Safety early warning methods, systems and storage media for fishing vessel operations

Abstract

This invention discloses a method, system, and storage medium for safety early warning of fishing vessel operations. Applied to the field of ship safety early warning technology, it enables accurate safety early warning during fishing vessel operations, effectively reducing the probability of ship accidents. The method includes: acquiring crew behavior data collected by a wearable device worn by the crew member; identifying crew behavior based on the crew behavior data to obtain crew status data; analyzing the crew status data to obtain a crew defense level; acquiring preset ship navigation data, including target fishing vessel navigation data and other ship navigation data; performing collision analysis based on the preset ship navigation data to obtain a ship collision risk level; calculating a comprehensive safety risk level for the fishing vessel based on the crew defense level and the ship collision risk level; and issuing a ship safety early warning based on the comprehensive safety risk level.

Description

Technical Field

[0001] This invention relates to the field of ship safety early warning technology, and in particular to a method, system and storage medium for early warning of safety during fishing vessel operations. Background Technology

[0002] Collisions between fishing boats and other vessels consistently account for a significant proportion of ship collision accidents. The main reasons for these collisions are: during the day, when crew members are engaged in fishing operations, the wheelhouse is unattended, and the crew is too busy to receive warning signals. Other vessels, such as merchant ships, may use searchlights or sound horns to warn the fishing boat, but the crew is either resting, sleeping, or fishing in the cabins, leaving no one on watch and preventing the fishing boat from escaping danger. Furthermore, at night, tired crew members rest in the cabins and become less vigilant, with no one on watch in the wheelhouse to receive collision warnings and take preventative measures. Additionally, fishing boats may encounter emergencies during navigation, such as strong winds or high waves, causing crew members to fall overboard. Existing shipboard communication equipment is insufficient to promptly warn other crew members, and the inability to quickly ascertain the crew's location can delay rescue efforts. Sometimes, even when someone is discovered to have fallen overboard, the inability to accurately locate them and send a distress signal can lead to drowning. Safety issues during fishing vessel operations are paramount. In related technologies, communication between departments mainly relies on outdated communication equipment such as walkie-talkies and marine telephones. This results in low communication efficiency, poor portability, and high communication costs, making it difficult to respond to emergencies such as collision avoidance and crew drowning. Therefore, how to provide timely and accurate safety warnings during fishing vessel operations has become an urgent problem to be solved. Summary of the Invention

[0003] To address at least one of the aforementioned technical problems, this invention proposes a method, system, and storage medium for safety early warning during fishing vessel operations, which can provide accurate safety warnings during fishing vessel operations and effectively reduce the probability of ship accidents.

[0004] On one hand, embodiments of the present invention provide a method for early warning of safety during fishing vessel operations, comprising the following steps:

[0005] Acquire crew behavior data collected by wearable devices; wherein the wearable devices are worn by the crew members.

[0006] Based on the crew behavior data, crew behavior identification is performed to obtain crew status data;

[0007] The crew's defense level is determined based on the analysis of the crew status data.

[0008] Acquire preset vessel navigation data; wherein, the preset vessel navigation data includes target fishing vessel navigation data and other vessel navigation data;

[0009] Collision analysis is performed based on the preset ship navigation data to obtain the ship collision risk level;

[0010] The comprehensive safety risk level of the fishing vessel is calculated based on the crew defense level and the collision risk level of the vessel.

[0011] Ship safety warnings are issued based on the comprehensive safety risk level of the fishing vessels.

[0012] A fishing vessel operation safety early warning method according to an embodiment of the present invention has at least the following beneficial effects: First, this embodiment collects crew behavior data using wearable devices worn by the crew members. Based on this data, the crew members' behavior is identified to obtain crew status data. Then, this embodiment analyzes the identified crew status data to obtain the crew members' ability to defend against danger, i.e., their defense level. Simultaneously, this embodiment acquires preset vessel navigation data and performs collision analysis using the acquired target fishing vessel navigation data and other vessel navigation data to obtain the real-time collision risk between the fishing vessel and other vessels, i.e., the vessel collision risk level. Furthermore, this embodiment calculates the comprehensive safety risk level of the fishing vessel by combining the crew defense level and the vessel collision risk level. By combining the crew members' own status with the collision risk status during the fishing vessel's navigation, the accuracy of the safety risk assessment of the fishing vessel is effectively improved. Next, this embodiment provides a vessel safety early warning based on the comprehensive safety risk level of the fishing vessel, achieving accurate safety warnings during fishing vessel operations, thereby effectively reducing the probability of vessel accidents.

