Jacket roll-on and roll-off transportation safety evaluation system based on multi-parameter monitoring

Abstract

The invention discloses a jacket roll-on and roll-off transportation safety evaluation system based on multi-parameter monitoring. The system comprises a multi-parameter monitoring module, a data acquisition and transmission module, a safety evaluation module and an early warning and feedback module. The multi-parameter monitoring module is installed in a roll-on and roll-off transportation operation area and used for monitoring displacement, stress, inclination angle and environmental parameters in the roll-on and roll-off process of the jacket in real time. The data acquisition and transmission module is connected with the multi-parameter monitoring module and is used for acquiring monitoring data and transmitting the monitoring data to the safety evaluation module; the safety evaluation module is used for processing and analyzing the collected data and evaluating the safety of roll-on and roll-off transportation; through comprehensive and real-time monitoring of multi-dimensional information such as displacement, stress, inclination angle and environmental parameters in the roll-on and roll-off transportation process, and in combination with an advanced data processing and evaluation algorithm, accurate and dynamic safety evaluation of roll-on and roll-off shipping operation of the offshore wind power jacket is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of offshore wind power jacket roll-on roll-off transportation, in particular to a jacket roll-on roll-off transportation safety evaluation system based on multi-parameter monitoring. BACKGROUND

[0002] (I) Importance of offshore wind power industry and jacket With the growing demand for clean energy worldwide, offshore wind power, as an important part of renewable energy, is entering a stage of rapid development. In offshore wind power projects, the jacket foundation is a key structure that supports wind turbine generators. It not only needs to have sufficient strength and stability to withstand complex loads in the marine environment, such as wind waves, currents, earthquakes, etc., but also needs to ensure accurate positioning and installation quality during installation. In recent years, the scale of offshore wind farms has been expanding, and the single capacity of wind turbine generators has been increasing, which has led to a significant increase in the size and weight of jackets. For example, the height of the jacket in some large offshore wind power projects can reach more than 100 meters, and the weight can reach several thousand tons, with unprecedented complexity.

[0003] Such large and precise jackets in offshore wind power project construction, their roll-on roll-off transportation is a key link in the entire project process. Roll-on roll-off transportation is widely used in large-scale offshore wind foundation transportation due to its efficiency and flexibility. However, the roll-on roll-off transportation of large jackets faces many challenges. On the one hand, the jacket itself has large size and high center of gravity, which is prone to tilting, instability and other safety accidents during roll-on roll-off; on the other hand, roll-on roll-off operations are usually carried out in limited spaces such as wharfs, with complex on-site environments and many uncertain factors such as ground flatness, equipment failure, and weather condition changes, which pose a threat to the safety of roll-on roll-off transportation. According to statistics, in the past offshore wind power project construction, there have been many cases of project delays and economic losses due to problems in the roll-on roll-off transportation link, highlighting the urgent need for roll-on roll-off transportation safety evaluation technology.

[0004] (II) Limitations of traditional roll-on roll-off transportation methods Traditional roll-on roll-off transportation methods mainly rely on the on-site experience of operators and some basic monitoring means. For example, operators use visual observation and simple measuring tools to judge the displacement and attitude change of the jacket, which is not only low in accuracy but also easily affected by human factors, making it difficult to accurately reflect the real state of the jacket during roll-on roll-off. In terms of stress monitoring, traditional methods can only measure a limited part of the jacket structure, and cannot fully grasp the stress distribution of the entire structure during roll-on roll-off, which poses a great safety hazard.

[0005] In addition, the consideration of environmental factors in traditional roll-on transportation is relatively insufficient. Meteorological conditions such as wind speed, wind direction, temperature, humidity, etc. have an important influence on the safety of roll-on operations. For example, strong winds may cause the jacket to swing significantly during roll-on, increasing the risk of instability; temperature changes may affect the performance of jacket materials and the operating state of related equipment. However, in previous roll-on transportation operations, the monitoring and use of these environmental parameters were not sufficient, and there was a lack of effective means to incorporate environmental factors into the overall safety assessment system, making roll-on operations relatively passive in the face of complex and changing environments.

