Method and system for cost and performance synergy optimization of export railway wagon painting scheme

Abstract

The application provides a cost and efficiency synergistic optimization method and system for export railway freight car coating schemes, and the method comprises the following steps: establishing a risk level matrix according to the corrosion environment level and corrosion risk of the export target country, determining the environment severity coefficient of each part and the part difference coefficient; establishing a comprehensive performance index system comprising corrosion resistance performance, weather resistance performance and mechanical performance, and determining the weight coefficient of each performance index; calculating the comprehensive performance index of each candidate coating scheme according to the environment severity coefficient, the part difference coefficient and the performance index weight; constructing a whole life cycle cost model of the coating scheme, and calculating the whole life cycle cost of each candidate scheme; calculating the performance-cost ratio of each candidate scheme, sorting each candidate scheme according to the performance-cost ratio value from large to small, and selecting the scheme with the largest performance-cost ratio as the optimal recommended process scheme. The application realizes the quantitative screening and optimization of the coating scheme, and reduces the comprehensive cost under the premise of meeting the protection requirements.

Description

Technical Field

[0001] This invention relates to the field of rail transit equipment manufacturing and coating protection, and more specifically, to a method and system for cost and efficiency synergistic optimization of coating schemes for exported railway freight cars. Background Technology

[0002] As a crucial component of rail transit equipment, the coating and protection system of railway freight cars directly impacts their corrosion resistance, service life, and total life-cycle cost in complex service environments. Exported railway freight cars face diverse service environments—from the high temperatures and humidity of Southeast Asia to the extreme cold of Russia, from ore abrasion in Australia to strong ultraviolet radiation in the Middle East—placing higher demands on coating and protection.

[0003] Current railway freight car painting technology mainly employs a multi-layer coating system: the primer is mostly epoxy zinc-rich or epoxy zinc phosphate coating, providing basic corrosion protection through cathodic protection or passivation; the intermediate coat commonly uses epoxy micaceous iron oxide coating, enhancing corrosion resistance through the shielding effect of lamellar micaceous iron oxide; the topcoat uses polyurethane or fluorocarbon coating, providing weather resistance and decorative effect. The total coating thickness is generally between 160 and 260 μm.

[0004] Coating process design primarily relies on experience-based analogies and standard comparisons. Process engineers select similar solutions based on the environmental requirements of the target export country and industry standards such as TB / T2879.4. For special environments, a conservative "higher is better" strategy is typically adopted, selecting a higher-grade coating solution to ensure compliance with contract requirements. Quality control mainly focuses on the film thickness, adhesion, and appearance of the initial coating, employing testing methods such as wet film cards, magnetic thickness gauges, and cross-cut tests.

[0005] For specific application scenarios, there are already targeted solutions. For example, the inner wall of an ore open wagon is coated with a polyurea elastomer coating. The coating, which is several millimeters thick, is sprayed using specialized equipment, which significantly improves wear resistance.

[0006] Traditional coating scheme design relies heavily on engineers' experience or simple standard comparisons, making it difficult to accurately adapt to the complex environments of different export target countries. This often leads to either over- or under-design in a "one-size-fits-all" manner. Furthermore, the corrosion risk varies significantly when the same coating scheme is applied to different parts of a vehicle (underframe, sidewalls, roof), but current technologies do not differentiate between these areas. To meet export contract requirements, a conservative strategy is typically adopted, choosing the highest-grade coating scheme, resulting in inflated per-vehicle coating costs. Conversely, excessively pursuing cost reduction may lead to insufficient protective effectiveness and premature failure. Moreover, existing schemes only focus on the initial coating cost, neglecting subsequent costs such as operation and maintenance, overhaul recoating, and disposal, failing to accurately reflect the economic viability of the coating scheme. Summary of the Invention

[0007] This invention addresses the technical problems existing in the prior art by providing a method and system for cost and efficiency synergistic optimization of coating schemes for export railway freight cars. By constructing a multi-factor coupled efficiency evaluation system and a full life cycle cost model, it achieves quantitative screening and optimization of coating schemes, thereby reducing overall costs while meeting protection requirements.

