Blockchain-based method and apparatus for determining product carbon footprint

Abstract

Embodiments of the present specification provide a product carbon emission determination method and device based on a blockchain, wherein the method comprises: receiving non-confidential resource consumption data corresponding to a target product and first carbon emission results corresponding to confidential resource consumption data; determining a carbon emission calculation model corresponding to the target product, calculating the non-confidential resource consumption data using the carbon emission calculation model, and obtaining calculation process information of the non-confidential resource consumption data and second carbon emission results; and uploading the non-confidential resource consumption data, the calculation process information, the second carbon emission results, and the first carbon emission results corresponding to the confidential resource consumption data to a blockchain. While ensuring that data that needs to be kept confidential is not leaked, the method reduces manual costs, avoids data tampering, facilitates data tracing, and further improves the accuracy of the final second carbon emission results and the first carbon emission results corresponding to the confidential resource consumption data.

Description

TECHNICAL FIELD

[0001] Embodiments of the present specification relate to the technical field of blockchain, and particularly relate to a product carbon emission determination method based on blockchain. BACKGROUND

[0002] With the improvement of environmental protection consciousness, more and more enterprises begin to pay attention to the reduction of carbon emission behavior. Determining the carbon footprint of a product can help to efficiently reduce carbon emission behavior. The carbon footprint is the greenhouse gas impact caused by a product based on the full life cycle of the product. Using product carbon footprint can provide a data basis for subsequent determination of emission reduction measures. However, the current determination of product carbon footprint mainly relies on manual collection of data in the full life cycle of the product and calculation, which will cost a lot of labor cost. SUMMARY

[0003] Therefore, the embodiments of the present specification provide a product carbon emission determination method based on blockchain. One or more embodiments of the present specification also relate to a product carbon emission determination device based on blockchain, another product carbon emission determination method based on blockchain, another product carbon emission determination device based on blockchain, a product carbon emission determination system based on blockchain, a computing device, a computer readable storage medium and a computer program to solve the technical defects in the prior art.

[0004] receive non-confidential resource consumption data corresponding to a target product and a first carbon emission result corresponding to confidential resource consumption data;

[0005] determine a carbon emission calculation model corresponding to the target product, calculate the non-confidential resource consumption data using the carbon emission calculation model, and obtain calculation process information of the non-confidential resource consumption data and a second carbon emission result;

[0006] upload the non-confidential resource consumption data, the calculation process information, the second carbon emission result and the first carbon emission result to a blockchain.

[0007] According to a second aspect of the embodiments of the present specification, a product carbon emission determination device based on blockchain is provided, comprising:

[0008] The receiving module is configured to receive non-confidential resource consumption data corresponding to a target product and a first carbon emission result corresponding to confidential resource consumption data;

[0009] The calculation module is configured to determine a carbon emission calculation model corresponding to the target product, calculate the non-confidential resource consumption data using the carbon emission calculation model, and obtain calculation process information of the non-confidential resource consumption data and a second carbon emission result;

[0010] an uploading module configured to upload the non-confidential resource consumption data, the computing process information, the second carbon emission result, and the first carbon emission result to a blockchain.

[0011] According to a third aspect of the embodiments of the present specification, a product carbon emission determination method based on a blockchain is provided, applied to a data collection end, comprising:

[0012] collecting non-confidential resource consumption data and confidential resource consumption data corresponding to a target product;

[0013] calculating a first carbon emission result corresponding to the confidential resource consumption data according to the confidential resource consumption data;

[0014] sending the non-confidential resource consumption data and the first carbon emission result to a product carbon emission determination platform.

[0015] According to a fourth aspect of the embodiments of the present specification, a product carbon emission determination device based on a blockchain is provided, applied to a data collection end, comprising:

[0016] a collecting module configured to collect non-confidential resource consumption data and confidential resource consumption data corresponding to a target product;

[0017] a calculating module configured to calculate a first carbon emission result corresponding to the confidential resource consumption data according to the confidential resource consumption data;

[0018] a sending module configured to send the non-confidential resource consumption data and the first carbon emission result to a product carbon emission determination platform.

[0019] According to a fifth aspect of the embodiments of the present specification, a product carbon emission determination system based on a blockchain is provided, comprising a data collection end and a product carbon emission determination platform, wherein,

[0020] the data collection end is configured to collect non-confidential resource consumption data and confidential resource consumption data corresponding to a target product, calculate a first carbon emission result corresponding to the confidential resource consumption data according to the confidential resource consumption data, and send the non-confidential resource consumption data and the first carbon emission result to the product carbon emission determination platform;

[0021] The product carbon emission determination platform is configured to receive non-confidential resource consumption data corresponding to a target product and a first carbon emission result; determine a carbon emission calculation model corresponding to the target product, calculate the non-confidential resource consumption data by using the carbon emission calculation model, obtain calculation process information of the non-confidential resource consumption data and a second carbon emission result; and upload the non-confidential resource consumption data, the calculation process information, the second carbon emission result, and the first carbon emission result to a blockchain.

[0022] According to a sixth aspect of an embodiment of the present specification, a computing device is provided, comprising:

[0023] a memory and a processor;

[0024] The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, which, when executed by the processor, implement the steps of the above product carbon emission determination method based on a blockchain.

[0025] According to a seventh aspect of an embodiment of the present specification, a computer readable storage medium is provided, which stores computer executable instructions, which, when executed by a processor, implement the steps of the above product carbon emission determination method based on a blockchain.

[0026] According to an eighth aspect of an embodiment of the present specification, a computer program is provided, which, when executed in a computer, causes the computer to perform the steps of the above product carbon emission determination method based on a blockchain.

[0027] One embodiment of the present specification provides a product carbon emission determination method based on a blockchain, which receives non-confidential resource consumption data corresponding to a target product and a first carbon emission result corresponding to confidential resource consumption data; determines a carbon emission calculation model corresponding to the target product, calculates the non-confidential resource consumption data by using the carbon emission calculation model, obtains calculation process information of the non-confidential resource consumption data and a second carbon emission result; and uploads the non-confidential resource consumption data, the calculation process information, the second carbon emission result, and the first carbon emission result to a blockchain.

[0028] The method receives the first carbon emission result corresponding to the non-confidential resource consumption data and the confidential resource consumption data of the target product, calculates the non-confidential resource consumption data by using a carbon emission calculation model, obtains a second carbon emission result and calculation process information when the non-confidential resource consumption data is calculated, and uploads the second carbon emission result and the calculation process information to a block chain. The first carbon emission result corresponding to the confidential resource consumption data is received, and the confidential resource consumption data is not received. The data that needs to be kept confidential is prevented from being leaked, the artificial cost is reduced, the data is prevented from being tampered, the data traceability is facilitated, and the accuracy of the final second carbon emission result and the first carbon emission result is further improved. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 is a scenario diagram of a product carbon emission determination method based on a block chain provided by an embodiment of the present specification;

[0030] Figure 2 is a flowchart of a product carbon emission determination method based on a block chain provided by an embodiment of the present specification;

[0031] Figure 3 is a schematic diagram of uploading a block chain in a product carbon emission determination method based on a block chain provided by an embodiment of the present specification;

[0032] Figure 4 is a process flowchart of a product carbon emission determination method based on a block chain provided by an embodiment of the present specification;

[0033] Figure 5 is a structural schematic diagram of a product carbon emission determination device based on a block chain provided by an embodiment of the present specification;

[0034] Figure 6 is a flowchart of another product carbon emission determination method based on a block chain provided by an embodiment of the present specification;

[0035] Figure 7 is a structural schematic diagram of another product carbon emission determination device based on a block chain provided by an embodiment of the present specification;

[0036] Figure 8 is a structural schematic diagram of a product carbon emission determination system based on a block chain provided by an embodiment of the present specification;

[0037] Figure 9 is a structural block diagram of a computing device provided by an embodiment of the present specification. DETAILED DESCRIPTION

[0038] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present description. However, the present description can be practiced without the specific details. In other instances, well-known methods have not been described in detail in order not to unnecessarily obscure aspects of the present description.

