Green product analysis system and method based on life cycle assessment

Abstract

The present invention discloses a green product analysis system and method based on life cycle assessment, which relates to the field of data processing technology. The system includes: constructing a periodic time series chain based on the life cycle of green products; configuring node operators to perform environmental impact analysis; obtaining environmental impact analysis results of life cycle nodes; constructing a Markov chain model framework to obtain the transfer impact probability of each node; performing environmental impact analysis of each life cycle node and the entire life cycle to obtain green product evaluation results. The system solves the technical problem that the existing green product analysis is based on static environmental impact assessment, focuses on analyzing the impact of specific stages of products, and lacks comprehensive and accurate analysis and consideration of the entire life cycle of products, and realizes comprehensive and accurate quantitative analysis of various potential environmental impacts within the product life cycle, achieving the technical effect of improving the accuracy and reliability of the product environmental performance analysis results.

Description

Technical Field

[0001] The present application relates to data processing related fields, and specifically to a green product analysis system and method based on life cycle assessment. Background Art

[0002] Under the current global environmental challenges, the concept of green products has gradually moved from theory to practice and has become an important force in promoting sustainable development. Green products not only pay attention to environmental impacts during production and use, but also pursue efficient use of resources and minimum environmental load throughout the product life cycle. With the increasingly severe global environmental problems and the public's in-depth understanding of the concept of sustainable development, enterprises and all sectors of society have begun to pay attention to the impact of products on the environment throughout their life cycle, and have realized that it is not enough to only focus on a certain stage of product production and use. It is necessary to comprehensively consider the environmental impact of products throughout their entire life cycle, from raw material acquisition, production and manufacturing, use and maintenance to waste disposal. Traditional green product analysis mainly focuses on the environmental impact of products in the design and production stages, and judges the environmental performance of products through evaluations of material selection, production processes, energy consumption and waste emissions, lacking comprehensive consideration of the entire life cycle of products.

[0003] Therefore, at the current stage, the green product analysis related technologies exist based on static environmental impact assessment, which focuses on analyzing the impact of specific stages of the product, resulting in the lack of comprehensive and accurate analysis and consideration of the entire life cycle of the product. Summary of the invention

[0004] This application provides a green product analysis system and method based on life cycle assessment, and adopts technical means such as building a periodic timing chain and Markov chain model framework, environmental impact analysis, etc., to solve the technical problem that the existing green product analysis is based on static environmental impact assessment, focuses on analyzing the impact of specific stages of the product, and lacks comprehensive and accurate analysis and consideration of the entire life cycle of the product. It realizes comprehensive and accurate quantitative analysis of various potential environmental impacts within the product life cycle, and achieves the technical effect of improving the accuracy and reliability of the product environmental performance analysis results.

[0005] The present application provides a green product analysis system based on life cycle assessment, the system comprising: a periodic timing chain construction module, the periodic timing chain construction module is based on the life cycle of the green product, takes the life cycle node as the timing chain node, and constructs the periodic timing chain according to the temporal relationship of the life cycle; a node operator configuration module, the node operator configuration module is based on the periodic timing chain, configures the node operator, the node operator is used to perform environmental impact analysis on each life cycle node; an environmental impact analysis result acquisition module, the environmental impact analysis result acquisition module is used to obtain the environmental impact analysis result of the life cycle node through the node operator; a node transfer impact probability acquisition module, the node transfer impact probability acquisition module is used to construct a Markov chain model framework according to the periodic timing chain, perform node transfer probability learning through sample cases, and obtain the transfer impact probability of each node; a green product evaluation result acquisition module, the green product evaluation result acquisition module is used to use the transfer impact probability and the environmental impact analysis result of the life cycle node to perform environmental impact analysis of each life cycle node and the entire life cycle, and obtain a green product evaluation result, the green product evaluation result includes the environmental impact of each life cycle node and the environmental impact of the entire life cycle.

[0006] In a possible implementation, the green product evaluation result acquisition module also performs the following processing: using the green product environmental target to screen the green product evaluation results to obtain abnormal nodes, wherein the abnormal nodes are life cycle nodes whose environmental impact does not meet the green product environmental target; decomposing the node influence factors based on the abnormal nodes, and fitting the influence coefficients of the influence factors; configuring the optimization variables according to the influence coefficients, and constructing the optimization module; adding the optimization module to the node operator, optimizing the abnormal nodes, and obtaining the node optimization strategy.

[0007] In a possible implementation, the green product evaluation result acquisition module also performs the following processing: when the green product environmental target is an environmental protection target for the entire life cycle, the impact probability of each life cycle node is traced based on the Markov chain model, and the weight of each node is configured according to the transfer impact probability of each life cycle node; the green product environmental target is decomposed into nodes based on the node weights to obtain the environmental node target of each life cycle node.

