Marine Area Integration Digital Intelligent Rescue System

Abstract

The present invention relates to the technical field of maritime rescue, specifically to an integrated digital intelligent rescue system for maritime areas, which includes an information collection module, a data analysis module, a rescue decision-making module, an execution operation module, and a central control unit; among them, the information collection module: is used to collect sea area environmental data and rescue object information through a variety of sensing devices and summarize them; the data analysis module: identifies the urgency of rescue needs and the specific location of rescue objects through a preset spatio-temporal sequence prediction algorithm; the rescue decision-making module: evaluates various rescue plans and formulates the optimal rescue action plan; the execution operation module: executes specific rescue operations by commanding rescue vessels, unmanned aerial vehicles, and life-saving equipment. The present invention significantly improves the rescue response speed and decision-making accuracy through real-time data integration and efficient module coordination, while enhancing the transparency and adaptability of rescue operations, greatly improving the efficiency and success rate of maritime rescue.

Description

Technical Field

[0001] The present invention relates to the technical field of maritime rescue, and particularly to an integrated digital intelligent rescue system for maritime areas. Background Art

[0002] Maritime rescue operations face numerous challenges, especially in complex and variable marine environments, such as rapidly changing weather conditions and vast rescue areas. Traditional rescue systems often rely on manual coordination and linear command processes, resulting in extended rescue response times and inefficient resource allocation. In addition, existing rescue systems often cannot update the rescue status in real time or effectively integrate information from different rescue units (such as ships, life-saving equipment, drones), making it difficult for rescue operations to adapt to rapidly changing situations and accurately locate rescue targets.

[0003] In the face of these challenges, the limitations of existing technologies are mainly manifested in information integration, real-time performance, and dynamic decision-making support for rescue operations. There is an urgent need for an advanced system that can collect and analyze data from multiple sensors in real time, make rapid rescue decisions, and effectively coordinate various rescue resources. In addition, as the complexity of rescue scenarios increases, traditional methods are unable to cope in terms of data processing and adaptability of rescue strategies.

[0004] Therefore, developing an integrated digital intelligent rescue system that can provide cross-module real-time data streams and status updates to ensure the efficiency and accuracy of rescue operations has become a key requirement for improving the success rate of maritime rescue. Summary of the Invention

[0005] Based on the above purposes, the present invention provides an integrated digital intelligent rescue system for maritime areas.

[0006] The integrated digital intelligent rescue system for maritime areas includes an information collection module, a data analysis module, a rescue decision-making module, an execution operation module, and a central control unit; among them,

[0007] The information collection module: is used to collect sea area environment data and rescue target information through various sensing devices such as personal bodyguard bracelets, intelligent positioning work cards, intelligent positioning alarm life jackets, drones, satellites, and marine buoys, and summarize them;

[0008] The data analysis module: performs real-time analysis, processing, and classification on the collected and summarized information, and identifies the urgency of rescue needs and the specific location of rescue targets through a preset spatio-temporal sequence prediction algorithm;

[0009] The rescue decision-making module: evaluates various rescue plans based on the output of the data analysis module and formulates an optimal rescue action plan;

[0010] Operation Execution Module: Based on the rescue plan formulated by the Rescue Decision Module, it executes specific rescue operations by commanding rescue boats, drones, and life-saving equipment.

[0011] Central Control Unit: Used to coordinate the operations of each module and provide real-time rescue status updates through a preset user interface.

[0012] Furthermore, the Information Collection Module includes a Data Reception Unit and a Data Summarization Unit; among them,

[0013] Data Reception Unit: It includes various sensor interfaces for receiving various types of data from different sensing devices, specifically including:

[0014] The Personal Bodyguard Bracelet provides physiological state data of the rescue target, including heart rate and exercise intensity, through the built-in accelerometer and heart rate monitor.

[0015] The Smart Location Work Card uses a Global Positioning System sensor to provide precise geographical location data of the rescue target.

[0016] The Smart Location Alarm Life Jacket, through the built-in GPS and water pressure sensors, is used to provide location information and detect whether the life jacket has inflated, thereby determining whether the rescue target is in the water.

[0017] The drone captures high-definition videos and night images through the onboard camera and infrared sensor, for monitoring the sea area environment and the dynamics of the rescue target.

[0018] The satellite provides a wide range of geographical and meteorological data, including climate conditions and ocean current information.

[0019] The marine buoy monitors environmental parameters such as seawater temperature, salinity, turbidity, wind speed, and wind direction through the built-in water quality analyzer and meteorological sensor.

[0020] The above devices all transmit data to the Data Reception Unit in real time through wireless communication technology.

[0021] Data Summarization Unit: It performs advanced data fusion processing on the received data. Specifically, it first unifies the formats of various sensor data and synchronizes them according to the time stamp to ensure the consistency of data time; then it conducts quality assessment on the collected data, including checking the signal strength of GPS data and outliers of heart rate data, and screening out low-quality or irrelevant data; finally, according to predefined algorithms, it comprehensively processes the high-quality data that has been screened and synchronized to generate a comprehensive data packet including the detailed location, physiological state, and surrounding environmental conditions of the rescue target, providing accurate input for the Data Analysis Module.