[0039] The terminology used in this description of one or more embodiments is for the purpose of describing particular embodiments only and is not intended to be limiting of one or more embodiments of the present description. As used in this description and the appended claims, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0040] It will be understood that, although the terms first, second, etc. can be used herein to describe various information, these terms are not intended to denote a temporal or chronological order. Rather, these terms are used solely to distinguish one from another only. For example, a first item could be termed a second item, and, similarly, a second item could be termed a first item without departing from the scope of one or more embodiments of the present description. As used herein, the term "if' can be construed to mean "when" or "upon" or "in response to determining" terms that indicate a logical relationship between an act and a condition or an event.

[0041] First, the noun terms related to one or more embodiments of the present description are explained.

[0042] Product Carbon Footprint: The sum of GHG emissions and GHG removals in a product system, expressed as carbon dioxide equivalents, and based on life cycle assessment using the single category of climate change impact.

[0043] GHG: Green House Gas, greenhouse gas.

[0044] LCA: Life cycle Assesment, life cycle assessment.

[0045] UPR: unit process, single unit process, refers to the definition of a process for an activity in the whole life cycle, which contains what inputs and outputs, etc.

[0046] PCR: Product Category Rules, product life cycle assessment technical specification.

[0047] IOT: Internet of Things, is the Internet, traditional telecommunications network, etc. Information carrier, let all the ordinary objects that can exercise independent function realize interconnection network.

[0048] In the present specification, a product carbon emission determination method based on a blockchain is provided. The present specification also relates to a product carbon emission determination device based on a blockchain, another product carbon emission determination method based on a blockchain, another product carbon emission determination device based on a blockchain, a product carbon emission determination system based on a blockchain, a computing device, and a computer-readable storage medium. Each is described in detail in the following embodiments.

[0049] Referring to Figure 1 , Figure 1 A scenario diagram of a product carbon emission determination method based on a blockchain according to an embodiment of the present specification is shown.

[0050] Figure 1 It includes a client 102, a server 104 and a blockchain node 106. Among them, the client 102 is in communication connection with the server 104, and the server 104 can execute a product carbon emission determination method based on a blockchain.

[0051] In specific implementation, a user can send a product carbon emission determination request to the server 104 through the client 102. After receiving the product carbon emission determination request, the server 104 can receive the product's confidential resource consumption data corresponding to the product carbon emission determination request and the first carbon emission result corresponding to the confidential resource consumption data, and calculate the non-confidential resource consumption data using the product's corresponding carbon emission calculation model, obtain the second carbon emission result, and retain the calculation process information and non-confidential resource consumption data of the non-confidential resource consumption data. According to the non-confidential resource consumption data, the calculation process information of the non-confidential resource consumption data, the first carbon emission result and the second carbon emission result, a carbon emission report is generated, and the carbon emission report is uploaded to the blockchain node 106. The carbon footprint of the product is calculated, and the authenticity of the carbon footprint report is ensured.

[0052] Referring to Figure 2 , Figure 2 A flowchart of a product carbon emission determination method based on a blockchain according to an embodiment of the present specification is shown, which specifically includes the following steps.

[0053] Step 202: receiving non-confidential resource consumption data corresponding to a target product and first carbon emission result corresponding to confidential resource consumption data.

[0054] Specifically, the carbon emission of the product based on the blockchain can be understood as the carbon footprint of the product in the production process.

[0055] The non-confidential resource consumption data corresponding to the target product can be understood as the resource consumption data without security requirements in the production process of the target product. For example, the non-confidential resource consumption data can be the electricity consumption, gas consumption, water consumption, etc. in the production of the target product, and can also be the usage of raw materials, transportation information, product yield, and waste production, etc. in the production of the target product. The first carbon emission result corresponding to the confidential resource consumption data can be understood as the carbon footprint generated by the resource consumption data with security requirements in the production process of the target product. The resource consumption data with security requirements can be, for example, the data of the production formula of the target product.

[0056] In actual application, the non-confidential resource consumption data corresponding to the target product and the first carbon emission result corresponding to the confidential resource consumption data can be received by the data collection end. The data collection end can be understood as a client, such as an IOT data collection device of an enterprise. For example, for a product production enterprise, the enterprise needs to calculate the carbon footprint in the production process of the product. Therefore, the enterprise can use the data collection device to collect and calculate the confidential resource consumption data.

[0057] Step 204: determining a carbon emission calculation model corresponding to the target product, calculating the non-confidential resource consumption data by using the carbon emission calculation model, and obtaining calculation process information of the non-confidential resource consumption data and a second carbon emission result.

[0058] The carbon emission calculation model corresponding to the target product can be understood as the calculation formula of the carbon footprint of the target product. The calculation process information of the non-confidential resource consumption data can be understood as the record of the process of calculating the carbon footprint of the non-confidential resource consumption data. The second carbon emission result can be understood as the calculation result of the carbon footprint of the target product calculated by using the non-confidential resource consumption data.

[0059] In actual application, the calculation framework of the carbon emission calculation model can be generated according to the PCR file corresponding to the target product and the existing model corresponding to the target product in the UPR model library. The collected non-confidential resource consumption data can be imported into the calculation framework, and the carbon footprint of the target product can be obtained. The UPR model library records the production process and emission inventory data of various products, and constructs a probability distribution model for each product based on the emission inventory data. There is a correlation between the list data, and the probability distribution of the initial resource consumption data of the product in any period of time obeys a multivariate Gaussian distribution model. The probability distribution model of each product can be used to check the collected initial resource consumption data.

[0060] In particular implementation, the calculation of the non-confidential resource consumption data by using the carbon emission calculation model obtains calculation process information of the non-confidential resource consumption data and a second carbon emission result, including:

[0061] The non-confidential resource consumption data is divided to obtain non-confidential resource consumption data of at least two stages;

[0062] The non-confidential resource consumption data of the first stage is subjected to data checking to obtain checked non-confidential resource consumption data, wherein the first stage is any one of the at least two stages;

[0063] The checked non-confidential resource consumption data is input into the carbon emission calculation model to obtain the calculation process information of the non-confidential resource consumption data and the second carbon emission result.

[0064] Specifically, when the non-confidential resource consumption data is divided, it can be divided according to the production process of the target product. For example, for the production process of clothing products, it is divided into four production stages of spinning, weaving, printing and dyeing, and garment making. Therefore, according to the production process, the non-confidential resource consumption data of clothing products can be divided into four stages, and each stage includes non-confidential resource consumption data of the stage. The non-confidential resource consumption data of each stage is subjected to data checking, and abnormal data is deleted to obtain checked non-confidential resource consumption data.

[0065] In particular implementation, the non-confidential resource consumption data of the first stage includes at least one data set;

[0066] Correspondingly, the data checking of the non-confidential resource consumption data of the first stage to obtain the checked non-confidential resource consumption data includes:

[0067] According to the probability distribution model of the resource consumption data corresponding to the target product, the credibility probability of the first data set is calculated, wherein the first data set is any one of the at least one data set, and the first data set includes non-confidential resource consumption data of a sub-stage in the first stage;

[0068] In the case where the credibility probability of the first data set is less than a preset credibility threshold, the first data set is regarded as an abnormal data set;

[0069] In the case where the number of the abnormal data set is less than a preset number threshold, the abnormal data set is deleted, and the data set in the at least one data set except the first data set is regarded as the checked non-confidential resource consumption data;

[0070] In a case where the number of the abnormal data sets is greater than a preset number threshold, the non-confidential resource consumption data of the first stage is deleted, and the data of the non-confidential resource consumption data of the at least one stage except the non-confidential resource consumption data of the first stage is taken as the non-confidential resource consumption data after verification.

[0071] In the first stage, at least two sub-stages can be divided, and the non-confidential resource consumption data in each sub-stage is taken as a data set. For example, for the textile stage of a clothing product, the textile stage includes 30 days, and the resource consumption data of each day can be taken as a data set, that is, the non-confidential resource consumption data of the textile stage includes 30 data sets.