[0008] In a possible implementation, the node operator configuration module also performs the following processing: collecting a historical evaluation sample set of life cycle nodes; constructing a training data set based on the historical evaluation sample set, which includes various influencing factors and environmental impact assessment results; using the training data set for learning to obtain a node environment evaluation mathematical model, configuring the node operator based on the node environment evaluation mathematical model, and inserting the node operator into the corresponding node of the periodic timing chain.

[0009] In a possible implementation, the node transfer impact probability acquisition module also performs the following processing: determining the dependent node relationship according to the periodic timing chain, the dependent node relationship includes two life cycle nodes, namely a predecessor node and a successor node, and the predecessor node and the successor node are in a front-and-back adjacent timing relationship in the periodic timing chain; allocating and setting the state space of the two life cycle nodes, the state space includes multiple states, and the environmental evaluation result of each state is different; based on the sample cases of the two life cycle nodes, clustering is performed according to multiple states to construct a multi-state clustering set; using the multi-state clustering set, calculating the impact probability of the data change of the predecessor node on the transition of the successor node from one state to another, and obtaining the transfer impact probability of the predecessor node on the successor node.

[0010] In a possible implementation, the node transfer impact probability acquisition module also performs the following processing: respectively obtain the impact probabilities of the multi-state clustering sets and establish an impact probability distribution; based on the impact probability distribution, set multiple probability intervals; integrate the impact probabilities according to the multiple probability intervals to obtain the transfer impact probability of the preceding node on the succeeding node, including: setting weights of the multiple probability intervals, performing weighted calculation based on the set weights, and obtaining the transfer impact probability of the preceding node on the succeeding node; or, establishing a mapping relationship between the multi-state clustering set of the preceding node and the multiple probability intervals to generate an adaptive matching module.

[0011] In a possible implementation, the green product evaluation result acquisition module also performs the following processing: obtaining the transfer impact probability of the current life cycle node, the transfer impact probability being the transfer impact probability of the previous node on the current life cycle node; using the transfer impact probability as a compensation coefficient to perform compensatory adjustments on the environmental impact analysis results to obtain the environmental impact of the current life cycle node; using the environmental impact of all life cycle nodes, performing weighted calculations according to the environmental impact relationships of the cycle nodes to obtain the environmental impact of the entire life cycle.

[0012] The present application also provides a green product analysis method based on life cycle assessment, the method comprising: based on the life cycle of the green product, using the life cycle nodes as timing chain nodes and the chronological relationship of the life cycle to construct a periodic timing chain; based on the periodic timing chain, configuring node operators, the node operators are used to perform environmental impact analysis on each life cycle node; through the node operators, the environmental impact analysis results of the life cycle nodes are obtained; according to the periodic timing chain, a Markov chain model framework is constructed, and node transfer probability learning is performed through sample cases to obtain the transfer impact probability of each node; using the transfer impact probability and the environmental impact analysis results of the life cycle nodes to perform environmental impact analysis of each life cycle node and the entire life cycle to obtain a green product evaluation result, the green product evaluation result includes the environmental impact of each life cycle node and the environmental impact of the entire life cycle.

[0013] The green product analysis system and method based on life cycle assessment proposed in this application is intended to build a cycle time chain based on the life cycle of green products; configure node operators to conduct environmental impact analysis; obtain environmental impact analysis results of life cycle nodes; build a Markov chain model framework to obtain the transfer impact probability of each node; conduct environmental impact analysis of each life cycle node and the entire life cycle to obtain green product evaluation results. It solves the technical problem that the existing green product analysis is based on static environmental impact assessment, focuses on analyzing the impact of specific stages of the product, and lacks comprehensive and accurate analysis and consideration of the entire life cycle of the product, and realizes comprehensive and accurate quantitative analysis of various potential environmental impacts within the product life cycle, achieving the technical effect of improving the accuracy and reliability of the product environmental performance analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0015] Figure 1 A schematic diagram of the structure of a green product analysis system based on life cycle assessment provided in an embodiment of the present application;

[0016] Figure 2 A schematic flow chart of a green product analysis method based on life cycle assessment provided in an embodiment of the present application.

[0017] Explanation of the reference numerals: periodic timing chain construction module 10, node operator configuration module 20, environmental impact analysis result acquisition module 30, node transfer impact probability acquisition module 40, green product evaluation result acquisition module 50. DETAILED DESCRIPTION

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0019] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0020] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0021] The present application embodiment provides a green product analysis system based on life cycle assessment, such as Figure 1 As shown, the system comprises:

[0022] The cycle timing chain construction module 10, the cycle timing chain construction module 10 is based on the life cycle of green products, takes the life cycle nodes as the timing chain nodes, and constructs the cycle timing chain according to the temporal relationship of the life cycle. The cycle timing chain is constructed based on the life cycle of green products, mainly connecting the green products from raw material collection, processing, processing, manufacturing, transportation, distribution, use, maintenance, recycling, final disposal after disposal and other links in series according to their temporal relationship in the natural life cycle of the product. The cycle timing chain takes the life cycle nodes as the timing chain nodes, and the temporal relationship of the life cycle. Specifically, the cycle timing chain may include the following important links: raw material collection and processing (the starting point of the green product life cycle, involving the selection and collection of raw materials with less impact on the environment, and the necessary pretreatment of them), processing and manufacturing (raw materials Being processed into final products, ensuring the use of environmentally friendly manufacturing technology and materials, reducing energy consumption and waste generation), transportation and distribution (after the product is completed, it reaches the hands of consumers through the transportation and distribution network, optimizing the logistics process, reducing energy consumption and carbon emissions), use (when consumers use the product, ensure that the product is used in an environmentally friendly way, and is easy to repair and reuse), repair and recycling (when the product fails, repair and maintain it, and at the end of its life, recycle it to reduce the impact of waste on the environment), post-waste treatment (for waste that cannot be recycled, safe and environmentally friendly treatment is carried out to reduce pollution to the environment). Building such a cycle timing chain helps to fully understand the impact of green products on the environment throughout their life cycle, so as to formulate more effective environmental protection strategies, and at the same time helps companies to comprehensively consider the performance of green products from a full life cycle perspective.

[0023] The node operator configuration module 20 configures the node operator based on the periodic timing chain, and the node operator is used to perform environmental impact analysis on each life cycle node. The node operator is configured based on the periodic timing chain of the green product's life cycle in order to perform environmental impact analysis on each life cycle node, wherein the node operator is actually used to quantitatively evaluate the potential or actual impact of each life cycle node on the environment. Specifically, configuring the node operator means collecting environmental data related to each life cycle node, such as resource consumption, waste generation, pollutant emissions, etc. According to the evaluation objectives, appropriate node operators are selected, and the selected node operators are configured to the corresponding life cycle nodes to ensure that they can accurately reflect the environmental impact of the node. The configured node operators are used to perform environmental impact analysis on each life cycle node to obtain the results. The environmental impact score of each node. The node operators may include but are not limited to resource consumption operators, waste generation operators, pollutant emission operators and ecological impact operators. The resource consumption operator is used to evaluate the environmental impact of each node in terms of resource (such as water, energy, raw materials, etc.) consumption. The waste generation operator is used to quantify the quantity and type of waste (including solid, liquid and gaseous waste) generated by each node. The pollutant emission operator is used to evaluate the environmental impact of each node in terms of pollutant emissions (such as greenhouse gases, hazardous substances, etc.). The ecological impact operator is used to evaluate the impact of each node on the structure and function of the ecosystem, including biodiversity loss, ecosystem degradation, etc.

[0024] The module 30 for obtaining the environmental impact analysis results is used to obtain the environmental impact analysis results of the life cycle nodes through the node operators. By using the configured node operators, the potential or actual impact of each life cycle node (such as raw material collection, production, transportation, use, waste, etc.) on the environment is quantitatively evaluated, so as to obtain the specific data and results of the environmental impact of the life cycle nodes, that is, the environmental impact analysis results. Specifically, these environmental impact analysis results are usually presented in the form of data, charts or reports to help enterprises or decision makers understand the specific impact of green products on the environment at various life cycle stages. Through the analysis results, enterprises can identify key nodes with greater environmental impacts, so as to formulate targeted improvement measures to reduce the impact of products on the environment and improve the environmental friendliness of green products.

[0025] The node transfer influence probability acquisition module 40 is used to construct a Markov chain model framework according to the periodic timing chain, perform node transfer probability learning through sample cases, and obtain the transfer influence probability of each node. A Markov chain model framework is constructed based on the periodic time series chain of the green product life cycle. Each stage or node of the product life cycle is regarded as a state in the Markov chain, and sample cases are used to learn the transition probabilities between these states. Specifically, constructing the Markov chain model framework refers to determining the main stages or nodes of the green product life cycle, such as raw material collection, production, transportation, use, and disposal, and considering these stages or nodes as states in the Markov chain. Then, the transition probability matrix of the Markov chain is defined, which represents the probability of transitioning from one state (life cycle stage) to another. Node transition probability learning refers to estimating unknown probability parameters based on observed data using sample cases (actual product life cycle data) and using maximum likelihood estimation or Bayesian estimation to learn the transition probabilities between states. For example, the actual frequency of each state to other states is counted and converted into probability. After learning the transition probability through sample cases, the transfer impact probability of each node is obtained. These transition probabilities represent how a specific life cycle stage (state) affects the possibility of a product entering the next stage (state). For example, if a product is well maintained and maintained during the use stage, the possibility of it entering the disposal stage may be reduced, and the possibility of entering the recycling stage may increase. This impact can be quantitatively evaluated through the transition probability.

