Assessment methods and related equipment for carbon emissions throughout the life cycle of the biopharmaceutical enterprise chain

Abstract

The present invention provides a method for evaluating carbon emissions of a biopharmaceutical enterprise chain throughout its life cycle and related equipment, which are applied to the field of data processing technology. The present application obtains information on biopharmaceutical enterprises to be evaluated; processes the information on biopharmaceutical enterprises to be evaluated, and generates information on the carbon chain of products to be evaluated; processes the target enterprise based on the full life cycle of the target drug, and generates a set of evaluation factors; processes the set of evaluation factors based on a preset multi-factor judgment matrix, and generates weight information of the evaluation factors and evaluation grades of the evaluation factors; generates a fuzzy matrix based on the weight information of the evaluation factors and the evaluation grades of the evaluation factors; processes several evaluation factors based on the fuzzy matrix, and generates discriminant factors; constructs a carbon footprint accounting model for the supply chain of biopharmaceutical enterprises based on the discriminant factors; processes the information on biopharmaceutical enterprises to be evaluated, and generates a carbon footprint of the biopharmaceutical enterprise chain and a green quantitative evaluation system for the entire enterprise chain.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method for evaluating carbon emissions over the entire life cycle of a biopharmaceutical enterprise chain and related equipment. Background Art

[0002] my country is making every effort to promote low-carbon development in all corporate chains. Currently, most companies are unable to quickly and accurately assess their own carbon emissions. There is an urgent need to develop a standardized, intelligent and efficient carbon footprint basic database and assessment method so that companies can quickly and accurately assess their own carbon emissions.

[0003] In terms of the carbon emission assessment method of the enterprise chain throughout its life cycle, although the full life cycle assessment method has been widely developed at home and abroad, the input-output method for my country's biopharmaceutical industry is still focused on the carbon footprint measurement of the pharmaceutical manufacturing industry at the macro level; at the micro level, most of the full life cycle carbon footprint measurement methods only focus on the carbon emission measurement of the drug production stage. Unlike other manufacturing industries, the pollution emissions from the collection of raw materials of biopharmaceutical companies are relatively large, and 70% of the carbon emissions of the entire enterprise chain come from the supply chain stage. Therefore, the carbon emission measurement of the upstream of biological drug production cannot be ignored. The existing traditional full life cycle assessment method is difficult to accurately calculate the emissions of each link in the biopharmaceutical enterprise chain, especially the emissions in the upstream supply chain stage. Therefore, improving and enhancing the refinement of the full life cycle assessment method of biopharmaceuticals is of great significance to comprehensively promote the low-carbon optimization of my country's biopharmaceutical companies.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The purpose of this application is to provide a method for evaluating carbon emissions throughout the life cycle of a biopharmaceutical enterprise chain and related equipment and systems, which at least to a certain extent overcomes the problems existing in the prior art. By constructing a carbon footprint accounting model for the supply chain of biopharmaceutical enterprises based on discriminant factors, and integrating economic, social and environmental multi-dimensional indicators, an overall evaluation of the green efficiency of the biopharmaceutical enterprise chain is conducted, breaking through the limitations of single enterprise efficiency measurement and promoting the green and sustainable development of the enterprise chain.

[0006] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present invention.

[0007] According to one aspect of the present application, a method for evaluating carbon emissions of a biopharmaceutical enterprise chain throughout its life cycle is provided, comprising: obtaining information on a biopharmaceutical enterprise to be evaluated, wherein the information on the biopharmaceutical enterprise to be evaluated is data related to a target enterprise; processing the information on the biopharmaceutical enterprise to be evaluated to generate information on a product carbon chain to be evaluated, wherein the information to be evaluated includes the entire life cycle of a target drug; processing the target enterprise based on the entire life cycle of the target drug to generate an evaluation factor set, wherein the evaluation factor set includes a number of evaluation factors and the correlation between each evaluation factor and the target drug; and evaluating the evaluation factor set based on a preset multi-factor judgment matrix. The evaluation factors are processed to generate weight information of the evaluation factors and evaluation grades of the evaluation factors, wherein the preset multi-factor judgment matrix is ​​composed of economic factors, environmental factors and social factors; a fuzzy matrix is ​​generated based on the weight information of the evaluation factors and the evaluation grades of the evaluation factors; a number of evaluation factors are processed based on the fuzzy matrix to generate discriminant factors; a carbon footprint accounting model for the supply chain of biopharmaceutical enterprises is constructed based on the discriminant factors; the information of the biopharmaceutical enterprises to be evaluated is processed based on the carbon footprint accounting model for the supply chain of biopharmaceutical enterprises to generate the carbon footprint of the biopharmaceutical enterprise chain and the green quantitative evaluation system of the entire enterprise chain.

[0008] In one embodiment of the present application, the processing of the evaluation factor set based on a preset multi-factor judgment matrix to generate weight information of the evaluation factors and evaluation levels of the evaluation factors includes: the method includes a calculation formula for calculating the evaluation level of the evaluation factor, and the calculation formula is: in, is the maximum eigenvalue, which is used to characterize the influence of different evaluation factors on carbon emissions. Corresponding to the weights of different evaluation factors.

[0009] In one embodiment of the present application, the information of the biopharmaceutical enterprise to be evaluated is processed based on the carbon footprint accounting model of the biopharmaceutical enterprise supply chain to generate the carbon footprint of the biopharmaceutical enterprise chain and the green quantitative evaluation system of the whole enterprise chain, including: processing the information of the biopharmaceutical enterprise to be evaluated based on the carbon footprint accounting model of the biopharmaceutical enterprise supply chain to generate a multi-factor comprehensive evaluation result of each evaluation object; the method also includes a calculation formula for calculating the multi-factor comprehensive evaluation result, and the calculation formula is: Among them, W is the fuzzy vector, R is the fuzzy relationship matrix, Represents the relationship between activities and products in an enterprise.

[0010] In one embodiment of the present application, the information of the biopharmaceutical enterprise to be evaluated is processed based on the carbon footprint accounting model of the biopharmaceutical enterprise supply chain to generate the carbon footprint of the biopharmaceutical enterprise chain and the green quantitative evaluation system of the whole enterprise chain, and further includes: processing the information of the biopharmaceutical enterprise to be evaluated based on the carbon footprint accounting model of the biopharmaceutical enterprise supply chain to generate the carbon footprint of the biopharmaceutical enterprise chain and the green quantitative evaluation system of the whole enterprise chain including the carbon footprint of the enterprise supply chain; the method further includes a calculation formula for calculating the carbon footprint of the enterprise supply chain, and the calculation formula is: in, is the total carbon footprint in the supply chain, Represents the collection of all enterprises in the supply chain, Represents the collection of all departments in the enterprise, Represents a collection of activities for a department, express Corporate In the department, Energy consumption generated by an activity, represents the emission factor of the energy source, Representative activities The discriminant factor.

[0011] In one embodiment of the present application, the information of the biopharmaceutical enterprise to be evaluated is processed based on the carbon footprint accounting model of the biopharmaceutical enterprise supply chain to generate the carbon footprint of the biopharmaceutical enterprise chain and the green quantitative evaluation system of the whole enterprise chain, and further includes: processing the information of the biopharmaceutical enterprise to be evaluated based on the carbon footprint accounting model of the biopharmaceutical enterprise supply chain to generate the carbon footprint of the biopharmaceutical enterprise chain and the green quantitative evaluation system of the whole enterprise chain including the efficiency value of a single decision-making unit; the method also includes a calculation formula for calculating the efficiency value of a single decision-making unit, and the calculation formula is: in, is the efficiency value of a single decision-making unit, x and y are the input and output values, respectively, and b is the directional distance of the frontier surface.

