Electronic and electrical product green design method and system

Abstract

The invention discloses a green design method and system for electronic and electrical products, and aims to reduce environmental burden through multi-dimensional optimization. The method comprises the following steps: firstly, establishing a function environment influence matrix, associating each function of a product with an environment influence factor, and evaluating the influence of the function on the environment; based on the user experience evaluation, core functions are identified and non-core functions are optimized or reduced, thereby reducing the overall environmental burden. According to the design method, by further selecting environment-friendly materials, designing a modular structure and utilizing iterative optimization calculation, collaborative optimization of material use and structural design is achieved, the material use amount is reduced, and the product recoverability is improved. And meanwhile, parts which are easy to age or damage are designed into independent modules which are easy to replace, so that the service life of the product is prolonged. In addition, a product integrated environment sensor monitors the use environment in real time, performance parameters are dynamically adjusted according to environment data and a functional environment influence matrix, and continuous optimization is achieved through remote firmware updating.

Description

Technical Field

[0001] The present invention relates to the technical field of green design of electronic products, and in particular to a green design method and system for electronic and electrical products. Background Art

[0002] In the existing technology, the design of electronic and electrical products is gradually developing towards energy conservation and environmental protection. Many products reduce their impact on the environment by using efficient materials, optimizing energy consumption management systems and extending product life. Designers usually introduce intelligent control systems to improve product operating efficiency and achieve sustainable development through material selection and product structure optimization. In addition, modular design has been applied in some products to simplify component replacement and maintenance.

[0003] However, the design methods in the prior art still have some problems in dealing with the environmental impact of multifunctional electronic products. First, the lack of a comprehensive assessment of the environmental impact of each function leads to the fact that some non-core functions still consume a lot of resources. Second, in terms of the coordinated optimization of material use and structural design, the prior art fails to fully reduce material waste or improve recyclability. Finally, although modular design has been applied, it still has shortcomings in the combination of dynamically adjusting product performance and extending product life.

[0004] Therefore, there is an urgent need to develop a new green design method and system for electronic and electrical products. Summary of the invention

[0005] The present application provides a green design method and system for electronic and electrical products to reduce the environmental burden of electronic and electrical products.

[0006] The present application provides a green design method for electronic and electrical products, including:

[0007] Establishing a product's functional environmental impact matrix, wherein the functional environmental impact matrix associates each function of the product with its corresponding environmental impact factor to assess the environmental impact of each function;

[0008] Based on the user experience assessment and the functional environmental impact matrix, identify the core functions that are important to the user experience, and optimize or reduce non-core functions to reduce the environmental burden;

[0009] According to the identified core functions, environmentally friendly materials are selected and modular structures are designed. Through iterative optimization calculations, the coordinated optimization of material use and structural design is achieved to reduce material usage and improve product recyclability.

[0010] The components in the product that are prone to aging or damage are designed as independent modules that are easy to replace, and a performance optimization mechanism is adopted to extend the overall service life of the product;

[0011] Integrate environmental sensors in the product to monitor the environment in which the product is used; adjust product performance parameters based on the functional environmental impact matrix and environmental data in the use environment;

[0012] Regularly collect and analyze user usage data and environmental impact data; based on the analysis results, dynamically adjust product performance parameters through remote firmware updates, and feed the results of dynamic adjustments back to the functional environmental impact matrix.

[0013] Furthermore, the establishment of the functional environmental impact matrix includes weighting the usage frequency, energy consumption, carbon emissions, and resource use of each function, quantifying the environmental impact factors into numerical values, and calculating the comprehensive environmental impact index based on the weight of each environmental impact factor.

[0014] Furthermore, the functional environmental impact matrix also takes into account the environmental impacts during the product life cycle, including the environmental impacts from raw material acquisition, manufacturing process, transportation, use to scrapping, and generates the final functional environmental impact matrix through a comprehensive evaluation of the environmental data of each stage.

[0015] Furthermore, the identification of core functions that are important to user experience includes generating a user function priority list through user surveys, questionnaires and user behavior data analysis, and screening out non-core functions that have a greater burden on the environment based on the environmental impact values ​​in the functional environmental impact matrix for optimization or reduction.

[0016] Furthermore, the optimization or reduction of non-core functions includes adopting low-energy consumption algorithms, reducing power consumption requirements of non-essential functions, and reducing contributions to the overall environmental impact by limiting the maximum operating frequency of designated functions.

[0017] The present application provides a green design system for electronic and electrical products, including:

[0018] An establishing unit, used to establish a functional environmental impact matrix of the product, wherein the functional environmental impact matrix associates each function of the product with its corresponding environmental impact factor to evaluate the environmental impact of each function;

[0019] An identification unit, used to identify core functions important to user experience based on the user experience evaluation and the functional environmental impact matrix, and optimize or reduce non-core functions to reduce environmental burden;

[0020] The selection unit is used to select environmentally friendly materials and design modular structures based on the identified core functions; and through iterative optimization calculations, the coordinated optimization of material use and structural design is achieved to reduce material usage and improve product recyclability;

[0021] Design unit, which is used to design the components in the product that are prone to aging or damage into independent modules that are easy to replace or upgrade, and adopt performance optimization mechanism to extend the overall service life of the product;

[0022] An integration unit, for integrating an environmental sensor in the product to monitor the use environment of the product; adjusting product performance parameters according to the functional environment impact matrix and environmental data in the use environment;

[0023] The feedback unit is used to regularly collect and analyze user usage data and environmental impact data; based on the analysis results, dynamically adjust product performance parameters through remote firmware updates, and feed back the results of the dynamic adjustment into the functional environmental impact matrix.

[0024] The beneficial effects of the technical solution provided by this application include:

[0025] (1) By establishing a functional environmental impact matrix, the environmental impact of each product function can be comprehensively evaluated, thereby optimizing or reducing non-core functions, significantly reducing resource consumption and environmental burden, and improving the green design level of the product. (2) By using environmentally friendly materials and modular design, and performing iterative optimization, the amount of materials used can be significantly reduced, while the recyclability of the product can be improved, promoting environmentally friendly treatment and reuse of the product at the end of its life cycle. (3) By designing wearing parts as replaceable or upgradeable independent modules and introducing performance optimization mechanisms, the service life of the product can be extended, reducing resource waste and the impact of frequent replacement on the environment. (4) By integrating environmental sensors to monitor the use environment, combining real-time data and the functional environmental impact matrix, dynamically adjusting product performance parameters, and achieving continuous optimization through remote firmware updates, the energy efficiency and adaptability of the product are improved, further reducing energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a flow chart of a green design method for electronic and electrical products provided in the first embodiment of the present application.

[0027] Figure 2 It is a schematic diagram of a green design system for electronic and electrical products provided in the second embodiment of the present application. DETAILED DESCRIPTION

[0028] Many specific details are described in the following description to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present application, so the present application is not limited by the specific implementation disclosed below.

[0029] The first embodiment of the present application provides a green design method for electronic and electrical products. Figure 1, which is a schematic diagram of the first embodiment of the present application. Figure 1 A green design method for electronic and electrical products is described in detail in the first embodiment of the present application.

[0030] S101: Establish a product function environmental impact matrix, wherein the function environmental impact matrix associates each function of the product with its corresponding environmental impact factor to evaluate the environmental impact of each function.

[0031] In step S101, establishing the product's functional environmental impact matrix is ​​the basis of the entire green design method, which can effectively evaluate and quantify the impact of each product function on the environment. Specifically, this step first requires a detailed analysis of the product's functions, classifying and describing each function of the product, and clarifying the specific role and implementation of each function. For example, in an electronic device, the functions may include switch control, display output, data processing, heat dissipation management, etc.

