A method and system for evaluating eco-design of express packaging products based on big data

Abstract

The present invention discloses a method and system for evaluating the ecological design of express packaging products based on big data, including collecting ecological test data and production data of express packaging products, preprocessing the ecological test data and the production data, obtaining environmental ecological data through the ecological test data, performing ecological analysis on the production data to obtain process ecological data, performing fusion analysis on the environmental ecological data and the process ecological data to obtain an ecological function, constructing an ecological design evaluation model for express packaging products according to the ecological function, inputting express packaging product design data into the express packaging product ecological design evaluation model, and outputting an evaluation result. This method can not only improve the accuracy of ecological design evaluation of express packaging products based on big data, but also has good interpretability, and can be directly applied to the ecological design evaluation system of express packaging products.

Description

Technical Field

[0001] The present invention relates to the field of evaluation, and in particular to an ecological design evaluation method and system for express packaging products based on big data. Background Art

[0002] With the rapid development of e-commerce, the express delivery industry has experienced unprecedented growth, and a large number of express parcels play an important role in daily life. However, the rapid growth of this industry has also brought many challenges, especially in the ecological design of express packaging products. Traditional express packaging product design often focuses on cost, transportation efficiency and packaging protection, while the consideration of environmental impact is relatively insufficient. This has led to excessive consumption of resources, large amounts of waste and increased environmental pollution.

[0003] At present, the ecological design evaluation method of express packaging products based on big data is still imperfect. On the one hand, the existing evaluation methods are often limited to a single indicator or local link, lacking comprehensiveness and systematicness; on the other hand, the application of big data technology in the field of ecological design of express packaging products is still in its infancy, and a mature method and system has not yet been formed.

[0004] Therefore, developing an eco-design evaluation method for express packaging products based on big data has important practical significance and application value. This method will combine big data technology and eco-design concepts to comprehensively consider and evaluate the design of express packaging products, achieve efficient use of resources, minimize environmental impact, and optimize consumer experience. It can not only improve the sustainable development level of the express industry, but also provide consumers with more environmentally friendly and convenient express packaging products. Summary of the invention

[0005] The purpose of this invention is to provide an eco-design evaluation method for express packaging products based on big data.

[0006] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0007] The present invention comprises the following steps:

[0008] Collecting ecological test data and production data of express packaging products, and preprocessing the ecological test data and the production data;

[0009] Environmental ecological data are obtained through the ecological test data; the environmental ecological data include first ecological data, second ecological data and third ecological data; the first ecological data represents the ecological impact quality value in the environmental ecological data; the second ecological data represents the cycle ratio of the product in the environmental ecological data; the third ecological data represents the environmental friendliness of the product in the environmental ecological data; the cycle ratio represents the ratio of the actual product life cycle to the standard life cycle; the environmental friendliness represents the ratio of the biomass in the unpolluted area to the biomass in the polluted area;

[0010] Performing ecological analysis on the production data to obtain process ecological data, and performing fusion analysis on the environmental ecological data and the process ecological data to obtain an ecological evaluation function;

[0011] An express packaging product ecological design evaluation model is constructed according to the ecological evaluation function, the express packaging product design data is input into the express packaging product ecological design evaluation model, and the evaluation result is output.

[0012] Furthermore, the method for obtaining the impact quality value includes:

[0013] Calculate the entropy of ecological experiment data:

[0014]

[0015] The number of ecological test data is The wth ecological experiment data is The control constant is b;

[0016] The ecological test data with entropy values ​​greater than 0.587 were used as impact data, and the center of gravity shift analysis was performed on the impact data. The expression is:

[0017]

[0018] The wth influence center data is The amount of data affected is The weight of the wth influencing data is The wth impact data is Impact data The standard value is The regulatory constant is z and the heritability coefficient is μ;

[0019] Calculate the kernel density:

[0020]

[0021] The kernel density is The bandwidth is g, the kernel function is L(·), and the reference value affecting the center of gravity data is