[0022] Furthermore, the Data Summarization Unit includes:

[0023] Data Quality Assessment Sub-unit: It is used to perform quality control on the data after format unification and synchronization. Specifically, it first evaluates the signal quality of GPS data using the signal-to-noise ratio calculation formula. The SNR calculation formula is: where P signal is the signal power, and P noise is the noise power; SNR is the signal-to-noise ratio. Data with a signal intensity lower than the preset threshold will be marked as low-quality and screened out; then the Z-score method is used to identify abnormal heart rate values. The calculation formula is: where x is a single heart rate measurement value, μ is the average heart rate, and σ is the standard deviation of the heart rate; data points with an absolute Z-score exceeding 3 are regarded as outliers and excluded;

[0024] Data Comprehensive Generation Sub-unit: It is used to process the filtered high-quality data according to a predefined algorithm to generate a comprehensive data packet; specifically, it first uses the weighted average method to fuse location and environmental data to improve the accuracy and reliability of the data. The weighted average calculation formula for location data is: where L avg is the weighted average location coordinate; L i is the location data provided by each sensor; w i is the weight calculated based on the signal-to-noise ratio of each data source; finally, combined with the environmental data provided by all sensors, the principal component analysis method is used to extract environmental features to simplify the data representation while retaining the predefined information, and finally a comprehensive data packet is formed.

[0025] Furthermore, the data analysis module includes a real-time analysis and processing unit and an urgency identification unit; where

[0026] Real-time Analysis and Processing Unit: It is used to perform real-time processing and classification on the comprehensive data packet from the data aggregation unit. Specifically, it first verifies the time stamp in the data packet to ensure the real-time nature of the data, and then classifies the data into multiple categories through the application of the decision tree classification method, including environmental category, physiological status category of the rescue object, and location information category;

[0027] Urgency Identification Unit: It uses a preset spatio-temporal sequence prediction algorithm to analyze the classified data to determine the urgency of the rescue need and the specific location of the rescue object. The spatio-temporal sequence prediction algorithm combines time data and space data to analyze the behavior pattern of the rescue object and the environmental change trend, and then predicts the future location of the rescue object and potential emergency situations, including sudden health problems or the impact of bad weather.

[0028] Furthermore, the real-time analysis and processing unit includes:

[0029] Timestamp Verification Subunit: Used to verify the timestamps in the comprehensive data packets received from the data aggregation unit. Specifically, it first compares the timestamps in each received data packet with the Coordinated Universal Time (UTC), and ensures the accuracy and consistency of each timestamp by synchronizing with the built-in time server of the system. Then, it calculates the time difference between the reception time of the data packet and its timestamp to evaluate the data latency. When the time difference exceeds the preset threshold of the system, the data is considered not to be real-time, and it is marked as obsolete and excluded from the urgency analysis.

[0030] Data Classification Subunit: Classifies the data through the decision tree algorithm. The specific steps include:

[0031] Feature Extraction: First, extracts features from each data packet, including environmental parameters, physiological state parameters of the rescue target, and location information.

[0032] Decision Tree Construction: Constructs a decision tree using the extracted features. The splitting of each node is based on the principle of maximizing information gain. By selecting features that can distinguish different classes for splitting, the information gain calculation formula is: where IG(T, F) is the information gain of feature F for dataset T; H(T) is the entropy of dataset T, representing the uncertainty of the data; T i represents the subset under a value of feature F; H(T i ) represents the entropy of subset T i ; |T i | / |T| represents the proportion of the number of elements in subset T i to the total dataset T.

[0033] Classification Decision: Through the constructed decision tree, the data is automatically classified into environmental categories, rescue target physiological state categories, and location information categories. Each leaf node in the decision tree represents a classification, and the category of the data is determined by the path to reach the leaf node.

[0034] Furthermore, the Urgency Identification Unit includes:

[0035] Spatio-Temporal Data Analysis Subunit: Uses a preset spatio-temporal sequence prediction algorithm to deeply analyze the data classified by the real-time analysis and processing subunit. Specifically, it first performs spatial interpolation on the data in the location information category to fill in any missing geographical location data points and ensure the integrity and continuity of the data. Then, it combines the time data with the spatial data and applies a spatio-temporal autoregressive model for analysis. The spatio-temporal autoregressive model formula is expressed as: Y t = ρWY t + βX t + ε t , where Y t represents the target variable at time t; ρ is the spatial dependence parameter; W is the spatial weight matrix; Xt is the explanatory variable at time t; β is the regression coefficient, indicating the influence intensity of the explanatory variable on the target variable; ε t is the error term; then use historical rescue data to train the model to learn the mutual dependence and patterns of time and space data, and finally apply the trained model to predict the future event development and potential location changes of the rescue targets;

[0036] Urgency assessment subunit: According to the output of the spatio-temporal data analysis subunit, assess the urgency of rescue needs. Specifically, first analyze the location change trend of the rescue target and the changes in environmental conditions, including meteorological changes and sea current dynamics; then conduct an emergency situation determination. By combining the physiological state changes of the rescue target and the environmental trend, and by setting a predetermined threshold, automatically determine the urgency of rescue needs.

