Intelligent wearable device for people on water and method for positioning people on water

Abstract

The invention provides an intelligent wearable device for an overwater person and an overwater person positioning method. The intelligent wearable device for the overwater person comprises a main control unit, a positioning unit, a multi-dimensional monitoring unit, a long-distance communication unit, a high-energy-density battery unit and an alarm unit. The positioning unit obtains high-precision positioning information of the water personnel in the current water area; the multi-dimensional monitoring unit monitors multi-dimensional data of the overwater personnel and judges whether the overwater personnel are in a drowning risk state or not; the long-distance communication unit communicates with a user terminal through satellite communication and 5G communication; the high-energy density battery unit adopts a novel lithium-sulfur battery, and the main control unit dynamically distributes electric quantity according to the operation state of each unit; the alarm unit generates a rescue alarm signal, and the main control unit sends out the rescue alarm signal when judging that the overwater person is in a drowning risk state. According to the invention, the positioning precision, drowning risk pre-judgment capability and communication performance of the intelligent wearable device for the overwater personnel are improved, and the safety guarantee of the overwater personnel is improved.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to an intelligent wearable device for water personnel and a method for locating water personnel. Background Art

[0002] With the increasing frequency of water activities (such as water sports, water operations, maritime rescue, etc.), the safety of people on the water has become an important concern.

[0003] Existing smart wearable devices focus on location tracking functions, but the positioning accuracy is limited, especially in complex water environments (such as multiple islands and areas with strong water flow interference), which are prone to large deviations, making it difficult for rescuers to quickly and accurately locate the precise location of people in distress. In addition, in terms of vital signs monitoring, most devices can only monitor simple physiological indicators, such as heart rate and body temperature, and lack the ability to accurately predict the risk of drowning. They cannot analyze multi-dimensional data such as people's posture and movement changes in the water, and the pressure of water on the human body in real time, making it difficult to issue alarms in time at the critical stage before a drowning accident occurs. In terms of communication functions, the communication distance between existing wearable devices and the outside world is short and the signal stability is poor. In an open water environment, once a certain range is exceeded, it is easy to lose contact with the rescue command center or nearby ships, affecting the timeliness and effectiveness of rescue. Summary of the invention

[0004] The present invention provides an intelligent wearable device for water personnel and a method for positioning water personnel, aiming to solve the problems of low positioning accuracy, insufficient drowning risk prediction capability and poor communication performance of existing intelligent wearable devices for water personnel, so as to improve the safety of water personnel.

[0005] In a first aspect, the present invention provides an intelligent wearable device for water personnel, comprising a main control unit, a positioning unit, a multi-dimensional monitoring unit, a long-distance communication unit, a high-energy density battery unit and an alarm unit; the main control unit is respectively connected to the positioning unit, the multi-dimensional monitoring unit, the long-distance communication unit, the high-energy density battery unit and the alarm unit, and is used to receive and process data from each unit;

[0006] The positioning unit adopts a multi-satellite positioning system and an underwater positioning base station for collaborative positioning. The multi-satellite positioning system includes GPS, Beidou and GLONASS satellite positioning systems. The underwater positioning base station is deployed at the bottom of a conventionally planned water area, and communicates with the intelligent wearable device of the water personnel through acoustic wave signals to obtain high-precision positioning information of the water personnel in the current water area;

[0007] The multi-dimensional monitoring unit includes a plurality of sensors for real-time monitoring of multi-dimensional data of people on the water, and transmitting the multi-dimensional data to the main control unit for comprehensive analysis to determine whether the people on the water are in a state of drowning risk; the multi-dimensional data includes heart rate information, body temperature information, blood pressure information, underwater posture information, limb movement information, and water pressure information on the human body;

[0008] The long-distance communication unit adopts satellite communication and 5G communication. If the signal strength of the satellite communication in the current water area is greater than the signal strength of the 5G communication, the communication is performed with the user terminal based on the satellite communication; if the signal strength of the satellite communication in the current water area is less than the signal strength of the 5G communication, or the distance between the current water area and the shore is less than or equal to the preset distance, the communication is performed with the user terminal based on the 5G communication;

[0009] The high energy density battery unit adopts a new type of lithium sulfur battery, and the main control unit dynamically distributes power according to the operating status of each unit;

[0010] The alarm unit includes multiple alarm signal generating modules for generating multiple rescue alarm signals. When the main control unit determines that the person on the water is in a drowning risk state, it sends out a rescue alarm signal.

[0011] In a second aspect, the present invention further provides a method for locating a person on water, which is applied to the smart wearable device for a person on water described in the first aspect, and the method for locating a person on water comprises:

[0012] Real-time acquisition of satellite positioning signals from multiple satellite positioning systems, multi-dimensional data of people on the water, and reception of acoustic wave signals sent by multiple underwater positioning base stations;

[0013] Based on the satellite positioning signal of each satellite positioning system and the sound wave signal of each underwater positioning base station, the high-precision positioning information of the people on the water in the current water area is determined;

[0014] Determining whether the person on the water is in a drowning risk state based on the multi-dimensional data;

[0015] If the person on the water is in a state of drowning risk, a rescue alarm signal is generated based on the high-precision positioning information, and the rescue alarm signal is sent to the user terminal based on the long-distance communication unit.

[0016] In a third aspect, the present invention further provides an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby implementing any of the above-mentioned methods for locating personnel on water.

[0017] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium, wherein the storage medium stores a computer software program, and when the computer software program is executed by a processor, the method for locating personnel on water as described in any one of the above-mentioned methods.

[0018] In a fifth aspect, the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-described methods for locating personnel on water.

[0019] The intelligent wearable device for water personnel provided by the embodiment of the present invention is coordinated with the underwater positioning base station by a multi-satellite positioning system. In areas with good satellite signals, the multi-satellite positioning system (GPS, Beidou and GLONASS) can provide relatively accurate planar position information. In complex waters, such as when multiple islands block satellite signals or strong water flow interference causes the satellite positioning accuracy to decrease, the underwater positioning base station can play a role. Since the sound wave propagates relatively stably in water and is not easily affected by interference factors above the water surface, the distance and orientation between the wearable device and the base station can be accurately determined, thereby correcting and supplementing the satellite positioning information and improving the positioning accuracy. On the other hand, the multiple sensors in the multi-dimensional monitoring unit can comprehensively collect the physiological and motion status data of the water personnel. The main control unit can perform a comprehensive analysis of the multi-dimensional data to predict the risk of drowning in advance, and issue an alarm in time at the critical stage of danger when the drowning accident has not yet completely occurred, thereby improving the ability to predict the risk of drowning. Furthermore, by combining satellite communication with 5G communication, in vast waters, satellite communication can ensure that the connection between the device and the outside world is not limited by geographical distance, and can achieve global communication coverage. When people on the water approach the shore or enter the coverage area of ​​5G base stations, they will automatically switch to 5G communication. Taking advantage of the high speed and low latency of 5G communication, a large amount of data can be quickly transmitted, which not only ensures the stability of long-distance communication, but also improves the efficiency and quality of data transmission and communication performance. Therefore, it effectively solves the problems of low positioning accuracy, insufficient drowning risk prediction ability and poor communication performance of existing smart wearable devices for people on the water, and significantly improves the safety level of people on the water and the efficiency of emergency rescue. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a structural schematic diagram of the intelligent wearable device for water personnel provided by the present invention;

[0021] Figure 2 It is a schematic flow chart of the method for locating personnel on water provided by the present invention;

[0022] Figure 3 An embodiment diagram of an electronic device provided by an embodiment of the present invention;

[0023] Figure 4An embodiment diagram of a computer-readable storage medium provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0025] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0026] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0027] Optional, see Figure 1 As shown, Figure 1 : is a schematic diagram of the structure of the intelligent wearable device for water personnel provided by the present invention, which includes a main control unit, a positioning unit, a multi-dimensional monitoring unit, a long-distance communication unit, a high-energy density battery unit and an alarm unit. Among them, the main control unit in the embodiment of the present invention is respectively connected to the positioning unit, the multi-dimensional monitoring unit, the long-distance communication unit, the high-energy density battery unit and the alarm unit, and is used to receive and process data from each unit.