[0022] Calculate the impact quality value of the impact data:

[0023]

[0024] The shape parameter estimate is The random number between 0 and 1 is a, and the influence degree of the wth influencing data is The standard value of the w-th influencing data influence is The impact quality value of the wth impact data is

[0025] Furthermore, the environmental friendliness includes:

[0026]

[0027] The biomass of the uncontaminated area of ​​the fth express packaging block and the biomass of the contaminated area of ​​the fth express packaging block are Q w,f The biomass of the non-express packaging contaminated area in block sf is U w,sf , the environmental friendliness of the fth block.

[0028] Furthermore, the method of performing ecological analysis on the production data to obtain process ecological data includes:

[0029] Input the production data into the production analysis model based on the decision tree, adjust the simulated ecological environment system and production energy consumption system according to the production data, and obtain the production ecological pollution amount;

[0030] The life cycle assessment method is used to calculate the degree of ecological impact based on production data and the amount of production ecological pollution:

[0031]

[0032] The cth production data is y c , production data y c The carbon emissions are Production data c The number of species reduced in the i-th ecological species is Production data c The important coefficient is The adjustment constant is

[0033] The coefficient of variation method is used to obtain the difference ratio weight of production data, and the difference ratio weight is optimized through grey correlation analysis. The objective function of the production analysis model is constructed according to the optimized difference ratio weight:

[0034]

[0035]

[0036] The objective function is The balance factor is η, and the production data is y c The actual ecological production pollution is Production data c The predicted ecological production pollution is Actual ecological production pollution and predicting ecological production pollution The loss function is The number of production data is m, the number of production data is y c The difference weight is Production data c The optimization weight is Production data c The grey correlation degree is The regularization term is ξ, and the offset constant is r;

[0037] Compute the regularization term:

[0038]

[0039] The first penalty coefficient is ρ, the second penalty coefficient is δ, the number of leaves that controls the complexity of the tree is U, the complexity is g, and the L2 norm function is ‖·‖ 2 ;

[0040] The winning data are sorted according to the objective function value, and the objective function value greater than 0.513 is output as process ecology data.

[0041] Furthermore, the method of fusing and analyzing the environmental ecological data and the process ecological data to obtain an ecological function includes:

[0042] Input the environmental ecological data and process ecological data into the fusion analysis model based on deep neural network, and use the environmental ecological data, process ecological data and hierarchical automatic encoder to hierarchically train the fusion analysis model;

[0043] Calculate data weights:

[0044]

[0045] The weight of the data τ is χ τ , the jth data τ j The correlation degree is Data τ j The probability of affecting the ecology is The correlation degree of data τ is H τ , the probability that data τ affects the ecology is P τ , the number of data τ is

[0046] Construct an ecological evaluation function based on data weights:

[0047]

[0048] The ecological function of the u-th express package is The weight of the quality-influencing data of the u-th express package is χ 1,u , the cycle ratio of the u-th express package is K 2,u , the cycle ratio weight of the u-th express package is χ 2,u , the environmental friendliness of the u-th express package is K 3,u , the environmental friendliness weight of the u-th express package is χ 3,u , the process ecological data of the u-th express package is G u , the weight of the process ecological data of the u-th express package is χ 4,u , the number of data for the u-th express packaging cycle ratio is q 2,u The reference value of the cycle ratio is The attenuation coefficient of the u-th express packaging cycle ratio is γ 2,u , the penalty factor is The slack variable of the environmental friendliness of the u-th express package is

[0049] The ecological function is used to create a modal matrix, the feature hashing algorithm is used to extract the ecological characteristics of environmental ecological data and process ecological data, and the shared feature matrix is ​​constructed using the ecological characteristics;

[0050] Initialize the shared feature matrix, initialize the ecological features in a random manner, and obtain the deep ecological features of the input data through the forward propagation of the deep neural network;

[0051] Construct a consistent coding matrix, create a data sharing space, and endow a deep ecological matrix;

[0052] Correct the basis matrix and jointly fine-tune the deep matching networks of each modality during the back-propagation process until the objective function converges and outputs the ecological evaluation function.