[0013] According to some embodiments of the present invention, the wearable device is provided with an optical volumetric sensor, an accelerometer and a gyroscope, and the crew behavior data includes physiological data, acceleration data and angular velocity data;

[0014] The acquisition of crew behavior data collected by wearable devices includes:

[0015] The physiological data is acquired through the optical volumetric sensor; wherein the physiological data includes heart rate and blood oxygen saturation data;

[0016] The acceleration data is acquired through the acceleration sensor;

[0017] The angular velocity data is obtained through the gyroscope.

[0018] According to some embodiments of the present invention, before performing the step of identifying crew behavior based on the crew behavior data to obtain crew status data, the method further includes:

[0019] The wearable device is used to acquire simulated work data of the test subjects;

[0020] A set of crew behavior actions is constructed based on the simulated operation data;

[0021] Based on the set of crew behavior actions, corresponding crew behavior state features are extracted; wherein, the crew behavior state features include physiological change rate features and posture change rate features;

[0022] The crew behavior state features are fused using a multi-feature fusion action recognition algorithm to generate a crew behavior state recognition model.

[0023] According to some embodiments of the present invention, the step of identifying crew behavior based on the crew behavior data to obtain crew status data includes:

[0024] The crew behavior data is identified by the crew behavior status recognition model to obtain crew status data.

[0025] According to some embodiments of the present invention, the step of performing collision analysis based on the preset ship navigation data to obtain the ship collision risk level includes:

[0026] Based on the preset ship navigation data, the collision risk between the fishing vessel and each other vessel is calculated to obtain collision risk data;

[0027] The collision risk data of the fishing vessel and other vessels are summed to obtain the vessel collision risk level; wherein, the vessel collision risk level includes emergency response level, navigation emergency organization level, remote monitoring level, and vessel self-inspection level.

[0028] According to some embodiments of the present invention, the calculation of the comprehensive safety risk level of the fishing vessel based on the crew defense level and the ship collision risk level includes:

[0029] The comprehensive safety risk level of the fishing vessel is obtained by multiplying the crew's defense level and the vessel's collision risk level.

[0030] According to some embodiments of the present invention, the step of providing ship safety early warning based on the comprehensive safety risk level of the fishing vessel includes:

[0031] When the comprehensive safety risk level of the fishing vessel is at the first preset level, an early warning reminder will be issued through the wearable device;

[0032] Alternatively, when the comprehensive safety risk level of the fishing vessel is the second preset level, the early warning reminder will be issued through the ship's broadcasting station;

[0033] Alternatively, when the overall safety risk level of the fishing vessel is at the third preset level, a distress message can be sent through the wearable device.

[0034] On the other hand, embodiments of the present invention also provide a fishing vessel operation safety early warning system, including:

[0035] The first acquisition module is used to acquire crew behavior data collected by a wearable device; wherein the wearable device is worn by the crew member.

[0036] The identification module is used to identify crew behavior based on the crew behavior data to obtain crew status data;

[0037] The first analysis module is used to analyze the crew status data to obtain the crew defense level;

[0038] The second acquisition module is used to acquire preset ship navigation data; wherein, the preset ship navigation data includes target fishing vessel navigation data and other ship navigation data;

[0039] The second analysis module is used to perform collision analysis based on the preset ship navigation data to obtain the ship collision risk level.

[0040] The risk calculation module is used to calculate the comprehensive safety risk level of the fishing vessel based on the crew defense level and the ship collision risk level.

[0041] The safety early warning module is used to provide ship safety early warnings based on the comprehensive safety risk level of the fishing vessel.

[0042] On the other hand, embodiments of the present invention also provide a fishing vessel operation safety early warning system, including:

[0043] At least one processor;

[0044] At least one memory for storing at least one program;

[0045] When the at least one program is executed by the at least one processor, the at least one processor implements the fishing vessel operation safety early warning method as described in the above embodiments.