[0026] The green product evaluation result acquisition module 50 is used to use the transfer impact probability and the environmental impact analysis results of the life cycle nodes to perform environmental impact analysis of each life cycle node and the entire life cycle to obtain green product evaluation results. The green product evaluation results include the environmental impact of each life cycle node and the environmental impact of the entire life cycle. The environmental impact analysis of green products using the transfer impact probability and the environmental impact analysis results of the life cycle nodes is intended to comprehensively evaluate the impact of the product on the environment at each life cycle node and throughout the life cycle, and finally obtain the green product evaluation results. Specifically, based on the environmental impact analysis results of the life cycle nodes, the specific impact of each node (such as raw material collection, production, transportation, use, waste, etc.) on the environment is quantitatively evaluated, including resource consumption, waste generation, pollutant emissions, and ecological impacts. Combined with the transfer impact probability, the environmental impact of each node is further analyzed when the state is transferred to the next node. How does it change accordingly? This helps to identify which nodes have the greatest impact on the environment and how these impacts are accumulated and propagated throughout the life cycle; after analyzing the environmental impact of each node After the impact is determined, these results are integrated to evaluate the environmental impact of the product throughout its entire life cycle. For example, the environmental impact of each node is weighted and accumulated to obtain a comprehensive environmental impact indicator. At the same time, the mutual influence and correlation between different nodes are considered. For example, the environmental impact of the production stage may affect the environmental impact of the transportation and use stages, and the environmental impact of the disposal stage may be affected by the previous stages. The environmental impact of each life cycle node and the environmental impact of the entire life cycle together constitute the green product evaluation result, including a detailed evaluation of the environmental impact of each node and a comprehensive evaluation of the environmental impact throughout the product life cycle. This can help companies understand which links of the product have major environmental problems, provide important information about the environmental performance of the product, and thus formulate corresponding improvement strategies.

[0027] The green product analysis system based on life cycle assessment according to the embodiment of the present invention is used to solve the technical problem that the existing green product analysis is based on static environmental impact assessment, focuses on analyzing the impact of a specific stage of the product, and lacks comprehensive and accurate analysis and consideration of the entire life cycle of the product, and achieves a comprehensive and accurate quantitative analysis of various potential environmental impacts within the product life cycle, achieving the technical effect of improving the accuracy and reliability of the product environmental performance analysis results. The green product analysis system based on life cycle assessment includes: a periodic timing chain construction module 10, a node operator configuration module 20, an environmental impact analysis result acquisition module 30, a node transfer impact probability acquisition module 40, and a green product evaluation result acquisition module 50.

[0028] The specific configuration of the green product evaluation result acquisition module 50 will be described in detail below. The green product evaluation result acquisition module 50 may further include: using the green product environmental target to screen the green product evaluation results to obtain abnormal nodes, and the abnormal nodes are life cycle nodes whose environmental impact does not meet the green product environmental target. Using the green product environmental target to screen the green product evaluation results, identify abnormal nodes of life cycle nodes whose environmental impact does not meet the green product environmental target. Specifically, abnormal nodes refer to specific stages or links in the entire life cycle of green products whose environmental impact exceeds the preset environmental target or standard, which may include raw material collection, production, transportation, use, disposal and other links. For example, if it is found that the environmental impact of a node exceeds the preset environmental target, then this node is regarded as an abnormal node. It also includes node influence factor decomposition based on the abnormal node and fitting the influence coefficient of each influence factor. After identifying the abnormal nodes in the green product life cycle, further analyze the specific factors that cause the environmental impact of these nodes to exceed the standard, and quantify the contribution of these factors to the environmental impact. Specifically, the influencing factors refer to the factors that affect the environmental impact of abnormal nodes, which may include the selection of raw materials, production process, energy efficiency, waste treatment methods, transportation methods, etc. Next, we will deeply understand the detailed information such as the production process, raw material source, and waste treatment process of the product, decompose each influencing factor, and analyze how they affect the environmental impact of abnormal nodes. Use regression analysis, principal component analysis, neural network, etc. to fit the influence coefficient of each influencing factor, which reflects the contribution of each factor to the environmental impact. It also includes configuring optimization variables and building an optimization module based on the influence coefficient. Analyze the influence coefficients, determine the key factors that have a significant impact on the environment, and configure these factors as optimization variables, that is, parameters that need to be continuously adjusted during the optimization process to minimize or maximize a certain objective function (such as environmental impact). When constructing the optimization module, first clarify the goal of the optimization problem, that is, the environmental impact that needs to be minimized, determine the constraints in the optimization problem, such as product performance, cost, production process, etc., and finally select optimization algorithms such as particle swarm optimization or gradient descent to search for the optimal solution. The goal of the optimization module is to find a set of optimization variable values ​​that minimize the environmental impact of the product. It also includes adding the optimization module to the node operator, optimizing the abnormal node, and obtaining the node optimization strategy.The constructed optimization module is integrated into the node operator that processes the life cycle nodes. When the node operator is used to evaluate or process a life cycle node, the optimization module can be automatically called to find the optimization solution for the node. The node operator will traverse the entire life cycle chain according to the predetermined rules or processes, identify those abnormal nodes whose environmental impact does not meet the environmental goals of green products, and trigger the optimization module to optimize these nodes. The optimization module will search for the optimization solution that minimizes the environmental impact under the constraints based on the previously configured optimization variables and the set objective function. After calculation and search by the optimization module, the node operator will output the optimization strategy for each abnormal node, that is, the node optimization strategy, for example, replacing raw materials, improving production processes, improving energy utilization efficiency, optimizing waste treatment methods, etc.

