A method, system, terminal, and storage medium for typhoon early warning of ships and large equipment in shipyards based on contingency plan activation and rule matching scanning.

Abstract

This invention discloses a method, system, terminal, and storage medium for typhoon early warning of ships and large equipment in shipyards based on contingency plan activation and rule matching scanning. The method includes: collecting multi-source key parameters through a shipyard multi-dimensional typhoon early warning management platform; filtering valid data through rule matching scanning; sequentially calling a contingency plan hierarchical triggering model, a wind load nonlinear calculation model, and a ship mooring force analysis algorithm to complete early warning threshold generation, wind load calculation, and mooring force analysis; the collaborative linkage of various models to explore parameter coupling relationships; and optimizing the calculation process step by step through indicator system construction, feature parameter extraction, multi-source data substitution, and result verification; the platform integrates the results of multiple modules to generate corresponding level early warning signals and emergency guidance. This invention achieves precise and efficient typhoon early warning, providing full-process technical support for typhoon prevention of ships and large equipment in shipyards, and improving the operability of early warning and risk management capabilities.

Description

Technical Field

[0001] This invention relates to the field of typhoon prevention and early warning technology for ships and large equipment, and in particular to a method, system, terminal and storage medium for typhoon prevention and early warning of ships and large equipment in shipyards based on pre-plan activation and rule matching scanning. Background Technology

[0002] Shipyards, as the core sites for ship construction and berthing, are located in waters frequently exposed to severe convective weather such as typhoons. Due to their large size, fixed berthing status, and complex structures, ships and large equipment are prone to safety risks such as mooring failure and structural damage under strong wind loads, directly threatening personnel safety and property interests. Traditional typhoon early warning systems rely on single-parameter monitoring and manual experience judgment, making it difficult to integrate multi-dimensional data such as meteorological conditions, ship status, equipment structure, and aquatic environment. Furthermore, with the increasing tonnage of ships and the growing automation of large equipment, higher demands are placed on the accuracy, real-time performance, and tiered response capabilities of typhoon early warning systems. There is an urgent need to construct an integrated solution that combines multi-source parameter acquisition, professional model calculation, and intelligent early warning management to provide full-process technical support from parameter acquisition to early warning output, meeting the shipyard's needs for refined typhoon emergency management.

[0003] Existing technologies suffer from two key drawbacks: First, the accuracy of parameter fusion and rule matching is insufficient. They often rely on single-dimensional data monitoring or simple linear calculations, failing to fully integrate the coupling relationship between the characteristics of ship mooring systems, the structural parameters of large equipment, and the dynamic changes in marine weather. This results in a lack of scientific rigor in setting warning thresholds, making it difficult to accurately reflect the actual typhoon risk level. Second, the coordination between model calculation and warning response is lacking. A closed-loop linkage mechanism has not been established for tiered triggering of contingency plans, wind load calculation, mooring stress analysis, and warning generation. Furthermore, a unified multi-dimensional typhoon warning management platform has not been formed, leading to data fragmentation across different stages. This makes it impossible to quickly integrate calculation results and output targeted emergency operation guidelines, affecting the timeliness and practicality of typhoon warnings. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of the existing technology, the present invention provides a method, system, terminal and storage medium for typhoon prevention early warning of shipyard vessels and large equipment based on pre-plan activation and rule matching scanning.

[0005] The technical solution adopted in this invention is a typhoon early warning method for shipyard vessels and large equipment based on contingency plan activation and rule matching scanning, comprising the following steps: S1, collecting real-time meteorological parameters of the sea area where the shipyard is located, vessel berthing location parameters, fixed structure parameters of large equipment, and historical typhoon emergency data through a shipyard multi-dimensional typhoon early warning management platform to establish a multi-source parameter collection library; S2, performing rule matching scanning on the multi-source parameters in the collection library based on a preset typhoon prevention rule library, filtering calibration parameters related to typhoon early warning, and eliminating invalid interference data; S3, calling the contingency plan graded triggering simulation model, and constructing a graded triggering indicator system by combining the filtered calibration parameters, and through multiple... S4: Dimensional parameter weighted calculation generates the trigger threshold range for the contingency plan; S5: Activate the nonlinear calculation model for wind load, inputting the ship's draft, equipment windward area, structural stiffness parameters, and real-time wind speed and direction parameters to complete the nonlinear calculation of the wind load on the ship and large equipment; S6: Utilize the ship mooring force analysis algorithm, integrating the tensile strength parameters of mooring cables, ship displacement parameters, and current velocity parameters of the mooring area to calculate the real-time force state of the mooring system; S7: Integrate the S3 graded triggering results, S4 wind load calculation results, and S5 mooring force analysis results through the shipyard's multi-dimensional typhoon warning management platform to generate corresponding level typhoon warning signals and emergency operation guidelines.

[0006] Furthermore, the expression for the tiered triggering simulation model of the contingency plan is as follows: ,in, For the tiered trigger coefficient of the contingency plan, As a weighting factor for meteorological parameters, For the first Measured values ​​of calibration parameters For the first Class labeling parameters affect weights. This is a correction factor for historical data. For ship parameter sensitivity coefficient, For the parameters of ship berthing dispersion, For the structural safety factor of large equipment, For sea area water depth parameters, As an emergency response correction factor, The frequency of activation of historical typhoon prevention plans;

[0007] Furthermore, the expression for the nonlinear calculation model of wind load is: ,in, This represents the comprehensive value of wind loads on ships and large equipment. This is the wind load shape coefficient. This refers to the windward projected area parameter. For real-time wind speed parameters, This is the wind direction correction factor. The angle between the wind direction and the equipment axis is a parameter. This is the structural damping coefficient. These are the bending stiffness parameters of the equipment structure. For the equipment's windward height parameter, This refers to the amplitude parameter of wind speed fluctuations.

[0008] Furthermore, the expression for the ship mooring force analysis algorithm is as follows: ,in, This represents the total force on the mooring system. The elastic coefficient of the cable. These are the initial tension parameters of the cable. The parameter is the angle between the cable and the horizontal direction. The coefficient of water flow resistance. For water flow velocity parameters, For the ship's inertia coefficient, For ship displacement parameters, For the water depth parameters of the mooring area, For ship length parameters, This is the mooring angle correction factor. This is a parameter representing the change in the ship's yaw angle;

[0009] Furthermore, the parameter fusion model expression of the shipyard's multi-dimensional typhoon early warning management platform is as follows: ,in, The result of multi-source parameter fusion. , , , , , Output weight coefficients for each module. This refers to the platform's data transmission latency parameter. For parameter acquisition accuracy parameters, for The real-time data update rate parameter at any given moment. This is the data collection duration parameter.

