Full-life-cycle carbon footprint monitoring analysis method and system based on supply chain

Abstract

The invention relates to the technical field of carbon emission analysis, in particular to a full-life-cycle carbon footprint monitoring analysis method and system based on a supply chain, and the method comprises the following steps: collecting the carbon emission accounting data of each link in the full life cycle of the supply chain; calculating a carbon emission result of each link according to the carbon emission accounting data of each link; the carbon emission reduction opportunity of each link is quantified by using the carbon emission result and the carbon emission accounting data of each link; and packaging a mapping relation between the carbon emission reduction opportunity and the carbon emission accounting data to obtain a monitoring analysis model of the carbon emission reduction opportunity according to the carbon emission accounting data. According to the method, the carbon emission reduction opportunity of each link is quantified by using the carbon emission result and the carbon emission accounting data of each link, and the neural network is used for packaging, so that the carbon emission reduction opportunity is automatically calculated according to the carbon emission accounting data, and the objectivity and the standard of calculation of the emission reduction opportunity are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission analysis, and in particular to a method and system for monitoring and analyzing carbon footprint throughout the entire life cycle based on a supply chain. Background Art

[0002] Carbon emission reduction is in line with both the economic benefits of enterprises and the social environmental protection values. Effective emission reduction measures are essential to achieve sustainable development of enterprises. Since supply chain management is an important part of enterprise operations, monitoring and analyzing carbon emission reduction opportunities throughout the life cycle of the supply chain is an important step in planning emission reduction measures.

[0003] Existing technologies usually only focus on the accounting of carbon emissions when conducting carbon footprint monitoring, and do not objectively and standardizedly identify the key links and emission reduction opportunities of carbon emissions. The judgment of emission reduction opportunities is mostly based on human subjective conjecture, and it is unable to accurately help companies identify emission reduction opportunities, making it difficult to optimize supply chain management and achieve sustainable development of the company. Summary of the invention

[0004] The purpose of the present invention is to provide a full life cycle carbon footprint monitoring and analysis method and system based on the supply chain, so as to solve the technical problems that the key links and emission reduction opportunities of carbon emissions are not objectively and standardized in the prior art, and the judgment of emission reduction opportunities is mostly based on human subjective conjecture.

[0005] In order to solve the above technical problems, the present invention specifically provides the following technical solutions:

[0006] A full life cycle carbon footprint monitoring and analysis method based on a supply chain includes the following steps:

[0007] Collect carbon emission accounting data for each link in the entire life cycle of the supply chain;

[0008] Calculate the carbon emission results of each link based on the carbon emission accounting data of each link;

[0009] Quantify the carbon emission reduction opportunities at each stage by using the carbon emission results and carbon emission accounting data of each stage;

[0010] The mapping relationship between carbon emission reduction opportunities and carbon emission accounting data is encapsulated to obtain a monitoring and analysis model for carbon emission reduction opportunities based on carbon emission accounting data.

[0011] As a preferred embodiment of the present invention, the entire life cycle of the supply chain includes raw material procurement, production and manufacturing, logistics and transportation, warehousing management, product use and waste disposal.

[0012] As a preferred solution of the present invention, the carbon emission factor method is used to calculate the carbon emission results.

[0013] As a preferred solution of the present invention, the method for quantifying carbon emission reduction opportunities in each link includes:

[0014] Quantify the spatial information content of carbon emission accounting data;

[0015] Quantify the temporal information content of carbon emission accounting data;

[0016] Based on the spatial and temporal information of carbon emission accounting data, carbon emission reduction opportunities are quantified in combination with carbon emission results;

[0017] Among them, the first quantitative formula for carbon emission reduction opportunities is:

[0018]

[0019] Where M k is the carbon emission reduction opportunity of the kth link, E k is the carbon emission result of the kth link, X k is the spatial information of the carbon emission accounting data of the kth link, Y k is the time information of the carbon emission accounting data of the kth link;

[0020] The second quantification of carbon emission reduction opportunities is:

[0021]

[0022] Where M k is the carbon emission reduction opportunity of the kth link, E k is the carbon emission result of the kth link, X k is the spatial information of the carbon emission accounting data of the kth link, Y k is the time information of the carbon emission accounting data of the kth link.