[0008] According to a first aspect of the present invention, a cost-efficiency synergistic optimization method for painting export railway freight cars is provided, comprising the following steps: Based on the corrosion environment level, temperature range, cargo medium type, and stress conditions and corrosion risks of different parts of the vehicle in the export destination country, a risk level matrix is ​​established to determine the environmental severity coefficient of each part. Coefficient of variation in location ; A comprehensive performance index system including corrosion resistance, weather resistance, and mechanical performance was established, and the weight coefficients of each performance index were determined by combining the analytic hierarchy process (AHP) with the entropy method. The comprehensive performance index of each candidate coating scheme is calculated based on the environmental severity coefficient, the part difference coefficient, and the performance index weight. Construct a lifecycle cost model for coating solutions and calculate the lifecycle cost of each candidate solution; Calculate the efficiency-cost ratio of each candidate scheme, sort the candidate schemes from largest to smallest efficiency-cost ratio, and select the scheme with the largest efficiency-cost ratio as the optimal recommended process scheme.

[0009] Based on the above technical solution, the present invention can also be improved as follows.

[0010] Optionally, the method further includes: performing sensitivity analysis on key parameters to evaluate the robustness of the optimization results; the key parameters include paint price, discount rate, cleaning cycle, and maintenance threshold.

[0011] Optionally, the corrosion environment level is classified according to ISO 12944-2 as: low-pollution atmosphere, urban atmosphere, industrial area, high salt spray industrial area and offshore platform, and the environmental severity coefficient k1 of each part is quantified and assigned according to the corrosion level.

[0012] Optionally, the stress conditions of different parts of the vehicle are divided into heavy corrosion protection zone, medium corrosion protection zone and light corrosion protection zone, and the difference coefficient k2 of each part is assigned a different value according to the corrosion risk of different areas of the vehicle.

[0013] Optionally, the weighting coefficients of each performance indicator are determined by combining the analytic hierarchy process (AHP) with the entropy method. include: A review panel composed of coating experts, process engineers, and quality engineers was invited to conduct pairwise comparisons and scoring of each performance indicator to construct a judgment matrix; the analytic hierarchy process (AHP) was used to calculate the subjective weight of each indicator. When there is actual test data for multiple candidate solutions, the entropy method is used to calculate the objective weight of each indicator, and finally, the combined weight is used to determine the final weight.

[0014] Optionally, the combined weight formula is expressed as:

[0015] In the formula, W j The combined weights of each indicator, This is an empirical coefficient. The subjective weights of each indicator, The objective weights of each indicator.

[0016] Optionally, the comprehensive performance index of each candidate coating scheme is calculated based on the environmental severity coefficient, the part difference coefficient, and the performance index weight, expressed by the formula:

[0017] in, Represented as a comprehensive performance index; This is expressed as the environmental severity coefficient of the location; Expressed as the coefficient of variation for different body parts; Let be the measured value of the j-th indicator of the scheme to be evaluated; This represents the benchmark value of the j-th indicator in the benchmark scheme; Let be the weight coefficient of the j-th indicator, satisfying =1; This is expressed as the number of performance indicators.

[0018] Optionally, the total life cycle cost includes the initial coating cost, operation and maintenance cost, overhaul and recoating cost, and disposal cost.

[0019] Optionally, the efficiency-cost ratio of each candidate solution is calculated using the following formula:

[0020] in, Reflects the protective effectiveness value obtained per unit cost input. Represented as a comprehensive performance index, C LCC Expressed as the total lifecycle cost of the coating scheme, it serves as a core indicator for scheme selection, categorizing candidate schemes according to... Sort the values ​​from largest to smallest and select The option with the largest value is selected as the optimal recommended process option.

