General assembly shop transformation layout optimization method based on fan unit capacity selection

Abstract

The invention belongs to the technical field of wind power generation equipment, and particularly discloses a general assembly workshop transformation layout optimization method based on fan unit capacity selection, which comprises the following steps: S1, data collection and analysis: collecting equipment parameters, process flows and material handling data of an existing general assembly workshop, and analyzing the equipment parameters, the process flows and the material handling data; comparing and analyzing with production demand data of a new capacity unit, and identifying key difference items; s2, designing layout schemes, and generating a plurality of sets of layout schemes based on an analysis result; s3, scheme evaluation and selection: performing simulation operation on each layout scheme through a digital simulation platform, and evaluating a plurality of indexes including production efficiency, logistics cost and space utilization rate; s4, performing scheme implementation and verification, executing equipment adjustment and personnel training according to the selected scheme, and performing trial operation verification; and S5, performing effect evaluation and iterative optimization, and continuously optimizing the layout scheme based on actual operation data.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation equipment, and in particular to a method for optimizing the layout of an assembly workshop based on capacity selection of wind turbine units. Background Art

[0002] With the continuous advancement of wind power technology and the expansion of the market, wind turbine capacity is constantly being upgraded. This upgrade not only brings higher power generation efficiency and lower operation and maintenance costs, but also poses new challenges to the layout and production processes of wind turbine assembly workshops.

[0003] Traditional assembly shop layouts are often designed for wind turbine units of specific capacities. When wind turbine unit capacities change, the existing layout may not be able to meet the new production requirements. Traditional layouts often struggle to adapt to the production needs of newer units, leading to low production efficiency and increased logistics costs. Therefore, a method for optimizing assembly shop layouts that can adapt to the changing capacities of wind turbine units is needed. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for optimizing the layout of an assembly workshop based on capacity selection of wind turbine units, so as to solve the problem that the traditional layout method is difficult to adapt to the production needs of new capacity units, resulting in low production efficiency and increased logistics costs.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solution, which is a method for optimizing the layout of an assembly workshop based on capacity selection of wind turbine units, comprising the following steps:

[0006] S1. Data collection and analysis: Collect equipment parameters, process flow, and material handling data from the existing assembly workshop, and compare and analyze them with the production demand data of the new capacity units to identify key differences.

[0007] S2. Layout plan design: Generate multiple layout plans based on the analysis results. The layout plans include space planning plans adjusted according to the size of the new unit, equipment layout plans based on process relevance, and path optimization plans considering logistics efficiency.

[0008] S3. Scheme evaluation and selection: simulate the operation of each layout scheme through a digital simulation platform to evaluate multiple indicators including production efficiency, logistics costs and space utilization.

[0009] S4. Implementation and verification of the plan: carry out equipment adjustment and personnel training according to the selected plan, and conduct trial operation verification.

[0010] S5. Effect evaluation and iterative optimization: continuously optimize the layout plan based on actual operation data.

[0011] Preferably, the data analysis in S1 specifically includes identifying the size variation range of the core components of the new unit; analyzing the space requirements and installation conditions of the new equipment; evaluating the adaptability of the existing logistics system to the new unit; and predicting the changing trend of the process cycle time.

[0012] Preferably, the space planning scheme includes determining the minimum space requirement of the assembly area according to the maximum external dimensions of the new unit; planning the relative positions of the buffer area and the storage area to set up an expandable flexible production area; and reserving adjustment space for future capacity upgrades.

[0013] Preferably, the equipment layout scheme adopts the following steps:

[0014] S1. Establish an equipment correlation matrix to quantify the material flow frequency between each equipment.

[0015] S2. Apply genetic algorithm to solve the optimal location of equipment.

[0016] S3. Verify the foundation bearing capacity of heavy equipment.

[0017] S4. Set up safe passages and emergency evacuation routes.

[0018] Preferably, the logistics path optimization includes analyzing the flow paths and frequencies of major materials; designing one-way logistics channels to reduce cross-interference; optimizing the operating radius of lifting equipment; and planning the deployment location of the intelligent logistics system.

[0019] Preferably, the scheme evaluation adopts discrete event simulation technology, including constructing a three-dimensional digital workshop model; setting production scenarios and load conditions; simulating material flow and equipment operating status; and outputting an evaluation report including equipment utilization, personnel movement distance and logistics efficiency.