[0037] Furthermore, the rescue decision module includes a rescue plan evaluation unit and a rescue action plan formulation unit; among them,

[0038] Rescue plan evaluation unit: Used to evaluate various rescue plans according to the output of the spatio-temporal data analysis subunit. Specifically, according to the specific location, environmental conditions and urgency level of the rescue target, automatically generate a variety of rescue plans, and each plan includes a rescue path, required resources, rescue time and estimated cost; then use multi-criteria decision analysis technology to compare the feasibility and efficiency of different plans, and the comparison criteria include the shortest rescue time, the lowest cost, the highest success rate and the minimum risk; according to the comparison results, select the rescue plan with the highest comprehensive score as the recommended implementation plan;

[0039] Rescue action plan formulation unit: Used to formulate the final rescue action plan, specifically including the unit rescue plan and the regionalized group chain rescue plan.

[0040] Furthermore, the unit rescue plan is used for a single rescue target at close range, including a person falling into the water on an offshore oil platform or a ship, and then formulate a rapid response unit rescue plan, including dispatching rescue equipment and personnel from the nearest platform or ship;

[0041] The regionalized group chain rescue plan is for emergencies that occur widely or simultaneously at multiple points. Formulate a regionalized group chain rescue plan. By using remote and local servers, the rescue systems between various rescue units can be independently and adaptively grouped to form a coordinated rescue network. Even in the absence of an external network, effective rescue operations can be implemented.

[0042] Furthermore, the execution operation module includes a rescue resource scheduling unit and a real-time operation command unit; among them,

[0043] Rescue resource scheduling unit: used to schedule and configure rescue resources according to the optimal rescue plan recommended by the rescue decision module. Specifically, it first identifies and lists all available rescue resources, including rescue ships, drones, and smart lifeboats; then allocates resources, and allocates corresponding resources to perform rescue tasks according to the needs of the rescue plan, including rescue distance, estimated rescue time, and resource functions; finally, it sends detailed scheduling instructions to the selected rescue resources, including rescue location, operation details, and time requirements;

[0044] Real-time operation command subunit: used to command the actual execution of rescue operations and ensure the efficiency and safety of rescue operations. Specifically, it maintains real-time communication with all rescue units through a high-frequency communication system to ensure the timely transmission of command information. During the process, it dynamically adjusts the rescue plan and resource allocation based on real-time feedback from the rescue scene, including environmental changes and updates on the status of the rescue objects. During the execution of the rescue, the rescue is based on the use of drones and satellite images to monitor the progress of the rescue operation in real time, evaluate the rescue effect, and make adjustments or reinforcements as needed.

[0045] Furthermore, the central control unit includes a module coordination unit and a user interface updating unit; wherein,

[0046] Module coordination subunit: used to coordinate the activities of the information collection module, data analysis module, rescue decision module and execution operation module to ensure the smoothness and efficiency of the rescue operation. The module coordination subunit specifically includes information flow management, task synchronization and exception handling;

[0047] The information flow management is used to implement efficient data exchange protocols to ensure unimpeded information flow between modules, including receiving, processing, transmitting and responding data;

[0048] The task synchronization is used to synchronize the operation tasks of each module to ensure that the rescue activities are carried out in an orderly manner according to the predetermined plan. Specifically, the time synchronization technology is used to ensure that the clocks of all modules are accurately consistent;

[0049] The exception handling is used to monitor the operating status of each module. When abnormal activity or failure of any module is detected, the preset emergency procedure is immediately started to ensure the continuous operation of the rescue system;

[0050] User interface update subunit: used to provide real-time rescue status updates to operators through a preset user interface, specifically by displaying the real-time data and rescue status of each module in a graphical interface, including the location of the rescue object, the deployment of rescue resources and environmental conditions.

[0051] Beneficial effects of the present invention:

[0052] The present invention significantly improves the response speed and coordination of rescue operations by implementing an efficient data exchange and module coordination mechanism. The high integration and real-time communication of various rescue modules, such as information collection, data analysis, rescue decision-making, and execution operations, ensure instant data processing and decision-making, reducing rescue preparation and execution time. This is particularly important in a rapidly changing maritime environment, enabling the rapid deployment of necessary resources within the golden rescue window period and improving rescue efficiency.