[0053] Furthermore, a method for constructing an eco-design evaluation model for express packaging products according to the eco-function includes:

[0054] The objective function of the eco-design evaluation model for express packaging products is constructed based on the ecological function, and the expression is:

[0055]

[0056] The loss function of the ecological assessment of the u-th express packaging product is The objective function of the ecological assessment of the u-th express packaging product is: The ecological evaluation function of the u-th express packaging product ecological evaluation is:

[0057] The eco-design assessment models for express packaging products include random forest algorithm, time series analysis algorithm, autoencoder, recurrent neural network algorithm, and genetic algorithm;

[0058] The random forest algorithm divides the input data into training data and test data in a ratio of 5:2;

[0059] The time series analysis algorithm obtains the time series variation according to the time variation law of the training data;

[0060] The autoencoder extracts ecological change features based on the degree of change in the temporal variation through unsupervised learning;

[0061] The recurrent neural network algorithm learns the changing patterns between the ecological characteristics and ecology through the training data;

[0062] The genetic algorithm test data predicts the ecological assessment according to the changing rules, and the learning rate of the express packaging product ecological design assessment model is adjusted according to the predicted ecological assessment and the actual ecological assessment.

[0063] Second, an eco-design evaluation system for express packaging products based on big data, including:

[0064] Data collection module: used to collect ecological test data and production data of express packaging products, and pre-process the ecological test data and the production data;

[0065] Data processing module: used to obtain environmental ecological data through the ecological test data; the environmental ecological data includes first ecological data, second ecological data and third ecological data; the first ecological data represents the ecological impact quality value in the environmental ecological data; the second ecological data represents the cycle ratio of the product in the environmental ecological data; the third ecological data represents the environmental friendliness of the product in the environmental ecological data; the cycle ratio represents the ratio of the actual product life cycle to the standard life cycle; the environmental friendliness represents the ratio of the biomass of the unpolluted area to the biomass of the polluted area;

[0066] Data fusion module: used to perform ecological analysis on the production data to obtain process ecological data, and to perform fusion analysis on the environmental ecological data and the process ecological data to obtain an ecological evaluation function;

[0067] Model building module: used to build an express packaging product ecological design evaluation model according to the ecological evaluation function, input the express packaging product design data into the express packaging product ecological design evaluation model, and output the evaluation result.

[0068] In a third aspect, an embodiment of the present application further provides an electronic device, including:

[0069] A processor; and a memory arranged to store computer executable instructions, which when executed cause the processor to perform the method steps described in the first aspect.

[0070] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple applications, the electronic device executes the method steps described in the first aspect.

[0071] The beneficial effects of the present invention are:

[0072] The present invention is a method and system for evaluating the eco-design of express packaging products based on big data. Compared with the prior art, the present invention has the following technical effects:

[0073] The present invention can improve the accuracy of user intention recognition in multi-dimensional human-computer interaction scenarios through preprocessing, obtaining satisfactory deviation, obtaining fuzzy intention, obtaining corrected intention, obtaining target intention and model building steps, thereby improving the precision of user intention recognition in multi-dimensional human-computer interaction scenarios, and optimizing user intention recognition in multi-dimensional human-computer interaction scenarios, which can greatly save resources and improve work efficiency. It can realize automatic recognition of user intention in multi-dimensional human-computer interaction scenarios, and perform intention correction and knowledge enhancement on user intention recognition in multi-dimensional human-computer interaction scenarios in real time, which is of great significance to user intention recognition in multi-dimensional human-computer interaction scenarios, can adapt to user intention recognition in multi-dimensional human-computer interaction scenarios with different standards and user intention recognition needs in different multi-dimensional human-computer interaction scenarios, and has a certain universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 This is a flowchart of the steps of an eco-design evaluation method for express packaging products based on big data of the present invention;

