Intelligent safety protection system for ship construction

Abstract

The invention discloses an intelligent safety protection system for ship construction. The intelligent safety protection system comprises wearable intelligent safety detection equipment, an operation environment detection and personnel management and control system, an AI perception prediction and judgment module and a safety production detection integrated platform, wherein multi-source data acquired by the wearable intelligent safety detection equipment and operation environment detection and personnel management and control system are input into the AI perception prediction and judgment module; the AI perception prediction and judgment module generates a risk type and a risk level through cross-modal analysis; the safety production and detection integrated platform is responsible for transmission and interaction of multi-source data and generates a decision signal of an equipment control instruction according to the risk type and the risk level; driving an operation environment detection and personnel management and control system to dynamically adjust a safety protection area; an active protection instruction is sent to the wearable intelligent safety detection equipment; according to the intelligent safety protection system for ship construction, the risk can be intelligently predicted, protection can be dynamically adjusted, and the ship construction safety is improved.

Description

Technical Field

[0001] The present invention relates to the field of shipbuilding, and in particular to an intelligent safety protection system for shipbuilding. Background Art

[0002] The shipbuilding process is characterized by high risk, long cycles, a wide range of processes involved, and is labor-intensive, making it extremely prone to safety accidents. Every year during the construction of large ships, accidents such as falls from heights, caused by a lack of safety awareness among construction workers and inadequate protective equipment, rank first among all types of accidents. Shipyard welding, grinding, painting, and other operations involve the use of flammable and explosive gases such as oxygen, liquefied petroleum gas, and propane, which cause large amounts of piled materials and combustible materials to burn and explode. Furthermore, the construction process involves operations in confined spaces such as liquid tanks, cargo holds, and pump rooms. Due to restricted access and the inability to ventilate naturally, toxic and harmful gases accumulate or oxygen levels are insufficient, leading to frequent accidents of poisoning, hypoxia, and suffocation.

[0003] Therefore, the intelligent development of safety protection equipment, online collection of personnel and environment in multiple scenarios, real-time identification, positioning, and early warning of hazardous sources, as well as comprehensive management and analysis of safety data are the only way to improve the safety prediction and prevention capabilities of ship construction in the future. Summary of the Invention

[0004] In response to the above technical problems, the present invention proposes an intelligent safety protection system for ship construction, which can intelligently predict risks, dynamically adjust protection, and improve ship construction safety.

[0005] The present invention provides an intelligent safety protection system for ship construction, which is characterized in that it includes: wearable intelligent safety detection equipment, an operating environment detection and personnel management system, an AI perception prediction and evaluation module, and an integrated production safety detection platform; wherein: the multi-source data collected by the wearable intelligent safety detection equipment and the operating environment detection and personnel management system are input into the AI ​​perception prediction and evaluation module; the AI ​​perception prediction and evaluation module generates risk types and risk levels through cross-modal analysis; the integrated production safety detection platform is responsible for the transmission and interaction of the multi-source data, and generates decision signals for equipment control instructions according to the risk types and risk levels; drives the operating environment detection and personnel management system to dynamically adjust the safety protection area; and sends active protection instructions to the wearable intelligent safety detection equipment.

[0006] Furthermore, the wearable intelligent safety detection equipment includes: a smart safety helmet, a smart safety belt and a smart bracelet, and is configured to: collect at least part of the multi-source data through the multi-source wearable sensors built into the smart safety helmet, the smart safety belt and the smart bracelet, wherein the multi-source data includes personnel physiological, movement posture and environmental data; the working environment detection and personnel management system includes: an easy-to-deploy environment and status detection terminal, which collects at least other part of the multi-source data such as gas concentration, temperature and humidity, vital signs and audio and video data through multi-source environmental sensor fusion for limited spaces on ships, open safety protection areas and key protection areas in workshops; the AI ​​perception prediction and evaluation module uses edge computing unit cross-modal analysis based on the multi-source data to generate the risk type and risk level.

[0007] Furthermore, the working environment detection and personnel management system also includes a safety positioning and electronic fence subsystem; within the limited space of the ship, UWB ultra-wideband technology is used to achieve real-time personnel positioning and intrusion alarm; in the open / workshop protection area, RFID and photoelectric sensing are linked to trigger graded expulsion warnings.

[0008] Furthermore, the AI ​​perception prediction and evaluation module adopts a deep learning neural network method to detect and classify the multi-source data, adopts an offline data + difficult sample comprehensive learning method, supplemented by an integrated detection method combining software and hardware, to achieve real-time monitoring of personnel behavior and dangerous environments; establishes a real-time data stream monitoring and early warning model, based on behavioral action recognition and previous and next frame dynamic prediction models, uses a multi-scale fusion neural network to optimize the model generalization parameters, and combines edge computing equipment to achieve accurate identification of personnel behavior and the dangerous environment.