[0010] Furthermore, the comprehensive evaluation model expression for the typhoon warning parameters of the shipyard's vessels and large equipment is as follows: ,in, The comprehensive assessment value for typhoon prevention and early warning. To comprehensively evaluate the weighting coefficients, This is a correction factor for the warning level. For equipment safety redundancy factor, For the first Structural safety reserve parameters for this type of equipment For ship berthing stability parameters, For the complexity parameters of the marine environment, The number of equipment types participating in the evaluation.

[0011] Further, S3 includes the following sub-steps: S31, based on the structural characteristics, berthing status, and marine environment differences of shipyard vessels and large equipment, meteorological intensity parameters, equipment wind resistance level parameters, ship mooring reliability parameters, and historical emergency response effectiveness parameters are extracted from multi-source collected parameters to construct a set of basic indicators for graded triggering; S32, the importance of each parameter in the basic indicator set is ranked using the analytic hierarchy process (AHP), and the weight allocation value of each indicator is determined by combining expert scores to form a weighted indicator system; S33, each parameter in the weighted indicator system is substituted into the graded triggering simulation model of the contingency plan, and trigger threshold intervals corresponding to different warning levels are generated through multi-dimensional parameter coupling calculations to clarify the activation conditions of each level of the contingency plan; S34, based on the matching results of real-time collected parameters and threshold intervals, the corresponding contingency plan level is preliminarily determined to provide a graded basis for the subsequent generation of warning signals.

[0012] Further, step S4 includes the following sub-steps: S41, retrieving the ship's draft, hull cross-sectional area, windward shape parameters of large equipment, and elastic modulus parameters of structural materials from the shipyard's multi-dimensional typhoon early warning management platform as the basic input parameters for wind load calculation; S42, performing time series analysis on real-time wind speed and direction parameters, extracting peak wind speed, average wind speed, and wind direction change rate calibration characteristic parameters to eliminate the influence of instantaneous wind speed fluctuations on the calculation results; S43, substituting the basic input parameters and wind speed and direction characteristic parameters into the wind load nonlinear calculation model, and solving the wind load distribution values ​​of each calibrated stress-bearing part of the ship and large equipment through iterative calculation; S44, performing spatial mapping processing on the calculated wind load distribution values, converting them into a data format compatible with the equipment structure stress analysis, providing data support for subsequent structural safety assessment.

[0013] Further, S5 includes the following sub-steps: S51, collecting the material tensile strength, diameter, and length parameters of the mooring cables, as well as the mooring point layout parameters, to establish a basic parameter library for the mooring system; S52, acquiring the real-time flow velocity and direction parameters of the mooring water area, and the ship's displacement, draft, and center of gravity height parameters, to construct an environmental and ship parameter system for mooring stress analysis; S53, substituting the data from the basic parameter library and parameter system into the ship mooring stress analysis algorithm to calculate the tension distribution of the mooring cables, the ship's displacement, and the yaw angle; S54, validating the calculation results, removing abnormal data that exceeds the physical constraints, and ensuring the rationality of the mooring stress analysis results.

[0014] A typhoon early warning system for shipyard vessels and large equipment based on contingency plan activation and rule-matching scanning is disclosed. This system, applied to a typhoon early warning method for shipyard vessels and large equipment based on contingency plan activation and rule-matching scanning, includes: a multi-source parameter intelligent acquisition and filtering unit, used to collect meteorological parameters of the shipyard's sea area, vessel status parameters, large equipment structural parameters, and historical typhoon data through various sensors and data interfaces, and to filter and calibrate parameters based on preset rules; a contingency plan hierarchical triggering and calculation unit, connected to the multi-source parameter intelligent acquisition and filtering unit, which calls the contingency plan hierarchical triggering and calculation model, and generates a contingency plan triggering threshold range based on the filtered calibrated parameters; and a wind load nonlinear calculation unit, connected to the multi-source parameter intelligent acquisition and filtering unit, which receives vessel and equipment structural parameters and meteorological parameters, and... The system employs a nonlinear wind load calculation model to perform wind load calculations. A ship mooring stress analysis unit, connected to a multi-source parameter intelligent acquisition and filtering unit, integrates mooring system parameters, ship parameters, and aquatic environment parameters, calculating the mooring stress state using a ship mooring stress analysis algorithm. A multi-dimensional data fusion and early warning signal generation unit, connected to the pre-plan tiered triggering and simulation calculation unit, the wind load nonlinear calculation unit, and the ship mooring stress analysis unit, integrates the output results of each unit to generate corresponding typhoon early warning signals and emergency operation guidelines. A system data storage and interaction unit, connected to each unit, stores the collected raw data, intermediate calculation results, early warning signals, and emergency guidelines, while also interacting with the shipyard's existing management system to support the real-time transmission and sharing of early warning information.

[0015] A terminal device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a typhoon early warning method for shipyard vessels and large equipment based on pre-plan activation and rule matching scanning.

[0016] A computer-readable storage medium storing a computer program that, when executed by a processor, implements a typhoon early warning method for shipyard vessels and large equipment based on pre-plan activation and rule-matching scanning.

[0017] Beneficial Effects: This invention proposes a method, system, terminal, and storage medium for typhoon early warning of shipyard vessels and large equipment based on contingency plan activation and rule-matching scanning. It integrates multi-dimensional data such as meteorological data, vessel status, equipment structure, and aquatic environment. Through professional model linkage calculations involving contingency plan tiered triggering, nonlinear wind load calculation, and vessel mooring force analysis, it fully considers the coupling relationships between various parameters, making the early warning threshold setting more closely aligned with actual risk scenarios and significantly improving the scientific rigor and accuracy of risk level determination. Simultaneously, by constructing a full-process closed-loop linkage mechanism through a shipyard multi-dimensional typhoon early warning management platform, it breaks through the limitations of data fragmentation in traditional technologies, achieving integrated processing from parameter collection, filtering, model calculation to early warning generation and emergency guidance output. This significantly enhances the synergy between model calculation and early warning response, rapidly integrating multi-module calculation results to form targeted operational guidelines. It effectively compensates for the shortcomings of existing technologies in terms of timeliness and practicality, providing full-process, refined technical support for typhoon prevention of shipyard vessels and large equipment, comprehensively improving the accuracy, real-time nature, and operability of typhoon early warning, and providing reliable protection for personnel safety and property interests. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the overall process of the method of the present invention.

[0019] Figure 2 This is a flowchart of method step S3 of the present invention;

[0020] Figure 3 This is a flowchart of method step S4 of the present invention;

[0021] Figure 4 This is a flowchart of step S5 of the method of the present invention.

