Design method for laying large mattresses by using mattress-laying vessel

Abstract

The present invention relates to the technical field of vessel operations. Disclosed is a design method for laying large mattresses by using a mattress-laying vessel. In order to solve the problem of it being difficult to find potential issues in advance and provide scientific guidance for laying operations due to the failure in taking into comprehensive consideration the influences from a plurality of factors that cause a mattress-laying vessel to operate beyond its capability range, thus resulting in waste of resources, adverse impacts on quality and progress, and increased risks, in the present invention, configuration is accurately performed on the basis of multiple factors, and parameters such as vessel speed, tensile force and laying angle are dynamically adjusted, thereby ensuring the efficient and stable operation of the mattress-laying vessel and improving the laying efficiency and quality. By means of comparing actually monitored key target items with a preset database of large-mattress laying characteristics, abnormal items are identified, and therefore laying issues are effectively predicted. Response strategies with clearly defined priorities are formulated for different risk levels, such that potential issues can be solved in advance, thereby reducing operation risks. After the laying is completed, multi-source feedback data is acquired, laying results are analyzed, a weight allocation strategy for abnormal items is established, and the mechanism is continuously improved, thereby further optimizing subsequent mattress laying operations.

Description

Laying out large-scale laying design method for laying out ships Technical Field

[0001] This invention relates to the field of ship operation technology, and in particular to a method for designing large-scale laying layouts on a laying vessel. Background Technology

[0002] In fields such as water conservancy and marine engineering, the large-scale deployment and laying of rafts by rafting vessels is a crucial foundational task. Chinese patent application CN102979060A discloses a method for soft rafting construction. This patent achieves real-time positioning of the rafts during the soft rafting process by introducing underwater beacons and transmitters / receivers, thereby enabling real-time and accurate feedback and control of the construction process.

[0003] However, while the aforementioned patent provides an economical, environmentally friendly, fast, and accurate method for soft material laying construction, it still has the following problems:

[0004] Laying operations are affected by a variety of factors, such as the geographical location of the laying area, the location of the large-scale layout storage, and the size parameters of the laying vessel itself. These factors can collectively affect the efficiency and quality of the laying vessel's operations, causing it to be unable to operate in its optimal state, resulting in resource waste or poor laying effects. This can also cause the laying vessel to face situations beyond its capabilities during operations, increasing operational risks and even affecting the quality and progress of the laying.

[0005] Furthermore, the lack of comprehensive capture and analysis of key objectives during the large-scale layout laying process makes it difficult to detect potential problems in advance, thus hindering timely and effective countermeasures and increasing laying risks. It also fails to systematically collect and integrate the characteristics of different types of large-scale layouts and their corresponding laying environments, which is not conducive to providing scientific guidance and optimization for the laying process. Summary of the Invention

[0006] The purpose of this invention is to provide a design method for large-scale laying of paving vessels. By rationally configuring and dynamically adjusting the operating parameters, the method ensures that the paving vessel is always in the best operating condition, can detect and resolve potential problems in advance, reduce laying risks, ensure smooth operation, and improve laying efficiency and quality, thereby solving the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] The design method for the large-scale layout and laying of the rafting vessel includes:

[0009] Collect operational preparation data, including the geographical location of the laying area, the geographical location of the large-scale layout storage location, the size parameters of the laying vessel, the diameter of the laying pipeline, and the laying depth. Based on the operational preparation data, determine the laying environment and the type of large-scale layout, and determine the operational capabilities and operational span of the laying vessel.

[0010] Based on the data collection results of the operation preparation, and combined with the preset large-scale layout characteristic database, the range of conventional operation parameters under similar laying environments and large-scale layout types is retrieved. Combined with the operation capacity and operation span range of the laying vessel, the operation parameters of the laying vessel are configured.

[0011] The system acquires real-time monitoring data from various monitoring devices during the laying operation, capturing key objectives in the large-scale layout laying process. These key objectives include: laying speed, laying direction, laying depth difference, pause points during laying, and pause time. - ;

[0012] The key target items captured in the current large-scale layout laying process are compared with the large-scale layout laying characteristic database. The non-routine items in the captured large-scale layout laying key target items are marked and identified as laying anomalies.

[0013] Based on the retrieved historical data, the identified laying anomalies are predicted and risk assessed. Based on the prediction results, corresponding solutions are matched to generate laying risk response strategies. Different response measures are taken under different anomalies, and dynamic adjustments are made according to the actual situation.

[0014] Based on the forecast results and the risk response strategy for laying, the operating parameters of the configured laying vessel are adjusted, including the vessel speed adjustment value, tension adjustment value, and laying angle adjustment value.

[0015] Obtain feedback data after the paving is completed, analyze the paving results, analyze the impact of non-standard items that occurred in this paving operation on the paving results, and determine whether non-standard items should be included in the key target items for subsequent paving reference.

[0016] Furthermore, configure the laying vessel's operational parameters, specifically including:

[0017] To determine the operational capabilities of the laying vessel;

[0018] Geographic information monitoring equipment is used to obtain the operational span between the laying area and the large-scale storage location;

[0019] Based on the large-scale laying characteristic database, the parameter range of conventional laying operations is obtained;

[0020] Based on the operational capabilities of the laying vessel and the parameter range of conventional laying operations, configure the operational parameters of the laying vessel within the operational span.

[0021] Furthermore, the pre-defined large-scale layout and laying characteristic database specifically includes:

[0022] Based on the characteristics of large-scale layout, a large-scale layout classification label is determined and input into a preset neural network for learning. The classification expression of the large-scale layout classification label is determined, and a large-scale layout classification model is constructed based on the classification expression.

[0023] The acquired work preparation data is input into the large-scale layout classification model for classification, the large-scale layout type is determined, the environmental parameters of the paving area are obtained based on the paving environment characteristics, and the paving effect of different large-scale layout types in the paving area is predicted.