[0009] Furthermore, the AI ​​perception prediction and evaluation module also includes a high-precision AI model for perception of multi-source data in complex environments and an AI model for monitoring dangerous abnormal sound sources, which uses a single-model routing rule or a dual-model collaborative triggering mechanism to generate the risk type and the risk level.

[0010] Furthermore, the integrated production safety detection platform also collects the multi-source data for production safety monitoring based on multi-protocol data transmission technology, and realizes stable transmission and interaction of the multi-source data in a complex industrial environment based on a heterogeneous communication mechanism; the multi-source data is centrally processed through the AI ​​perception prediction and evaluation module, and the abnormal monitoring data is extracted in conjunction with the alarm rule model, and the risk type and the risk level are generated; the safety scheduling rules are matched to generate the decision signal of the equipment control instruction.

[0011] Furthermore, after the decision signal of the equipment control instruction is generated, it is also connected to the wearable intelligent protective equipment through a Bluetooth gateway to achieve comprehensive monitoring and warning of the working environment and the status of the operators.

[0012] Furthermore, it also includes forming alarm thresholds and data models based on historical data and industry specifications to generate decision signals for the equipment control instructions.

[0013] Furthermore, the easy-to-deploy environment and status detection terminal adopts a narrow space adaptive deployment configuration, and compensates for the signal attenuation caused by metal equipment blocking through the ultrasonic echo delay model of the multi-source environmental sensor, thereby solving the problem of blind spot monitoring in cabin corners.

[0014] Furthermore, the integrated production safety detection platform also dynamically draws a heat map of personnel trajectories based on the positioning data of the safety positioning and electronic fence subsystem, and traces back high-risk operation paths.

[0015] The beneficial effects of the present invention compared with the prior art are as follows: through the shipbuilding intelligent safety protection system of the present invention, a dynamic protection system of the entire chain of "people-environment-equipment" is constructed, and three major breakthrough effects are achieved through the collaboration of four major modules: Accurate risk prediction: Integrating multi-source data from wearable devices (personnel status) and environmental monitoring systems (operation scenarios), AI cross-modal analysis enables real-time identification of risk types and levels, transforming traditional passive protection into active early warning. Dynamic protection response: The safe production platform drives the environmental control system to automatically adjust the protection area based on risk instructions, and sends active protection instructions to personnel and equipment, realizing dynamic optimization of protection strategies based on risks; Integrated management efficiency: Break down information silos and complete the closed loop of data integration, decision-making, and command execution through a unified platform, significantly improving safety management and control efficiency in complex construction scenarios.

[0016] The ultimate goal is to achieve the comprehensive benefits of reduced construction accident rates, increased emergency response speed, and optimized safety management costs; providing full-coverage, adaptive, intelligent safety protection for ship construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The overall framework diagram of the intelligent safety protection system for ships of the present invention is provided; Figure 2 A data composition diagram of the shipbuilding intelligent safety protection system of the present invention; Figure 3 Data flow chart of the intelligent safety protection system for ship construction of the present invention. DETAILED DESCRIPTION

[0018] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0019] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature identified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0020] In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "connect," "fixed," etc. should be understood broadly. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediary; or internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0021] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0022] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0023] The present invention will be described in detail below with reference to the accompanying drawings: The present invention discloses an intelligent safety protection system for shipbuilding, such as Figure 1 As shown, four applications are used in three complex scenarios during the entire ship construction process: limited space on board, open safety protection areas, and key safety protection areas in workshops. Intelligent safety protection, operational safety monitoring, personnel management and AI identification are realized, forming two systems, seven types of equipment, two models and one platform.

[0024] Specifically, the two systems of the shipbuilding intelligent safety protection system of the present invention include wearable intelligent safety detection equipment and an operating environment detection and personnel management system. The multi-source data collected by the wearable intelligent safety detection equipment and the operating environment detection and personnel management system are input into an AI perception prediction and evaluation module. The AI ​​perception prediction and evaluation module includes a high-precision AI model for multi-source data perception in complex environments and an AI model for monitoring dangerous abnormal sound sources (two sets of models). The AI ​​perception prediction and evaluation module generates risk types and risk levels based on the specific types of multi-source data through cross-modal analysis. Finally, the integrated production safety detection platform (one platform) generates decision signals for equipment control instructions based on the risk type and risk level; drives the operating environment detection and personnel management system to dynamically adjust the safety protection area; and sends active protection instructions to the wearable intelligent safety detection equipment. The integrated production safety detection platform is responsible for the transmission and interaction of the multi-source data.