[0023] As a preferred solution of the present invention, the method for quantifying the spatial information amount of the carbon emission accounting data includes:

[0024] The discrete degree of carbon emission accounting data of each link is measured respectively, and after normalization, the spatial information of carbon emission accounting data is obtained;

[0025] The spatial information volume of the carbon emission accounting data is:

[0026]

[0027] Where, X k is the spatial information volume of carbon emission accounting data of the kth link, d i is the i-th data item in the carbon emission accounting data of the k-th link, dj is the jth data item in the carbon emission accounting data of the kth link, m k is the total number of data items of the carbon emission accounting data of the kth link, N is the total number of links in the entire life cycle of the supply chain, max is the maximum operator, and min is the minimum operator.

[0028] As a preferred solution of the present invention, the method for quantifying the time information amount of the carbon emission accounting data includes:

[0029] The discreteness of the carbon emission accounting data of each link and the historical carbon emission accounting data are measured respectively, and after normalization, the time information of the carbon emission accounting data is obtained;

[0030] The time information volume of the carbon emission accounting data is:

[0031]

[0032] Where Y k is the time information of the carbon emission accounting data of the kth link, d i is the i-th data item in the carbon emission accounting data of the k-th link, do i is the i-th data item in the historical carbon emission accounting data of the k-th link, m k is the total number of data items of the carbon emission accounting data of the kth link, N is the total number of links in the entire life cycle of the supply chain, max is the maximum operator, and min is the minimum operator.

[0033] As a preferred solution of the present invention, the method for constructing the monitoring and analysis model includes:

[0034] The carbon emission accounting data of each link is used as the input of the BP neural network, and the carbon emission reduction opportunities of each link are used as the output of the BP neural network;

[0035] The monitoring and analysis model is obtained by learning the mapping relationship between input items and output items through a BP neural network;

[0036] The monitoring and analysis model is:

[0037] M k =BP({d i |i∈[1,m k ]});

[0038] Where M k is the carbon emission reduction opportunity of the kth link, d i is the i-th data item in the carbon emission accounting data of the k-th link, m kis the total number of data items of carbon emission accounting data in the kth link, and BP is the BP neural network.

[0039] As a preferred solution of the present invention, the loss function and evaluation index of the monitoring and analysis model are measured using mean square error.

[0040] As a preferred solution of the present invention, the method for analyzing carbon emission reduction opportunities after the monitoring and analysis model is built includes:

[0041] The carbon emission accounting data of each link is input into the monitoring and analysis model, and the monitoring and analysis model outputs the carbon emission reduction opportunities of each link.

[0042] As a preferred solution of the present invention, the present invention provides a full life cycle carbon footprint monitoring and analysis system based on a supply chain, which is applied to the full life cycle carbon footprint monitoring and analysis method based on a supply chain, and the system includes:

[0043] Data input module, used to collect carbon emission accounting data at all stages of the supply chain life cycle;

[0044] The carbon emission accounting module is used to calculate the carbon emission results of each link based on the carbon emission accounting data of each link;

[0045] The result analysis module is used to quantify the carbon emission reduction opportunities in each link by using the carbon emission results and carbon emission accounting data of each link;

[0046] and using monitoring and analysis models to quantify carbon emission reduction opportunities at all stages based on carbon emission accounting data;

[0047] The report generation module is used to generate a detailed carbon emission report based on the carbon emission reduction opportunities output by the result analysis module to provide decision support.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention utilizes the carbon emission results of each link and the carbon emission accounting data to quantify the carbon emission reduction opportunities of each link, and uses a neural network for encapsulation to realize automatic calculation of carbon emission reduction opportunities based on the carbon emission accounting data, thereby ensuring the objectivity and standardization of the calculation of emission reduction opportunities. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0051] Figure 1 A flow chart of a method for monitoring and analyzing carbon footprint throughout the life cycle provided by an embodiment of the present invention;

[0052] Figure 2 A block diagram of a full life cycle carbon footprint monitoring and analysis system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] like Figure 1 As shown, the present invention provides a full life cycle carbon footprint monitoring and analysis method based on a supply chain, comprising the following steps:

[0055] Collect carbon emission accounting data for each link in the entire life cycle of the supply chain;

[0056] Calculate the carbon emission results of each link based on the carbon emission accounting data of each link;

[0057] Quantify the carbon emission reduction opportunities at each stage by using the carbon emission results and carbon emission accounting data of each stage;

[0058] The mapping relationship between carbon emission reduction opportunities and carbon emission accounting data is encapsulated to obtain a monitoring and analysis model for carbon emission reduction opportunities based on carbon emission accounting data.

