Design optimization method for laying vessel construction

Abstract

The present application relates to the technical field of equipment operation design optimization, and particularly relates to a design optimization method for a laying ship construction, which comprises the following steps: in the historical operation information of the laying ship, identifying the operation equipment in each task type, analyzing the data of the operation equipment, determining an independent operation area, obtaining operation data in the independent operation area, identifying an optimal point of the operation data, marking the number of operators corresponding to the optimal point as preferred data of the independent operation area, and finally selecting the independent operation area of a real-time construction task according to the real-time construction task of the laying ship, integrating the number of operators in the independent operation area, obtaining design optimization data, and optimizing and managing the real-time construction task according to the design optimization data, so that the number of operators is matched with the area work task amount. The present application can save the construction cost, improve the work progress and work quality of the laying ship construction.

Description

Technical Field

[0001] This invention relates to the field of equipment operation design optimization technology, and in particular to a design optimization method for the construction of a laying vessel. Background Technology

[0002] The raft, also known as the sinking raft, is a construction project that combines the laying of soft-foundation (sand, etc.) riverbed structures with the slope protection / bottom protection of the Yangtze River to meet the engineering needs of the Yangtze River and coastal waterway improvement.

[0003] The prior art CN118427964A discloses a method for optimizing the layout of equipment inside a ship's cabin, including the following steps: Step 1) Collect data on cabins and equipment to be arranged, analyze the cabin structure and the external attributes of the equipment inside the cabin, and conduct a comprehensive relationship analysis between the equipment; Step 2) Establish a cabin equipment layout design model, divide the cabin into areas within the model, allocate the equipment to be arranged to each area, establish spatial connections between the equipment and the areas, and form different cabin equipment layout design schemes; Step 3) Construct a quantitative model of personnel operation and movement intensity; Step 4) Calculate the design schemes of cabin equipment layout using the minimum personnel operation and movement intensity function F(P) as the objective function to obtain the optimized design scheme; Step 5) Model the optimized cabin equipment layout based on the optimized design scheme.

[0004] When the laying vessel is under construction, the operation process of each work area is different, and different numbers of workers are needed to maintain the work progress. However, under normal working conditions, the number of workers in each work area is usually adjusted by human experience. This can cause some work areas to have a mismatch between the number of workers and the workload of the area when the laying vessel is working in real time, which will affect the work progress and quality of the laying vessel. Summary of the Invention

[0005] The purpose of this invention is to solve the problems in the background art by proposing a design optimization method for the construction of a laying vessel.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A design optimization method for laying-out vessels, which specifically includes the following steps:

[0008] Step 1: Collect the design information and historical operation information of the laying vessel. The design information refers to the 3D model of the laying vessel, and the historical operation information includes the construction tasks performed by the laying vessel in the past and the construction process of each construction task.

[0009] Step 2: Based on the design information of the laying vessel, determine the construction equipment and basic operation work of the laying vessel, obtain the execution tasks in the historical operation information of the laying vessel, and divide the historical operation information of the laying vessel into multiple task types;

[0010] Within each task type, identify the running devices appearing in all subtasks, and select key devices from among the running devices based on the frequency of their appearance.

[0011] Obtain the basic operational tasks of key equipment, calculate the operational similarity value between key equipment based on the basic operational tasks, and obtain the path distance between key equipment. Determine the independent operating area based on the operational similarity value and the path distance.

[0012] Step 3: Obtain the operation data of the independent operating area, and create a visualization image based on the operation data. Then, determine the optimal point in the visualization image and mark the number of operators corresponding to the optimal point as the preferred data of the independent operating area.

[0013] Step 4: Obtain the real-time construction task of the laying vessel, determine the task type corresponding to the real-time construction task, obtain the independent operating area and corresponding optimization data in this task type, and integrate them into the design optimization data transmission to the terminal display device. Afterwards, relevant personnel optimize and manage the real-time construction task based on the design optimization data in the terminal display device.