[0021] According to a second aspect of the present invention, a cost-efficiency synergistic optimization system for export railway freight car painting schemes is provided, comprising: The Environment-Component Risk Matrix Construction Module is used to establish a risk level matrix based on the corrosion environment level, temperature range, cargo medium type, and stress conditions and corrosion risks of different parts of the vehicle in the export destination country, and to determine the environmental severity coefficient and part difference coefficient of each part. The module for constructing and determining the weights of the performance index system is used to establish a comprehensive performance index system that includes corrosion resistance, weather resistance, and mechanical performance. The weight coefficients of each performance index are determined by combining the analytic hierarchy process (AHP) with the entropy method. The comprehensive performance index calculation module is used to calculate the comprehensive performance index of each candidate coating scheme based on the environmental severity coefficient, the part difference coefficient, and the performance index weight. The lifecycle cost calculation module is used to build a lifecycle cost model for coating solutions and calculate the lifecycle cost of each candidate solution. The efficiency-cost ratio calculation and scheme selection module is used to calculate the efficiency-cost ratio of each candidate scheme, sort the candidate schemes from largest to smallest efficiency-cost ratio, and select the scheme with the largest efficiency-cost ratio as the optimal recommended process scheme.

[0022] The technical effects and advantages of this invention are as follows: This invention provides a method and system for cost-efficiency synergistic optimization of coating for export railway freight cars. By constructing a multi-factor coupled efficiency evaluation system and a full life-cycle cost model, the coating scheme design is transformed from traditional "qualitative experience" to "quantitative data." Through the multi-factor coupled efficiency evaluation system and the full life-cycle cost model, precise quantitative screening and optimization of coating schemes are achieved; and an efficiency-cost ratio is constructed. As a core criterion, the most cost-effective solution is selected while meeting the protection performance requirements, avoiding resource waste caused by over-design, and ensuring that insufficient protection is not caused by cost control; an environmental severity coefficient is introduced. Coefficient of variation of location It precisely adapts to the differentiated needs of different export target countries and different parts of vehicles, and incorporates subsequent costs such as operation and maintenance, overhaul and repainting, and scrapping into the model to truly reflect the full life cycle economy of the coating solution.

[0023] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0024] Figure 1 This is an overall flowchart of the method for synergistic optimization of cost and efficiency of coating schemes provided in the embodiments of the present invention; Figure 2 This is a diagram of a coating cost-efficiency synergistic optimization decision model provided in an embodiment of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Understandably, based on the deficiencies in the background technology, this invention proposes a cost-efficiency synergistic optimization method for painting export railway freight cars, specifically as follows: Figure 1 As shown, it includes the following steps: Step 1: Based on the corrosion environment level, temperature range, cargo medium type, and stress conditions and corrosion risks of different parts of the vehicle in the target export country, establish a risk level matrix and determine the environmental severity coefficient of each part. Coefficient of variation of location ; The corrosion environment levels are divided into five levels: C2, C3, C4, C5, and CX, according to ISO 12944-2. An environmental severity coefficient k1 is introduced, which is quantified and assigned according to the corrosion levels in ISO 12944-2: 0.8 for C2, 1.0 for C3, 1.2 for C4, 1.4 for C5, and 1.6 for CX.

[0027]

[0028] The vehicle parts are divided into three categories: Category I (heavy corrosion protection area), Category II (medium corrosion protection area), and Category III (light corrosion protection area), and a part difference coefficient k2 is introduced. The part difference coefficient k2 is assigned a different value according to the corrosion risk of different areas of the vehicle: 1.2~1.5 for Category I, 1.0~1.2 for Category II, and 0.8~0.9 for Category III.

[0029]

[0030] The types of cargo media include chemicals, coal / ore, grain, cement, etc. For special media environments, under severe environmental conditions... Add a medium correction factor to the basis The value ranges from 1.1 to 1.5.

[0031] Step 2: Establish a comprehensive performance index system that includes corrosion resistance, weather resistance, and mechanical performance. Use the analytic hierarchy process (AHP) combined with the entropy method to determine the weight coefficients of each performance index. ; The benchmark value of the performance indicator system The values ​​are taken from measured values ​​of currently mature process solutions or values ​​specified in industry standards. The performance index system includes salt spray resistance. Moist heat resistance aging resistance Gloss and color retention Adhesion abrasion resistance Stone impact resistance One or more of them.