[0059] The present invention quantifies the carbon emission reduction opportunities in each link with specific numerical values, so that the carbon emission reduction opportunities in each link can be digitally reflected, which is concise and objective. Moreover, it can provide data support for discovering the key links of carbon emission reduction based on the comparison of numerical values, without relying on the personal experience, knowledge and judgment of analysts to make decisions, and is more standardized and systematic.

[0060] In a popular sense, the greater the carbon emission result of a certain link, the more important the link is in carbon emission analysis. The greater the carbon emission, the more important the link is in controlling carbon emission reduction. Therefore, the greater the carbon emission result, the greater the opportunity for carbon emission reduction should be.

[0061] Similarly, the more data a link's carbon emission accounting data contains, the more parameters that can be adjusted in controlling carbon emission reduction. For example, the carbon emission accounting data includes fossil fuel consumption, limestone raw material consumption, net purchased electricity, and net purchased steam. Four parameters can be adjusted for carbon emission reduction. As the amount of data increases, the more emission reduction control parameters there are, the greater the operability of the control, and the greater the opportunity for carbon emission reduction. Therefore, the more data the carbon emission accounting data contains, the greater the opportunity for carbon emission reduction should be.

[0062] In addition, the greater the difference between the carbon emission accounting data of a certain link and the historical data, the greater the numerical change of the parameter quantity that can be adjusted in controlling carbon emission reduction in this link. For example, the greater the change in fossil fuel consumption, limestone raw material consumption, net purchased electricity, and net purchased steam in the carbon emission accounting data, the larger the numerical range of fossil fuel consumption, limestone raw material consumption, net purchased electricity, and net purchased steam will be. In other words, if the historical carbon emission accounting data is A and the current carbon emission accounting data is B, the numerical range of the parameter quantity that can be adjusted for controlling carbon emission reduction should be [A, B]. When the range between [A, B] is larger, the values ​​of the four parameters that can be adjusted within the larger numerical range for carbon emission reduction, the greater the operability of emission reduction regulation, and the greater the opportunity for carbon emission reduction. Therefore, when the range of changes in the data contained in the carbon emission accounting data is larger, the opportunity for carbon emission reduction should also be greater.

[0063] In summary, the present invention relates the quantification of carbon emission reduction opportunities to carbon emission results and carbon emission accounting data, which is consistent with actual scenarios and public perception.

[0064] The present invention uses the data amount contained in the carbon emission accounting data as the spatial information amount of the carbon emission accounting data, and uses the numerical adjustment range of the data amount contained in the carbon emission accounting data as the temporal information amount of the carbon emission accounting data, and characterizes the data characteristics of the carbon emission accounting data by the spatial information amount and the temporal information amount.

[0065] The present invention combines carbon emission results with spatial information and temporal information to quantify carbon emission reduction opportunities, with carbon emission results as the main parameter and spatial information and temporal information as powers, that is, carbon emission reduction opportunities are mainly associated with carbon emission results and are secondarily associated with carbon emission accounting data, thereby focusing attention on carbon emission reduction on links with high carbon emission result stock values. The higher the carbon emissions, the better the effect obtained after the carbon emission reduction operation, and ultimately the better the carbon emission reduction benefits over the entire life cycle of the supply chain as a whole. On the basis of high carbon emission result stock values, when the spatial information and temporal information of carbon emission accounting data are higher, the carbon emission reduction opportunities will be greater, and emission reduction operations on links with higher spatial information and temporal information of carbon emission accounting data will also achieve better benefits, which will be superimposed on the emission reduction benefits generated by links with high carbon emission result stock values. The combination of the two benefits makes the carbon emission reduction benefits over the entire life cycle of the supply chain optimal.