[0032] After completing the functional classification, the next step is to determine the environmental impact factors associated with each function. Environmental impact factors generally refer to the negative impact of the function on the environment throughout the product life cycle, including but not limited to energy consumption, resource consumption, carbon emissions, waste generation, water resource use, etc. In order to effectively associate each function with its environmental impact, it is necessary to obtain relevant environmental data through experimental data, industry standards or life cycle assessment (LCA) tools. For example, for the operating energy consumption of a certain function, its power consumption can be determined through actual testing or historical data accumulation, and used as the energy consumption impact factor of the function. For carbon emissions, the carbon emissions calculation method specified in the environmental standards can be used as the carbon footprint impact factor of the function.

[0033] Next, the process of building an environmental impact matrix is ​​to associate each function with its corresponding environmental impact factor. The specific approach is: all functions are listed in one column of the matrix, and environmental impact factors (such as energy consumption, carbon emissions, resource consumption, etc.) are listed in another column of the matrix. Each cell of the matrix is ​​filled in according to the actual operation of the function and the specific impact of the function on the environment. For example, if the energy consumption of a display output function is high, the energy consumption cell corresponding to the function in the matrix will be filled with a larger value to reflect its greater energy consumption impact; and for a low-energy standby function, the energy consumption factor may correspond to a smaller value.

[0034] To further quantify the overall impact of a function on the environment, weights can be assigned to each environmental impact factor. The weights can be set based on the product's design goals, regulatory requirements, or the company's environmental policy. For example, if a design goal is to reduce a product's carbon emissions, the weight of the carbon emissions factor can be set higher; if the focus is on energy consumption, the weight of the energy consumption factor can be higher. By assigning different weights, the relative importance of different environmental impacts in the final assessment can be flexibly adjusted.

[0035] After the functional environmental impact matrix is ​​established, the values ​​of each function and environmental impact factor in the matrix can be weighted and summarized to generate a comprehensive environmental impact score for each function. This score can not only help designers identify functions with greater environmental impact, but also provide a quantitative basis for subsequent design optimization.

[0036] Ultimately, the establishment of the functional environmental impact matrix is ​​not only a process of data collection and organization, but also an analytical tool that relates functions to environmental burdens, which provides a scientific basis for functional optimization and material selection in subsequent steps.

[0037] Furthermore, the establishment of the functional environmental impact matrix includes weighting the usage frequency, energy consumption, carbon emissions, and resource use of each function, quantifying the environmental impact factors into numerical values, and calculating the comprehensive environmental impact index based on the weight of each environmental impact factor.

[0038] Furthermore, the functional environmental impact matrix also takes into account the environmental impacts during the product life cycle, including the environmental impacts from raw material acquisition, manufacturing process, transportation, use to scrapping, and generates the final functional environmental impact matrix through a comprehensive evaluation of the environmental data of each stage.

[0039] In the green design method of electronic and electrical products, the establishment of the functional environmental impact matrix is ​​a complex process that aims to quantify the overall impact of each function of the product on the environment. Specifically, this step first requires a detailed analysis of each function of the product to identify key environmental impact factors such as the frequency of use, energy consumption, carbon emissions, and resource use of each function. Each environmental impact factor will be assigned a weight based on its different degrees of impact on the environment, and the size of the weight value is determined based on its importance. For example, carbon emissions may be given a higher weight in a design because it is closely related to global climate change, while other factors such as water use may be given a lower weight, depending on the product design goals and environmental protection strategies.

[0040] For each function, designers first need to collect data on the frequency of use. This can be obtained by analyzing historical data, experimental tests, or user feedback. The frequency of use reflects how frequently a function is used throughout the product life cycle, which can indirectly assess the potential impact of the function on energy consumption and resource use. Energy consumption is determined by actually measuring the electricity or other forms of energy consumed by each function during operation, while carbon emissions are calculated or consulted from a standard database based on the source and type of energy used.

[0041] Resource usage mainly refers to the materials or other natural resources consumed during the production process of the function. In order to ensure the accuracy of the data, these data can usually be obtained through surveys of raw material suppliers, life cycle assessment (LCA) tools or analysis of standard industry data. After each environmental impact factor is quantified into a numerical value, the designer will calculate the comprehensive environmental impact index of the function based on these numerical values ​​and the pre-set weights. By summarizing the environmental impact indexes of all functions, the comprehensive environmental burden of the entire product can be obtained.

[0042] In addition, the functional environmental impact matrix needs to further consider the environmental impact of the product throughout its life cycle. The life cycle covers every stage of the product from raw material acquisition, manufacturing process, transportation, use to scrap disposal. In the raw material acquisition stage, designers need to evaluate the sustainability of the materials used, the impact of mining on the environment, and the related energy consumption. In the manufacturing process, it is necessary to calculate the energy consumption of production equipment, the treatment of manufacturing waste, and the carbon emissions generated during the manufacturing process. The transportation stage involves the entire logistics process of the product from the factory to the hands of the consumer, and its main environmental impact comes from the carbon emissions and energy use of the transportation tools. The data of the use stage is equally important, mainly evaluating the energy consumption of the product when it is actually used in the hands of the consumer and the total carbon emissions during the life cycle. Finally, the scrapping stage considers the recycling, disassembly and waste disposal methods of the product at the end of its life cycle, whether it can be handled or reused in an environmentally friendly way.

[0043] By comprehensively evaluating the environmental data of the product at different stages, designers can generate the final functional environmental impact matrix. This matrix comprehensively covers the entire life cycle of the product, helping designers identify the most environmentally burdensome aspects of the product during its life cycle, thereby providing a basis for design optimization and improvement.

[0044] S102: Based on the user experience evaluation and the functional environmental impact matrix, identify the core functions that are important to the user experience, and optimize or reduce non-core functions to reduce the environmental burden.

[0045] In step S102, based on the user experience evaluation and the functional environmental impact matrix, the core functions that are important to the user experience are identified, and non-core functions are optimized or reduced to reduce the environmental burden. This process first requires a detailed user experience evaluation of all functions of the product. User experience evaluation is mainly carried out in two ways: one is to obtain user feedback on the frequency and importance of different functions of the product through quantitative methods, such as questionnaires and user behavior analysis; the other is to use qualitative methods, such as user interviews and focus group discussions, to gain an in-depth understanding of which functions users rely on most in actual use and which functions are relatively less important.

[0046] During the user experience evaluation process, designers can score or quantify the frequency of use, response speed, operating difficulty, and impact on the overall user experience of each function. For example, in a smart appliance product, users may rely most on the core operating functions, while for some auxiliary functions (such as automatic brightness adjustment or certain special modes), users use them less frequently and have relatively fewer experience requirements. This information will help identify the core functions that are critical to the user experience and those non-core functions that can be optimized or cut.

[0047] After the user experience evaluation is completed, these user data need to be compared with the functional environmental impact matrix. The functional environmental impact matrix has listed the environmental burden of each function, including energy consumption, resource consumption, carbon emissions, etc. By combining the priority of user experience and the environmental burden of the function, it is possible to identify those functions that are important to the user experience but also have a greater environmental burden. For these core functions, designers will give priority to retaining and further optimizing them to reduce their environmental burden without compromising the user experience. For example, a core function with high power consumption can reduce energy consumption by optimizing algorithms and adjusting working modes.

[0048] At the same time, for those non-core functions, that is, functions that have little impact on user experience, especially functions with a greater environmental burden, you can consider reducing, simplifying or even removing them. For example, if some auxiliary functions or functions that are rarely used by users have a greater environmental impact, they can be cut or adjusted without affecting the overall user experience. This optimization method can not only effectively reduce the environmental burden of the product, but also improve the overall green design efficiency of the product.