[0032] The comprehensive performance indicator system includes three dimensions: corrosion resistance, weather resistance, and mechanical performance. Each indicator specifically includes: Corrosion resistance: Salt spray resistance Moist heat resistance h ; Weather resistance: aging resistance Gloss and color retention ; Mechanical performance: adhesion abrasion resistance Stone impact resistance ; The weighting coefficients of each performance indicator were determined using the analytic hierarchy process (AHP) combined with the entropy method. Specifically, it includes: Constructing a judgment matrix: Invite coating experts, process engineers, and quality engineers to form a review panel to compare and score each performance indicator in pairs, and construct judgment matrix A; Calculating subjective weights: The subjective weights of each indicator are calculated using the analytic hierarchy process (AHP). ; Calculating objective weights: When there is actual test data for multiple candidate solutions, the entropy method is used to calculate the objective weights of each indicator. ; Combination weighting: The final weights are determined using a combination weighting formula, which is expressed as follows:

[0033] in, Subjective weights calculated using the analytic hierarchy process (AHP); The objective weights calculated using the entropy method; This is an empirical coefficient, ranging from 0.5 to 0.7. This coefficient is an adjustable weighting coefficient in the method, and its function is to balance the proportion of subjective weight and objective weight in the overall weight. It is determined by the review group based on the characteristics of the project stage. For example, new models tend to be more subjective and experienced, while mature and established models tend to be more objective and data-driven.

[0034] Step 3: Based on the determined environmental severity coefficient Coefficient of variation of location In addition to the performance index weights, the overall performance index of each candidate coating scheme is calculated. ; The comprehensive efficiency index The formula is expressed as:

[0035] in, Let be the measured value of the j-th indicator of the scheme to be evaluated; This represents the benchmark value of the j-th indicator in the benchmark scheme; Let be the weight coefficient of the j-th indicator, satisfying = 1; This is expressed as the number of performance indicators.

[0036] If a certain indicator is a negative indicator (e.g., the lower the wear, the better), then adopt... Alternatively, it can be processed in reciprocal form.

[0037] Step 4: Construct a full life cycle cost model for the coating scheme and calculate the full life cycle cost of each candidate scheme; The life-cycle cost model formula for the coating scheme is expressed as follows:

[0038] Among them, C LCC This represents the total lifecycle cost of the coating solution, where T is the design life, r is the discount rate, and N is the number of recoatings within the design life. The year of the nth repainting.

[0039] For the initial painting cost, = ; C m For material costs, , Let be the unit price of the i-th type of paint. This refers to the amount of paint used per vehicle. C i For labor costs, , For the j-th process time, This refers to the hourly rate; Ce For energy consumption costs, , For the k-th type of energy consumption, This refers to the unit price of energy. Environmental protection costs include the costs of waste gas treatment, wastewater treatment, and solid waste disposal.

[0040] The total life cycle cost includes the initial coating cost, operation and maintenance cost, overhaul and recoating cost, and disposal cost. The formula for calculating paint usage in the initial coating cost is expressed as follows:

[0041] Where S is the coating area, For single-pass dry film thickness, For the number of spray coats, For the volume solids content of the coating, The coating utilization rate. For manual spraying, the percentage is 55% to 60%, and for electrostatic spraying, it is 70% to 75%.

[0042] Operation and maintenance costs :

[0043] Where T is the design life and r is the discount rate, which is 5% to 8%; , , These represent the costs for cleaning, inspection, and partial repairs in year t.

[0044] Overhaul and repainting costs :

[0045] Where N represents the number of recoating cycles within the design life. The year of the nth repainting. This is the cost of the nth repainting.

[0046] Waste disposal costs :

[0047] in, For coating peeling costs, This is for the environmental disposal fee of waste paint residue.