[0029] The specific configuration of the green product evaluation result acquisition module 50 will be described in detail below. The green product evaluation result acquisition module 50 may further include: when the green product environmental target is the environmental target of the whole life cycle, the impact probability of each life cycle node is traced based on the Markov chain model, and the weight of each node is configured according to the transfer impact probability of each life cycle node. When the environmental target of the green product is set as the environmental target of the whole life cycle, the Markov chain model is used to analyze the transfer probability and transfer impact probability between each node in the whole life cycle of the product, and trace and identify which nodes have an important impact on the environmental target of the whole life cycle, which is usually the main contributor to the environmental impact, or the key node in the environmental impact transmission path. Based on the result of the impact probability tracing, the corresponding weight is configured for each life cycle node. The configuration of the weight can be comprehensively evaluated according to the size of the transfer impact probability, the position of the node in the life cycle, the type and degree of the environmental impact, and other factors. For example, the node that contributes a lot to the environmental impact will be given a higher weight. It also includes node decomposition of the green product environmental target based on the weight of each node to obtain the environmental node target of each life cycle node. Through node decomposition, the environmental goals of the entire green product can be broken down into each specific life cycle node, thereby ensuring that each node can contribute to achieving the overall environmental goals. Specifically, the overall environmental goals of green products are clarified, such as reducing carbon emissions, reducing energy consumption, and improving resource utilization efficiency. According to the calculated weights of each node, the importance and contribution of each node in the process of achieving environmental goals are analyzed. According to the overall environmental goals and the weights of each node, specific environmental node goals are set for each life cycle node.

[0030] The specific configuration of the node operator configuration module 20 will be described in detail below. The node operator configuration module 20 may further include: collecting historical evaluation sample sets of life cycle nodes. Collecting and organizing historical evaluation data or samples related to each node in the green product life cycle, including actual data or evaluation results on the environmental impact, resource consumption, energy consumption, waste generation, etc. of the product at different life cycle stages (such as design, production, use, disposal, etc.). It also includes, according to the historical evaluation sample set, constructing a training data set, which includes various influencing factors and environmental impact assessment results. Based on the historical evaluation sample set, the relevant data of each life cycle node is sorted and converted into a training data set. When the training data set is used to train a machine learning model, the model can learn and identify the relationship between the influencing factors and the environmental impact assessment results, thereby predicting or optimizing the environmental impact of future products, wherein the influencing factors are various factors or variables that may affect the environmental impact of the product, and the environmental impact assessment results are the target variables of the data set, indicating the environmental impact assessment results of the corresponding samples. It also includes, using the training data set for learning, obtaining a node environmental evaluation mathematical model, configuring the node operator based on the node environmental evaluation mathematical model, and inserting the node operator into the corresponding node of the periodic timing chain. The constructed training data set is trained using neural networks, support vector machines, etc., to obtain a model that can accurately evaluate and predict the environmental impact of product life cycle nodes, namely, a node environmental evaluation mathematical model. The node operator is configured according to the node environmental evaluation mathematical model. For example, the node environmental evaluation mathematical model is embedded in the node operator, and then the configured node operator is inserted into the corresponding node of the periodic timing chain. This means that at each node, when the environmental impact needs to be evaluated, the node operator will be called and the node environmental evaluation mathematical model will be used for evaluation.

[0031] The specific configuration of the node transfer impact probability acquisition module 40 will be described in detail below. The node transfer impact probability acquisition module 40 may further include: according to the periodic timing chain, determining the dependent node relationship, the dependent node relationship includes two life cycle nodes, namely the front node and the rear node, and the front node and the rear node are in a front-to-back adjacent timing relationship in the periodic timing chain. In the whole life cycle of the product, according to the different stages or nodes it has experienced in sequence on the timeline, the dependency relationship between the nodes is determined, including two life cycle nodes, the front node and the rear node. Specifically, the front node refers to the node before a certain node in the periodic timing chain, which usually has a direct or indirect impact on the subsequent node. The rear node is opposite to the front node, and refers to the node after a certain node in the periodic timing chain. The rear node may rely on the output or result of the front node as input or reference. In a complete periodic timing chain, the nodes are usually arranged in chronological order, and each node may have its specific front node and rear node. It also includes allocating and setting the state space of the two life cycle nodes, the state space includes multiple states, and the environmental evaluation results of each state are different. A series of possible states are defined for these two nodes (pre-node and post-node), each state represents a different environmental impact assessment result. The state space is a set that includes all possible states. Each state is an environmental state that a node may reach under specific conditions. For example, in the production process of a product, the pre-node may be the raw material procurement stage, and its post-node may be the product manufacturing stage. For these two nodes, the following state space can be defined: for the pre-node, state 1 (using environmentally friendly materials, good environmental impact assessment results), state 2 (using traditional materials, general environmental impact assessment results), state 3 (using highly polluting materials, poor environmental impact assessment results), and for the post-node, state A (using efficient and energy-saving production processes, excellent environmental impact assessment results), state B (using general production processes, qualified environmental impact assessment results), and state C (using high-energy consumption, high-pollution processes, unqualified environmental impact assessment results). It also includes clustering according to multiple states based on sample cases of the two life cycle nodes to construct a multi-state clustering set. Using clustering algorithms such as K-means, hierarchical clustering, and DBSCAN, based on the status information of two life cycle nodes (predecessor node and post-node) in historical data or sample cases, they are divided into different cluster sets to obtain multi-state cluster sets, each of which represents a specific state combination, that is, the combination of the predecessor node and the post-node in a certain state. Using the multi-state cluster set, the probability of the influence of the data change of the predecessor node on the transition of the post-node from one state to another is calculated to obtain the transfer influence probability of the predecessor node on the post-node.The states of the predecessor node and the successor node are regarded as random variables, and each state is a possible value of the random variable. The transfer influence probability describes the possibility of the successor node transferring from one state to another when the predecessor node is in a certain state. Specifically, according to the multi-state clustering set, the frequency of the successor node state transfer under different predecessor node states is counted. For each state of the predecessor node, the number of samples of its successor node transferring to each state is counted, and then the number of transfers to each state is divided by the total number of samples of the predecessor node state to obtain the conditional probability, that is, the transfer influence probability of the predecessor node on the successor node.