[0066] Therefore, the present invention relates the quantification of carbon emission reduction opportunities to carbon emission results and the amount of spatial information and temporal information, so that the quantified carbon emission reduction opportunities have practical application value, and emission reduction regulation decisions are made based on the quantified carbon emission reduction opportunities, which can achieve the best overall carbon emission reduction benefits throughout the life cycle of the supply chain.

[0067] Furthermore, the present invention also encapsulates the above-mentioned carbon emission reduction opportunity quantification process, realizes the automatic calculation of carbon emission reduction opportunities based on carbon emission accounting data, skips the carbon emission accounting process, simplifies the carbon emission reduction opportunity quantification process, and utilizes modeling to encapsulate the carbon emission reduction opportunity quantification process, thereby ensuring the systematic and standardized calculation of emission reduction opportunities.

[0068] The entire life cycle of the supply chain includes raw material procurement, production and manufacturing, logistics and transportation, warehousing management, product use and waste disposal.

[0069] The carbon emission factor method is used to calculate the carbon emission results.

[0070] Methods for quantifying carbon emission reduction opportunities at each stage include:

[0071] Quantify the spatial information content of carbon emission accounting data;

[0072] Quantify the temporal information content of carbon emission accounting data;

[0073] Based on the spatial and temporal information of carbon emission accounting data, carbon emission reduction opportunities are quantified in combination with carbon emission results;

[0074] Among them, the first quantitative formula for carbon emission reduction opportunities is:

[0075]

[0076] Where M k is the carbon emission reduction opportunity of the kth link, E k is the carbon emission result of the kth link, X k is the spatial information of the carbon emission accounting data of the kth link, Y k is the time information of the carbon emission accounting data of the kth link;

[0077] The second quantification of carbon emission reduction opportunities is:

[0078]

[0079] Where M k is the carbon emission reduction opportunity of the kth link, E k is the carbon emission result of the kth link, X k is the spatial information of the carbon emission accounting data of the kth link, Y k is the time information of the carbon emission accounting data of the kth link.

[0080] Methods for quantifying the spatial information content of carbon emission accounting data include:

[0081] The discrete degree of carbon emission accounting data of each link is measured respectively, and after normalization, the spatial information of carbon emission accounting data is obtained;

[0082] The amount of spatial information of carbon emission accounting data is:

[0083]

[0084] Where, X k is the spatial information volume of carbon emission accounting data of the kth link, d i is the i-th data item in the carbon emission accounting data of the k-th link, d j is the jth data item in the carbon emission accounting data of the kth link, m k is the total number of data items of the carbon emission accounting data of the kth link, N is the total number of links in the entire life cycle of the supply chain, max is the maximum operator, and min is the minimum operator.

[0085] Therefore, the more data a link's carbon emission accounting data contains, the more parameters that can be adjusted in controlling carbon emission reduction. For example, the carbon emission accounting data includes fossil fuel consumption, limestone raw material consumption, net purchased electricity, and net purchased steam. Four parameters can be adjusted for carbon emission reduction. As the amount of data increases, the more emission reduction control parameters there are, the greater the operability of the control, and the greater the opportunity for carbon emission reduction. Therefore, the more data the carbon emission accounting data contains, the greater the opportunity for carbon emission reduction should be.

[0086] Methods for quantifying the temporal information content of carbon emission accounting data include:

[0087] The discreteness of the carbon emission accounting data of each link and the historical carbon emission accounting data are measured respectively, and after normalization, the time information of the carbon emission accounting data is obtained;

[0088] The amount of time information of carbon emission accounting data is:

[0089]

[0090] Where Y k is the time information of the carbon emission accounting data of the kth link, d i is the i-th data item in the carbon emission accounting data of the k-th link, do i is the i-th data item in the historical carbon emission accounting data of the k-th link, m k is the total number of data items of the carbon emission accounting data of the kth link, N is the total number of links in the entire life cycle of the supply chain, max is the maximum operator, and min is the minimum operator.