[0049] In summary, in step S102, by combining user experience data with the functional environmental impact matrix, designers can systematically evaluate the importance and environmental impact of each function and optimize or reduce the function based on the evaluation results.

[0050] Furthermore, the identification of core functions that are important to user experience includes generating a user function priority list through user surveys, questionnaires and user behavior data analysis, and screening out non-core functions that have a greater burden on the environment based on the environmental impact values ​​in the functional environmental impact matrix for optimization or reduction.

[0051] Furthermore, the optimization or reduction of non-core functions includes adopting low-energy consumption algorithms, reducing power consumption requirements of non-essential functions, and reducing contributions to the overall environmental impact by limiting the maximum operating frequency of designated functions.

[0052] Identifying core functions that are important to the user experience is mainly achieved through a series of quantitative and qualitative methods. This process first involves user surveys, questionnaires, and user behavior data analysis. Designers usually design detailed questionnaires or survey forms to understand the importance and frequency of use of various functions when using the product. The design of the questionnaire should include the use of various functions, such as which functions are most frequently used in daily use, which functions are considered indispensable by users, and which functions are used less frequently but still need to be retained.

[0053] In addition to user questionnaires, user behavior data analysis is also a key step. Through the built-in data monitoring tools in the product, designers can obtain user behavior data in actual use. This data includes the startup frequency, continuous running time, energy consumption records of each function, etc., which helps to more accurately understand the user's usage habits in the actual environment. This kind of behavior data can reflect the user's dependence on the function and usage preferences, and this information is often more accurate than the user's subjective feedback.

[0054] After collecting these user surveys and behavioral data, designers will generate a user feature priority list. This list ranks the relative importance of each feature to the user experience based on the user's score of the feature. The priority list clearly indicates which features are core features and which features have less impact on the user's overall experience.

[0055] While generating the user function priority list, designers also need to combine the data in the function environmental impact matrix. The function environmental impact matrix provides the specific impact of each function on the environment during operation, such as energy consumption, carbon emissions, resource consumption, etc. Therefore, designers can screen out non-core functions that have a heavy burden on the environment by analyzing the priority list and the function environmental impact matrix. These functions are not important in user use, but they may have an adverse impact on the environment due to high energy consumption or high resource consumption. Ultimately, these screened non-core functions will become the target of optimization or reduction to reduce their negative impact on the environment.

[0056] There are many ways to optimize or reduce these non-core functions. First, designers can reduce the resource consumption of these functions by adopting low-power algorithms. For example, some functions may consume a lot of power when running in the background. By introducing more efficient algorithms, reducing the processing complexity and data transmission volume when they work, the energy consumption can be reduced.

[0057] In addition, reducing the power requirements of non-essential functions is another effective optimization method. This can be done by analyzing the power consumption patterns of non-core functions to find opportunities or conditions where power consumption can be reduced. For example, certain functions can be automatically turned off in the product's low power mode or standby mode, or their operating power can be reduced when it is not necessary.

[0058] Finally, designers can further reduce the environmental impact of certain specified functions by limiting their maximum operating frequency. For some functions that are not frequently used but are essential, resource consumption can be reduced by limiting their frequent operation under high load conditions. For example, a high-power graphics processing function can run in a low-frequency mode under normal operation and switch to a high-performance mode only when needed. In this way, the system can not only retain the function to meet user needs, but also reduce its dependence on energy in daily use.

[0059] Through the above optimization measures, the overall environmental impact of non-core functions on the product will be significantly reduced without having a significant negative impact on the user experience.

[0060] S103: Based on the identified core functions, select environmentally friendly materials and design modular structures; and through iterative optimization calculations, achieve coordinated optimization of material use and structural design to reduce material usage and improve product recyclability.

[0061] In step S103, for the core functions identified previously, designers need to select appropriate environmentally friendly materials and design modular structures to better achieve the coordinated optimization of material use and structural design. The first step in this process is to give priority to the environmental characteristics when selecting materials based on the characteristics and requirements of the core functions. Environmentally friendly materials include renewable materials, recyclable materials, biodegradable materials, etc. Designers need to ensure that the selected materials meet green design standards, such as reducing energy consumption and reducing carbon emissions. When selecting materials, the functional adaptability of the materials, such as mechanical strength, durability, high temperature resistance or corrosion resistance, etc., must also be considered to meet the needs of the core functions.

[0062] Next, in the modular structure design, designers need to divide the various components of the core functions into independent modules. These modules should be easy to disassemble, repair and replace, and at the same time simplify the recycling and reuse of the product. A key advantage of modular design is that when a module fails, only the module needs to be replaced without replacing the entire product, which significantly reduces resource waste. Modular design can also support flexible product upgrades. Users can upgrade the performance of a single module without replacing the entire device, extending the product life cycle.

[0063] In the design process, the core of this step is to achieve the coordinated optimization of material usage and structural design through iterative optimization calculations. This collaborative optimization usually uses algorithmic simulation and calculation tools to gradually adjust and optimize the amount of material used and the geometric design of the module. In each iteration, designers can calculate and analyze different material combinations and structural schemes to evaluate their performance indicators such as strength, durability and energy efficiency. At the same time, the optimization process will focus on reducing the amount of materials used, especially high-energy consumption and high-pollution materials, while ensuring that the function and performance of the product are not affected. Through tools such as finite element analysis (FEA), the performance of materials and structures in various usage environments can be simulated, thereby helping designers find the best design solution.

[0064] In addition, in collaborative optimization, it is necessary to ensure the recyclability of the structural design. For example, when designing, it is necessary to consider how to ensure that at the end of the product life cycle, the modules can be easily decomposed and their materials can be effectively recycled or processed through reasonable material combinations and modular design. Modular design can also significantly reduce waste at the time of scrapping and reduce the environmental burden of the entire product.

[0065] In short, the S103 step involves everything from material selection to modular structure design, and then to material and structure coordinated optimization through iterative calculation. Through this series of design and optimization processes, it is possible to effectively reduce material usage, improve product recyclability, and ensure that the performance of core functions is combined with environmental friendliness.

[0066] Furthermore, the iterative optimization calculation is used to achieve the coordinated optimization of material use and structural design, including:

[0067] Step P101: Establish a multi-objective optimization model, which includes an objective function f1 for minimizing material usage, an objective function f2 for maximizing product recyclability, an objective function f3 for minimizing product life cycle environmental impact, and an objective function f4 for maximizing product structural strength:

[0068] The objective function f1 of minimizing material usage is implemented using the following formula 1:

[0069]

[0070] Among them, m i is the density of the i-th material; V i is the volume of the i-th material; n is the total number of types of materials used in the product;

[0071] The objective function f2 of maximizing the recyclability of the product is implemented using the following formula 2:

[0072]

[0073] Among them, R i is the recovery rate of the i-th material; V i is the volume of the i-th material; n is the total number of types of materials used in the product;

[0074] The objective function f3 of minimizing the environmental impact of the product life cycle is implemented using the following formula 3:

[0075]

[0076] Among them, E i is the production energy consumption of the i-th material; C i is the carbon emission of the i-th material; n is the total number of materials used in the product;

[0077] The objective function f4 of maximizing product structural strength is implemented by the following formula 4:

[0078]

[0079] Among them, σ y is the yield strength of the material; σ max is the maximum stress of the structure under load;

[0080] Step P102: Introduce a collaborative optimization method combining an adaptive multi-objective ant colony optimization algorithm and finite element analysis. The specific steps include:

[0081] (a) Initial generation optimization model step: Initialize the ant colony into multiple groups of possible material-structure combination schemes, where each ant represents a design scheme, which includes material type and structural geometric parameters;