[0072] The probability distribution model can be understood as a probability distribution model constructed based on the existing emission inventory data of the target product, which is subject to a multivariate Gaussian distribution model, as shown in formulas (1), (2) and (3).

[0073]

[0074]

[0075]

[0076] wherein x (i) represents the random vector of the i-th emission inventory data, μ is the mean vector thereof, ∑ is the covariance matrix of the multivariate Gaussian distribution model, and p(x) is the probability density function. For different data sets, due to the differences in production process and technical level of the product, different credibility thresholds are provided.

[0077] Therefore, in actual application, the credibility probability of each data set can be calculated by using the above formula (3), and in a case where the credibility probability of the data set is less than a preset credibility threshold, it is indicated that the data set is an abnormal data set.

[0078] In addition, for the data sets included in each stage, when the number of abnormal data sets is less than a preset number threshold, the abnormal data sets can be deleted. When the number of abnormal data sets is greater than the preset number threshold, it is indicated that the data of the stage is unqualified, and therefore all the data sets of the stage are deleted.

[0079] In actual application, the abnormal data sets included in a stage with a ratio less than a preset ratio threshold can be deleted, and all the data sets included in a stage with a ratio greater than the preset ratio threshold can be deleted, according to the comparison between the ratio of the number of abnormal data sets to the number of all data sets included in each stage and the preset ratio threshold.

[0080] For example, for the non-confidential resource consumption data of product A in stage 1 and stage 2, stage 1 includes 30 days, and stage 2 includes 10 days, then the non-confidential resource consumption data in stage 1 includes 30 data sets, and the non-confidential resource consumption data in stage 2 includes 10 data sets. After calculating the credibility probability of each data set, it is determined that there are 10 abnormal data sets in stage 1 and 1 abnormal data set in stage 2, then the ratio of abnormal data sets to all data sets in stage 1 is 10 / 30 equal to 0.33, the ratio of abnormal data sets to all data sets in stage 2 is 1 / 10 equal to 0.1, the preset proportion threshold is 0.2, the ratio in stage 1 is greater than the preset proportion threshold, at this time all the non-confidential resource consumption data in stage 1 is deleted, that is, the non-confidential resource consumption data in stage 1 is not selected for the calculation of carbon footprint. The ratio in stage 2 is less than the preset proportion threshold, so delete the 1 abnormal data set in stage 2, and the remaining 9 data sets are the non-confidential resource consumption data after verification.

[0081] In summary, by performing data verification on the non-confidential resource consumption data, the abnormal data can be removed, so that the subsequent calculation of the carbon footprint is more accurate.

[0082] In practical application, when calculating the second carbon emission result, the influence factor of the target product can be calculated, and the specific implementation is as follows:

[0083] Determine the influence factor corresponding to the target product;

[0084] Using the carbon emission calculation model, according to the influence factor and the verified non-confidential resource consumption data, the second carbon emission result is calculated, and the calculation process information of the non-confidential resource consumption data is generated.

[0085] Wherein, the influence factor can be understood as an emission factor that affects the calculation result of the carbon footprint, which can be, for example, raw materials, transportation or energy in the production process of the target product.

[0086] In practical application, the influence factor with the highest matching degree can be selected from the LCA database according to the geographical, time, production technology and other information of product production, and the corresponding second carbon emission result (i.e. product carbon footprint) is calculated using the following formula (4).

[0087]

[0088] Wherein, CF is the product carbon footprint, n is the total number of emission lists, S i is the activity data of the i-th emission list, and the corresponding emission factor is EF i .

[0089] In practical applications, after obtaining the calculation process information of the non-confidential resource consumption data and the second carbon emission result, emission reduction measures can be determined, and the specific implementation is as follows:

[0090] An influence factor of at least one dimension corresponding to the target product is determined.

[0091] In the case that the influence factor of the first dimension changes, the sensitivity of the second carbon emission result is calculated, wherein the first dimension is any one of the at least one dimension.

[0092] According to the calculation result, a target influence factor is determined from the influence factors of the at least one dimension.

[0093] According to the target influence factor, a target emission reduction strategy is generated.

[0094] The target emission reduction strategy can be understood as a strategy for reducing the carbon emission of the target product.

[0095] Specifically, the influence factor of each dimension can be changed, and the sensitivity of the second carbon emission result is calculated according to the change value. The influence factor reaching a preset sensitivity threshold is determined as the target influence factor.

[0096] In practical applications, according to the carbon footprint calculation result (i.e., the second carbon emission result), the sensitivity of the carbon footprint result can be analyzed from three dimensions of raw materials, energy and transportation.

[0097] For the raw material dimension, the use amount of raw materials can be reduced by adjusting and optimizing the production process, recycling and other methods, i.e., reducing the activity level D i , and the sensitivity of the product carbon footprint result to the raw material dimension can be calculated by the following formula (5):

[0098] E m,i = ΔCF / ΔD i (5)

[0099] Wherein, ΔCF is the change value of the product carbon footprint after changing the raw material activity level, and ΔD i is the change amount of the raw material activity level.

[0100] The use of raw materials can also be replaced by selecting a more low-carbon material, i.e., changing the emission factor EF i of the material, and the sensitivity of the product carbon footprint result to the raw material dimension can be calculated by the following formula (6):

[0101] E m,ef,i = ΔCF / ΔEF i (6)

[0102] Where, ΔCF is the change of product carbon footprint after replacing raw materials, ΔEF i is the change of material emission factor.

[0103] For the energy dimension, the use of energy can be reduced by adjusting the production process, changing the use habits, etc., that is, reducing the energy activity level D i The sensitivity of the product carbon footprint result to it can be calculated by formula (7) as follows:

[0104] E en,i = ΔCF / ΔD en,i (7)

[0105] Where, ΔCF is the change of product carbon footprint after changing the energy activity level, ΔD en,i is the change of energy activity level.

[0106] The energy structure can also be changed by using green energy, etc., that is, changing the emission factor EF en,i The sensitivity of the product carbon footprint result to it can be calculated by formula (8) as follows:

[0107] E en,ef,i = ΔCF / ΔEF en,i (8)

[0108] Where, ΔCF is the change of product carbon footprint after changing the energy structure, ΔEF en,i is the change of energy emission factor.

[0109] For the transportation dimension, the transportation distance can be adjusted by selecting different suppliers (delivery locations) of raw materials, that is, changing the transportation distance data D T,i The sensitivity of the product carbon footprint result to it can be calculated by formula (9) as follows:

[0110] E T,i = ΔCF / ΔD T,i (9)

[0111] Where, ΔCF is the change of product carbon footprint after changing the transportation distance, ΔD T,i is the change of transportation distance.

[0112] Different material transportation methods or different fuel types can also be selected, that is, the emission factor EF T,i corresponding to transportation is changed. The sensitivity of the product carbon footprint result to it can be calculated by formula (10) as follows:

[0113] E T,ef,i = ΔCF / ΔEF T,i (10)

[0114] Wherein, AFC is the change value of product carbon footprint after changing the material transportation mode, AEF is the change value of transportation emission factor. T,i is the change value of transportation emission factor.

[0115] In the case of changing the influence factors of different dimensions, the sensitivity of the product carbon footprint calculation result can be calculated, and from the calculated multiple sensitivities, the influence factors whose sensitivity reaches the preset sensitivity threshold are selected, and these influence factors are determined as target influence factors, and then a target emission reduction strategy is generated according to the target influence factors.

[0116] In summary, by calculating the sensitivity of the carbon footprint calculation result in the case of changing the influence factors of different dimensions, and selecting the influence factors with higher sensitivity according to the sensitivity, the accuracy and optimization of the subsequent target emission reduction strategy generation are realized.

[0117] In specific implementation, the generating a target emission reduction strategy according to the target influence factors comprises:

[0118] determining at least one initial emission reduction strategy according to the target influence factors;

[0119] determining a cost value corresponding to a first initial emission reduction strategy, wherein the first initial emission reduction strategy is any one of the at least one initial emission reduction strategy;

[0120] selecting an initial emission reduction strategy with the minimum cost value as the target emission reduction strategy.