[0046] On the other hand, embodiments of the present invention also provide a computer storage medium storing a processor-executable program, which, when executed by the processor, is used to implement the fishing vessel operation safety early warning method as described in the above embodiments. Attached Figure Description

[0047] Figure 1 This is a flowchart of the fishing vessel operation safety early warning method provided in the embodiments of the present invention;

[0048] Figure 2 This is a schematic diagram of the fishing vessel operation safety early warning system provided in an embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram of a fishing vessel operation safety early warning system provided in another embodiment of the present invention. Detailed Implementation

[0050] The embodiments described in this application should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0051] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0053] Before describing the embodiments of this application, the relevant terms involved in this application will be explained.

[0054] Optical volumetric plethysmography (PPG) sensors are sensors that measure real-time changes in light reflection or transmission within a region or volume of interest using optical volumetric plethysmography. PPG is an optical measurement technique that measures physiological data by analyzing the differences in light absorption between blood and surrounding tissues. For example, changes in blood volume with each heartbeat affect light reflection or transmission; by detecting the transmittance and reflectance at different wavelengths, information such as heart rate, respiration, and blood oxygen saturation can be determined.

[0055] Collisions between fishing boats and other vessels consistently account for a significant proportion of ship collision accidents. The main reasons for these collisions are: during the day, when crew members are engaged in fishing operations, the wheelhouse is unattended, and the crew is too busy to receive warning signals. Other vessels, such as merchant ships, may use searchlights or sound horns to warn the fishing boat, but the crew is either resting, sleeping, or fishing in the cabins, leaving no one on watch and preventing the fishing boat from escaping danger. Furthermore, at night, tired crew members rest in the cabins and become less vigilant, with no one on watch in the wheelhouse to receive collision warnings and take preventative measures. Additionally, fishing boats may encounter emergencies during navigation, such as strong winds or high waves, causing crew members to fall overboard. Existing shipboard communication equipment is insufficient to promptly warn other crew members, and the inability to quickly ascertain the crew's location can delay rescue efforts. Sometimes, even when someone is discovered to have fallen overboard, the inability to accurately locate them and send a distress signal can lead to drowning. Safety issues during fishing vessel operations are paramount. In related technologies, communication between departments mainly relies on outdated communication equipment such as walkie-talkies and marine telephones. This results in low communication efficiency, poor portability, and high communication costs, making it difficult to respond to emergencies such as collision avoidance and crew drowning. Therefore, how to provide timely and accurate safety warnings during fishing vessel operations has become an urgent problem to be solved.

[0056] One embodiment of the present invention provides a method, system, and storage medium for safety early warning during fishing vessel operations, which can achieve accurate safety early warning during fishing vessel operations and effectively reduce the probability of ship accidents. (See also...) Figure 1 The method in this embodiment of the invention includes, but is not limited to, steps S110, S120, S130, S140, S150, S160 and S170.

[0057] Specifically, the application process of the method in this embodiment of the invention includes, but is not limited to, the following steps:

[0058] S110: Acquire crew behavior data collected by wearable devices. The wearable devices are worn by the crew members.

[0059] S120: Based on the crew behavior data, identify crew behavior to obtain crew status data.

[0060] S130: The crew defense level is determined based on the analysis of crew status data.

[0061] S140: Obtain preset vessel navigation data. This preset vessel navigation data includes the target fishing vessel's navigation data and other vessel navigation data.

[0062] S150: Collision analysis is performed based on preset ship navigation data to obtain the ship collision risk level.

[0063] S160: The comprehensive safety risk level of a fishing vessel is calculated based on the crew's defense level and the vessel's collision risk level.

[0064] S170: Conduct ship safety early warning based on the comprehensive safety risk level of fishing vessels.