[0075] Figure 2 It is a schematic diagram of the structure of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION

[0076] The present invention is further described below by means of specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0077] The present invention provides an express packaging product ecological design evaluation method and system based on big data, comprising the following steps:

[0078] like Figure 1 As shown, in this embodiment, the following steps are included:

[0079] Collect ecological test data and production data of express packaging products, and pre-process the ecological test data and production data; the ecological test data include biodegradability, life cycle, toxicity, photodegradability, chemical stability, recyclability, energy consumption, carbon emissions, biomass before the test, biomass after the test, environmental conditions, and the reduction ratio of green vegetation in the environmental conditions; the production data include raw materials, process schemes, energy consumption, carbon emissions, chemical substances used, waste generated, strength, durability, and production efficiency;

[0080] In the actual evaluation, the ecological test data include biodegradability 77%, life cycle 4 years, low toxicity, photodegradability 69%, medium chemical stability, high recyclability, energy consumption 8MJ / kg, carbon emissions 1.8kg CO2 / kg, 85 kinds of biomass 100 grams before the test, 53 kinds of biomass 115 grams after the test, and environmental conditions. The green vegetation reduction ratio is 23%; production data include renewable resources for raw materials, green manufacturing process for process solutions, energy consumption 12MJ / unit, carbon emissions 2.5kg CO2 / unit, environmentally friendly use of a small amount of chemical substances, extremely low waste generation, high strength, durability for 6 years, and production efficiency 87%;

[0081] Environmental ecological data are obtained through the ecological test data; the environmental ecological data include first ecological data, second ecological data and third ecological data; the first ecological data represents the ecological impact quality value in the environmental ecological data; the second ecological data represents the cycle ratio of the product in the environmental ecological data; the third ecological data represents the environmental friendliness of the product in the environmental ecological data; the cycle ratio represents the ratio of the actual product life cycle to the standard life cycle; the environmental friendliness represents the ratio of the biomass in the unpolluted area to the biomass in the polluted area;

[0082] In the actual evaluation, the first ecological data is 0.346, the second ecological data is 1.2, and the third ecological data is 0.741;

[0083] Performing ecological analysis on the production data to obtain process ecological data, and performing fusion analysis on the environmental ecological data and the process ecological data to obtain an ecological evaluation function;

[0084] In the actual evaluation, the process ecological data include biodegradability, life cycle, toxicity, photodegradability, chemical stability, recyclability, energy consumption, carbon emissions, biomass before the test, biomass after the test, environmental conditions, environmental conditions, green vegetation reduction ratio, process plan, energy consumption, carbon emissions, strength, durability, and production efficiency;

[0085] Constructing an eco-design evaluation model for express packaging products according to the eco-evaluation function, inputting the express packaging product design data into the eco-design evaluation model for express packaging products, and outputting the evaluation result;

[0086] In the actual evaluation, the ecological test data include biodegradability of 65%, life cycle of 6 years, low toxicity, photodegradability of 62%, medium chemical stability, medium recyclability, energy consumption of 10MJ / kg, carbon emissions of 2kg CO2 / kg, 93 types of biomass 135 grams before the test, 37 types of biomass 157 grams after the test, and environmental conditions. The proportion of green vegetation reduction is 44%; production data include renewable resources for raw materials, green manufacturing process for process solutions, energy consumption of 15MJ / unit, carbon emissions of 3kg CO2 / unit, small amounts of slightly toxic chemicals used, extremely low waste generation, medium strength, durability of 4 years, and production efficiency of 67%; the evaluation result is 0.478.

