Laying ship large row cloth laying design method

Abstract

The present invention discloses a laying design method for large-scale fabric laying ships, which relates to the technical field of ship operations. In order to solve the problems that the influence of multiple factors is not comprehensively considered, it is easy for the laying ship operation to exceed the capacity range, resulting in waste of resources, affecting the quality and progress, increasing risks, and it is difficult to discover problems in advance and scientifically guide the laying; the present invention accurately configures and dynamically adjusts parameters such as ship speed, tension and laying angle by comprehensively considering multiple factors to ensure the efficient and stable operation of the laying ship, improve the laying efficiency and quality, compare the actually monitored key target items with the preset large-scale fabric laying characteristic database, mark the non-conventional items, and realize the effective prediction of laying problems. A response strategy with clear priorities is formulated for different risk levels, which can solve potential problems in advance and reduce operation risks. After the laying is completed, multi-source feedback data is obtained, the laying results are analyzed, a weight allocation strategy for non-conventional items is established, and the continuous improvement mechanism can continuously optimize the subsequent laying operations.

Description

Technical Field

[0001] The present invention relates to the technical field of ship operations, and particularly to a design method for laying large-scale fabrics on a fabric-laying ship. Background Art

[0002] In the fields of water conservancy projects, ocean engineering, etc., the operation of laying large-scale fabrics on a fabric-laying ship is a crucial basic task. Chinese Patent Application No. CN102979060A discloses a construction method for laying a flexible mattress. This patent realizes real-time positioning of the mattress body during the construction process of laying the flexible mattress through the introduction of underwater beacons and transmitting and receiving receivers, and then conducts real-time and accurate feedback and control on the construction process.

[0003] However, although the above patent provides an economic, environmentally friendly, fast, and accurate construction method for laying flexible mattresses, there are still the following problems:

[0004] The fabric-laying operation is affected by various factors, such as the geographical location of the fabric-laying area, the storage location of the large-scale fabric, the size parameters of the fabric-laying ship itself, etc. These factors will jointly affect the operation efficiency and quality of the fabric-laying ship, resulting in the fabric-laying ship being unable to operate in the best state, causing waste of resources or poor laying effects, making the fabric-laying ship face situations beyond its capacity range during the operation process, increasing the operation risk, and even affecting the laying quality and progress;

[0005] Moreover, there is a lack of comprehensive capture and analysis of key target items during the process of laying large-scale fabrics, making it difficult to detect potential problems that may occur during the laying process in advance, so that effective countermeasures cannot be taken in time, increasing the laying risk. Also, the characteristics of different types of large-scale fabrics and the corresponding laying environments are not systematically collected and integrated, which is not conducive to scientifically guiding and optimizing the laying process. Summary of the Invention

[0006] The purpose of the present invention is to provide a design method for laying large-scale fabrics on a fabric-laying ship. By reasonably configuring operation parameters and dynamically adjusting the operation parameters, it is ensured that the fabric-laying ship is always in the best operation state, can detect and solve potential problems in advance, reduce the laying risk, ensure the smooth progress of the operation, and improve the laying efficiency and quality, so as to solve the problems raised in the above background art.

[0007] To achieve the above purpose, the present invention provides the following technical solutions:

[0008] A design method for laying large-scale fabrics on a fabric-laying ship, comprising:

[0009] Collect operation preparation data, including the geographical location of the fabric-laying area, the geographical location of the storage location of the large-scale fabric, the size parameters of the fabric-laying ship, the diameter of the fabric-laying pipeline, and the laying depth. Based on the operation preparation data, determine the laying environment and the type of large-scale fabric, and determine the operation capacity and operation span range of the fabric-laying ship;

[0010] Based on the results of job preparation data collection, in combination with the preset large layout laying characteristic database, retrieve the range of conventional operation parameters under similar laying environments and large layout types, and configure the operation parameters of the laying vessel in combination with the operation capabilities and operation span range of the laying vessel;

[0011] Obtain the monitoring data fed back by each monitoring device during the operation of the laying vessel in real time, and capture the key target items during the laying process of the large layout. The key target items include: the laying speed of the large layout, the laying direction of the large layout, the depth difference of the large layout during laying, the pause points and the pause time during the laying process;

[0012] Compare the key target items captured during the current laying process of the large layout with the large layout laying characteristic database, mark the non-conventional items among the captured key target items of the large layout laying, and determine them as laying abnormal situations;

[0013] Based on the retrieved historical data, predict and conduct risk assessment on the identified laying abnormal situations, match corresponding solutions based on the prediction results, generate a laying risk response strategy, take different response measures under different abnormal situations, and make dynamic adjustments according to the actual situation;

[0014] Adjust the operation parameters of the laying vessel configured according to the prediction results and the laying risk response strategy, including the ship speed adjustment value, the tension adjustment value, and the laying angle adjustment value;

[0015] Obtain the feedback data after the laying is completed, and analyze the laying results, analyze the influence degree of the non-conventional items that appear in this laying operation on the laying results, and judge whether the non-conventional items are included in the key target items for subsequent laying reference.

[0016] Furthermore, configure the operation parameters of the laying vessel, specifically including:

[0017] Obtain the operation capability range of the laying vessel;

[0018] Use a geographic information monitoring device to obtain the operation span range formed between the laying area and the storage location of the large layout;

[0019] Based on the large layout laying characteristic database, obtain the parameter range of the conventional laying operation;

[0020] According to the operation capability range of the laying vessel and the parameter range of the conventional laying operation, configure the operation parameters of the laying vessel within the operation span range.