[0025] In an embodiment of the present invention, an AI perception, prediction, and assessment module responds to a multi-source data collaborative decision-making mechanism. Data from wearable devices (personnel status) and environmental systems (space status) are fed into the AI ​​model, generating a unified risk assessment (type + level) through cross-modal analysis. The integrated production safety monitoring platform uses this data to drive dynamic hardware protection. This improves response speed: from data collection to command execution, the entire process is ≤ 500ms. False alarm rates are reduced through multi-source cross-validation (for example, helmet impact data combined with cabin oxygen concentration to determine coma risk). This achieves a closed loop from "personnel status - space environment - AI decision - hardware execution," breaking the traditional fragmented nature of security subsystems.

[0026] In an embodiment of the present invention, wearable intelligent safety detection equipment is specifically embodied in seven types of equipment, including smart safety helmets, smart safety belts, and smart bracelets of the wearable intelligent safety protection and quality inspection equipment system, and easy-to-deploy environment and status detection terminals of the easy-to-deploy environment and status detection subsystem. The wearable intelligent safety detection equipment collects personnel and environmental data and transmits them to the complex environment multi-source data high-precision perception AI model and dangerous abnormal sound source monitoring AI model in the AI ​​perception prediction and evaluation module for analysis and processing, and then transmits the results to the integrated production safety detection platform to realize data aggregation and display, safety warning and reverse control of equipment.

[0027] In an embodiment of the present invention, the operating environment detection and personnel management system also includes an easily deployable environment and status detection subsystem and a safety positioning and electronic fence subsystem; the safety positioning and electronic fence subsystem includes personnel positioning equipment in closed cabins, high-precision personnel positioning electronic fence equipment in open areas, and personnel approach and intrusion alarm equipment in protection areas; for limited spaces on ships, open safety protection areas and key protection areas in workshops, multi-source data such as gas concentration, temperature and humidity, vital signs, and audio and video data are collected through the fusion of multi-source environmental sensors.

[0028] Furthermore, if Figure 2 As shown in the figure, multi-source data can be classified into personnel data, operation data and environmental data. Personnel data specifically includes behavioral data, health data and posture data; among them, health data includes heart rate, body temperature, movement status, etc.; environmental data includes lighting data, temperature and humidity data, gas composition data, noise data, and gas composition data may include oxygen data, flammable and explosive data, toxic gas data, etc.; operation data includes operation process data, equipment operation data, and operation execution data, among which equipment operation data includes equipment status and equipment parameters.

[0029] In an embodiment of the present invention, during the operation of the intelligent safety protection system for shipbuilding, data routing decisions follow the principle of scenario-driven classification and collaborative analysis, and are accurately allocated to a high-precision perception AI model for complex environment multi-source data (referred to as the perception model) or an AI model for monitoring dangerous abnormal sound sources (referred to as the sound source model) through a judgment mechanism. The specific logic is as follows (the following are some embodiments and do not limit other embodiments not mentioned): 1. Single Model Routing Rules Sound source model exclusive data Judgment conditions: The original data carries audio spectrum characteristics or equipment mechanical vibration characteristics; Data types: noise data in environmental data (such as high-frequency sound patterns of metal cutting > 10kHz) and equipment operation data in operation data (such as abnormal friction noise characteristics of main engine bearings); Scenario example: When an acoustic sensor in a sealed cabin captures the continuous 8-12kHz sound of metal fatigue crack expansion, the data packet is automatically labeled with an audio identifier, directly connected to the sound source model for wavelet packet energy entropy analysis, and outputs a "structural damage risk" conclusion.

[0030] Perception model-specific data Judgment conditions: The data has spatial correlation and non-audio characteristics; Data Type: Personnel data: behavioral data (helmet posture angle > 45°), health data (wristband heart rate > 150bpm), posture data (UWB positioning coordinates); Environmental data: gas composition (methane concentration > 1% LEL), lighting data (arc intensity > 10,000 lux), temperature and humidity (temperature rise rate > 3°C / min); Job data: operation execution data (welding mask not worn event); Example scenario: The tension sensor on the safety belt of a worker working at height returns 12.8 kN exceeding the limit (numeric value). This is combined with the UWB positioning speed > 1.5 m / s motion feature to trigger a climbing and falling risk assessment.

[0031] 2. Dual-model collaborative triggering mechanism Judgment conditions: There are cross-modal risk coupling characteristics or sound source events that require spatial verification.

[0032] Sound source events trigger spatial perception (the sound source model triggers the perception model): When the sound source model identifies that earmuffs are not worn during polishing work (for example, continuous exposure to 6.8kHz), the video analysis thread of the perception model is automatically activated, the nearest camera is called to focus on the direction of the sound source, and the earmuffs are detected.

[0033] Output compound decision: "illegal operation + hearing damage risk".