[0082] (b) Finite element analysis evaluation step: Perform finite element analysis on each ant’s design scheme to calculate its stress distribution, deformation, and structural strength to ensure that each scheme meets the requirements of the objective function f4;

[0083] (c) Fitness function calculation step: Calculate the fitness based on the following formula 5:

[0084]

[0085] Among them, Fitness represents the fitness value; w1, w2, w3, w4 are weight factors of the objective function; α, β, γ, δ are smoothing coefficients;

[0086] (d) Updating the optimal solution and the local optimal solution step: by calculating the fitness value of each ant, updating the global optimal solution and the local optimal solution, and recording the current optimal material-structure combination;

[0087] (e) Pheromone update step: According to the fitness value, the pheromone concentration is updated using the following formula 6:

[0088] τ ij (t+1)=(1-ρ)·τ ij (t)+Δτ ij (6)

[0089] Among them, τ ij (t+1) represents the pheromone concentration between the i-th point and the j-th point at time t+1; τ ij (t) represents the pheromone concentration between the i-th point and the j-th point at time t; ρ is the pheromone volatility coefficient; Δτ ij is the pheromone increment, which indicates the incremental pheromone value left by the current optimal ant on the path from i to j;

[0090] (f) Adaptive parameter adjustment step: According to the following formulas 7 and 8, dynamically adjust the pheromone importance factor and heuristic information factor in the ant colony algorithm:

[0091]

[0092]

[0093] Among them, α(t+1) represents the pheromone importance factor at the t+1th iteration; α(t) represents the pheromone importance factor at the tth iteration; λ is the learning rate; F best (t) is the optimal fitness value in the tth iteration; F best (t-1) is the optimal fitness value in the t-1th iteration; β(t+1) represents the heuristic information factor at the t+1th iteration; β(t) represents the heuristic information factor at the tth iteration;

[0094] (g) Local search and perturbation mechanism step: perform a small-scale perturbation on the current optimal solution, and evaluate the feasibility of the material and structure combination after the perturbation through fast finite element analysis to ensure the precision of local optimization;

[0095] (h) Taboo search strategy step: maintain a taboo table to record the most recently visited solutions and avoid repeated visits to the same solution within a specified number of iterations, preventing the algorithm from falling into local optimality and improving global search capabilities;

[0096] (i) Iteration stop step: repeat steps b to h until the maximum number of iterations is reached or the convergence condition is met, ensuring that the optimal solution is searched through multiple iterations;

[0097] Step P103: Conduct manufacturability analysis on the final optimization results to ensure that the selected materials and structural designs can meet the actual production process requirements and verify their feasibility in existing production equipment;

[0098] Step P104: If the manufacturability analysis fails, adjust the constraints of the multi-objective optimization model, including the allowed material types, processing methods or structural details, and re-execute steps P102 and P103 until the optimal solution that meets the green design goals and can be actually produced is found.

[0099] In this embodiment, the entire process of the green design method realizes the coordinated optimization of material usage and structural design through multi-objective optimization calculation.

[0100] First, in step P101, a multi-objective optimization model is established, which aims to optimize multiple design goals simultaneously, including material usage, product recyclability, product life cycle environmental impact, and structural strength. Each goal is represented and achieved through a corresponding mathematical formula.

[0101] The first objective function f1 is to minimize the material usage, which is achieved by formula 1. i is the density of the ith material, which represents the relationship between the weight and volume of each material; V i is the volume of the i-th material, indicating the space occupied by the material in the product; n is the total number of materials used in the product. By multiplying and adding the density and volume of each material, designers can calculate the total usage of all materials in the product. This goal is to reduce the total consumption of materials by reducing the volume of materials or selecting low-density materials, thereby achieving green design.

[0102] The second objective function f2 is to maximize the recyclability of the product, which is achieved by formula 2. i is the recycling rate of the i-th material, reflecting the extent to which the material can be reused after the product life cycle ends; V iIt is still the volume of the i-th material, representing its actual usage in the product. This formula reflects the recyclability of each material through multiplication, and estimates the overall recyclability of the product by accumulating the recyclability of different materials. Through this method, designers can give priority to materials with high recycling rates to improve the environmental friendliness of the product.

[0103] The third objective function f3 is to minimize the environmental impact of the product life cycle. In formula 3, E i is the production energy consumption of the i-th material, indicating the energy consumed in the process of material acquisition and processing; C i is the carbon emission of the i-th material, representing the carbon footprint of the material on the environment during the production process. By summing up the production energy consumption and carbon emissions of each material, designers can estimate the total environmental impact of the entire product during its life cycle. The purpose of this formula is to reduce the overall environmental burden by reducing materials with high energy consumption or large carbon emissions.

[0104] The fourth objective function f4 is to maximize the structural strength of the product, which is implemented using formula 4. y,i is the yield strength of the i-th material; ρ max,i is the maximum stress of the i-th material in the structure; V i is the volume of the i-th material; n is the total number of materials used in the product; this formula can comprehensively consider the contribution of different materials to the structural strength by combining the yield strength and maximum stress of each material with its volume ratio in the structure. This method can more reasonably reflect the structural strength of a product using multiple materials.

[0105] Step P102 involves collaborative optimization by combining an adaptive multi-objective ant colony optimization algorithm with finite element analysis (FEA). First, the ant colony algorithm is initialized to multiple possible material and structure combinations, with each ant representing a design solution. Finite element analysis is used to calculate the stress distribution, deformation, and structural strength of each solution to ensure that the requirements of objective function f4 are met.

[0106] If a solution performs poorly in f4 and cannot meet the structural strength requirements, the fitness value of the solution will be low. In the ant colony optimization algorithm, the fitness value determines the probability of the solution being selected in subsequent iterations. Solutions with low fitness may be eliminated, and ants will gradually tend to choose better design solutions. In other words, solutions that do not meet the f4 requirements may be eliminated from the optimization process, allowing the optimization to continue to focus on solutions with qualified strength.

[0107] The fitness function is calculated using Formula 5, where w1, w2, w3, and w4 are weight factors of the objective function, which determine the importance of each objective in the overall optimization. The weight factors can be manually adjusted according to the design priority. α, β, γ, and δ are smoothing coefficients. By introducing these coefficients, the fitness function changes more smoothly to avoid excessive impact on the overall optimization due to the extreme value of a single objective.

[0108] In the pheromone update step, the pheromone concentration is updated by Formula 6 to reflect the accumulated experience of the ants in path selection. The volatility coefficient ρ of the pheromone controls its decay rate over time, ensuring that the algorithm can explore new solutions and utilize historical experience.

[0109] Adaptive parameter adjustment uses formulas 7 and 8 to dynamically adjust the pheromone importance factor α and the heuristic information factor β. The formula adjusts the parameters by the change in the optimal fitness, so that the algorithm maintains exploratory nature during the search process and ensures convergence to the optimal solution.

[0110] Finally, the local search and perturbation mechanism ensures the local optimization of the solution by making subtle adjustments to the current optimal solution, combined with finite element analysis. The taboo search strategy prevents the algorithm from falling into the local optimum by maintaining a taboo table to avoid repeated exploration of solutions that have already been visited.

[0111] The whole process is carried out in an iterative manner until the maximum number of iterations is reached or the convergence condition is met, and the optimal solution is finally obtained. Finally, the feasibility of the design scheme is verified through manufacturability analysis, and the constraints are adjusted when necessary to ensure that the product design not only meets the green design goals, but can also be realized under existing production conditions.