[0021] Furthermore, the preset large layout laying characteristic database specifically includes:

[0022] Determine the large layout classification identifier based on the characteristics of the large layout, and input it into a preset neural network for learning to determine the classification expression of the large layout classification identifier. Based on the classification expression, construct a large layout classification model;

[0023] Input the obtained job preparation data into the large layout classification model for classification to determine the large layout type. Obtain the environmental parameters of the laying area based on the laying environment characteristics, and predict the laying effect of different large layout types in the laying area;

[0024] Based on the monitoring device to capture the status information during the large layout laying process in real time, extract the laying characteristic features, identify the laying characteristics in different situations based on the extraction results, and identify possible abnormal situations;

[0025] Integrate the large layout type, laying environment, and corresponding laying characteristics, and map the integrated data into a vector space with a fixed dimension based on the large layout type to generate a large layout laying characteristic database.

[0026] Furthermore, the key target items during the large layout laying process also include:

[0027] Calculate the displacement volume of the laying barge based on the size parameters of the laying barge, determine the displacement of the laying barge, and calculate the effective load of the laying barge according to the displacement and the empty ship weight of the laying barge;

[0028] Calculate the weight per unit length of the pipeline of the laying barge according to the pipeline diameter and laying depth, and calculate the pipeline length that the laying barge can lay according to the effective load of the laying barge and the weight per unit length of the pipeline;

[0029] Determine the starting and ending coordinates of the laying area according to the geographical location of the laying area, determine the center line of the laying area based on the starting and ending coordinates, and determine the navigation route of the laying barge based on the center line and the large layout laying direction;

[0030] Calculate the number of voyages of the laying barge according to the navigation route and the pipeline length, and calculate the number of laying barges according to the number of voyages and the working efficiency of the laying barge where N represents the number of laying barges, N c represents the number of voyages, E f represents the working efficiency of the laying barge;

[0031] Determine the layout spacing of the laying barges according to the navigation route and the number of layout, and determine the layout position of the laying barges according to the layout spacing and the size parameters of the laying barges.

[0032] Furthermore, mark the non-conventional items in the captured key target items of the large layout laying, specifically including:

[0033] Determine the large layout laying speed and the difference in large layout laying depth as conventional target items, and obtain the range values of each conventional target item based on historical data and industry standards;

[0034] Determine the pause points, large layout laying direction, and pause time during the laying process as variable target items, obtain the monitoring values of each variable target item through real-time monitoring equipment, and use the monitoring values of each variable target item as the marking values of non-conventional items;

[0035] Compare and analyze the monitored non-conventional items based on the range values of the conventional target items to identify abnormal large layout laying situations.

[0036] Furthermore, generating a laying risk response strategy further includes: establishing a risk assessment matrix for different abnormal situations and predicted laying problems, dividing 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.

[0037] Furthermore, establishing a risk assessment matrix for different abnormal situations and predicted laying problems, and dividing risk levels based on the risk assessment matrix, including:

[0038] Retrieve the occurrence probability corresponding to each abnormal situation during the large layout laying process;

[0039] Retrieve the occurrence probability corresponding to each predicted laying problem;

[0040] Retrieve the large layout laying delay rate and economic loss rate corresponding to the abnormal situation and the laying problem;

[0041] Obtain the abnormal coefficient corresponding to the abnormal situation and the laying problem by using the large layout laying delay rate and economic loss rate corresponding to the abnormal situation and the laying problem;

[0042] Among them, the abnormal coefficient corresponding to the abnormal situation is obtained through the following formula:

[0043]

[0044] Among them, R 01 represents the abnormal coefficient corresponding to the abnormal situation; T and J represent the large layout laying delay rate and economic loss rate corresponding to the abnormal situation; T c and J c represent the reference values of the large layout laying delay rate and economic loss rate corresponding to the preset abnormal situation; k 01 represents the first adjustment coefficient, which is obtained through experiments according to the actual application scenario, and is used to adjust the relative importance between the large layout laying delay rate and the economic loss rate when the abnormal situation occurs, and the value range of the adjustment coefficient is 0.35 - 0.72;

[0045] Moreover, the anomaly coefficient corresponding to the laying problem is obtained through the following formula:

[0046]

[0047] where R 02 represents the anomaly coefficient corresponding to the laying problem; G and H represent the large layout laying delay rate and economic loss rate corresponding to the laying problem; k 02 represents the second adjustment coefficient, which is obtained through experiments according to the actual application scenario, and is used to adjust the relative importance between the large layout laying delay rate and the economic loss rate when the laying problem occurs. Moreover, the value range of the adjustment coefficient is 0.21 - 0.74;

[0048] Establish a risk assessment matrix using the abnormal situation and the anomaly coefficient corresponding to the laying problem, and conduct risk level classification.

[0049] Furthermore, establishing a risk assessment matrix using the abnormal situation and the anomaly coefficient corresponding to the laying problem, and conducting risk level classification, includes:

[0050] When the anomaly coefficient exceeds the first anomaly coefficient threshold, it is determined that the severity level corresponding to the abnormal situation and the laying problem is of a relatively high severity; when the anomaly coefficient does not exceed the first anomaly coefficient threshold, but exceeds the second anomaly coefficient threshold, it is determined that the severity level corresponding to the abnormal situation and the laying problem is of medium severity; when the anomaly coefficient does not exceed the second anomaly coefficient threshold, it is determined that the severity level corresponding to the abnormal situation and the laying problem is of general severity;

[0051] Extract the severity level parameters corresponding to each abnormal situation, where the severity levels include relatively high severity, medium severity, and general severity. Moreover, the severity level parameter corresponding to relatively high severity is 0.8, the severity level parameter corresponding to medium severity is 0.6, and the severity level parameter corresponding to general severity is 0.4;

[0052] Extract the severity level parameters corresponding to each laying problem, where the severity levels include relatively high severity, medium severity, and general severity. Moreover, the severity level parameter corresponding to relatively high severity is 1, the severity level parameter corresponding to medium severity is 0.8, and the severity level parameter corresponding to general severity is 0.5;

[0053] Establish a risk assessment matrix by combining the occurrence probability corresponding to each abnormal situation and the severity level parameter corresponding to each abnormal situation with the occurrence probability corresponding to each laying problem and the severity level parameter corresponding to each laying problem;

[0054] Among them, the structure of the risk assessment matrix is as follows:

[0055]

[0056] Among them, A represents the risk assessment matrix; Y1, Y2... Y n respectively represent the occurrence probabilities corresponding to n abnormal situations; S y1 , S y2 ... S yn respectively represent the severity level parameters corresponding to n abnormal situations; W1, W2... W m respectively represent the occurrence probabilities corresponding to m laying problems; S w1 , S w2 ... S wm respectively represent the severity level parameters corresponding to m laying problems; when m ≠ n, the vacancy elements in the matrix are filled with the occurrence probability 0 and the severity level parameter 0.