[0022] Figure 5 This is a diagram showing the system unit composition of the present invention. Detailed Implementation

[0023] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] like Figure 1 As shown, a typhoon early warning method for shipyard vessels and large equipment based on contingency plan activation and rule matching scanning includes the following steps:

[0025] S1, through the shipyard's multi-dimensional typhoon early warning management platform, collects real-time meteorological parameters of the sea area where the shipyard is located, ship berthing location parameters, fixed structure parameters of large equipment, and historical typhoon emergency data to establish a multi-source parameter collection library;

[0026] Specifically, step S1 uses the shipyard's multi-dimensional typhoon warning management platform, equipped with meteorological sensors, GPS positioning modules, structural stress monitoring equipment, and data acquisition terminals, to comprehensively collect real-time meteorological parameters of the sea area where the shipyard is located. These parameters include 12 core meteorological data types, such as 10-minute average wind speed, instantaneous maximum wind speed, wind direction change angle, air pressure, and precipitation, with a collection frequency set to once per minute. Simultaneously, it collects ship berthing position parameters, including six positioning information items such as the ship's longitudinal coordinates, lateral coordinates, berthing anchorage number, and distance from the edge of the dock. It also collects fixed structure parameters for large equipment, including the tightening force of the base fixing bolts for equipment such as cranes and gantry cranes. The system collects 15 structural data items, including the spacing of structural support beams, the dimensions of the windward side, and the distribution of equipment weight. It also retrieves nearly five years of historical typhoon emergency data, including eight categories of historical records such as warning response time, damaged equipment locations, and mooring system failures during each typhoon. All collected data is uploaded to the platform database in real time via a 5G transmission module, establishing a multi-source parameter collection library with 41 categories of parameters across four dimensions: meteorology, ships, equipment, and historical data. This provides comprehensive and continuous raw data support for subsequent rule matching and model calculations. The accuracy of the collected data is controlled within ±0.1, ensuring that the data accurately reflects the actual environment and equipment status.

[0027] S2, based on the preset typhoon prevention rule base, performs rule matching scans on the multi-source parameters in the collection database, filters out calibration parameters related to typhoon warning, and removes invalid interference data;

[0028] Specifically, step S2, based on the 32 core rules included in the preset typhoon prevention rule base, performs a line-by-line matching scan of the multi-source parameters in the data collection database. The rule base includes four main categories: meteorological parameter threshold rules, ship safety rules, equipment structure tolerance rules, and data validity judgment rules. Among these, the meteorological parameter threshold rules clarify the range of parameters such as wind speed and wind direction change rate corresponding to different warning levels; the ship safety rules define the safety boundaries of ship berthing positions and the basic state requirements of the mooring system; the equipment structure tolerance rules specify the normal value ranges of structural parameters for various parts of large equipment; and the data validity judgment rules set data quality judgment standards such as data missing rate and fluctuation amplitude. During the scanning process, the system... The system compares collected parameters with rule base entries through logical operations to filter out key parameters that meet the needs of typhoon early warning analysis. These parameters include 23 categories of core parameters such as real-time wind speed, wind direction, ship berthing coordinates, and equipment base bolt tightening force. At the same time, it automatically removes invalid interference data with a missing rate of more than 5% or fluctuations exceeding three times the normal range, such as abnormal values ​​caused by sensor failures or distorted data generated during transmission. The filtered key parameters are stored according to data type to establish a structured data subset. The data filtering time is controlled within 3 seconds to ensure that subsequent model calculations can be carried out based on high-quality, highly relevant data, avoiding invalid data from consuming computing resources and affecting the accuracy of early warning results.

[0029] S3, call the contingency plan hierarchical triggering simulation model, combine the screened calibration parameters to construct a hierarchical triggering indicator system, and generate the contingency plan triggering threshold range through multi-dimensional parameter weighting calculation;

[0030] Specifically, step S3 invokes the tiered triggering simulation model of the contingency plan. First, based on the 23 selected key parameters, a tiered triggering indicator system is constructed, comprising three primary indicators (meteorological risk, ship safety, and equipment tolerance), eight secondary indicators, and 23 tertiary indicators. The primary meteorological risk indicator includes three secondary indicators such as wind speed intensity and wind direction stability; the primary ship safety indicator includes two secondary indicators such as berthing location safety and mooring foundation status; and the primary equipment tolerance indicator involves three secondary indicators such as structural strength and anchorage reliability. Subsequently, using the Delphi method, 10 experts in typhoon prevention are invited to score the importance of each indicator. The weight allocation value of each indicator is calculated using the analytic hierarchy process (AHP), with meteorological risk accounting for 45% of the primary indicators. The system assigns weights to the following indicators: ship safety (30%), equipment durability (25%), and secondary and tertiary indicators according to their impact. Based on this weighted indicator system, a multi-dimensional parameter coupling weighted calculation is performed. The measured values ​​of each indicator are multiplied by their weight values ​​and then summed to generate four warning levels: Level I, Level II, Level III, and Level IV. The Level I warning threshold range corresponds to the lowest risk level, and the Level IV warning threshold range corresponds to the highest risk level. The boundary values ​​of each threshold range are calibrated through historical data regression analysis to ensure that the range division meets the shipyard's actual typhoon prevention needs. Parallel computing is used to improve efficiency during the calculation process, with the threshold range generation time not exceeding 5 seconds, providing a clear and scientific basis for subsequent warning level determination.

[0031] S4, enable the nonlinear calculation model of wind load, input the ship's draft, the windward area of ​​the equipment, the structural stiffness parameters and the real-time wind speed and direction parameters, and complete the nonlinear calculation of the wind load on the ship and large equipment.

[0032] Specifically, step S4 activates the nonlinear wind load calculation model. First, 11 basic input parameters are extracted from the structured data subset, including ship draft, underwater cross-sectional area of ​​the hull, projected area of ​​the windward side of large equipment, elastic modulus of the equipment structural materials, and structural support stiffness. The ship draft is updated in real-time based on the ship's load, and the projected area of ​​the windward side of large equipment is calculated using the angle between the equipment's 3D model and the real-time wind direction. Simultaneously, real-time wind speed and direction parameters are retrieved, including five types of dynamic parameters: 10-minute average wind speed, peak instantaneous wind speed, and the angle between the wind direction and the equipment axis. All input parameters undergo data standardization to ensure that the parameter format conforms to the model's calculation requirements. Requirements: During the model calculation process, the nonlinear effects of wind speed fluctuations and wind direction changes on wind loads should be fully considered. Through piecewise calculation, the steady-state wind load component should be calculated first, and then the fluctuating wind load component should be superimposed. At the same time, the distribution of wind loads on the structural surface should be corrected by combining the elastic deformation characteristics of the equipment structure. Finally, the accurate calculation of wind loads on 28 key areas, including the ship hull surface, the main structure of large equipment, and key connection parts, should be completed. The calculation results include the magnitude, direction, and distribution density of wind loads in each area. The calculation error should be controlled within ±3%, and the calculation time should not exceed 8 seconds, providing accurate stress data support for subsequent structural safety assessment and emergency response measures.