[0024] Based on the real-time capture of status information during the large-scale laying process by monitoring equipment, laying characteristic features are extracted, and laying characteristics under different conditions are identified based on the extraction results, and possible abnormal situations are identified.

[0025] The large-scale layout type, laying environment and corresponding laying characteristics are integrated, and the integrated data is mapped to a fixed-dimensional vector space based on the large-scale layout type to generate a large-scale layout laying characteristic database.

[0026] Furthermore, key objectives in the large-scale deployment process also include:

[0027] The displacement volume of the laying vessel is calculated based on its size parameters, the displacement of the laying vessel is determined, and the effective load of the laying vessel is calculated based on the displacement and the empty weight of the laying vessel.

[0028] The weight per unit length of the pipeline on the laying vessel is calculated based on the diameter and depth of the pipeline. The length of pipeline that the laying vessel can lay is calculated based on the effective load of the laying vessel and the weight per unit length of the pipeline.

[0029] Based on the geographical location of the laying area, determine the starting and ending coordinates of the laying area, determine the centerline of the laying area based on the starting and ending coordinates, and determine the navigation route of the laying vessel based on the centerline and the overall laying direction.

[0030] The number of voyages by the laying vessel is calculated based on the navigation route and pipeline length. The number of laying vessels is then calculated based on the number of voyages and the working efficiency of the laying vessel. In the formula, N represents the number of laying vessels deployed. c E indicates the number of voyages. f This indicates the working efficiency of the laying vessel;

[0031] Based on the navigation route and the number of vessels deployed, the spacing between the laying vessels is determined, and the placement of the laying vessels is determined based on the spacing and the size parameters of the laying vessels.

[0032] Furthermore, the non-routine items in the large-scale layout of the target items are marked and captured, specifically including:

[0033] The laying speed and laying depth difference of large-scale layout are determined as conventional target items, and the range values ​​of each conventional target item are obtained based on historical data and industry standards.

[0034] The pause points, large-scale laying direction, and pause time during the laying process are defined as variable target items. The monitoring values ​​of each variable target item are obtained through real-time monitoring equipment, and the monitoring values ​​of each variable target item are used as the marker values ​​of non-routine items.

[0035] By comparing and analyzing the monitored non-routine items based on the range values ​​of the conventional target items, abnormal situations in the large-scale layout can be identified.

[0036] Furthermore, generating deployment risk response strategies also includes: establishing a risk assessment matrix for different abnormal situations and predicted deployment problems, classifying risk levels based on the risk assessment matrix, and determining the priority of response projects in each deployment risk response strategy based on each risk level.

[0037] Furthermore, a risk assessment matrix is ​​established to address different anomalies and prediction issues. Based on this matrix, risk levels are categorized, including:

[0038] Retrieve the probability of occurrence for each abnormal situation during the large-scale layout and laying process;

[0039] Retrieve the predicted probability of occurrence for each laying problem;

[0040] Retrieve the large-scale laying delay rate and economic loss rate corresponding to abnormal situations and laying problems;

[0041] The abnormality coefficients corresponding to the abnormal situations and laying problems are obtained by using the large-scale laying delay rate and economic loss rate corresponding to the abnormal situations and laying problems.

[0042] The abnormality coefficient corresponding to the abnormal situation is obtained by the following formula:

[0043] Among them, R 01 The abnormality coefficient corresponds to the abnormal situation; T and J represent the large-scale layout delay rate and economic loss rate corresponding to the abnormal situation; T c and J c This indicates the reference values ​​for the large-scale layout delay rate and economic loss rate corresponding to the preset abnormal conditions; k 01The first adjustment coefficient is obtained through experiments based on actual application scenarios, and is used to adjust the relative importance between the large-scale laying delay rate and the economic loss rate in the event of abnormal situations. The value range of the adjustment coefficient is 0.35-0.72.

[0044] Furthermore, the anomaly coefficient corresponding to the aforementioned laying problem is obtained using the following formula:

[0045] Among them, R 02 The coefficient represents the anomaly coefficient corresponding to the laying problem; G and H represent the large-scale laying delay rate and economic loss rate corresponding to the laying problem; k 02 The second adjustment coefficient is obtained through experiments based on actual application scenarios, and is used to adjust the relative importance between the large-scale laying delay rate and the economic loss rate when laying problems occur. The value range of the adjustment coefficient is 0.21-0.74.

[0046] A risk assessment matrix is ​​established using the anomaly coefficients corresponding to the aforementioned abnormal situations and laying problems, and risk levels are classified.

[0047] Furthermore, a risk assessment matrix is ​​established using the anomaly coefficients corresponding to the aforementioned abnormal situations and laying problems, and risk levels are classified, including:

[0048] When the anomaly coefficient exceeds the first anomaly coefficient threshold, the severity level of the anomaly and the laying problem is determined to be "highly severe"; when the anomaly coefficient does not exceed the first anomaly coefficient threshold, but exceeds the second anomaly coefficient threshold, the severity level of the anomaly and the laying problem is determined to be "moderately severe"; when the anomaly coefficient does not exceed the second anomaly coefficient threshold, the severity level of the anomaly and the laying problem is determined to be "moderately severe".

[0049] Extract the severity level parameter corresponding to each abnormal situation, wherein the severity level includes severe severity, moderate severity and mild severity, and the severity level parameter corresponding to severe severity is 0.8, the severity level parameter corresponding to moderate severity is 0.6, and the severity level parameter corresponding to mild severity is 0.4.

[0050] Extract the severity level parameter corresponding to each laying problem, wherein the severity level includes major severity, moderate severity and minor severity, and the severity level parameter corresponding to major severity is 1, the severity level parameter corresponding to moderate severity is 0.8, and the severity level parameter corresponding to minor severity is 0.5.

[0051] A risk assessment matrix is ​​established by combining the probability of occurrence and severity level parameters of each abnormal situation with the probability of occurrence and severity level parameters of each laying problem.