[0087] In this embodiment, the method for obtaining the impact quality value includes:

[0088] Calculate the entropy of ecological experiment data:

[0089]

[0090] The number of ecological test data is The wth ecological experiment data is The control constant is b;

[0091] The ecological test data with entropy values ​​greater than 0.587 were used as impact data, and the center of gravity shift analysis was performed on the impact data. The expression is:

[0092]

[0093] The wth influence center data is The amount of data affected is The weight of the wth influencing data is The wth impact data is Impact data The standard value is The control constant is z and the heritability coefficient is μ;

[0094] Calculate the kernel density:

[0095]

[0096] The kernel density is The bandwidth is g, the kernel function is L(·), and the reference value affecting the center of gravity data is

[0097] Calculate the impact quality value of the impact data:

[0098]

[0099] The shape parameter estimate is The random number between 0 and 1 is a, and the influence degree of the wth influencing data is The standard value of the w-th influencing data influence is The impact quality value of the wth impact data is

[0100]

[0101] In this embodiment, the environmental friendliness includes:

[0102]

[0103] The biomass of the uncontaminated area of ​​the fth express packaging block and the biomass of the contaminated area of ​​the fth express packaging block are Q w,f The biomass of the non-express packaging contaminated area in block sf is U w,sf , the environmental friendliness of the fth block.

[0104] In this embodiment, the method of performing ecological analysis on the production data to obtain process ecological data includes:

[0105] Input the production data into the production analysis model based on the decision tree, adjust the simulated ecological environment system and production energy consumption system according to the production data, and obtain the production ecological pollution amount;

[0106] The life cycle assessment method is used to calculate the degree of ecological impact based on production data and the amount of production ecological pollution:

[0107]

[0108] The cth production data is y c , production data y c The carbon emissions are Production data c The number of species reduced in the i-th ecological species is Production data c The important coefficient is The adjustment constant is

[0109] The coefficient of variation method is used to obtain the difference ratio weight of production data, and the difference ratio weight is optimized through grey correlation analysis. The objective function of the production analysis model is constructed according to the optimized difference ratio weight:

[0110]

[0111]

[0112] The objective function is The balance factor is η, and the production data is y c The actual ecological production pollution is Production data c The predicted ecological production pollution is Actual ecological production pollution and predicting the amount of ecological production pollution The loss function is The number of production data is m, the number of production data is y c The difference weight is Production data c The optimization weight is Production data c The grey correlation degree is The regularization term is ξ, and the offset constant is r;

[0113] Compute the regularization term:

[0114]

[0115] The first penalty coefficient is ρ, the second penalty coefficient is δ, the number of leaves that controls the complexity of the tree is U, the complexity is g, and the L2 norm function is ‖·‖ 2 ;

[0116] The winning data are sorted according to the objective function value, and the objective function value greater than 0.513 is output as process ecology data.

[0117] In this embodiment, the method of performing fusion analysis on the environmental ecological data and the process ecological data to obtain an ecological function includes:

[0118] Input the environmental ecological data and process ecological data into the fusion analysis model based on deep neural network, and use the environmental ecological data, process ecological data and hierarchical automatic encoder to hierarchically train the fusion analysis model;

[0119] Calculate data weights:

[0120]

[0121] The weight of the data τ is χ τ, the jth data τ j The correlation degree is Data τ j The probability of affecting the ecology is The correlation degree of data τ is H τ , the probability that data τ affects the ecology is P τ , the number of data τ is

[0122] Construct an ecological evaluation function based on data weights:

[0123]

[0124] The ecological function of the u-th express package is The weight of the quality-influencing data of the u-th express package is χ 1,u , the cycle ratio of the u-th express package is K 2,u , the cycle ratio weight of the u-th express package is χ 2,u , the environmental friendliness of the u-th express package is K 3,u , the environmental friendliness weight of the u-th express package is χ 3,u , the process ecological data of the u-th express package is G u , the weight of the process ecological data of the u-th express package is χ 4,u , the number of data for the u-th express packaging cycle ratio is q 2,u The reference value of the cycle ratio is The attenuation coefficient of the u-th express packaging cycle ratio is γ 2,u , the penalty factor is The slack variable of the environmental friendliness of the u-th express package is