[0121] It can be understood that the influence factors whose sensitivity reaches the preset sensitivity threshold can include at least two, that is, the determined target influence factors can include at least two, and then multiple initial emission reduction strategies can be determined according to each target influence factor. In order to obtain an optimized strategy that takes into account economic efficiency and emission reduction benefits, the cost value corresponding to each initial emission reduction strategy can be calculated, and an initial emission reduction strategy with the minimum cost value is selected as the target emission reduction strategy.

[0122] In actual application, the minimum cost value in one operation cycle can be taken as a target function for solving the optimization problem. Specifically, the following formula (11) can be used for calculation.

[0123] min c sch = c op - c off - c re (11)

[0124] Wherein, c sch is the equal annual cost, c op is the cost of implementing the emission reduction optimization scheme, c off is the carbon offset cost corresponding to the reduced product emission, and c rec op , c off , c re may be calculated by the following equations (12), (13) and (14) respectively:

[0125]

[0126]

[0127]

[0128] wherein c op,i is the cost of implementing the optimization scheme of the i-th emission reduction measure based on sensitivity analysis, β i is the implementation coefficient of the i-th emission reduction measure, ΔCF op,i is the change amount of carbon footprint brought by the i-th emission reduction measure, c CO2 is the price of market carbon offset quota, and α is the emission reduction benefit coefficient set after evaluation, and V is the total value of the current product.

[0129] In the optimization solution of the above problem, the following constraints need to be met:

[0130]

[0131] If β i = 0, it means that the emission reduction measure is not performed, and if β i = 1, it means that the emission reduction measure is performed.

[0132] In the optimization solution, a solver can be used to solve the problem, and an optimization scheme of economic efficiency and emission reduction benefit can be proposed according to the solution result.

[0133] In summary, through the optimization problem solution, the target emission reduction strategy with the minimum cost value is obtained, so that the generated carbon emission reduction strategy can take into account the economic efficiency and emission reduction benefit, so that the subsequent use of the carbon emission reduction strategy can ensure the emission reduction effect and consider the cost.

[0134] In actual application, in order to ensure the visualization of data, after obtaining the calculation process information of the non-confidential resource consumption data and the second carbon emission result, the method further includes:

[0135] According to the non-confidential resource consumption data, the calculation process information of the non-confidential resource consumption data, the second carbon emission result and the first carbon emission result corresponding to the confidential resource consumption data, a preset report template is used to generate a display interface and a carbon emission report containing the second carbon emission result and the first carbon emission result.

[0136] The carbon emission report can be understood as a carbon footprint certification report of the target product. The content of the carbon emission report can include a detailed process of life cycle product carbon footprint calculation, initial resource consumption data of the production process, selection of influencing factors, automatic analysis of emission results, and suggestions for emission reduction strategies. The display interface can be used for low-carbon promotion of the product, etc.

[0137] In summary, by generating the carbon emission report, data filling errors caused by manual report filling can be avoided, human resources can be reduced, and rapid carbon footprint certification can be achieved.

[0138] In addition, in order to ensure the authenticity and timeliness of the carbon footprint calculation result, the carbon emission report can be sent to the authentication end, and the carbon emission report authentication result sent by the authentication end can be received.

[0139] The carbon emission report authentication result can be, for example, a carbon footprint certificate. The authentication end can upload the certificate number, certificate validity period, product information, carbon footprint certification result, and audit person in charge of the carbon footprint certificate to the blockchain.

[0140] Step 206: uploading the non-confidential resource consumption data, the calculation process information, the second carbon emission result, and the first carbon emission result to the blockchain.

[0141] Specifically, after obtaining the second carbon emission result, the non-confidential resource consumption data, the calculation process information, the second carbon emission result, and the first carbon emission result corresponding to the confidential resource consumption data can be uploaded to the blockchain.

[0142] In addition, after obtaining the non-confidential resource consumption data, the calculation process information, and the second carbon emission result, the method further includes:

[0143] generating a carbon emission set corresponding to the target product according to the non-confidential resource consumption data, the calculation process information of the non-confidential resource consumption data, the second carbon emission result, and the first carbon emission result;

[0144] uploading the carbon emission set to the blockchain.

[0145] The carbon emission set can be understood as a set of carbon footprint calculation results corresponding to the target product.

[0146] In addition, the created carbon emission set can also be uploaded to the UPR model library.

[0147] By uploading the established carbon emission set to the blockchain, it can be used as a template or reference in the modeling process of subsequent similar products.

[0148] In practical applications, different data can be uploaded to different data layers in the blockchain. As shown in Figure 3 Figure 3 A schematic diagram of uploading a blockchain in a blockchain-based product carbon emission determination method provided by one embodiment of the present specification is shown. As shown in Figure 3 The data uploaded to the blockchain can be divided into a source data layer 302, a carbon emission set layer 304, a carbon footprint data layer 306, and a carbon footprint authentication layer 308.

[0149] The data layers are divided according to different data sources. The source data layer 302 comes from the data collection end, the carbon emission set layer 304 comes from the data stored in the model, the carbon footprint data layer 306 comes from the result value calculated by the carbon emission calculation model, and the data of the carbon footprint authentication layer 308 comes from the authentication end upload. Each layer can trace back the data of the previous layer.

[0150] The source data layer 302 includes real-time collected data and offline uploaded data, the carbon emission set layer 304 includes emission inventory activity data and selected emission factors, the carbon footprint data layer 306 is divided into first carbon emission results corresponding to secret resource consumption data and second carbon emission results, and the carbon footprint authentication layer 308 includes authentication information of carbon emission report authentication results.

[0151] When it is necessary to review the product carbon footprint results and the authentication process, the first carbon emission results and the second carbon emission results corresponding to the secret resource consumption data in the carbon footprint data layer 306 can be traced back according to the authentication information of the carbon footprint authentication layer 308; the emission inventory activity data and the emission factor data in the carbon emission set layer 304 associated with the second carbon emission results can be traced back through the second carbon emission results; but for the first carbon emission results corresponding to the secret resource consumption data, since the first carbon emission results corresponding to the secret resource consumption data are directly stored after real-time calculation of the secret resource consumption data in the source data layer 302 when the secret resource consumption data is collected by the data collection end, it is not possible to trace back the corresponding production activity data; the real-time collected data and the offline uploaded data associated with the emission inventory activity data in the carbon emission set layer 304 can be traced back. Through data hierarchical tracing, the traceability of carbon footprint data, calculation process and authentication information can be ensured, and the credibility can be improved.

[0152] ​In summary, the above method receives the first carbon emission result corresponding to the non-confidential resource consumption data and the confidential resource consumption data of the target product, calculates the non-confidential resource consumption data by using the carbon emission calculation model, obtains the second carbon emission result and the calculation process information when the non-confidential resource consumption data is calculated, and uploads to the blockchain. Only the first carbon emission result corresponding to the confidential resource consumption data is received for the confidential data, and the confidential resource consumption data is not received. While ensuring that the data that needs to be kept confidential will not be leaked, the artificial cost is reduced, and the data can be avoided from being tampered with, facilitating the traceability of the data, and further improving the accuracy of the final second carbon emission result and the first carbon emission result corresponding to the confidential resource consumption data.

[0153] The following describes the embodiments of the present application in conjunction with the accompanying Figure 4 The application of the product carbon footprint calculation based on the product carbon emission determination method based on the blockchain provided in the specification is taken as an example to further illustrate the product carbon emission determination method based on the blockchain. Among them, Figure 4 A processing process flow diagram of a product carbon emission determination method based on a blockchain provided by an embodiment of the present specification is shown, which specifically includes the following steps.

[0154] Step 402: receiving non-confidential resource consumption data corresponding to a target product and first carbon emission results corresponding to confidential resource consumption data.