[0053] The present invention makes rescue decision-making more accurate and scientific by utilizing advanced spatio-temporal sequence prediction algorithms and decision support technologies. Through in-depth analysis of the behavior patterns of rescue targets and trends in environmental changes, the system can predict the future positions of rescue targets and potential emergencies, such as sudden health problems or the impact of adverse weather. This predictive ability not only optimizes the allocation of rescue resources but also improves the adaptability and success rate of rescue plans.

[0054] The present invention enhances the transparency and monitorability of rescue operations by implementing a user-friendly real-time update interface. The rescue team can obtain dynamic updates on the rescue status through a real-time graphical interface, including information such as the current deployment of rescue resources, the status of rescue targets, and environmental conditions. This not only helps the command center make more reasonable dispatching decisions but also enables rescue personnel to respond more effectively to on-site changes, ensuring the safety and efficiency of rescue operations. Brief Description of the Drawings

[0055] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings described below are only those of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0056] Figure 1 Schematic diagram of the integrated digital intelligent rescue system in the maritime area for the embodiment of the present invention;

[0057] Figure 2 Schematic diagram of the data analysis module for the embodiment of the present invention. Detailed Embodiment

[0058] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the following further details the present invention in conjunction with specific embodiments.

[0059] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention shall have the ordinary meanings understood by those of ordinary skill in the field to which the present invention pertains. The "first", "second" and similar terms used in the present invention do not denote any order, quantity or importance, but are only used to distinguish different components. The terms such as "comprising" or "including" mean that the elements or items appearing before this word cover the elements or items listed after this word and their equivalents, without excluding other elements or items.

[0060] As Figure 1 - Figure 2 shown, the integrated digital intelligent rescue system for the marine area includes an information collection module, a data analysis module, a rescue decision-making module, an execution operation module and a central control unit; among them,

[0061] The information collection module: is used to collect sea area environment data and rescue object information through various sensing devices such as bodyguard bracelets, intelligent positioning work cards, intelligent positioning alarm life jackets, drones, satellites and marine buoys, and summarize them;

[0062] The data analysis module: performs real-time analysis, processing and classification on the collected and summarized information, and identifies the urgency of rescue needs and the specific location of the rescue object through a preset spatio-temporal sequence prediction algorithm;

[0063] The rescue decision-making module: evaluates various rescue plans according to the output of the data analysis module, and formulates the optimal rescue action plan;

[0064] The execution operation module: based on the rescue plan formulated by the rescue decision-making module, performs specific rescue operations by commanding rescue vessels, drones and life-saving equipment;

[0065] The central control unit: is used to coordinate the operations of each module, and provides real-time rescue status updates through a preset user interface.

[0066] The information collection module includes a data receiving unit and a data summarizing unit; among them,

[0067] The data receiving unit: includes a variety of sensor interfaces, and is used to receive various types of data from different sensing devices, specifically including:

[0068] The personal bodyguard bracelet provides physiological state data of the rescue target, including heart rate and exercise intensity, through built-in accelerometers and heart rate monitors. The personal bodyguard bracelet also includes a water-sensitive sensor for real-time monitoring of the contact between the bracelet and water. When the sensor detects continuous contact with water exceeding a preset time threshold, it is determined that the rescue target may have fallen into the water, and the alarm mechanism is automatically triggered. The water-sensitive sensor senses the presence of moisture by using changes in conductivity or capacitance. Under normal circumstances, the bracelet remains dry, and the conductivity or capacitance remains at the baseline level; once the bracelet is immersed in water, the conductivity or capacitance changes sharply, and the sensor outputs a signal to trigger the alarm; after the water detection sub-unit triggers the alarm, the built-in GPS is used to calculate and provide the precise geographical location data of the rescue target, and these data are then sent to the central control unit through the wireless communication module for precisely locating the rescue target and coordinating the rescue operation;

[0069] The intelligent positioning work card uses a Global Positioning System (GPS) sensor to provide the precise geographical location data of the rescue target;

[0070] The intelligent positioning alarm life jacket uses built-in GPS and water pressure sensors to provide location information and detect whether the life jacket has inflated, so as to determine whether the rescue target is in the water;

[0071] The drone captures high-definition videos and night images through the equipped cameras and infrared sensors for monitoring the sea area environment and the dynamics of the rescue target;

[0072] The satellite provides a wide range of geographical and meteorological data, including climate conditions and ocean current information;

[0073] The marine buoy monitors environmental parameters such as seawater temperature, salinity, turbidity, wind speed and wind direction through built-in water quality analyzers and meteorological sensors;

[0074] The above devices all transmit data to the data receiving unit in real time through wireless communication technologies (such as Wi-Fi, Bluetooth, satellite communication);

[0075] Data aggregation unit: It performs advanced data fusion processing on the received data. Specifically, it first unifies the formats of various sensor data and synchronizes them according to the timestamps to ensure the consistency of data time. Then, it conducts quality assessment on the collected data, including checking the signal strength of GPS data and outliers in heart rate data, and screening out low-quality or irrelevant data. Finally, according to the predefined algorithm, it comprehensively processes the high-quality data that has been screened and synchronized, generates a comprehensive data packet including the detailed location, physiological status, and surrounding environmental conditions of the rescue object, and provides accurate input for the data analysis module. By configuring the data reception unit and the data aggregation unit in detail, it can ensure that the data received from multiple sensing devices is not only sufficient in quantity but also high in quality, effectively supporting real-time and accurate rescue decisions. This efficient data processing and integration ability significantly improves the response speed and success rate of rescue operations, ensuring the safety and effectiveness of rescue operations.