[0020] Preferably, the implementation of the plan includes formulating a phased implementation plan; designing the sequence of equipment relocation; and planning a temporary production transition plan.

[0021] Preferably, the effect evaluation includes collecting equipment operation data in actual production; monitoring material flow time and distance; recording abnormal situations and treatment plans; and establishing a feedback mechanism for continuous improvement.

[0022] Use digital twin technology to achieve real-time mapping of the physical status of the workshop; dynamically optimize layout plans; simulate production scenarios of units with different capacities; and support remote collaborative decision-making and optimization.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] The assembly workshop layout optimization method and system based on wind turbine unit capacity iteration provided by the present invention can adapt to the continuous iterative upgrade of wind turbine unit capacity, improve production efficiency, reduce logistics costs, and provide an effective layout optimization solution for the wind power equipment manufacturing field. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0027] See also Figure 1 The present invention provides a technical solution, a method for optimizing the layout of an assembly workshop based on capacity selection of wind turbine units, comprising the following steps:

[0028] Step 1: Data collection and analysis: Collect equipment parameters, process flow, and material handling data from the existing assembly workshop, and compare and analyze them with the production demand data of the new capacity units to identify key differences.

[0029] Data analysis specifically includes identifying the size variation range of the core components of the new unit; analyzing the space requirements and installation conditions of the new equipment; evaluating the adaptability of the existing logistics system to the new unit; and predicting the changing trend of the process cycle time.

[0030] Step 2: Layout plan design. Based on the analysis results, multiple layout plans are generated. The layout plans include space planning plans adjusted according to the size of the new unit, equipment layout plans based on process correlation, and path optimization plans considering logistics efficiency.

[0031] The space planning plan includes determining the minimum space requirements for the assembly area based on the maximum external dimensions of the new unit; planning the relative positions of the buffer and storage areas to set up expandable flexible production areas; and reserving adjustment space for future capacity upgrades.

[0032] The equipment layout plan adopts the following steps:

[0033] S1. Establish an equipment correlation matrix to quantify the material flow frequency between each equipment.

[0034] S2. Apply genetic algorithm to solve the optimal location of equipment.

[0035] S3. Verify the foundation bearing capacity of heavy equipment.

[0036] S4. Set up safe passages and emergency evacuation routes.

[0037] The logistics path optimization includes analyzing the flow paths and frequencies of major materials; designing one-way logistics channels to reduce cross-interference; optimizing the operating radius of lifting equipment; and planning the deployment location of the intelligent logistics system.

[0038] Step 3: Scheme evaluation and selection: simulate the operation of each layout scheme through the digital simulation platform to evaluate multiple indicators including production efficiency, logistics costs and space utilization.

[0039] The scheme evaluation uses discrete event simulation technology, including building a three-dimensional digital workshop model; setting production scenarios and load conditions; simulating material flow and equipment operating status; and outputting an evaluation report including equipment utilization, personnel movement distance and logistics efficiency.

[0040] Use digital twin technology to achieve real-time mapping of the physical status of the workshop; dynamically optimize layout plans; simulate production scenarios of units with different capacities; and support remote collaborative decision-making and optimization.

[0041] Step 4: Implementation and verification of the plan: perform equipment adjustment and personnel training according to the selected plan, and conduct trial operation verification.

[0042] The implementation of the plan includes formulating a phased implementation plan; designing the sequence of equipment relocation; and planning a temporary production transition plan.

[0043] Step 5: Effect evaluation and iterative optimization: Continuously optimize the layout plan based on actual operation data.

[0044] Effectiveness evaluation includes collecting equipment operation data in actual production; monitoring material flow time and distance; recording abnormal situations and treatment plans; and establishing a feedback mechanism for continuous improvement.

[0045] In summary, the assembly shop layout optimization method for renovation is based on iterative wind turbine unit capacity requirements and achieves adaptive adjustments to the shop layout through a data-driven, multi-stage optimization process. The core principle is to balance space constraints, production efficiency, and expansion flexibility through a technical approach of variance analysis, intelligent optimization, simulation verification, and dynamic iteration.

[0046] Data-driven difference identification compares production demand data (such as size, logistics volume, and cycle time) between new and old units to identify key constraints for layout adjustments (such as minimum assembly space and logistics bottlenecks). This ensures that the layout design addresses the core contradictions of capacity upgrades and avoids excessive modifications.