[0033] S5 utilizes a ship mooring stress analysis algorithm, integrating mooring cable tensile strength parameters, ship displacement parameters, and mooring water flow velocity parameters to calculate the real-time stress state of the mooring system.

[0034] Specifically, step S5 employs a ship mooring force analysis algorithm. First, it integrates core parameters of the mooring system, including six types of cable characteristic parameters such as material tensile strength, elastic modulus, diameter, and length; four types of arrangement parameters such as the location, number, and fixing strength of mooring points; simultaneously, it extracts seven types of basic ship parameters, including ship displacement, draft, ship center of gravity height, and hull transverse projected area; and three types of aquatic environmental parameters, including real-time flow velocity, flow direction, and water flow pulsation intensity of the mooring area. All parameters are synchronously acquired from structured data subsets and real-time acquisition channels to ensure data timeliness and consistency. The algorithm then... The calculation process first calculates the force exerted by the water flow on the ship, then analyzes the ship's force balance state in conjunction with the ship's own state parameters, and then derives the tension distribution of the mooring lines through the mechanical equilibrium equations. At the same time, it considers the elastic deformation of the lines and the influence of the ship's small displacements on the force, and completes a comprehensive analysis of the overall force state of the mooring system. The calculation results include key data such as the real-time tension value of each line, the overall force balance coefficient of the mooring system, the maximum possible displacement of the ship, and the yaw angle. The calculation accuracy is controlled within ±2%, and the calculation time does not exceed 6 seconds, providing a scientific basis for judging whether there is a risk of failure in the mooring system.

[0035] S6 integrates the S3 graded triggering results, S4 wind load calculation results, and S5 mooring force analysis results through the shipyard's multi-dimensional typhoon early warning management platform to generate corresponding typhoon early warning signals and emergency operation guidelines.

[0036] Specifically, step S6 integrates the preliminary judgment results of the warning level and trigger threshold range generated in step S3, the wind load data of key areas of ships and large equipment calculated in step S4, and the real-time stress status data of the mooring system obtained in step S5 through the central processing module of the shipyard's multi-dimensional typhoon warning management platform. Simultaneously, it calls upon the platform's built-in database of warning signal generation rules and emergency operation guidelines. This database includes 28 specific warning signal descriptions corresponding to four warning levels and 36 targeted emergency operation measures, covering aspects such as equipment reinforcement, mooring adjustment, and personnel evacuation. The platform first performs a secondary verification of the wind load data, mooring stress data, and preliminary judgment results of the warning level to confirm the rationality of the warning level. If the data exceeds the threshold range corresponding to the current warning level, the warning level will be automatically adjusted. Subsequently, based on the final determined warning level, corresponding operational measures will be matched from the emergency operation guidance database. Combined with the specific location and status parameters of the ship and equipment, personalized emergency operation guidance will be generated, including information such as warning level, risk location, operation steps, and execution priority. Warning signals and emergency guidance will be simultaneously released through multiple channels such as the platform's visual terminals, audible and visual alarms, and staff mobile terminals, with a release delay of no more than 2 seconds. This ensures that relevant personnel can obtain warning information in a timely manner and quickly carry out emergency response work, realizing closed-loop management of the entire process from data integration to warning output and operation guidance, and improving the timeliness and effectiveness of shipyard's typhoon emergency response.

[0037] Preferably, the expression for the tiered triggering model of the contingency plan is: ,in, For the tiered trigger coefficient of the contingency plan, As a weighting factor for meteorological parameters, For the first Measured values ​​of calibration parameters For the first Class labeling parameters affect weights. This is a correction factor for historical data. For ship parameter sensitivity coefficient, For the parameters of ship berthing dispersion, For the structural safety factor of large equipment, For sea area water depth parameters, As an emergency response correction factor, The frequency of activation of historical typhoon prevention plans;

[0038] Specifically, the implementation of the tiered triggering simulation model for the contingency plan involves setting meteorological parameter weighting factors based on the typhoon activity patterns in the sea area where the shipyard is located. The measured values ​​of key parameters are derived from 23 types of core data screened in step S2. The influence weight of each type of key parameter is determined by 10 experts in the field of typhoon prevention through three rounds of scoring using the Delphi method. The historical data correction coefficient is calibrated based on regression analysis of eight types of historical typhoon emergency data from the past five years. The sensitivity coefficient of ship parameters is set with three gradient values ​​based on the typhoon risk characteristics of ships of different tonnages. The ship berthing dispersion parameter is calculated using the longitudinal and transverse coordinates of the ship. The structural safety factor of large equipment is determined based on the equipment structural strength test report. The sea area water depth parameter is collected once per hour by real-time hydrological monitoring equipment. The emergency response correction factor is set with reference to past emergency response efficiency data. The frequency of activation of historical typhoon prevention plans is statistically analyzed based on the number of times the plans are activated for various warning levels in the past five years. During model implementation, each parameter is first substituted into the calculation process. The basic value is obtained by weighted summation of multi-dimensional parameters. Then, the coupled influence of ship berthing dispersion and sea area depth is integrated by square root operation. The influence of historical plan activation frequency is superimposed by logarithmic operation correction. Finally, the plan grading trigger coefficient is generated. This coefficient corresponds to the warning threshold range of Level I to Level IV. By comparing with the calculation results of real-time parameters, the activation level of the plan is accurately determined. The model calculation time is controlled within 3 seconds to ensure the timeliness and scientific nature of grading triggering, providing a core basis for subsequent warning level determination.

[0039] Preferably, the expression for the nonlinear calculation model of wind load is: ,in, This represents the comprehensive value of wind loads on ships and large equipment. This is the wind load shape coefficient. This refers to the windward projected area parameter. For real-time wind speed parameters, This is the wind direction correction factor. The angle between the wind direction and the equipment axis is a parameter. This is the structural damping coefficient. These are the bending stiffness parameters of the equipment structure. For the equipment's windward height parameter, This refers to the amplitude parameter of wind speed fluctuations.

[0040] Specifically, in the implementation of the nonlinear calculation model for wind load, the wind load shape coefficient is divided into four categories based on the external structural characteristics of the ship hull and large equipment. The windward projected area parameter is calculated by comparing the equipment's three-dimensional model with the real-time wind direction angle. The real-time wind speed parameter uses the 10-minute average wind speed and the instantaneous maximum wind speed collected once per minute. The wind direction correction coefficient is set into five intervals based on the variation range of the wind direction and the equipment axis. The wind direction and the equipment axis angle are monitored in real time by meteorological sensors. The structural damping coefficient is determined with reference to the mechanical performance test data of the equipment's structural materials. The equipment's structural bending stiffness parameter is obtained based on the equipment design drawings and structural strength test reports. The equipment's windward height parameter is measured by laser ranging equipment. The wind speed fluctuation amplitude parameter is calculated by analyzing the wind speed fluctuation data within 10 minutes. During the model implementation, the product of the real-time wind speed squared and the windward projected area and wind load shape coefficient is first calculated. Then, the nonlinear correction term is calculated by combining the wind direction correction coefficient and the sine square of the wind direction angle. At the same time, the structural influence term is calculated by multiplying the bending stiffness of the equipment structure, the cube ratio of the windward height and the structural damping coefficient. Finally, the three results are integrated to obtain the comprehensive value of wind load on ships and large equipment. This value accurately reflects the stress differences in different parts, and the calculation error is controlled within ±3%, providing data support for judging the structural endurance of equipment.