[0057] Obtain the norm of the risk assessment matrix according to the structure of the risk assessment matrix;

[0058] Compare the norm of the risk assessment matrix with a preset norm threshold;

[0059] When the norm of the risk assessment matrix is lower than the preset first norm threshold, it is determined that the risk level is low;

[0060] When the norm of the risk assessment matrix is not lower than the preset first norm threshold but lower than the second norm threshold, it is determined that the risk level is medium;

[0061] When the norm of the risk assessment matrix is not lower than the preset second norm threshold, it is determined that the risk level is high.

[0062] Furthermore, adjust the operation parameters of the laying barge, specifically including:

[0063] Determine the type of operation parameters of the laying barge that need to be adjusted according to the predicted laying problems and the formulated countermeasures, and determine the adjustment direction and target of each operation parameter of the laying barge;

[0064] According to the risk level of the laying problem and the priority of the response items in the laying risk countermeasure, combined with the operation parameters of the laying barge, the characteristics of the large fabric, and the laying environment, calculate the ship speed adjustment value, the tension adjustment value, and the laying angle adjustment value;

[0065] Adjust the corresponding operation parameters of the laying barge according to the calculated adjustment values, and compare and evaluate the adjusted operation parameters to determine whether the adjustment effect meets the expectations.

[0066] Further, obtain the feedback data after the laying is completed, including the actual topographical and geomorphic data of the laying area, the actual laying position and status data of the large layout, the operation data of the laying vessel during the entire operation process, and the laying quality inspection data.

[0067] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0068] Comprehensively configure and dynamically adjust parameters such as ship speed, tension, and laying angle considering multiple factors to ensure the efficient and stable operation of the laying vessel, improve the laying efficiency and quality. By comparing the actually monitored key target items with the preset large layout laying characteristic database, mark the non-conventional items to effectively predict laying problems. Develop a response strategy with clear priorities for different risk levels to solve potential problems in advance and reduce operation risks. After the laying is completed, obtain multi-source feedback data, analyze the laying results, establish a weight allocation strategy for non-conventional items, and determine whether to include them in subsequent references. The continuous improvement mechanism can continuously optimize subsequent laying operations, accumulate experience, make the entire laying process more scientific and reliable, and provide strong guarantee for related projects. Description of the Drawings

[0069] Figure 1 It is a flowchart of the large layout laying design method of the laying vessel of the present invention. Detailed Embodiments

[0070] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0071] In order to solve the technical problems that the laying is not comprehensively considered affected by multiple factors such as regional location and ship body parameters, which easily causes the laying vessel to operate beyond its capacity range, resulting in waste of resources, affecting quality and progress, and increasing risks, and lacking comprehensive monitoring and analysis of key laying target items and systematic integration of large layout characteristics and environmental data, making it difficult to discover problems in advance and scientifically guide laying, please refer to Figure 1 , the present embodiment provides the following technical solutions:

[0072] A large layout laying design method for a laying vessel, including:

[0073] Collect the data for the laying operation preparation, i.e., the basic information and relevant scope related to the laying operation, including obtaining the geographical location of the laying area (clarifying its longitude and latitude range, topographical features, etc.), the geographical location of the large mat storage location (planning the transportation route), the dimensional parameters of the laying vessel (such as the ship length, ship width, ship height, and draft), the diameter of the laying pipeline, and the laying depth. Based on the operation preparation data, determine the laying environment and the type of large mat, and determine the operation capacity and operation span range of the laying vessel;

[0074] Based on the results of collecting the operation preparation data, in combination with the preset database of the laying characteristics of the large mat, retrieve the range of conventional operation parameters under similar laying environments and large mat types, and in combination with the operation capacity and operation span range of the laying vessel, configure the operation parameters of the laying vessel;

[0075] Real-time obtain the monitoring data fed back by each monitoring device during the operation of the laying vessel, and capture the key target items during the laying process of the large mat. The key target items include: the laying speed of the large mat, which is the average speed of the large mat laid within the laying area; the laying direction of the large mat, which is the angle formed by the laying path of the large mat and different axis directions after establishing a spatial rectangular coordinate system in the laying area; the laying depth difference of the large mat, which refers to the difference formed by the laying depths of the large mat captured at two monitoring time points; the pause point during the laying process, that is, the position point of the target object in the laying area where the large mat stays during the laying process; the pause time, which is the time consumed for the large mat to stay at the position point of the target object in the laying area;

[0076] Compare the currently captured key target items during the laying process of the large mat with the database of the laying characteristics of the large mat, mark the non-conventional items among the captured key target items of the large mat laying, and determine them as laying abnormal situations;

[0077] Based on the retrieved historical data, predict and conduct risk assessment on the identified laying abnormal situations, and match the corresponding solutions based on the prediction results, such as adjusting the operation parameters of the laying vessel, adjusting the type of large mat, adjusting the laying method, conducting equipment maintenance, etc., generate a laying risk response strategy, take different response measures under different abnormal situations, and dynamically adjust according to the actual situation;