[0006] (Three) Limitations of existing safety assessment technologies Currently, there are some related technologies in the field of roll-on transportation safety assessment, but these technologies still have many limitations. Some technologies can monitor certain parameters of the jacket, such as displacement or stress, but the monitoring parameters are relatively single and cannot comprehensively assess the safety of roll-on transportation. This one-sided evaluation method is difficult to accurately predict potential safety risks, because the various factors in the roll-on transportation process are interrelated and interdependent, and only a single parameter cannot fully describe the safety state of the entire system.

[0007] Some technologies attempt to monitor multiple parameters, but there are deficiencies in data processing and analysis. The large amount of collected monitoring data cannot be effectively integrated and deeply mined, and cannot be converted into safety assessment results with practical guiding significance. For example, simple data listing and statistics cannot reveal the internal relationship between parameters and the trend of change, making it difficult to predict accident signs in advance and provide timely and accurate warning information for operators, resulting in these technologies not being able to fully play their role in practical applications, and not meeting the requirements of high precision, comprehensiveness and real-time safety assessment for large-scale offshore wind power jacket roll-on transportation.

[0008] In summary, the existing technologies have obvious limitations in the safety assessment of offshore wind power jacket roll-on transportation, and there is an urgent need for a system that can consider multiple parameters, monitor in real time and accurately assess the safety of roll-on transportation, to ensure the smooth construction and safe operation of offshore wind power projects. SUMMARY

[0009] In view of the deficiencies of the prior art, the present application provides a kind of based on the safety evaluation system of multi-parameter monitoring of pipe rack roll-on / roll-off transport, solve the problems of single monitoring parameter, insufficient data processing and analysis capability, unable to comprehensively and accurately assess safety risks and other problems in the prior art of offshore wind power pipe rack roll-on / roll-off transport safety evaluation technology.The system integrates multiple high-precision sensors to monitor multi-dimensional information such as displacement, stress, inclination, and environmental parameters during roll-on / roll-off transport in real time, and combines advanced data processing and evaluation algorithms to achieve comprehensive, accurate, and dynamic evaluation of roll-on / roll-off transport safety, timely warning of potential risks, and providing scientific basis for operators to ensure safe and efficient roll-on / roll-off operations of large offshore wind power pipe racks.

[0010] To achieve the above object, the present application is realized by the following technical scheme: a kind of based on the safety evaluation system of multi-parameter monitoring of pipe rack roll-on / roll-off transport, comprising: multi-parameter monitoring module, data acquisition and transmission module, safety evaluation module, early warning and feedback module;The multi-parameter monitoring module is installed in the roll-on / roll-off transport operation area, for real-time monitoring of displacement, stress, inclination and environmental parameters during pipe rack roll-on / roll-off process;The data acquisition and transmission module is connected with the multi-parameter monitoring module, for collecting monitoring data and transmitting to the safety evaluation module;The safety evaluation module is used for processing and analyzing the collected data, and evaluating the safety of roll-on / roll-off transport;The early warning and feedback module is connected with the safety evaluation module, for triggering early warning and feeding back information to the operator according to the evaluation result.

[0011] Preferably, the multi-parameter monitoring module includes displacement monitoring submodule, stress monitoring submodule, inclination monitoring submodule and environmental parameter monitoring submodule;The displacement monitoring submodule installs high-precision displacement sensors at key positions of the pipe rack, each vehicle group of self-propelled module transport vehicle (SPMT) and key nodes of roll-on / roll-off track, for real-time tracking of three-dimensional displacement changes of the pipe rack during roll-on / roll-off process, relative position changes of SPMT vehicle group and deformation displacement of roll-on / roll-off track after stress;The stress monitoring submodule is arranged with strain gauge stress sensors on the main structural members of the pipe rack, key connection positions and stress contact areas with SPMT, for real-time monitoring of stress distribution and size of each part during roll-on / roll-off process;The inclination monitoring submodule is equipped with high-precision inclinometers at the top, bottom and near the center of gravity of the pipe rack, for real-time monitoring of three-dimensional spatial attitude changes of the pipe rack during roll-on / roll-off operation;The environmental parameter monitoring submodule installs temperature and humidity sensors, wind speed and direction sensors, etc., for comprehensive monitoring of weather conditions at the roll-on / roll-off site.