[0041] Preferably, the expression for the ship mooring force analysis algorithm is: ,in, This represents the total force on the mooring system. The elastic coefficient of the cable. These are the initial tension parameters of the cable. The parameter is the angle between the cable and the horizontal direction. The coefficient of water flow resistance. For water flow velocity parameters, For the ship's inertia coefficient, For ship displacement parameters, For the water depth parameters of the mooring area, For ship length parameters, This is the mooring angle correction factor. This is a parameter representing the change in the ship's yaw angle;

[0042] Specifically, in the implementation of the ship mooring force analysis algorithm, the elastic coefficient of the mooring line is determined based on the material type and specifications of the mooring line. The initial tension parameter of the mooring line is collected in real time by the tension monitoring equipment of the mooring system. The angle between the mooring line and the horizontal direction is obtained by taking pictures with a high-definition camera and combining image analysis technology. The water flow resistance coefficient is determined based on the flow velocity and direction data of the mooring area and the ship's draft. The water flow velocity parameter is collected once per minute by the hydrological monitoring equipment. The ship inertia coefficient is set with reference to the ship's displacement and hull structure characteristics. The ship's displacement parameter is calculated in real time based on the ship's load register and draft. The water depth parameter of the mooring area is consistent with that collected in step S1. The ship's length parameter is retrieved from the ship's basic information register. The mooring angle correction coefficient is set according to the arrangement of the mooring lines. The change in the ship's yaw angle is calculated by the change in the ship's position monitored by the GPS positioning module. When implementing the algorithm, the product of the initial tension of the mooring line and the cosine of the angle is first calculated. The coupling effect of the water flow resistance coefficient and the square of the flow velocity is then used for correction. The inertial influence term is then calculated by the square root of the ratio of the ship's displacement, water depth and length. Finally, the product of the mooring angle correction coefficient and the change in yaw angle is superimposed to obtain the total force value of the mooring system. This value includes the tension distribution of each mooring line and the overall force balance state. The calculation accuracy is controlled within ±2%, and the calculation time does not exceed 4 seconds, providing accurate data for judging whether there is a risk of failure in the mooring system.

[0043] Preferably, the parameter fusion model expression of the shipyard multi-dimensional typhoon early warning management platform is as follows: ,in, The result of multi-source parameter fusion. , , , , , Output weight coefficients for each module. This refers to the platform's data transmission latency parameter. For parameter acquisition accuracy parameters, for The real-time data update rate parameter at any given moment. This is the data collection duration parameter.

[0044] Specifically, the parameter fusion model of the shipyard's multi-dimensional typhoon early warning management platform was implemented. The weighting of the trigger coefficients for different typhoon levels, the comprehensive wind load value, and the total force value of the mooring system was determined using the analytic hierarchy process (AHP) combined with expert scoring. Data transmission delay was monitored in real-time using network monitoring tools. Parameter acquisition accuracy was determined with reference to the technical specifications of various sensors and data acquisition equipment. The real-time data update rate was calculated as the number of effective updates from each data source per minute. The data acquisition duration was set to the entire period from the issuance of the typhoon warning to the end of the typhoon's impact. During model implementation, the trigger coefficients for different typhoon levels, the comprehensive wind load value, and the total force value of the mooring system were weighted separately. Then, the effects of data transmission delay and acquisition accuracy were integrated using square root operations. The cumulative effect of the real-time data update rate, obtained through integral operations, was superimposed to generate the final multi-source parameter fusion result. This result comprehensively reflects the risk coupling status of multiple dimensions, including meteorology, ships, equipment, and mooring. Data fusion time was controlled within 2 seconds, ensuring efficient integration of data from each module and providing comprehensive parameter support for subsequent early warning signal generation, thus improving the accuracy and comprehensiveness of the early warning results.

[0045] Preferably, the comprehensive evaluation model expression for the typhoon warning parameters of the shipyard's vessels and large equipment is as follows: ,in, The comprehensive assessment value for typhoon prevention and early warning. To comprehensively evaluate the weighting coefficients, This is a correction factor for the warning level. For equipment safety redundancy factor, For the first Structural safety reserve parameters for this type of equipment For ship berthing stability parameters, For the complexity parameters of the marine environment, The number of equipment types participating in the evaluation.

[0046] Specifically, a comprehensive evaluation model for typhoon warning parameters of ships and large equipment in shipyards was implemented. The comprehensive evaluation weight coefficients were determined by 10 experts using the Delphi method. The warning level correction factor was set with gradient values ​​based on the risk levels of warnings from Level I to Level IV. The equipment safety redundancy coefficient was determined with reference to the equipment structural safety reserve test report and design standards. The structural safety reserve parameters of the j-th type of equipment were divided into three levels based on the strength test data of various parts of the equipment. The ship berthing stability parameters were calculated comprehensively based on the ship's draft, displacement, and berthing position parameters. The marine environmental complexity parameters were determined by combining meteorological and hydrological data such as wind speed, current velocity, and water depth. The number of equipment types participating in the evaluation included eight major large equipment types in the shipyard, such as cranes and gantry cranes. During model implementation, the sum of the square of the trigger coefficient of the contingency plan level and the product of the comprehensive wind load value and the total force value of the mooring system was first calculated. The multi-source parameter fusion results and the correction terms of the ratio of the square root of the ship berthing stability and the marine environmental complexity were then superimposed. Finally, the comprehensive impact of the structural safety reserves of various equipment types was integrated through product calculation to generate the comprehensive evaluation value for typhoon warning. This value corresponds to different warning level thresholds. By comparing it with the set threshold, the overall typhoon risk level can be accurately determined. The model calculation takes no more than 3 seconds, providing core decision-making basis for the generation of warning signals and the formulation of emergency operation guidelines.

