observed data and the predicted value, and then adds the weighted correction to the estimated position to obtain the predicted position of the diving bell. This state correction mechanism based on Kalman filtering makes full use of the uncertainty information provided by the second covariance matrix. By optimally weighting and fusing the predicted and observed values, it retains the global estimation results provided by the particle algorithm while strengthening the local correction brought by short-term observation data.

[0221] When the observation data is relatively reliable, the system will assign a larger correction weight, making the predicted position closer to the observed value; conversely, when there is a lot of noise in the observation, the system will retain the original estimation result more, and finally obtain the predicted position of the diving bell.

[0222] For example, the above process can be represented as:

[0223] ;

[0224] In the formula, Indicates the predicted location. This indicates measurement error.

[0225] By incorporating a Kalman filter mechanism to correct the estimation results of the particle algorithm, the system effectively solves the positioning jitter and accuracy instability problems caused by relying solely on the particle algorithm for state estimation in existing technologies. The system first defines a complete state vector containing three-dimensional position and velocity components based on the estimated position of the diving bell, thus providing richer kinematic information for the state estimation. By predicting the state using a motion model and calculating the first covariance matrix, the system can accurately assess the uncertainty of the prediction results, providing reliable prior information for subsequent state correction.

[0226] After receiving the emergency communication signal, the system calculates the Kalman gain based on prediction uncertainty and measurement noise characteristics, achieving adaptive weighting between predicted and observed values. This dynamic weighting mechanism automatically adjusts the filter correction strength according to changes in signal quality, avoiding over-reliance on observations when noise is high while ensuring timely state updates when the signal is reliable. By adjusting the first covariance matrix through the Kalman gain, the system accurately quantifies the improvement effect of observation information on the accuracy of state estimation, enabling the final second covariance matrix to truly reflect the uncertainty level of the state estimation.

[0227] The estimated position is corrected based on the updated second covariance matrix. While retaining the global search capability provided by the particle algorithm, the system effectively suppresses high-frequency fluctuations in the state estimate through the local smoothing effect of the Kalman filter. This hybrid estimation strategy, combining global search and local smoothing, overcomes the shortcomings of relying solely on the particle algorithm, which is susceptible to observation noise, and avoids the performance degradation of the traditional Kalman filter in nonlinear scenarios, significantly improving the accuracy of the diving bell's predicted position.

[0228] In complex scenarios where multiple diving bells operate simultaneously, the system needs to accurately distinguish and identify the identity of each diving bell. This is a crucial prerequisite for achieving precise positioning and coordinated rescue. If the signal source cannot be accurately identified, the positioning results may not correspond correctly to the actual diving bell, affecting the accuracy of rescue decisions.

[0229] Based on the above embodiments, as an optional embodiment, the above method may further include the following steps:

[0230] Step S601: In the case of multiple diving bells, the propagation delay, signal amplitude, and signal phase of each emergency communication signal are determined based on the center frequency, transmission time slot, and pseudo-random code carried by each emergency communication signal.

[0231] Step S602: Based on the propagation delay, signal amplitude, and signal phase of each emergency communication signal, determine the diving bell corresponding to each emergency communication signal.

[0232] The propagation delay of emergency communication signals refers to the time interval required for the signal to travel from the transmitting end to the receiving end. In this embodiment, it can be understood as the time position corresponding to the correlation peak obtained through correlation matching calculation. This time position reflects the time delay experienced by the signal from the diving bell to the mother ship, and is used to calculate the straight-line distance from the diving bell to the mother ship.

[0233] Here, signal amplitude refers to the strength of the received signal. In the embodiments of this application, it can be understood as the peak amplitude of the correlated matching output. This amplitude reflects the energy level of the signal after being affected by acoustic attenuation and multipath effects during underwater propagation, and is used to assess the reliability of the signal and estimate the rough range of the diving bell.

[0234] Here, signal phase refers to the phase angle information of the received signal. In the embodiments of this application, it can be understood as the complex phase angle of the correlated matching output, which includes the geometric features of the signal propagation path and Doppler frequency shift information, and is used to help determine the motion state and relative orientation of the diving bell.

[0235] Specifically, in order to accurately distinguish and identify signals from multiple diving bells, this application assigns a unique center frequency, transmission time slot, and pseudo-random code to each diving bell. This multi-dimensional coding strategy ensures that the signals transmitted by different diving bells have distinguishable characteristics during the signal design phase, laying the foundation for subsequent signal processing and parameter extraction.