[0034] Environmental anomalies activate voiceprint verification (the perception model triggers the sound source model): When the perception model detects a benzene concentration of 0.9ppm (dynamic threshold 0.8ppm) in the paint shop, it sends a gas leak voiceprint matching request to the sound source model. The sound source model scans the "hissing" leakage characteristics in the audio stream (such as 500-800Hz broadband noise) and collaboratively outputs: "Toxic gas leakage + diffusion risk."

[0035] Through specialized division of labor and cross-modal verification, the dual-model collaborative architecture offers three core advantages in shipbuilding safety protection: First, the acoustic model focuses on processing audio stream features (such as equipment noise or leak soundprints), while the perception model integrates visual and environmental parameters (such as personnel posture and gas concentration). This division of labor significantly reduces the computational complexity of a single model. Second, when the acoustic model detects an abnormal sound (such as the sound of metal fatigue crack propagation), it automatically triggers the perception model to review the spatial state (such as personnel location and equipment operating conditions), forming a multi-source evidence chain and improving the reliability of risk assessment. Third, the modular design facilitates the expansion of additional sensor data streams (such as vibration spectrum incorporating into the acoustic model and temperature and humidity fluctuations being rerouted to the perception model), enhancing the system's adaptability to complex industrial scenarios. This architecture essentially decouples heterogeneous data types and establishes collaborative triggering logic, ensuring real-time performance while strengthening the robustness of risk identification.

[0036] During the data collection phase, visual and audio sensors are used to comprehensively collect visual and audio data related to personnel behavior, posture, and hazardous environments within the vessel's confined space. The sensors transmit this hazardous environment data in real time to the AI ​​model. Upon receiving the data, the AI ​​model uses the YOLO object detection and SORT multi-target tracking algorithms to accurately detect and track targets in the visual data, quickly identifying information such as the location and posture of the operator. Simultaneously, a transform algorithm for abnormal sound detection analyzes and identifies abnormal sounds in the audio data, such as those caused by equipment failure or dangerous operations. For operator behavior data, the AI ​​model uses specialized extraction techniques combined with a confidence calculation method to conduct an in-depth analysis of personnel behavior, determining whether the operator has engaged in any illegal operations, the compliance with regulations, and the safety of their behavior. During this process, the AI ​​model integrates visual, audio, and other multi-source sensor data based on a hardware-software integrated deep learning and sensor fusion model. Through the model's deep learning and reasoning, the AI ​​model achieves comprehensive, coordinated, and accurate multi-dimensional visual and audio detection and identification within the vessel's confined space, providing reliable assurance for safe vessel operations.

[0037] It should be noted that the multi-source sensors of the present invention include multi-source wearable sensors and multi-source environmental sensors; the multi-source wearable sensors are built into wearable intelligent safety detection equipment such as the smart safety helmet, the smart safety belt and the smart bracelet; and the multi-source environmental sensors are deployed in the working environment detection and personnel management system.

[0038] In an embodiment of the present invention, the smart helmet is integrated with a pressure sensor. When encountering an unexpected collision, it can quickly sense and feedback the information in a timely manner, buying valuable time for subsequent medical treatment; the smart safety belt not only ensures the physical fixation of the workers when working at heights, but also monitors its own stress state in real time. Through the built-in pressure sensor, the tension borne by the safety belt is accurately sensed. Once it exceeds the safety threshold, an alarm is immediately issued to remind the workers and managers of potential dangers. In addition, it can also combine the personnel's motion sensor data to determine whether the workers are in an unstable working posture and give corrective prompts in time; the health monitoring bracelet focuses on monitoring the physical health of the workers. It can continuously and in real time collect key physiological parameters such as heart rate, blood pressure, and body temperature. When an abnormal increase in heart rate is detected or blood pressure fluctuations exceed the normal range, an alarm is quickly issued so that appropriate medical measures can be taken in time. At the same time, it can also record the number of steps, activity intensity and other data of the workers to provide a basis for evaluating the fatigue level of the workers; Based on multi-source data such as environmental parameters, personnel movement and physiological parameters collected by wearable equipment, the data quality is improved through multi-sensor data fusion algorithms, and data mining methods are used to analyze data to predict dangerous areas, behaviors and personnel health and fatigue conditions. An anomaly detection model based on machine learning and deep learning is constructed to identify various anomalies and provide support for safety decision-making.

[0039] Wearable alarm and abnormal data interaction technology focuses on identifying irregular wear and abnormal operations. It uses image recognition and sensor data analysis to monitor equipment wearing conditions and identifies abnormal operations based on operational specifications. It uses a variety of alarm methods to alert operators. It also establishes an efficient abnormal data interaction mechanism, pushing abnormal data in real time to operator terminals and management monitoring platforms. The platform triggers emergency responses based on rules to ensure the safety of shipbuilding personnel.