[0047] Preferred, such as Figure 2 As shown, S3 includes the following sub-steps: S31, based on the structural characteristics, berthing status, and marine environment differences of shipyard vessels and large equipment, meteorological intensity parameters, equipment wind resistance level parameters, ship mooring reliability parameters, and historical emergency response effectiveness parameters are extracted from multi-source collected parameters to construct a set of basic indicators for graded triggering; S32, the importance of each parameter in the basic indicator set is ranked using the analytic hierarchy process (AHP), and the weight allocation value of each indicator is determined by combining expert scores to form a weighted indicator system; S33, each parameter in the weighted indicator system is substituted into the graded triggering simulation model of the contingency plan, and trigger threshold intervals corresponding to different warning levels are generated through multi-dimensional parameter coupling calculations to clarify the activation conditions of each level of the contingency plan; S34, based on the matching results of real-time collected parameters and threshold intervals, the corresponding contingency plan level is preliminarily determined to provide a graded basis for the subsequent generation of warning signals.

[0048] Specifically, in step S3, S31 is executed first. Based on the structural characteristics of ships of different tonnages within the shipyard, three types of berthing states (dock berthing, anchorage berthing, and dock berthing), and four types of differences in the marine environment (wind speed, current speed, water depth, and seabed topography), meteorological intensity parameters, equipment wind resistance level parameters, ship mooring reliability parameters, and historical emergency response effectiveness parameters are extracted from the 23 key parameters selected in step S2. These constitute four core indicators, constructing a hierarchical triggering basic indicator set including four primary indicators, 12 secondary indicators, and 23 tertiary indicators. Then, in step S32, the importance of each parameter in the basic indicator set is ranked using the analytic hierarchy process (AHP). Ten experts in typhoon prevention are invited to conduct three rounds of scoring. Based on the scoring results, the weight of meteorological intensity parameters in the primary indicators is determined to be 45%, equipment wind resistance level parameters 25%, and ship... The system assigns 20% to ship mooring reliability parameters and 10% to historical emergency response effectiveness parameters. Secondary and tertiary indicators are allocated according to their corresponding weights to form a complete weighted indicator system. Next, step S33 is executed, where each parameter in the weighted indicator system is substituted into the pre-set data format into the pre-planned tiered triggering simulation model. Through multi-dimensional parameter coupling calculations, trigger threshold intervals corresponding to four warning levels, from Level I to Level IV, are generated. The boundary values ​​of each interval are calibrated using eight types of historical typhoon data from the past five years to ensure the rationality of the interval division. Finally, step S34 is implemented, where the real-time collected parameters selected in step S2 are compared one by one with the generated threshold intervals. Based on the matching results, the corresponding pre-planned level is initially determined. The entire step-by-step implementation process takes less than 5 seconds, providing accurate and scientific tiered basis for subsequent warning signal generation and improving the pertinence and reliability of warning level determination.

[0049] Preferred, such as Figure 3 As shown, step S4 includes the following sub-steps: S41, retrieving the ship's draft, hull cross-sectional area, windward shape parameters of large equipment, and elastic modulus parameters of structural materials from the shipyard's multi-dimensional typhoon early warning management platform as the basic input parameters for wind load calculation; S42, performing time series analysis on real-time wind speed and direction parameters, extracting peak wind speed, average wind speed, and wind direction change rate calibration characteristic parameters to eliminate the influence of instantaneous wind speed fluctuations on the calculation results; S43, substituting the basic input parameters and wind speed and direction characteristic parameters into the wind load nonlinear calculation model, and solving the wind load distribution values ​​of each calibrated stress-bearing part of the ship and large equipment through iterative calculation; S44, performing spatial mapping processing on the calculated wind load distribution values, converting them into a data format compatible with the equipment structure stress analysis, providing data support for subsequent structural safety assessment.

[0050] Specifically, step S4 proceeds sequentially from S41 to S44. In stage S41, eight types of parameters related to the ship's draft, hull cross-sectional area, and windward shape of large equipment, as well as three types of mechanical parameters such as the elastic modulus of structural materials, are retrieved from the database of the shipyard's multi-dimensional typhoon warning management platform. These parameters serve as the basic input data for wind load calculation, with data acquisition accuracy controlled within ±0.1. After entering S42, time series analysis is performed on the real-time wind speed and wind direction parameters collected in step S1, with the analysis duration set at 10 minutes. Five key characteristic parameters, including peak wind speed, average wind speed, and wind direction change rate, are extracted. The moving average method is used to eliminate interference caused by instantaneous wind speed fluctuations, ensuring parameter stability. Subsequently, S43 is executed, and the basic parameters obtained in S41 are... The input parameters, along with the wind speed and direction characteristic parameters extracted in S42, are sequentially input into the wind load nonlinear calculation model according to the model requirements. An iterative calculation method is used to solve for the wind load distribution values ​​of 28 key areas on the ship's hull surface, the main structure of large equipment, and 12 key connection parts. The number of iterations is set to 15 to ensure calculation accuracy. Finally, S44 is implemented to perform spatial mapping processing on the calculated wind load distribution values, converting the three-dimensional stress data into a two-dimensional data format compatible with the equipment structure stress analysis software. The data conversion takes no more than 2 seconds, ensuring that the data can be directly used in subsequent structural safety assessments. This provides accurate and standardized data support for judging the equipment structure's tolerance in typhoon environments and improves the practicality of wind load calculation results.

[0051] Preferred, such as Figure 4 As shown, step S5 includes the following sub-steps: S51, collecting the material tensile strength, diameter, and length parameters of the mooring cables, as well as the mooring point layout parameters, to establish a basic parameter library for the mooring system; S52, acquiring the real-time flow velocity and direction parameters of the mooring water area, and the ship's displacement, draft, and center of gravity height parameters, to construct an environmental and ship parameter system for mooring stress analysis; S53, substituting the data from the basic parameter library and parameter system into the ship mooring stress analysis algorithm to calculate the tension distribution of the mooring cables, the ship's displacement, and the yaw angle; S54, validating the calculation results, removing abnormal data that exceeds the physical constraints, and ensuring the rationality of the mooring stress analysis results.

[0052] Specifically, step S5 includes the following sub-steps: In stage S51, six core parameters of the mooring cables, such as material tensile strength, diameter, and length, and four arrangement parameters, such as mooring point location, quantity, and fixing strength, are collected to establish a basic parameter library for the mooring system, including ten parameters. The parameter collection frequency is set to once every 30 minutes to ensure data timeliness. In stage S52, through hydrological monitoring equipment and a ship condition monitoring system, three environmental parameters of the mooring area, such as real-time flow velocity, flow direction, and water flow pulsation intensity, and seven basic ship parameters, such as ship displacement, draft, and center of gravity height, are obtained to construct an environmental and ship parameter system for mooring force analysis. This system includes ten key parameters, with data errors controlled within ±2%. After proceeding to S53, the parameters from S5... All data from the established basic parameter library and the parameter system constructed in S52 are substituted into the ship mooring force analysis algorithm according to the logical order required by the algorithm. Six key data are calculated, including the real-time tension value of each cable, the overall force balance coefficient of the mooring system, the maximum possible displacement of the ship, and the yaw angle. The calculation process adopts a parallel processing method to improve efficiency. Finally, S54 is executed to verify the validity of the calculation results of S53 based on physical constraints such as the tensile strength limit of the mooring cable material and the safe displacement range of the ship at mooring. Abnormal data that exceeds the constraint range is eliminated. The verification pass rate must reach more than 98% to ensure the rationality and reliability of the mooring force analysis results and provide an accurate decision-making basis for judging whether there is a risk of failure in the mooring system.