[0091] Therefore, the greater the difference between the carbon emission accounting data of a certain link and the historical data, the greater the numerical change of the parameter quantity that can be adjusted in controlling carbon emission reduction in this link. For example, the greater the change in fossil fuel consumption, limestone raw material consumption, net purchased electricity, and net purchased steam in the carbon emission accounting data, the larger the numerical range of fossil fuel consumption, limestone raw material consumption, net purchased electricity, and net purchased steam will be. In other words, if the historical carbon emission accounting data is A and the current carbon emission accounting data is B, the numerical range of the parameter quantity that can be adjusted for controlling carbon emission reduction should be [A, B]. When the range between [A, B] is larger, the values ​​of the four parameters that can be adjusted within the larger numerical range for carbon emission reduction, the greater the operability of emission reduction regulation, and the greater the opportunity for carbon emission reduction. Therefore, when the range of changes in the data contained in the carbon emission accounting data is larger, the opportunity for carbon emission reduction should also be greater.

[0092] The present invention uses the data amount contained in the carbon emission accounting data as the spatial information amount of the carbon emission accounting data, and uses the numerical adjustment range of the data amount contained in the carbon emission accounting data as the temporal information amount of the carbon emission accounting data, and characterizes the data characteristics of the carbon emission accounting data by the spatial information amount and the temporal information amount.

[0093] The present invention combines carbon emission results with spatial information and temporal information to quantify carbon emission reduction opportunities, with carbon emission results as the main parameter and spatial information and temporal information as powers, that is, carbon emission reduction opportunities are mainly associated with carbon emission results and are secondarily associated with carbon emission accounting data, thereby focusing attention on carbon emission reduction on links with high carbon emission result stock values. The higher the carbon emissions, the better the effect obtained after the carbon emission reduction operation, and ultimately the better the carbon emission reduction benefits over the entire life cycle of the supply chain as a whole. On the basis of high carbon emission result stock values, when the spatial information and temporal information of carbon emission accounting data are higher, the carbon emission reduction opportunities will be greater, and emission reduction operations on links with higher spatial information and temporal information of carbon emission accounting data will also achieve better benefits, which will be superimposed on the emission reduction benefits generated by links with high carbon emission result stock values. The combination of the two benefits makes the carbon emission reduction benefits over the entire life cycle of the supply chain optimal.

[0094] Therefore, the present invention relates the quantification of carbon emission reduction opportunities to carbon emission results and the amount of spatial information and temporal information, so that the quantified carbon emission reduction opportunities have practical application value, and emission reduction regulation decisions are made based on the quantified carbon emission reduction opportunities, which can achieve the best overall carbon emission reduction benefits throughout the life cycle of the supply chain.

[0095] The present invention also encapsulates the quantification process of the above-mentioned carbon emission reduction opportunities, realizes the automatic calculation of carbon emission reduction opportunities based on carbon emission accounting data, skips the carbon emission accounting process, simplifies the quantification process of carbon emission reduction opportunities, and uses modeling to encapsulate the quantification process of carbon emission reduction opportunities, which also ensures the systematic and standard nature of the calculation of emission reduction opportunities, as follows:

[0096] The construction method of the monitoring and analysis model includes:

[0097] The carbon emission accounting data of each link is used as the input of the BP neural network, and the carbon emission reduction opportunities of each link are used as the output of the BP neural network;

[0098] The mapping relationship between input items and output items is learned through BP neural network to obtain the monitoring and analysis model;

[0099] The monitoring and analysis model is:

[0100] M k =BP({d i|i∈[1,m k ]});

[0101] Where M k is the carbon emission reduction opportunity of the kth link, d i is the i-th data item in the carbon emission accounting data of the k-th link, m k is the total number of data items of carbon emission accounting data in the kth link, and BP is the BP neural network.

[0102] The loss function and evaluation index of the monitoring and analysis model are measured using mean square error.

[0103] After the monitoring and analysis model is built, the carbon emission reduction opportunity analysis methods include:

[0104] The carbon emission accounting data of each link is input into the monitoring and analysis model, and the monitoring and analysis model outputs the carbon emission reduction opportunities of each link.

[0105] BP (back propagation) neural network is a concept proposed by scientists led by Rumelhart and McClelland. It is a multi-layer feedforward neural network trained according to the error back propagation algorithm and is one of the most widely used neural network models.