[0065] In this specific embodiment, the first step is to acquire crew behavior data collected by wearable devices. Specifically, in this embodiment, the wearable devices are worn by the crew members. For example, the wearable devices are worn tightly on the wrists of crew members, including the captain, helmsman, chief engineer, engineer, and other crew members. By wearing wearable devices on the fishing vessel crew members, this embodiment can acquire real-time crew behavior data for each crew member. Next, this embodiment performs crew behavior recognition based on the crew behavior data to obtain crew status data. Specifically, this embodiment identifies the behavioral status of the corresponding crew member based on the crew behavior data collected by the wearable devices worn by each crew member. For example, the current behavioral status of a crew member includes piloting the ship, fishing operations, sleeping, and being in the water. It is easy to understand that the crew member's response to danger warnings differs depending on their behavioral status. For example, when a crew member is piloting the ship, they may be in a highly focused state and can respond promptly to dangerous situations. However, when a crew member is sleeping, it is difficult to respond promptly to danger warnings. Therefore, this embodiment identifies the crew behavior data to obtain the behavioral status of each crew member, i.e., crew status data. Next, this embodiment analyzes crew status data to obtain the crew defense level. Specifically, this embodiment analyzes the identified crew status data to obtain the corresponding crew defense level. In this embodiment, the crew defense status level, or crew defense level, is divided into four levels from low to high. The fourth level represents a crew member in a dangerous state with no defense capability whatsoever; this is the lowest level of defense capability. The third level represents a crew member who is unaware of the risk and whose reaction and behavioral decisions are slow, or even unable to make correct behavioral decisions before danger arrives. The second level represents a crew member who is unaware of the risk and whose behavioral decisions are slow. The first level represents a crew member who is conscious and can react and make behavioral decisions quickly without needing to be alerted; this is the highest level of defense capability.

[0066] Furthermore, this embodiment acquires preset vessel navigation data. Specifically, the preset vessel navigation data in this embodiment includes the target fishing vessel's navigation data and other vessel navigation data. This embodiment acquires the target fishing vessel (i.e., this fishing vessel) and other vessels' navigation information in real time, including speed, heading, and position. By acquiring the target fishing vessel's and other vessels' speed, heading, and position data, this embodiment can calculate the relative speed, relative heading, and relative distance between this fishing vessel and other vessels. For example, in this embodiment, each vessel can obtain its own position, speed, and heading through the Global Positioning System (GPS). Then, each vessel can send its own position, speed, and heading to other vessels within a preset range via AIS. In this way, the target fishing vessel can receive the relevant data on the other vessels' speed and heading. Furthermore, this embodiment performs collision analysis based on the preset vessel navigation data to obtain the vessel collision risk level. Specifically, this embodiment analyzes the collision risk between the fishing vessel and other vessels using pre-set navigation data of each vessel, thereby obtaining the risk of collision between the fishing vessel and other vessels, i.e., the vessel collision risk level. Next, this embodiment calculates the comprehensive safety risk level of the fishing vessel based on the crew's defense level and the vessel collision risk level. This embodiment combines the crew's defense level, identified from crew behavior data collected by wearable devices, with the vessel collision risk level obtained from the collision risk between the fishing vessel and other vessels, comprehensively considering the crew's personal status and the collision risk between vessels, thus obtaining relatively accurate safety risk status data for the fishing vessel, i.e., the comprehensive safety risk level. Then, this embodiment provides vessel safety warnings based on the comprehensive safety risk level. By analyzing the comprehensive safety risk level of the fishing vessel and providing corresponding vessel safety warnings, this embodiment can achieve accurate safety warnings during fishing operations, effectively reducing the probability of vessel accidents.

[0067] It should be noted that, in some embodiments of the present invention, "other vessels" refers to vessels within a predetermined range of the target fishing vessel, i.e., the fishing vessel itself. For example, vessels within 8 nautical miles of the fishing vessel.

[0068] It should be noted that, in some embodiments of the present invention, the crew being in the water is set as the fourth level of the defense status level, the crew being asleep is set as the third level of the defense status level, the crew being fishing is set as the second level of the defense status level, and the crew being piloting the ship on the slipway is set as the first level of the defense status level.

[0069] In some embodiments of the present invention, the wearable device is equipped with an optical volumetric sensor, an accelerometer, and a gyroscope, and the crew behavior data includes physiological data, acceleration data, and angular velocity data. Accordingly, the crew behavior data acquired by the wearable device in this embodiment includes, but is not limited to:

[0070] Physiological data is acquired using an optical volumetric sensor. This physiological data includes heart rate and blood oxygen saturation data.