[0012] Preferably, the data acquisition and transmission module comprises a multi-channel data acquisition card and a data transmission network; the multi-channel data acquisition card is precisely connected with each monitoring sub-module sensor, and uniformly collects multi-parameter original data according to a preset high-frequency acquisition rate; the data transmission network utilizes a wireless transmission network combined with a wired Ethernet to build a high-speed, stable and low-delay data transmission channel, and losslessly transmits the collected data to the safety evaluation module.

[0013] Preferably, the safety evaluation module comprises a data preprocessing unit, a safety evaluation algorithm unit and a safety evaluation model training and optimization unit; the data preprocessing unit removes noise interference by using a filtering algorithm, uniformly processes different dimension data to a standard interval through normalization, and integrates multi-source data by means of a data fusion technology to reconstruct a complete data chain of real-time working conditions of the jacket roll-on and roll-off transportation; the safety evaluation algorithm unit simulates the stress deformation characteristics of the jacket by fusing a finite element analysis algorithm, compares the measured stress and displacement data with the theoretical safety threshold, introduces a posture solving algorithm to determine the posture stability of the jacket according to the inclination angle data, combines an environmental adaptability algorithm to quantify the reduction coefficient of the roll-on safety of the environmental factors, and outputs quantitative safety evaluation indexes covering the structure safety, posture stability, environmental adaptation and other dimensions; the safety evaluation model training and optimization unit continuously trains and iterates the safety evaluation model by using a machine learning algorithm based on the past roll-on case data, simulation data and real-time monitoring new data, and strengthens the generalization ability and evaluation accuracy of the model.

[0014] Preferably, the early warning and feedback module comprises a warning threshold setting unit, a warning triggering and pushing unit and a feedback information collection and optimization unit; the warning threshold setting unit finely calibrates the corresponding warning threshold of each safety evaluation index according to the industry specifications, jacket design parameters and historical experience data; the warning triggering and pushing unit compares the safety evaluation index with the warning threshold in real time, triggers the warning as soon as the threshold is broken, and pushes to the handheld terminal of the operator and the on-site display screen according to the risk level, and the pushing content includes the risk source, risk level and brief disposal suggestion; the feedback information collection and optimization unit collects the disposal results and suggestions of the operator on the warning feedback, and feeds back to the safety evaluation module and the warning threshold setting unit to dynamically optimize the evaluation model and adjust the warning threshold.

[0015] Beneficial effects The application provides a jacket roll-on and roll-off transportation safety evaluation system based on multi-parameter monitoring. 1. Comprehensive monitoring and accurate assessment: The multi-parameter monitoring module monitors key indicators such as displacement, stress, inclination, and environmental parameters during the roll-on process of the jacket, capturing various state information of the jacket during the roll-on process. The safety assessment module uses advanced data processing and analysis algorithms to convert monitoring data into safety assessment results with practical guiding significance, providing scientific basis for roll-on transportation decision-making, effectively improving the safety and reliability of roll-on transportation. Compared with traditional single parameter monitoring technology, this system can more comprehensively reflect the safety risks in the roll-on process, avoiding accidents caused by incomplete monitoring.

[0016] 2. Timely warning and rapid response: The early warning and feedback module can trigger early warning and push it to the operator in time according to the real-time evaluation results, so that the operator can take measures to deal with potential risks and avoid accidents or reduce accident losses. At the same time, the feedback information collection and optimization unit can continuously optimize the early warning threshold and evaluation model to improve the accuracy and effectiveness of early warning, and realize dynamic safety control of roll-on transportation process. This technical effect can significantly shorten the response time and reduce the serious consequences caused by delayed response, providing more solid safety protection for roll-on transportation process.

[0017] 3. Adapt to complex working conditions and continuous optimization: The system fully considers the complex and variable environmental conditions of offshore wind farms and the diversified process requirements of jacket roll-on transportation, and can adapt to different roll-on scenes and working conditions. By continuously collecting roll-on case data and real-time monitoring data, and using machine learning algorithms to train and optimize the safety evaluation model, the system can continuously learn and adapt to new roll-on working conditions, realize self-improvement and performance improvement, and provide long-term and stable safety protection for offshore wind jacket roll-on transportation. Compared with existing fixed evaluation models, the adaptive optimization capability of this system enables it to better cope with various new roll-on working conditions and challenges that may arise in the future, with wider applicability and foresight.