[0076] The data aggregation unit includes:

[0077] Data quality assessment subunit: It is used to perform quality control on the data after format unification and synchronization. Specifically, it first uses the Signal-to-Noise Ratio (SNR) calculation formula to evaluate the signal quality of GPS data. The SNR calculation formula is: where, P signal is the signal power, and P noise is the noise power; SNR is the signal-to-noise ratio, which is an index to measure the GPS signal quality and represents the ratio of the signal strength to the background noise strength. The data with signal strength lower than the preset threshold will be marked as low-quality and screened out. Then, the Z-score method is used to identify abnormal heart rate values. The calculation formula is: where, x is a single heart rate measurement value, μ is the average heart rate, and σ is the standard deviation of the heart rate; when the absolute Z-score exceeds 3 for a data point, it is regarded as an outlier and excluded;

[0078] Data comprehensive generation subunit: It is used to process the screened high-quality data according to the predefined algorithm and generate a comprehensive data packet. Specifically, it first uses the weighted average method to fuse the location and environmental data to improve the accuracy and reliability of the data. The weighted average calculation formula for location data is: where, L avg is the weighted average location coordinate, which is the estimated location of the rescue object obtained by integrating the location information provided by multiple data sources; L i is the location data provided by each sensor; w iThe weights are calculated based on the signal-to-noise ratios of each data source; finally, combining the environmental data (such as temperature, wind speed, etc.) provided by all sensors, the principal component analysis (PCA) method is used to extract environmental features to simplify the data representation while retaining the predetermined information, and finally a comprehensive data packet is formed; by introducing a data quality assessment subunit and a data integration generation subunit, the reliability and accuracy of the rescue data can be effectively improved. The data quality assessment ensures that only high-quality data is used for further analysis and decision-making, thereby reducing the risk of incorrect rescue decisions. Data fusion and comprehensive processing make the rescue decision-making more accurate and timely, significantly improving the efficiency and success rate of the rescue operation, and at the same time increasing the system's adaptability to complex environmental factors. By using advanced calculation methods and algorithms, the present invention realizes the efficient processing and application of key data in maritime rescue, and improves the intelligent level of the entire rescue system.

[0079] The data analysis module includes a real-time analysis and processing unit and an urgency recognition unit; among them,

[0080] The real-time analysis and processing unit: is used to perform real-time processing and classification on the comprehensive data packet from the data aggregation unit. Specifically, it first verifies the time stamp in the data packet to ensure the real-time nature of the data, and then, by applying the decision tree classification method, classifies the data into multiple categories, including environmental category, physiological state category of the rescue object, and location information category, to assist the system in quickly identifying and sorting the data and providing a structured input for urgency analysis;

[0081] The urgency recognition unit: uses a preset spatio-temporal sequence prediction algorithm to analyze the classified data to determine the urgency of the rescue requirement and the specific location of the rescue object. The spatio-temporal sequence prediction algorithm combines time data (such as the incident time, data collection time) and space data (such as the position change of the rescue object), analyzes the behavior pattern of the rescue object and the environmental change trend, and then predicts the future position of the rescue object and potential emergency situations, including sudden health problems or the impact of bad weather; through the setting of the real-time analysis and processing unit and the urgency recognition unit, a large amount of rescue data can be processed quickly and accurately, ensuring that the rescue operation can respond to the most urgent needs. The real-time processing and effective classification of the data speed up the recognition speed of the emergency situation, and the application of the spatio-temporal sequence prediction algorithm improves the rescue accuracy, enabling the rescue team to deploy resources more effectively and make preparations for the rescue in advance.

[0082] The real-time analysis and processing unit includes:

[0083] Timestamp Verification Subunit: Used to verify the timestamps in the comprehensive data packets received from the Data Aggregation Unit. Specifically, it first compares the timestamps in each received data packet with Coordinated Universal Time (UTC), and ensures the accuracy and consistency of each timestamp by synchronizing with the built-in time server of the system. Then, it calculates the time difference between the reception time of the data packet and its timestamp to evaluate the data latency. When the time difference exceeds the system preset threshold (e.g., 10 seconds), the data is considered not to be real-time, and it is marked as obsolete and excluded from the urgency analysis.

[0084] Data Classification Subunit: Classifies data through the decision tree algorithm. The specific steps include:

[0085] Feature Extraction: First, extracts features from each data packet, including environmental parameters (such as temperature, humidity), physiological state parameters of the rescue target (such as heart rate, blood pressure), and location information (such as latitude and longitude coordinates).