[0053] like Figure 5As shown, a typhoon early warning system for shipyard vessels and large equipment based on contingency plan activation and rule-matching scanning is presented. This system is applied to a typhoon early warning method for shipyard vessels and large equipment based on contingency plan activation and rule-matching scanning. It includes: a multi-source parameter intelligent acquisition and filtering unit, used to collect meteorological parameters of the shipyard's sea area, vessel status parameters, large equipment structural parameters, and historical typhoon data through various sensors and data interfaces, and to filter and calibrate parameters based on preset rules; a contingency plan hierarchical triggering and calculation unit, connected to the multi-source parameter intelligent acquisition and filtering unit, which calls the contingency plan hierarchical triggering and calculation model, and generates a contingency plan triggering threshold range based on the filtered calibrated parameters; and a wind load nonlinear calculation unit, connected to the multi-source parameter intelligent acquisition and filtering unit, which receives vessel and equipment structural parameters and meteorological parameters. The system employs a nonlinear wind load calculation model to perform wind load calculations. A ship mooring stress analysis unit, connected to a multi-source parameter intelligent acquisition and filtering unit, integrates mooring system parameters, ship parameters, and aquatic environment parameters, and calculates the mooring stress state using a ship mooring stress analysis algorithm. A multi-dimensional data fusion and early warning signal generation unit, connected to the pre-plan tiered triggering and simulation calculation unit, the wind load nonlinear calculation unit, and the ship mooring stress analysis unit, integrates the output results of each unit to generate corresponding typhoon early warning signals and emergency operation guidelines. A system data storage and interaction unit, connected to each unit, stores the collected raw data, intermediate calculation results, early warning signals, and emergency guidelines, while also interacting with the shipyard's existing management system to support the real-time transmission and sharing of early warning information.

[0054] A terminal device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a typhoon early warning method for shipyard vessels and large equipment based on pre-plan activation and rule matching scanning.

[0055] A computer-readable storage medium storing a computer program that, when executed by a processor, implements a typhoon early warning method for shipyard vessels and large equipment based on pre-plan activation and rule-matching scanning.

[0056] Furthermore, when the processes described above in this invention are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0057] This invention presents a method, system, terminal, and storage medium for typhoon early warning of ships and large equipment in shipyards, based on contingency plan activation and rule-matching scanning. Addressing the issue of insufficient accuracy in parameter fusion, it comprehensively integrates key data such as meteorological conditions, ship status, equipment structure, and aquatic environment through a multi-source parameter acquisition and rule-matching scanning mechanism. It combines the coordinated calculations of three professional models—contingency plan tiered triggering simulation, wind load nonlinear calculation, and ship mooring force analysis—to deeply explore the inherent coupling relationships between various parameters. This allows the setting of early warning thresholds to overcome the limitations of a single dimension, better aligning with the actual typhoon risk scenarios in shipyards and significantly improving the scientific rigor of risk level assessment. Furthermore, addressing the lack of coordination between model calculation and early warning response, it constructs a closed-loop system through a multi-dimensional typhoon early warning management platform for shipyards. This breaks down the barriers of data fragmentation at each stage, achieving integrated connection from parameter acquisition, screening, and model calculation to early warning generation and emergency guidance output. It rapidly integrates the calculation results of multiple modules to form targeted operational guidelines, effectively compensating for the shortcomings of existing technologies in terms of timeliness and practicality.

[0058] This invention combines multi-source parameter integration with rule-matching scanning to ensure the comprehensiveness and effectiveness of key data, providing high-quality data support for subsequent model calculations and avoiding invalid interference data from affecting early warning results. Three types of professional models work in synergy, fully leveraging their respective technical advantages to achieve multi-dimensional and in-depth analysis of typhoon risks, offering greater comprehensiveness compared to traditional single-model calculations. The integrated design of the shipyard's multi-dimensional typhoon early warning management platform enables efficient connection between data flow, model calculations, and early warning output, improving the overall efficiency of the early warning process. The entire process's technical design is tailored to the shipyard's actual application scenarios, generating highly practical emergency operation guidelines that directly provide clear guidance for frontline typhoon prevention work, comprehensively enhancing the overall effectiveness of typhoon early warning for shipyard vessels and large equipment.

[0059] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0060] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A typhoon early warning method for shipyard vessels and large equipment based on contingency plan activation and rule-matching scanning, characterized in that, Includes the following steps: S1. Collect real-time meteorological parameters of the sea area where the shipyard is located, ship berthing location parameters, fixed structure parameters of large equipment, and historical typhoon emergency data through the shipyard's multi-dimensional typhoon early warning management platform to establish a multi-source parameter collection library; S2. Based on a preset typhoon prevention rule library, perform rule matching scanning on the multi-source parameters in the collection library, filter calibration parameters related to typhoon early warning, and eliminate invalid interference data; S3. Call the contingency plan graded triggering simulation model, combine it with the filtered calibration parameters to construct a graded triggering indicator system, and generate the contingency plan triggering threshold range through multi-dimensional parameter weighted calculation; S4. Activate wind load. The nonlinear calculation model takes the ship's draft, equipment windward area, structural stiffness parameters, and real-time wind speed and direction parameters as input to perform nonlinear calculations of the wind loads on the ship and large equipment; S5 uses a ship mooring stress analysis algorithm to calculate the real-time stress state of the mooring system by integrating the tensile strength parameters of the mooring cables, the ship's displacement parameters, and the current velocity parameters of the mooring area; S6 integrates the S3 graded triggering results, the S4 wind load calculation results, and the S5 mooring stress analysis results through the shipyard's multi-dimensional typhoon warning management platform to generate corresponding level typhoon warning signals and emergency operation guidelines.

2. The method for typhoon early warning of shipyard vessels and large equipment based on pre-plan activation and rule matching scanning according to claim 1, characterized in that, The expression for the tiered triggering simulation model of the contingency plan is: ,in, For the tiered trigger coefficient of the contingency plan, As a weighting factor for meteorological parameters, For the first Measured values ​​of calibration parameters For the first Class labeling parameters affect weights. This is a correction factor for historical data. For ship parameter sensitivity coefficient, For the parameters of ship berthing dispersion, For the structural safety factor of large equipment, For sea area water depth parameters, As an emergency response correction factor, The frequency of activation of historical typhoon prevention plans; The expression for the nonlinear calculation model of wind load is: ,in, This represents the comprehensive value of wind loads on ships and large equipment. This is the wind load shape coefficient. This refers to the windward projected area parameter. For real-time wind speed parameters, This is the wind direction correction factor. The angle between the wind direction and the equipment axis is a parameter. The structural damping coefficient is... These are the bending stiffness parameters of the equipment structure. For the equipment's windward height parameter, This refers to the amplitude parameter of wind speed fluctuations.