[0012] In one embodiment of the present application, the information of the biopharmaceutical enterprise to be evaluated is processed based on the carbon footprint accounting model of the biopharmaceutical enterprise supply chain to generate the carbon footprint of the biopharmaceutical enterprise chain and the green quantitative evaluation system of the entire enterprise chain, and further includes: processing the information of the biopharmaceutical enterprise to be evaluated based on the carbon footprint accounting model of the biopharmaceutical enterprise supply chain to generate the carbon footprint of the biopharmaceutical enterprise chain and the green quantitative evaluation system of the entire enterprise chain including the efficiency measurement of the entire enterprise chain; the method also includes a calculation formula for calculating the efficiency measurement of the entire enterprise chain, and the calculation formula is:

[0013]

[0014] in, It represents the efficiency measurement of the entire enterprise chain, the z variable represents the input and output factors, and the u variable is the corresponding weight;

[0015] It is the rth output in the R,-dimensional output vector of the pth stage of the model. This output is a normal output and does not serve as the input of the next stage but directly leaves the model;

[0016] It is the kth output in the S,-dimensional output vector of the p-th stage of the model. This output is the intermediate output and serves as part of the input of the next stage p+1;

[0017] is the i-th output in the input vector of dimension p+1 of the model. This input is a normal input and enters the model from the p+1 stage. In the last stage p, all outputs are regarded as normal outputs z and all outputs leave the model.

[0018] The weight vectors corresponding to the above input and output are defined as: Output The corresponding weights; Output is the corresponding weight; For input The corresponding weight.

[0019] In one embodiment of the present application, the target enterprise is processed based on the entire life cycle of the target drug to generate a set of evaluation factors, including: extracting features from the information of the biopharmaceutical enterprise to be evaluated to determine an original feature library, wherein the original feature library includes non-continuous variable data and continuous variable data; processing the target enterprise based on the non-continuous variable data to generate initial evaluation factors; processing the initial evaluation factors based on the continuous variable data to generate target evaluation factors; and generating a set of evaluation factors based on the target evaluation factors.

[0020] Another aspect of the present application is a device for evaluating carbon emissions of a biopharmaceutical enterprise chain throughout its life cycle, characterized in that it includes: an acquisition module for acquiring information about a biopharmaceutical enterprise to be evaluated, wherein the information about the biopharmaceutical enterprise to be evaluated is data related to a target enterprise; a processing module for processing the information about the biopharmaceutical enterprise to be evaluated to generate information about a product carbon chain to be evaluated, wherein the information to be evaluated includes the entire life cycle of a target drug; processing the target enterprise based on the entire life cycle of the target drug to generate an evaluation factor set, wherein the evaluation factor set includes a number of evaluation factors and the correlation between each evaluation factor and the target drug; and a method for evaluating the carbon emissions of a target drug based on a preset multi-factor system. The set of evaluation factors is processed by a prime judgment matrix to generate weight information of the evaluation factors and evaluation levels of the evaluation factors, wherein the preset multi-factor judgment matrix is ​​composed of economic factors, environmental factors and social factors; a fuzzy matrix is ​​generated based on the weight information of the evaluation factors and the evaluation levels of the evaluation factors; a number of evaluation factors are processed based on the fuzzy matrix to generate discriminant factors; a carbon footprint accounting model for the supply chain of biopharmaceutical enterprises is constructed based on the discriminant factors; the information of the biopharmaceutical enterprises to be evaluated is processed based on the carbon footprint accounting model for the supply chain of biopharmaceutical enterprises to generate a carbon footprint of the biopharmaceutical enterprise chain and a green quantitative evaluation system for the entire enterprise chain.

[0021] According to another aspect of the present application, an electronic device is characterized in that it includes: a first processor; and a memory for storing executable instructions of the first processor; wherein the first processor is configured to execute the above-mentioned method for evaluating carbon emissions of the entire life cycle of the bio-pharmaceutical enterprise chain by executing the executable instructions.

[0022] According to another aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a second processor, the above-mentioned method for evaluating carbon emissions of the entire life cycle of the biopharmaceutical enterprise chain is implemented.

[0023] According to another aspect of the present application, a computer program product is provided, including a computer program, characterized in that when the computer program is executed by a third processor, the computer program implements the above-mentioned method for evaluating carbon emissions of the entire life cycle of the biopharmaceutical enterprise chain.

[0024] The present application provides a method and related equipment for evaluating carbon emissions of a biopharmaceutical enterprise chain over its entire life cycle. The server obtains information about the biopharmaceutical enterprise to be evaluated, wherein the information about the biopharmaceutical enterprise to be evaluated is data related to the target enterprise; the information about the biopharmaceutical enterprise to be evaluated is processed to generate information about the product carbon chain to be evaluated, wherein the information to be evaluated includes the entire life cycle of the target drug; the target enterprise is processed based on the entire life cycle of the target drug to generate an evaluation factor set, wherein the evaluation factor set includes several evaluation factors and the correlation between each evaluation factor and the target drug; the evaluation factor set is processed based on a preset multi-factor judgment matrix to generate weight information of the evaluation factors and evaluation levels of the evaluation factors, wherein the preset multi-factor judgment matrix is ​​composed of economic factors, environmental factors and social factors; a fuzzy matrix is ​​generated based on the weight information of the evaluation factors and the evaluation levels of the evaluation factors; several evaluation factors are processed based on the fuzzy matrix to generate discriminant factors; a carbon footprint accounting model for the supply chain of biopharmaceutical enterprises is constructed based on the discriminant factors; the information about the biopharmaceutical enterprise to be evaluated is processed based on the carbon footprint accounting model for the supply chain of biopharmaceutical enterprises to generate a carbon footprint of the biopharmaceutical enterprise chain and a green quantitative evaluation system for the entire enterprise chain. By constructing a carbon footprint accounting model for the supply chain of biopharmaceutical enterprises based on discriminant factors and integrating multi-dimensional indicators of economy, society and environment, an overall assessment of the green efficiency of the biopharmaceutical enterprise chain is conducted, which broadens the limitations of single enterprise efficiency measurement and promotes the green and sustainable development of the enterprise chain.

[0025] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A flow chart showing a method for evaluating carbon emissions of a biopharmaceutical enterprise chain throughout its life cycle provided by an embodiment of the present application;

[0027] Figure 2 A schematic diagram of the structure of a device for evaluating carbon emissions of a biopharmaceutical enterprise chain throughout its life cycle provided by an embodiment of the present application is shown;

[0028] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present application is shown;

[0029] Figure 4 A schematic diagram of a storage medium provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0030] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0031] Combine the following Figure 1 To describe the method for assessing carbon emissions of the entire life cycle of a biopharmaceutical enterprise chain according to an exemplary embodiment of the present application. It should be noted that the following application scenarios are only shown to facilitate understanding of the spirit and principles of the present application, and the embodiments of the present application are not limited in this regard. On the contrary, the embodiments of the present application can be applied to any applicable scenario.

[0032] In one embodiment, the present application also proposes a method and related equipment for evaluating carbon emissions throughout the life cycle of a biopharmaceutical enterprise chain. Figure 1 The following schematically shows a flow chart of a method for evaluating carbon emissions of a biopharmaceutical enterprise chain throughout its life cycle according to an embodiment of the present application. Figure 1 As shown, the method is applied to a server, comprising:

[0033] S101, obtaining information on biopharmaceutical companies to be evaluated.

[0034] In one embodiment, the information of the biopharmaceutical enterprise to be evaluated is data related to the target enterprise (such as the overall enterprise of the biopharmaceutical enterprise chain), including but not limited to raw material collection data: collecting information about the source of raw materials, including the collection location and method of biological raw materials and their impact on the ecological environment; R&D stage data: recording relevant data on energy consumption, chemical reagent use, experimental animal breeding and other activities in the drug development process; production process data: obtaining energy use data of production facilities, including electricity, water, steam and fuel consumption, and the amount of waste generated in the production process; supply chain information: collecting data on each link in the supply chain, such as carbon emission data on raw material transportation, semi-finished product storage and product distribution; product use data: collecting carbon emission data that may be generated by medical products during the use stage, such as the use of medical equipment and the treatment of medical waste; waste and recycling data: obtaining carbon emission data during product waste treatment and recycling, including incineration, landfill and recycling.