[0112] In step P104, constraints refer to restrictions imposed in the multi-objective optimization model. These restrictions affect the solution space of the model, thereby determining which design solutions are feasible and which are not. The specific content of the constraints will be set according to the design goals of the product and the actual production capacity.

[0113] Specifically, the constraints mainly include the following aspects:

[0114] 1. Material Type:

[0115] This is an important constraint that determines which materials can be used in the design. The choice of material type is subject to many constraints, such as material availability, material cost, material processability, and its environmental performance. Some materials may have good environmental performance, but if these materials are difficult to obtain on the market or their processing requires specialized equipment, these restrictions will become constraints on material selection.

[0116] 2. Processing method:

[0117] The choice of processing method is also a key constraint that affects the manufacturability of the design. The processing method depends on the existing manufacturing equipment, process flow and production cost of the factory. For example, some structures may only be processed by complex multi-axis machine tools, while the existing factory equipment may only support traditional stamping or injection molding processes. This constraint will limit the structures and shapes that can be used in the design, forcing designers to choose reasonable structural designs within the existing processing capabilities.

[0118] 3.Structural details:

[0119] The constraints of structural details are mainly reflected in the product's geometry, size, connection method, etc. These limitations may come from the capabilities of manufacturing equipment, assembly accuracy, or physical requirements during use. Overly complex or sophisticated structures may exceed the processing capabilities of existing equipment or add unnecessary difficulties during assembly. Therefore, designers need to simplify the structure to adapt to the production process while meeting performance and function.

[0120] 4. Environmental protection goals and regulatory requirements:

[0121] Sometimes, product design also needs to meet specific environmental regulations or standards, such as limiting the use of certain hazardous substances, requiring products to achieve a certain level of energy efficiency, or mandating that products must be recyclable. These external mandatory requirements also constitute constraints in the design process.

[0122] In step P104, if the manufacturability analysis fails, that is, the design cannot be actually manufactured under the existing production process or equipment conditions, the designer needs to adjust the constraints of the multi-objective optimization model and re-execute the previous optimization and analysis steps. The key to this process is how to adjust the design model according to production constraints so that it can meet the green design goals and adapt to the actual production environment.

[0123] First, the adjustment of constraints involves multiple aspects, mainly including material types, processing methods and structural details. The adjustment of material types is an important step. If the manufacturability analysis finds that the selected material is not suitable for the existing production process or supply chain conditions, such as the inability to obtain the required material, the material cost is too high or the material is difficult to process, then the designer needs to choose a new alternative material. These alternative materials should have good processing performance and supply feasibility while meeting the green design standards. For example, designers can choose low-cost and easy-to-process materials based on the material's machinability, market supply and environmental characteristics, while meeting certain recycling rates and low carbon emissions.

[0124] Secondly, the adjustment of processing methods is also the key to solving manufacturability problems. At this stage, designers need to re-evaluate whether the designed product structure matches the existing production equipment and processes. If the original design requires special production equipment or complex processing technology, which makes manufacturing difficult, designers can simplify the processing technology and adopt a more standardized and industrialized processing method. For example, some complex parts may require expensive or difficult-to-operate processing tools in the original plan, but by modifying the design and structural details, they can be converted to more common processing technologies, such as stamping, injection molding or CNC machining, thereby reducing production difficulty and cost.

[0125] In addition, the adjustment of structural details is an important aspect to ensure manufacturability. If some structures in the original design are too complex or difficult to implement, designers need to simplify or optimize these structures. Specifically, the entire manufacturing process can be simplified by reducing unnecessary complex geometric shapes, optimizing component connection methods, or using standardized parts. For example, a component that originally required multiple processes to form can improve production efficiency and reduce costs by improving structural design, reducing processing steps, or using a mold for one-time molding.

[0126] When the material, processing method or structural details are adjusted, the designer needs to re-execute the previous steps P102 and P103. That is to say, the new material-structure combination is first optimized and calculated again through the adaptive multi-objective ant colony optimization algorithm and finite element analysis. In this process, various indicators in the multi-objective optimization model are still considered, such as material usage, recyclability, life cycle environmental impact and structural strength, and the fitness function is recalculated to determine whether the adjusted design can maintain the best state in terms of environmental impact and product performance.

[0127] When performing the manufacturability analysis in step P103, the designer needs to evaluate whether the new design can adapt to the existing manufacturing conditions. If the new material and structural design can pass the test of the manufacturing equipment and meet the requirements of the production process, the design will be considered manufacturable. If the new design still fails the manufacturability analysis, the designer needs to adjust the constraints again and repeat the above process until the optimal solution that meets the green design goals and can be actually produced is found.

[0128] This iterative process ensures that the feasibility of product design is balanced with environmental goals.

[0129] The following is a reference implementation code for iterative optimization calculation. This code uses Python to implement a collaborative optimization method that combines a multi-objective optimization model, an ant colony optimization algorithm, and finite element analysis.

[0130]

[0131]

[0132]

[0133]

[0134]

[0135] S104: Components in the product that are prone to aging or damage are designed as independent modules that are easy to replace or upgrade, and performance optimization mechanisms are adopted to extend the overall service life of the product.

[0136] Furthermore, the easily replaceable independent modules adopt a standardized interface design, so that the modules are interchangeable in products of different models and versions, and a quick-release mechanical structure is used at the module connection to reduce replacement time.

[0137] In step S104, the design goal is to ensure that the components in the product that are prone to aging or damage can be designed as independent modules that are easy to replace or upgrade, and to adopt performance optimization mechanisms to extend the overall service life of the product. First of all, identifying which components are most likely to age or damage during the product life cycle is the key to this step. Usually, these components include electronic components, mechanical structures, connectors, batteries, etc. By analyzing the product's working environment, frequency of use, and historical failure data, the expected life and common failure modes of these components can be determined.

[0138] Once the components that are prone to damage or aging are identified, designers need to modularize these components into independent and easy-to-disassemble, repair or replace modules. This can be achieved by using standardized interfaces, plug-in designs or quick-release mechanisms. For example, designing a standardized snap-on or plug-in connector allows users or maintenance personnel to quickly replace damaged modules without damaging other components or the housing. Such a design also allows users to upgrade certain components on their own as needed, such as upgrading to a more efficient processor, storage unit or battery, thereby extending the service life of the product.

[0139] In addition to modular design, performance optimization mechanism is also a key factor. By integrating self-diagnosis function and sensor system, the product can monitor the working status of each module in real time, such as temperature, voltage, current and other operating parameters, to detect potential failures or performance degradation. For example, the sensor can monitor the number of battery charging cycles and battery health status. Once the battery performance is detected to drop to a certain threshold, the system will automatically remind the user to replace it. The self-diagnosis function can not only help extend the service life of the product, but also avoid interruptions caused by sudden failures and improve the user experience.

[0140] In addition, the performance optimization mechanism can also reduce component wear by adjusting product operating parameters. For example, when the system detects that certain modules are overheated, it can automatically reduce the operating frequency of the processor or reduce the use of certain high-load functions to reduce the long-term loss of heat to components. This dynamic adjustment mechanism can extend the service life of each module and reduce failures caused by excessive use or environmental factors.

[0141] By designing components that are prone to aging or damage as independent, replaceable modules, combined with performance optimization mechanisms, not only the overall service life of the product is extended, but also the maintainability and upgradeability of the product are significantly improved. Users do not need to replace the entire product, but only need to replace or upgrade the faulty module, which not only reduces the waste of resources, but also effectively improves the environmental benefits of the product.

[0142] S105: Integrate an environmental sensor in the product to monitor the use environment of the product; and adjust product performance parameters according to the functional environment impact matrix and environmental data in the use environment.