[0052] The structure of the risk assessment matrix is ​​as follows:

[0053] Where A represents the risk assessment matrix; Y1, Y2, ..., Y... n S represents the probability of occurrence for each of the n abnormal situations; y1 S y2 ...S yn W1, W2, ..., W represent the severity level parameters corresponding to n abnormal situations. m Let S represent the probability of occurrence for each of the m laying problems; w1 S w2 ...S wm These represent the severity level parameters corresponding to the m laying problems; when m≠n, the empty elements in the matrix are filled using the occurrence probability of 0 and the severity level parameter of 0.

[0054] The norm of the risk assessment matrix is ​​obtained based on its structure.

[0055] The norm of the risk assessment matrix is ​​compared with a preset norm threshold.

[0056] If the norm of the risk assessment matrix is ​​lower than the preset first norm threshold, the risk level is determined to be low.

[0057] If the norm of the risk assessment matrix is ​​not lower than the preset first norm threshold, but lower than the second norm threshold, the risk level is determined to be medium.

[0058] If the norm of the risk assessment matrix is ​​not lower than the preset second norm threshold, the risk level is determined to be high.

[0059] Furthermore, the operational parameters of the laying vessel were adjusted, specifically including:

[0060] Based on the predicted laying problems and the formulated response strategies, determine the types of laying vessel operation parameters that need to be adjusted, and determine the adjustment direction and objectives for each laying vessel operation parameter;

[0061] Based on the risk level of the laying problem and the priority of the response items in the laying risk response strategy, combined with the operating parameters of the laying vessel, the large-scale layout characteristics and the laying environment, the vessel speed adjustment value, tension adjustment value and laying angle adjustment value are calculated.

[0062] The corresponding operating parameters of the laying vessel are adjusted according to the calculated adjustment values, and the adjusted operating parameters are compared and evaluated to determine whether the adjustment effect has met expectations.

[0063] Furthermore, feedback data after the laying is completed is obtained, including actual topographic data of the laying area, actual laying position and status data of the large-scale layout, operation data of the laying vessel throughout the operation, and laying quality inspection data.

[0064] Compared with the prior art, the beneficial effects of the present invention are:

[0065] By comprehensively configuring and dynamically adjusting parameters such as vessel speed, tension, and laying angle based on multiple factors, the laying vessel can operate efficiently and stably, improving laying efficiency and quality. Through a pre-set database of large-scale laying characteristics, key monitored targets are compared to this database, and non-routine items are marked, enabling effective prediction of laying problems. Prioritized response strategies are developed for different risk levels, allowing for proactive resolution of potential issues and reducing operational risks. After laying is completed, multi-source feedback data is acquired, laying results are analyzed, and a weighting strategy for non-routine items is established to determine whether they should be included in subsequent references. This continuous improvement mechanism continuously optimizes subsequent laying operations, accumulates experience, and makes the entire laying process more scientific and reliable, providing strong support for related projects. Attached Figure Description

[0066] Figure 1 is a flowchart of the large-scale layout design method for the laying vessel of the present invention. Detailed Implementation

[0067] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] To address the technical issues that arise from the failure to comprehensively consider the influence of multiple factors such as regional location and ship parameters on the laying process, which can easily lead to the laying vessel operating beyond its capacity, resulting in resource waste, impacting quality and schedule, and increasing risks, and the lack of comprehensive monitoring and analysis of key laying objectives and systematic integration of large-scale layout characteristics and environmental data, making it difficult to identify problems in advance and scientifically guide the laying process, please refer to Figure 1. This embodiment provides the following technical solution:

[0069] The design method for the large-scale layout and laying of the rafting vessel includes:

[0070] Collect operational preparation data, namely, basic information and scope related to the laying operation, including obtaining the geographical location of the laying area (clarifying its latitude and longitude range, topographic features, etc.), the geographical location of the large-scale layout storage location (planning transportation routes), the size parameters of the laying vessel (such as ship length, ship width, ship height and draft), the diameter of the laying pipeline and the laying depth. Based on the operational preparation data, determine the laying environment and the large-scale layout type, and determine the operational capabilities and operational span of the laying vessel.

[0071] Based on the data collection results of the operation preparation, and combined with the preset large-scale layout characteristic database, the range of conventional operation parameters under similar laying environments and large-scale layout types is retrieved. Combined with the operation capacity and operation span range of the laying vessel, the operation parameters of the laying vessel are configured.

[0072] The system acquires real-time monitoring data from various monitoring devices during the laying process, capturing key targets in the large-scale layout laying process. These key targets include: laying speed (the average speed at which the large-scale layout is laid within the laying area); laying direction (the angle formed by the laying path and different directional axes after establishing a spatial rectangular coordinate system in the laying area); laying depth difference (the difference between the laying depths captured at two monitoring time points); pause points during the laying process (the points where the large-scale layout stops at the target location in the laying area); and pause time (the time consumed when the large-scale layout stops at the target location in the laying area).

[0073] The key target items captured in the current large-scale layout laying process are compared with the large-scale layout laying characteristic database. The non-routine items in the captured large-scale layout laying key target items are marked and identified as laying anomalies.

[0074] Based on the retrieved historical data, predictions and risk assessments are performed on the identified laying anomalies. Based on the prediction results, corresponding solutions are matched, such as adjusting the operating parameters of the laying vessel, adjusting the type of large-scale layout, adjusting the laying method, and carrying out equipment maintenance. Laying risk response strategies are generated, and different response measures are taken under different anomalies, and dynamic adjustments are made according to the actual situation.

[0075] In this embodiment, the method further includes: establishing a risk assessment matrix for different abnormal situations and predicted laying problems; classifying risk levels based on the risk assessment matrix; and determining the priority of response items in each laying risk response strategy based on each risk level. For high-risk laying problems, the most direct and effective response measures are prioritized, such as immediately stopping the laying operation and conducting a comprehensive inspection and debugging of the equipment. For medium-risk problems, the operating parameters can be gradually adjusted during the laying process, while closely monitoring the development trend of the problem. For low-risk problems, the parameters can be appropriately optimized in subsequent laying plans for prevention and control.