[0028] In one embodiment, the positioning unit in the embodiment of the present invention adopts a multi-satellite positioning system and an underwater positioning base station for collaborative positioning, wherein the multi-satellite positioning system includes GPS (Global Navigation Satellite System), Beidou (Beidou Navigation Satellite System) and GLONASS (GalileoNavigation Satellite System) satellite positioning systems. The underwater positioning base station is deployed on the bottom of a conventionally planned designated water area and communicates with the smart wearable device of the personnel on the water through acoustic wave signals. Therefore, when the positioning unit positions the smart wearable device of the personnel on the water, it can perform positioning based on the satellite positioning signals of each satellite positioning system and the acoustic wave signals of each underwater positioning base station. For example, in an area with good satellite signals, the multi-satellite positioning system can provide relatively accurate planar position information. In complex waters, such as when multiple islands block satellite signals or strong water currents interfere, causing the satellite positioning accuracy to decrease, underwater positioning base stations can play a role. Since sound waves propagate relatively stably in water and are not easily affected by interference factors above the water surface, they can accurately measure the distance and direction between the wearable device and the base station, thereby correcting and supplementing the satellite positioning information, thereby obtaining high-precision positioning information for people on the water in the current waters.

[0029] In one embodiment, the multi-dimensional monitoring unit in the embodiment of the present invention includes multiple sensors, such as a heart rate sensor, a body temperature sensor, a blood pressure acquisition sensor, an underwater posture acquisition sensor, a limb movement acquisition sensor, and a water pressure on the human body acquisition sensor. Therefore, the multi-dimensional monitoring unit can obtain the heart rate information, body temperature information, blood pressure information, underwater posture information, limb movement information, and water pressure information on the human body of the people on the water, and transmit the above data to the main control unit.

[0030] Among them, heart rate sensors are usually based on photoplethysmography (PPG) technology. The sensor emits light of a specific wavelength (such as green light) to the surface of the skin, and the absorption and reflection of light by the blood will change periodically with the heartbeat. By detecting the change in the intensity of the reflected light, the sensor converts it into an electrical signal and then calculates the heart rate value. For example, in each heartbeat cycle, when blood fills the blood vessels, it absorbs more light and the intensity of the reflected light decreases; when the heart relaxes, the intensity of the reflected light increases. The sensor samples this light intensity change at a certain sampling frequency (such as 100Hz) and collects a series of discrete data points to form the original sequence of heart rate data HR(t).

[0031] The body temperature sensor uses a temperature sensor such as a thermistor or a thermocouple. The body temperature sensor is based on the thermoelectric properties of the material, and its resistance value or electromotive force will change with temperature. The body temperature sensor is attached to a suitable part of the human body (such as the inside of the wrist or the chest) to sense the body surface temperature in a contact manner. The body temperature sensor collects temperature data at a relatively low sampling frequency (such as 1Hz) to obtain a body temperature data sequence BT(t).

[0032] The blood pressure sensor uses the oscillometric blood pressure measurement principle to detect the pressure changes caused by the pulse fluctuations of the blood vessel wall during the inflation and deflation of the cuff. There is a pressure sensitive element inside the blood pressure sensor. When the cuff pressure changes, the electrical characteristics of the sensitive element (such as capacitance, resistance, etc.) change, thereby converting the pressure change into an electrical signal to obtain the systolic pressure SBP (t) and diastolic pressure DBP (t) values.

[0033] The underwater attitude acquisition sensor uses an inertial measurement unit (IMU), which usually integrates an accelerometer, a gyroscope, and a magnetometer. The accelerometer is used to measure the acceleration of the human body in the three coordinate axes. For example, when the human body rises or sinks in the water, the vertical acceleration will change; when there is a translation or rotation movement, the horizontal acceleration will also change. The gyroscope mainly measures the angular velocity of the human body around the three coordinate axes, and can reflect the rotational movement of the human body, such as rolling, yaw, and other movements. The magnetometer can assist in determining the direction information. The IMU collects data at a higher sampling frequency (such as 100Hz) to obtain the pitch angle θ(t), roll angle θ(t), and pitch angle θ(t). The original data sequence of the yaw angle ψ(t) is used to obtain the posture changes of the human body in water.

[0034] Limb motion collection sensors rely on accelerometers installed at key parts of the human limbs (such as wrists, elbows, knees, ankles, etc.). When the limbs move, the accelerometer will detect the corresponding acceleration changes. For example, when the arm is paddling, the accelerometer at the wrist will collect periodic acceleration changes, and its acceleration components A in different directions are x (t),A y (t),A z (t) It can reflect the direction, amplitude and frequency of limb movement. The sensor collects data at a high sampling frequency (such as 100Hz).

[0035] The pressure acquisition sensor uses an array of pressure sensors distributed at different locations on the human body surface to sense the pressure of water on various parts of the human body. For example, pressure sensors are placed on the chest, back, abdomen, etc. When the human body is in different postures in the water (such as standing, floating, sinking, etc.), the water pressure on various parts will change. The pressure sensor converts the pressure into an electrical signal and collects data at a certain sampling frequency (such as 10Hz) to obtain a water pressure data sequence P(t).

[0036] Furthermore, the main control unit conducts a comprehensive analysis based on the heart rate information, body temperature information, blood pressure information, underwater posture information, limb movement information, and water pressure information of the water person to determine whether the water person is at risk of drowning. It should be noted that the surface of each sensor in the multi-dimensional monitoring unit has a waterproof and corrosion-resistant coating to adapt to the long-term underwater use environment.

[0037] In one embodiment, the long-distance communication unit adopts satellite communication and 5G communication. If the signal strength of the satellite communication in the current water area is greater than the signal strength of the 5G communication, communication with the user terminal is performed based on the satellite communication; if the signal strength of the satellite communication in the current water area is less than the signal strength of the 5G communication, or the distance between the current water area and the shore is less than or equal to the preset distance, communication with the user terminal is performed based on the 5G communication.

[0038] Therefore, the smart wearable device for water personnel can transmit the high-precision positioning information of water personnel in the current water area, as well as the real-time heart rate information, body temperature information, blood pressure information, underwater posture information, limb movement information, water pressure on the human body information, and whether the water personnel are at risk of drowning to the user terminal in real time through the long-distance communication unit. It should be noted that the long-distance communication unit also includes a signal enhancement antenna, which adopts a foldable design and can be stored in a specific part of the wearable device when not in use, and unfolded when in use to enhance the reception and transmission capabilities of communication signals.

[0039] In one embodiment, the high energy density battery unit in the embodiment of the present invention adopts a new type of lithium sulfur battery, and the main control unit dynamically allocates power according to the operating status of each unit. It should be noted that the high energy density battery unit also has a wireless charging function, and can be wirelessly charged through a wireless charging base station when the water personnel are in a specific area (such as a dock, a charging area on a rescue ship).

[0040] In one embodiment, the alarm unit includes multiple alarm signal generating modules, which can generate multiple rescue alarm signals, such as light alarm signals, sound alarm signals, light and sound alarm signals, etc. When the main control unit determines that the people on the water are at risk of drowning, it sends out a rescue alarm signal.