[0106] BP neural network has the ability to classify patterns of any complexity and excellent multi-dimensional function mapping ability. It learns certain rules through its own training and obtains the result closest to the expected output value when the input value is given, that is, it determines the mapping relationship between input and output. The BP algorithm includes two processes: forward propagation of signals and reverse propagation of errors. That is, when calculating the error output, it is done from input to output, while adjusting the weights and thresholds from output to input. During forward propagation, the input signal acts on the output node through the hidden layer, and after nonlinear transformation, the output signal is generated. If the actual output does not match the expected output, the error reverse propagation process is entered. Error back propagation is to propagate the output error through the hidden layer to the input layer layer by layer, and distribute the error to all units in each layer, and use the error signal obtained from each layer as the basis for adjusting the weights of each unit. By adjusting the connection strength between the input node and the hidden layer node, the connection strength between the hidden layer node and the output node, and the threshold, the error is reduced along the gradient direction. After repeated learning and training, the network parameters (weights and thresholds) corresponding to the minimum error are determined, and the training stops. At this point, the trained neural network can process the input information of similar samples and obtain the output information by itself after nonlinear transformation with the smallest output error.

[0107] By utilizing the function mapping property of the BP neural network, the present invention applies the BP neural network to the mapping process of carbon emission accounting data and carbon emission reduction opportunities, and trains the BP neural network to encapsulate the calculation of carbon emission reduction opportunities.

[0108] like Figure 2 As shown, the present invention provides a full life cycle carbon footprint monitoring and analysis system based on a supply chain, which is applied to a full life cycle carbon footprint monitoring and analysis method based on a supply chain. The system includes:

[0109] Data input module, used to collect carbon emission accounting data at all stages of the supply chain life cycle;

[0110] The carbon emission accounting module is used to calculate the carbon emission results of each link based on the carbon emission accounting data of each link;

[0111] The result analysis module is used to quantify the carbon emission reduction opportunities in each link by using the carbon emission results and carbon emission accounting data of each link;

[0112] and using monitoring and analysis models to quantify carbon emission reduction opportunities at all stages based on carbon emission accounting data;

[0113] The report generation module generates a detailed carbon emission report based on the carbon emission reduction opportunities output by the result analysis module to provide decision support.

[0114] The present invention utilizes the carbon emission results of each link and the carbon emission accounting data to quantify the carbon emission reduction opportunities of each link, and uses a neural network for encapsulation to realize automatic calculation of carbon emission reduction opportunities based on the carbon emission accounting data, thereby ensuring the objectivity and standardization of the calculation of emission reduction opportunities.

[0115] The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and protection scope of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present application.

Claims

1. A full life cycle carbon footprint monitoring and analysis method based on a supply chain, characterized in that: The following steps are involved: Collect carbon emission accounting data for each link in the entire life cycle of the supply chain; Calculate the carbon emission results of each link based on the carbon emission accounting data of each link; Quantify the carbon emission reduction opportunities at each stage by using the carbon emission results and carbon emission accounting data of each stage; The mapping relationship between carbon emission reduction opportunities and carbon emission accounting data is encapsulated to obtain a monitoring and analysis model for carbon emission reduction opportunities based on carbon emission accounting data.

2. The method for monitoring and analyzing the carbon footprint of a supply chain throughout its life cycle according to claim 1, characterized in that: The entire life cycle of the supply chain includes raw material procurement, production and manufacturing, logistics and transportation, warehousing management, product use and waste disposal.

3. The method for monitoring and analyzing the carbon footprint of a supply chain throughout its life cycle according to claim 1, characterized in that: The carbon emission factor method is used to calculate the carbon emission results.