[0014] As a further aspect of the present invention, the method for determining key equipment includes:

[0015] S1: Based on the design information of the laying vessel, identify all the construction equipment on the laying vessel, and determine the basic operation of each construction equipment according to the equipment model;

[0016] S2: Obtain the executed tasks from the historical operation information of the laying vessel. Based on the task type, divide the historical operation information of the laying vessel according to the task type. The task types include slope protection, riverbed protection, soft soil treatment, and pump station foundation protection.

[0017] S3: In the divided task types, arbitrarily select a task type and mark it as the target execution type. Taking this target execution type as an example, obtain the subtasks in the target execution type, identify the running equipment in each subtask and the basic running work performed by the running equipment. Furthermore, the subtask refers to the elements that make up the target execution type, i.e., the construction task.

[0018] Mark the running devices appearing in the subtasks as target devices. Randomly select a target device and count the number of times this target device appears in the subtasks of the target execution type. Mark the number of appearances as CSi. Then obtain the number of subtasks in the target execution type and mark it as CZi. Use the formula CSi÷CZi=fi to obtain the frequency of appearance of target device i in the subtasks of the target execution type, where i represents the device number of different running devices and i∈[1,I], indicating that there are I different types of running devices in the target execution type.

[0019] S4: Compare the frequency fi of running device i in the target execution type with the frequency threshold fy. If fi < fy, then in the target execution type, running device i is marked as a frequently used device. Otherwise, if fi ≥ fy, then in the target execution type, running device i is marked as a key device.

[0020] As a further aspect of the present invention, when counting the number of times the target device appears, CSi, the basic operation of the target device is the same.

[0021] As a further aspect of the present invention, the method for obtaining the independent operating region includes:

[0022] Select key equipment from the target execution type and obtain the basic operation work of the key equipment. Then, use the cosine similarity processing method to process the basic operation work of the key equipment to obtain the operation similarity value Gs.

[0023] Take the values ​​of the work similarity values ​​that are greater than or equal to the similarity threshold X1, and obtain the key equipment corresponding to this work similarity value, and mark it as equipment a1 and equipment a2. Then, based on the three-dimensional model of the laying vessel, obtain the path distance DL between equipment a1 and equipment a2. Compare the path distance DL with the distance threshold Dy. If DL≤Dy, then merge the areas where equipment a1 and equipment a2 are located into an independent operating area. Otherwise, if DL>Dy, then mark the areas where equipment a1 and equipment a2 are located as independent operating areas respectively.

[0024] As a further aspect of the present invention, if the operating similarity value of a key device is less than the similarity threshold, the area where this key device is located is directly marked as an independent operating area.

[0025] As a further aspect of the present invention, the method for determining preferred data for an independent operating region includes:

[0026] SS1: Select any independent operating area in a task type and mark it as the specified analysis area. Then, based on historical operation information, obtain the operation data of the specified analysis area. The operation data includes the number of operators, construction speed and operation cost in the specified analysis area.

[0027] SS2: Set the number of operators, construction speed, and operating cost as the X-axis, Y-axis, and Z-axis, respectively. Then, based on the historical operating data in the independent operating area, construct a visualization image and determine the optimal point based on the visualization image. Then, obtain the number of operators corresponding to the optimal point and mark it as the preferred data for the independent operating area.

[0028] As a further aspect of the present invention, the construction speed corresponding to each construction task in an independent operating area is obtained, and the construction speed is averaged, and the obtained average result is marked as the speed standard value;

[0029] The minimum operating cost is identified and marked as the minimum cost. Then, the point corresponding to the minimum cost is obtained, and the construction speed corresponding to the minimum cost is obtained. The construction speed is subtracted from the standard speed value to obtain the speed difference. The speed difference is compared with the standard deviation range. If the speed difference is within the standard deviation range, the minimum cost is marked as the optimal point. Otherwise, if the speed difference is not within the standard deviation range, the point corresponding to the minimum cost is deleted. The minimum operating cost is then re-identified in the visualization image, and the above method is repeated until the optimal point is determined. The standard deviation range is a threshold.