[0236] When multiple diving bells conduct emergency communications simultaneously, the mother ship receives a superposition of multiple signals. This composite signal contains the signal components transmitted by each diving bell, each carrying corresponding signal amplitude, pseudo-random code modulation, propagation delay, and phase information, while also being superimposed with the effects of environmental noise. To accurately extract the signal parameters of each diving bell from the superimposed signal, the system employs a multi-level signal processing strategy.

[0237] First, the system performs a short-time Fourier transform on the received superimposed signals to obtain their time-frequency representation. This transform maps the signals onto the time-frequency plane, making the diving bell signals operating in different frequency bands separable in the frequency domain. Subsequently, the system designs a set of bandpass filters corresponding to different center frequencies, using frequency domain filtering to initially separate the signal components of each diving bell. This frequency-domain filtering-based preprocessing effectively reduces mutual interference between signals.

[0238] For each frequency band, the system further extracts signal parameters through correlation matching operations. By calculating the correlation function between the signal and the standard pseudo-random code, the system can determine key parameters such as the signal's propagation delay, amplitude, and phase. The propagation delay is determined by the peak position of the correlation function, directly reflecting the signal propagation time; the signal amplitude is determined by the magnitude of the correlation peak, which can be used to estimate the distance to the diving bell; and the signal phase is determined by the complex phase angle of the correlation output, containing geometric information about the signal propagation path.

[0239] Finally, the system determines the sender's identity for each signal component by comparing the extracted signal parameters with a pre-stored diving bell feature library. This multi-parameter matching-based identification method fully utilizes the signal's frequency, delay, and coding characteristics, improving the reliability of the identification.

[0240] To distinguish between the multiple diving bells, each diving bell Use a unique encoding parameter:

[0241] (1) Center frequency (2) Launch time slot (3) Pseudo-random code ;

[0242] The signal received by the mother ship is a superposition of multiple diving bell signals:

[0243] ;

[0244] In the formula, Indicates signal amplitude. This represents the pseudo-random code sequence of the k-th diving bell. Indicates the propagation delay. Indicates the signal phase. Indicates noise;

[0245] For the received signal Perform a short-time Fourier transform (STFT) to obtain the time-frequency representation:

[0246] ;

[0247] Then, a frequency domain filter is used to extract different center frequencies. Corresponding signals:

[0248] ;

[0249] In the formula, This indicates a bandpass filter, corresponding to the center frequency. , This represents the extracted k-th diving bell signal;

[0250] The signal is calculated using correlation matching and a standard pseudo-random code. Similarity:

[0251] ;

[0252] In the formula, the calculation process for the time delay is as follows:

[0253] ;

[0254] signal amplitude Calculated from the peak value of the matched filter output:

[0255] ;

[0256] This value can be used to estimate the distance of the diving bell.

[0257] ;

[0258] signal phase Calculated from the complex phase angle output by the matched filter:

[0259] ;

[0260] By matching signal parameters, it can be determined which diving bell emitted the signal:

[0261] .

[0262] By employing the embodiments of this application, and assigning a unique center frequency, transmission time slot, and pseudo-random code to each diving bell, the technical problems of signal interference and difficulty in accurately identifying the signal source when multiple diving bells communicate simultaneously in the prior art are effectively solved. This scheme introduces a multi-dimensional coding strategy of frequency division multiple access, time division multiple access, and code division multiple access during the signal design phase, ensuring that the signals transmitted by different diving bells have distinguishable characteristics, fundamentally avoiding the signal aliasing defects easily caused by traditional single coding methods.

[0263] Specifically, this application employs a combination of short-time Fourier transform and bandpass filtering to map the superimposed signals onto the time-frequency plane and achieve preliminary separation, overcoming the signal extraction difficulties caused by directly processing superimposed signals in the time domain in existing technologies. By extracting key parameters such as propagation delay, signal amplitude, and signal phase of each signal component through correlation matching operations, the system can not only accurately calculate the distance information of the diving bell but also analyze the geometric characteristics of the signal propagation path.

[0264] Furthermore, by comprehensively comparing the matching degree between propagation delay, signal amplitude, and signal phase with a pre-stored feature library, this application enables the system to maintain a high recognition accuracy even in complex scenarios with multiple signal sources. This multi-dimensional feature fusion recognition strategy not only enhances the system's ability to resist environmental interference but also provides reliable technical support for underwater multi-target collaborative rescue.