[0071] Acceleration data is acquired through an accelerometer.

[0072] Angular velocity data is obtained using a gyroscope.

[0073] In this specific embodiment, the wearable device is equipped with an optical volumetric plethysmography (PPG) sensor, an accelerometer, and a gyroscope. Accordingly, the crew behavior data collected by the wearable device in this embodiment includes physiological data, acceleration data, and angular velocity data. Specifically, this embodiment first acquires the crew's physiological data, including heart rate and blood oxygen saturation data, using the PPG sensor. The PPG sensor in the wearable device detects the light absorption of blood and surrounding tissues using PPG, thereby acquiring the crew's heart rate and blood oxygen saturation data in real time. Simultaneously, this embodiment acquires corresponding acceleration data using the accelerometer and corresponding angular velocity data using the gyroscope. For example, this embodiment wears the wearable device on the crew member's wrist, acquiring the corresponding wrist acceleration data using the accelerometer and the wrist angular velocity data using the gyroscope. It is easy to understand that using acceleration and angular velocity data for motion recognition can effectively improve the accuracy of motion recognition. In addition, the PPG signal acquired by the optical volumetric plethysmography sensor is less affected by background noise. Therefore, this embodiment can effectively improve the accuracy of action recognition by combining acceleration data, angular velocity data and physiological data acquired through PPG.

[0074] In some embodiments of the present invention, before performing the step of identifying crew behavior based on crew behavior data to obtain crew status data, the fishing vessel operation safety early warning method provided in this embodiment also includes, but is not limited to:

[0075] Data on simulated work activities of test subjects is obtained through wearable devices.

[0076] A set of crew behavior actions is constructed based on simulated operation data.

[0077] The corresponding crew behavior state features are extracted from the crew behavior action set. These features include physiological change rate features and posture change rate features.

[0078] A crew behavior state recognition model is generated by fusing crew behavior state features using a multi-feature fusion action recognition algorithm.

[0079] In this specific embodiment, before recognizing crew behavior using crew behavior data, a crew behavior state recognition model is first constructed. Specifically, this embodiment first acquires simulated work data of the test subjects through wearable devices and constructs a set of crew behavior actions based on the simulated work data. Next, this embodiment extracts corresponding crew behavior state features from the set of crew behavior actions, including physiological change rate features and posture change rate features. Further, this embodiment fuses the crew behavior features using a multi-feature fusion action recognition algorithm to generate a crew behavior state recognition model. For example, this embodiment uses a ship model as an experimental platform and constructs four scenarios, including driving in the cockpit of a fishing boat, fishing operations on deck, sleeping in the lounge at night, and crew falling into the water, to conduct a simulated fishing boat operation experiment. Simulated fishing boat crew operation data of the test subjects are acquired through wearable devices. In this embodiment, several healthy test subjects perform various related gestures with moderate force, and the duration of each action is based on the time required to complete the action under natural conditions. In this experiment, the same action was performed 10 times, with a 2-second interval between each two repetitions. After each action task was completed, the participants relaxed for 2 minutes to prevent muscle fatigue and other factors from adversely affecting the experimental results, and to minimize the influence of irrelevant factors. Next, this embodiment constructs a set of crew behavior actions based on the collected simulated operation data. Then, feature extraction is performed on the data in the ship-type behavior action set to obtain corresponding physiological change rate features and posture change rate features. Further, the extracted physiological change rate features and posture change rate features are fused using a multi-feature fusion action recognition algorithm, and a classifier is trained to generate a corresponding crew behavior state recognition model.

[0080] In some embodiments of the present invention, crew behavior identification is performed based on crew behavior data to obtain crew status data, including but not limited to:

[0081] Crew behavior data is obtained by identifying crew behavior data through a crew behavior status recognition model.

[0082] In this specific embodiment, crew behavior recognition is performed using a constructed crew behavior state recognition model. Specifically, after training the crew behavior action set to generate the crew behavior state recognition model, the model is used to identify crew behavior data, thereby obtaining crew state data and recognizing the current state of each crew member. Furthermore, by training the model with the crew behavior action set constructed through crew behavior simulation by test subjects, the accuracy of crew state recognition can be effectively improved.