[0032] The specific configuration of the node transfer influence probability acquisition module 40 will be described in detail below. The node transfer influence probability acquisition module 40 can further include: respectively obtaining the influence probability of the multi-state cluster set and establishing the influence probability distribution. Obtain the influence probability of each state cluster set, organize these probability values ​​into a probability distribution, for example, organize into a two-dimensional matrix, the row represents the state of the front node, the column represents the state of the rear node, and the elements in the matrix are the corresponding transition probabilities, systematically representing and analyzing the influence of the front node state on the rear node state transition. It also includes, based on the influence probability distribution, setting multiple probability intervals. Divide the range of the probability distribution into several continuous or discontinuous intervals, each interval corresponding to a specific probability range. It also includes, according to the multiple probability intervals, integrating the influence probability, and obtaining the transfer influence probability of the front node on the rear node. On the basis of the multi-state cluster set and the corresponding influence probability distribution, by comprehensively considering the information of multiple probability intervals, a comprehensive probability value reflecting the influence of the front node state on the rear node state transition is finally obtained, that is, the transfer influence probability of the front node on the rear node.

[0033] The specific configuration of the node transfer influence probability acquisition module 40 will be described in detail below. The node transfer influence probability acquisition module 40 may further include: setting the weights of the multiple probability intervals, performing weighted calculation based on the set weights, obtaining the transfer influence probability of the preceding node on the following node, or establishing a mapping relationship between the multi-state clustering set of the preceding node and the multiple probability intervals, and generating an adaptive matching module. The different states of the preceding node are associated with the corresponding probability intervals, and a module that can automatically match the preceding node state to the corresponding probability interval is developed. Specifically, it is determined which probability interval or intervals each preceding node state is associated with, which may be a one-to-one, one-to-many or many-to-one relationship. An adaptive matching module is designed and developed, which can receive the state of the preceding node as input, and automatically find the probability interval that matches the input state according to the stored mapping relationship. If there are multiple matching probability intervals, the module can select or merge according to certain rules (such as priority, probability value size, etc.), and the output of the module will be the probability interval or probability value corresponding to the input preceding node state.

[0034] The specific configuration of the green product evaluation result acquisition module 50 will be described in detail below. The green product evaluation result acquisition module 50 can further include: obtaining the transfer impact probability of the current life cycle node, the transfer impact probability is the transfer impact probability of the previous node on the current life cycle node. It also includes using the transfer impact probability as a compensation coefficient to compensate and adjust the environmental impact analysis result to obtain the environmental impact of the current life cycle node. Environmental impact analysis is an analysis of the data of the product life cycle. The previous node of the life cycle has a certain impact on the subsequent node. Its impact coefficient is calculated and added to the evaluation result. Specifically, the compensation coefficient is a parameter used to adjust or correct a value or result. Using the transfer impact probability as a compensation coefficient means that this probability value will be used to correct or adjust the result of the environmental impact analysis to more accurately reflect the environmental impact of the current life cycle node. According to the size and direction of the transfer impact probability value, the analysis result is increased or decreased. If the transfer impact probability is high, it means that the state change of the previous node has a greater impact on the subsequent node, and the result of the environmental impact analysis is increased; on the contrary, if the transfer impact probability is low, the result of the environmental impact analysis is reduced. It also includes using the environmental impact of all life cycle nodes and performing weighted calculations according to the environmental impact relationships of the cycle nodes to obtain the environmental impact of the entire life cycle. It is necessary to obtain the environmental impact of each life cycle node of the product or project, clarify the environmental impact relationships between these nodes, such as the interactions and dependencies between different nodes, and their contribution to the overall environmental impact, determine the weight of each node based on factors such as the severity, duration, and scope of impact of the environmental impact, perform weighted calculations, and ultimately obtain the environmental impact of the entire life cycle.