[0143] Furthermore, the environmental sensors include temperature sensors, humidity sensors, light sensors and air quality sensors, which feed back real-time data to the control system and dynamically adjust product performance parameters in response to environmental changes.

[0144] In step S105, designers need to integrate environmental sensors into the product to monitor the product's usage environment and adjust the product's performance parameters based on the collected environmental data and the functional environmental impact matrix. First of all, choosing the right environmental sensor is the key. Typically, environmental sensors can include temperature sensors, humidity sensors, light sensors, air quality sensors, etc., which can monitor the environmental conditions around the product in real time. These sensors should be selected according to the specific functions and usage scenarios of the product. For example, if the product is used outdoors, temperature and humidity sensors will be necessary; in indoor environments, air quality sensors may be more important.

[0145] Once sensors are integrated into a product, they will continuously collect real-time environmental data. For example, a temperature sensor can record temperature changes in the environment in which the device is operating, while a humidity sensor can detect humidity levels in the air. This data will be transmitted to the product's control system and compared with the standard environmental parameters previously set in the functional environmental impact matrix. The functional environmental impact matrix provides the operating performance of each function under different environmental conditions and the degree of impact on the environment, such as energy consumption, heat dissipation requirements, etc. Therefore, by comparing and analyzing the real-time environmental data with the preset data in the matrix, the system can make corresponding adjustments.

[0146] Based on environmental data, the product's performance parameters can be adjusted dynamically. For example, if the ambient temperature is high and the sensor detects that the temperature is outside the normal range, the system can automatically reduce the processor's operating frequency, reduce power consumption and heat generation to avoid excessive energy consumption and overheating of the device. Similarly, if the sensor detects that the indoor humidity is too high, the product's air conditioning or dehumidification function can be automatically activated to optimize the device's operating environment.

[0147] The adjustment process is based on the matching degree of environmental data with the functional environmental impact matrix. The real-time data of the sensor is constantly compared with the ideal working conditions in the matrix, and the system adjusts the operating mode of the product according to this difference. For example, when the ambient temperature is too low, some devices may not need to operate efficiently, and the system can reduce the power consumption of the device and extend the battery life. In a dimly lit environment, the display device may automatically reduce the brightness to save energy.

[0148] In this way, the product can intelligently adapt to the changing external environment, achieve energy saving and efficient operation. At the same time, this dynamic adjustment helps to extend the service life of the product and reduce the wear and tear of the equipment under extreme environmental conditions.

[0149] S106: Regularly collect and analyze user usage data and environmental impact data; and based on the analysis results, dynamically adjust product performance parameters through remote firmware updates, and feed back the results of the dynamic adjustment into the functional environmental impact matrix.

[0150] In step S106, designers need to regularly collect and analyze user usage data and environmental impact data, which will be used to further optimize product performance. First, the product should have the ability to continuously collect usage data. Usage data includes but is not limited to the operating time of the equipment, frequency of function use, energy consumption, and fault records. These data can be collected in real time through the product's built-in monitoring system and stored in the product's memory, or transmitted to the cloud or external server through wireless communication technology. This data is collected to understand the user's actual usage habits, so that the product's performance can be optimized to better meet user needs while reducing unnecessary energy consumption.

[0151] In terms of environmental impact data, in addition to the data previously collected by environmental sensors in real time, it also includes a cumulative analysis of the impact of long-term operation of the product under different environmental conditions. For example, sensors can record the operating performance of the equipment under different temperature, humidity and other conditions. By accumulating and analyzing these data, it is possible to identify under which conditions the performance of the equipment decreases or the energy consumption increases, so as to further adjust the operating mode of the equipment.

[0152] By analyzing usage data and environmental impact data, designers can obtain the performance of the device in actual use and identify potential optimization opportunities. This analysis process usually requires the use of big data analysis tools, combined with machine learning or statistical methods, to extract valuable patterns and trends from large amounts of data. For example, if the analysis results show that certain functions are used less frequently and consume more power under specific environmental conditions, the system can reduce unnecessary resource waste by adjusting the operation of these functions. Or, if it is found that the failure rate of the device increases under certain environmental conditions, the operating parameters of the device can be adjusted in time to improve its reliability.

[0153] After completing the data analysis, dynamically adjusting the product through remote firmware updates is a key step. Remote firmware updates mean that the product can automatically obtain and install the latest performance optimization solutions through a network connection without manual user intervention. This update method can quickly respond to feedback from usage data and environmental impact data to ensure that the product can always operate in the best state. For example, through remote updates, the algorithms of certain functions can be adjusted to improve their efficiency under special environmental conditions, or reduce the use of functions that consume too much energy during unnecessary periods.

[0154] The updated performance parameters not only directly affect the operation of the equipment, but can also be further fed back into the product's functional environmental impact matrix. In this way, the functional environmental impact matrix can be continuously updated and optimized based on the latest usage and environmental data to ensure that it always reflects the actual operation of the equipment. This closed-loop feedback mechanism allows the design and operation mode of the product to continue to evolve to maximize the realization of green design goals.

[0155] Furthermore, the adjusting product performance parameters according to the functional environment impact matrix and the environmental data in the use environment includes:

[0156] According to the following formula 9, the adaptive environmental impact index AEII of the product is calculated:

[0157]

[0158] Among them, W i is the weight factor of the ith function in the functional environment impact matrix; F i is the current usage frequency of the i-th function; E i is the actual environmental impact value of the ith function under the current environmental conditions; i is the function adaptation factor, which is used to dynamically adjust the sensitivity of the function to the environmental impact; n is the total number of functions in the function-environment impact matrix; E base is the baseline environmental impact value of the product under standard environmental conditions; κ is the correction factor, which is used to balance the overall environmental impact under different usage environments;

[0159] Based on the calculated adaptive environmental impact index AEII, the adjusted product performance parameter P is obtained according to the following formula 10:

[0160] P=P base ×(1-α×(AEII-1) β ×θ) (10)

[0161] Among them, P base is the reference power parameter; α is the adjustment sensitivity coefficient; β is the nonlinear adjustment index; θ is the performance adjustment elasticity factor;

[0162] According to the following formula 11, the environment adaptation coefficient EAC is calculated:

[0163]

[0164] Among them, γ j is the impact weight of the jth environmental factor; C j is the current measured value of the jth environmental factor; C jbase is the reference value of the jth environmental factor; δ j is the dynamic correction coefficient; m is the total number of environmental factors monitored by the sensor;

[0165] According to the following formula 12, the final performance parameter P after dynamic adjustment is obtained: final :

[0166] P final =P×EAC(12).

[0167] The green design method for electronic and electrical products in this embodiment dynamically adjusts the performance parameters of the product by calculating the adaptive environmental impact index (AEII) and combining the functional environmental impact matrix and real-time environmental data of the product to achieve optimal energy efficiency and environmental friendliness.

[0168] In the first step, the adaptive environmental impact index (AEII) of the product is calculated by formula 9. In formula 9, W i is the weight factor of the ith function in the function-environmental impact matrix, which is used to indicate the importance of the function to the overall product design. The weight factor is usually set based on user needs, the frequency of use of the function, and the importance of the environmental impact. For example, a function with higher energy consumption may be given a larger weight to occupy a more important position in the optimization.

[0169] F iis the current usage frequency of the ith function, indicating how active the function is in actual use. This parameter can be obtained by real-time monitoring of the product's operating status, for example, by capturing the usage of each function through user behavior data or built-in sensors. The usage frequency range is generally between 0 and 1, with 0 indicating no use and 1 indicating full load operation.