[0040] In the embodiments of the present invention, the work environment detection and personnel management system can solve the safety problems existing in multi-scenario safety protection zones, such as difficulty in identifying personnel status, difficulty in tracking movements, difficulty in isolating areas, difficulty in identifying hazard sources, and difficulty in environmental monitoring. The easy-to-deploy environmental and status detection subsystem integrates multiple sensors, including gas, temperature, audio, and video, enabling quick and easy deployment at the work site. This subsystem collects multi-dimensional environmental data in real time and leverages advanced data analysis techniques to accurately identify hazards within the work environment, such as hazardous gas leaks and temperature anomalies. Furthermore, multi-sensor data fusion algorithms effectively improve data accuracy, providing a reliable basis for environmental monitoring. The collected personnel data, analyzed through multi-scenario personnel management and protective configuration design, helps address the challenges of personnel status identification and movement statistics, enabling real-time monitoring of personnel status and accurate tracking of movement. The security positioning and electronic fence subsystem, leveraging UWB ultra-wideband wireless communication, Bluetooth, and RFID wireless carrier communication technologies, is developing technologies for personnel positioning in confined spaces, personnel intrusion detection in open areas, and personnel approach warning in key laboratories. Research is underway on models for personnel positioning and intrusion warning, resulting in a series of virtual electronic fence systems. This research, based on electronic fences and UWB wireless carriers, effectively enables personnel positioning and area control.

[0041] Furthermore, within the limited space of the ship, UWB ultra-wideband technology is used to achieve real-time positioning of personnel and intrusion alarms; in open / workshop protection areas, RFID and photoelectric sensing are linked to trigger graded expulsion warnings.

[0042] Through the embodiments of the present invention, differentiated positioning and protection strategies offer three core advantages tailored to the specific characteristics of shipbuilding scenarios: First, within confined spaces with complex electromagnetic environments, UWB technology, leveraging nanosecond pulse signals to penetrate metal structures and resist multipath interference, achieves high-precision positioning, effectively resolving the positioning failure issues caused by signal attenuation in confined compartments associated with traditional Bluetooth / RFID technology. Second, in open areas, RFID batch recognition and photoelectric sensing are linked, creating a physical-virtual dual barrier through tag scanning combined with laser boundary projection, balancing the economic viability of large-scale monitoring with the real-time nature of intrusion response. Finally, a hierarchical response mechanism (e.g., wristband vibration → virtual boundary measurement → acoustic alarm) avoids excessive interference with normal operations and significantly improves the operability of protective measures. Essentially, through technical adaptation and hierarchical response design, two major industry challenges in shipbuilding, namely, reliable positioning in confined spaces and cost-effective protection in open areas, have been simultaneously addressed.

[0043] In an embodiment of the present invention, the AI ​​perception prediction and evaluation module adopts a deep learning neural network method to detect and classify the multi-source data, adopts an offline data + difficult sample comprehensive learning method, supplemented by an integrated detection method combining software and hardware, to achieve real-time monitoring of personnel behavior and dangerous environments; establishes a real-time data stream monitoring and early warning model, based on behavioral action recognition and previous and next frame dynamic prediction models, uses a multi-scale fusion neural network to optimize the model generalization parameters, and combines edge computing equipment to achieve accurate identification of personnel behavior and the dangerous environment.

[0044] The AI ​​perception prediction and evaluation module achieves precise monitoring of high-risk ship scenarios through multi-stage technology integration: first, it adopts offline basic training and incremental learning of difficult samples (such as adversarial generation of false detection samples of mask detachment under strong light), combined with a multi-scale spatiotemporal fusion network to synchronously process wide-angle global perspective and helmet first-person image, integrates different resolution features through spatial pyramid pooling, and uses temporal convolution to analyze the continuity of climbing actions; secondly, relying on the collaborative software and hardware architecture, a lightweight YOLO model is deployed at the edge of the helmet to realize mask wearing detection, the regional base station runs the 3D skeleton algorithm to calculate the center of gravity offset, and the cloud-based collaborative voiceprint analysis completes multimodal decision-making; while ensuring the accuracy of behavior recognition, the edge energy consumption is reduced, significantly overcoming the industry bottlenecks of detection delay and generalization attenuation in the complex environment of ships.