[0086] Decision Tree Construction: Constructs a decision tree using the extracted features. The splitting of each node is based on the principle of maximizing information gain. By selecting features that can distinguish different classes for splitting, the information gain calculation formula is: where, IG(T, F) is the information gain of feature F for dataset T; H(T) is the entropy of dataset T, representing the uncertainty of the data; T i represents the subset under a value of feature F; H(T i ) represents the entropy of subset T i ; |T i | / |T| represents the proportion of the number of elements in subset T i to the total dataset T.

[0087] Classification Decision: Through the constructed decision tree, the data is automatically classified into environmental categories, rescue target physiological state categories, and location information categories. Each leaf node in the decision tree represents a classification, and the category of the data is determined by the path to reach the leaf node. Through the precise operations of the Timestamp Verification Subunit and the Data Classification Subunit, the real-time nature and accuracy of the data are ensured. The precise verification of timestamps reduces the risk of incorrect decisions caused by data latency, while the efficient data classification improves the system's response speed and accuracy to rescue needs. This structured and automated data processing method significantly enhances the overall efficiency and success rate of rescue operations.

[0088] The Urgency Identification Unit includes:

[0089] Spatio-temporal data analysis sub-unit: It deeply analyzes the data classified by the real-time analysis and processing sub-unit using a preset spatio-temporal sequence prediction algorithm. Specifically, it first performs spatial interpolation on the data of the location information category to fill in any missing geographical location data points, ensuring the integrity and continuity of the data. Then, it combines the time data (such as the specific time point of data collection) with the spatial data (such as the geographical location coordinates of the rescue target), and applies the Spatio-Temporal Autoregressive Model (STAR) for analysis. The formula of the spatio-temporal autoregressive model is expressed as: Y t = ρWY t + βX t + ε t , where Y t represents the target variable at time t (such as the location of the rescue target); ρ is the spatial dependence parameter, measuring the mutual influence between different locations; W is the spatial weight matrix, defining the intensity of the interaction between geographical locations; X t is the explanatory variable at time t (such as environmental factors and time variables); β is the regression coefficient, indicating the influence intensity of the explanatory variable on the target variable; ε t is the error term, representing other random influences not explained by the model. Then, it uses historical rescue data to train the model to learn the mutual dependence and patterns of time and spatial data. Finally, it applies the trained model to predict the future event development and potential location changes of the rescue target;

[0090] Urgency assessment sub-unit: According to the output of the spatio-temporal data analysis sub-unit, it assesses the urgency of the rescue demand. Specifically, it first analyzes the location change trend of the rescue target and the environmental condition changes, including meteorological changes and sea current dynamics. Then, it makes an emergency situation determination. By combining the physiological state changes of the rescue target and the environmental trend, and by setting a predetermined threshold (such as the heart rate exceeding the safe range), it automatically determines the urgency of the rescue demand. The specific determination formula is: U = f(x1, x2,..., x n ), where U is the urgency rating; x n are factors including physiological parameters and environmental parameters. Through the refined operations of the spatio-temporal data analysis sub-unit and the urgency assessment sub-unit, it can efficiently analyze and predict the location of the rescue target and the urgency of the rescue demand. The application of the spatio-temporal sequence prediction algorithm not only improves the accuracy of prediction, but also enables the rescue operation to respond quickly and more pertinently, greatly enhancing the rescue efficiency and success rate.

[0091] The rescue decision-making module includes a rescue plan evaluation unit and a rescue action plan formulation unit; among them,

[0092] Rescue Plan Evaluation Unit: It is used to evaluate various rescue plans according to the output of the spatio-temporal data analysis subunit. Specifically, according to the specific location, environmental conditions and emergency level of the rescue object, multiple rescue plans are automatically generated. Each plan includes a rescue path, required resources (such as rescue boats, drones), rescue time and estimated cost. Then, multi-criteria decision-making analysis (MCDM) technology is used to compare the feasibility and efficiency of different plans. The comparison criteria include the shortest rescue time, the lowest cost, the highest success rate and the minimum risk. According to the comparison results, the rescue plan with the highest comprehensive score is selected as the recommended implementation plan.

[0093] Rescue Action Plan Formulation Unit: It is used to formulate the final rescue action plan, specifically including the unit rescue plan and the regionalized chain rescue plan.

[0094] The unit rescue plan is used for a single rescue object at close range, including a person falling into the water on an offshore oil platform or a ship. Furthermore, a rapid-response unit rescue plan is formulated, including dispatching rescue equipment and personnel from the nearest platform or ship.