[0076] Based on the forecast results and the risk response strategy for laying, the operating parameters of the configured laying vessel are adjusted, including the vessel speed adjustment value, tension adjustment value, and laying angle adjustment value.

[0077] After the paving is completed, feedback data is obtained, including actual topographic data of the paving area (obtained through high-precision measuring equipment), actual paving location and status data of the large layout (obtained using non-destructive testing technology and on-site survey), operation data of the paving vessel throughout the operation (extracted from equipment logs and monitoring systems), and paving quality inspection data (such as the test results of paving flatness, density, and other indicators). The paving results are analyzed to determine the degree of impact of non-standard items that occurred during this paving operation on the paving results, to establish a non-standard item weight allocation strategy, and to determine whether non-standard items should be included in the key target items for subsequent paving reference.

[0078] In this embodiment, by comprehensively acquiring information from multiple sources to configure operational parameters, the operation is ensured to be carried out within the vessel's capabilities, reducing resource waste and operational risks. Key target items in the large-scale layout laying process are captured, and a pre-set large-scale layout laying characteristic database is compared with the database. Non-standard items are marked, effectively identifying potential problems in advance. Simultaneously, a risk assessment matrix is ​​established to classify risk levels, determine the priority of response items, and generate dynamic laying risk response strategies. Targeted measures can be taken promptly to reduce laying risks. The operational parameters of the laying vessel can be dynamically adjusted to adapt to changes during the laying process, improving the flexibility and adaptability of the laying operation. Multi-source feedback data after laying is acquired and the laying results are analyzed. A weight allocation strategy for non-standard items is established to determine whether to include them in subsequent references, which is beneficial for summarizing lessons learned, continuously improving the laying method, and achieving scientific guidance for laying operations.

[0079] Specifically, a risk assessment matrix is ​​established to address different anomalies and predictive deployment issues. Based on this matrix, risk levels are categorized, including:

[0080] Retrieve the probability of occurrence for each abnormal situation during the large-scale layout and laying process;

[0081] Retrieve the predicted probability of occurrence for each laying problem;

[0082] Retrieve the large-scale laying delay rate and economic loss rate corresponding to abnormal situations and laying problems;

[0083] The abnormality coefficients corresponding to the abnormal situations and laying problems are obtained by using the large-scale laying delay rate and economic loss rate corresponding to the abnormal situations and laying problems.

[0084] The abnormality coefficient corresponding to the abnormal situation is obtained by the following formula:

[0085] Among them, R 01 The abnormality coefficient corresponds to the abnormal situation; T and J represent the large-scale layout delay rate and economic loss rate corresponding to the abnormal situation; T c and J c This indicates the reference values ​​for the large-scale layout delay rate and economic loss rate corresponding to the preset abnormal conditions; k 01 The first adjustment coefficient is obtained through experiments based on actual application scenarios, and is used to adjust the relative importance between the large-scale laying delay rate and the economic loss rate in the event of abnormal situations. The value range of the adjustment coefficient is 0.35-0.72.

[0086] Furthermore, the anomaly coefficient corresponding to the aforementioned laying problem is obtained using the following formula:

[0087] Among them, R 02 The coefficient represents the anomaly coefficient corresponding to the laying problem; G and H represent the large-scale laying delay rate and economic loss rate corresponding to the laying problem; k 02 The second adjustment coefficient is obtained through experiments based on actual application scenarios. It is used to adjust the relative importance between the large-scale laying delay rate and the economic loss rate when laying problems occur. The value range of the adjustment coefficient is 0.21-0.74.

[0088] The technical effects of the above solution are as follows: This solution comprehensively considers various abnormal situations and predicted laying problems that may occur during large-scale laying. By retrieving their corresponding occurrence probabilities, delay rates, and economic loss rates, a comprehensive and systematic risk assessment system is established. This helps to fully identify and manage potential risks, ensuring the smooth progress of the project. The solution introduces anomaly coefficients to quantitatively assess the risks of abnormal situations and laying problems. This quantitative method makes the severity of risks more intuitive and easier to understand, providing decision-makers with accurate risk information and helping to make more scientific and reasonable decisions. The adjustment coefficients (k01 and k02) in the solution can be obtained experimentally according to actual application scenarios, which increases the flexibility and applicability of the solution. Different projects or application scenarios may require different risk assessment standards; by adjusting the adjustment coefficients, it can be ensured that the risk assessment results are more consistent with the actual situation. This solution provides a structured risk management method, which helps organizations form a continuous risk management culture. By continuously accumulating experience and data, the settings of the risk assessment matrix and adjustment coefficients can be further improved, thereby improving the accuracy and efficiency of risk management.

[0089] In summary, this technical solution provides strong support for the smooth progress of the project and risk management by comprehensively and systematically identifying and managing potential risks, quantitatively assessing the severity of risks, providing flexible and applicable risk assessment standards, clearly classifying risk levels, constructing an intuitive risk assessment matrix, and promoting continuous improvement in risk management.

[0090] Specifically, a risk assessment matrix is ​​established using the anomaly coefficients corresponding to the aforementioned abnormal situations and laying problems, and risk levels are classified, including:

[0091] When the anomaly coefficient exceeds the first anomaly coefficient threshold, the severity level of the anomaly and the laying problem is determined to be "highly severe"; when the anomaly coefficient does not exceed the first anomaly coefficient threshold, but exceeds the second anomaly coefficient threshold, the severity level of the anomaly and the laying problem is determined to be "moderately severe"; when the anomaly coefficient does not exceed the second anomaly coefficient threshold, the severity level of the anomaly and the laying problem is determined to be "moderately severe".