[0041] The embodiment of the present invention uses a multi-satellite positioning system and an underwater positioning base station to coordinate positioning. In areas with good satellite signals, the multi-satellite positioning system can provide relatively accurate planar position information. In complex waters, such as when multiple islands block satellite signals or strong water flow interference causes the satellite positioning accuracy to decrease, the underwater positioning base station can play a role. Since the propagation of sound waves in water is relatively stable and not easily affected by interference factors above the water surface, the distance and orientation between the wearable device and the base station can be accurately measured, thereby correcting and supplementing the satellite positioning information and improving the positioning accuracy. On the other hand, the multiple sensors in the multi-dimensional monitoring unit can comprehensively collect the physiological and motion status data of the people on the water. The main control unit can perform a comprehensive analysis of the multi-dimensional data to predict the risk of drowning in advance, and issue an alarm in time at the critical stage of danger when the drowning accident has not yet completely occurred, thereby improving the ability to predict the risk of drowning. Furthermore, by combining satellite communication with 5G communication, in vast waters, satellite communication can ensure that the connection between the device and the outside world is not limited by geographical distance, and can achieve global communication coverage. When people on the water approach the shore or enter the coverage area of ​​5G base stations, they will automatically switch to 5G communication. Taking advantage of the high speed and low latency of 5G communication, a large amount of data can be quickly transmitted, which not only ensures the stability of long-distance communication, but also improves the efficiency and quality of data transmission and communication performance. Therefore, it effectively solves the problems of low positioning accuracy, insufficient drowning risk prediction ability and poor communication performance of existing smart wearable devices for people on the water, and significantly improves the safety level of people on the water and the efficiency of emergency rescue.

[0042] Optional, see Figure 2 , Figure 2 The flowchart of the method for locating personnel on water provided by the present invention is shown in FIG. The executor of the method for locating personnel on water in the embodiment of the present invention is a smart wearable device for personnel on water. The following embodiment briefly describes the above-mentioned smart wearable device for personnel on water with a wearable device. Therefore, the method for locating personnel on water provided by the embodiment of the present invention includes:

[0043] Step 10, acquiring satellite positioning signals of a multi-satellite positioning system, multi-dimensional data of people on the water in real time, and receiving sound wave signals sent by multiple underwater positioning base stations.

[0044] Specifically, the wearable device in the embodiment of the present invention can receive signals from the GPS satellite system, the Beidou satellite system and the GLONASS satellite system in real time. Therefore, the wearable device can obtain the satellite positioning signal of the multi-satellite positioning system in real time, wherein the satellite positioning signal includes ephemeris data, pseudorange information and Doppler frequency shift data. Therefore, it can be understood that the wearable device can obtain the ephemeris data, pseudorange information and Doppler frequency shift data of the multi-satellite positioning system in real time.

[0045] Furthermore, wearable devices can obtain multi-dimensional data of people on the water in real time through internal sensors, where the multi-dimensional data includes heart rate information, body temperature information, blood pressure information, underwater posture information, limb movement information, and information on the pressure of water on the human body. Therefore, it can be understood that wearable devices can obtain heart rate information, body temperature information, blood pressure information, underwater posture information, limb movement information, and information on the pressure of water on the human body of people on the water in real time.

[0046] Furthermore, the wearable device can receive sound wave signals sent by multiple underwater positioning base stations in real time, wherein the sound wave signals include the base station number information of the underwater positioning base station and the signal sending time of the sound wave. Therefore, it can be understood that the wearable device receives the sound wave signals sent by multiple underwater positioning base stations in real time, and analyzes the sound wave signals sent by each underwater positioning base station to obtain the base station number information and signal sending time of each underwater positioning base station.

[0047] Step 20, based on the satellite positioning signal of each satellite positioning system and the sound wave signal of each underwater positioning base station, determine the high-precision positioning information of the people on the water in the current water area.

[0048] Furthermore, the wearable device performs positioning analysis based on the ephemeris data, pseudorange information and Doppler frequency shift data of each satellite positioning system, as well as the signal transmission time of each underwater positioning base station, to obtain high-precision positioning information of the people on the water in the current waters, as specifically described in steps 201 to 204.

[0049] Step 30, determining whether the person on the water is at risk of drowning based on the multi-dimensional data.

[0050] Furthermore, the wearable device analyzes the status of the people on the water based on the real-time heart rate information, body temperature information, blood pressure information, underwater posture information, limb movement information and water pressure information on the human body, and determines in real time whether the people on the water are at risk of drowning, as described in steps 301 to 304.

[0051] Step 40: If the person on the water is in a state of drowning risk, a rescue alarm signal is generated based on the high-precision positioning information, and the rescue alarm signal is sent to the user terminal based on the long-distance communication unit.

[0052] Furthermore, if it is determined that the person on the water is at risk of drowning, the wearable device generates a rescue alarm signal based on the high-precision positioning information. If the signal strength of the satellite communication in the current water area is greater than the signal strength of the 5G communication, the wearable device sends the rescue alarm signal to the user terminal through the satellite communication in the long-distance communication unit. If the signal strength of the satellite communication in the current water area is less than the signal strength of the 5G communication, or the distance between the current water area and the shore is less than or equal to the preset distance, the wearable device sends the rescue alarm signal to the user terminal through the 5G communication in the long-distance communication unit.

[0053] The embodiment of the present invention uses a multi-satellite positioning system and an underwater positioning base station to coordinate positioning. In areas with good satellite signals, the multi-satellite positioning system can provide relatively accurate planar position information. In complex waters, such as when multiple islands block satellite signals or strong water flow interference causes the satellite positioning accuracy to decrease, the underwater positioning base station can play a role. Since the propagation of sound waves in water is relatively stable and not easily affected by interference factors above the water surface, the distance and orientation between the wearable device and the base station can be accurately measured, thereby correcting and supplementing the satellite positioning information and improving the positioning accuracy. On the other hand, the multiple sensors in the multi-dimensional monitoring unit can comprehensively collect the physiological and motion status data of the people on the water. The main control unit can perform a comprehensive analysis of the multi-dimensional data to predict the risk of drowning in advance, and issue an alarm in time at the critical stage of danger when the drowning accident has not yet completely occurred, thereby improving the ability to predict the risk of drowning. Furthermore, by combining satellite communication with 5G communication, in vast waters, satellite communication can ensure that the connection between the device and the outside world is not limited by geographical distance, and can achieve global communication coverage. When people on the water approach the shore or enter the coverage area of ​​5G base stations, they will automatically switch to 5G communication. Taking advantage of the high speed and low latency of 5G communication, a large amount of data can be quickly transmitted, which not only ensures the stability of long-distance communication, but also improves the efficiency and quality of data transmission and communication performance. Therefore, it effectively solves the problems of low positioning accuracy, insufficient drowning risk prediction ability and poor communication performance of existing smart wearable devices for people on the water, and significantly improves the safety level of people on the water and the efficiency of emergency rescue.

[0054] In one embodiment, the description of steps 201 to 204 is as follows:

[0055] Step 201, based on the ephemeris data and Doppler frequency shift data of each satellite positioning system, respectively calculate the satellite clock bias and relative velocity change of each satellite positioning system.

[0056] Optionally, the ephemeris data in the embodiment of the present invention includes information related to satellite orbit parameters and satellite clocks. The satellite clock deviation is mainly caused by the difference between the physical properties of the satellite clock (such as the frequency stability of the oscillator, etc.) and the ideal clock. Therefore, the wearable device calculates the satellite clock deviation of each satellite positioning system based on the ephemeris data of each satellite positioning system. The specific process is: extracting the basic parameters of the satellite clock from the ephemeris data, wherein the ephemeris data provides the polynomial coefficients of the satellite clock, and the polynomial of the satellite clock deviation is expressed as Δt s =a0+a1(t-t0)+a2(t-t0) 2 +....+a n (t-t0) n , where t0 represents the reference time, a0, a1, a2, ...., a n are the polynomial coefficients obtained from the ephemeris data.