[0170] λ i is the function adaptation factor, which is used to dynamically adjust the sensitivity of each function to environmental influences. Different functions may have different responsiveness to changes in the external environment, and the adaptation factor reflects this difference. For example, some functions are more sensitive to temperature changes, so their λ i may be set higher, while other insensitive functions λ i Lower.

[0171] E i It is the actual environmental impact value of the ith function under the current environmental conditions, usually indicating energy consumption, carbon emissions or other forms of environmental burden. Through sensor data or estimation models, the environmental impact of different functions under different environmental conditions can be obtained in real time.

[0172] The numerator of the formula multiplies the weight factor, usage frequency, and adaptation factor of each function by its actual environmental impact value and sums them up to represent the total environmental impact of all functions. The denominator is the sum of the function weight, usage frequency, and adaptation factor to obtain a normalized factor. Finally, multiply it by the baseline environmental impact value E base The inverse of κ is used to adjust κ (correction coefficient) to balance the overall impact in different environments.

[0173] In the second step, the calculated AEII is used to dynamically adjust the performance parameters of the product through formula 10. base It is the benchmark power parameter of the product under standard conditions, indicating the performance or power consumption of the product under normal environment. The benchmark power parameter is generally set through experimental testing or equipment specifications.

[0174] α is the adjustment sensitivity coefficient, which controls the amplitude of product performance adjustment. The size of the sensitivity coefficient determines the response intensity of product performance to environmental changes. A larger α value indicates a stronger adjustment intensity.

[0175] β is the nonlinear adjustment index, which controls the nonlinear curve of performance adjustment. This index determines whether the degree of product performance adjustment is linear or nonlinear when AEII deviates from the standard. For example, β greater than 1 means that the adjustment is nonlinear, and the performance will change dramatically with the change of AEII, especially when AEII is close to the limit value.

[0176] θ is the elasticity factor of performance adjustment, which is used to further fine-tune the amplitude of performance adjustment. It determines the smoothness of the adjustment process and ensures that the adjustment will not be too drastic and affect the stability of the product and user experience.

[0177] Next, the environmental adaptation coefficient is calculated using formula 11. j is the impact weight of the jth environmental factor, reflecting the importance of this factor in adjusting performance. For example, environmental conditions such as temperature, humidity, and light intensity may have a greater impact on the performance of certain products, so the corresponding environmental factors will be given a higher weight.

[0178] C j is the current measured value of the jth environmental factor, measured in real time by a sensor, such as the current data provided by a temperature sensor, humidity sensor or other environmental monitoring equipment.

[0179] C jbase is the reference value of the jth environmental factor, indicating the normal value of the environmental factor under standard use environment. This reference value is usually set during product design or determined according to industry standards.

[0180] δ j It is a dynamic correction factor used to balance the effects of different environmental factors on performance. Changes in some environmental factors may not have a direct impact on product performance, so the correction factor can correct or reduce the effects of these environmental factors. The calculation results of EAC reflect the difference between the current environmental conditions and the standard conditions, and convert them into adaptive adjustments to performance.

[0181] Finally, the final performance parameter after dynamic adjustment is obtained through formula 12. In this step, the previously adjusted product performance parameter P is multiplied by the environmental adaptation coefficient EAC to reflect the further correction of the external environment to the product performance. The final performance parameter P final It reflects the actual operating status and energy efficiency optimization results of the product in the current environment.

[0182] In short, through these formulas, the performance parameters of the product can be monitored and adjusted in real time to keep it in the best operating state under different environmental conditions. This method ensures the green design and efficient operation of the product by combining weight factors, frequency of use, environmental data and correction factors.

[0183] In the above-mentioned embodiment, a green design method for electronic and electrical products is provided. Correspondingly, the present application also provides a green design system for electronic and electrical products. Figure 2, which is a schematic diagram of an embodiment of a green design system for electronic and electrical products of the present application. Since this embodiment, i.e., the second embodiment, is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The system embodiment described below is only illustrative.

[0184] The second embodiment of the present application provides a green design system for electronic and electrical products, including:

[0185] Establishing unit 201, used to establish a functional environmental impact matrix of a product, wherein the functional environmental impact matrix associates each function of the product with its corresponding environmental impact factor to evaluate the environmental impact of each function;

[0186] An identification unit 202, configured to identify core functions important to user experience based on the user experience evaluation and the function-environment impact matrix, and optimize or reduce non-core functions to reduce environmental burden;

[0187] The selection unit 203 is used to select environmentally friendly materials and design modular structures according to the identified core functions; and to achieve coordinated optimization of material use and structural design through iterative optimization calculations to reduce material usage and improve product recyclability;

[0188] Design unit 204, used for designing components in the product that are prone to aging or damage into independent modules that are easy to replace or upgrade, and adopting a performance optimization mechanism to extend the overall service life of the product;

[0189] The integration unit 205 is used to integrate an environmental sensor in the product to monitor the use environment of the product; adjust the product performance parameters according to the functional environment impact matrix and the environmental data in the use environment;

[0190] The feedback unit 206 is used to regularly collect and analyze the user's usage data and environmental impact data; and based on the analysis results, dynamically adjust the product performance parameters through remote firmware updates, and feed back the results of the dynamic adjustment into the functional environmental impact matrix.

[0191] A third embodiment of the present application provides an electronic device, the electronic device comprising:

[0192] processor;

[0193] The memory is used to store a program, and when the program is read and executed by the processor, the green design method for electronic and electrical products provided in the first embodiment of the present application is executed.

[0194] The fourth embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the green design method for electronic and electrical products provided in the first embodiment of the present application is executed.

[0195] Although the present application is disclosed as above in the form of a preferred embodiment, it is not intended to limit the present application. Any technical personnel in this field may make possible changes and modifications without departing from the spirit and scope of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims of the present application.

Claims

1. A green design method for electronic and electrical products, characterized in that: include: Establishing a product's functional environmental impact matrix, wherein the functional environmental impact matrix associates each function of the product with its corresponding environmental impact factor to evaluate the environmental impact of each function; Based on the user experience assessment and the functional environmental impact matrix, identify the core functions that are important to the user experience, and optimize or reduce non-core functions to reduce the environmental burden; According to the identified core functions, environmentally friendly materials are selected and modular structures are designed; And through iterative optimization calculations, the coordinated optimization of material use and structural design is achieved to reduce material usage and improve product recyclability; The components in the product that are prone to aging or damage are designed as independent modules that are easy to replace, and a performance optimization mechanism is adopted to extend the overall service life of the product; Integrate environmental sensors in the product to monitor the environment in which the product is used; adjust product performance parameters based on the functional environmental impact matrix and environmental data in the use environment; Regularly collect and analyze user usage data and environmental impact data; based on the analysis results, dynamically adjust product performance parameters through remote firmware updates, and feed the results of dynamic adjustments back to the functional environmental impact matrix.

2. The green design method for electronic and electrical products according to claim 1, characterized in that: The establishment of the functional environmental impact matrix includes weighting the usage frequency, energy consumption, carbon emissions, and resource use of each function, quantifying the environmental impact factors into numerical values, and calculating the comprehensive environmental impact index based on the weight of each environmental impact factor.

3. The green design method for electronic and electrical products according to claim 2, characterized in that: The functional environmental impact matrix also takes into account the environmental impact during the product life cycle, including the environmental impact from raw material acquisition, manufacturing process, transportation, use to scrapping stage. The final functional environmental impact matrix is ​​generated by comprehensively evaluating the environmental data of each stage.

4. The green design method for electronic and electrical products according to claim 1, characterized in that: The identification of core functions that are important to user experience includes generating a user function priority list through user surveys, questionnaires and user behavior data analysis, and screening out non-core functions that have a greater burden on the environment based on the environmental impact values ​​in the functional environmental impact matrix for optimization or reduction.