[0078] In this embodiment, it also includes: establishing a risk assessment matrix for different abnormal situations and predicted laying problems, conducting risk level classification based on the risk assessment matrix, and determining the priority of the response items in each laying risk response strategy based on each risk level. For high-risk laying problems, give priority to taking the most direct and effective response measures, such as immediately stopping the laying operation and conducting a comprehensive inspection and debugging of the equipment; for medium-risk problems, the operation 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 the subsequent laying plan for prevention and control;

[0079] Adjust the operation parameters of the laying barge according to the prediction results and the laying risk response strategy, including the ship speed adjustment value, the tension adjustment value, and the laying angle adjustment value;

[0080] Obtain the feedback data after the laying is completed, including the actual topographic and geomorphic data of the laying area (obtained by high-precision measurement equipment), the actual laying position and status data of the large fabric (obtained by non-destructive testing technology and on-site investigation), the operation data of the laying barge during the entire operation process (extracted from the equipment log and monitoring system), and the laying quality inspection data (such as the inspection results of laying flatness, density, etc.), and analyze the laying results. Analyze the influence degree of the non-conventional items that appear in this laying operation on the laying results, establish a weight distribution strategy for non-conventional items, and judge whether the non-conventional items are included in the key target items for subsequent laying reference.

[0081] In this embodiment, by comprehensively obtaining various information to configure the operation parameters, ensuring that the operation is carried out within the ship's capabilities, reducing resource waste and operation risks, capturing the key target items during the laying process of the large fabric, presetting the large fabric laying characteristic database, and comparing the two, marking the non-conventional items, it can effectively discover potential problems in the laying process in advance. At the same time, establish a risk assessment matrix to divide the risk levels, determine the priority of response items, generate a dynamic laying risk response strategy, and can take targeted measures in a timely manner to reduce the laying risk. It can dynamically adjust the operation parameters of the laying barge to adapt to the changes during the laying process, improve the flexibility and adaptability of the laying, obtain multi-source feedback data after the laying is completed and analyze the laying results, establish a weight distribution strategy for non-conventional items, and judge whether to include them in the subsequent reference, which is conducive to summarizing experience and lessons, continuously improving the laying method, and realizing scientific guidance for the laying operation.

[0082] Specifically, for different abnormal situations and predicted laying problems, establish a risk assessment matrix, and based on the risk assessment matrix, conduct risk level division, including:

[0083] Retrieve the occurrence probability corresponding to each abnormal situation during the laying process of the large fabric;

[0084] Retrieve the occurrence probability corresponding to each predicted laying problem;

[0085] Retrieve the laying delay rate and economic loss rate of the large fabric corresponding to the abnormal situation and the laying problem;

[0086] Use the laying delay rate and economic loss rate of the large fabric corresponding to the abnormal situation and the laying problem to obtain the abnormal coefficient corresponding to the abnormal situation and the laying problem;

[0087] Among them, the abnormal coefficient corresponding to the abnormal situation is obtained through the following formula:

[0088]

[0089] Among them, R 01 represents the abnormal coefficient corresponding to the abnormal situation; T and J represent the large layout laying delay rate and economic loss rate corresponding to the abnormal situation; T c and J c represent the reference values of the large layout laying delay rate and economic loss rate corresponding to the preset abnormal situation; k 01 represents the first adjustment coefficient, which is obtained through experiments according to the actual application scenario, and is used to adjust the relative importance between the large layout laying delay rate and economic loss rate when the abnormal situation occurs. And the value range of the adjustment coefficient is 0.35 - 0.72;

[0090] And the abnormal coefficient corresponding to the laying problem is obtained through the following formula:

[0091]

[0092] Among them, R 02 represents the abnormal coefficient corresponding to the laying problem; G and H represent the large layout laying delay rate and economic loss rate corresponding to the laying problem; k 02 represents the second adjustment coefficient, which is obtained through experiments according to the actual application scenario, and is used to adjust the relative importance between the large layout laying delay rate and economic loss rate when the laying problem occurs. And the value range of the adjustment coefficient is 0.21 - 0.74.

[0093] The technical effects of the above technical solution are as follows: This solution comprehensively considers various abnormal situations that may occur during the large layout laying process and predicted laying problems. By retrieving their corresponding occurrence probabilities, delay rates, and economic loss rates, a comprehensive and systematic risk assessment system is established. This helps to comprehensively identify and manage potential risks and ensure the smooth progress of the project. The solution quantifies and evaluates the risks of abnormal situations and laying problems by introducing the abnormal coefficient. This quantification method makes the severity of the risk more intuitive and easy to understand, provides accurate risk information for decision-makers, and helps to make more scientific and reasonable decisions. The adjustment coefficients (k01 and k02) in the solution can be obtained through experiments according to the actual application scenario, which increases the flexibility and applicability of the solution. Different projects or application scenarios may require different risk assessment criteria. By adjusting the adjustment coefficients, it can be ensured that the risk assessment results are more in line with the actual situation. This solution provides a structured risk management method, which helps the organization to form a continuous risk management culture. By continuously accumulating experience and data, the risk assessment matrix and the setting of the adjustment coefficients can be further improved, thereby improving the accuracy and efficiency of risk management.

[0094] In summary, through the technical effects of comprehensively and systematically identifying and managing potential risks, quantitatively evaluating the severity of risks, providing flexible and applicable risk assessment criteria, clearly dividing risk levels, constructing an intuitive risk assessment matrix, and promoting the continuous improvement of risk management, etc., this technical solution provides strong support for the smooth progress of the project and risk management.