[0083] In some embodiments of the present invention, collision analysis is performed based on preset ship navigation data to obtain a ship collision risk level, including but not limited to:

[0084] The collision risk between the fishing vessel and each other vessel is calculated based on the preset vessel navigation data, and the collision risk data is obtained.

[0085] The collision risk data between fishing vessels and other vessels are summed to obtain the vessel collision risk level. This level includes emergency response response level, navigation emergency organization level, remote monitoring level, and vessel self-inspection level.

[0086] In this specific embodiment, the collision risk between the fishing vessel and other vessels is first calculated based on preset vessel navigation data. Then, the corresponding collision risk data are accumulated to obtain the vessel collision risk level. Specifically, when there are two or more other vessels within a preset range (e.g., within 8 nautical miles) of the fishing vessel, the real-time collision risk between the fishing vessel and each of these other vessels is analyzed. This embodiment first determines whether the maximum value of the obtained real-time collision risk is greater than a preset risk threshold. If the maximum value is less than or equal to the risk threshold, the fishing vessel is considered relatively safe, with a low probability of collision with other vessels, and no need to alert the crew; it is at a low risk level. Conversely, if the maximum value is greater than the risk threshold, the fishing vessel is considered to have a potential collision with other vessels. Next, this embodiment accumulates the risk levels of the fishing vessel with those of other vessels to obtain the final risk level of the fishing vessel, i.e., the vessel collision risk level. In this embodiment, the vessel collision risk threshold is determined based on the safety risks and management realities of the fishing vessel. Multiple real-time collision risk zones are pre-defined, each corresponding to a different risk level. In this embodiment, the risk level of the fishing vessel can be determined based on the real-time collision risk zone into which the real-time collision risk falls. Accordingly, in this embodiment, the vessel collision risk level is divided into four levels from severe to minor, including emergency response level, navigation emergency organization level, remote monitoring level, and vessel self-inspection level.

[0087] In some embodiments of the present invention, the comprehensive safety risk level of the fishing vessel is calculated based on the crew's defense level and the vessel collision risk level, including but not limited to:

[0088] The comprehensive safety risk level of the fishing vessel is obtained by multiplying the crew's defense level and the vessel's collision risk level.

[0089] In this specific embodiment, the comprehensive safety risk level of the fishing vessel is calculated by multiplying the crew's defense level and the ship's collision risk level. Specifically, the comprehensive safety risk level of the fishing vessel is calculated by multiplying the crew's defense level and the ship's collision risk level. For example, this embodiment obtains 256 comprehensive safety risk values ​​for fishing vessel operations after multiplying the ship's defense level and the ship's collision risk level. When this result is greater than 2, the wearable device worn by the crew will issue a corresponding warning message. It is easy to understand that after obtaining the comprehensive safety risk level of the fishing vessel, this embodiment outputs a warning message associated with the comprehensive safety risk level to the crew, enabling the crew to take collision avoidance measures based on the real-time collision risk level. Specific operations can be determined according to the specific situation, such as changing course, reducing speed, or a combination of both.

[0090] In some embodiments of the present invention, vessel safety warnings are issued based on the comprehensive safety risk level of fishing vessels, including but not limited to:

[0091] When the overall safety risk level of a fishing vessel reaches the first preset level, a warning will be issued via wearable devices.

[0092] Alternatively, when the overall safety risk level of a fishing vessel reaches the second preset level, an early warning will be issued via the ship's broadcasting station.

[0093] Alternatively, when the overall safety risk level of the fishing vessel is at the third preset level, a distress message can be sent via wearable devices.