[0048] Step 5: Calculate the efficiency-cost ratio of each candidate scheme and select the scheme with the highest efficiency-cost ratio as the optimal recommended process scheme.

[0049] Calculate the performance-cost ratio (REC) for each candidate solution:

[0050] in, Reflects the protective effectiveness value obtained per unit cost input. Represented as a comprehensive performance index, C LCC This is expressed as the total lifecycle cost of the coating scheme; it serves as a core indicator for scheme selection. Candidate schemes are categorized according to... Sort the values ​​from largest to smallest and select The largest possible solution is selected as the optimal recommended process solution.

[0051] When multiple options When values ​​are close (difference ≤ 5%), choose according to risk preference: Risk-averse: Choose the more efficient solution; Cost-sensitive: Choose the lower-cost option.

[0052] Step 6: Perform sensitivity analysis on key parameters to assess the robustness of the optimization results.

[0053] The key parameters include one or more of the following: paint price, discount rate, cleaning cycle, and maintenance threshold. The sensitivity analysis employs a single-factor analysis method, changing each key parameter individually and observing the results. The magnitude of value changes is used to identify sensitive parameters and assess the robustness of decisions.

[0054] Industrial application verification The method of this invention has been applied and verified in multiple export projects, including Australian ore cars, Gabonese tank cars, and UAE flatcars. Application results show that: 1. The average overall cost of painting a single vehicle decreased by 12.5%, achieving the expected target; 2. Improved coating quality consistency led to a 60% decrease in early failure complaints for exported vehicles; 3. The process design cycle is shortened by more than 50%, shifting from experience-based design to vector-based design; 4. The application rate of water-based coatings has increased, resulting in a significant reduction in VOC emissions.

[0055] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, a further detailed description is provided below in conjunction with the accompanying drawings and specific embodiments.

[0056] Example 1: Optimization of the coating scheme for the open wagon chassis of ore exported to Australia; This embodiment focuses on the painting of open wagon chassis for exporting ore to Australia, and optimizes the solution using the method of the present invention.

[0057] Step 1: Constructing the environment-component risk matrix; The target export country is the coastal region of Australia, with a corrosion environment level of C5 (high salt spray industrial area) and an environmental severity coefficient k1 of 1.4.

[0058] The base frame is located in Class I area (heavy corrosion protection area), which is subjected to high-frequency impact, mud and sand erosion and salt water corrosion. The difference coefficient k2 of the parts is taken as 1.3.

[0059] Step 2: Candidate solutions are determined; Retrieve four candidate paint schemes that meet the requirements from the Task 1 modular database:

[0060] Step 3: Construction and weight determination of the performance indicator system; Establish an effectiveness system comprising five key indicators, and use the analytic hierarchy process (AHP) to determine the weights:

[0061] Using Scheme A as the baseline (with all indicator values ​​set to 1.00), the relative values ​​of the performance indicators for each scheme are as follows:

[0062] Step 4: Calculate the overall performance index; The formula for calculating the overall effectiveness index of each scheme is as follows:

[0063] = 1.4×1.3×(0.30×1.00+0.20×1.00+0.20×1.00+0.20×1.00+0.10×1.00) = 1.4×1.3×1.00=1.82; = 1.4 × 1.3 × (0.30×0.85 + 0.20×0.90 + 0.20×0.95 + 0.20×1.10 + 0.10×1.00) = 1.4×1.3×0.95 = 1.73; = 1.4 × 1.3 × (0.30 × 1.10 + 0.20 × 1.50 + 0.20 × 1.00 + 0.20 × 0.90 + 0.10 × 1.00) = 1.4 × 1.3 × 1.12 = 2.04; = 1.4 × 1.3 × (0.30×1.20 + 0.20×0.95 + 0.20×1.10 + 0.20×2.50 + 0.10×1.50) = 1.4×1.3×1.46 = 2.66; Step 5: Calculate the total lifecycle cost; Collect various cost data and calculate the total life cycle cost of each option (design life of 20 years, discount rate of 6%):