[0035] Transportation and logistics data: record the transportation mode, distance and related energy consumption of the entire logistics process from production to end users; Facility energy and emission data: collect energy use and greenhouse gas emission data of production and R&D facilities, including direct and indirect emissions; Employee commuting and business trip data: record employee commuting methods and business trip frequency, and evaluate their contribution to carbon emissions; Product life cycle data: collect data from the entire life cycle of products from concept to market withdrawal to evaluate the environmental impact of products; Market and sales data: obtain data on product sales channels and market distribution, and understand the geographical scope and transportation mode of product distribution; Policy and regulatory compliance data: collect data related to environmental policies and regulatory compliance, and evaluate the company's environmental management and emission reduction measures; Third-party certification and reporting: obtain evaluation reports from third-party certification agencies, such as ISO14001 environmental management system certification, etc.; Through these detailed data collection, we can fully understand the carbon emissions of biopharmaceutical companies and provide a basis for formulating emission reduction strategies and improving energy efficiency. At the same time, this will also help companies better respond to regulatory requirements, enhance environmental responsibility and market competitiveness.

[0036] S102, processing the information of the biopharmaceutical enterprise to be evaluated to generate the information of the product carbon chain to be evaluated.

[0037] In one implementation, the information to be evaluated includes the entire life cycle of the target drug, and all links of the enterprise chain are included in the scope of carbon emission measurement. All links of the enterprise chain include upstream, midstream, and downstream. Specifically for the biopharmaceutical enterprise chain, the enterprise chain is divided into three main links: upstream, midstream, and downstream, and the specific activities of each link are clarified: Upstream: API mining, involving the collection and primary processing of raw materials. Midstream: Drug production, including drug research and development, production, processing, and packaging. Downstream: Drug sales, involving logistics, distribution, retail, and end-user use.

[0038] Consider the pollution emissions of the entire production process from the environmental dimension, incorporate the carbon emissions of the environmental dimension into the evaluation system, and add the evaluation indicators of the economic and social dimensions to conduct a comprehensive efficiency evaluation. Consider the pollution emissions of the entire production process from the environmental dimension, including the emissions of waste gas, wastewater, solid waste and chemical substances; add the evaluation indicators of the economic dimension, such as cost-benefit analysis, return on investment and corporate economic benefits; consider the evaluation indicators of the social dimension, such as employment impact, public health and social responsibility. Combine the evaluation indicators of the three dimensions of environment, economy and society to conduct a comprehensive efficiency evaluation to comprehensively evaluate the sustainability of biopharmaceutical companies, integrate and analyze the collected data, and determine the carbon emission intensity and potential emission reduction opportunities of each link.

[0039] S103, processing the target enterprise based on the entire life cycle of the target drug to generate a set of evaluation factors.

[0040] In one embodiment, the evaluation factor set includes several evaluation factors and the correlation between each evaluation factor and the target drug. In the enterprise supply chain, the evaluation object of the product carbon chain can be established according to the characteristics of the enterprise, and the evaluation object of the product carbon chain can be determined according to the characteristics of the enterprise: D represents the set of all activities of a certain enterprise, for example, d1 represents the upstream supplier's raw material collection, d2 represents the midstream pharmaceutical enterprise workshop production activities, and d3 represents the downstream drug sales. The above is the activity set of the overall enterprise in the biopharmaceutical enterprise chain. Specific to different pharmaceutical enterprise chains, such as liraglutide drugs, its upstream may be pharmaceutical intermediate research and development, midstream is drug synthesis, and downstream is drug sales; and traditional Chinese medicine such as Cordyceps sinensis powder, its upstream d1 is the supplier's Cordyceps sinensis raw material collection, midstream is grinding, and downstream d3 is the sale of pharmaceutical intermediates to another enterprise. The activity set D of different specific drugs is determined according to the characteristics of the drugs, .

[0041] Based on the drug life cycle, determine whether an activity is related to drug production and establish a set of evaluation factors. The applicant needs to determine the factors to be evaluated. These factors represent different aspects that the applicant is concerned about, such as the production process of the product, carbon emission sources, and equipment use. The purpose of this step is to create a set that includes all the factors that need to be evaluated. You can think of it as a list of all the factors that the applicant needs to consider. It represents a set of evaluation factors corresponding to the characteristics of the enterprise. It can include production time, carbon emission sources of products, equipment involved in the process, etc. .

[0042] In another embodiment, feature extraction is performed on the information of the biopharmaceutical company to be evaluated to determine an original feature library, wherein the original feature library includes non-continuous variable data and continuous variable data; the target company is processed based on the non-continuous variable data to generate initial evaluation factors; the initial evaluation factors are processed based on the continuous variable data to generate target evaluation factors; and an evaluation factor set is generated based on the target evaluation factors.

[0043] Extract key features from the information of the biopharmaceutical companies to be evaluated to form the basis for evaluation. Divide the extracted features into non-continuous variable data (such as categorical data, qualitative data) and continuous variable data (such as numerical data, time series data). Analyze the non-continuous variable data to identify the characteristics of the company in different categories or attributes, such as company size, ownership type, market positioning, etc. Use the non-continuous variable data to process the target company and generate initial evaluation factors. These factors may include the company's carbon emission policy, historical emission records, environmental management system certification, etc. Analyze the continuous variable data to identify the company's characteristics in terms of carbon emissions, energy consumption, production efficiency, etc.

[0044] The initial evaluation factors are further processed using continuous variable data to generate target evaluation factors, which may include specific carbon emissions, energy efficiency, emission intensity, etc. The target evaluation factors are integrated to construct an evaluation factor set, which will be used to evaluate the company's carbon emission performance and sustainability. Weights are assigned to each factor in the evaluation factor set to reflect its importance in the overall evaluation.

[0045] S104: Process the evaluation factor set based on a preset multi-factor judgment matrix to generate weight information of the evaluation factors and evaluation grades of the evaluation factors.

[0046] After determining the set of evaluation factors, the applicant assigns an evaluation level to the above factors. This evaluation level indicates the relevance of each factor to the product. For example, the applicant can define four levels: very relevant, relevant, irrelevant and very irrelevant. Each level has a corresponding range, such as very relevant is between 75% and 100%. The purpose of this step is to quantify the relevance of each factor so that it can be used for subsequent analysis. The relevance of product activities is quantitatively classified. Indicates different Indicates the degree of relevance to product production, extremely relevant , related , not relevant Very unrelated ;

[0047] In addition, the applicant needs to determine the importance of each factor in the overall evaluation and use a variety of methods to calculate weights, such as the analytic hierarchy process (AHP). The weight of each factor is assigned through mathematical methods to ensure that each factor has a reasonable proportion in the comprehensive evaluation. Weight calculation methods include entropy method, analytic hierarchy process (AHP), Delphi method, Topsis method, etc. This application is based on the combination of analytic hierarchy process and weight distribution method. This method optimizes the weight distribution process and obtains a more accurate weight distribution. According to the analytic hierarchy process that establishes the hierarchy of supply chain enterprise activities, the relative importance of each factor under a single criterion is calculated (factors include economic factors such as enterprise output, environmental factors such as carbon emissions, social factors such as the age of senior executives, etc.), and a judgment matrix is ​​established for each criterion. (Assuming there is activities), right The relative importance of is expressed in a matrix ( and The range is 1 to ), the single factor matrix satisfies the following conditions:

[0048]

[0049]

[0050] when ;

[0051] The purpose of calculating the maximum eigenvalue is to ensure that the matrix set constructed in the previous steps is mathematically valid. Since there will be inconsistencies between the ideal matrix and the actual matrix for comparing different factors, in order to avoid the situation where the logical order cannot be satisfied, the maximum eigenvalue of the comparison matrix needs to be calculated. For each single standard, the eigenvalue of the matrix is ​​compared with the corresponding eigenvector, and a consistency check is performed on each judgment matrix. The eigenvalue represents the degree of influence of different indicator weights on carbon emissions. The maximum eigenvalue is for