[0094] In this specific embodiment, due to the numerous and varied comprehensive safety risks associated with fishing vessels, a tiered early warning system is used for vessel safety alerts. Specifically, when the comprehensive safety risk level of the fishing vessel is at the first preset level, this embodiment uses wearable devices to issue early warnings. For example, if the calculated comprehensive safety risk level is at the first preset level, after analysis, this embodiment issues early warnings to the crew member, such as vibrations or sounds, to alert them to take timely measures to avoid danger. Alternatively, when the comprehensive safety risk level is at the second preset level, this embodiment issues early warnings via the ship's public address system. For example, in the event of a particularly urgent incident, such as when the crew member is asleep and unable to perceive the risk and therefore unable to operate the vessel, this embodiment issues early warnings via the ship's public address system to broaden the impact and alert the crew member. Furthermore, when the comprehensive safety risk level is at the third preset level, this embodiment sends a distress signal via wearable devices. For example, when the crew can no longer receive information, or when they receive information but cannot take action, or when a safety accident has occurred, the wearable device system analyzes the information and automatically sends a distress signal to surrounding vessels, thereby locating the crew's position in a timely manner and carrying out rescue operations.

[0095] It should be noted that, in some embodiments of the present invention, the principle block diagram of the fishing vessel operation safety early warning system is as follows: Figure 3 As shown in the diagram. This embodiment uses wearable devices worn by fishing boat crew members to acquire their behavioral status data in real time via a client-side device (the fisherman's end) and feed it back to the fishing boat. The client in this embodiment includes a beacon and the wearable device worn by the fisherman. The wearable device is equipped with a crew behavior data acquisition unit, including an optical volumetric sensor, an accelerometer, and a gyroscope. For example, this embodiment uses a wristband worn by the crew member to acquire their behavioral data in real time and transmit the collected data to the host computer on the fishing boat for processing. In this embodiment, the fishing boat and the client communicate via a first communication link, such as wireless communication via Bluetooth. Correspondingly, the fishing boat also includes an AIS terminal. This embodiment acquires the ship's AIS data via the AIS terminal. The fishing boat and the shore communicate via a second communication link, such as communication via BeiDou communication. Simultaneously, this embodiment includes a monitoring unit on the shore. This monitoring unit receives and monitors the data sent from the fishing boat and provides safety warnings, thereby enabling accurate safety warnings during fishing operations and effectively reducing the probability of ship accidents.

[0096] An embodiment of the present invention also provides a safety early warning system for fishing vessel operations, comprising:

[0097] The first acquisition module is used to acquire crew behavior data collected by wearable devices worn by crew members.

[0098] The identification module is used to identify crew behavior based on crew behavior data to obtain crew status data.

[0099] The first analysis module is used to analyze crew status data to determine the crew's defense level.

[0100] The second acquisition module is used to acquire preset vessel navigation data. This preset vessel navigation data includes the target fishing vessel's navigation data and other vessel navigation data.

[0101] The second analysis module is used to perform collision analysis based on preset ship navigation data to obtain the ship collision risk level.

[0102] The risk calculation module is used to calculate the comprehensive safety risk level of fishing vessels based on the crew's defense level and the ship's collision risk level.

[0103] The safety early warning module is used to provide ship safety warnings based on the comprehensive safety risk level of fishing vessels.

[0104] Reference Figure 2 An embodiment of the present invention also provides a fishing vessel operation safety early warning system, comprising:

[0105] At least one processor 210.

[0106] At least one memory 220 is used to store at least one program.

[0107] When at least one program is executed by at least one processor 210, the at least one processor 210 implements the fishing vessel operation safety early warning method as described in the above embodiments.

[0108] An embodiment of the present invention also provides a computer-readable storage medium storing computer-executable instructions that are executed by one or more control processors, for example, performing the steps described in the above embodiments.

[0109] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0110] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A method for early warning of safety during fishing vessel operations, characterized in that, Includes the following steps: Acquire crew behavior data collected by wearable devices; wherein the wearable devices are worn by the crew members. Crew behavior is identified based on the crew behavior data to obtain crew status data; wherein, the crew status data includes shipboard operation, fishing operations, sleeping, and falling into the water; The crew defense level is obtained by analyzing the crew status data; wherein, the crew defense level characterizes the speed at which the crew responds to danger warnings; Acquire preset vessel navigation data; wherein, the preset vessel navigation data includes target fishing vessel navigation data and other vessel navigation data; Collision analysis is performed based on the preset ship navigation data to obtain the ship collision risk level; The comprehensive safety risk level of the fishing vessel is calculated based on the crew defense level and the collision risk level of the vessel. Ship safety warnings are issued based on the comprehensive safety risk level of the fishing vessels.