[0057] According to the special theory of relativity, due to the high-speed motion of the satellite, the satellite clock will slow down relative to the ground clock, and its frequency change is Δf s / f=-v 2 / 2c 2 , where v is the speed of the satellite, c is the speed of light, and f is the nominal frequency of the satellite clock. According to general relativity, since the satellite is in a weaker gravitational field, the satellite clock will become faster relative to the ground clock, and its frequency change is Δf g / f=ΔU / 2c 2 , where ΔU is the gravitational potential difference between the satellite and the ground. Therefore, considering the relativistic effect, the total change in the satellite clock frequency is Δf / f = Δf s / f+Δf g / f.

[0058] In one embodiment, the frequency deviation Δf(t1) of the satellite clock at a certain time t1 is known, then the change in the satellite clock deviation within a period of time Δt is By continuously updating this deviation value, the satellite clock deviation corrected based on the ephemeris data and the relativistic effect is obtained, and the satellite clock deviation of each satellite positioning system is obtained. n represents the number of satellite positioning systems.

[0059] Furthermore, the wearable device calculates the relative speed change of each satellite positioning system based on the Doppler frequency shift data of each satellite positioning system. The specific analysis is as follows: The Doppler frequency shift is caused by the relative motion between the satellite and the wearable device. When the satellite is close to the wearable device, the received signal frequency will increase; when the satellite is far away from the wearable device, the received signal frequency will decrease. Therefore, the frequency f0 of the satellite transmission signal is obtained, and the frequency of the signal received by the wearable device is ft , according to the Doppler frequency shift formula f t =f0(1+v r / c), where v r is the radial relative velocity between the satellite and the wearable device (i.e. the relative velocity in the direction of the line connecting the two), and c is the speed of light. Further, the radial relative velocity v at a certain moment is calculated r =c(f t -f0) / f0. Further, the received frequency is continuously measured. In one embodiment, the received frequency at time t1 is f1, and the received frequency at time t2 is f2. Then the radial relative velocity change during time t1 and t2 is Δv r =c(f2-f1) / f0, thereby obtaining the relative velocity change Δv of each satellite positioning system i .

[0060] Step 202, based on the coordinates, pseudorange information, satellite clock bias and relative speed change of each satellite positioning system, construct a first objective function for solving the positioning information of the water personnel and the clock bias of the smart wearable device of the water personnel.

[0061] Furthermore, the wearable device obtains the coordinates of each satellite positioning system (x i ,y i ,z i ), a preliminary positioning equation is constructed according to the coordinates of each satellite positioning system, the satellite clock bias and the relative speed change, as well as the coordinates of the water personnel to be solved and the clock bias of the smart wearable device of the water personnel to be solved. The preliminary positioning equation can be expressed as:

[0062]

[0063] Among them, d i The preliminary positioning equation representing the true distance between the i-th satellite positioning system and the wearable device, (x i ,y i ,z i ) represents the coordinates of the i-th satellite positioning system, (x, y, z) represents the coordinates of the personnel on the water to be solved, Δt u represents the clock deviation of the smart wearable device of the water personnel to be solved, f i represents the satellite signal frequency of the i-th satellite positioning system, t i represents the signal propagation time of the i-th satellite positioning system.

[0064] Furthermore, the wearable device constructs an error equation for each satellite positioning system based on the preliminary positioning equation and the pseudorange information of each satellite positioning system, wherein the error equation can be expressed as:

[0065]

[0066] Among them, e i represents the error equation of the i-th satellite positioning system, ρ i Represents the pseudorange information of the i-th satellite positioning system.

[0067] Furthermore, the above nonlinear equations are linearized by Taylor series expansion to obtain a linearized matrix equation, which is the first objective function for solving the positioning information of the water personnel and the clock deviation of the smart wearable device of the water personnel. Therefore, the first objective function can be expressed as:

[0068] E=A·X+B.

[0069] Among them, the first objective function E represents the error equation e of all satellite positioning systems i The matrix is ​​formed by combining them, so E = [e1, e2, ..., e n ] T ; A represents the coefficient matrix constructed by the coordinates of the water personnel to be solved and the clock deviation of the smart wearable device of the water personnel to be solved, that is, A=[x,y,z,Δt u ] T ; B represents a vector containing information such as satellite coordinates and pseudorange.

[0070] Step 203, solving the first objective function to obtain the initial positioning information of the water personnel and the clock deviation correction number of the smart wearable device of the water personnel;

[0071] Furthermore, the wearable device solves the first objective function by the least square method, that is, X=(A T A) -1 A T E, get the initial positioning information (x0, y0, z0) of the personnel on the water and the clock deviation correction number Δt of the smart wearable device of the personnel on the water u0 .

[0072] Step 204, based on the clock deviation correction number and the sound wave signal of each underwater positioning base station, the initial positioning information is corrected to obtain high-precision positioning information of the people on the water in the current water area.

[0073] Furthermore, the wearable device corrects the initial positioning information according to the clock deviation correction number and the sound wave signal of each underwater positioning base station to obtain high-precision positioning information of the people on the water in the current water area, as described in steps 2041 to 2045.

[0074] The embodiment of the present invention uses a multi-satellite positioning system and an underwater positioning base station to coordinate positioning. In areas with good satellite signals, the multi-satellite positioning system can provide relatively accurate planar position information. In complex waters, such as when multiple islands block satellite signals or strong water flow interference causes satellite positioning accuracy to decrease, the underwater positioning base station can play a role. Since sound waves propagate relatively stably in water and are not easily affected by interference factors above the water surface, the distance and direction between the wearable device and the base station can be accurately measured, thereby correcting and supplementing the satellite positioning information, improving positioning accuracy, and thus improving the safety level of people on the water and the efficiency of emergency rescue.

[0075] In one embodiment, the description of steps 2041 to 2045 is as follows:

[0076] Step 2041, based on the signal sending time in the sound wave signal of each underwater positioning base station, the signal receiving time when the smart wearable device of the water personnel receives the sound wave signal of each underwater positioning base station, and the water temperature of the current water area at each signal receiving time, determine the distance between the smart wearable device of the water personnel and each underwater positioning base station.

[0077] Optionally, the wearable device can determine the signal sending time in the sound wave signal of each underwater positioning base station according to the information carried in the sound wave signal of each underwater positioning base station.

[0078] Optionally, the wearable device can receive sound wave signals from m underwater positioning base stations, so the signal receiving time when the wearable device receives the sound wave signal from each underwater positioning base station can be determined, and the signal receiving time when the wearable device receives the sound wave signal from the m underwater positioning base stations is recorded as t r1 ,t r2 ,...,t rm .

[0079] Furthermore, the wearable device obtains the water temperature of the current water area at each signal receiving moment, and determines the propagation speed of the sound wave signal at each signal receiving moment according to the water temperature at each signal receiving moment, wherein the calculation formula of the propagation speed of the sound wave signal at each signal receiving moment is as follows:

[0080]

[0081] Among them, v wj represents the jth signal receiving time t rj The propagation speed of the acoustic wave signal, t rj represents the signal receiving time when the wearable device receives the acoustic wave signal from the jth underwater positioning base station; T j Indicates that the current water area is at the jth signal reception time t rj The water temperature in degrees Celsius.

[0082] Furthermore, the wearable device determines the distance between the smart wearable device of the water personnel and each underwater positioning base station according to the signal sending time in the sound wave signal of each underwater positioning base station, the signal receiving time when the smart wearable device of the water personnel receives the sound wave signal of each underwater positioning base station, and the water temperature of the current water area at each signal receiving time. The specific calculation formula is as follows:

[0083] d j =v wj (t rj -t sendj ), where d j represents the distance between the smart wearable device of the water personnel and the jth underwater positioning base station, t sendj Indicates the signal sending time of the jth underwater positioning base station.

[0084] Step 2042, based on the coordinates of each underwater positioning base station and the distance between the smart wearable device of the water personnel and each underwater positioning base station, construct a second objective function for solving the positioning information of the water personnel.

[0085] Step 2043, solving the second objective function to obtain the positioning correction value.