[0064] Step 6: Performance-cost ratio calculation and solution selection; Calculate the efficiency-cost ratio of each option. :

[0065] Combination such as Figure 2 As shown, the calculation results indicate that Option D (partial polyurea reinforcement) has the highest performance-to-cost ratio, with a 46% performance improvement over Option A and a cost increase of only 16%, making it the most cost-effective option and the preferred choice for ore truck chassis exported to Australia. Option C (fluorocarbon topcoat) is the second best option, while Option B has a similar performance-to-cost ratio to Option A but a lower cost, and can be considered as an alternative when the budget is limited.

[0066] Step 7: Sensitivity analysis; Sensitivity analysis was conducted on coating price ±20%, discount rate 4%~8%, and cleaning cycle 1~5 years. The results showed that option D... The value remains optimal, indicating good decision-making robustness.

[0067] Example 2: Optimization of painting scheme for boxcars exported to Southeast Asia; This embodiment focuses on the outer surface coating of special shed trucks for exporting rice to Southeast Asia, and optimizes the solution using the method of the present invention.

[0068] Step 1: Constructing the environment-component risk matrix; The target export country is Southeast Asia, with a corrosion environment level of C3 (urban atmosphere, high humidity and heat), and an environmental severity coefficient k1 of 1.0. The sidewall area belongs to Class II area, and the location difference coefficient k2 of 1.0.

[0069] Step 2: Candidate solutions are determined;

[0070] Step 3: Construction and Weight Determination of Performance Indicator System Establish an effectiveness system comprising five key indicators, and use the analytic hierarchy process (AHP) to determine the weights:

[0071] Using Scheme A as the baseline (with all indicator values ​​set to 1.00), the relative values ​​of the performance indicators for each scheme are as follows:

[0072] Step 4: Calculate the overall performance index; Calculate the overall effectiveness index of each scheme

[0073] Option A: = 0.28×1.00+0.47×1.00 + 0.16×1.00+0.09×1.00 = 1.00→E_{total}(A)=1.00, = 1.00; Option B: =0.28×0.90 + 0.47×1.05+0.16×1.00 + 0.09×1.10 =1.0045, = 1.0045; Option C: =0.28×0.85 + 0.47×0.95 + 0.16×0.90 + 0.09×0.80 =0.9005, = 0.9005.

[0074] Step 5: Calculate the total lifecycle cost; Basic data: coating area 200㎡, paint utilization rate 60%, labor cost 80 yuan / hour, electricity cost 0.8 yuan / kWh, design life 15 years, discount rate 6%.

[0075]

[0076] Step 6: Performance-Cost Ratio Calculation and Solution Optimization Calculate the efficiency-cost ratio of each option. :

[0077] The calculation results show that Scheme A has the highest efficiency-cost ratio and is recommended as the preferred scheme.

[0078] Step 7: Sensitivity analysis; If the exporting country has strict environmental regulations (such as the EU), water-based coatings should be given priority. In this case, options B and C can be weighed. Option C has a significant cost advantage and can be considered as the second-best choice under environmental requirements.

[0079] By applying the method of this invention, the overall cost of painting a single export railway freight car can be reduced by more than 10%, while ensuring that the protective performance of the coating meets the design life requirements, which has significant economic and social benefits.

[0080] According to a second aspect of the present invention, a cost-efficiency synergistic optimization system for export railway freight car painting schemes is provided, comprising: The Environment-Component Risk Matrix Construction Module is used to establish a risk level matrix based on the corrosion environment level, temperature range, cargo medium type, and stress conditions and corrosion risks of different parts of the vehicle in the export destination country, and to determine the environmental severity coefficient and part difference coefficient of each part. The module for constructing and determining the weights of the performance index system is used to establish a comprehensive performance index system that includes corrosion resistance, weather resistance, and mechanical performance. The weight coefficients of each performance index are determined by combining the analytic hierarchy process (AHP) with the entropy method. The comprehensive performance index calculation module is used to calculate the comprehensive performance index of each candidate coating scheme based on the environmental severity coefficient, the part difference coefficient, and the performance index weight. The lifecycle cost calculation module is used to build a lifecycle cost model for coating solutions and calculate the lifecycle cost of each candidate solution. The efficiency-cost ratio calculation and scheme selection module is used to calculate the efficiency-cost ratio of each candidate scheme, sort the candidate schemes from largest to smallest efficiency-cost ratio, and select the scheme with the largest efficiency-cost ratio as the optimal recommended process scheme.