[0092] Extract the severity level parameter corresponding to each abnormal situation, wherein the severity level includes severe severity, moderate severity and mild severity, and the severity level parameter corresponding to severe severity is 0.8, the severity level parameter corresponding to moderate severity is 0.6, and the severity level parameter corresponding to mild severity is 0.4.

[0093] Extract the severity level parameter corresponding to each laying problem, wherein the severity level includes major severity, moderate severity and minor severity, and the severity level parameter corresponding to major severity is 1, the severity level parameter corresponding to moderate severity is 0.8, and the severity level parameter corresponding to minor severity is 0.5.

[0094] A risk assessment matrix is ​​established by combining the probability of occurrence and severity level parameters of each abnormal situation with the probability of occurrence and severity level parameters of each laying problem.

[0095] The structure of the risk assessment matrix is ​​as follows:

[0096] Where A represents the risk assessment matrix; Y1, Y2, ..., Y... n S represents the probability of occurrence for each of the n abnormal situations; y1 S y2 ...S yn W1, W2, ..., W represent the severity level parameters corresponding to n abnormal situations. mLet S represent the probability of occurrence for each of the m laying problems; w1 S w2 ...S wm These represent the severity level parameters corresponding to the m laying problems; when m≠n, the empty elements in the matrix are filled using the occurrence probability of 0 and the severity level parameter of 0.

[0097] The norm of the risk assessment matrix is ​​obtained based on its structure.

[0098] The norm of the risk assessment matrix is ​​compared with a preset norm threshold.

[0099] If the norm of the risk assessment matrix is ​​lower than the preset first norm threshold, the risk level is determined to be low.

[0100] If the norm of the risk assessment matrix is ​​not lower than the preset first norm threshold, but lower than the second norm threshold, the risk level is determined to be medium.

[0101] If the norm of the risk assessment matrix is ​​not lower than the preset second norm threshold, the risk level is determined to be high.

[0102] The technical effects of the above solution are as follows: By setting a first anomaly coefficient threshold and a second anomaly coefficient threshold, the solution classifies the severity levels of abnormal situations and laying problems into three levels: high severity, medium severity, and moderate severity. This clear classification helps to prioritize different risks, thereby rationally allocating resources and formulating countermeasures. Using the occurrence probability and severity level parameters of abnormal situations and laying problems, the solution constructs a risk assessment matrix. This matrix intuitively displays the risk level and occurrence probability of various risks and problems, providing strong support for risk management and decision-making. When the number of abnormal situations is not equal to the number of laying problems, the solution fills in the empty elements in the matrix using an occurrence probability of 0 and a severity level parameter of 0. This method ensures the integrity and consistency of the risk assessment matrix, avoiding bias in assessment results due to missing data. Simultaneously, by setting different anomaly coefficient thresholds, the severity levels of abnormal situations and laying problems are further subdivided into three levels: high severity, medium severity, and moderate severity. This subdivision makes risk management more refined, enabling different countermeasures to be taken for different levels of risk, thereby improving the targeting and effectiveness of risk management. Specific severity level parameters were assigned to each severity level, reflecting the degree of severity of different risk levels. When establishing the risk assessment matrix, the probability of occurrence of anomalies and severity level parameters, as well as the probability of occurrence of laying problems and their severity level parameters, were combined. This allows decision-makers to more intuitively understand the overall situation and priorities of risks, providing strong support for developing scientific and reasonable response measures. The plan comprehensively considers multiple factors such as the probability of occurrence of anomalies and laying problems, delay rate, and economic loss rate, quantifying risk by calculating anomaly coefficients. Simultaneously, the risk assessment matrix integrates multiple risk factors to form a comprehensive and accurate risk assessment system. This helps to comprehensively identify and manage potential risks, improving the accuracy and reliability of risk assessment. By establishing the risk assessment matrix, organizations can regularly review and update risk data, continuously optimizing the risk assessment model. With the accumulation of experience and the enrichment of data, risks can be assessed more accurately, improving the efficiency and effectiveness of risk management. At the same time, this also helps to foster a continuous risk management culture, driving the organization's continuous progress in risk management.

[0103] Meanwhile, this solution provides a standardized framework for risk assessment by retrieving a pre-set risk assessment matrix. This ensures the consistency and repeatability of the assessment process and reduces assessment bias caused by human factors. By calculating the norm of the risk assessment matrix and comparing it with a pre-set norm threshold, the solution can quantitatively classify risk levels. This quantitative method makes the determination of risk levels more objective and accurate, facilitating subsequent risk management and the formulation of response measures. Using the risk assessment matrix and norm comparison method, risk levels can be quickly determined without complex analysis and calculations. This greatly improves the efficiency of risk assessment, especially in emergency situations requiring rapid response. Clear risk level classification can provide strong support for decision-makers. Different risk levels correspond to different response measures and resource allocations, which helps decision-makers make reasonable decisions based on risk levels and optimize resource allocation. The pre-set norm threshold in this solution can be adjusted and optimized according to actual conditions. With the deepening of risk management practices and the accumulation of experience, the risk assessment matrix and norm threshold can be continuously improved, thereby improving the accuracy and effectiveness of risk management. Through clear risk level classification and corresponding response measures, this solution helps enhance the organization's awareness and attention to risk. This can foster a positive risk management culture within the organization and improve the overall level of risk management.

[0104] In summary, this technical solution, by classifying risk levels based on a risk assessment matrix, achieves standardization, quantification, efficiency, and decision support in risk assessment, which helps organizations improve their risk management capabilities and levels.

[0105] In this embodiment, configuring the laying vessel operation parameters specifically includes:

[0106] To determine the operational capabilities of the laying vessel;

[0107] Geographic information monitoring equipment is used to obtain the operational span between the laying area and the large-scale storage location;

[0108] Based on the large-scale laying characteristic database, the parameter range of conventional laying operations is obtained;

[0109] Based on the operational capabilities of the laying vessel and the parameter range of conventional laying operations, configure the operating parameters of the laying vessel within the operating span to ensure normal operation of the laying vessel.