[0052]

[0053] The weight matrix is ​​normalized, and the corresponding eigenvector is The values ​​of the vector correspond to the values ​​in the criterion layer The weight of the element. is the product of the standardized weights of the judgment matrix D and the evaluation factors W. The judgment matrix D is the weight of each evaluation index compared with each other. N is the number of dimensions, indicating how many indicators there are. Wi is the weight of a certain indicator in the overall index. wj and wj are a single indicator in the index. When calculating the relative importance weights of each factor under a single standard, a consistency test is performed. When the eigenvector corresponding to the maximum eigenvalue is used as the weight vector of the influence of the comparison factor on the upper factor, the greater the inconsistency, the greater the judgment error. Therefore, inconsistency can be detected by , is the largest eigenvalue, is the number of dimensions:

[0054]

[0055] The consistency index is used to determine whether the evaluators' judgments are consistent when making paired comparisons. Suppose the applicant is evaluating the relative importance of three factors A, B, and C: if the applicant believes that A is more important than B, and B is more important than C, then it stands to reason that the applicant should believe that A is more important than C. However, in practice, due to the subjectivity of human judgment, inconsistencies may occur, such as the applicant believing that C is more important than A. In order to detect this inconsistency, the applicant uses the consistency index (CI) by calculating the consistency index ( ), to measure the consistency of the judgment matrix. The size of the value describes the consistency. The smaller the value, the better the consistency. Its purpose is that when the eigenvector corresponding to the maximum eigenvalue is used as a comparison factor to illustrate the degree of influence on the upper-level factors, the larger the error will be judged. In order to eliminate this error, a consistency test is required to prove the correctness of the weight index.

[0056] S105: Generate a fuzzy matrix based on the weight information of the evaluation factors and the evaluation levels of the evaluation factors.

[0057] In one implementation, a fuzzy matrix is ​​created based on the weight and evaluation level of each factor. The fuzzy matrix contains the fuzzy values ​​of each factor (such as production equipment factors and energy consumption factors) at different evaluation levels. The fuzzy matrix is ​​a two-dimensional table. Each row of the table represents a factor and each column represents an evaluation level. By combining the weight and the fuzzy matrix, a comprehensive evaluation result vector can be calculated. This vector represents the performance of each factor in the comprehensive evaluation. In order to make the correlation between production evaluation factors and production activities correspond one to one, the vectors between the two need to be mapped. From the evaluation set, To relevance The vector of the fuzzy mapping is , The range is from 1 to ,This is On the fuzzy subset, all are combined to obtain the fuzzy matrix , the fuzzy matrix By assigning object sets, factor sets and corresponding weights, the fuzzy matrix is .

[0058] S106, processing a number of evaluation factors based on the fuzzy matrix to generate a discriminant factor.

[0059] In one implementation, the discriminant factor represents the final score of the comprehensive evaluation result. According to the fuzzy matrix and weights, the applicant can obtain the score of each factor at different evaluation levels, and then perform weighted average on these scores to obtain the final comprehensive evaluation score. In order to simplify the judgment, a threshold value can be set, such as 50 points. If the comprehensive evaluation score is greater than or equal to this threshold value, the applicant considers that the factor is important (the discriminant factor is 1), otherwise it is considered unimportant (the discriminant factor is 0).

[0060] The fuzzy matrix maps the production activity related sets, and then constructs the discriminant factor by setting the correlation between different production activity sets and drug production, that is, the membership of the evaluation set. The higher the membership, the more relevant the factor is to drug production. The membership degree is determined by the correlation, and the fuzzy comprehensive evaluation results are normalized. The 0-1 distribution of the discriminant factor is: if Greater than 50, then is 1, otherwise, is 0.

[0061] S107, constructing a carbon footprint accounting model for the supply chain of biopharmaceutical companies based on the discriminant factors.

[0062] In one implementation, in biopharmaceutical companies, building a comprehensive supply chain carbon footprint measurement model to form a carbon chain is a complex task, which requires that every link from raw material procurement to product waste disposal be taken into consideration. In addition to the direct energy consumption of the production workshop, other activities such as employee commuting and meetings are difficult to quantify, but their correlation with carbon emissions can be evaluated through discriminant factors. The carbon chain is composed of a collection of nodes of various companies in the supply chain, including suppliers, manufacturers, logistics companies, consumers, and secondary processing companies. By collecting and analyzing the carbon emission data of these nodes, emission hotspots and emission reduction potential can be identified, and then effective emission reduction strategies can be formulated. This process emphasizes cross-departmental collaboration and continuous monitoring to ensure transparency and enhance corporate environmental responsibility, and jointly promote the green and sustainable development of enterprises.

[0063] S108, processing the information of the biopharmaceutical enterprise to be evaluated based on the biopharmaceutical enterprise supply chain carbon footprint accounting model, and generating a biopharmaceutical enterprise chain carbon footprint and a green quantitative evaluation system for the entire enterprise chain.

[0064] In one embodiment, the information of the biopharmaceutical enterprise to be evaluated is processed based on the carbon footprint accounting model of the biopharmaceutical enterprise supply chain, and a multi-factor comprehensive evaluation result of each evaluation object is generated. The fuzzy comprehensive operator is used to calculate the fuzzy vector and fuzzy relationship matrix A comprehensive evaluation of the multi-factors of each evaluation object (such as production equipment factors, energy consumption factors) was established. The purpose of the multi-factor evaluation result is to consider various factors that affect production activities, not just the energy consumption of production. For example, the satisfaction of workshop employees or the education level of senior executives will affect the drug productivity and thus affect the carbon emissions of drugs. Represents the relationship between activities and products in an enterprise.

[0065] Specifically, the calculation formula for the multi-factor comprehensive evaluation result is:

[0066] .

[0067] This application not only focuses on direct energy consumption through in-depth processing of the information of biopharmaceutical companies to be evaluated, but also comprehensively considers the impact of multiple factors such as production equipment, employee satisfaction, and executive education on drug productivity and carbon emissions. Fuzzy comprehensive operators are used to perform comprehensive operations on fuzzy vectors and fuzzy relationship matrices, and a multi-factor comprehensive evaluation result vector including production equipment factors and energy consumption factors is established. This method can reveal the complex relationship between internal corporate activities and product carbon emissions, thereby more accurately evaluating the overall performance of the company in the production process. Through this comprehensive evaluation system, companies can identify emission reduction potential and improvement directions, and promote biopharmaceutical companies to achieve green and sustainable development goals.

[0068] In another embodiment, the information of the biopharmaceutical enterprise to be evaluated is processed based on the carbon footprint accounting model of the biopharmaceutical enterprise supply chain to generate the carbon footprint of the biopharmaceutical enterprise chain and the green quantitative evaluation system of the entire enterprise chain including the carbon footprint of the enterprise supply chain. In addition, the method also includes a calculation formula for calculating the carbon footprint of the enterprise supply chain, which is: in, The total carbon footprint in the supply chain, Represents the collection of all enterprises in the supply chain, Represents the collection of all departments in the enterprise, Represents a collection of activities for a department, express Corporate In the department, Energy consumption generated by an activity, represents the emission factor of the energy source, Representative activities The discriminant factor.

[0069] A carbon chain is formed based on the previously constructed enterprise supply chain carbon footprint calculation model. In drug production, carbon emissions are generated not only in the production workshop, but also in the procurement of raw materials by employees or commuting to drug decision-making meetings, which also belong to the emissions of the entire pharmaceutical enterprise chain. However, since it is impossible to quantify the correlation between carbon emissions from production activities such as commuting to meetings, a carbon chain is constructed based on discriminant factors to illustrate the degree of affiliation with carbon emissions. The carbon chain consists of a set of carbon chain nodes of each enterprise in the supply chain, including suppliers, manufacturers, logistics companies, consumers, and secondary processing companies.