5. The green design method for electronic and electrical products according to claim 1, characterized in that: The optimization or reduction of non-core functions includes adopting low-energy consumption algorithms, reducing the power consumption requirements of non-essential functions, and reducing the contribution to the overall environmental impact by limiting the maximum operating frequency of designated functions.

6. The green design method for electronic and electrical products according to claim 1, characterized in that: The iterative optimization calculation is used to achieve the coordinated optimization of material use and structural design, including: Step P101: Establish a multi-objective optimization model, which includes an objective function f1 for minimizing material usage, an objective function f2 for maximizing product recyclability, an objective function f3 for minimizing product life cycle environmental impact, and an objective function f4 for maximizing product structural strength: The objective function f1 of minimizing material usage is implemented using the following formula 1: Among them, m i is the density of the i-th material; V i is the volume of the i-th material; n is the total number of types of materials used in the product; The objective function f2 of maximizing the recyclability of the product is implemented using the following formula 2: Among them, R i is the recovery rate of the i-th material; V i is the volume of the i-th material; n is the total number of types of materials used in the product; The objective function f3 of minimizing the environmental impact of the product life cycle is implemented using the following formula 3: Among them, E i is the production energy consumption of the i-th material; C i is the carbon emission of the i-th material; n is the total number of materials used in the product; The objective function f4 of maximizing product structural strength is implemented by the following formula 4: Among them, σ y,i is the yield strength of the i-th material; σ max,i is the maximum stress of the i-th material in the structure; V i is the volume of the i-th material; n is the total number of types of materials used in the product; Step P102: Introduce a collaborative optimization method combining an adaptive multi-objective ant colony optimization algorithm and finite element analysis. The specific steps include: (a) Initial generation optimization model step: Initialize the ant colony into multiple groups of possible material-structure combination schemes, where each ant represents a design scheme, which includes material type and structural geometric parameters; (b) Finite element analysis evaluation step: Perform finite element analysis on each ant’s design scheme to calculate its stress distribution, deformation, and structural strength to ensure that each scheme meets the requirements of the objective function f4; (c) Fitness function calculation step: Calculate the fitness based on the following formula 5: Among them, Fitness represents the fitness value; w1, w2, w3, w4 are weight factors of the objective function; α,=,γ,δ are smoothing coefficients; (d) Updating the optimal solution and the local optimal solution step: by calculating the fitness value of each ant, updating the global optimal solution and the local optimal solution, and recording the current optimal material-structure combination; (e) Pheromone update step: According to the fitness value, the pheromone concentration is updated using the following formula 6: t ij (t+1)=(1-ρ)·τ ij (t)+Δτ ij (6) Among them, τ ij (t+1) represents the pheromone concentration between the i-th point and the j-th point at time t+1; τ ij (t) represents the pheromone concentration between the i-th point and the j-th point at time t; ρ is the pheromone volatility coefficient; Δτ ij is the pheromone increment, which indicates the incremental pheromone value left by the current optimal ant on the path from i to j; (f) Adaptive parameter adjustment step: According to the following formulas 7 and 8, dynamically adjust the pheromone importance factor and heuristic information factor in the ant colony algorithm: Among them, α(t+1) represents the pheromone importance factor at the t+1th iteration; α(t) represents the pheromone importance factor at the tth iteration; λ is the learning rate; F best (t) is the optimal fitness value in the tth iteration; F best (t-1) is the optimal fitness value in the t-1th iteration; β(t+1) represents the heuristic information factor at the t+1th iteration; β(t) represents the heuristic information factor at the tth iteration; (g) Local search and perturbation mechanism step: perform a small-scale perturbation on the current optimal solution, and evaluate the feasibility of the material and structure combination after the perturbation through fast finite element analysis to ensure the precision of local optimization; (h) Taboo search strategy step: maintain a taboo table to record the most recently visited solutions and avoid repeated visits to the same solution within a specified number of iterations, preventing the algorithm from falling into local optimality and improving global search capabilities; (i) Iteration stop step: repeat steps b to h until the maximum number of iterations is reached or the convergence condition is met, ensuring that the optimal solution is searched through multiple iterations; Step P103: Conduct manufacturability analysis on the final optimization results to ensure that the selected materials and structural designs can meet the actual production process requirements and verify their feasibility in existing production equipment; Step P104: If the manufacturability analysis fails, adjust the constraints of the multi-objective optimization model, including the allowed material types, processing methods or structural details, and re-execute steps P102 and P103 until the optimal solution that meets the green design goals and can be actually produced is found.

7. The green design method for electronic and electrical products according to claim 1, characterized in that: The easily replaceable independent modules adopt a standardized interface design, so that the modules are interchangeable in products of different models and versions, and a quick-release mechanical structure is adopted at the module connection to reduce replacement time.

8. The green design method for electronic and electrical products according to claim 1, characterized in that: The environmental sensors include temperature sensors, humidity sensors, light sensors and air quality sensors, which feed back real-time data to the control system and dynamically adjust product performance parameters in response to environmental changes.

9. The green design method for electronic and electrical products according to claim 1, characterized in that: The adjusting of product performance parameters according to the functional environment impact matrix and the environmental data in the use environment includes: According to the following formula 9, the adaptive environmental impact index AEII of the product is calculated: Among them, W i is the weight factor of the ith function in the functional environment impact matrix; F i is the current usage frequency of the ith function; E i is the actual environmental impact value of the ith function under the current environmental conditions; i is the function adaptation factor, which is used to dynamically adjust the sensitivity of the function to the environmental impact; n is the total number of functions in the function-environment impact matrix; E base is the baseline environmental impact value of the product under standard environmental conditions; κ is the correction factor, which is used to balance the overall environmental impact under different usage environments; Based on the calculated adaptive environmental impact index AEII, the adjusted product performance parameter P is obtained according to the following formula 10: P=P base ×(1-α×(AEII-1) β ×θ) (10) Among them, P base is the reference power parameter; α is the adjustment sensitivity coefficient; β is the nonlinear adjustment index; θ is the performance adjustment elasticity factor; According to the following formula 11, the environment adaptation coefficient EAC is calculated: Among them, γ j is the impact weight of the jth environmental factor; C j is the current measured value of the jth environmental factor; C jbase is the reference value of the jth environmental factor; δ j is the dynamic correction coefficient; m is the total number of environmental factors monitored by the sensor; According to the following formula 12, the final performance parameter P after dynamic adjustment is obtained: final : P final =P×EAC(12)。 10. A green design system for electronic and electrical products, characterized in that: include: An establishing unit, used to establish a functional environmental impact matrix of the product, wherein the functional environmental impact matrix associates each function of the product with its corresponding environmental impact factor to evaluate the environmental impact of each function; An identification unit, used to identify core functions important to user experience based on the user experience evaluation and the functional environmental impact matrix, and optimize or reduce non-core functions to reduce environmental burden; Selection unit, used to select environmentally friendly materials and design modular structures for the identified core functions; And through iterative optimization calculations, the coordinated optimization of material use and structural design is achieved to reduce material usage and improve product recyclability; Design unit, which is used to design the components in the product that are prone to aging or damage into independent modules that are easy to replace or upgrade, and adopt performance optimization mechanism to extend the overall service life of the product; An integration unit, for integrating an environmental sensor in the product to monitor the use environment of the product; adjusting product performance parameters according to the functional environment impact matrix and environmental data in the use environment; Feedback unit, used to regularly collect and analyze user usage data and environmental impact data; Based on the analysis results, product performance parameters are dynamically adjusted through remote firmware updates, and the results of the dynamic adjustments are fed back into the functional environmental impact matrix.

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