3. The method for typhoon early warning of shipyard vessels and large equipment based on pre-plan activation and rule matching scanning according to claim 1, characterized in that, The expression for the ship mooring force analysis algorithm is as follows: ,in, This represents the total force on the mooring system. The elastic coefficient of the cable. These are the initial tension parameters of the cable. The parameter is the angle between the cable and the horizontal direction. The coefficient of water flow resistance. For water flow velocity parameters, For the ship's inertia coefficient, For ship displacement parameters, For the water depth parameters of the mooring area, For ship length parameters, This is the mooring angle correction factor. This is a parameter representing the change in the ship's yaw angle; The parameter fusion model expression of the shipyard's multi-dimensional typhoon early warning management platform is as follows: ,in, The result of multi-source parameter fusion. Output weight coefficients for each module. This refers to the platform's data transmission latency parameter. For parameter acquisition accuracy parameters, for The real-time data update rate parameter at any given moment. This is the data collection duration parameter.

4. The method for typhoon early warning of shipyard vessels and large equipment based on pre-plan activation and rule matching scanning according to claim 1, characterized in that, The comprehensive evaluation model expression for typhoon warning parameters of ships and large equipment in shipyards is as follows: ,in, The comprehensive assessment value for typhoon prevention and early warning. To comprehensively evaluate the weighting coefficients, This is a correction factor for the warning level. For equipment safety redundancy factor, For the first Structural safety reserve parameters for this type of equipment For ship berthing stability parameters, This is a parameter representing the complexity of the marine environment. The number of equipment types participating in the evaluation.

5. A typhoon early warning method for shipyard vessels and large equipment based on pre-plan activation and rule matching scanning according to claim 1, characterized in that, S3 includes the following steps: S31, based on the structural characteristics, berthing status, and marine environment differences of shipyard vessels and large equipment, meteorological intensity parameters, equipment wind resistance level parameters, ship mooring reliability parameters, and historical emergency response effectiveness parameters are extracted from multi-source collected parameters to construct a set of basic indicators for graded triggering; S32, the importance of each parameter in the basic indicator set is ranked using the analytic hierarchy process (AHP), and the weight allocation value of each indicator is determined by combining expert scores to form a weighted indicator system; S33, each parameter in the weighted indicator system is substituted into the graded triggering simulation model of the contingency plan, and trigger threshold intervals corresponding to different warning levels are generated through multi-dimensional parameter coupling calculations to clarify the activation conditions of each level of the contingency plan; S34, based on the matching results of real-time collected parameters and threshold intervals, the corresponding contingency plan level is preliminarily determined to provide a graded basis for the generation of warning signals.

6. A method for typhoon early warning of ships and large equipment in shipyards based on pre-plan activation and rule matching scanning according to claim 1, characterized in that, S4 includes the following steps: S41, retrieving the ship's draft, hull cross-sectional area, windward shape parameters of large equipment, and elastic modulus parameters of structural materials from the shipyard's multi-dimensional typhoon early warning management platform as the basic input parameters for wind load calculation; S42, performing time series analysis on real-time wind speed and direction parameters, extracting peak wind speed, average wind speed, and wind direction change rate calibration characteristic parameters to eliminate the influence of instantaneous wind speed fluctuations on the calculation results; S43, substituting the basic input parameters and wind speed and direction characteristic parameters into the wind load nonlinear calculation model, and solving the wind load distribution values ​​of each calibrated stress-bearing part of the ship and large equipment through iterative calculation; S44, performing spatial mapping processing on the calculated wind load distribution values, converting them into a data format compatible with the equipment structure stress analysis, and providing data support for structural safety assessment.

7. A typhoon early warning method for shipyard vessels and large equipment based on pre-plan activation and rule matching scanning according to claim 1, characterized in that, S5 includes the following sub-steps: S51, collecting the material tensile strength, diameter, and length parameters of the mooring cables, as well as the mooring point layout parameters, to establish a basic parameter library for the mooring system; S52, acquiring the real-time flow velocity and direction parameters of the mooring area, and the ship's displacement, draft, and center of gravity height parameters, to construct an environmental and ship parameter system for mooring stress analysis; S53, substituting the data from the basic parameter library and parameter system into the ship mooring stress analysis algorithm to calculate the tension distribution of the mooring cables, the ship's displacement, and the yaw angle; S54, validating the calculation results, removing abnormal data that exceeds the physical constraints, and ensuring the rationality of the mooring stress analysis results.

8. A typhoon early warning system for shipyard vessels and large equipment based on pre-plan activation and rule-matching scanning, characterized in that, This system is applied to the typhoon early warning method for shipyard vessels and large equipment based on pre-plan activation and rule matching scanning as described in claim 1. It includes: a multi-source parameter intelligent acquisition and filtering unit, used to acquire meteorological parameters of the shipyard sea area, vessel status parameters, large equipment structural parameters, and historical typhoon data through various sensors and data interfaces, and to filter and calibrate parameters based on preset rules; a pre-plan hierarchical triggering and calculation unit, connected to the multi-source parameter intelligent acquisition and filtering unit, which calls the pre-plan hierarchical triggering and calculation model and generates a pre-plan triggering threshold range based on the filtered calibration parameters; and a wind load nonlinear calculation unit, connected to the multi-source parameter intelligent acquisition and filtering unit, which receives vessel and equipment structural parameters and meteorological parameters, and completes the calculation using the wind load nonlinear calculation model. The system includes: a wind load calculation unit; a ship mooring stress analysis unit, connected to a multi-source parameter intelligent acquisition and filtering unit, which integrates mooring system parameters, ship parameters, and aquatic environment parameters, and calculates the mooring stress state through a ship mooring stress analysis algorithm; a multi-dimensional data fusion and early warning signal generation unit, connected to the pre-plan graded triggering and simulation calculation unit, the wind load nonlinear calculation unit, and the ship mooring stress analysis unit, which integrates the output results of each unit to generate corresponding level typhoon early warning signals and emergency operation guidelines; and a system data storage and interaction unit, connected to each unit, used to store the collected raw data, intermediate calculation results, early warning signals, and emergency guidelines, while also interacting with the shipyard's existing management system to support the real-time transmission and sharing of early warning information.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements a typhoon early warning method for shipyard vessels and large equipment based on pre-plan activation and rule matching scanning as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements a typhoon early warning method for shipyard vessels and large equipment based on pre-plan activation and rule matching scanning as described in any one of claims 1 to 7.

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