[0018] 4. Data-driven decision support: The large amount of roll-on transportation process data collected not only serves real-time safety assessment and early warning, but also provides rich data support for subsequent roll-on process optimization, jacket design improvement, transportation equipment selection, etc. Through in-depth analysis and mining of these data, potential problems and improvement space in the roll-on transportation process can be found, providing strong data-driven decision support for the technological upgrading and innovative development of the offshore wind power industry, and promoting the sustainable development of the entire industry. This technical effect helps to realize the intelligent and fine management of the offshore wind power industry, improve the overall production efficiency and quality, and enhance the competitiveness of the industry. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1The application is based on a multi-parameter monitoring system for roll-on roll-off transportation safety evaluation of a jacket. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the application will be clearly and completely described in connection with the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the application.

[0021] Please refer to Figure 1 A multi-parameter monitoring system for roll-on roll-off transportation safety evaluation of a jacket includes: (I) System construction and initial configuration At a roll-on roll-off wharf for offshore wind power jackets, each module is installed and deployed according to the design architecture. The various sensors of the multi-parameter monitoring module need to be accurately positioned and firmly fixed to ensure that they can accurately monitor the corresponding parameters. For example, displacement sensors are installed at key support points of the jacket, connection points of the vehicle group of the SPMT, and key positions at both ends and in the middle of the roll-on roll-off track; stress sensors are closely attached to the main beam of the jacket, key welds, and positions in contact with the SPMT; inclinometers are installed at the top platform, bottom foundation, and near the center of gravity of the jacket; and environmental parameter sensors are reasonably arranged around the roll-on roll-off operation area to ensure that the on-site weather conditions can be fully monitored.

[0022] The multi-channel data acquisition card in the data acquisition and transmission module is accurately connected with the sensors of each monitoring submodule through a dedicated cable, and signal connection debugging is performed to ensure that the acquired signals are accurate. At the same time, a data transmission channel is built using a wireless transmission network (such as 5G technology) combined with wired Ethernet, network connectivity testing is performed, and the high speed, stability, and low latency of data transmission are ensured. The server of the safety evaluation module loads a pre-trained safety evaluation model, and according to the specific parameters of this roll-on roll-off task (such as the model of the jacket, the number and configuration of the SPMT, etc.), the model is initially configured, and the initial evaluation parameters and warning thresholds are set. The pre-warning and feedback module is associated with the handheld terminal device (such as a smartphone, a tablet computer, etc.) of the operator and the on-site display screen, and the pre-warning push channel is calibrated to ensure that the pre-warning information can be timely and accurately delivered to the operator.

[0023] (II) Monitoring initialization before roll-on Before the jacket is in place and the SPMTs are arranged in the starting position, each monitoring sub-module is turned on. After the sensors are briefly preheated and stabilized, the initial state full parameter data is collected. These initial data include the displacement values of the jacket at the initial position (usually with the installation position as the reference zero point), the initial stress state of each key position, the initial attitude inclination angle of the jacket, and the initial environmental parameters (such as temperature, humidity, wind speed, wind direction, etc.) on site. The data acquisition and transmission module transmits the collected initial data to the safety evaluation module, and the safety evaluation algorithm unit establishes an initial safety evaluation benchmark according to these initial data, compares the deviation of each monitoring data with the benchmark value, and initializes zero, to create a benchmark reference for formal rolling operation evaluation. For example, the initial displacement data will be used as the benchmark for subsequent displacement change calculation, the initial stress state is used to judge the trend and amplitude of stress change during rolling, and the initial inclination angle is used to detect the change of the jacket attitude.