[0086] Furthermore, the wearable device constructs a second objective function for solving the positioning information of the water personnel according to the coordinates of each underwater positioning base station, the positioning information of the water personnel to be solved, and the distance between the smart wearable device of the water personnel and each underwater positioning base station. The second objective function can be expressed as:

[0087]

[0088] Among them, (x j ,y j ,z j ) represents the coordinates of the jth underwater positioning base station, (x, y, z) represents the coordinates of the personnel on the water to be solved, η j Represents the acoustic wave propagation measurement error of the jth underwater positioning base station.

[0089] Furthermore, for m, m ≥ 3 base stations, a nonlinear equation group can be obtained, and after linearization through Taylor series expansion, the positioning correction based on the underwater base station is solved based on the least squares method, where the positioning correction can be expressed as Δx b ,Δy b ,Δz b .

[0090] Step 2044, correct the initial positioning information based on the positioning correction value to obtain the final positioning information of the people on the water.

[0091] Furthermore, the wearable device adds the positioning correction value to the initial positioning information, corrects the initial positioning information, and obtains the final positioning information of the person on the water. Therefore, the final positioning information of the person on the water (x f ,y f ,z f ) can be expressed as:

[0092] (x f ,y f ,z f )=(x0+Δx b ,y0+Δy b ,z0+Δz b ).

[0093] Step 2045, the initial positioning information and the final positioning information are merged based on the clock bias correction number to obtain high-precision positioning information.

[0094] Furthermore, the wearable device fuses the initial positioning information and the final positioning information according to the clock deviation correction number to obtain high-precision positioning information, as specifically described in steps 20451 to 20452.

[0095] The embodiment of the present invention uses a multi-satellite positioning system and an underwater positioning base station to coordinate positioning. In areas with good satellite signals, the multi-satellite positioning system can provide relatively accurate planar position information. When the satellite positioning accuracy decreases in complex waters, the underwater positioning base station can play a role. Since sound waves propagate relatively stably in water and are not easily affected by interference factors above the water surface, the distance and direction between the wearable device and the base station can be accurately measured, thereby correcting and supplementing the satellite positioning information, improving the positioning accuracy, and thus improving the safety level of people on the water and the efficiency of emergency rescue.

[0096] In one embodiment, the description of step 20451 to step 20452 is as follows:

[0097] Step 20451, determining a first weight coefficient of the initial positioning information and a second weight coefficient of the final positioning information based on the clock bias correction number;

[0098] Specifically, the wearable device corrects the clock deviation Δt u0 Calculate the first weight coefficient of the initial positioning information, wherein the specific calculation process of the first weight coefficient is as follows: the wearable device calculates the first weight coefficient according to the clock deviation correction number Δt u0 The satellite signal strength S1 and the satellite signal noise ratio SNR1 of the multi-satellite positioning system are calculated, wherein the calculation formula of the satellite signal strength S1 is as follows:

[0099] S1=S 10*exp(-αΔt u0 ).

[0100] Among them, S 10 It represents the signal strength under ideal conditions without clock error, exp() represents the exponential function, and α represents the preset attenuation coefficient.

[0101] The calculation formula of satellite signal signal-to-noise ratio SNR1 is as follows:

[0102] SNR1=S1 / [N0*(1+β|Δt u0 |).

[0103] Wherein, N0 represents the basic noise level and β represents the noise influence coefficient.

[0104] Furthermore, the wearable device obtains the transmission power of the sound wave signal of each underwater positioning base station, the gain of the smart wearable device of the personnel on the water, and the propagation attenuation of the sound wave in the water.

[0105] Furthermore, the wearable device calculates the base station signal strength and base station signal-to-noise ratio of each underwater positioning base station according to the transmission power of the sound wave signal of each underwater positioning base station, the gain of the smart wearable device of the water personnel, and the propagation attenuation of the sound wave in the water. The calculation formula of the base station signal strength of each underwater positioning base station is as follows:

[0106] A j =A j *(μf j +λ).

[0107] Among them, S 2j represents the base station signal strength of the jth underwater positioning base station, P tj represents the transmission power of the acoustic signal of the jth underwater positioning base station, G r represents the gain of the wearable device, d j represents the distance between the wearable device and the jth underwater positioning base station, A j represents the propagation attenuation of the acoustic wave signal of the jth underwater positioning base station in water, f j represents the acoustic frequency of the acoustic wave signal of the jth underwater positioning base station, μ represents the absorption coefficient of water in the current area, and λ represents the scattering coefficient of water in the current area.

[0108] The calculation formula of the base station signal noise ratio of each underwater positioning base station is as follows:

[0109]

[0110] Among them, SNR 2j A represents the signal-to-noise ratio of the base station signal of the jth underwater positioning base station,r Represents the effective area of ​​the wearable device receiving sound waves; P 1j Indicates the current area at the jth signal reception time t rj The ambient background noise under the water environment is determined based on the water environment type; P 2j Indicates the current area at the jth signal reception time t rj Water flow noise under j Indicates that the current area receives the jth signal at time t rj The density of water below, v j Indicates the current area at the jth signal reception time t rj where L represents the size of the wearable device.

[0111] Furthermore, the wearable device obtains the base station signal strength and base station signal noise ratio of the underwater positioning base station according to the average of the base station signal strength and base station signal noise ratio of each underwater positioning base station, wherein the calculation formula of the base station signal strength S2 of the underwater positioning base station is as follows

[0112]

[0113] The calculation formula of the base station signal noise ratio of the underwater positioning base station is as follows:

[0114]

[0115] Furthermore, the wearable device determines a first weight coefficient ω1 according to the base station signal strength, the base station signal signal-to-noise ratio, the satellite signal strength and the satellite signal signal-to-noise ratio. The specific calculation formula is as follows:

[0116]

[0117] Furthermore, the wearable device determines a second weight coefficient ω2 of the final positioning information according to the first weight coefficient, that is, the second weight coefficient ω2=1-ω1.

[0118] Step 20452, determine high-precision positioning information based on the initial positioning information and its corresponding first weight coefficient and the final positioning information and its corresponding second weight coefficient.

[0119] Furthermore, the wearable device performs weighted calculation according to the initial positioning information and its corresponding first weight coefficient and the final positioning information and its corresponding second weight coefficient to obtain high-precision positioning information. Therefore, the high-precision positioning information (x final ,y final ,z final ) can be expressed as:

[0120] (x final ,y final ,zfinal )=(ω1x0+ω2x f ,ω1y0+ω2y f ,ω1z0+ω2z f ).

[0121] The embodiment of the present invention uses a multi-satellite positioning system and an underwater positioning base station to coordinate positioning. In areas with good satellite signals, the multi-satellite positioning system can provide relatively accurate planar position information. When the satellite positioning accuracy decreases in complex waters, the underwater positioning base station can play a role. Since sound waves propagate relatively stably in water and are not easily affected by interference factors above the water surface, the distance and direction between the wearable device and the base station can be accurately measured, thereby correcting and supplementing the satellite positioning information, improving the positioning accuracy, and thus improving the safety level of people on the water and the efficiency of emergency rescue.

[0122] In one embodiment, the description of steps 301 to 304 is as follows:

[0123] Step 301 , filtering the multi-dimensional data to obtain filtered multi-dimensional data, and extracting risk assessment data features related to drowning risk from the filtered multi-dimensional data.

[0124] Optionally, in the multi-dimensional data of the embodiment of the present invention, heart rate data HR(t) (unit: times / minute), body temperature data BT(t) (unit: degrees Celsius), blood pressure data (systolic blood pressure SBP(t) and diastolic blood pressure DBP(t), unit: mmHg), underwater posture data (pitch angle θ(t), roll angle and yaw angle ψ(t), unit: degree), limb movement data (A x (t),A y (t),A z (t), unit: m / s 2 ), and the water pressure data on the human body P(t) (unit: Pascal), where t represents the collection time.