[0155] Specifically, the first carbon emission results corresponding to the non-confidential resource consumption data and the confidential resource consumption data sent by the data collection end can be received.

[0156] Step 404: determining a carbon emission calculation model corresponding to the target product.

[0157] Specifically, the carbon footprint calculation formula corresponding to the target product and the emission factor corresponding to the target product can be determined.

[0158] Step 406: calculating the non-confidential resource consumption data by using the carbon emission calculation model, obtaining the calculation process information of the non-confidential resource consumption data and the second carbon emission result.

[0159] Step 408: determining at least one dimension of the influence factor corresponding to the target product, calculating the sensitivity of the second carbon emission result in the case of changing the influence factor of each dimension, determining the target influence factor from the influence factor of at least one dimension according to the calculation result, and generating a target emission reduction strategy according to the target influence factor.

[0160] Step 410: generating a display interface and a carbon emission report containing the second carbon emission result and the first carbon emission result according to the non-confidential resource consumption data, the calculation process information of the non-confidential resource consumption data, the second carbon emission result, and the first carbon emission result, and using a preset report template.

[0161] Step 412: sending the carbon emission report to an authentication end and receiving a carbon emission report authentication result sent by the authentication end.

[0162] In summary, the above method receives a first carbon emission result corresponding to non-confidential resource consumption data and confidential resource consumption data of a target product, calculates the non-confidential resource consumption data by using a carbon emission calculation model, obtains a second carbon emission result and calculation process information when the non-confidential resource consumption data is calculated, and uploads to a blockchain. Only the first carbon emission result corresponding to the confidential resource consumption data is received for the confidential data, and the confidential resource consumption data is not received. The data that needs to be kept confidential is not leaked, the artificial cost is reduced, the data is not tampered with, the data traceability is facilitated, and the accuracy of the final second carbon emission result and the first carbon emission result corresponding to the confidential resource consumption data is further improved.

[0163] Corresponding to the above method embodiment, the present specification also provides a product carbon emission determination device based on a blockchain, Figure 5 A structure schematic diagram of a product carbon emission determination device based on a blockchain is shown, which is provided by one embodiment of the present specification. As shown in the figure, Figure 5 The device comprises:

[0164] The receiving module 502 is configured to receive non-confidential resource consumption data corresponding to a target product and a first carbon emission result corresponding to confidential resource consumption data.

[0165] The calculation module 504 is configured to determine a carbon emission calculation model corresponding to the target product, calculate the non-confidential resource consumption data by using the carbon emission calculation model, and obtain calculation process information of the non-confidential resource consumption data and a second carbon emission result.

[0166] The uploading module 506 is configured to upload the non-confidential resource consumption data, the calculation process information, the second carbon emission result, and the first carbon emission result to a blockchain.

[0167] In an optional embodiment, the calculation module 504 is further configured to:

[0168] divide the non-confidential resource consumption data to obtain non-confidential resource consumption data of at least two stages.

[0169] performing data verification on the non-confidential resource consumption data of the first stage to obtain verified non-confidential resource consumption data, wherein the first stage is any one of the at least two stages;

[0170] inputting the verified non-confidential resource consumption data into the carbon emission calculation model to obtain the calculation process information of the non-confidential resource consumption data and a second carbon emission result.

[0171] In an optional embodiment, the non-confidential resource consumption data of the first stage includes at least one data set, and the calculation module 504 is further configured to:

[0172] calculate a credibility probability of a first data set according to a probability distribution model of the resource consumption data corresponding to the target product, wherein the first data set is any one of the at least one data set, and the first data set includes non-confidential resource consumption data of a sub-stage in the first stage;

[0173] in a case where the credibility probability of the first data set is less than a preset credibility threshold, regarding the first data set as an abnormal data set;

[0174] in a case where the number of abnormal data sets is less than a preset number threshold, deleting the abnormal data sets and regarding data sets in the at least one data set except the first data set as verified non-confidential resource consumption data;

[0175] in a case where the number of abnormal data sets is greater than the preset number threshold, deleting the non-confidential resource consumption data of the first stage and regarding non-confidential resource consumption data in the non-confidential resource consumption data of the at least one stage except the non-confidential resource consumption data of the first stage as verified non-confidential resource consumption data.

[0176] In an optional embodiment, the calculation module 504 is further configured to:

[0177] determine an influence factor corresponding to the target product;

[0178] calculate a second carbon emission result according to the influence factor and the verified non-confidential resource consumption data by using the carbon emission calculation model, and generate calculation process information of the non-confidential resource consumption data.

[0179] In an optional embodiment, the apparatus further includes a generation module configured to:

[0180] determine an influence factor of at least one dimension corresponding to the target product;

[0181] In the case of a change in the influence factor of the first dimension, the sensitivity of the second carbon emission result is calculated, wherein the first dimension is any one of the at least one dimension;

[0182] According to the calculation result, a target influence factor is determined from the influence factors of the at least one dimension;

[0183] According to the target influence factor, a target emission reduction strategy is generated.

[0184] In an optional embodiment, the generation module is further configured to:

[0185] According to the target influence factor, at least one initial emission reduction strategy is determined;

[0186] A cost value corresponding to a first initial emission reduction strategy is determined, wherein the first initial emission reduction strategy is any one of the at least one initial emission reduction strategy;

[0187] The initial emission reduction strategy with the minimum cost value is taken as the target emission reduction strategy.

[0188] In an optional embodiment, the generation module is further configured to:

[0189] According to the non-confidential resource consumption data, the calculation process information of the non-confidential resource consumption data, the second carbon emission result and the first carbon emission result, a preset report template is used to generate a display interface and a carbon emission report containing the second carbon emission result and the first carbon emission result.

[0190] In an optional embodiment, the apparatus further includes a sending module configured to:

[0191] The carbon emission report is sent to an authentication end, and a carbon emission report authentication result sent by the authentication end is received.

[0192] In an optional embodiment, the uploading module 506 is further configured to:

[0193] According to the non-confidential resource consumption data, the calculation process information of the non-confidential resource consumption data, the second carbon emission result and the first carbon emission result, a carbon emission set corresponding to the target product is generated;

[0194] The carbon emission set is uploaded to the blockchain.

[0195] In summary, the above device receives the non-confidential resource consumption data of the target product and the first carbon emission result corresponding to the confidential resource consumption data, calculates the non-confidential resource consumption data using the carbon emission calculation model, obtains the second carbon emission result and the calculation process information when calculating the non-confidential resource consumption data, and uploads to the blockchain. For confidential data, only the first carbon emission result corresponding to the confidential resource consumption data is received, and the confidential resource consumption data is not received. While ensuring that the data that needs to be kept confidential cannot be leaked, the artificial cost is reduced, and the data can be avoided from being tampered with, facilitating the traceability of the data, and further improving the accuracy of the final second carbon emission result and the first carbon emission result corresponding to the confidential resource consumption data.

[0196] The above is a schematic scheme of a product carbon emission determination device based on a blockchain according to an embodiment. It should be noted that the technical scheme of the product carbon emission determination device based on a blockchain belongs to the same concept as the technical scheme of the product carbon emission determination method based on a blockchain described above. The technical scheme of the product carbon emission determination device based on a blockchain, which is not described in detail, can be referred to the description of the technical scheme of the product carbon emission determination method based on a blockchain described above.

[0197] Corresponding to the method embodiment described above, the present specification also provides a product carbon emission determination method based on a blockchain, applied to a data collection end, referring to Figure 6 , Figure 6 A flowchart of another product carbon emission determination method based on a blockchain according to an embodiment of the present specification is shown, which specifically includes the following steps.

[0198] Step 602: Collecting non-confidential resource consumption data and confidential resource consumption data corresponding to the target product.