[0081] It is understood that the cost and efficiency synergistic optimization system for the export railway freight car painting scheme provided by the present invention corresponds to the cost and efficiency synergistic optimization method for the export railway freight car painting scheme provided in the foregoing embodiments. The relevant technical features of the cost and efficiency synergistic optimization system for the export railway freight car painting scheme can be referred to the relevant technical features of the cost and efficiency synergistic optimization method for the export railway freight car painting scheme, and will not be repeated here.

[0082] Compared with the prior art, the present invention has the following beneficial effects: 1. Scientific decision-making and precise quantification: The design of coating schemes is transformed from the traditional "qualitative experience" to "quantitative data". Through a multi-factor coupled performance evaluation system and a full life cycle cost model, the precise quantitative screening and optimization of coating schemes can be achieved.

[0083] 2. Efficiency-Cost Co-optimization: Constructing an efficiency-cost ratio As the core criterion, the optimal cost-effective solution should be selected under the premise of meeting the protection performance requirements, avoiding the waste of resources caused by over-design, while ensuring that the protection is not insufficient due to cost control.

[0084] 3. Environmental-part differentiation treatment: Introducing environmental severity coefficients. Coefficient of variation of location To precisely adapt to the different needs of the environment and different parts of the vehicle in different export target countries, we can achieve "tailored to local conditions and different parts".

[0085] 4. Comprehensive life-cycle consideration: Breaking through the limitations of traditional methods that only focus on the initial coating cost, this model incorporates subsequent costs such as operation and maintenance, overhaul and recoating, and disposal, to truly reflect the full life-cycle economics of the coating solution.

[0086] 5. Dynamic expansion and continuous optimization: The model has good scalability and can update parameters as new materials and processes emerge, continuously optimizing coating schemes and adapting to technological advancements and market changes.

[0087] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0088] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0089] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0090] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for cost-efficiency synergistic optimization of painting schemes for export railway freight cars, characterized in that, Includes the following steps: Based on the corrosion environment level, temperature range, cargo medium type, and stress conditions and corrosion risks of different parts of the vehicle in the export destination country, a risk level matrix is ​​established to determine the environmental severity coefficient of each part. Coefficient of variation of location ; A comprehensive performance index system including corrosion resistance, weather resistance, and mechanical performance was established, and the weight coefficients of each performance index were determined by combining the analytic hierarchy process (AHP) with the entropy method. The comprehensive performance index of each candidate coating scheme is calculated based on the environmental severity coefficient, the part difference coefficient, and the performance index weight. Construct a lifecycle cost model for coating solutions and calculate the lifecycle cost of each candidate solution; Calculate the efficiency-cost ratio of each candidate scheme, sort the candidate schemes from largest to smallest efficiency-cost ratio, and select the scheme with the largest efficiency-cost ratio as the optimal recommended process scheme.

2. The method for cost and efficiency synergistic optimization of the painting scheme for export railway freight cars according to claim 1, characterized in that, Also includes: Sensitivity analysis was performed on key parameters to assess the robustness of the optimization results; The key parameters include paint price, discount rate, cleaning cycle, and maintenance threshold.

3. The method for cost and efficiency synergistic optimization of the export railway freight car painting scheme according to claim 1, characterized in that, The corrosion environment levels are classified according to ISO 12944-2 as follows: low-pollution atmosphere, urban atmosphere, industrial area, high-salt spray industrial area, and offshore platform, with environmental severity coefficients for each location. Quantitative values ​​are assigned based on the corrosion level.