[0125] Furthermore, the wearable device performs filtering processing on the collected multi-dimensional data to remove noise interference in the multi-dimensional data to obtain filtered multi-dimensional data.

[0126] For heart rate data, since PPG signals are susceptible to noise interference such as motion artifacts, the embodiment of the present invention adopts an adaptive filtering algorithm, such as an adaptive filter based on the least mean square (LMS) algorithm, to dynamically adjust the filter coefficients according to the error between the input signal and the expected signal (noise-free heart rate signal). In one embodiment, the input original heart rate data sequence is HR raw(t), the reference signal (which can be a signal related to heart rate collected by other relatively stable sensors or a model signal built based on historical heart rate data) is HR ref (t), the coefficient vector of the filter is ω(t), then the output HR of the filter is filtered (t) = ω T (t)HR raw (t), error e(t) = HR ref (t)-HR filtered (t), by continuously updating the coefficient ω(t+1)=ω(t)+μe(t)HR raw (t), so that the filter gradually adapts to the signal characteristics, removes noise interference, and obtains more accurate heart rate data HR(t), where μ is the step size factor, which is used to control the convergence speed.

[0127] For body temperature data, since it changes relatively slowly and the collection environment is relatively stable, the embodiment of the present invention adopts a simple mean filter. In one embodiment, the original body temperature data sequence collected is BT raw (t), take the data within a certain time window (such as the past 10 sampling points) and calculate the average value as the filtered body temperature data Where N is the window size.

[0128] For blood pressure data, since the measurement process is relatively stable and the noise impact is small, the embodiment of the present invention adopts median filtering. raw (t) and DBP raw Each data point in (t) is replaced by the median of the data in its time window (such as the past 5 sampling points) to obtain the filtered systolic blood pressure SBP(t) and diastolic blood pressure DBP(t).

[0129] For the attitude data, the Kalman filter algorithm is used to fuse and filter the attitude data. In one embodiment, the state vector of the attitude angle measured by the IMU is in, are the angular velocity of the pitch angle, the angular velocity of the roll angle and the angular velocity of the yaw angle respectively, and the measurement vector is Among them, θ m , ψ m It is the attitude angle directly measured by IMU.

[0130] The state transfer matrix F of the system is constructed based on the human kinematic model and the characteristics of the sensor. The state transfer matrix F describes the transfer law of the state vector from one moment to the next when there is no external measurement input, that is, the change relationship between the attitude angle and its angular velocity in adjacent time intervals based on human motion and the physical characteristics of the sensor itself, such as the natural rotation and translation of the human body in water and the response characteristics of each component of the IMU. The measurement matrix H is determined according to the association between the attitude angle measured by the IMU and each element in the state vector, reflecting the relationship between the measurement vector and the state vector. The uncertainty change of the state vector caused by various unpredictable factors (such as irregular water flow disturbances on the human body in water, small performance fluctuations of the IMU itself, etc.) during the state transfer process is analyzed, and the process noise covariance matrix Q is constructed. The measurement noise covariance matrix R is constructed based on the statistical characteristics of the noise contained in each measurement value in the measurement vector Z, reflecting the size and correlation of the measurement error caused by factors such as sensor accuracy limitations and environmental interference during the measurement process.

[0131] Therefore, the prediction steps of Kalman filtering are:

[0132]

[0133] in. Indicates the estimated value based on the state at the previous moment The predicted a priori estimate of the state vector at the current moment, that is, the estimated state angle and angular velocity at the current moment based only on the state transition law without combining the measured data at the current moment; Represents the corresponding prior estimation error covariance matrix.

[0134] The update steps are:

[0135]

[0136]

[0137] Among them, K k represents the Kalman gain; Indicates that the current measurement data Z k The estimated value of the state vector at the current moment obtained after the update is the prior estimate The estimated result that is closer to the actual posture state after fusion optimization with the measured data; k represents the corresponding updated estimated error covariance matrix, and I represents the identity matrix.

[0138] The Kalman filter is used to fuse the data of the accelerometer, gyroscope and magnetometer to improve the accuracy and stability of the attitude data, and the filtered attitude data θ(t) and and ψ(t).

[0139] For body motion data, since body motion data has a lot of high-frequency noise, the embodiment of the present invention uses a Butterworth low-pass filter. According to the frequency characteristics of body motion, in one embodiment, the cutoff frequency is 10 Hz, and the noise components higher than this frequency are filtered out. The original body motion acceleration data sequence is A xraw (t),A yraw (t),A zraw (t), after low-pass filtering, we get A x (t),A y (t),A z (t).

[0140] For water pressure data, the embodiment of the present invention adopts weighted moving average filtering. Since the importance of pressure sensors at different locations to the overall drowning determination may be different, different weights ω are assigned to pressure sensors at each location based on experience or experiments. i In one embodiment, the original water pressure data sequence is P iraw (t), i represents sensors at different locations, and the filtered water pressure data is:

[0141]

[0142] Wherein, G is the number of pressure sensors.

[0143] Furthermore, the wearable device extracts risk assessment data features related to drowning risk from the filtered multi-dimensional data, wherein the risk assessment data features include heart rate change rate, body temperature change rate, blood pressure change rate, posture change characteristics, limb movement activity and water pressure change characteristics.

[0144] Among them, heart rate change rate: ΔHR(t) = [HR(t)-HR(t-Δt)] / Δt, where Δt represents the interval time of data collection. Body temperature change rate: ΔBT(t) = [BT(t)-BT(t-Δt)] / Δt. Blood pressure change rate: ΔSBP(t) = [SBP(t)-SBP(t-Δt)] / Δt, ΔDBP(t) = [DBP(t)-DBP(t-Δt)] / Δt. Posture change characteristics: Δθ(t) = θ(t)-θ(t-Δt), Δψ(t)=ψ(t)-ψ(t-Δt).

[0145] Physical activity: Physical activity energy is Calculate the limb movement energy change rate ΔE(t) = [E(t)-E(t-Δt)] / Δt.

[0146] Calculate the water pressure change characteristics: water pressure change rate ΔP(t) = [P(t)-P(t-Δt)] / Δt; water pressure distribution characteristics, divide the human body surface into multiple areas (head, chest, abdomen, limbs, etc.), and measure the pressure value of each area through multiple pressure sensors. The pressure value of the i-th area is P i (t), calculate the ratio of each area pressure to the total pressure Where n is the number of regions.

[0147] Step 302: input the risk assessment data features into a pre-built risk assessment model to obtain a risk assessment probability value output by the risk assessment model.

[0148] Optionally, a pre-trained risk assessment model is built into the wearable device of the embodiment of the present invention. The risk assessment model is trained based on the sample data features and the corresponding risk assessment label results. The specific catenary process is described in steps 50 to 70. Therefore, the wearable device inputs the risk assessment data features into the pre-built risk assessment model to obtain the risk assessment result output by the risk assessment model. The risk assessment result in the embodiment of the present invention is a risk assessment probability value, wherein the risk assessment probability value has a value range of [0,1], where 0 indicates no drowning risk and 1 indicates a high drowning risk.

[0149] Step 303, if the risk assessment probability value is greater than or equal to the preset probability threshold, it is determined that the person on the water is at risk of drowning; Step 304, if the risk assessment probability value is less than the preset probability threshold, it is determined that the person on the water is not at risk of drowning.

[0150] Further, the wearable device compares the risk assessment probability value with the preset probability threshold to obtain a comparison result, wherein the preset probability threshold is set according to actual conditions, such as 0.55, 0.58, etc. If the comparison result is determined to be that the risk assessment probability value is greater than or equal to the preset probability threshold, the wearable device determines that the person on the water is at risk of drowning. If the comparison result is determined to be that the risk assessment probability value is less than the preset probability threshold, the wearable device determines that the person on the water is not at risk of drowning.