[0045] like Figure 3As shown, it is a data flow chart of the intelligent safety protection system for shipbuilding according to an embodiment of the present invention. At the shipbuilding site, multi-source data is collected and transmitted through wearable intelligent safety detection equipment and the working environment detection and personnel management system. Data processing and analysis are performed in the AI ​​perception prediction and evaluation module to generate risk types and risk levels. Early warning judgment is performed on the integrated production safety detection platform. For risk-free data, detection continues. For risky data, an early warning mechanism is triggered and an emergency response is initiated. Data is stored on the integrated production safety detection platform to form a safety detection database. Subsequently, data analysis, display, decision-making, support and other data applications can be performed based on the current time period or historical time period data. Finally, production safety measures are optimized based on data applications (such as data analysis and decision-making).

[0046] In an embodiment of the present application, the integrated production safety detection platform can realize the data fusion and transmission of various monitoring equipment (environmental monitoring terminals, wearable protective equipment, etc.); in terms of technical implementation, it realizes the stable transmission and interaction of multi-source data in a complex industrial environment based on the heterogeneous communication mechanism; through the AI ​​perception prediction and evaluation module, multi-source data is centralized and processed, and in conjunction with the alarm rule model, abnormal monitoring data is extracted and generated including risk type and risk level; matching the safety scheduling rules, a decision signal of the equipment control instruction is generated. The heterogeneous communication mechanism (heterogeneous platform) data interaction protocol is designed to achieve smooth data interaction between various monitoring equipment (such as detection equipment based on multi-sensor Internet of Things, wearable smart protective equipment, etc.) and the integrated safety production monitoring platform; the data such as personnel behavior, posture and dangerous environment collected by these equipment are accurately transmitted to the platform through the developed heterogeneous communication mechanism data interaction protocol; through research to form alarm thresholds and data models, the data transmitted to the platform are analyzed and judged; when the monitoring data reaches the pre-set alarm threshold, the platform responds quickly according to the data model; at the same time, when the alarm is triggered, the early warning and emergency response technology platform can quickly start the early warning and emergency response according to the established technical route. An alarm mechanism is established, and precise guidance is provided for emergency response. A functional architecture for comprehensive collection and analysis of personnel, environment and operations is established, and data related to personnel, environment and operations from different equipment is integrated. A safety monitoring database system is constructed to store and manage various monitoring data, and provide data support for subsequent analysis. Based on this, multi-dimensional safety data analysis, display and alarm response functions are formed, so that the platform can not only meet the needs of normalized safety management and control, and monitor the safety status of the ship construction process in real time, but also provide strong support for emergency response business when safety accidents occur, and finally realize the close coordination between the integrated safety production monitoring platform and various monitoring equipment, and comprehensively improve the level of safety production protection in the ship construction process.

[0047] In the context of intelligent safety protection systems for shipbuilding, heterogeneous communication mechanisms refer to multi-protocol converged communication systems designed to adapt to complex industrial environments. Their core purpose is to solve transmission challenges that cannot be addressed by a single communication technology.

[0048] The essential difference from ordinary multimode communication.

[0049] Ordinary multi-mode communication: simple protocol stacking, requiring manual switching, each protocol works independently without coordination; The heterogeneous communication mechanism of this system includes: dynamic scheduling protocol of intelligent decision-making center (such as selecting transmission path according to data priority), data mutual verification between protocols (such as UWB positioning and Bluetooth RSSI combined to resist deception attacks), and energy collaborative management (the bracelet's low-power Bluetooth wakes up the 5G module and transmits only in high-risk events).

[0050] In an embodiment of the present invention, the integrated production safety detection platform can also form alarm thresholds and data models based on historical data and industry specifications to generate decision signals for equipment control instructions.

[0051] In an embodiment of the present invention, the integrated production safety detection platform realizes precise risk control in the shipbuilding scenario by constructing a dynamic alarm threshold system and a data-driven decision-making model: the platform integrates the historical accident database (such as the environmental parameters and equipment status records of 200 high-altitude fall incidents in a shipyard in the past five years) and industry safety regulations, uses time series analysis to mine accident precursor characteristics (such as the displacement acceleration pattern 10 seconds before the sudden increase in seat belt tension), and establishes a multivariate coupled risk probability model based on the Bayesian network; when the real-time monitoring data stream is input, the system first dynamically adjusts the threshold according to the environmental conditions (for example, in the closed cabin hypoxia warning, the threshold is set to 19.5% oxygen concentration in summer and 2% in winter). 0.2% to compensate for ventilation differences), and then match the preset decision rule tree (for example, when the welding mask is detected to be off + the arc light intensity exceeds the standard, a three-level response is automatically triggered: wristband vibration warning → activation of the safety helmet sunshade → cutting off the welding machine power); this mechanism significantly improves the effectiveness of the alarm, avoiding false alarms caused by fixed thresholds (such as a 57% reduction in the misjudgment rate of conventional temperature rise caused by high temperature weather) and missed alarms (such as an 82% increase in the detection rate of early vibration characteristics of fatigue fracture of lifting rigging). At the same time, through the direct connection between the decision signal and the hardware control system (such as the real-time scaling of the electronic fence radius with the risk level), a closed-loop management from risk perception to physical intervention is formed, ultimately achieving the forward shift of the accident prevention checkpoint while ensuring the continuity of operations.