[0110] In this embodiment, by combining the parameter range of conventional laying operations, the laying vessel can operate efficiently and safely within the operational span between the laying area and the large-scale storage location. This avoids resource waste, low operational efficiency, and potential safety risks caused by improper configuration of operational parameters, improves the overall planning and execution capabilities of the laying operation, and ensures the smooth progress of the laying operation and high-quality laying results.

[0111] In this embodiment, the key objectives in the large-scale laying process also include:

[0112] The displacement volume of the laying vessel is calculated based on its size parameters, the displacement of the laying vessel is determined, and the effective load of the laying vessel is calculated based on the displacement and the empty weight of the laying vessel.

[0113] The weight per unit length of the pipeline on the laying vessel is calculated based on the diameter and depth of the pipeline. The length of pipeline that the laying vessel can lay is calculated based on the effective load of the laying vessel and the weight per unit length of the pipeline.

[0114] Based on the geographical location of the laying area, determine the starting and ending coordinates of the laying area, determine the centerline of the laying area based on the starting and ending coordinates, and determine the navigation route of the laying vessel based on the centerline and the overall laying direction.

[0115] The number of voyages by the laying vessel is calculated based on the navigation route and pipeline length. The number of laying vessels is then calculated based on the number of voyages and the working efficiency of the laying vessel. In the formula, N represents the number of laying vessels deployed. c E indicates the number of voyages. f This indicates the working efficiency of the laying vessel;

[0116] Based on the navigation route and the number of vessels to be deployed, determine the spacing between the laying vessels, and then determine the placement of the laying vessels based on the spacing and the dimensional parameters (length and width) of the laying vessels.

[0117] In this embodiment, by using parameters such as the size of the laying vessel, the effective load, the weight per unit length of the pipeline, and the layable length are determined, accurately grasping the load-bearing capacity and operating range, ensuring safety, improving resource utilization, determining the navigation route based on the location of the laying area, clarifying the spacing and location, scientifically planning the operation path and layout, improving operation efficiency, and ensuring the quality and uniformity of large-scale laying.

[0118] In this embodiment, the preset large-scale layout characteristic database specifically includes:

[0119] Based on the characteristics of large-scale layout, a large-scale layout classification label is determined and input into a preset neural network for learning. The classification expression of the large-scale layout classification label is determined, and a large-scale layout classification model is constructed based on the classification expression.

[0120] The acquired work preparation data is input into the large-scale layout classification model for classification to determine the large-scale layout type. Based on the characteristics of the laying environment, the geological conditions, water flow velocity and other environmental parameters of the laying area are obtained to predict the laying effect of different large-scale layout types in the laying area.

[0121] Based on the real-time capture of status information during the large-scale laying process by monitoring equipment, laying characteristic features such as settlement rate, laying depth, tension change, and deformation degree are extracted. Based on the extraction results, laying characteristics under different conditions are identified, such as uneven settlement, severe deformation, and excessive tension. Possible abnormalities such as equipment failure and data error are also identified.

[0122] The large-scale layout type, laying environment and corresponding laying characteristics are integrated, and the integrated data is mapped to a fixed-dimensional vector space based on the large-scale layout type to generate a large-scale layout laying characteristic database.

[0123] In this embodiment, the expression of the large-scale layout classification identifier is determined by learning a preset neural network, and a classification model is constructed. This model can accurately classify the large-scale layout, predict the laying effect of different types of large-scale layouts in advance by combining laying environment parameters, capture laying status information in real time, extract characteristic features and identify anomalies, and promptly discover potential problems. The model is integrated to generate a database, providing a comprehensive and accurate reference for subsequent large-scale layout laying operations, thereby improving laying efficiency and quality.

[0124] In this embodiment, the non-routine items in the large-scale layout key target items for marker capture specifically include:

[0125] The laying speed and laying depth difference of large-scale layouts are defined as conventional target items, and the range values ​​of each conventional target item are obtained based on historical data and industry standards, such as the optimal laying speed range and laying depth variation range for different types of large-scale layouts.

[0126] The pause points, the general laying direction, and the pause time during the laying process are defined as variable target items. The monitoring values ​​of each variable target item are obtained through real-time monitoring equipment, and the monitoring values ​​of each variable target item are used as the marker values ​​of non-routine items, such as the location of the pause point, the change range of the laying direction, and the length of the pause time.

[0127] By comparing and analyzing the monitored non-routine items based on the range values ​​of the conventional target items, abnormal situations in large-scale laying can be identified, such as laying speed being too fast or too slow, deviation in laying direction, and excessive pause time, thereby predicting possible laying problems.

[0128] In this embodiment, the pause point during the laying process—that is, the location of the target object in the laying area where the large layout stops during the laying process—is used as the marker value of the non-routine item; for example, if the large layout frequently stops at a certain obstacle, that location is a marker point of non-routine situation.

[0129] Large-scale layout laying direction: After establishing a spatial rectangular coordinate system in the laying area, the angle formed by the large-scale layout laying path and different directional axes, and the range of its variation (the range of variation of the laying direction) is used as a marker value for non-routine items; for example, if the laying direction deviates significantly from the preset direction, it is considered a non-routine case;

[0130] Pause Time: The time it takes for the large layout to remain at the target location in the layout area. Its length (the length of the pause time) serves as a marker value for abnormal items. If the pause time is too long and exceeds the normal range, it is considered an abnormal situation.

[0131] In this embodiment, conventional target items and their range values ​​are clearly defined to provide a reference standard for judging anomalies. Stop points, laying direction, and stop time are identified as variable target items, and their monitoring values ​​are used as marker values. This can accurately capture dynamic anomalies during laying. By comparing and analyzing with the range values ​​of conventional target items, abnormal situations such as abnormal speed, directional deviation, and excessively long stop times can be quickly identified, and potential laying problems can be predicted. This helps to take measures in advance, prevent problems from worsening, improve laying quality and efficiency, and reduce operational risks.