[0024] (Three) Rolling operation real-time evaluation process Data acquisition and transmission process: With the issuance of the rolling instruction, the jacket begins to gradually roll onto the SPMT. At this time, each sub-module of the multi-parameter monitoring module works synchronously. The displacement monitoring sub-module real-time tracks the displacement changes of each key point of the jacket, accurately records its moving track in three-dimensional space, including horizontal displacement, vertical displacement and micro displacement changes in each direction, and the displacement accuracy can reach millimeter level. The stress monitoring sub-module synchronously senses the stress distribution and size change of each part of the jacket structure, and real-time captures the stress concentration area and stress mutation. The inclination monitoring sub-module dynamically quantifies the three-dimensional space attitude change of the jacket during rolling, and real-time monitors the slight changes of its inclination angle in each direction, providing data support for attitude stability evaluation. The environmental parameter monitoring sub-module continuously reports the meteorological information of the rolling site, including real-time wind speed, wind direction change, temperature and humidity data, etc. The data acquisition and transmission module collects the original data of each monitoring sub-module according to the preset high-frequency acquisition rate (such as collecting 10-100 groups of data per second, the specific frequency can be adjusted according to the rolling speed and accuracy requirement), and uses the constructed data transmission network to stably and losslessly transmit the data to the safety evaluation module.

[0025] Data processing and evaluation process: After receiving the transmitted data, the data preprocessing unit first uses digital filtering algorithms (such as Kalman filtering, wavelet transform filtering, etc.) to eliminate noise interference in the data, and remove abnormal data fluctuations caused by sensor errors, environmental interference and other factors. Then, through normalization processing, data of different dimensions are uniformly converted to a standard interval (such as [0, 1] or [-1, 1] interval) to facilitate subsequent data fusion and analysis. Data fusion technology integrates multi-source data, organically fuses different types of data such as displacement, stress, inclination and environmental parameters, reconstructs the complete data chain of the real-time working condition of the jacket during rolling and transportation, and forms a comprehensive and accurate description of the rolling process. The safety evaluation algorithm unit uses finite element analysis algorithm to simulate the stress deformation characteristics of the jacket under the current rolling state according to the fused data, compares the measured stress and displacement data with the theoretical safety threshold obtained by finite element analysis. At the same time, the attitude solving algorithm is introduced to accurately determine the attitude stability of the jacket according to the inclination data, and to determine whether it exceeds the preset safe attitude range. In addition, combined with the environmental adaptability algorithm, the reduction coefficient of environmental factors (such as the influence of wind speed on the swing of the jacket, the influence of temperature on the performance of materials, etc.) on the safety of rolling is quantified, and the actual influence degree of environmental factors on the safety of rolling is considered comprehensively. After the comprehensive operation of the above algorithms, the final output includes multi-dimensional quantitative safety evaluation indexes such as structural safety (such as stress safety factor, displacement safety margin, etc.), attitude stability (such as attitude stability evaluation index), environmental adaptation (such as environmental risk level), etc. The operation personnel are presented with intuitive visual methods (such as charts, curves, color coding, etc.) on the system display screen to assist them in real-time control of the transportation safety situation.

[0026] Early warning and feedback process: Once the safety evaluation index output by the safety evaluation module in real time approaches or exceeds the warning threshold, the early warning trigger and push unit responds immediately. According to the different risk levels, the early warning information is classified and pushed to the operator. For example, when the stress value of a key part of the jacket reaches 80% of the warning threshold, the system sends a level one early warning information to the operator's handheld terminal, prompting attention and preparation; if the stress value continues to rise and exceeds the warning threshold, the system immediately sends a level two early warning information to the on-site display screen and the operator's handheld terminal, showing the risk source location, risk level (such as high risk), and brief disposal suggestions (such as suspending the rolling operation, checking the stress concentration area, etc.). The operator makes decisions and takes appropriate measures to deal with the risk according to the pushed early warning information. For example, according to the stress warning, the rolling operation is suspended, the stress condition of the jacket is checked in detail, and the bearing state or rolling speed of the SPMT is adjusted to eliminate the stress hidden danger and ensure the safety of the rolling operation.