[0052] In an embodiment of the present invention, the easy-to-deploy environment and status detection terminal also adopts a narrow space adaptive deployment configuration, and compensates for the signal attenuation caused by metal equipment blocking through the ultrasonic echo delay model of the multi-source environmental sensor, thereby solving the problem of blind spot monitoring in cabin corners.

[0053] The easy-to-deploy environmental and status detection terminal overcomes the challenge of monitoring confined spaces on ships through its innovative configuration design: for traditional blind spots such as cabin corners and curved bulkheads, the terminal adopts a modular magnetic universal bracket structure and is equipped with a deformable articulated arm (6 degrees of freedom adjustment), allowing the sensor array to fit tightly to the hull structure with a curvature radius of 0.6 meters; based on a multi-source ultrasonic sensor network, a dedicated echo attenuation model for metal environments is established, and the signal strength is dynamically calibrated through a time-delay compensation algorithm to effectively offset signal distortion caused by metal obstacles such as pipelines and beams; this solution significantly eliminates monitoring blind spots and provides blind-spot-free safety monitoring for high-risk areas in ship construction.

[0054] In an embodiment of the present application, the integrated production safety monitoring platform also dynamically creates a heat map of personnel trajectories based on the positioning data from the safety positioning and electronic fencing subsystems, allowing for the retrieval of high-risk work paths. This integrated production safety monitoring platform uses personnel trajectory heat mapping technology to achieve refined management and control of high-risk shipbuilding operations. Based on the high-precision UWB location data stream provided by the safety positioning subsystem, the platform dynamically generates heat maps using a kernel density estimation algorithm, visually displaying hotspots of personnel activity using a color gradient (e.g., red indicates high-frequency paths, blue indicates low-activity areas). For confined space operations, the system automatically associates risk levels with electronic fencing-defined zones (e.g., flammable areas in paint shops and the edges of aerial work platforms). When a continuous trajectory is detected crossing a red high-risk zone (e.g., a worker approaches an unprotected porthole seven times within two hours), the retrospective analysis module is automatically triggered. This replays the work process through a timeline to identify illegal operating patterns (e.g., crossing restricted areas without following the prescribed path). This information allows managers to optimize operating procedures (e.g., adjust channel settings, add guardrails), and provide targeted safety training for individuals. This technology converts discrete positioning data into spatial risk distribution insights, significantly improving the ability to trace hidden dangers, providing data support for preventive safety management, and effectively reducing accident risks caused by unreasonable path planning or illegal operations.

[0055] Through the embodiments of the present invention, we address the challenges of high safety requirements for personnel working in complex working conditions such as confined spaces, open spaces, and key areas of workshops during shipbuilding, as well as the difficulties in identifying hazards and integrating multi-information for safety warning and control. We are conducting technical research on wearable intelligent safety detection equipment for multi-scenario applications, work environment detection and personnel control systems, and an integrated platform for AI perception, prediction, and evaluation, along with production safety monitoring, based on multi-sensor IoT. We are also pursuing breakthroughs in key technologies such as interactive fusion of heterogeneous multi-sensor data and edge computing, multi-target detection and tracking algorithms based on YOLO and SORT, positioning based on UWB ultra-wideband wireless carrier communication, and the construction and integrated application of a safety information database. We are developing engineering prototypes for wearable intelligent protective equipment for personnel (including helmets, safety belts, and smart wristbands), easily deployable environmental and status monitoring terminals (combining multi-sensor information collection, personnel positioning base stations, and edge computing), and dynamic electronic fences. Furthermore, we are developing a software system for an integrated safety detection and production platform. This system enables online data collection of personnel and environments in multiple scenarios, real-time identification, location, and warning of hazards, and comprehensive management and analysis of safety data. Effectively improve the ability to predict and prevent safety risks in ship construction, and provide equipment and system guarantees for the intelligent development of ship safety production.

[0056] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform, or the corresponding software can be implemented through a hardware platform.

[0057] Those skilled in the art will understand that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily required to implement the present application. Those skilled in the art will understand that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the implementation scenario description, or can be changed accordingly and located in one or more devices different from the implementation scenario. The modules of the above-mentioned implementation scenario can be combined into one module, or can be further split into multiple sub-modules.

[0058] The serial numbers of the above application are for descriptive purposes only and do not represent the advantages or disadvantages of the implementation scenarios. The above disclosure only discloses several specific implementation scenarios of the present application, but the present application is not limited thereto. Any changes that can be conceived by those skilled in the art should fall within the scope of protection of the present application.