[0095] Specifically, a risk assessment matrix is established by using the anomaly coefficients corresponding to the abnormal situations and laying problems, and risk levels are divided, including:

[0096] When the anomaly coefficient exceeds the first anomaly coefficient threshold, it is determined that the severity level corresponding to the abnormal situation and laying problem is of a relatively high severity; when the anomaly coefficient does not exceed the first anomaly coefficient threshold, but exceeds the second anomaly coefficient threshold, it is determined that the severity level corresponding to the abnormal situation and laying problem is of a medium severity; when the anomaly coefficient does not exceed the second anomaly coefficient threshold, it is determined that the severity level corresponding to the abnormal situation and laying problem is of a general severity;

[0097] Extract the severity level parameters corresponding to each abnormal situation, where the severity levels include relatively high severity, medium severity, and general severity, and the severity level parameter corresponding to relatively high severity is 0.8, the severity level parameter corresponding to medium severity is 0.6, and the severity level parameter corresponding to general severity is 0.4;

[0098] Extract the severity level parameters corresponding to each laying problem, where the severity levels include relatively high severity, medium severity, and general severity, and the severity level parameter corresponding to relatively high severity is 1, the severity level parameter corresponding to medium severity is 0.8, and the severity level parameter corresponding to general severity is 0.5;

[0099] A risk assessment matrix is established by combining the occurrence probability corresponding to each abnormal situation and the severity level parameter corresponding to each abnormal situation with the occurrence probability corresponding to each laying problem and the severity level parameter corresponding to each laying problem;

[0100] Among them, the structure of the risk assessment matrix is as follows:

[0101]

[0102] Among them, A represents the risk assessment matrix; Y1, Y2... Y n respectively represent the occurrence probabilities corresponding to n abnormal situations; S y1 , S y2 ... S ynrespectively represent the severity level parameters corresponding to n abnormal conditions; W1, W2... W m respectively represent the occurrence probabilities corresponding to m laying problems; S w1 , S w2 ... S wm respectively represent the severity level parameters corresponding to m laying problems; when m≠n, the vacancy elements in the matrix are filled with the occurrence probability 0 and the severity level parameter 0.

[0103] Obtain the norm of the risk assessment matrix according to the structure of the risk assessment matrix;

[0104] Compare the norm of the risk assessment matrix with a preset norm threshold;

[0105] When the norm of the risk assessment matrix is lower than the preset first norm threshold, it is determined that the risk level is low;

[0106] When the norm of the risk assessment matrix is not lower than the preset first norm threshold but lower than the second norm threshold, it is determined that the risk level is medium;

[0107] When the norm of the risk assessment matrix is not lower than the preset second norm threshold, it is determined that the risk level is high.

[0108] The technical effects of the above technical solution are as follows: By setting the first anomaly coefficient threshold and the second anomaly coefficient threshold, the severity levels of abnormal situations and laying problems are divided into three levels: relatively high severity, medium severity, and general severity. This clear division helps to prioritize different risks, thereby reasonably allocating resources and formulating countermeasures. Using the occurrence probability and severity level parameters of abnormal situations, as well as the occurrence probability and severity level parameters of laying problems, the solution constructs a risk assessment matrix. This matrix intuitively shows the risk levels and occurrence probabilities 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 the vacant elements in the matrix with the occurrence probability 0 and severity level parameter 0. This processing method ensures the integrity and consistency of the risk assessment matrix, avoiding evaluation result deviations caused by missing data. At the same time, by setting different anomaly coefficient thresholds, the severity levels of abnormal situations and laying problems are further divided into three levels: relatively high severity, medium severity, and general severity. This refinement makes risk management more meticulous, enabling different countermeasures to be taken for risks of different levels, thereby improving the pertinence and effectiveness of risk management. Specific severity level parameters are assigned to each severity level, and these parameters reflect the severity of risks at different levels. When establishing the risk assessment matrix, the occurrence probability and severity level parameters of abnormal situations, as well as the occurrence probability and severity level parameters of laying problems, are combined. This enables decision-makers to more intuitively understand the overall situation and priority of risks, providing strong support for formulating scientific and reasonable countermeasures. The solution comprehensively considers multiple factors such as the occurrence probability, delay rate, and economic loss rate of abnormal situations and laying problems, and quantifies risks by calculating the anomaly coefficient. At the same time, a comprehensive and accurate risk assessment system is formed by integrating multiple risk factors using the risk assessment matrix. This helps to comprehensively identify and manage potential risks, improving the accuracy and reliability of risk assessment. By establishing a risk assessment matrix, the organization 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 evaluated more accurately, improving the efficiency and effectiveness of risk management. At the same time, this also helps to form a continuous risk management culture, promoting the organization's continuous progress in risk management.

[0109] At the same time, the scheme provides a standardized framework for risk assessment by calling the preset risk assessment matrix. This ensures the consistency and repeatability of the assessment process and reduces the assessment bias caused by human factors. By calculating the norm of the risk assessment matrix and comparing it with the preset norm threshold, the scheme can quantitatively divide the risk level. This quantitative method makes the determination of risk level more objective and accurate, which is convenient for subsequent risk management and the formulation of countermeasures. Using the risk assessment matrix and the norm comparison method, the risk level can be quickly determined without complex analysis and calculation. This greatly improves the efficiency of risk assessment, especially in emergency situations that require rapid response. Clear risk level division can provide strong support for decision makers. Different levels of risk correspond to different countermeasures and resource inputs, which helps decision makers make reasonable decisions based on risk levels and optimize resource allocation. The preset norm threshold in the scheme can be adjusted and optimized according to actual conditions. With the deepening of risk management practice and the accumulation of experience, the risk assessment matrix and norm threshold can be continuously improved to improve the accuracy and effectiveness of risk management. Through clear risk level division and corresponding countermeasures, the scheme helps to enhance the awareness and attention to risks within the organization. This can promote the formation of a positive risk management culture in the organization and improve the overall risk management level.

[0110] In summary, this technical solution achieves standardization, quantification, efficiency and decision support of risk assessment by dividing risk levels based on the risk assessment matrix, which helps organizations improve their risk management capabilities and levels.