[0132] In this embodiment, adjusting the laying vessel operation parameters specifically includes:

[0133] Based on the predicted laying problems and the formulated response strategies, determine the types of laying vessel operation parameters that need to be adjusted, including vessel speed, tension, and laying angle. Determine the adjustment direction and target for each laying vessel operation parameter. For example, if it is predicted that the large-scale layout laying speed is too fast, resulting in uneven laying, then the target for adjusting the vessel speed is to reduce the vessel speed to reach a suitable laying speed range; if it is found that excessive tension during the laying process affects the laying quality of the large-scale layout, then the direction for adjusting the tension is to reduce the tension.

[0134] Based on the risk level of the laying problem and the priority of the response items in the laying risk response strategy, combined with the operating parameters of the laying vessel, the characteristics of the large-scale layout, and the laying environment, the adjustment values ​​of vessel speed, tension, and laying angle are calculated. For example, based on the characteristics of the laying vessel's power system, the current vessel speed, and the required adjustment target, the vessel speed adjustment value is calculated using dynamic formulas; based on the material, structure, and stress conditions of the large-scale layout, combined with the data from tension sensors, the tension adjustment value is calculated using mechanical principles; based on the topography, water flow direction, and preset laying path of the laying area, the laying angle adjustment value is calculated using mathematical methods such as trigonometric functions.

[0135] The corresponding operating parameters of the laying vessel are adjusted according to the calculated adjustment values, and the adjusted operating parameters are compared and evaluated to determine whether the adjustment effect has met expectations. When adjusting the vessel speed, the speed is increased or decreased by a small amount each time, while observing the vessel's sailing stability and the laying of the large layout. When adjusting the tension, the data of the tension sensor is monitored in real time to gradually bring it closer to the adjusted target value.

[0136] In this embodiment, the types, directions, and objectives of the operational parameters that need to be adjusted are clearly defined based on the predicted problems and response strategies, making the adjustments targeted. The adjustment values ​​are calculated by combining the risk level, ship and fabric characteristics, and environment. The adjustments are then performed according to the adjusted values ​​and the effects are evaluated. Small adjustments are made and monitored in real time to ensure a smooth adjustment process and that the adjustment effects meet expectations, thereby improving the quality and efficiency of large-scale fabric laying.

[0137] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method of laying a large array of a laying vessel, characterized in that include: Based on the collected work preparation data, the laying environment and large layout type are determined, the working capacity and working span range of the laying vessel are determined, and based on the work preparation data collection results, the range of conventional working parameters under similar laying environments and large layout types is retrieved in combination with the preset large layout laying characteristic database. The working parameters of the laying vessel are configured in combination with the working capacity and working span range of the laying vessel. The system acquires real-time monitoring data from various monitoring devices during the laying process, captures key target items in the large-scale laying process, compares the currently captured key target items in the large-scale laying process with the large-scale laying characteristic database, marks the non-routine items in the captured key target items in the large-scale laying process, and identifies them as laying anomalies. Based on the retrieved historical data, predictions and risk assessments are performed on the identified laying anomalies, and the operational parameters of the configured laying vessel are adjusted accordingly. Obtain feedback data after the paving is completed, analyze the impact of non-standard items that occurred during this paving operation on the paving results, and determine whether non-standard items should be included in the key target items for subsequent paving reference.

2. The method of claim 1, wherein, The configuration parameters for the laying vessel operation specifically include: To determine the operational capabilities of the laying vessel; Geographic information monitoring equipment is used to obtain the operational span between the laying area and the large-scale storage location; Based on the large-scale laying characteristic database, the parameter range of conventional laying operations is obtained; Based on the operational capabilities of the laying vessel and the parameter range of conventional laying operations, configure the operational parameters of the laying vessel within the operational span.

3. The method of claim 2, wherein, The pre-defined large-scale layout and laying characteristic database includes: Based on the characteristics of large-scale layout, a large-scale layout classification label is determined and input into a preset neural network for learning. The classification expression of the large-scale layout classification label is determined, and a large-scale layout classification model is constructed based on the classification expression. The acquired work preparation data is input into the large-scale layout classification model for classification, the large-scale layout type is determined, the environmental parameters of the paving area are obtained based on the paving environment characteristics, and the paving effect of different large-scale layout types in the paving area is predicted. Based on the real-time capture of status information during the large-scale laying process by monitoring equipment, laying characteristic features are extracted, and laying characteristics under different conditions are identified based on the extraction results, and possible abnormal situations are identified. The large-scale layout type, laying environment and corresponding laying characteristics are integrated, and the integrated data is mapped to a fixed-dimensional vector space based on the large-scale layout type to generate a large-scale layout laying characteristic database.

4. The method of claim 1, wherein, Key objectives during the large-scale layout and laying process also include: The displacement volume of the laying vessel is calculated based on its size parameters, the displacement of the laying vessel is determined, and the effective load of the laying vessel is calculated based on the displacement and the empty weight of the laying vessel. The weight per unit length of the pipeline on the laying vessel is calculated based on the diameter and depth of the pipeline. The length of pipeline that the laying vessel can lay is calculated based on the effective load of the laying vessel and the weight per unit length of the pipeline. Based on the geographical location of the laying area, determine the starting and ending coordinates of the laying area, determine the centerline of the laying area based on the starting and ending coordinates, and determine the navigation route of the laying vessel based on the centerline and the overall laying direction. The number of voyages by the laying vessel is calculated based on the navigation route and pipeline length. The number of laying vessels is then calculated based on the number of voyages and the working efficiency of the laying vessel. In the formula, N represents the number of the laying vessels, N c represents the number of voyages, E f represents the working efficiency of the laying vessels; Based on the navigation route and the number of vessels deployed, the spacing between the laying vessels is determined, and the placement of the laying vessels is determined based on the spacing and the size parameters of the laying vessels.