[0265] Figure 2 This schematic diagram illustrates a structural block diagram of an emergency communication system based on a diving system provided in this application, which may include:

[0266] The signal acquisition module is used to acquire emergency communication signals sent by the diving bell. The emergency communication signals include at least: underwater acoustic positioning signals, environmental signals, and optical signals.

[0267] The location determination module is used to determine the estimated location of the diving bell based on the particle algorithm and the emergency communication signal, wherein the environmental signal is used to adjust the distribution of particles, the optical signal is used to constrain the distribution range of particles, and the underwater acoustic positioning signal is used to adjust the weight of particles.

[0268] Based on the above embodiments, as an optional embodiment, the position determination module is further configured to obtain a first particle. The first particle is a Gaussian distribution state space sampling point randomly generated based on a multidimensional state vector and an initial covariance matrix, with the last known position of the diving bell as the reference center. This first particle characterizes the initial state distribution of the diving bell. The initial covariance matrix is ​​a symmetric positive definite matrix characterizing the degree of uncertainty of the initial state of the diving bell in multiple dimensions. The motion model corresponding to the first particle is then modified based on the environmental signal to obtain a second particle. This second particle is a discrete-time linear equation describing the evolution of the particle state over time, used to predict the change in the motion state of the diving bell within a given time step. The motion model includes a state transition matrix, control input, and process noise. The state transition matrix is ​​determined by the environmental signal. The control input represents the influence of water flow on particle motion, and the noise term represents the uncertainty of the motion model. The likelihood probability of the second particle is calculated based on the underwater acoustic positioning signal to obtain the weight of the second particle. The likelihood probability of the second particle represents the probability value of the degree of matching between the predicted state of the second particle and the actual predicted result corresponding to the underwater acoustic positioning signal. The distribution range of the second particle is determined based on the optical signal. Second particles that deviate from the distribution range and have a weight less than a weight threshold are removed to obtain a third particle. The estimated position of the diving bell is determined based on the third particle.

[0269] Based on the above embodiments, as an optional embodiment, the location determination module is further configured to calculate the Kalman gain of the emergency communication signal, so as to correct the estimated position by the Kalman gain and obtain the predicted position of the diving bell. The Kalman gain characterizes the correction factor matrix calculated based on the uncertainty of the estimated position based on the particle algorithm and the measurement noise of the emergency communication signal.

[0270] Based on the above embodiments, as an optional embodiment, the position determination module is further configured to define a state vector of the diving bell based on the estimated position of the diving bell, wherein the state vector represents the three-dimensional position and velocity components of the diving bell; predict the first covariance matrix of the diving bell based on the motion model and the estimated position; calculate the Kalman gain based on the emergency communication signal; adjust the first covariance matrix through the Kalman gain; and correct the estimated position based on the second covariance matrix to obtain the predicted position of the diving bell.

[0271] Based on the above embodiments, as an optional embodiment, the emergency communication system based on the diving system further includes a diving bell determination module, used to determine the propagation delay, signal amplitude, and signal phase of each emergency communication signal based on the center frequency, transmission time slot, and pseudo-random code carried by each emergency communication signal when multiple diving bells are included; and to determine the diving bell corresponding to the transmission of each emergency communication signal based on the propagation delay, signal amplitude, and signal phase of each emergency communication signal.

[0272] Based on the above embodiments, as an optional embodiment, the signal acquisition module is further configured to acquire environmental signals transmitted by the underwater acoustic communication device, the environmental signals including water flow velocity, underwater acoustic propagation speed, and environmental parameters, wherein the water flow velocity and underwater acoustic propagation speed are determined based on the environmental parameters, and the environmental parameters include at least one of water temperature, salinity, and pressure; acquire underwater acoustic positioning signals transmitted by the emergency positioning device, the underwater acoustic positioning signals including at least one of the distance from the diving bell to the mother ship, azimuth angle, and pitch angle; and acquire optical signals transmitted by the strobe light, the optical signals being used to calculate the angle of the strobe light relative to the mother ship.