[0027] (Four) System review and optimization after rolling After the single roll-on operation is successfully completed, the system automatically starts the review procedure, archives and stores the whole-process monitoring data, evaluation results, early warning feedback records and other information. The safety evaluation model training and optimization unit incorporates the data of this roll-on operation into the training set, combines with the past roll-on case data and simulation data, and uses machine learning algorithms (such as deep neural networks, support vector machines, etc.) to conduct a new round of training and optimization of the safety evaluation model. By analyzing the risk characteristics and model performance in this roll-on process, the algorithm parameters are fine-tuned, the model structure is optimized, and the model's ability to identify and evaluate roll-on safety risks is further improved. The feedback information collection and optimization unit summarizes the experience and suggestions accumulated by the operating personnel in the process of disposing the early warning, deeply analyzes the root cause of the evaluation deviation, and specifically corrects the safety evaluation model and the early warning threshold setting method. For example, if the operating personnel feedback that there is a deviation between the environmental risk level evaluated by the system and the actual on-site situation in a certain early warning, the related parameters in the environmental adaptability algorithm are adjusted and the environmental risk evaluation model is optimized in combination with the environmental parameters and roll-on state at that time. After continuous review and optimization, the system can increasingly accurately adapt to different types of jacket roll-on transportation needs, continuously improve the accuracy and reliability of the evaluation, and provide more high-quality and efficient safety evaluation services for subsequent offshore wind power jacket roll-on transportation.

[0028] The application provides a jacket roll-on transportation safety evaluation system based on multi-parameter monitoring, which comprehensively and real-timely monitors multi-dimensional information such as displacement, stress, inclination and environmental parameters in the roll-on transportation process, and combines advanced data processing and evaluation algorithms to realize accurate and dynamic safety evaluation of offshore wind power jacket roll-on loading operations. The system can timely warn potential safety risks, provide scientific decision-making basis for operating personnel, effectively improve the safety and reliability of roll-on transportation, and has significant technical innovation and practical application value.

[0029] Meanwhile, the contents not described in detail in the specification all belong to the prior art known by those skilled in the art.

[0030] It should be noted that, in this paper, relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.

[0031] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A safety assessment system for roll-on / roll-off transportation of jacketed structures based on multi-parameter monitoring, characterized in that, include: The multi-parameter monitoring module is installed in the roll-on / roll-off transportation operation area to monitor the displacement, stress, tilt angle and environmental parameters of the jacket during the roll-on / roll-off process in real time. The data acquisition and transmission module is connected to the multi-parameter monitoring module and is used to acquire monitoring data and transmit it to the safety assessment module; The safety assessment module is used to process and analyze the collected data to assess the safety of roll-on / roll-off transportation. The early warning and feedback module, connected to the safety assessment module, is used to trigger early warnings and provide feedback information to operators based on the assessment results.

2. The safety assessment system for roll-on / roll-off transportation of jacket structures based on multi-parameter monitoring according to claim 1, characterized in that: The multi-parameter monitoring module includes: The displacement monitoring submodule installs high-precision displacement sensors at key parts of the jacket, each unit of the self-propelled modular transport vehicle (SPMT), and key nodes of the roll-on / roll-off track. These sensors are used to track the three-dimensional displacement changes of the jacket during the roll-on / roll-off process in real time, as well as the relative position changes of the SPMT units and the deformation displacement of the roll-on / roll-off track after being subjected to force. The stress monitoring submodule deploys strain gauge stress sensors on the main structural components of the jacket, key connection parts, and stress-bearing areas in contact with SPMT, to monitor the stress distribution and magnitude of each part in real time throughout the roll-on / roll-off process. The tilt monitoring submodule is equipped with high-precision tilt meters at the top, bottom and near the center of gravity of the jacket structure, which are used to monitor the three-dimensional spatial attitude changes of the jacket structure in real time during roll-on / roll-off operations. The environmental parameter monitoring submodule is equipped with temperature and humidity sensors, wind speed and direction sensors, etc., to monitor the meteorological conditions at the roll-on / roll-off site in all aspects.

3. The safety assessment system for roll-on / roll-off transportation of jacketed structures based on multi-parameter monitoring according to claim 1, characterized in that: The data acquisition and transmission module includes: The multi-channel data acquisition card precisely interfaces with the sensors of each monitoring submodule and collects raw data of multiple parameters at a preset high-frequency acquisition rate. The data transmission network utilizes a combination of wireless and wired Ethernet to establish a high-speed, stable, and low-latency data transmission channel, enabling lossless transmission of collected data to the security assessment module.