[0035] In the above, refer to Figure 1 The green product analysis system based on life cycle assessment according to an embodiment of the present invention is described in detail. Figure 2 A green product analysis method based on life cycle assessment according to an embodiment of the present invention is described.

[0036] Green product analysis methods based on life cycle assessment, such as Figure 2As shown, the method includes: based on the life cycle of the green product, using the life cycle node as the timing chain node and the time sequence relationship of the life cycle to construct a periodic timing chain; based on the periodic timing chain, configuring the node operator, the node operator is used to perform environmental impact analysis on each life cycle node; through the node operator, obtaining the environmental impact analysis result of the life cycle node; according to the periodic timing chain, constructing a Markov chain model framework, performing node transfer probability learning through sample cases, and obtaining the transfer impact probability of each node; using the transfer impact probability and the environmental impact analysis result of the life cycle node to perform environmental impact analysis of each life cycle node and the entire life cycle, and obtain a green product evaluation result, the green product evaluation result includes the environmental impact of each life cycle node and the environmental impact of the entire life cycle.

[0037] In a possible implementation, the green product analysis method based on life cycle assessment also includes: using green product environmental goals to screen the green product assessment results to obtain abnormal nodes, wherein the abnormal nodes are life cycle nodes whose environmental impacts do not meet the green product environmental goals; decomposing node influence factors based on the abnormal nodes, and fitting the influence coefficients of each influence factor; configuring optimization variables according to the influence coefficients, and constructing an optimization module; adding the optimization module to the node operator, optimizing the abnormal nodes, and obtaining a node optimization strategy.

[0038] In a possible implementation, the green product analysis method based on life cycle assessment also includes: when the green product environmental target is an environmental protection target for the entire life cycle, tracing the impact probability of each life cycle node based on the Markov chain model, and configuring the weight of each node according to the transfer impact probability of each life cycle node; performing node decomposition of the green product environmental target based on the node weights to obtain the environmental node target of each life cycle node.

[0039] In a possible implementation, the green product analysis method based on life cycle assessment also includes: collecting historical assessment sample sets of life cycle nodes; constructing a training data set based on the historical assessment sample set, which includes various influencing factors and environmental impact assessment results; using the training data set for learning to obtain a node environmental evaluation mathematical model, configuring the node operator based on the node environmental evaluation mathematical model, and inserting the node operator into the corresponding node of the periodic timing chain.

[0040] In a possible implementation, the green product analysis method based on life cycle assessment also includes: determining a dependent node relationship according to the periodic timing chain, the dependent node relationship includes two life cycle nodes, namely a preceding node and a succeeding node, and the preceding node and the succeeding node are in a front-and-back adjacent timing relationship in the periodic timing chain; allocating and setting a state space of the two life cycle nodes, the state space includes multiple states, and the environmental evaluation result of each state is different; based on sample cases of the two life cycle nodes, clustering is performed according to multiple states to construct a multi-state clustering set; using the multi-state clustering set, calculating the impact probability of the data change of the preceding node on the transition of the succeeding node from one state to another, and obtaining the transfer impact probability of the preceding node on the succeeding node.

[0041] In a possible implementation, the green product analysis method based on life cycle assessment also includes: obtaining the influence probability of the multi-state clustering set respectively and establishing an influence probability distribution; based on the influence probability distribution, setting multiple probability intervals; integrating the influence probabilities according to the multiple probability intervals to obtain the transfer influence probability of the preceding node on the succeeding node, including: setting weights of the multiple probability intervals, performing weighted calculation based on the set weights, and obtaining the transfer influence probability of the preceding node on the succeeding node; or, establishing a mapping relationship between the multi-state clustering set of the preceding node and the multiple probability intervals to generate an adaptive matching module.

[0042] In a possible implementation, the green product analysis method based on life cycle assessment also includes: obtaining the transfer impact probability of the current life cycle node, the transfer impact probability is the transfer impact probability of the previous node on the current life cycle node; using the transfer impact probability as a compensation coefficient, compensating and adjusting the environmental impact analysis result to obtain the environmental impact of the current life cycle node; using the environmental impact of all life cycle nodes, performing weighted calculations according to the environmental impact relationship of the cycle nodes, and obtaining the environmental impact of the entire life cycle.