[0111] In this embodiment, the configuration of the laying ship operation parameters specifically includes:

[0112] Obtain the operating capacity range of the laying vessel;

[0113] Use geographic information monitoring equipment to obtain the operating span formed between the laying area and the large layout storage location;

[0114] Based on the large-scale laying characteristic database, obtaining a parameter range for a conventional laying operation;

[0115] According to the operating capacity range of the laying ship and the parameter range of conventional laying operations, the operating parameters of the laying ship are configured within the operating span to ensure normal operation of the laying ship.

[0116] In this embodiment, combined with the parameter range of conventional laying operations, the laying vessel can operate efficiently and safely within the operating span between the laying area and the large layout storage location, avoiding waste of resources, low operating efficiency and potential safety risks caused by improper configuration of operating parameters, improving the overall planning and execution capabilities of the laying operation, and ensuring the smooth progress of the laying operation and high-quality laying results.

[0117] In this embodiment, the key target items during the laying process of the large apron also include:

[0118] Calculate the displacement volume of the laying vessel based on the size parameters of the laying vessel, determine the displacement of the laying vessel, and calculate the effective load of the laying vessel according to the displacement and the empty ship weight of the laying vessel;

[0119] Calculate the weight per unit length of the pipeline of the laying vessel according to the pipeline diameter and laying depth, and calculate the pipeline length that the laying vessel can lay according to the effective load of the laying vessel and the weight per unit length of the pipeline;

[0120] Determine the starting and ending coordinates of the laying area according to the geographical location of the laying area, determine the center line of the laying area based on the starting and ending coordinates, and determine the navigation route of the laying vessel based on the center line and the laying direction of the large apron;

[0121] Calculate the number of voyages of the laying vessel according to the navigation route and the pipeline length, and calculate the number of laying vessels according to the number of voyages and the working efficiency of the laying vessel , where N represents the number of laying vessels, N c represents the number of voyages, and E f represents the working efficiency of the laying vessel;

[0122] Determine the laying spacing of the laying vessel according to the navigation route and the number of laying vessels, and determine the laying position of the laying vessel according to the laying spacing and the size parameters (length and width of the ship) of the laying vessel.

[0123] In this embodiment, through 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, the load-bearing and operation ranges are accurately grasped, safety is ensured, resource utilization is improved, the navigation route is determined in combination with the position of the laying area, the spacing and position are clarified, the operation path and layout are scientifically planned, the operation efficiency is improved, and the laying quality and uniformity of the large apron are ensured.

[0124] In this embodiment, the preset large apron laying characteristic database specifically includes:

[0125] Determine the classification identifier of the large apron based on the characteristics of the large apron, input it into the preset neural network for learning, determine the classification expression of the large apron classification identifier, and construct a large apron classification model based on the classification expression;

[0126] Input the obtained operation preparation data into the large apron classification model for classification to determine the large apron type, obtain environmental parameters such as the geological conditions and water flow velocity of the laying area based on the laying environment characteristics, and predict the laying effect of different large apron types in the laying area;

[0127] Based on the monitoring device to capture the status information in the process of large-scale cloth laying in real time, extract the laying characteristic features, such as settlement speed, laying depth, tension change, deformation degree, etc., identify the laying characteristics in different situations based on the extraction results, such as uneven settlement, serious deformation, excessive tension, etc., and identify possible abnormal situations, such as equipment failure, data error, etc.;

[0128] Integrate the large-scale cloth type, laying environment and the corresponding laying characteristics, and map the integrated data into a vector space with a fixed dimension based on the large-scale cloth type to generate a large-scale cloth laying characteristic database.

[0129] In this embodiment, by presetting neural network learning to determine the expression of the large-scale cloth classification identifier, constructing a classification model, it can accurately classify the large-scale cloth, predict the laying effect of different types of large-scale cloth in advance by combining the laying environment parameters, capture the laying status information in real time, extract characteristic features and identify abnormalities, can timely discover potential problems, integrate and generate a database, provide a comprehensive and accurate reference basis for subsequent large-scale cloth laying operations, and improve the laying efficiency and quality.

[0130] In this embodiment, mark the non-conventional items in the captured key target items of large-scale cloth laying, specifically including:

[0131] Determine the large-scale cloth laying speed and the difference in large-scale cloth laying depth as conventional target items, and obtain the range values of each conventional target item based on historical data and industry standards, such as the optimal laying speed range and laying depth change range of different types of large-scale cloth;

[0132] Determine the pause points, large-scale cloth laying direction and pause time during the laying process as variable target items, obtain the monitoring values of each variable target item through real-time monitoring equipment, and use the monitoring values of each variable target item as the marking values of non-conventional items, such as the position of the pause point, the change amplitude of the laying direction, the length of the pause time, etc.;

[0133] Based on the range values of the conventional target items, compare and analyze the monitored non-conventional items, identify abnormal situations in large-scale cloth laying, such as too fast or too slow laying speed, deviation of laying direction, too long pause time, etc., and then predict possible laying problems;

[0134] In this embodiment, the pause point during the laying process: that is, the position point of the target object in the laying area where the large-scale cloth stays during the laying process, and its specific position (the position of the pause point) is used as the marking value of the non-conventional item; for example, if the large-scale cloth frequently pauses at a certain specific obstacle, this position belongs to the marking point of the non-conventional situation;

[0135] Large layout laying direction: After establishing a spatial rectangular coordinate system in the laying area, it is the angle formed by the large layout laying path and different direction axes, and the range of its change (the range of the laying direction change) is used as the marking value of the non-conventional item; for example, if the laying direction deviates significantly from the preset direction, it belongs to the non-conventional situation;

[0136] Pause time: It is the time consumed by the large layout staying at the target position point in the laying area, and the length of it (the length of the pause time) is used as the marking value of the non-conventional item; if the pause time is too long and exceeds the normal range, it is regarded as a non-conventional situation.