[0199] In practical applications, when collecting resource consumption data, resource consumption data can be collected in real time according to time, such as collecting the daily electricity consumption, gas consumption, water consumption, and raw material consumption during the production of the target product. The source data of enterprise production can be collected by IOT data collection equipment, such as smart electricity meters for collecting metered electricity consumption, smart water meters for collecting metered water consumption, photovoltaic meters for collecting the power generation of photovoltaic power generation equipment, etc. For raw material consumption, transportation information, product yield, and waste generation data, data collection can also be achieved through offline uploading, such as uploading data through enterprise internal data systems, enterprise internal workshop production statistics tables, and enterprise internal warehouse account information.

[0200] In addition, for the real-time collected resource consumption data, the resource consumption data collected every day can be stored in the blockchain as a data set, and the resource consumption data can be carbon emission activity data, each data set corresponds to carbon emission activity data D i = Dt i，begin -Dt i，end , wherein Dt i，begin is the source data of the start time recorded by the ith data set, and Dt i，end is the source data of the end time recorded by the ith data set. For offline uploaded data, data sets can also be established according to time periods and stored in the blockchain to facilitate subsequent data tracing.

[0201] In practical applications, before collecting the non-confidential resource consumption data and confidential resource consumption data corresponding to the target product, the data source of the resource consumption data can be labeled, and the confidential data source and the non-confidential data source are determined according to the label, and the specific implementation manner is as follows:

[0202] The target product and the data source of the resource consumption data corresponding to the target product are displayed through a display interface, wherein the resource consumption data includes confidential resource consumption data and non-confidential resource consumption data;

[0203] Receive a labeling instruction for the data source, and determine a confidential data source and a non-confidential data source according to the labeling instruction;

[0204] Collect the confidential resource consumption data corresponding to the target product from the confidential data source, and collect the non-confidential resource consumption data corresponding to the target product from the non-confidential data source.

[0205] Wherein, the data source can be understood as the source of the collected resource consumption data, for example, for the collected electricity, the corresponding data source is the electric meter, for the collected water consumption, the corresponding data source is the water meter. For the uploaded raw material usage, the corresponding data source is the enterprise internal data system. The confidential data source can be used to indicate that the resource consumption data collected from the data source has a confidential requirement, which is confidential resource consumption data. And the non-confidential data source can be used to indicate that the resource consumption data collected from the data source does not have a confidential requirement, which is non-confidential resource consumption data.

[0206] Based on this, before collecting the confidential resource consumption data and the non-confidential resource consumption data, the target product that needs to be calculated for carbon emissions and the data source of the resource consumption data that needs to be collected can be displayed through the display interface, and a labeling instruction for the data source is received. The data source labeled with the preset confidential label is determined as a confidential data source, and the data source labeled with the preset non-confidential label is determined as a non-confidential data source, such as the data source labeled with 1 is determined as a confidential data source, and the data source labeled with 0 is determined as a non-confidential data source. The confidential resource consumption data corresponding to the target product is collected from the confidential data source, and the non-confidential resource consumption data corresponding to the target product is collected from the non-confidential data source.

[0207] In summary, by labeling the data source, the confidential resource consumption data and the non-confidential resource consumption data are distinguished, so that the real-time calculation of the confidential resource consumption data at the data collection end is facilitated, and the security of the confidential resource consumption data is further improved.

[0208] Alternatively, the confidential resource consumption data can also be determined through the attribute information of the target product, and the specific implementation manner is as follows

[0209] Collect resource consumption data, wherein the resource consumption data includes confidential resource consumption data and non-confidential resource consumption data

[0210] According to the attribute information of the target product, determine the confidential information;

[0211] According to the confidential information, the resource consumption data corresponding to the target product is parsed to determine the non-confidential resource consumption data and the confidential resource consumption data.

[0212] The attribute information of the target product may be, for example, type information of the target product, such as a food type product, a clothing type product, etc. For a food type product, its confidential information may be, for example, information related to production formula data.

[0213] Based on this, the confidential information of the target product can be determined according to the type information of the target product, and the resource consumption data can be parsed according to the confidential information, and the non-confidential resource consumption data and the confidential resource consumption data can be determined according to the parsing result.

[0214] In summary, by determining the non-confidential resource consumption data and the confidential resource consumption data according to the attribute information of the target product, different confidential requirements of different products can be met, the individualization of the confidential requirements of the products is realized, and the protection of the confidential resource consumption data of different products is further realized.

[0215] Step 604: According to the confidential resource consumption data, a first carbon emission result corresponding to the confidential resource consumption data is calculated.

[0216] In actual application, for the calculation of the confidential resource consumption data, a real-time algorithm can be set at the data collection end to perform real-time calculation during data collection, and only the first carbon emission result corresponding to the confidential resource consumption data (i.e., the carbon footprint calculation result of the confidential resource consumption data) is retained, as shown in the following formula (16).

[0217]

[0218] The confidential carbon emission source data has K items, S i,s is the i-th confidential source data in the selected calculation period, f i,s is the emission calculation factor corresponding to the i-th confidential source data, E total,s is the total emission of the K items of confidential carbon emission sources in the selected calculation period. For the confidential source data, only the total carbon emission E of all confidential emission source data is displayed at the model calculation end. total,s .

[0219] Step 606: sending the non-confidential resource consumption data and the first carbon emission result to a product carbon emission determination platform.

[0220] In summary, the above method calculates the first carbon emission result corresponding to the confidential resource consumption data at the data collection end, and only sends the first carbon emission result corresponding to the confidential resource consumption data to the product carbon emission determination platform, so as to ensure that the confidential resource consumption data cannot be leaked, thereby ensuring the security of the confidential resource consumption data.

[0221] Corresponding to the above method embodiment, the present specification also provides a product carbon emission determination device based on a block chain, which is applied to a data collection end, Figure 7 Fig. 6 shows a structure schematic diagram of another product carbon emission determination device based on a block chain provided by an embodiment of the present specification. As shown in Figure 7 The device comprises:

[0222] The collection module 702 is configured to collect non-confidential resource consumption data and confidential resource consumption data corresponding to a target product;

[0223] The calculation module 704 is configured to calculate a first carbon emission result corresponding to the confidential resource consumption data according to the confidential resource consumption data;

[0224] The sending module 706 is configured to send the non-confidential resource consumption data and the first carbon emission result to a product carbon emission determination platform.

[0225] In an optional embodiment, the collection module 702 is further configured to:

[0226] display the data sources of the target product and the resource consumption data corresponding to the target product through the display interface, wherein the resource consumption data includes confidential resource consumption data and non-confidential resource consumption data;

[0227] receive a labeling instruction for the data sources, and determine confidential data sources and non-confidential data sources according to the labeling instruction;

[0228] collect confidential resource consumption data corresponding to the target product from the confidential data sources, and collect non-confidential resource consumption data corresponding to the target product from the non-confidential data sources.

[0229] In an optional embodiment, the collection module 702 is further configured to:

[0230] collect resource consumption data, wherein the resource consumption data includes confidential resource consumption data and non-confidential resource consumption data

[0231] determine confidential information according to the attribute information of the target product;

[0232] analyze the resource consumption data corresponding to the target product according to the confidential information, to determine non-confidential resource consumption data and confidential resource consumption data.

[0233] In summary, the above device calculates the first carbon emission result corresponding to the confidential resource consumption data at the data collection end, and only sends the first carbon emission result corresponding to the confidential resource consumption data to the product carbon emission determination platform, thereby ensuring that the confidential resource consumption data cannot be leaked, and further ensuring the security of the confidential resource consumption data.

[0234] Corresponding to the above method embodiments, the present specification also provides a product carbon emission determination system based on a block chain, which comprises a data collection end and a product carbon emission determination platform, wherein,

[0235] the data collection end is configured to collect non-confidential resource consumption data and confidential resource consumption data corresponding to a target product, calculate a first carbon emission result corresponding to the confidential resource consumption data according to the confidential resource consumption data, and send the non-confidential resource consumption data and the first carbon emission result to the product carbon emission determination platform;

[0236] The product carbon emission determination platform is configured to receive non-confidential resource consumption data corresponding to a target product and a first carbon emission result; determine a carbon emission calculation model corresponding to the target product, calculate the non-confidential resource consumption data by using the carbon emission calculation model, obtain calculation process information of the non-confidential resource consumption data and a second carbon emission result; and upload the non-confidential resource consumption data, the calculation process information, the second carbon emission result and the first carbon emission result to a blockchain.