[0070] In another embodiment, the information of the biopharmaceutical enterprise to be evaluated is processed based on the carbon footprint accounting model of the biopharmaceutical enterprise supply chain to generate the carbon footprint of the biopharmaceutical enterprise chain and the green quantitative evaluation system of the entire enterprise chain, including the efficiency value of a single decision-making unit, and also including a calculation formula for calculating the efficiency value of a single decision-making unit. The specific calculation formula is: in, is the efficiency value of a single decision-making unit, x and y are the input and output values, respectively, and b is the directional distance of the frontier surface, indicating how far the inefficient individual is from the current ideal technical production level, that is, the degree of inefficiency. Units with an efficiency score below 100% are called inefficient units.

[0071] Assume that there are n decision-making units, and each DMU has two input-output vectors: input vector x∈Rqm, output vector y∈Rq1, then the matrix X=[x1,…,xn]∈Rmxn, Y=[y1,…,yn]∈Rq1xn. Among them, X>0, Y>0, s represents the slack variables of input and output; is the weight vector.

[0072] D is the overall efficiency of the decision-making unit. The efficiency value of the corresponding decision-making unit is obtained by incorporating the input and output quantities into the measurement framework. The significance of this formula is that each indicator in the following enterprise green supply chain management evaluation index system table (economic such as total enterprise output, environmental such as carbon emissions) is used as input and output for measurement to measure the efficiency value of a single decision-making unit (such as a midstream pharmaceutical company).

[0073] The economic dimension includes four modules: R&D efficiency, production efficiency, logistics efficiency and sales efficiency. Among them, R&D efficiency uses R&D cost, cycle and number of projects as the main evaluation indicators, which can be used to measure an enterprise's innovation ability and the efficiency and benefits of R&D. The production efficiency evaluation is based on factors such as production cost, capacity utilization and number of production lines, which can reflect the effectiveness and efficiency of the enterprise's production. Logistics efficiency examines the company's situation in terms of transportation costs, storage area and the number of logistics suppliers, which is directly related to the company's logistics operation efficiency. Sales efficiency measures the company's market operation capabilities from several aspects such as sales revenue, number of customers, and number of sales channels.

[0074] The evaluation content of the social dimension consists of three modules: employee development, supplier management and customer service. Employee development mainly focuses on the number of employee training hours, employee satisfaction survey results and employee turnover rate. These indicators directly reflect the company's investment in employees and the degree of employee recognition of the company. The supplier management module includes indicators such as supplier selection costs, social responsibility assessment results and the number of suppliers, which can evaluate the company's ability to manage the supply chain. Customer service evaluates the company's customer service level through statistics on the number of customer complaints, service response time and satisfaction survey results.

[0075] The environmental dimension is to evaluate the company's green performance, focusing on the status of raw material acquisition, production process, logistics and transportation, and waste disposal. The assessment of raw material acquisition mainly examines the company's raw material procurement amount, the impact of procurement on the environment, and the recovery rate of raw materials, which is related to the company's sustainable use of resources. The production process focuses on carbon footprint, water resource utilization, and waste volume, which can measure the impact of the company's production on the environment. Logistics and transportation examine the environmental protection of the company's logistics stage from the perspectives of transportation carbon footprint, storage carbon footprint, and transportation energy consumption. Waste disposal focuses on the waste recovery amount, processing cost, and processing rate, reflecting the company's waste processing capabilities and environmental awareness.

[0076] Specifically, the enterprise green supply chain management evaluation index system is shown as follows:

[0077]

[0078]

[0079] The economic, social, and environmental indicators in the above table are input into the carbon footprint accounting model of the biopharmaceutical enterprise supply chain. For example, input indicators include the raw material purchase amount, water resource utilization, etc., and output indicators include the number of production lines, sales revenue, etc. The secondary indicators under the above different dimensions are incorporated into the decision-making unit for efficiency measurement, which effectively evaluates the company's green supply chain management, helps to conduct in-depth analysis and research on the company from different angles and levels, and provides a scientific basis for the company's decision-making.

[0080] In another embodiment, the information of the biopharmaceutical enterprise to be evaluated is processed based on the carbon footprint accounting model of the biopharmaceutical enterprise supply chain to generate the carbon footprint of the biopharmaceutical enterprise chain and the green quantitative evaluation system of the entire enterprise chain, including the efficiency measurement of the entire enterprise chain, the purpose of which is to quantitatively compare the input and output of the industrial chain; in addition, the application also includes a calculation formula for calculating the efficiency measurement of the entire enterprise chain, and the specific calculation formula is:

[0081]

[0082] in, It represents the efficiency measurement of the entire enterprise chain, the z variable represents the input and output factors, and the u variable is the corresponding weight;

[0083] (r=1,2,…,R,) is the rth output in the R,-dimensional output vector of the pth stage of the model. This output is a normal output and does not serve as the input of the next stage but directly leaves the model;

[0084] (k=1,2,…,S,) is the kth output in the S,-dimensional output vector of the p-th stage of the model. This output is an intermediate output and serves as part of the input of the next stage p+1;

[0085] (i=1,2,…,) is the i-th output in the input vector of the p+1th stage of the model. This input is a normal input and enters the model from the p+1th stage. In the last stage p, all outputs are regarded as normal outputs z and all outputs leave the model.

[0086] The weight vectors corresponding to the above input and output are defined as: Output The corresponding weights; Output is the corresponding weight; For input Corresponding weights. Specifically, the formula after ST is a constraint condition, the purpose of which is to be consistent with actual production. If there is only one output, R dimension is one dimension, and R inputs are R dimensions.

[0087] The supply chain carbon footprint accounting model for biopharmaceutical companies evaluates the efficiency of the entire enterprise chain by comprehensively considering the carbon emissions of raw material collection, production, distribution and other links. The model not only focuses on energy consumption, but also incorporates multiple factors such as production equipment, employee satisfaction and executive education. It uses fuzzy comprehensive operators to process complex factor relationships and generate a comprehensive evaluation result vector. This multi-dimensional analysis reveals the connection between internal corporate activities and product carbon emissions, helping companies identify efficiency bottlenecks and emission reduction opportunities. Through continuous monitoring and model optimization, companies can formulate and implement strategies to improve efficiency and reduce carbon footprint, and promote the green and sustainable development of the company.

[0088] In the construction of a green quantitative evaluation system, previous scholars have selected a single input-output indicator from the environmental dimension when measuring the green efficiency of biopharmaceutical companies, and have not taken multiple input-output indicators from the economic, social and environmental dimensions for comprehensive consideration. At the same time, in terms of efficiency measurement, existing literature uses a single enterprise as a decision-making unit, that is, an individual efficiency value for measurement, and some scholars use a two-stage DEA model for efficiency measurement. The industrial chain is often composed of three main parts, namely the upstream, midstream and downstream. Therefore, the present invention adopts a three-stage serial DEA model, incorporates the entire industrial chain into the efficiency measurement, and comprehensively evaluates the green efficiency of the biopharmaceutical industry chain through economic, environmental and social dimensions.

[0089] By incorporating social, economic and environmental aspects into the green quantitative evaluation system of the industrial chain, the comprehensive efficiency of the entire life cycle of the bio-pharmaceutical industry chain under future low-carbon optimization can be calculated, and the key influencing factors of carbon emissions in each link of the bio-pharmaceutical industry chain can be pointed out.

[0090] This application constructs a carbon footprint accounting model for the supply chain of biopharmaceutical enterprises based on discriminant factors, integrates economic, social and environmental multi-dimensional indicators, and conducts an overall assessment of the green efficiency of the biopharmaceutical enterprise chain, breaking through the limitations of single enterprise efficiency measurement and promoting the green and sustainable development of the enterprise chain.