[0137] In this embodiment, the conventional target items and their range values are clarified to provide a reference standard for judging abnormalities. The pause point, laying direction, pause time, etc. are determined as the variable target items, and their monitoring values are used as the marking values, which can accurately capture the dynamic abnormalities during laying. By comparing and analyzing with the range values of the conventional target items, abnormal situations such as speed abnormalities, direction deviations, and excessive pauses can be quickly identified, and potential laying problems can be predicted, which helps to take measures in advance to avoid the deterioration of problems, improve the laying quality and efficiency, and reduce the operation risks.

[0138] In this embodiment, the operation parameters of the laying barge are adjusted, specifically including:

[0139] Determine the types of operation parameters of the laying barge that need to be adjusted according to the predicted laying problems and the formulated countermeasures, including ship speed, tension, laying angle, etc., and determine the adjustment directions and targets of each operation parameter of the laying barge. For example, if it is predicted that the laying speed of the large layout is too fast, resulting in uneven laying, then the target of the ship speed adjustment value is to reduce the ship speed to make it reach the appropriate laying speed range; if it is found that the tension is too large during laying, affecting the laying quality of the large layout, then the direction of the tension adjustment value is to reduce the tension;

[0140] Calculate the ship speed adjustment value, tension adjustment value, and laying angle adjustment value according to the risk level of the laying problem and the priority of the response items in the laying risk response strategy, combined with the operation parameters of the laying barge, the characteristics of the large layout, and the laying environment. For example, calculate the ship speed adjustment value using the dynamics formula according to the characteristics of the power system of the laying barge, the current ship speed, and the required adjustment target; calculate the tension adjustment value based on the material, structure, and force-bearing situation of the large layout, combined with the data of the tension sensor, using the mechanical principle; calculate the laying angle adjustment value according to the topography and geomorphology of the laying area, the water flow direction, and the preset laying path, through mathematical methods such as trigonometric functions;

[0141] Adjust the corresponding operation parameters of the laying vessel according to the calculated adjustment value, compare and evaluate the adjusted operation parameters, and determine whether the adjustment effect meets the expectation. When adjusting the ship speed, increase or decrease the ship speed in a small amplitude each time, and at the same time observe the navigation stability of the ship and the laying condition of the large layout; when adjusting the tension, monitor the data of the tension sensor in real time to make it gradually approach the adjusted target value.

[0142] In this embodiment, clarify the types, directions and targets of the operation parameters to be adjusted based on the prediction problems and coping strategies, so that the adjustment is targeted. Combine the risk level, the characteristics of the ship and the layout, and the environment to calculate the adjustment value, operate according to the adjustment value and evaluate the effect, make small adjustments and monitor in real time to ensure that the adjustment process is stable and the adjustment effect meets the expectation, and improve the laying quality and efficiency of the large layout.

[0143] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A large-scale laying design method for laying vessels, characterized in that: include: Determine the laying environment and large-scale layout type based on the collected operation preparation data, determine the laying ship's operating capacity and operating span range, and based on the operation preparation data collection results, retrieve the conventional operating parameter range under similar laying environments and large-scale layout types in combination with the preset large-scale layout laying characteristic database, and configure the laying ship's operating parameters in combination with the laying ship's operating capacity and operating span range; Acquire the monitoring data fed back by each monitoring device during the operation of the laying ship in real time, capture the key target items in the large-scale laying process, compare the key target items in the large-scale laying process currently captured with the large-scale laying characteristic database, mark the unconventional items in the captured key target items of the large-scale laying, and determine them as abnormal laying conditions; Based on the retrieved historical data, the identified laying anomalies are predicted and risk assessed, and the configured laying vessel operation parameters are adjusted; Obtain feedback data after the laying is completed, analyze the impact of unconventional items in this laying operation on the laying results, and determine whether unconventional items should be included in the key target items for subsequent laying references; The key objectives in the large-scale laying process also include: The displacement volume of the laying ship is calculated based on the size parameters of the laying ship, the displacement of the laying ship is determined, and the effective load of the laying ship is calculated according to the displacement and the empty ship weight of the laying ship; Calculate the weight per unit length of the pipeline on the laying vessel based on the diameter of the pipeline and the laying depth; calculate the length of the pipeline that can be laid by the laying vessel based on the effective load of the laying vessel and the weight per unit length of the pipeline; Determine the starting and ending coordinates of the paving area according to the geographical location of the paving area, determine the center line of the paving area based on the starting and ending coordinates, and determine the navigation route of the paving ship based on the center line and the large-scale paving direction; Calculate the number of voyages of the laying ship according to the sailing route and pipeline length, and calculate the number of laying ships to be deployed according to the number of voyages and the working efficiency of the laying ship. , where N represents the number of laying vessels, N c Indicates the number of voyages, E f Indicates the working efficiency of the laying ship; The layout spacing of the laying ships is determined according to the navigation route and the number of laying ships, and the layout position of the laying ships is determined according to the layout spacing and the size parameters of the laying ships.

2. The large-scale laying design method for laying vessels according to claim 1 is characterized in that: The configuration of the laying ship operation parameters specifically includes: Obtain the operating capacity range of the laying vessel; Use geographic information monitoring equipment to obtain the operating span formed between the laying area and the large layout storage location; Based on the large-scale laying characteristic database, obtaining a parameter range for a conventional laying operation; According to the operating capacity range of the laying vessel and the parameter range of conventional laying operations, the operating parameters of the laying vessel are configured within the operating span.