[0043] The green product analysis system based on life cycle assessment provided in the embodiment of the present invention can execute the green product analysis method based on life cycle assessment provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0044] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0045] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A green product analysis system based on life cycle assessment, characterized by: The system comprises: A cycle timing chain construction module, wherein the cycle timing chain construction module is based on the life cycle of the green product, takes the life cycle nodes as the timing chain nodes, and constructs the cycle timing chain based on the temporal sequence of the life cycle; A node operator configuration module, wherein the node operator configuration module configures a node operator based on the periodic timing chain, and the node operator is used to perform environmental impact analysis on each life cycle node; An environmental impact analysis result acquisition module, the environmental impact analysis result acquisition module is used to obtain the environmental impact analysis result of the life cycle node through the node operator; A node transfer influence probability acquisition module, wherein the node transfer influence probability acquisition module is used to construct a Markov chain model framework according to the periodic timing chain, perform node transfer probability learning through sample cases, and obtain the transfer influence probability of each node; A green product evaluation result acquisition module, the green product evaluation result acquisition module is used to use the transfer impact probability and the environmental impact analysis results of the life cycle nodes to perform environmental impact analysis of each life cycle node and the entire life cycle to obtain a green product evaluation result, the green product evaluation result includes the environmental impact of each life cycle node and the environmental impact of the entire life cycle; The node transfer impact probability acquisition module performs the following steps: According to the periodic timing chain, a dependent node relationship is determined, wherein the dependent node relationship includes two lifecycle nodes, namely a preceding node and a succeeding node, and the preceding node and the succeeding node are in a front-and-back adjacent timing relationship in the periodic timing chain; Allocating and setting a state space of the two life cycle nodes, the state space including a plurality of states, each state having a different environmental evaluation result; Based on the sample cases of the two life cycle nodes, clustering is performed according to multiple states to construct a multi-state clustering set; Using the multi-state clustering set, calculating the influence probability of the data change of the preceding node on the transition of the succeeding node from one state to another state, and obtaining the transition influence probability of the preceding node on the succeeding node; The step of obtaining the transfer influence probability of the preceding node on the succeeding node includes: Obtain the influence probabilities of multi-state clustering sets respectively and establish influence probability distribution; Based on the impact probability distribution, setting a plurality of probability intervals; Integrating the influence probabilities according to the multiple probability intervals to obtain the transfer influence probability of the preceding node on the succeeding node includes: Setting weights of the multiple probability intervals, performing weighted calculation based on the set weights, and obtaining the transfer influence probability of the preceding node on the succeeding node; or, Establishing a mapping relationship between the multi-state clustering set of the front node and the multiple probability intervals to generate an adaptive matching module; The green product evaluation result obtaining module performs the following steps: Obtaining the transfer impact probability of the current life cycle node, where the transfer impact probability is the transfer impact probability of the previous node on the current life cycle node; Using the transfer impact probability as a compensation coefficient, performing compensation adjustment on the environmental impact analysis result to obtain the environmental impact of the current life cycle node; The environmental impact of all life cycle nodes is utilized, and weighted calculation is performed according to the environmental impact relationship of the cycle nodes to obtain the environmental impact of the entire life cycle.

2. The green product analysis system based on life cycle assessment according to claim 1, characterized in that: The execution steps of the system also include: The green product evaluation results are screened using the green product environmental target to obtain abnormal nodes, where the abnormal nodes are life cycle nodes whose environmental impact does not meet the green product environmental target; Decomposing node impact factors based on the abnormal nodes and fitting the impact coefficients of the impact factors; According to the influence coefficient, the optimization variables are configured and the optimization module is constructed; The optimization module is added to the node operator to optimize the abnormal node and obtain the node optimization strategy.

3. The green product analysis system based on life cycle assessment according to claim 2, characterized in that: The execution steps of the system also include: When the green product environmental target is the environmental target of the entire life cycle, the impact probability of each life cycle node is traced based on the Markov chain model, and the weight of each node is configured according to the transfer impact probability of each life cycle node; The green product environmental target is node-decomposed based on the node weights to obtain the environmental node target of each life cycle node.

4. The green product analysis system based on life cycle assessment according to claim 1, characterized in that: The node operator configuration module performs the following steps: Collect historical evaluation sample sets at lifecycle nodes; Based on the historical assessment sample set, a training data set is constructed, which includes various influencing factors and environmental impact assessment results; The training data set is used for learning to obtain a node environment evaluation mathematical model, the node operator is configured based on the node environment evaluation mathematical model, and the node operator is inserted into the corresponding node of the periodic timing chain.

5. The green product analysis method based on life cycle assessment is characterized by: The method is applied to the green product analysis system based on life cycle assessment according to claim 1, and the method comprises: Based on the life cycle of green products, the life cycle nodes are used as the timing chain nodes, and the time sequence relationship of the life cycle is used to build the cycle timing chain; Based on the periodic timing chain, a node operator is configured, and the node operator is used to perform environmental impact analysis on each life cycle node; Obtaining the environmental impact analysis result of the life cycle node through the node operator; According to the periodic time series chain, a Markov chain model framework is constructed, and node transfer probability learning is performed through sample cases to obtain the transfer influence probability of each node; The environmental impact analysis results of each life cycle node and the entire life cycle are used to perform environmental impact analysis of each life cycle node and the entire life cycle to obtain a green product evaluation result, which includes the environmental impact of each life cycle node and the environmental impact of the entire life cycle.

Images