[0151] The embodiment of the present invention can quickly and accurately predict the risk assessment probability value based on the comprehensive physiological and motion status data of the people on the water in combination with the risk assessment model, so that it can quickly and accurately determine whether the people on the water are in a state of drowning risk based on the risk assessment probability value, thereby significantly improving the safety level of the people on the water and the efficiency of emergency rescue.

[0152] In one embodiment, the training constraint condition is that the loss function value of each sample data feature is less than or equal to a preset loss threshold, and the loss difference between two adjacent loss function values ​​is less than a preset difference threshold, and the loss difference is in a decreasing trend. The description of steps 50 to 70 is as follows:

[0153] Step 50, input each sample data feature into the initially trained initial assessment model to obtain the risk assessment prediction result of each sample data feature output by the initial assessment model.

[0154] Optionally, the number of input layer nodes M of the initial evaluation model in the embodiment of the present invention is model Consistent with the characteristic dimensions of the sample data features, the sample data features in the embodiment of the present invention include heart rate, body temperature, blood pressure, posture, limb movement and water pressure. Therefore, the number of input layer nodes M of the initial evaluation model is model = 6. Number of hidden layer nodes Among them, l model is the number of nodes in the output layer, represents a round-up function, embodiment 1 of the present invention model =1, a model is a regulation constant between 1 and 10, for example, a model =5, the number of output layer nodes is 1, which represents the risk assessment probability value, and the value range is 0-1, where 0 represents no drowning risk and 1 represents a high drowning risk.

[0155] Therefore, the wearable device inputs each sample data feature into the initially trained initial assessment model to obtain the risk assessment prediction result of each sample data feature output by the initial assessment model.

[0156] Step 60, based on the loss function of the initial assessment model combined with the risk assessment label result and the risk assessment prediction result of each sample data feature, calculate the loss function value of each sample data feature.

[0157] Furthermore, the wearable device calculates the loss function value of each sample data feature according to the loss function of the initial evaluation model combined with the risk assessment label result and the risk assessment prediction result of each sample data feature, wherein the loss function can be expressed as:

[0158] Among them, Loss i Represents the loss function value of the i-th sample data feature, represents the risk assessment label result of the i-th sample data feature, represents the risk assessment prediction result of the i-th sample data feature, δ x Indicates the preset weight coefficient.

[0159] Step 70: If it is determined that the loss function value based on each sample data feature does not meet the training constraints, then based on the risk assessment label result and the risk assessment prediction result of each sample data feature, adjust the model parameters of the initial assessment model until the loss function value of each sample data feature output by the adjusted assessment model meets the training constraints, thereby obtaining a risk assessment model.

[0160] Further, determine whether the loss function value of each sample data feature satisfies the training constraint condition. If it is determined that the training constraint condition is not satisfied according to the loss function value of each sample data feature, that is, the loss function value of at least one sample data feature is greater than the preset loss threshold, or / and, there is at least one loss difference between the loss function value of the i-th sample data feature and the loss function value of the i+1-th sample data feature that is greater than or equal to the preset difference threshold, or / and, there is at least one loss difference that does not show a decreasing trend, that is, the loss difference between the loss function value of the i+1-th sample data feature and the loss function value of the i+2-th sample data feature is greater than or equal to the loss difference between the loss function value of the i-th sample data feature and the loss function value of the i+1-th sample data feature, then calculate the adjustment coefficient according to the risk assessment label result and the risk assessment prediction result of each sample data feature, wherein the calculation formula of the adjustment coefficient is as follows:

[0161]

[0162] Among them, A model Represents the adjustment coefficient, N samlpe represents the number of sample data features, σ(y ture ) represents the standard deviation of the risk assessment label result, σ(y pre ) represents the standard deviation of the risk assessment prediction results, represents the mean of the risk assessment label results, represents the mean of the risk assessment prediction results, min[·] represents the minimum function, and max[·] represents the maximum function.

[0163] Furthermore, by adjusting the model parameters of the initial evaluation model through the adjustment coefficient, the embodiment of the present invention can adjust the weight matrix and bias vector of the activation function in the hidden layer in the initial evaluation model, for example, each weight coefficient in the weight matrix of the kth hidden layer is adjusted to Each bias term in the bias vector is adjusted to

[0164] Furthermore, each sample data feature is input into the adjusted evaluation model to obtain the loss function value of each sample data feature output by the adjusted evaluation model, until the loss function value of each sample data feature satisfies the training constraint, that is, the loss function values ​​of all sample data features are less than or equal to the preset loss threshold, and the loss difference between the loss function value of all adjacent i-th sample data feature and the loss function value of the i+1-th sample data feature is less than the preset difference threshold, and all loss differences show a decreasing trend, that is, the loss difference between the loss function value of the i+1-th sample data feature and the loss function value of the i+2-th sample data feature is less than the loss difference between the loss function value of the i-th sample data feature and the loss function value of the i+1-th sample data feature, thereby obtaining a risk assessment model.

[0165] The embodiment of the present invention trains a risk assessment model, so that the risk assessment probability value can be quickly and accurately predicted based on the comprehensive physiological and motion status data of the people on the water in combination with the risk assessment model, so that it can be quickly and accurately determined whether the people on the water are in a state of drowning risk based on the risk assessment probability value, which significantly improves the safety level of the people on the water and the efficiency of emergency rescue.

[0166] See also Figure 3 , Figure 3 FIG. 1 is an embodiment diagram of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, an embodiment of the present invention provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor 320. When the processor 320 executes the computer program 311, the following steps are implemented:

[0167] Real-time acquisition of satellite positioning signals from multiple satellite positioning systems, multi-dimensional data of people on the water, and reception of acoustic wave signals sent by multiple underwater positioning base stations;

[0168] Based on the satellite positioning signal of each satellite positioning system and the sound wave signal of each underwater positioning base station, the high-precision positioning information of the people on the water in the current water area is determined;

[0169] Determine whether people on the water are at risk of drowning based on multi-dimensional data;

[0170] If people on the water are at risk of drowning, a rescue alarm signal is generated based on the high-precision positioning information, and the rescue alarm signal is sent to the user terminal based on the long-distance communication unit.

[0171] See also Figure 4 , Figure 4 Detailed description of an embodiment of a computer-readable storage medium provided by an embodiment of the present invention. Figure 4As shown, this embodiment provides a computer-readable storage medium 400, on which a computer program 311 is stored. When the computer program 311 is executed by a processor, the following steps are implemented:

[0172] Real-time acquisition of satellite positioning signals from multiple satellite positioning systems, multi-dimensional data of people on the water, and reception of acoustic wave signals sent by multiple underwater positioning base stations;

[0173] Based on the satellite positioning signal of each satellite positioning system and the sound wave signal of each underwater positioning base station, the high-precision positioning information of the people on the water in the current water area is determined;

[0174] Determine whether people on the water are at risk of drowning based on multi-dimensional data;

[0175] If people on the water are at risk of drowning, a rescue alarm signal is generated based on the high-precision positioning information, and the rescue alarm signal is sent to the user terminal based on the long-distance communication unit.

[0176] On the other hand, the present invention further provides a computer program product, the computer program product includes a computer program, the computer program can be stored in a non-transitory computer-readable storage medium, when the computer program is executed by a processor, the computer can execute the method for locating a person on water provided by the above methods, the method for locating a person on water includes:

[0177] Real-time acquisition of satellite positioning signals from multiple satellite positioning systems, multi-dimensional data of people on the water, and reception of acoustic wave signals sent by multiple underwater positioning base stations;

[0178] Based on the satellite positioning signal of each satellite positioning system and the sound wave signal of each underwater positioning base station, the high-precision positioning information of the people on the water in the current water area is determined;

[0179] Determine whether people on the water are at risk of drowning based on multi-dimensional data;

[0180] If people on the water are at risk of drowning, a rescue alarm signal is generated based on the high-precision positioning information, and the rescue alarm signal is sent to the user terminal based on the long-distance communication unit.