4. The method for cost and efficiency synergistic optimization of the export railway freight car painting scheme according to claim 1, characterized in that, The stress conditions of different parts of the vehicle are divided into heavy corrosion protection zone, medium corrosion protection zone, and light corrosion protection zone; the difference coefficient of each part Different values ​​are assigned based on the corrosion risk in different areas of the vehicle.

5. The method for cost and efficiency synergistic optimization of the export railway freight car painting scheme according to claim 1, characterized in that, The method of determining the weighting coefficients of each performance indicator using the analytic hierarchy process combined with the entropy method includes: A review panel composed of coating experts, process engineers, and quality engineers was invited to conduct pairwise comparisons and scoring of each performance indicator to construct a judgment matrix. The subjective weights of each indicator were calculated using the analytic hierarchy process (AHP). When there is actual test data for multiple candidate solutions, the entropy method is used to calculate the objective weight of each indicator; finally, the combined weight is used to determine the final weight.

6. The method for cost and efficiency synergistic optimization of the painting scheme for export railway freight cars according to claim 5, characterized in that, The combined weight formula is expressed as follows: In the formula, W j The combined weights of each indicator, This is an empirical coefficient. The subjective weights of each indicator, The objective weights of each indicator.

7. The method for cost and efficiency synergistic optimization of the export railway freight car painting scheme according to claim 1, characterized in that, The formula for calculating the comprehensive performance index of each candidate coating scheme based on the environmental severity coefficient, the location difference coefficient, and the performance index weight is expressed as follows: in, Represented as a comprehensive performance index; This is expressed as the environmental severity coefficient of the location; Expressed as the coefficient of variation for different body parts; This represents the measured value of the j-th indicator of the scheme to be evaluated; This represents the benchmark value of the j-th indicator in the benchmark scheme; Let the weight coefficient of the j-th indicator be denoted as , satisfying . =1; This is expressed as the number of performance indicators.

8. The method for cost and efficiency synergistic optimization of the export railway freight car painting scheme according to claim 1, characterized in that, The total life cycle cost includes the initial coating cost, operation and maintenance cost, overhaul and recoating cost, and disposal cost.

9. The method for cost and efficiency synergistic optimization of the painting scheme for export railway freight cars according to claim 1, characterized in that, The formula for calculating the efficiency-cost ratio of each candidate solution is as follows: in, Reflects the protective effectiveness value obtained per unit cost input. Represented as a comprehensive performance index, C LCC Expressed as the total lifecycle cost of the coating scheme, it serves as a core indicator for scheme selection, categorizing candidate schemes according to... Sort the values ​​from largest to smallest and select The largest possible solution is selected as the optimal recommended process solution.

10. A cost-efficiency synergistic optimization system for export railway freight car painting schemes, characterized in that it includes: The Environment-Component Risk Matrix Construction Module is used to establish a risk level matrix based on the corrosion environment level, temperature range, cargo medium type, and stress conditions and corrosion risks of different parts of the vehicle in the export destination country, and to determine the environmental severity coefficient and part difference coefficient of each part. The module for constructing and determining the weights of the performance index system is used to establish a comprehensive performance index system that includes corrosion resistance, weather resistance, and mechanical performance. The weight coefficients of each performance index are determined by combining the analytic hierarchy process (AHP) with the entropy method. The comprehensive performance index calculation module is used to calculate the comprehensive performance index of each candidate coating scheme based on the environmental severity coefficient, the part difference coefficient, and the performance index weight. The lifecycle cost calculation module is used to build a lifecycle cost model for coating solutions and calculate the lifecycle cost of each candidate solution. The efficiency-cost ratio calculation and scheme selection module is used to calculate the efficiency-cost ratio of each candidate scheme, sort the candidate schemes from largest to smallest efficiency-cost ratio, and select the scheme with the largest efficiency-cost ratio as the optimal recommended process scheme.

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