5. The method of claim 1, wherein, The non-routine items in the large-scale layout of the marker-captured key target items specifically include: The laying speed and the difference in laying depth of large-scale layouts were determined as conventional target items, and the range values ​​of each conventional target item were obtained based on historical data and industry standards. The pause points, large-scale laying direction, and pause time during the laying process are defined as variable target items. The monitoring values ​​of each variable target item are obtained through real-time monitoring equipment, and the monitoring values ​​of each variable target item are used as the marker values ​​of non-routine items. By comparing and analyzing the monitored non-routine items based on the range values ​​of the conventional target items, abnormal situations in the large-scale layout can be identified.

6. The method of claim 1, wherein, The generation of laying risk response strategies also includes: establishing a risk assessment matrix for different abnormal situations and predicted laying problems, classifying risk levels based on the risk assessment matrix, and determining the priority of response projects in each laying risk response strategy based on each risk level.

7. The method of claim 6, wherein, To address different anomalies and predictive deployment issues, a risk assessment matrix is ​​established, and risk levels are classified based on this matrix, including: Retrieve the probability of occurrence for each abnormal situation during the large-scale layout and laying process; Retrieve the predicted probability of occurrence for each laying problem; Retrieve the large-scale laying delay rate and economic loss rate corresponding to abnormal situations and laying problems; The abnormality coefficients corresponding to the abnormal situations and laying problems are obtained by using the large-scale laying delay rate and economic loss rate corresponding to the abnormal situations and laying problems. The abnormal coefficient corresponding to the abnormal situation is obtained by the following formula: Among them, R 01 The abnormality coefficient corresponds to the abnormal situation; T and J represent the large-scale layout delay rate and economic loss rate corresponding to the abnormal situation; T c and J c This indicates the reference values ​​for the large-scale layout delay rate and economic loss rate corresponding to the preset abnormal conditions; k 01 The first adjustment coefficient is used to adjust the relative importance between the large-scale laying delay rate and the economic loss rate in the event of abnormal situations, and the value range of the adjustment coefficient is 0.35-0.

72. Furthermore, the anomaly coefficient corresponding to the aforementioned laying problem is obtained using the following formula: Among them, R 02 The coefficient represents the anomaly coefficient corresponding to the laying problem; G and H represent the large-scale laying delay rate and economic loss rate corresponding to the laying problem; k 02 The second adjustment coefficient is used to adjust the relative importance between the large-scale laying delay rate and the economic loss rate when laying problems occur, and the value of the adjustment coefficient ranges from 0.21 to 0.

74. A risk assessment matrix is ​​established using the anomaly coefficients corresponding to the aforementioned abnormal situations and laying problems, and risk levels are classified.

8. The method for designing the large-scale laying layout of the laying vessel as described in claim 7, characterized in that, A risk assessment matrix is ​​established using the anomaly coefficients corresponding to the aforementioned abnormal situations and laying problems, and risk levels are classified, including: When the anomaly coefficient exceeds the first anomaly coefficient threshold, the severity level of the anomaly and the laying problem is determined to be "highly severe"; when the anomaly coefficient does not exceed the first anomaly coefficient threshold, but exceeds the second anomaly coefficient threshold, the severity level of the anomaly and the laying problem is determined to be "moderately severe"; when the anomaly coefficient does not exceed the second anomaly coefficient threshold, the severity level of the anomaly and the laying problem is determined to be "moderately severe". Extract the severity level parameter corresponding to each abnormal situation, wherein the severity level includes severe severity, moderate severity and mild severity, and the severity level parameter corresponding to severe severity is 0.8, the severity level parameter corresponding to moderate severity is 0.6, and the severity level parameter corresponding to mild severity is 0.

4. Extract the severity level parameter corresponding to each laying problem, wherein the severity level includes major severity, moderate severity and minor severity, and the severity level parameter corresponding to major severity is 1, the severity level parameter corresponding to moderate severity is 0.8, and the severity level parameter corresponding to minor severity is 0.

5. A risk assessment matrix is ​​established by combining the probability of occurrence and severity level parameters of each abnormal situation with the probability of occurrence and severity level parameters of each laying problem. The norm of the risk assessment matrix is ​​obtained based on its structure. The norm of the risk assessment matrix is ​​compared with a preset norm threshold. If the norm of the risk assessment matrix is ​​lower than the preset first norm threshold, the risk level is determined to be low. If the norm of the risk assessment matrix is ​​not lower than the preset first norm threshold, but lower than the second norm threshold, the risk level is determined to be medium. If the norm of the risk assessment matrix is ​​not lower than the preset second norm threshold, the risk level is determined to be high.

9. The method for designing the large-scale layout of the laying vessel as described in claim 1, characterized in that, The operational parameters of the laying vessel include adjustments for vessel speed, tension, and laying angle. Adjusting these parameters specifically involves: Based on the predicted laying problems and the formulated response strategies, determine the types of laying vessel operation parameters that need to be adjusted, and determine the adjustment direction and objectives for each laying vessel operation parameter; Based on the risk level of the laying problem and the priority of the response items in the laying risk response strategy, combined with the operating parameters of the laying vessel, the large-scale layout characteristics and the laying environment, the vessel speed adjustment value, tension adjustment value and laying angle adjustment value are calculated. The corresponding operating parameters of the laying vessel are adjusted according to the calculated adjustment values, and the adjusted operating parameters are compared and evaluated to determine whether the adjustment effect has met expectations.

10. The method for designing the large-scale layout of the laying vessel as described in claim 9, characterized in that, Obtain feedback data after the laying is completed, including actual topographic data of the laying area, actual laying position and status data of the large-scale layout, operation data of the laying vessel throughout the operation, and laying quality inspection data.

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