[0030] Compared with existing technologies, the advantages of this invention are:

[0031] This invention analyzes the design and historical operational information of the laying vessel, sets multiple task types based on the historical operational information, identifies the operating equipment in each task type, analyzes the data of the operating equipment, determines independent operating areas, obtains operational data in the independent operating areas, identifies the optimal point of the operational data, and marks the number of operators corresponding to the optimal point as the preferred data for the independent operating area. Finally, based on the real-time construction task of the laying vessel, the independent operating area of ​​the real-time construction task is selected, and the number of operators in the independent operating area is integrated to obtain design optimization data. The staff optimizes the management of the real-time construction task based on the design optimization data, thereby matching the number of workers with the workload of the area, saving construction costs while improving the work progress and quality of laying vessel construction. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the method flow structure of the present invention. Detailed Implementation

[0033] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0034] Reference Figure 1 A design optimization method for the construction of laying-out vessels, which specifically includes the following steps:

[0035] Step 1: Collect the design information and historical operation information of the laying vessel. The design information of the laying vessel refers to the three-dimensional model of the laying vessel. Furthermore, the three-dimensional model includes the construction equipment existing in the laying vessel and the corresponding position of each construction equipment in the laying vessel. The historical operation information includes the construction tasks performed by the laying vessel in the past and the construction process of each construction task.

[0036] Step Two: Based on the design information and historical operation information of the laying vessel, an independent operating area is set up for the laying vessel. The method for setting up the independent operating area is as follows:

[0037] S1: Based on the design information of the laying vessel, identify all the construction equipment on the laying vessel, and determine the basic operation of each construction equipment according to the equipment model. It should be further explained that, due to the different instructions executed by the construction equipment, there may be multiple basic operation instructions within a single construction equipment. For example, the laying vessel is equipped with a drum, the main function of which is to wind up and unwind geotextile or other slab materials. In another basic operation, the drum is used to assist in the tension control of the slab.

[0038] S2: Obtain the executed tasks from the historical operation information of the laying vessel. Based on the task type, divide the historical operation information of the laying vessel according to the task type. The task types include slope protection, riverbed protection, soft soil treatment, and pump station foundation protection, etc.

[0039] S3: In the divided task types, arbitrarily select a task type and mark it as the target execution type. Taking this target execution type as an example, obtain the subtasks in the target execution type, identify the running equipment in each subtask and the basic running work performed by the running equipment. Furthermore, the subtask refers to the elements that make up the target execution type, i.e., the construction task.

[0040] Mark the running devices appearing in the subtasks as target devices. Randomly select a target device and count the number of times this target device appears in the subtasks of the target execution type. Mark the number of appearances as CSi. Then obtain the number of subtasks in the target execution type and mark it as CZi. Use the formula CSi÷CZi=fi to obtain the frequency of appearance of target device i in the subtasks of the target execution type, where i represents the device number of different running devices and i∈[1,I], indicating that there are I different types of running devices in the target execution type.

[0041] It should be further noted that when counting the number of times the target device appears (CSi), the basic operation of the target device is the same.

[0042] S4: Compare the frequency fi of running device i in the target execution type with the frequency threshold fy. If fi < fy, then in the target execution type, running device i is marked as a frequently used device. Conversely, if fi ≥ fy, then in the target execution type, running device i is marked as a key device. Furthermore, the specific value of the frequency threshold fy is obtained by those skilled in the art through big data calculation.

[0043] S5: Select key equipment in the target execution type and obtain the basic operation work of the key equipment. Then, perform similarity processing on the basic operation work of the key equipment to obtain the work similarity value Gs. It should be further explained that the similarity processing method is set to cosine similarity processing method. The specific processing process of cosine similarity processing method is existing technology and will not be described in detail here.