4. The method for monitoring and analyzing the carbon footprint of a supply chain throughout its life cycle according to claim 1, characterized in that: The quantification methods for carbon emission reduction opportunities in each link include: Quantify the spatial information content of carbon emission accounting data; Quantify the temporal information content of carbon emission accounting data; Based on the spatial and temporal information of carbon emission accounting data, carbon emission reduction opportunities are quantified in combination with carbon emission results; Among them, the first quantitative formula for carbon emission reduction opportunities is: Where M k is the carbon emission reduction opportunity of the kth link, E k is the carbon emission result of the kth link, X k is the spatial information of the carbon emission accounting data of the kth link, Y k is the time information of the carbon emission accounting data of the kth link; The second quantification of carbon emission reduction opportunities is: Where M k is the carbon emission reduction opportunity of the kth link, E k is the carbon emission result of the kth link, X k is the spatial information of the carbon emission accounting data of the kth link, Y k is the time information of the carbon emission accounting data of the kth link.

5. The method for monitoring and analyzing the carbon footprint of a supply chain throughout its life cycle according to claim 4 is characterized in that: The method for quantifying the spatial information amount of the carbon emission accounting data includes: The discrete degree of carbon emission accounting data of each link is measured respectively, and after normalization, the spatial information of carbon emission accounting data is obtained; The spatial information volume of the carbon emission accounting data is: In the formula, X k is the spatial information volume of carbon emission accounting data of the kth link, d i is the i-th data item in the carbon emission accounting data of the k-th link, d j is the jth data item in the carbon emission accounting data of the kth link, m k is the total number of data items of the carbon emission accounting data of the kth link, N is the total number of links in the entire life cycle of the supply chain, max is the maximum operator, and min is the minimum operator.

6. The method for monitoring and analyzing the carbon footprint of a supply chain throughout its life cycle according to claim 4, characterized in that: The method for quantifying the time information amount of the carbon emission accounting data includes: The discreteness of the carbon emission accounting data of each link and the historical carbon emission accounting data are measured respectively, and after normalization, the time information of the carbon emission accounting data is obtained; The time information volume of the carbon emission accounting data is: Where Y k is the time information of the carbon emission accounting data of the kth link, d i is the i-th data item in the carbon emission accounting data of the k-th link, do i is the i-th data item in the historical carbon emission accounting data of the k-th link, m k is the total number of data items of the carbon emission accounting data of the kth link, N is the total number of links in the entire life cycle of the supply chain, max is the maximum operator, and min is the minimum operator.

7. The method for monitoring and analyzing the carbon footprint of a supply chain throughout its life cycle according to claim 1, characterized in that: The method for constructing the monitoring and analysis model includes: The carbon emission accounting data of each link is used as the input of the BP neural network, and the carbon emission reduction opportunities of each link are used as the output of the BP neural network; The monitoring and analysis model is obtained by learning the mapping relationship between input items and output items through a BP neural network; The monitoring and analysis model is: M k =BP({d i |i∈[1,m k ]}); Where M k is the carbon emission reduction opportunity of the kth link, d i is the i-th data item in the carbon emission accounting data of the k-th link, m k is the total number of data items of carbon emission accounting data in the kth link, and BP is the BP neural network.

8. The method for monitoring and analyzing the carbon footprint of a supply chain throughout its life cycle according to claim 7, characterized in that: The loss function and evaluation index of the monitoring and analysis model are both measured using mean square error.

9. The method for monitoring and analyzing the carbon footprint of a supply chain throughout its life cycle according to claim 1, characterized in that: After the monitoring and analysis model is built, the carbon emission reduction opportunity analysis methods include: The carbon emission accounting data of each link is input into the monitoring and analysis model, and the monitoring and analysis model outputs the carbon emission reduction opportunities of each link.

10. A full life cycle carbon footprint monitoring and analysis system based on the supply chain, characterized in that: A method for monitoring and analyzing the carbon footprint of a supply chain throughout its life cycle as described in any one of claims 1 to 9, the system comprising: Data input module, used to collect carbon emission accounting data at all stages of the supply chain life cycle; The carbon emission accounting module is used to calculate the carbon emission results of each link based on the carbon emission accounting data of each link; The result analysis module is used to quantify the carbon emission reduction opportunities in each link by using the carbon emission results and carbon emission accounting data of each link; and using monitoring and analysis models to quantify carbon emission reduction opportunities at all stages based on carbon emission accounting data; The report generation module is used to generate a detailed carbon emission report based on the carbon emission reduction opportunities output by the result analysis module to provide decision support.

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