[0273] Based on the above embodiments, as an optional embodiment, the signal acquisition module is further configured to obtain a clock drift error, wherein the clock drift error characterizes the relative time difference between the reference clock of the mother ship and the local clock of the diving bell when recording the same time period; perform linear interpolation on the environmental signal based on the clock drift error; and perform cubic spline interpolation on the underwater acoustic positioning signal based on the clock drift error.

[0274] It should be noted that the emergency communication system part based on the diving system in the embodiments of this application corresponds to the emergency communication method part based on the diving system in the embodiments of this application. The description of the data processing system part is specifically referred to in the data processing method part, and will not be repeated here.

[0275] Figure 3 The diagram illustrates a structural block diagram of an electronic device suitable for implementing the methods described above, according to an embodiment of this application. Figure 3 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0276] like Figure 3 As shown, an electronic device 300 according to an embodiment of this application includes a processor 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage portion 308 into a random access memory (RAM) 303. The processor 301 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 301 may also include onboard memory for caching purposes. The processor 301 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this application.

[0277] RAM 303 stores various programs and data required for the operation of electronic device 300. Processor 301, ROM 302, and RAM 303 are interconnected via bus 304. Processor 301 executes various operations of the method flow according to embodiments of this application by executing programs in ROM 302 and / or RAM 303. It should be noted that the programs may also be stored in one or more memories other than ROM 302 and RAM 303. Processor 301 may also execute various operations of the method flow according to embodiments of this application by executing programs stored in said one or more memories.

[0278] According to embodiments of this application, the electronic device 300 may further include an input / output (I / O) interface 305, which is also connected to a bus 304. The system 300 may also include one or more of the following components connected to the input / output (I / O) interface 305: an input section 306 including a keyboard, mouse, etc.; an output section 307 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN card, modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the input / output (I / O) interface 305 as needed. A removable medium 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 310 as needed so that computer programs read from it can be installed into the storage section 308 as needed.

[0279] According to embodiments of this application, the method flow according to embodiments of this application can be implemented as a computer software program. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by processor 301, it performs the functions defined in the system of embodiments of this application. According to embodiments of this application, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0280] This application also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0281] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium. Examples include, but are not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0282] For example, according to embodiments of this application, a computer-readable storage medium may include the ROM 302 and / or RAM 303 described above and / or one or more memories other than ROM 302 and RAM 303.

[0283] Embodiments of this application also include a computer program product comprising a computer program containing program code for performing the methods provided in the embodiments of this application. When the computer program product is run on an electronic device, the program code is used to enable the electronic device to implement the methods provided in the embodiments of this application.

[0284] When the computer program is executed by the processor 301, it performs the functions defined in the system / apparatus of this application embodiment. According to the embodiments of this application, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0285] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via communication section 309, and / or installed from removable medium 311. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0286] According to embodiments of this application, program code for executing the computer programs provided in the embodiments of this application can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0287] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions. Those skilled in the art will understand that the features recited in the various embodiments and / or claims of this application can be combined and / or combined in various ways, even if such combinations or combinations are not expressly stated in this application. In particular, the various embodiments and / or features described in the claims of this application may be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application.

[0288] The embodiments of this application have been described above. However, these embodiments are merely illustrative and not intended to limit the scope of this application. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of this application is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this application, and all such substitutions and modifications should fall within the scope of this application.

Claims

1. An emergency communication method based on a diving system, applied to a diving system, comprising: The emergency communication signal transmitted by the diving bell is obtained, and the emergency communication signal includes at least: underwater acoustic positioning signal, environmental signal and optical signal; The estimated location of the diving bell is determined based on the particle algorithm and the emergency communication signal. The environmental signal is used to adjust the distribution of particles, the optical signal is used to constrain the distribution range of particles, and the underwater acoustic positioning signal is used to adjust the weight of particles. The step of determining the estimated location of the diving bell based on the particle algorithm and the emergency communication signal includes: The first particle is obtained. The first particle is a state space sampling point randomly generated based on a Gaussian distribution using the last known position of the diving bell as the reference center and the multidimensional state vector and the initial covariance matrix. Based on the environmental signal, the motion model corresponding to the first particle is modified to obtain the second particle. The motion model is a discrete-time linear equation describing the evolution of the particle state over time, which is used to predict the change of the motion state of the diving bell within a given time step. The likelihood probability of the second particle is calculated based on the underwater acoustic positioning signal to obtain the weight of the second particle. The likelihood probability of the second particle represents the probability value of the degree of matching between the predicted state of the second particle and the actual prediction result corresponding to the underwater acoustic positioning signal. The distribution range of the second particle is determined based on the optical signal; The third particle is obtained by removing the second particle that deviates from the distribution range and has a weight less than the weight threshold; The estimated position of the diving bell is determined based on the third particle.