[0237] In an optional embodiment, the system further comprises a data authentication end, wherein,

[0238] The product carbon emission determination platform is configured to generate a carbon emission report according to the non-confidential resource consumption data, the calculation process information, the second carbon emission result and the first carbon emission result, and send the carbon emission report to the data authentication end.

[0239] The data authentication end is configured to receive the carbon emission report, authenticate the carbon emission report, obtain a carbon emission report authentication result, and upload the carbon emission report authentication result to the blockchain node.

[0240] As shown in Figure 8 As shown in Figure 8 FIG. 1 shows a structural schematic diagram of a product carbon emission determination system based on a blockchain according to an embodiment of the present specification.

[0241] As shown in Figure 8As shown, the system 800 includes a data collection end 801, a blockchain node 802, a product carbon emission determination platform 803, and a data authentication end 804. The data collection end 801 is configured to collect non-confidential resource consumption data and confidential resource consumption data in the production process of a product, and to perform real-time calculation on the confidential resource consumption data to obtain a first carbon emission result corresponding to the confidential resource consumption data, and to send the non-confidential resource consumption data and the first carbon emission result corresponding to the confidential resource consumption data to the product carbon emission determination platform 803. The product carbon emission determination platform 803 is configured to calculate the non-confidential resource consumption data to obtain calculation process information of the non-confidential resource consumption data and a second carbon emission result, and to establish a carbon emission calculation model. Specifically, the carbon footprint calculation model, the PCR file, and the emission factor corresponding to the target product can be obtained from the LCA database, the UPR model library, the PCR library, and the calculation standard library, and the established carbon emission calculation model can be uploaded to the UPR model library, and a carbon footprint report and a corresponding emission reduction strategy can be generated. The non-confidential resource consumption data, the first carbon emission result corresponding to the confidential resource consumption data, the second carbon emission result, and the calculation process information of the non-confidential resource consumption data are uploaded to the blockchain node 802, and the carbon footprint report is sent to the data authentication end 804. After the data authentication end 804 performs online authentication on the carbon footprint report, the authentication result is uploaded to the blockchain node 802.

[0242] In summary, the system described above receives the first carbon emission result corresponding to the non-confidential resource consumption data and the confidential resource consumption data of the target product, calculates the non-confidential resource consumption data using the carbon emission calculation model to obtain the second carbon emission result and the calculation process information of the non-confidential resource consumption data, and uploads the results to the blockchain. For confidential data, only the first carbon emission result corresponding to the confidential resource consumption data is received, and the confidential resource consumption data is not received. This reduces the labor cost while ensuring that the data that needs to be kept confidential is not leaked. It also avoids data tampering, facilitates data traceability, and further improves the accuracy of the final second carbon emission result and the first carbon emission result corresponding to the confidential resource consumption data.

[0243] The above is a schematic scheme of a product carbon emission determination system based on a blockchain according to an embodiment. It should be noted that the technical scheme of the product carbon emission determination system based on a blockchain is the same as the technical scheme of the product carbon emission determination method based on a blockchain described above. The details of the technical scheme of the product carbon emission determination system based on a blockchain that are not described in detail can be referred to the description of the technical scheme of the product carbon emission determination method based on a blockchain described above.

[0244] Figure 9A structural block diagram of a computing device 900 is shown, which is provided according to one embodiment of the present specification. The components of the computing device 900 include, but are not limited to, a memory 910 and a processor 920. The processor 920 is connected with the memory 910 through a bus 930, and a database 950 is used to save data.

[0245] The computing device 900 further includes an access device 940, which enables the computing device 900 to communicate via one or more networks 960. Examples of these networks include the Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or a combination of communication networks such as the Internet. The access device 940 can include one or more of any type of network interface (e.g., a network interface card (NIC)) such as an IEEE 802.11 Wireless Local Area Network (WLAN) wireless interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, a Near Field Communication (NFC) interface, and the like, either wired or wireless.

[0246] In one embodiment of the present application, the above-mentioned components of the computing device 900 and other components not shown in the above-mentioned components can be connected with each other, for example, through a bus. It should be understood that Figure 9 the computing device 900 can be connected with each other through a bus. It should be understood that Figure 9 the computing device structure block diagram shown is only for the purpose of example, and is not a limitation on the scope of the present application. Other components can be added or replaced as needed by those skilled in the art.

[0247] The computing device 900 can be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smartphone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other type of mobile device, or a stationary computing device such as a desktop computer or a personal computer (PC). The computing device 900 can also be a mobile or stationary server.

[0248] The processor 920 is configured to execute computer-executable instructions to implement the steps of the above-described method for determining product carbon emissions based on a blockchain.

[0249] The above describes a schematic solution of a computing device according to an embodiment. It should be noted that the technical solution of the computing device and the technical solution of the above-described method for determining product carbon emissions based on a blockchain belong to the same concept. For details of the technical solution of the computing device that are not described in detail, reference can be made to the description of the technical solution of the above-described method for determining product carbon emissions based on a blockchain.

[0250] An embodiment of the present specification further provides a computer-readable storage medium storing computer-executable instructions. The computer-executable instructions, when executed by a processor, implement the steps of the above-described method for determining product carbon emissions based on a blockchain.

[0251] The above describes a schematic solution of a computer-readable storage medium according to an embodiment. It should be noted that the technical solution of the storage medium and the technical solution of the above-described method for determining product carbon emissions based on a blockchain belong to the same concept. For details of the technical solution of the storage medium that are not described in detail, reference can be made to the description of the technical solution of the above-described method for determining product carbon emissions based on a blockchain.

[0252] An embodiment of the present specification further provides a computer program. When the computer program is executed in a computer, the computer program causes the computer to perform the steps of the above-described method for determining product carbon emissions based on a blockchain.

[0253] The above describes a schematic solution of a computer program according to an embodiment. It should be noted that the technical solution of the computer program and the technical solution of the above-described method for determining product carbon emissions based on a blockchain belong to the same concept. For details of the technical solution of the computer program that are not described in detail, reference can be made to the description of the technical solution of the above-described method for determining product carbon emissions based on a blockchain.

[0254] The above-described embodiments of the application have several aspects, no single one of which is solely responsible for the application's desirable attributes. Without limiting the scope of the application as expressed by the claims which follow, some further embodiments make these aspects even more useful. Other embodiments can result in less desirable attributes.

[0255] The computer readable medium can include any entity or apparatus capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, software distribution medium, etc. It should be noted that the computer readable medium can include appropriate contents according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0256] It should be noted that for the foregoing method embodiments, the acts described can be performed in a different order than that described, and that various interlocking and / or parallel configurations are also possible according to the certain embodiments of the present specification. Furthermore, certain of the acts can be optional depending upon the particular embodiment of the method. The scope of the method of each method claim should not be limited to the specific embodiments set forth herein, but should be given the full scope of its corresponding jurisdictional patent claims and any equivalents thereof.

[0257] In the above embodiments, the description of each embodiment is focused on different aspects, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0258] The above disclosed preferred embodiments of the present specification are only used to help explain the present specification. Alternative embodiments do not describe all the details and do not limit the application to the specific embodiments described. Obviously, according to the content of the embodiments of the present specification, many modifications and changes can be made. The present specification selects and describes these embodiments in order to better explain the principles and practical applications of the embodiments of the present specification, so that those skilled in the art can well understand and use the present specification. The present specification is limited by the claims and their full scope and equivalents.