[0044] The similarity value of the work is taken as greater than or equal to the similarity threshold X1, and the key equipment corresponding to this similarity value is obtained and marked as equipment a1 and equipment a2. Then, based on the three-dimensional model of the laying vessel, the path distance DL between equipment a1 and equipment a2 is obtained. The path distance DL is compared with the distance threshold Dy. If DL≤Dy, the areas where equipment a1 and equipment a2 are located are merged into an independent operating area. Conversely, if DL>Dy, the areas where equipment a1 and equipment a2 are located are marked as independent operating areas respectively. It should be further noted that the specific value of the distance threshold Dy is obtained by those skilled in the art after big data calculation.

[0045] Furthermore, if the operational similarity values ​​of a key piece of equipment are all less than the similarity threshold, then the area where this key piece of equipment is located is directly marked as an independent operating area;

[0046] Then, the remaining task types are set as target execution types and processed according to the methods in steps S2 to S5 above, thereby setting up several independent running areas;

[0047] Step 3: Obtain construction tasks from historical operation information and analyze the construction process of each construction task to determine the optimal data for each independent operation area. Specifically, the methods for determining the optimal data for an independent operation area include:

[0048] SS1: Select any independent operating area in a task type and mark it as the specified analysis area. Then, based on historical operation information, obtain the operation data of the specified analysis area. The operation data includes the number of operators, construction speed and operation cost in the specified analysis area.

[0049] SS2: Set the number of operators, construction speed, and operating cost as the X-axis, Y-axis, and Z-axis, respectively. Then, based on the historical operating data in the independent operating area, construct a visualization image and determine the optimal point based on the visualization image. Then, obtain the number of operators corresponding to the optimal point and mark it as the preferred data in the independent operating area.

[0050] It should be further explained that, in this embodiment, the visualization image is set as a curve graph, wherein the method for identifying the optimal point in the curve graph includes: obtaining the construction speed corresponding to each construction task in the independent operating area, averaging the construction speed, and marking the obtained average result as the speed standard value;

[0051] The minimum cost is identified and marked as the minimum cost. Then, the point corresponding to the minimum cost is obtained, and the construction speed corresponding to the minimum cost is obtained. The construction speed is subtracted from the standard speed value to obtain the speed difference. The speed difference is compared with the standard deviation range. If the speed difference is within the standard deviation range, the minimum cost is marked as the optimal point. Otherwise, if the speed difference is not within the standard deviation range, the point corresponding to the minimum cost is deleted. The minimum cost is then re-identified in the operating cost, and the above method is repeated until the optimal point is determined. The standard deviation range is a threshold, and the specific range is obtained by those skilled in the art through big data calculation.

[0052] Step 4: Obtain the real-time construction task of the laying vessel. Search for the real-time construction task in the task type to determine the corresponding task type. Obtain the independent operating area in this task type. Then, integrate the independent operating area in this task type and the corresponding preferred data into design optimization data and transmit it to the terminal display device. Afterwards, relevant personnel optimize and manage the real-time construction task based on the design optimization data in the terminal display device.