[0125] The ecological function is used to create a modal matrix, the feature hashing algorithm is used to extract the ecological characteristics of environmental ecological data and process ecological data, and the shared feature matrix is ​​constructed using the ecological characteristics;

[0126] Initialize the shared feature matrix, initialize the ecological features in a random manner, and obtain the deep ecological features of the input data through the forward propagation of the deep neural network;

[0127] Construct a consistent coding matrix, create a data sharing space, and endow a deep ecological matrix;

[0128] Correct the basis matrix and jointly fine-tune the deep matching networks of each modality during the back-propagation process until the objective function converges and outputs the ecological evaluation function.

[0129] In this embodiment, the method for constructing an eco-design evaluation model for express packaging products according to the eco-function includes:

[0130] The objective function of the eco-design evaluation model for express packaging products is constructed based on the ecological function, and the expression is:

[0131]

[0132] The loss function of the ecological assessment of the u-th express packaging product is The objective function of the ecological assessment of the u-th express packaging product is: The ecological evaluation function of the u-th express packaging product ecological evaluation is:

[0133] The eco-design assessment models for express packaging products include random forest algorithm, time series analysis algorithm, autoencoder, recurrent neural network algorithm, and genetic algorithm;

[0134] The random forest algorithm divides the input data into training data and test data in a ratio of 5:2;

[0135] The time series analysis algorithm obtains the time series variation according to the time variation law of the training data;

[0136] The autoencoder extracts ecological change features based on the degree of change in the temporal variation through unsupervised learning;

[0137] The recurrent neural network algorithm learns the changing patterns between the ecological characteristics and ecology through the training data;

[0138] The genetic algorithm test data predicts the ecological assessment according to the changing rules, and the learning rate of the express packaging product ecological design assessment model is adjusted according to the predicted ecological assessment and the actual ecological assessment.

[0139] Second, an eco-design evaluation system for express packaging products based on big data, including:

[0140] Data collection module: used to collect ecological test data and production data of express packaging products, and pre-process the ecological test data and the production data;

[0141] Data processing module: used to obtain environmental ecological data through the ecological test data; the environmental ecological data includes first ecological data, second ecological data and third ecological data; the first ecological data represents the ecological impact quality value in the environmental ecological data; the second ecological data represents the cycle ratio of the product in the environmental ecological data; the third ecological data represents the environmental friendliness of the product in the environmental ecological data; the cycle ratio represents the ratio of the actual product life cycle to the standard life cycle; the environmental friendliness represents the ratio of the biomass of the unpolluted area to the biomass of the polluted area;

[0142] Data fusion module: used to perform ecological analysis on the production data to obtain process ecological data, and to perform fusion analysis on the environmental ecological data and the process ecological data to obtain an ecological evaluation function;

[0143] Model building module: used to build an express packaging product ecological design evaluation model according to the ecological evaluation function, input the express packaging product design data into the express packaging product ecological design evaluation model, and output the evaluation result.

[0144] Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 2 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0145] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0146] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0147] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming an express packaging product eco-design assessment device based on big data at the logical level. The processor executes the program stored in the memory and is specifically used to execute any of the above-mentioned express packaging product eco-design assessment methods based on big data.

[0148] The above application Figure 1The embodiment shown discloses an eco-design assessment method for express packaging products based on big data, which can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or instructions in the form of software. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present application can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in a decoding processor. The software module can be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0149] The electronic device may also perform Figure 1 A big data-based eco-design evaluation method for express packaging products is proposed and implemented Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0150] An embodiment of the present application also proposes a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions. When the instructions are executed by an electronic device including multiple applications, any one of the aforementioned express packaging product eco-design assessment methods based on big data is executed.