[0091] Optionally, in another embodiment of the method of the present application, the step of constructing a carbon footprint accounting model for the supply chain of a biopharmaceutical enterprise based on the discriminant factor includes:

[0092] Obtain the total training set for the carbon footprint accounting model of the biopharmaceutical enterprise supply chain;

[0093] Processing the target training set to generate an initial candidate group and an initial fixed group;

[0094] The initial candidate group and the initial fixed group are processed respectively according to a first preset rule to generate a preset candidate group and a preset fixed group, wherein the preset candidate group and the preset fixed group respectively carry identification information representing different covariate priority information;

[0095] Generate a loop iteration rule based on the identification information, wherein the loop iteration rule is set based on the number of covariates to be matched that is preset by a user;

[0096] Processing the covariates in the preset candidate group and the covariates in the preset fixed group based on the loop iteration rule to generate a preprocessed candidate group and a preprocessed fixed group, wherein the covariates include continuous variables and categorical variables;

[0097] Processing the preprocessing selected group and the preprocessing fixed group based on a preset matching rule to generate a matching result;

[0098] Processing the matching results based on a variable numerical calculation model to generate statistical values ​​of the preprocessed selected group and statistical values ​​of the preprocessed fixed group;

[0099] If the statistical value of the pre-processed candidate group and the statistical value of the pre-processed fixed group meet the matching requirements respectively, generating a target candidate group and a target fixed group;

[0100] The biopharmaceutical enterprise supply chain carbon footprint accounting model is processed based on the target candidate group and the target fixed group to generate a target biopharmaceutical enterprise supply chain carbon footprint accounting model.

[0101] In one implementation, the total training set is subjected to preprocessing operations such as cleaning and standardization to prepare for generating a target training set and a target test set, and the preprocessed data set is divided into two parts: a target training set for model training and a target test set for model evaluation. In the target training set, an initial candidate group and a fixed group are generated according to specific standards or rules. The initial candidate group and the fixed group are processed according to a first preset rule to generate a preset candidate group and a preset fixed group, which carry identifiers of different covariate priority information. Based on the number of covariates set by the user, a loop iteration rule is generated for subsequent covariate processing, and the covariates (including continuous variables and categorical variables) in the preset candidate group and the preset fixed group are processed according to the loop iteration rule to generate a preprocessed candidate group and a preprocessed fixed group.

[0102] Based on the preset matching rules, the preprocessed candidate groups and fixed groups are matched to generate matching results. The matching results are processed using the variable numerical calculation model to generate statistical values ​​of the preprocessed candidate groups and preprocessed fixed groups. The statistical values ​​of the preprocessed candidate groups and preprocessed fixed groups are checked to see if they meet the matching requirements. If the statistical values ​​meet the requirements, these groups are defined as the target candidate groups and target fixed groups, which will be used for the final model training. The data of the target candidate groups and target fixed groups are used to prepare for model training and optimization. The performance of the model is evaluated on the target test set, and adjustments and optimizations are made as needed. The verified and optimized model is applied to actual problems for prediction and analysis. Through this process, the quality and representativeness of the model training data can be ensured, thereby improving the accuracy and reliability of the model. At the same time, this process also supports the interpretability and stability evaluation of the model.

[0103] In one embodiment, if Figure 2 As shown, the present application also provides a device for evaluating carbon emissions of a biopharmaceutical enterprise chain throughout its life cycle, including:

[0104] An acquisition module 201 is used to acquire information of a biopharmaceutical enterprise to be evaluated, wherein the information of the biopharmaceutical enterprise to be evaluated is data related to the target enterprise;

[0105] The processing module 202 is used to process the information of the biopharmaceutical enterprise to be evaluated and generate the information to be evaluated of the product carbon chain, wherein the information to be evaluated includes the entire life cycle of the target drug; based on the entire life cycle of the target drug, the target enterprise is processed to generate a set of evaluation factors, wherein the set of evaluation factors includes several evaluation factors and the correlation between each evaluation factor and the target drug; based on a preset multi-factor judgment matrix, the set of evaluation factors is processed to generate weight information of the evaluation factors and evaluation levels of the evaluation factors, wherein the preset multi-factor judgment matrix is ​​composed of economic factors, environmental factors and social factors; based on the weight information of the evaluation factors and the evaluation levels of the evaluation factors, a fuzzy matrix is ​​generated; based on the fuzzy matrix, several evaluation factors are processed to generate discriminant factors; based on the discriminant factors, a carbon footprint accounting model for the supply chain of biopharmaceutical enterprises is constructed; based on the carbon footprint accounting model for the supply chain of biopharmaceutical enterprises, the information of the biopharmaceutical enterprise to be evaluated is processed to generate a carbon footprint of the biopharmaceutical enterprise chain and a green quantitative evaluation system for the entire enterprise chain.

[0106] In another embodiment of the present application, the processing module 202 is configured to process the evaluation factor set based on the preset multi-factor judgment matrix to generate weight information of the evaluation factors and evaluation levels of the evaluation factors, including:

[0107] The method includes a calculation formula for calculating the evaluation level of the evaluation factor, and the calculation formula is:

[0108]

[0109] in, is the maximum eigenvalue, which is used to characterize the influence of different evaluation factors on carbon emissions. Corresponding to the weights of different evaluation factors.

[0110] In another embodiment of the present application, the processing module 202 is configured to process the information of the biopharmaceutical enterprise to be evaluated based on the biopharmaceutical enterprise supply chain carbon footprint accounting model to generate a biopharmaceutical enterprise chain carbon footprint and a green quantitative evaluation system for the entire enterprise chain, including:

[0111] Processing the information of the biopharmaceutical enterprise to be evaluated based on the biopharmaceutical enterprise supply chain carbon footprint accounting model to generate a multi-factor comprehensive evaluation result of each evaluation object;

[0112] The method also includes a calculation formula for calculating a multi-factor comprehensive evaluation result, and the calculation formula is:

[0113]

[0114] Among them, W is the fuzzy vector, R is the fuzzy relationship matrix, Represents the relationship between activities and products in an enterprise.

[0115] In another embodiment of the present application, the processing module 202 is configured to process the information of the biopharmaceutical enterprise to be evaluated based on the biopharmaceutical enterprise supply chain carbon footprint accounting model to generate a biopharmaceutical enterprise chain carbon footprint and a green quantitative evaluation system for the entire enterprise chain, and further includes:

[0116] Based on the carbon footprint accounting model of the biopharmaceutical enterprise supply chain, the information of the biopharmaceutical enterprise to be evaluated is processed to generate the carbon footprint of the biopharmaceutical enterprise chain and the green quantitative evaluation system of the entire enterprise chain including the carbon footprint of the enterprise supply chain;

[0117] The method also includes a calculation formula for calculating the carbon footprint of the enterprise supply chain, the calculation formula is:

[0118]

[0119] in, is the total carbon footprint in the supply chain, Represents the collection of all enterprises in the supply chain, Represents the collection of all departments in the enterprise, Represents a collection of activities for a department, express Corporate In the department, Energy consumption generated by an activity, represents the emission factor of the energy source, Representative activities The discriminant factor.

[0120] In another embodiment of the present application, the processing module 202 is configured to process the information of the biopharmaceutical enterprise to be evaluated based on the biopharmaceutical enterprise supply chain carbon footprint accounting model to generate a biopharmaceutical enterprise chain carbon footprint and a green quantitative evaluation system for the entire enterprise chain, and further includes:

[0121] Processing the information of the biopharmaceutical enterprise to be evaluated based on the biopharmaceutical enterprise supply chain carbon footprint accounting model, generating the biopharmaceutical enterprise chain carbon footprint and the green quantitative evaluation system of the entire enterprise chain including the efficiency value of a single decision-making unit;

[0122] The method further includes a calculation formula for calculating the efficiency value of a single decision-making unit, and the calculation formula is:

[0123]

[0124] Among them, D is the overall efficiency of the decision-making unit, x and y are the input and output values, respectively, and b is the directional distance of the frontier surface.