2. The fishing vessel operation safety early warning method according to claim 1, characterized in that, The wearable device is equipped with an optical volumetric sensor, an accelerometer, and a gyroscope. The crew behavior data includes physiological data, acceleration data, and angular velocity data. The acquisition of crew behavior data collected by wearable devices includes: The physiological data is acquired through the optical volumetric sensor; wherein the physiological data includes heart rate and blood oxygen saturation data; The acceleration data is acquired through the acceleration sensor; The angular velocity data is obtained through the gyroscope.

3. The fishing vessel operation safety early warning method according to claim 1, characterized in that, Before performing the step of identifying crew behavior based on the crew behavior data to obtain crew status data, the method further includes: The wearable device is used to acquire simulated work data of the test subjects; A set of crew behavior actions is constructed based on the simulated operation data; Based on the set of crew behavior actions, corresponding crew behavior state features are extracted; wherein, the crew behavior state features include physiological change rate features and posture change rate features; The crew behavior state features are fused using a multi-feature fusion action recognition algorithm to generate a crew behavior state recognition model.

4. The fishing vessel operation safety early warning method according to claim 3, characterized in that, The step of identifying crew behavior based on the crew behavior data to obtain crew status data includes: The crew behavior data is identified by the crew behavior status recognition model to obtain crew status data.

5. The fishing vessel operation safety early warning method according to claim 1, characterized in that, The step of performing collision analysis based on the preset ship navigation data to obtain the ship collision risk level includes: Based on the preset ship navigation data, the collision risk between the fishing vessel and each other vessel is calculated to obtain collision risk data; The collision risk data of the fishing vessel and other vessels are accumulated to obtain the vessel collision risk level; wherein, the vessel collision risk level includes emergency response level, navigation emergency organization level, remote monitoring level, and vessel self-inspection level; wherein, multiple real-time collision risk intervals are pre-set, each real-time collision risk interval corresponds to a different vessel collision risk level, and the vessel collision risk level is determined according to the real-time collision risk interval in which the collision risk data falls.

6. The fishing vessel operation safety early warning method according to claim 1, characterized in that, The comprehensive safety risk level of the fishing vessel, calculated based on the crew's defense level and the vessel's collision risk level, includes: The comprehensive safety risk level of the fishing vessel is obtained by multiplying the crew's defense level and the vessel's collision risk level.

7. The fishing vessel operation safety early warning method according to claim 1, characterized in that, The method of issuing ship safety warnings based on the comprehensive safety risk level of the fishing vessel includes: When the comprehensive safety risk level of the fishing vessel is at the first preset level, an early warning reminder will be issued through the wearable device; Alternatively, when the comprehensive safety risk level of the fishing vessel is the second preset level, the early warning reminder will be issued through the ship's broadcasting station; Alternatively, when the overall safety risk level of the fishing vessel is at the third preset level, a distress message can be sent through the wearable device.

8. A safety early warning system for fishing vessel operations, characterized in that, include: The first acquisition module is used to acquire crew behavior data collected by a wearable device; wherein the wearable device is worn by the crew member. The identification module is used to identify crew behavior based on the crew behavior data to obtain crew status data; wherein, the crew status data includes shipboard operation, fishing operations, sleeping, and falling into the water; The first analysis module is used to analyze the crew status data to obtain the crew defense level; wherein, the crew defense level characterizes the speed of the crew's response to danger warnings; The second acquisition module is used to acquire preset ship navigation data; wherein, the preset ship navigation data includes target fishing vessel navigation data and other ship navigation data; The second analysis module is used to perform collision analysis based on the preset ship navigation data to obtain the ship collision risk level. The risk calculation module is used to calculate the comprehensive safety risk level of the fishing vessel based on the crew defense level and the ship collision risk level. The safety early warning module is used to provide ship safety early warnings based on the comprehensive safety risk level of the fishing vessel.

9. A safety early warning system for fishing vessel operations, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the fishing vessel operation safety early warning method as described in any one of claims 1 to 7.

10. A computer storage medium storing a processor-executable program, characterized in that, The program executable by the processor is used, when executed by the processor, to implement the fishing vessel operation safety early warning method as described in any one of claims 1 to 7.

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