[0151] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0152] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0153] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0154] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0155] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0156] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0157] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0158] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0159] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0160] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for evaluating the eco-design of express packaging products based on big data, characterized in that: The following steps are involved: Collecting ecological test data and production data of express packaging products, and preprocessing the ecological test data and the production data; Environmental ecological data are obtained through the ecological test data; the environmental ecological data include first ecological data, second ecological data and third ecological data; the first ecological data represents the ecological impact quality value in the environmental ecological data; the second ecological data represents the cycle ratio of the product in the environmental ecological data; the third ecological data represents the environmental friendliness of the product in the environmental ecological data; the cycle ratio represents the ratio of the actual product life cycle to the standard life cycle; The environmental friendliness level indicates the ratio of biomass in unpolluted areas to biomass in polluted areas; Performing ecological analysis on the production data to obtain process ecological data, and performing fusion analysis on the environmental ecological data and the process ecological data to obtain an ecological evaluation function; Constructing an eco-design evaluation model for express packaging products according to the eco-evaluation function, inputting the express packaging product design data into the eco-design evaluation model for express packaging products, and outputting the evaluation result; The method for obtaining the impact quality value includes: Calculate the entropy of ecological experiment data: The number of ecological test data is The wth ecological experiment data is The control constant is b; The ecological test data with entropy values ​​greater than 0.587 were used as impact data, and the center of gravity shift analysis was performed on the impact data. The expression is: The wth influence center data is The amount of data affected is The weight of the wth influencing data is The wth impact data is Impact data The standard value is The regulatory constant is z and the heritability coefficient is μ; Calculate the kernel density: The kernel density is The bandwidth is g, the kernel function is L(·), and the reference value affecting the center of gravity data is Calculate the impact quality value of the impact data: The shape parameter estimate is The random number between 0 and 1 is a, and the influence degree of the wth influencing data is The standard value of the w-th influencing data influence is The impact quality value of the wth impact data is The environmental friendliness includes: The biomass of the uncontaminated area of ​​the fth express package is Q s,f , the biomass of the area after the express packaging pollution in the fth block is Q w,f The biomass of the non-express packaging contaminated area in block sf is U w,sf , the environmental friendliness of the fth block is β(f); The method of performing ecological analysis on the production data to obtain process ecological data comprises: Input the production data into the production analysis model based on the decision tree, adjust the simulated ecological environment system and production energy consumption system according to the production data, and obtain the production ecological pollution amount; The life cycle assessment method is used to calculate the degree of ecological impact based on production data and the amount of production ecological pollution: The cth production data is y c , production data y c The carbon emissions are Production data c The number of species reduced in the i-th ecological species is Production data c The important coefficient is The adjustment constant is The coefficient of variation method is used to obtain the difference ratio weight of production data, and the difference ratio weight is optimized through grey correlation analysis. The objective function of the production analysis model is constructed according to the optimized difference ratio weight: The objective function is The balance factor is η, and the production data is y c The actual ecological production pollution is Production data c The predicted ecological production pollution is Actual ecological production pollution and predicting ecological production pollution The loss function is The number of production data is m, the number of production data is y c The difference weight is Production data c The optimization weight is Production data c The grey correlation degree is The regularization term is ξ, and the offset constant is r; Compute the regularization term: The first penalty coefficient is ρ, the second penalty coefficient is δ, the number of leaves that controls the complexity of the tree is U, the complexity is g, and the L2 norm function is ||·|| 2 ; The winning data are sorted according to the objective function value, and the objective function value greater than 0.513 is output as process ecology data.