[0047] Multi-objective intelligent layout optimization quantifies the material flow frequency between equipment (correlation matrix), minimizing logistics costs and using genetic algorithms to solve for optimal non-overlapping equipment locations. The layout problem is transformed into a combinatorial optimization problem, using heuristic algorithms to address high-dimensional nonlinear constraints (such as safe passages and foundation loads). Logistics path optimization is based on unidirectional flow and lifting operation radius design to reduce cross-logistics (lean production principles).

[0048] Digital simulation verification, combining discrete event simulation with digital twins, builds 3D models to simulate dynamic production (e.g., equipment utilization and personnel movement distances) and verify the robustness of layout solutions under varying production loads. Virtual trial and error reduces actual transformation costs, while digital twins enable real-time feedback and dynamic optimization.

[0049] Flexible design and iterative mechanisms allow for expansion space reservation, leaving buffers and upgrade space within the layout (modular design principles). A continuous improvement feedback loop: Optimize the layout through actual operational data (such as exception records and logistics efficiency), forming a PDCA (Plan-Do-Check-Act) cycle.

[0050] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0051] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the layout of an assembly workshop based on capacity selection of wind turbine units, characterized in that: The following steps are involved: S1. Data collection and analysis: Collect equipment parameters, process flow, and material handling data from the existing assembly workshop, and compare and analyze them with the production demand data of the new capacity units to identify key differences; S2. Layout design: Generate multiple layout plans based on the analysis results. These plans include space planning based on the size of the new units, equipment layout based on process relevance, and route optimization considering logistics efficiency. S3. Scheme evaluation and selection: Simulate various layout schemes through a digital simulation platform to evaluate multiple indicators including production efficiency, logistics costs, and space utilization; S4. Implementation and verification of the plan: equipment adjustment and personnel training shall be carried out according to the selected plan, and trial operation verification shall be conducted; S5. Effect evaluation and iterative optimization: continuously optimize the layout plan based on actual operation data.

2. The method for optimizing the layout of an assembly workshop based on capacity selection of wind turbine units according to claim 1 is characterized in that: The data analysis in S1 specifically includes: Identify the dimensional variation range of core components of the new unit; analyze the space requirements and installation conditions of the new equipment; evaluate the adaptability of the existing logistics system to the new unit; and predict the changing trend of process cycle time.

3. The method for optimizing the layout of an assembly workshop based on capacity selection of wind turbine units according to claim 1 is characterized in that: The spatial planning scheme includes: Determine the minimum space requirement for the assembly area based on the maximum external dimensions of the new unit; plan the relative positions of the buffer zone and storage area to set up an expandable flexible production area; and reserve adjustment space for future capacity upgrades.

4. The method for optimizing the layout of an assembly workshop based on wind turbine unit capacity selection according to claim 1 is characterized in that: The equipment layout plan adopts the following steps: S1. Establish an equipment correlation matrix to quantify the material flow frequency between each equipment; S2, applying genetic algorithm to solve the optimal location of equipment; S3. Verify the foundation bearing capacity of heavy equipment; S4. Set up safe passages and emergency evacuation routes.

5. The method for optimizing the layout of an assembly workshop based on capacity selection of wind turbine units according to claim 1 is characterized in that: The logistics path optimization includes: Analyze the flow paths and frequencies of major materials; design one-way logistics channels to reduce cross-interference; optimize the operating radius of lifting equipment; and plan the deployment location of the intelligent logistics system.

6. The method for optimizing the layout of an assembly workshop based on capacity selection of wind turbine units according to claim 1 is characterized in that: The scheme evaluation uses discrete event simulation technology, including: Build a 3D digital workshop model; set production scenarios and load conditions; simulate material flow and equipment operating status; and output evaluation reports including equipment utilization, personnel movement distance, and logistics efficiency.

7. The method for optimizing the layout of an assembly workshop based on capacity selection of wind turbine units according to claim 1 is characterized in that: The implementation of the program includes: Develop a phased implementation plan; design the sequence of equipment relocation; and plan a temporary production transition plan.

8. The method for optimizing the layout of an assembly workshop based on capacity selection of wind turbine units according to claim 1 is characterized in that: The effectiveness evaluation includes: Collect equipment operation data in actual production; monitor material flow time and distance; record abnormal situations and treatment plans; and establish a feedback mechanism for continuous improvement.

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