[0095] The regionalized chain rescue plan is for emergencies that occur over a wide range or simultaneously at multiple points. A regionalized chain rescue plan is formulated. By using remote and local servers, the rescue systems between various rescue units (such as different offshore oil platforms, mobile ships) can independently and adaptively form a chain, forming a coordinated rescue network. Even in the absence of an external network, effective rescue operations can be implemented. Through the rescue plan evaluation unit and the rescue action plan formulation unit, the optimal rescue action plan can be quickly formulated and implemented according to the actual situation. This not only improves the rescue efficiency and success rate but also significantly reduces the rescue cost and time. Especially in a complex and dynamically changing offshore rescue environment, the systematic and automated rescue decision-making process of the present invention provides a high degree of flexibility and adaptability, making rescue operations more scientific and effective.

[0096] The execution operation module includes a rescue resource scheduling unit and a real-time operation command unit; among them,

[0097] Rescue Resource Scheduling Unit: It is used to schedule and allocate rescue resources according to the optimal rescue plan recommended by the rescue decision module. Specifically, first identify and list all available rescue resources, including rescue boats, drones, intelligent lifeboats; then perform resource allocation. According to the requirements of the rescue plan, including the rescue distance, estimated rescue time and functions of the resources, allocate corresponding resources to perform rescue tasks; finally, send detailed scheduling instructions to the selected rescue resources, including the rescue location, operation details and time requirements.

[0098] Real-time operation command subunit: used to command the actual execution of rescue operations and ensure the efficiency and safety of rescue operations. Specifically, it maintains real-time communication with all rescue units through a high-frequency communication system to ensure the timely transmission of command information. During the process, it dynamically adjusts the rescue plan and resource allocation based on real-time feedback from the rescue scene, including environmental changes and updates on the status of the rescue targets. During the execution of the rescue, the rescue is based on the use of drones and satellite images to monitor the progress of the rescue operation in real time, evaluate the rescue effect, and make adjustments or reinforcements as needed. Through the efficient operation of the rescue resource scheduling unit and the real-time operation command unit, it can ensure the rapid response and efficient execution of the rescue operation. The precise scheduling of resources and real-time command not only improve the success rate of the rescue operation, but also significantly reduce the rescue time and potential risks. In addition, the system's dynamic adjustment capability enables rescue operations to flexibly respond to complex and changeable maritime rescue environments, greatly improving the rescue efficiency and the survival rate of the rescue targets.

[0099] The central control unit includes a module coordination unit and a user interface update unit; wherein,

[0100] Module coordination subunit: used to coordinate the activities of the information collection module, data analysis module, rescue decision module and execution operation module to ensure the smoothness and efficiency of the rescue operation. The module coordination subunit specifically includes information flow management, task synchronization and exception handling;

[0101] Information flow management is used to implement efficient data exchange protocols to ensure unimpeded information flow between modules, including receiving, processing, transmitting and responding data;

[0102] Task synchronization is used to synchronize the operation tasks of each module to ensure that the rescue activities are carried out in an orderly manner according to the predetermined plan. Specifically, time synchronization technology, such as the Network Time Protocol (NTP), is used to ensure that the clocks of all modules are accurately consistent;

[0103] Exception handling is used to monitor the operating status of each module. When abnormal activity or failure of any module is detected, the preset emergency procedure is immediately activated to ensure the continuous operation of the rescue system;

[0104] User interface update subunit: used to provide real-time rescue status updates to operators through a preset user interface, specifically by displaying the real-time data and rescue status of each module in a graphical interface, including the location of the rescue object, the deployment of rescue resources and environmental conditions; through the fine management and real-time feedback of the module coordination subunit and the user interface update subunit, the central control unit can ensure that all rescue modules work together and efficiently, while providing intuitive and immediate data support for rescue command.