2. The method according to claim 1, further comprising: The Kalman gain of the emergency communication signal is calculated to correct the estimated position, thereby obtaining the predicted position of the diving bell. The Kalman gain characterizes the correction factor matrix calculated based on the uncertainty of the estimated position using the particle algorithm and the measurement noise of the emergency communication signal.

3. The method according to claim 2, wherein calculating the Kalman gain of the emergency communication signal to correct the estimated position using the Kalman gain to obtain the predicted position of the diving bell comprises: Based on the estimated position of the diving bell, a state vector of the diving bell is defined, which represents the three-dimensional position and velocity components of the diving bell. The first covariance matrix of the diving bell is predicted based on the motion model and estimated position. Calculate the Kalman gain based on the emergency communication signal; The first covariance matrix is ​​adjusted by the Kalman gain; The estimated position is corrected based on the second covariance matrix to obtain the predicted position of the diving bell.

4. The method according to claim 1, further comprising: In the case of multiple diving bells, the propagation delay, signal amplitude, and signal phase of each emergency communication signal are determined based on the center frequency, transmission time slot, and pseudo-random code carried by each emergency communication signal. Based on the propagation delay, signal amplitude, and signal phase of each emergency communication signal, the diving bell corresponding to the issuance of each emergency communication signal is determined.

5. The method according to claim 1, wherein the emergency communication system of the diving bell includes an underwater acoustic communicator, an emergency positioning device, and a strobe light, and the step of obtaining the emergency communication signal sent by the diving bell includes: The environmental signal transmitted by the underwater acoustic communication device is obtained. The environmental signal includes water flow velocity, underwater acoustic propagation speed and environmental parameters. The water flow velocity and underwater acoustic propagation speed are determined based on the environmental parameters, which include at least one of water temperature, salinity and pressure. Obtain the underwater acoustic positioning signal sent by the emergency positioning device, wherein the underwater acoustic positioning signal includes at least one of the distance from the diving bell to the mother ship, the azimuth angle, and the pitch angle; The optical signal transmitted by the strobe light is obtained, and the optical signal is used to calculate the angle of the strobe light relative to the mother ship.

6. The method according to claim 5, further comprising, after obtaining the emergency communication signal sent by the diving bell: The clock drift error is obtained, which characterizes the relative time difference between the mother ship's reference clock and the diving bell's local clock when recording the same time period. The environmental signal is linearly interpolated based on the clock drift error. The underwater acoustic positioning signal is subjected to cubic spline interpolation based on the clock drift error.

7. An emergency communication system based on a diving system, comprising: The signal acquisition module is used to acquire emergency communication signals sent by the diving bell. The emergency communication signals include at least: underwater acoustic positioning signals, environmental signals, and optical signals. The location determination module is used to determine the estimated location of the diving bell based on the particle algorithm and the emergency communication signal, wherein the environmental signal is used to adjust the distribution of particles, the optical signal is used to constrain the distribution range of particles, and the underwater acoustic positioning signal is used to adjust the weight of particles. The location determination module is further configured to obtain a first particle, which is a state-space sampling point randomly generated based on a Gaussian distribution using the last known position of the diving bell as a reference center and an initial covariance matrix; to modify the motion model corresponding to the first particle based on the environmental signal to obtain a second particle, wherein the motion model is a discrete-time linear equation describing the evolution of the particle state over time, used to predict the change in the motion state of the diving bell within a given time step; to calculate the likelihood probability of the second particle based on the underwater acoustic positioning signal to obtain the weight of the second particle, wherein the likelihood probability of the second particle represents the probability value of the degree of matching between the predicted state of the second particle and the actual prediction result corresponding to the underwater acoustic positioning signal; to determine the distribution range of the second particle based on the optical signal; to remove second particles that deviate from the distribution range and whose weight is less than the weight threshold to obtain a third particle; and to determine the estimated position of the diving bell based on the third particle.

8. An electronic device, comprising: One or more processors; Memory, used to store one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1 to 6.

9. A computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the method of any one of claims 1 to 6.

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