3. The large-scale laying design method for laying vessels according to claim 2 is characterized in that: The preset large-scale laying characteristics database includes: Determine the large arrangement classification mark based on the characteristics of the large arrangement, input it into a preset neural network for learning, determine the classification expression of the large arrangement classification mark, and construct a large arrangement classification model based on the classification expression; Input the obtained work preparation data into the large-scale arrangement classification model for classification, determine the large-scale arrangement type, obtain the environmental parameters of the paving area based on the paving environment characteristics, and predict the paving effects of different large-scale arrangement types in the paving area; Based on the monitoring equipment, the status information of the large-scale laying process is captured in real time, the laying characteristics are extracted, and the laying characteristics under different conditions are identified based on the extraction results, and possible abnormal conditions are identified; The large-scale arrangement type, laying environment and corresponding laying characteristics are integrated, and the integrated data are mapped into a vector space of fixed dimension based on the large-scale arrangement type to generate a large-scale arrangement laying characteristic database.

4. The large-scale laying design method for laying vessels according to claim 1, characterized in that: Mark and capture the unconventional items in the key target items of large-scale layout laying, including: Determine the large-scale laying speed and large-scale laying depth difference as conventional target items, and obtain the range value of each conventional target item based on historical data and industry standards; The pause points, large-scale laying directions and pause times in the laying process are determined as change target items, and the monitoring values ​​of each change target item are obtained through real-time monitoring equipment, and each change target item monitoring value is used as a marking value of an abnormal item; Based on the range values ​​of conventional target items, the monitored unconventional items are compared and analyzed to identify abnormal situations in large-scale laying.

5. The large-scale laying design method for laying vessels according to claim 1, characterized in that: Generating a laying risk response strategy also includes: establishing a risk assessment matrix for different abnormal situations and predicted laying problems, dividing the risk levels based on the risk assessment matrix, and determining the priority of the response items in each laying risk response strategy based on each risk level.

6. The large-scale laying design method for laying vessels according to claim 5, characterized in that: According to different abnormal situations and predicted laying problems, a risk assessment matrix is ​​established, and risk levels are divided based on the risk assessment matrix, including: Retrieve the probability of occurrence of each abnormal situation during the large-scale laying process; Retrieve the predicted probability of occurrence of each laying problem; Retrieve the large-scale laying delay rate and economic loss rate corresponding to abnormal situations and laying problems; Obtaining an abnormal coefficient corresponding to the abnormal situation and the laying problem by using the large-scale laying delay rate and economic loss rate corresponding to the abnormal situation and the laying problem; The abnormal coefficient corresponding to the abnormal situation is obtained by the following formula: ； Among them, R 01 represents the abnormal coefficient corresponding to the abnormal situation; T and J represent the large-scale laying delay rate and economic loss rate corresponding to the abnormal situation; T c and J c Indicates the reference value of the large-scale laying delay rate and the reference value of the economic loss rate corresponding to the preset abnormal situation; k 01 represents a first adjustment coefficient, which is used to adjust the relative importance between the large-scale laying delay rate and the economic loss rate when abnormal conditions occur, and the value range of the adjustment coefficient is 0.35-0.72; In addition, the abnormal coefficient corresponding to the laying problem is obtained by the following formula: ； Among them, R 02 represents the abnormal 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 represents a second adjustment coefficient, which 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 range of the adjustment coefficient is 0.21-0.74; A risk assessment matrix is ​​established using the abnormal conditions and abnormal coefficients corresponding to the laying problems, and risk levels are divided.

7. The large-scale laying design method for laying vessels according to claim 6, characterized in that: A risk assessment matrix is ​​established using the abnormal conditions and abnormal coefficients corresponding to the laying problems, and risk levels are divided, including: When the abnormal coefficient exceeds the first abnormal coefficient threshold, the severity level corresponding to the abnormal situation and the laying problem is determined to be of high severity; when the abnormal coefficient does not exceed the first abnormal coefficient threshold, but exceeds the second abnormal coefficient threshold, the severity level corresponding to the abnormal situation and the laying problem is determined to be of medium severity; when the abnormal coefficient does not exceed the second abnormal coefficient threshold, the severity level corresponding to the abnormal situation and the laying problem is determined to be of general severity; Extracting a severity level parameter corresponding to each abnormal situation, wherein the severity level includes a high severity, a medium severity, and a general severity, and the severity level parameter corresponding to the high severity is 0.8, the severity level parameter corresponding to the medium severity is 0.6, and the severity level parameter corresponding to the general severity is 0.4; Extracting the severity level parameter corresponding to each paving problem, wherein the severity level includes a high severity, a medium severity and a general severity, and the severity level parameter corresponding to the high severity is 1, the severity level parameter corresponding to the medium severity is 0.8, and the severity level parameter corresponding to the general severity is 0.5; Establishing a risk assessment matrix using the occurrence probability corresponding to each abnormal situation and the severity level parameter corresponding to each abnormal situation in combination with the occurrence probability corresponding to each laying problem and the severity level parameter corresponding to each laying problem; Acquiring a norm of the risk assessment matrix according to the structure of the risk assessment matrix; Comparing the norm of the risk assessment matrix with a preset norm threshold; When the norm of the risk assessment matrix is ​​lower than a preset first norm threshold, the risk level is determined to be low; When 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; When the norm of the risk assessment matrix is ​​not lower than a preset second norm threshold, the risk level is determined to be high.

8. The large-scale laying design method for laying vessels according to claim 1, characterized in that: The operation parameters of the laying ship include the ship speed adjustment value, tension adjustment value and laying angle adjustment value. Adjusting the operation parameters of the laying ship includes: According to the predicted laying problems and the formulated response strategies, determine the types of laying ship operating parameters that need to be adjusted, and determine the adjustment direction and goals of each laying ship operating parameter; According to the risk level of laying problems and the priority of the response items in the laying risk response strategy, combined with the operation parameters of the laying ship, the large-scale layout characteristics and the laying environment, the ship speed adjustment value, the tension adjustment value and the 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 meets the expectations.

9. The large-scale laying design method for laying vessels according to claim 8, characterized in that: Obtain feedback data after the laying is completed, including the actual topographic data of the laying area, the actual laying position and status data of the large-scale laying, the operation data of the laying ship during the entire operation process, and the laying quality inspection data.

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