[0105] The present invention is intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Accordingly, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. Maritime regional integrated digital intelligent rescue system, characterized in that, It includes an information collection module, a data analysis module, a rescue decision-making module, an execution operation module, and a central control unit. Among them, Information collection module: It is used to collect sea area environment data and rescue object information through various sensing devices such as personal bodyguard bracelets, intelligent positioning work cards, intelligent positioning alarm life jackets, drones, satellites, and marine buoys, and summarize them. The information collection module includes a data reception unit and a data summarization unit. Among them, Data reception unit: It includes a variety of sensor interfaces and is used to receive various types of data from different sensing devices. Data summarization unit: It performs data fusion processing on the received data, conducts quality assessment on the collected data, and comprehensively processes the filtered and synchronized data according to predefined algorithms to generate a comprehensive data packet including the detailed location, physiological state, and surrounding environmental conditions of the rescue object, providing accurate input for the data analysis module. Data analysis module: It performs real-time analysis processing and classification on the collected and summarized information, and identifies the urgency of rescue needs and the specific location of the rescue object through a preset spatio-temporal sequence prediction algorithm. The data analysis module includes a real-time analysis processing unit and an urgency identification unit. Among them, Real-time analysis processing unit: It is used to first verify the time stamp in the data packet to ensure the real-time nature of the data, and then classify the data into multiple categories, including environmental category, rescue object physiological state category, and location information category, by applying the method of decision tree classification. Urgency identification unit: It uses a preset spatio-temporal sequence prediction algorithm to analyze the classified data to determine the urgency of rescue needs and the specific location of the rescue object. The spatio-temporal sequence prediction algorithm combines time data and space data to analyze the behavior pattern of the rescue object and the environmental change trend, and then predicts the future location of the rescue object and potential emergency situations, including the impact of sudden health problems or bad weather. The urgency identification unit includes: Spatio-temporal data analysis sub-unit: It uses a preset spatio-temporal sequence prediction algorithm to deeply analyze the data classified by the real-time analysis processing unit. Specifically, it first performs spatial interpolation on the data of the location information category to fill any missing geographical location data points to ensure the integrity and continuity of the data; then combines the time data and space data and applies a spatio-temporal autoregressive model for analysis; then uses historical rescue data to train the model to learn the mutual dependence and pattern of time and space data, and finally applies the trained model to predict the future event development and potential location change of the rescue object. Urgency assessment sub-unit: According to the output of the spatio-temporal data analysis sub-unit, it assesses the urgency of rescue needs. Specifically, it first analyzes the location change trend of the rescue object and the change of environmental conditions, including meteorological changes and sea current dynamics; then conducts emergency situation determination, and automatically determines the urgency of rescue needs by combining the physiological state change of the rescue object and the environmental trend through setting a predetermined threshold. Rescue decision-making module: According to the output of the data analysis module, it evaluates various rescue plans and formulates the optimal rescue action plan. Operation Execution Module: Based on the rescue plan formulated by the Rescue Decision Module, it executes specific rescue operations by commanding rescue vessels, drones, and life-saving equipment. Central Control Unit: Used to coordinate the operations of each module and provide real-time rescue status updates through a preset user interface.

2. The integrated digital intelligent rescue system for maritime areas according to claim 1, characterized in that The Rescue Decision Module includes a Rescue Plan Evaluation Unit and a Rescue Action Plan Formulation Unit; among them, Rescue Plan Evaluation Unit: Used to evaluate various rescue plans according to the output of the Urgency Identification Unit. Specifically, it automatically generates multiple rescue plans based on the specific location, environmental conditions, and urgency level of the rescue target. Each plan includes a rescue path, required resources, rescue time, and estimated cost. Then, it uses multi-criteria decision analysis techniques to compare the feasibility and efficiency of different plans. The comparison criteria include the shortest rescue time, the lowest cost, the highest success rate, and the minimum risk. According to the comparison results, the rescue plan with the highest comprehensive score is selected as the recommended plan for execution. Rescue Action Plan Formulation Unit: Used to formulate the final rescue action plan.

3. The integrated digital intelligent rescue system for the maritime area according to claim 1, characterized in that, The rescue action plan includes a unit rescue plan and a regionalized chain rescue plan. The unit rescue plan is used for a single rescue target at close range, including a person falling into the water on an offshore oil platform or a ship. Then, a rapid-response unit rescue plan is formulated, including dispatching rescue equipment and personnel from the nearest platform or ship. The regionalized chain rescue plan is for emergencies occurring over a wide range or simultaneously at multiple points. A regionalized chain rescue plan is formulated. By using remote and local servers, the rescue systems among various rescue units can be independently and adaptively chained to form a coordinated rescue network. Even without an external network, effective rescue operations can be carried out.

4. The integrated digital intelligent rescue system for maritime areas according to claim 1, characterized in that, The Operation Execution Module includes a Rescue Resource Scheduling Unit and a Real-Time Operation Command Unit; among them, Rescue Resource Scheduling Unit: Used to identify and list all available rescue resources, including rescue vessels, drones, and intelligent lifeboats. Then, it conducts resource allocation. According to the requirements of the rescue plan, including the rescue distance, estimated rescue time, and functions of the resources, it allocates corresponding resources to perform rescue tasks. Finally, it sends detailed scheduling instructions to the selected rescue resources, including the rescue location, operation details, and time requirements. Real-Time Operation Command Unit: Used to maintain real-time communication with all rescue units through a high-frequency communication system to ensure the timely transmission of command information. During the process, according to the real-time feedback of the rescue scene, including environmental changes and updates on the status of the rescue target, it dynamically adjusts the rescue plan and resource allocation. It uses drones and satellite images to monitor the progress of the rescue operation in real time, evaluate the rescue effect, and make adjustments or reinforcements as needed.

5. The integrated digital intelligent rescue system for maritime areas according to claim 1, wherein, The Central Control Unit includes a Module Coordination Unit and a User Interface Update Unit; among them, Module Coordination Unit: Used to coordinate the activities of the Information Collection Module, Data Analysis Module, Rescue Decision Module, and Operation Execution Module, including information flow management, task synchronization, and exception handling. User interface update unit: used to provide real-time rescue status updates to the operator through a preset user interface, including the location of the rescue target, the deployment of rescue resources, and the environmental conditions.

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