4. The safety assessment system for roll-on / roll-off transportation of jacketed structures based on multi-parameter monitoring according to claim 1, characterized in that: The security assessment module includes: The data preprocessing unit uses filtering algorithms to remove noise interference, normalizes data of different dimensions to a standard range, and integrates multi-source data with the help of data fusion technology to reconstruct a complete data chain of real-time working conditions for the roll-on / roll-off transportation of jackets. The safety assessment algorithm unit integrates finite element analysis algorithms to simulate the stress and deformation characteristics of the jacket structure, compares the measured stress and displacement data with the theoretical safety threshold, introduces an attitude calculation algorithm to determine the attitude stability of the jacket structure based on the tilt angle data, and combines an environmental adaptability algorithm to quantify the reduction factor of environmental factors on roll-on / roll-off safety, outputting quantitative safety assessment indicators covering dimensions such as structural safety, attitude stability, and environmental adaptability. The safety assessment model training and optimization unit relies on past roll-on / roll-off case data, simulation data, and newly added data from real-time monitoring. It uses machine learning algorithms to continuously train and iterate the safety assessment model, thereby enhancing the model's generalization ability and assessment accuracy.

5. A safety assessment system for roll-on / roll-off transportation of jacket structures based on multi-parameter monitoring as described in claim 1, characterized in that: The early warning and feedback module includes: The warning threshold setting unit precisely calibrates the warning thresholds corresponding to each safety assessment indicator based on industry standards, jacket design parameters, and historical experience data. The early warning triggering and push unit compares the safety assessment indicators with the early warning thresholds in real time. When the threshold is exceeded, an early warning is triggered immediately and pushed to the operator's handheld terminal and on-site display screen according to the risk level. The push content includes the risk source, risk level, and brief handling suggestions. The feedback information collection and optimization unit collects the results and suggestions from operators regarding the handling of early warning feedback, and feeds them back to the safety assessment module and the early warning threshold setting unit to dynamically optimize the assessment model and adjust the early warning threshold.

6. The safety assessment system for roll-on / roll-off transportation of jacket structures based on multi-parameter monitoring according to claim 2, characterized in that: The high-precision displacement sensor of the displacement monitoring submodule has an accuracy of no less than 1 mm, a sampling frequency of no less than 10 Hz, and is waterproof, dustproof, and resistant to electromagnetic interference to adapt to the harsh environmental conditions of the roll-on / roll-off site.

7. A safety assessment system for roll-on / roll-off transportation of jacket structures based on multi-parameter monitoring as described in claim 2, characterized in that: The strain gauge stress sensor in the stress monitoring submodule uses a high-precision alloy strain gauge with a stable strain sensitivity coefficient and a measurement accuracy within ±0.1%. It can also compensate for the influence of temperature on the measurement results in real time, ensuring the accuracy of stress measurement under different ambient temperatures.

8. A safety assessment system for roll-on / roll-off transportation of jacketed structures based on multi-parameter monitoring according to claim 2, characterized in that: The high-precision inclinometer of the tilt monitoring submodule has a measurement range of ±90° and an accuracy of no less than 0.05°. It has a fast dynamic response capability, can accurately capture tilt changes when the jacket posture changes rapidly, and has an automatic calibration function to eliminate installation errors and zero drift during long-term use.

9. A safety assessment system for roll-on / roll-off transportation of jacket structures based on multi-parameter monitoring according to claim 2, characterized in that: The environmental parameter monitoring submodule's temperature and humidity sensors have a temperature measurement range of -40℃ to 80℃ with an accuracy of ±0.5℃ and a humidity measurement range of 0-100%RH with an accuracy of ±3%RH. The wind speed and direction sensors have a wind speed measurement range of 0-70m / s with an accuracy of ±0.3m / s and a wind direction measurement range of 0-360° with an accuracy of ±3°. All environmental parameter sensors have data storage capabilities, enabling them to temporarily store data during network failures and automatically re-upload it after network recovery.

10. A safety assessment system for roller-on / roll-off transportation of jacketed structures based on multi-parameter monitoring according to claim 4, characterized in that: The machine learning algorithm used in the safety assessment model training and optimization unit includes a deep neural network algorithm. Its network structure contains at least 5 hidden layers. The Adam optimization algorithm is used for model training, and the learning rate is dynamically adjusted within the range of 0.0001-0.

01. The model performance is evaluated by the k-fold cross-validation method to ensure the model's generalization ability and prediction accuracy under different roll-on / roll-off conditions. The model training cycle does not exceed 24 hours, and the model can be updated in a timely manner to adapt to new roll-on / roll-off task requirements.

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