2. According to the big data-based express packaging product eco-design evaluation method of claim 1, it is characterized by: The method of fusing and analyzing the environmental ecological data and the process ecological data to obtain an ecological assessment function comprises: Input the environmental ecological data and process ecological data into the fusion analysis model based on deep neural network, and use the environmental ecological data, process ecological data and hierarchical automatic encoder to hierarchically train the fusion analysis model; Calculate data weights: The weight of the data τ is χ τ , the jth data τ j The correlation degree is Data τ j The probability of affecting the ecology is The correlation degree of data τ is H τ , the probability that data τ affects the ecology is P τ , the number of data τ is Construct an ecological evaluation function based on data weights: The ecological evaluation function of the u-th express package is The weight of the quality-influencing data of the u-th express package is χ 1,u , the cycle ratio of the u-th express package is K 2,u , the cycle ratio weight of the u-th express package is χ 2,u , the environmental friendliness of the u-th express package is K 3,u , the environmental friendliness weight of the u-th express package is χ 3,u , the process ecological data of the u-th express package is G u , the weight of the process ecological data of the u-th express package is χ 4,u , the number of data for the u-th express packaging cycle ratio is q 2,u The reference value of the cycle ratio is The attenuation coefficient of the u-th express packaging cycle ratio is γ 2,u , the penalty factor is The slack variable of the environmental friendliness of the u-th express package is The ecological evaluation function is used to create a modal matrix, the feature hashing algorithm is used to extract the ecological characteristics of environmental ecological data and process ecological data, and the shared feature matrix is ​​constructed using the ecological characteristics; Initialize the shared feature matrix, initialize the ecological features in a random manner, and obtain the deep ecological features of the input data through the forward propagation of the deep neural network; Construct a consistent coding matrix, create a data sharing space, and endow a deep ecological matrix; Correct the basis matrix and jointly fine-tune the deep matching networks of each modality during the back-propagation process until the objective function converges and outputs the ecological evaluation function.

3. According to the big data-based express packaging product eco-design evaluation method of claim 1, it is characterized in that: The method for constructing an eco-design assessment model for express packaging products according to the eco-assessment function comprises: The objective function of the eco-design evaluation model for express packaging products is constructed based on the ecological evaluation function, and the expression is: The loss function of the ecological assessment of the u-th express packaging product is The objective function of the ecological assessment of the u-th express packaging product is: The ecological evaluation function of the u-th express packaging product ecological evaluation is: The eco-design assessment models for express packaging products include random forest algorithm, time series analysis algorithm, autoencoder, recurrent neural network algorithm, and genetic algorithm; The random forest algorithm divides the input data into training data and test data in a ratio of 5:2; The time series analysis algorithm obtains the time series variation according to the time variation law of the training data; The autoencoder extracts ecological change features based on the degree of change in the temporal variation through unsupervised learning; The recurrent neural network algorithm learns the changing patterns between the ecological characteristics and ecology through the training data; The genetic algorithm test data predicts the ecological assessment according to the changing rules, and the learning rate of the express packaging product ecological design assessment model is adjusted according to the predicted ecological assessment and the actual ecological assessment.

4. A big data-based express packaging product ecological design evaluation system, used to execute the big data-based express packaging product ecological design evaluation method described in claims 1-3, characterized in that: include: Data collection module: used to collect ecological test data and production data of express packaging products, and pre-process the ecological test data and the production data; Data processing module: used to obtain environmental ecological data through the ecological test data; the environmental ecological data includes first ecological data, second ecological data and third ecological data; the first ecological data represents the ecological impact quality value in the environmental ecological data; the second ecological data represents the cycle ratio of the product in the environmental ecological data; the third ecological data represents the environmental friendliness of the product in the environmental ecological data; the cycle ratio represents the ratio of the actual product life cycle to the standard life cycle; The environmental friendliness level indicates the ratio of biomass in unpolluted areas to biomass in polluted areas; Data fusion module: used to perform ecological analysis on the production data to obtain process ecological data, and to perform fusion analysis on the environmental ecological data and the process ecological data to obtain an ecological evaluation function; Model building module: used to build an express packaging product ecological design evaluation model according to the ecological evaluation function, input the express packaging product design data into the express packaging product ecological design evaluation model, and output the evaluation result.

5. An electronic device comprising: processor; as well as A memory arranged to store computer executable instructions, which when executed cause the processor to perform the method according to any one of claims 1 to 3.

6. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of application programs, enables the electronic device to execute the method according to any one of claims 1 to 3.

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