[0181] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0182] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0183] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent wearable device for water personnel, characterized in that: It includes a main control unit, a positioning unit, a multi-dimensional monitoring unit, a long-distance communication unit, a high-energy density battery unit and an alarm unit; the main control unit is connected to the positioning unit, the multi-dimensional monitoring unit, the long-distance communication unit, the high-energy density battery unit and the alarm unit respectively, and is used to receive and process data from each unit; The positioning unit adopts a multi-satellite positioning system and an underwater positioning base station for collaborative positioning. The multi-satellite positioning system includes GPS, Beidou and GLONASS satellite positioning systems. The underwater positioning base station is deployed at the bottom of a conventionally planned water area, and communicates with the intelligent wearable device of the water personnel through acoustic wave signals to obtain high-precision positioning information of the water personnel in the current water area; The multi-dimensional monitoring unit includes a plurality of sensors for real-time monitoring of multi-dimensional data of people on the water, and transmitting the multi-dimensional data to the main control unit for comprehensive analysis to determine whether the people on the water are in a state of drowning risk; The multi-dimensional data includes heart rate information, body temperature information, blood pressure information, underwater posture information, limb movement information, and water pressure information on the human body; The long-distance communication unit adopts satellite communication and 5G communication. If the signal strength of the satellite communication in the current water area is greater than the signal strength of the 5G communication, the communication is performed with the user terminal based on the satellite communication; if the signal strength of the satellite communication in the current water area is less than the signal strength of the 5G communication, or the distance between the current water area and the shore is less than or equal to the preset distance, the communication is performed with the user terminal based on the 5G communication; The high energy density battery unit adopts a new type of lithium sulfur battery, and the main control unit dynamically distributes power according to the operating status of each unit; The alarm unit includes multiple alarm signal generating modules for generating multiple rescue alarm signals. When the main control unit determines that the person on the water is in a drowning risk state, it sends out a rescue alarm signal.

2. A method for locating personnel on water, implemented based on the intelligent wearable device for personnel on water as claimed in claim 1, characterized in that: The method for locating personnel on water comprises: Real-time acquisition of satellite positioning signals from multiple satellite positioning systems, multi-dimensional data of people on the water, and reception of acoustic wave signals sent by multiple underwater positioning base stations; Based on the satellite positioning signal of each satellite positioning system and the sound wave signal of each underwater positioning base station, the high-precision positioning information of the people on the water in the current water area is determined; Determining whether the person on the water is in a drowning risk state based on the multi-dimensional data; If the person on the water is in a state of drowning risk, a rescue alarm signal is generated based on the high-precision positioning information, and the rescue alarm signal is sent to the user terminal based on the long-distance communication unit.

3. The method for locating personnel on water according to claim 2, characterized in that: The satellite positioning signal includes ephemeris data, pseudorange information and Doppler frequency shift data; The method of determining the high-precision positioning information of the personnel on the water in the current water area based on the satellite positioning signal of each satellite positioning system and the sound wave signal of each underwater positioning base station includes: Based on the ephemeris data and Doppler frequency shift data of each satellite positioning system, the satellite clock bias and relative velocity change of each satellite positioning system are calculated respectively; Based on the coordinates, pseudorange information, satellite clock bias and relative velocity change of each satellite positioning system, a first objective function is constructed for solving the positioning information of the water personnel and the clock bias of the smart wearable device of the water personnel; Solving the first objective function to obtain initial positioning information of the personnel on the water and a clock deviation correction number of the smart wearable device of the personnel on the water; The initial positioning information is corrected based on the clock deviation correction number and the sound wave signal of each underwater positioning base station to obtain high-precision positioning information of the water personnel in the current water area.

4. The method for locating personnel on water according to claim 3, characterized in that: The initial positioning information is corrected based on the clock deviation correction number and the sound wave signal of each underwater positioning base station to obtain high-precision positioning information of the water personnel in the current water area, including: Determine the distance between the smart wearable device for the water personnel and each underwater positioning base station based on the signal sending time in the sound wave signal of each underwater positioning base station, the signal receiving time when the smart wearable device for the water personnel receives the sound wave signal of each underwater positioning base station, and the water temperature of the current water area at each signal receiving time; Based on the coordinates of each underwater positioning base station and the distance between the smart wearable device of the water personnel and each underwater positioning base station, a second objective function for solving the positioning information of the water personnel is constructed; Solving the second objective function to obtain a positioning correction value; Correcting the initial positioning information based on the positioning correction amount to obtain final positioning information of the person on the water; The initial positioning information and the final positioning information are fused based on the clock deviation correction number to obtain the high-precision positioning information.

5. The method for locating personnel on water according to claim 4, characterized in that: The fusing the initial positioning information and the final positioning information based on the clock deviation correction number to obtain the high-precision positioning information includes: Determine a first weight coefficient of the initial positioning information and a second weight coefficient of the final positioning information based on the clock deviation correction number; The high-precision positioning information is determined based on the initial positioning information and its corresponding first weight coefficient and the final positioning information and its corresponding second weight coefficient.

6. The method for locating personnel on water according to claim 5, characterized in that: The determining of the first weight coefficient of the initial positioning information based on the clock deviation correction number includes: Based on the transmission power of the acoustic wave signal of each underwater positioning base station, the gain of the smart wearable device of the personnel on the water, and the propagation attenuation of the acoustic wave in the water, the base station signal strength and the base station signal-to-noise ratio are determined; Determine satellite signal strength and satellite signal-to-noise ratio based on clock bias corrections; The first weight coefficient is determined based on the base station signal strength, the base station signal signal-to-noise ratio, the satellite signal strength, and the satellite signal signal-to-noise ratio.

7. The method for locating personnel on water according to any one of claims 2 to 6, characterized in that: The multi-dimensional data includes heart rate information, body temperature information, blood pressure information, underwater posture information, limb movement information, and water pressure information on the human body; The determining whether the person on the water is in a drowning risk state based on the multi-dimensional data includes: Performing filtering processing on the multi-dimensional data to obtain filtered multi-dimensional data, and extracting risk assessment data features related to drowning risk from the filtered multi-dimensional data; Inputting the risk assessment data features into a pre-built risk assessment model to obtain a risk assessment probability value output by the risk assessment model; the risk assessment model is trained based on the sample data features and their corresponding risk assessment label results; If the risk assessment probability value is greater than or equal to a preset probability threshold, it is determined that the person on the water is in a state of drowning risk; If the risk assessment probability value is less than the preset probability threshold, it is determined that the person on the water is not at risk of drowning.

8. The method for locating personnel on water according to claim 7, characterized in that: The training constraints are: the loss function value of each sample data feature is less than or equal to the preset loss threshold, and the loss difference between two adjacent loss function values ​​is less than the preset difference threshold, and the loss difference shows a decreasing trend; The training process of the risk assessment model is as follows: Input each sample data feature into the initially trained initial assessment model to obtain a risk assessment prediction result for each sample data feature output by the initial assessment model; Calculate the loss function value of each sample data feature based on the loss function of the initial assessment model combined with the risk assessment label result and the risk assessment prediction result of each sample data feature; If it is determined that the loss function value based on each sample data feature does not satisfy the training constraints, the model parameters of the initial assessment model are adjusted based on the risk assessment label results and risk assessment prediction results of each sample data feature until the loss function value of each sample data feature output by the adjusted assessment model satisfies the training constraints, thereby obtaining the risk assessment model.

9. An electronic device, comprising: A memory and a processor, characterized in that a computer software program is stored in the memory, and when the processor reads and executes the computer software program, the method for locating personnel on water as described in any one of claims 2 to 8 is implemented.

10. A non-transitory computer-readable storage medium, characterized in that: The storage medium stores a computer software program, which, when executed by a processor, implements the method for locating personnel on water as described in any one of claims 2 to 8.

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