[0125] In another embodiment of the present application, the processing module 202 is configured to process the information of the biopharmaceutical enterprise to be evaluated based on the biopharmaceutical enterprise supply chain carbon footprint accounting model to generate a biopharmaceutical enterprise chain carbon footprint and a green quantitative evaluation system for the entire enterprise chain, and further includes:

[0126] Based on the carbon footprint accounting model of the biopharmaceutical enterprise supply chain, the information of the biopharmaceutical enterprise to be evaluated is processed to generate the carbon footprint of the biopharmaceutical enterprise chain and the green quantitative evaluation system of the entire enterprise chain, including the efficiency measurement of the entire enterprise chain;

[0127] The method also includes a calculation formula for calculating the efficiency measurement of the entire enterprise chain, and the calculation formula is:

[0128]

[0129] in, It represents the efficiency measurement of the entire enterprise chain, the z variable represents the input and output factors, and the u variable is the corresponding weight;

[0130] (r=1,2,…,R,) is the rth output in the R,-dimensional output vector of the pth stage of the model. This output is a normal output and does not serve as the input of the next stage but directly leaves the model;

[0131] (k=1,2,…,S,) is the kth output in the S,-dimensional output vector of the p-th stage of the model. This output is an intermediate output and serves as part of the input of the next stage p+1;

[0132] (i=1,2,…,) is the i-th output in the input vector of the p+1th stage of the model. This input is a normal input and enters the model from the p+1th stage. In the last stage p, all outputs are regarded as normal outputs z and all outputs leave the model.

[0133] The weight vectors corresponding to the above input and output are defined as: Output The corresponding weights; Output is the corresponding weight; For input The corresponding weight.

[0134] In another embodiment of the present application, the processing module 202 is configured to process the target enterprise based on the full life cycle of the target drug and generate a set of evaluation factors, including:

[0135] Extracting features from the biopharmaceutical enterprise information to be evaluated to determine an original feature library, wherein the original feature library includes non-continuous variable data and continuous variable data;

[0136] Processing the target enterprise based on the non-continuous variable data to generate initial evaluation factors;

[0137] Processing the initial evaluation factors based on the continuous variable data to generate target evaluation factors;

[0138] An evaluation factor set is generated based on the target evaluation factor.

[0139] The present application embodiment provides an electronic device, such as Figure 3 As shown, the electronic device 3 includes a first processor 300, a memory 301, a bus 302 and a communication interface 303, and the first processor 300, the communication interface 303 and the memory 301 are connected via the bus 302; the memory 301 stores a computer program that can be run on the first processor 300, and when the first processor 300 runs the computer program, it executes the method for assessing carbon emissions of the entire life cycle of the biopharmaceutical enterprise chain provided in any of the aforementioned embodiments of the present application.

[0140] The memory 301 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 303 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0141] The bus 302 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. Among them, the memory 301 is used to store programs, and the first processor 300 executes the program after receiving the execution instruction. The method for assessing the carbon emissions of the entire life cycle of the biopharmaceutical enterprise chain disclosed in any implementation of the embodiment of the present application may be applied to the first processor 300, or implemented by the first processor 300.

[0142] The first processor 300 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the first processor 300. The above-mentioned first processor 300 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be embodied as being executed by a hardware decoding processor, or by a combination of hardware and software modules in a decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 301, and the first processor 300 reads the information in the memory 301 and completes the steps of the above method in combination with its hardware.

[0143] The electronic device provided in the above-mentioned embodiments of the present application and the method for assessing carbon emissions of a biopharmaceutical enterprise chain throughout its life cycle provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0144] The present application embodiment provides a computer-readable storage medium, such as Figure 4 As shown, the computer-readable storage medium stores 401 a computer program, and when the computer program is read and executed by the second processor 402, the aforementioned method for assessing carbon emissions during the entire life cycle of a biopharmaceutical enterprise chain is implemented.

[0145] The technical solution of the embodiment of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling an electronic device (which may be an air conditioner, a refrigeration device, a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the method described in the embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0146] The computer-readable storage medium provided in the above-mentioned embodiments of the present application and the method for assessing carbon emissions of a biopharmaceutical enterprise chain throughout its life cycle provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0147] An embodiment of the present application provides a computer program product, including a computer program, wherein the computer program is executed by a third processor to implement the method described above.

[0148] The computer program product provided in the above-mentioned embodiments of the present application and the method for assessing carbon emissions of a biopharmaceutical enterprise chain throughout its life cycle provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0149] It should be noted that, in this application, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or still includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0150] Each embodiment in this application is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the evaluation method, electronic device, electronic device, and readable storage medium embodiment for evaluating the carbon emissions of the entire life cycle of a biopharmaceutical enterprise chain, since they are basically similar to the above-mentioned evaluation method embodiment for the carbon emissions of the entire life cycle of a biopharmaceutical enterprise chain, the description is relatively simple, and the relevant parts can be referred to the partial description of the above-mentioned evaluation method embodiment for the carbon emissions of the entire life cycle of a biopharmaceutical enterprise chain.

[0151] Although the present application is disclosed as above, the present application is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application. Therefore, the protection scope of the present application shall be subject to the scope defined by the claims.

Claims

1. A method for assessing carbon emissions throughout the life cycle of a biopharmaceutical enterprise chain, characterized in that: include: Obtaining information about the biopharmaceutical company to be evaluated, wherein the information about the biopharmaceutical company to be evaluated is data related to the target company; Processing the information of the biopharmaceutical enterprise to be evaluated to generate the information of the product carbon chain to be evaluated, wherein the information of the product carbon chain to be evaluated includes the entire life cycle of the target drug; Processing the target enterprise based on the full life cycle of the target drug to generate an evaluation factor set, wherein the evaluation factor set includes a number of evaluation factors and the correlation between each evaluation factor and the target drug; The evaluation factor set is processed based on a preset multi-factor judgment matrix to generate weight information of the evaluation factors and evaluation levels of the evaluation factors, wherein the preset multi-factor judgment matrix is ​​composed of economic factors, environmental factors and social factors; generating a fuzzy matrix based on the weight information of the evaluation factors and the evaluation levels of the evaluation factors; Processing a number of evaluation factors based on the fuzzy matrix to generate a discriminant factor; Constructing a carbon footprint accounting model for the supply chain of biopharmaceutical enterprises based on the discriminant factors; Based on the carbon footprint accounting model of the biopharmaceutical enterprise supply chain, the information of the biopharmaceutical enterprise to be evaluated is processed to generate the carbon footprint of the biopharmaceutical enterprise chain and the green quantitative evaluation system of the entire enterprise chain; The method processes the information of the biopharmaceutical enterprise to be evaluated based on the carbon footprint accounting model of the biopharmaceutical enterprise supply chain to generate the carbon footprint of the biopharmaceutical enterprise chain and the green quantitative evaluation system of the whole enterprise chain, including: processing the information of the biopharmaceutical enterprise to be evaluated based on the carbon footprint accounting model of the biopharmaceutical enterprise supply chain to generate the carbon footprint of the biopharmaceutical enterprise chain and the green quantitative evaluation system of the whole enterprise chain including the efficiency value of a single decision-making unit; the method also includes a calculation formula for calculating the efficiency value of a single decision-making unit, and the calculation formula is: in, is the efficiency value of a single decision-making unit, x and y are the input and output values, respectively, and b is the directional distance of the frontier surface. The specific values ​​of input, output and frontier distance related to a specific decision-making unit. k represents different decision-making units, M is related to the number of input indicators, N is related to the number of output indicators, and P is related to the number of parameters related to the frontier distance. are the slack variables of input and output and the variables related to the distance from the frontier surface. represents the mth input value of the kth decision-making unit, represents the nth output value of the kth decision-making unit, It represents the pth correlation value of the kth decision unit in the frontier direction distance.