[0053] 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 design optimization method for the construction of a laying-out vessel, characterized in that, The method specifically includes the following steps: Step 1: Collect the design information and historical operation information of the laying vessel. The design information refers to the 3D model of the laying vessel, and the historical operation information includes the construction tasks performed by the laying vessel in the past and the construction process of each construction task. Step 2: Based on the design information of the laying vessel, determine the construction equipment and basic operation work of the laying vessel, obtain the execution tasks in the historical operation information of the laying vessel, and divide the historical operation information of the laying vessel into multiple task types; Within each task type, identify the running devices appearing in all subtasks, and select key devices from among the running devices based on the frequency of their appearance. Obtain the basic operational tasks of key equipment, calculate the operational similarity value between key equipment based on the basic operational tasks, and obtain the path distance between key equipment. Determine the independent operating area based on the operational similarity value and the path distance. The methods for determining independent operating regions include: Based on the design information of the laying vessel, identify all the construction equipment on the laying vessel, and determine the basic operation of each construction equipment according to the equipment model; The historical operation information of the laying vessel is obtained, and the historical operation information of the laying vessel is divided according to the task type. The task types include slope protection, riverbed protection, soft soil treatment and pump station foundation protection. In the divided task types, arbitrarily select a task type and mark it as the target execution type. Select key equipment in the target execution type and obtain the basic operation work of the key equipment. Then, use the cosine similarity processing method to process the basic operation work of the key equipment to obtain the operation similarity value Gs. Take the values ​​of work similarity values ​​that are greater than or equal to the similarity threshold X1, and obtain the key equipment corresponding to this work similarity value, and mark them as equipment a1 and equipment a2. Then, based on the three-dimensional model of the laying vessel, obtain the path distance DL between equipment a1 and equipment a2. Compare the path distance DL with the distance threshold Dy. If DL≤Dy, then merge the areas where equipment a1 and equipment a2 are located into an independent operating area. Otherwise, if DL>Dy, then mark the areas where equipment a1 and equipment a2 are located as independent operating areas respectively. Step 3: Obtain the operation data of the independent operating area, and create a visualization image based on the operation data. Then, determine the optimal point in the visualization image and mark the number of operators corresponding to the optimal point as the preferred data of the independent operating area. The methods for obtaining preferred data include: SS1: Select an independent operating area in a task type and mark it as a specified analysis area. Then, based on historical operation information, obtain the operation data of the specified analysis area. The operation data includes the number of operators, construction speed and operation cost in the specified analysis area. SS2: Set the number of operators, construction speed, and operating cost as the X-axis, Y-axis, and Z-axis, respectively. Then, based on the historical operating data in the independent operating area, construct a visualization image and determine the optimal point based on the visualization image. Then, obtain the number of operators corresponding to the optimal point and mark it as the preferred data in the independent operating area. Step 4: Obtain the real-time construction task of the laying vessel, determine the task type corresponding to the real-time construction task, obtain the independent operating area and corresponding optimization data in this task type, and integrate them into the design optimization data transmission to the terminal display device. Afterwards, relevant personnel optimize and manage the real-time construction task based on the design optimization data in the terminal display device.

2. The design optimization method for laying-out vessels according to claim 1, characterized in that, The methods for identifying key equipment include: Obtain the subtasks in the target execution type, identify the running devices in each subtask and the basic running tasks performed by the running devices. Subtasks refer to the elements that make up the target execution type, i.e., construction tasks. Mark the running devices appearing in the subtasks as target devices. Randomly select a target device and count the number of times this target device appears in the subtasks of the target execution type. Mark the number of appearances as CSi. Then obtain the number of subtasks in the target execution type and mark it as CZi. Use the formula CSi÷CZi=fi to obtain the frequency of appearance of target device i in the subtasks of the target execution type, where i represents the device number of different running devices and i∈[1,I], indicating that there are I different types of running devices in the target execution type. The frequency fi of running device i in the target execution type is compared with the frequency threshold fy. If fi < fy, then running device i is marked as a frequently used device in the target execution type. Otherwise, if fi ≥ fy, then running device i is marked as a key device in the target execution type.

3. The design optimization method for laying-out vessels according to claim 2, characterized in that, When counting the number of times the target device appears (CSi), the basic operation of the target device is the same.

4. The design optimization method for laying-out vessels according to claim 1, characterized in that, If the operating similarity value of a key piece of equipment is less than the similarity threshold, then the area where this key piece of equipment is located is directly marked as an independent operating area.

5. The design optimization method for laying-out vessels according to claim 1, characterized in that, Obtain the construction speed corresponding to each construction task in the independent operating area, average the construction speed, and mark the average result as the speed standard value; The minimum operating cost is identified and marked as the minimum cost. Then, the point corresponding to the minimum cost is obtained, and the construction speed corresponding to the minimum cost is obtained. The construction speed is subtracted from the standard speed value to obtain the speed difference. The speed difference is compared with the standard deviation range. If the speed difference is within the standard deviation range, the minimum cost is marked as the optimal point. Otherwise, if the speed difference is not within the standard deviation range, the point corresponding to the minimum cost is deleted. The minimum operating cost is then re-identified in the visualization image, and the above method is repeated until the optimal point is determined. The standard deviation range is a threshold.

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