Claims

1. An intelligent safety protection system for shipbuilding, characterized in that: include: Wearable intelligent safety detection equipment, working environment detection and personnel management system, AI perception prediction and evaluation module, and integrated production safety detection platform; including: The multi-source data collected by the wearable intelligent safety detection equipment and the working environment detection and personnel management system is input into the AI ​​perception prediction and evaluation module; The AI ​​perception prediction and assessment module generates risk types and risk levels through cross-modal analysis; The integrated production safety detection platform is responsible for the transmission and interaction of the multi-source data, and generates decision signals for equipment control instructions based on the risk type and risk level; drives the working environment detection and personnel management system to dynamically adjust the safety protection area; and sends active protection instructions to the wearable intelligent safety detection equipment.

2. The intelligent safety protection system for shipbuilding according to claim 1 is characterized in that: The wearable intelligent safety detection equipment includes: a smart helmet, a smart safety belt and a smart wristband, which are configured as follows: Collecting at least part of the multi-source data through the multi-source wearable sensors built into the smart helmet, the smart safety belt, and the smart bracelet, wherein the multi-source data includes physiological data, motion posture data, and environmental data of the person; The working environment detection and personnel management system includes: an easy-to-deploy environment and status detection terminal, which collects at least other parts of the multi-source data such as gas concentration, temperature and humidity, vital signs, audio and video data through multi-source environmental sensor fusion for confined spaces on ships, open safety protection areas and key protection areas in workshops; the AI ​​perception prediction and evaluation module uses edge computing unit cross-modal analysis based on the multi-source data to generate the risk type and the risk level.

3. The intelligent safety protection system for shipbuilding according to claim 2 is characterized in that: The working environment detection and personnel control system also includes a safety positioning and electronic fence subsystem; In the limited space of the ship, UWB ultra-wideband technology is used to achieve real-time personnel positioning and intrusion alarm; In the open / workshop protection area, RFID and photoelectric sensing are linked to trigger graded expulsion warnings.

4. The intelligent safety protection system for shipbuilding according to claim 2, characterized in that: The AI ​​perception prediction and evaluation module adopts a deep learning neural network method to detect and classify the multi-source data, adopts an offline data + difficult sample comprehensive learning method, supplemented by an integrated detection method combining software and hardware, to achieve real-time monitoring of personnel behavior and dangerous environments; establishes a real-time data stream monitoring and early warning model, based on behavioral action recognition and previous and next frame dynamic prediction models, uses a multi-scale fusion neural network to optimize the model generalization parameters, and combines edge computing equipment to achieve accurate identification of personnel behavior and the dangerous environment.

5. The intelligent safety protection system for shipbuilding according to any one of claims 1 to 4, characterized in that: The AI ​​perception prediction and evaluation module also includes a high-precision perception AI model for multi-source data in complex environments and an AI model for monitoring dangerous abnormal sound sources, which uses a single-model routing rule or a dual-model collaborative triggering mechanism to generate the risk type and the risk level.

6. The intelligent safety protection system for shipbuilding according to claim 1 is characterized in that: The integrated production safety detection platform also collects multi-source data for production safety monitoring based on multi-protocol data transmission technology, and realizes stable transmission and interaction of multi-source data in complex industrial environments based on heterogeneous communication mechanisms; The multi-source data is centrally processed through the AI ​​perception prediction and evaluation module, and the abnormal monitoring data is extracted in conjunction with the alarm rule model to generate the risk type and the risk level; the safety scheduling rules are matched to generate the decision signal of the equipment control instruction.

7. The intelligent safety protection system for shipbuilding according to claim 6, characterized in that: After the decision signal of the equipment control instruction is generated, it is also connected to the wearable intelligent protective equipment through a Bluetooth gateway to achieve comprehensive monitoring and warning of the working environment and the status of the operators.

8. The intelligent safety protection system for shipbuilding according to claim 6, characterized in that: It also includes forming alarm thresholds and data models based on historical data and industry specifications, and generating decision signals for the equipment control instructions.

9. The intelligent safety protection system for shipbuilding according to claim 2, characterized in that: The easy-to-deploy environment and status detection terminal adopts a narrow space adaptive deployment configuration, and compensates for the signal attenuation caused by metal equipment blocking through the ultrasonic echo delay model of the multi-source environmental sensor, thereby solving the problem of blind spot monitoring in cabin corners.

10. The intelligent safety protection system for shipbuilding according to claim 3, characterized in that: The integrated production safety detection platform also dynamically draws a heat map of personnel trajectories based on the positioning data of the safety positioning and electronic fence subsystem, and traces back high-risk operation paths.

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