2. The method according to claim 1, characterized in that The step of processing the evaluation factor set based on a preset multi-factor judgment matrix to generate weight information of the evaluation factors and evaluation levels of the evaluation factors includes: The method includes a calculation formula for calculating the evaluation level of the evaluation factor, and the calculation formula is: ； ； in, is the maximum eigenvalue, which is used to characterize the influence of different evaluation factors on carbon emissions. is the weight-related variable corresponding to one of the evaluation factors, is the weight-related variable corresponding to another evaluation factor, Corresponding to the weights of different evaluation factors, n represents the number of evaluation factors, Indicates the relative importance of the i-th evaluation factor relative to the j-th evaluation factor in the judgment matrix. It represents the standardized weighted product of the judgment matrix D and the evaluation factor w.

3. The method according to claim 1, characterized in that The processing of the information of the biopharmaceutical enterprise to be evaluated based on the biopharmaceutical enterprise supply chain carbon footprint accounting model to generate the biopharmaceutical enterprise chain carbon footprint and the green quantitative evaluation system of the entire enterprise chain also includes: Processing the information of the biopharmaceutical enterprise to be evaluated based on the biopharmaceutical enterprise supply chain carbon footprint accounting model to generate a multi-factor comprehensive evaluation result of each evaluation object; The method also includes a calculation formula for calculating a multi-factor comprehensive evaluation result, and the calculation formula is: ； Among them, B represents the multi-factor comprehensive evaluation result vector of each evaluation object, W is a fuzzy vector, and its elements represents the weight distribution of different evaluation factors in the comprehensive evaluation, R is the fuzzy relationship matrix, Indicates The evaluation factors and The degree of fuzzy relationship between the evaluation levels, Represents the relationship between activities and products in an enterprise.

4. The method according to claim 1, characterized in that The processing of the information of the biopharmaceutical enterprise to be evaluated based on the biopharmaceutical enterprise supply chain carbon footprint accounting model to generate the biopharmaceutical enterprise chain carbon footprint and the green quantitative evaluation system of the entire enterprise chain also includes: Based on the carbon footprint accounting model of the biopharmaceutical enterprise supply chain, the information of the biopharmaceutical enterprise to be evaluated is processed to generate the carbon footprint of the biopharmaceutical enterprise chain and the green quantitative evaluation system of the entire enterprise chain including the carbon footprint of the enterprise supply chain; The method also includes a calculation formula for calculating the carbon footprint of the enterprise supply chain, the calculation formula is: ； in, is the total carbon footprint in the supply chain, Represents the collection of all enterprises in the supply chain, Represents the collection of all departments in the enterprise, Represents a collection of activities for a department, express Corporate In the department, Energy consumption generated by the activity, represents the emission factor of the energy source, Representative activities The discriminant factor.

5. The method according to claim 1, characterized in that The processing of the information of the biopharmaceutical enterprise to be evaluated based on the biopharmaceutical enterprise supply chain carbon footprint accounting model to generate the biopharmaceutical enterprise chain carbon footprint and the green quantitative evaluation system of the entire enterprise chain also includes: Based on the carbon footprint accounting model of the biopharmaceutical enterprise supply chain, the information of the biopharmaceutical enterprise to be evaluated is processed to generate the carbon footprint of the biopharmaceutical enterprise chain and the green quantitative evaluation system of the entire enterprise chain, including the efficiency measurement of the entire enterprise chain; The method also includes a calculation formula for calculating the efficiency measurement of the entire enterprise chain, and the calculation formula is: ； ； ； ； ； in, It represents the efficiency measurement of the entire enterprise chain, the z variable represents the input and output factors, and the u variable is the corresponding weight; It is the rth output in the R,-dimensional output vector of the pth stage of the model. This output is a normal output and does not serve as the input of the next stage but directly leaves the model; It is the kth output in the S,-dimensional output vector of the p-th stage of the model. This output is the intermediate output and serves as part of the input of the next stage p+1; is the i-th output in the input vector of dimension p+1 of the model. This input is a normal input and enters the model from the p+1 stage. In the last stage p, all outputs are regarded as normal output z and all outputs leave the model. The weight vectors corresponding to the above input and output are defined as: Output The corresponding weights; Output is the corresponding weight; For input The corresponding weight.

6. The method according to claim 1, characterized in that The target enterprise is processed based on the full life cycle of the target drug to generate a set of evaluation factors, including: Extracting features from the biopharmaceutical enterprise information to be evaluated to determine an original feature library, wherein the original feature library includes non-continuous variable data and continuous variable data; Processing the target enterprise based on the non-continuous variable data to generate initial evaluation factors; Processing the initial evaluation factors based on the continuous variable data to generate target evaluation factors; An evaluation factor set is generated based on the target evaluation factor.

7. A device for assessing carbon emissions throughout the life cycle of a biopharmaceutical enterprise chain, characterized in that: The device comprises: An acquisition module, used to acquire information of the biopharmaceutical enterprise to be evaluated, wherein the information of the biopharmaceutical enterprise to be evaluated is data related to the target enterprise; A processing module is used to process the information of the biopharmaceutical enterprise to be evaluated and generate the information to be evaluated of the product carbon chain, wherein the information to be evaluated includes the full life cycle of the target drug; based on the full life cycle of the target drug, the target enterprise is processed to generate a set of evaluation factors, wherein the set of evaluation factors includes several evaluation factors and the correlation between each evaluation factor and the target drug; based on a preset multi-factor judgment matrix, the set of evaluation factors is processed to generate weight information of the evaluation factors and the evaluation level of the evaluation factors, wherein the preset multi-factor judgment matrix is ​​composed of economic factors, environmental factors and social factors; based on the weight information of the evaluation factors and the evaluation level of the evaluation factors, a fuzzy matrix is ​​generated; based on the fuzzy matrix, several evaluation factors are processed to generate discriminant factors; based on the discriminant factors, a carbon footprint accounting model for the supply chain of biopharmaceutical enterprises is constructed; based on the carbon footprint accounting model for the supply chain of biopharmaceutical enterprises, the information of the biopharmaceutical enterprise to be evaluated is processed to generate a carbon footprint of the biopharmaceutical enterprise chain and a green quantitative evaluation system for the entire enterprise chain; The information of the biopharmaceutical enterprise to be evaluated is processed based on the carbon footprint accounting model of the biopharmaceutical enterprise supply chain to generate the carbon footprint of the biopharmaceutical enterprise chain and the green quantitative evaluation system of the whole enterprise chain, including: the information of the biopharmaceutical enterprise to be evaluated is processed based on the carbon footprint accounting model of the biopharmaceutical enterprise supply chain to generate the carbon footprint of the biopharmaceutical enterprise chain and the green quantitative evaluation system of the whole enterprise chain including the efficiency value of a single decision-making unit; the calculation formula for calculating the efficiency value of a single decision-making unit is: in, is the efficiency value of a single decision-making unit, x and y are the input and output values, respectively, and b is the directional distance of the frontier surface. are the specific values ​​of input, output and frontier distance related to a specific decision-making unit, k represents different decision-making units, M is related to the number of types of input indicators, N is related to the number of types of output indicators, and P is related to the number of parameters related to the frontier distance. are the slack variables of input and output and the variables related to the distance from the frontier surface. represents the mth input value of the kth decision-making unit, represents the nth output value of the kth decision-making unit, It represents the pth correlation value of the kth decision unit in the frontier direction distance.

8. An electronic device, characterized in that: include: a first processor; and a memory for storing executable instructions of the first processor; Wherein, the first processor is configured to execute the method for evaluating carbon emissions of the entire life cycle of a biopharmaceutical enterprise chain as described in any one of claims 1 to 6 by executing the executable instructions.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the second processor, the method for evaluating carbon emissions of a biopharmaceutical enterprise chain throughout its life cycle as described in any one of claims 1 to 6 is implemented.

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