Claims

1. A blockchain-based product carbon emission determination method, comprising: receiving non-confidential resource consumption data corresponding to a target product and first carbon emission results corresponding to confidential resource consumption data sent by a data collection end; determining a carbon emission calculation model corresponding to the target product, calculating the non-confidential resource consumption data using the carbon emission calculation model, and obtaining calculation process information of the non-confidential resource consumption data and second carbon emission results; determining at least one dimension influence factor corresponding to the target product; calculating the sensitivity of the second carbon emission results under the condition that the influence factors of different dimensions are changed, and selecting a target influence factor whose sensitivity reaches a preset sensitivity threshold from the calculated sensitivities; generating a target emission reduction strategy according to the target influence factor; generating a display interface and a carbon emission report containing the second carbon emission results and the first carbon emission results using a preset report template according to the non-confidential resource consumption data, the calculation process information, the second carbon emission results, and the first carbon emission results, wherein the carbon emission report is used for authentication by an authentication end to obtain an authentication result; uploading the non-confidential resource consumption data, the calculation process information, the second carbon emission results, and the first carbon emission results to a blockchain, wherein the data uploaded to the blockchain is divided into a source data layer, a carbon emission set layer, a carbon footprint data layer, and a carbon footprint authentication layer, the data of the source data layer comes from the data collection end, the data of the carbon emission set layer comes from the data stored in the carbon emission calculation model, the data of the carbon footprint data layer comes from the result value calculated by the carbon emission calculation model, the carbon footprint data layer includes authentication information of the authentication result, and each layer can trace the data of the previous layer. 2.The method of claim 1, wherein the calculating the non-confidential resource consumption data using the carbon emission calculation model to obtain the calculation process information of the non-confidential resource consumption data and the second carbon emission results comprises: dividing the non-confidential resource consumption data to obtain non-confidential resource consumption data of at least two stages; performing data checking on the non-confidential resource consumption data of a first stage to obtain checked non-confidential resource consumption data, wherein the first stage is any one of the at least two stages; inputting the checked non-confidential resource consumption data into the carbon emission calculation model to obtain the calculation process information of the non-confidential resource consumption data and the second carbon emission results. 3.The method of claim 2, wherein the non-confidential resource consumption data of the first stage comprises at least one data set. Correspondingly, the performing data checking on the non-confidential resource consumption data of the first stage to obtain checked non-confidential resource consumption data comprises: According to the probability distribution model of the resource consumption data corresponding to the target product, a credibility probability of a first data set is calculated, wherein the first data set is any one of the at least one data set, and the first data set includes non-confidential resource consumption data of a sub-stage in the first stage; In a case where the credibility probability of the first data set is less than a preset credibility threshold, the first data set is taken as an abnormal data set; In a case where the number of the abnormal data sets is less than a preset number threshold, the abnormal data sets are deleted, and data sets in the at least one data set except the first data set are taken as non-confidential resource consumption data after verification; In a case where the number of the abnormal data sets is greater than the preset number threshold, the non-confidential resource consumption data of the first stage is deleted, and data in the non-confidential resource consumption data of the at least two stages except the non-confidential resource consumption data of the first stage is taken as non-confidential resource consumption data after verification.

4. The method of claim 2, wherein the inputting the non-confidential resource consumption data after verification into the carbon emission calculation model to obtain the calculation process information of the non-confidential resource consumption data and a second carbon emission result comprises: determining an influence factor corresponding to the target product; using the carbon emission calculation model, calculating a second carbon emission result according to the influence factor and the non-confidential resource consumption data after verification, and generating the calculation process information of the non-confidential resource consumption data.

5. The method of claim 1, wherein the generating a target emission reduction strategy according to the target influence factor comprises: determining at least one initial emission reduction strategy according to the target influence factor; determining a cost value corresponding to a first initial emission reduction strategy, wherein the first initial emission reduction strategy is any one of the at least one initial emission reduction strategy; taking an initial emission reduction strategy with a minimum cost value as the target emission reduction strategy.

6. The method of claim 1, further comprising, after the obtaining the calculation process information of the non-confidential resource consumption data and the second carbon emission result: generating a carbon emission set corresponding to the target product according to the non-confidential resource consumption data, the calculation process information of the non-confidential resource consumption data, the second carbon emission result, and the first carbon emission result; and uploading the carbon emission set to the blockchain.

7. A product carbon emission determination method based on a blockchain, applied to a data collection end, comprising: collecting non-confidential resource consumption data and confidential resource consumption data corresponding to a target product; calculating a first carbon emission result corresponding to the confidential resource consumption data according to the confidential resource consumption data; and sending the non-confidential resource consumption data and the first carbon emission result to a product carbon emission determination platform, wherein the non-confidential resource consumption data is used to calculate by using a carbon emission calculation model corresponding to the target product to obtain calculation process information of the non-confidential resource consumption data and a second carbon emission result; the second carbon emission result is used to calculate the sensitivity of different dimensions of influence factors, and a target influence factor with a sensitivity reaching a preset sensitivity threshold is selected from the calculated sensitivities, the target influence factor is used to generate a target emission reduction strategy; the non-confidential resource consumption data, the calculation process information, the second carbon emission result and the first carbon emission result are used to generate a display interface and a carbon emission report containing the second carbon emission result and the first carbon emission result based on a preset report template, the carbon emission report is used for authentication by an authentication end to obtain an authentication result; the non-confidential resource consumption data, the calculation process information, the second carbon emission result and the first carbon emission result are also used to upload to a blockchain; the data uploaded to the blockchain is divided into a source data layer, a carbon emission set layer, a carbon footprint data layer and a carbon footprint authentication layer, the data of the source data layer comes from the data collection end, the data of the carbon emission set layer comes from the data stored in the carbon emission calculation model, the data of the carbon footprint data layer comes from the result value calculated by the carbon emission calculation model, the carbon footprint data layer includes authentication information of the authentication result, and each layer can trace back to the data of a previous layer.

8. The method of claim 7, wherein the collecting of the non-confidential resource consumption data and the confidential resource consumption data corresponding to the target product comprises: displaying, through a display interface, a target product and data sources of resource consumption data corresponding to the target product, wherein the resource consumption data comprises confidential resource consumption data and non-confidential resource consumption data; receiving a labeling instruction for the data sources and determining confidential data sources and non-confidential data sources according to the labeling instruction; collecting confidential resource consumption data corresponding to the target product from the confidential data sources and collecting non-confidential resource consumption data corresponding to the target product from the non-confidential data sources.

9. The method of claim 7, wherein the collecting of the non-confidential resource consumption data and the confidential resource consumption data corresponding to the target product comprises: collecting resource consumption data, wherein the resource consumption data comprises confidential resource consumption data and non-confidential resource consumption data; determining confidential information according to attribute information of the target product; analyzing resource consumption data corresponding to the target product according to the confidential information to determine non-confidential resource consumption data and confidential resource consumption data.

10. A product carbon emission determination system based on a blockchain, comprising a data collection end and a product carbon emission determination platform, wherein The data collection end is configured to collect non-confidential resource consumption data and confidential resource consumption data corresponding to a target product, calculate a first carbon emission result corresponding to the confidential resource consumption data according to the confidential resource consumption data, and send the non-confidential resource consumption data and the first carbon emission result to the product carbon emission determination platform. The product carbon emission determination platform is configured to receive the non-confidential resource consumption data corresponding to the target product and the first carbon emission result sent by the data collection end. A carbon emission calculation model corresponding to the target product is determined, the non-confidential resource consumption data is calculated by using the carbon emission calculation model, and calculation process information of the non-confidential resource consumption data and a second carbon emission result are obtained. At least one influence factor of a dimension corresponding to the target product is determined. The sensitivity of the second carbon emission result is calculated under the condition that the influence factors of different dimensions are changed, and a target influence factor whose sensitivity reaches a preset sensitivity threshold is selected from the calculated sensitivities. A target emission reduction strategy is generated according to the target influence factor. A display interface and a carbon emission report containing the second carbon emission result and the first carbon emission result are generated by using a preset report template according to the non-confidential resource consumption data, the calculation process information, the second carbon emission result, and the first carbon emission result, wherein the carbon emission report is used for authentication by an authentication end to obtain an authentication result; and the non-confidential resource consumption data, the calculation process information, the second carbon emission result, and the first carbon emission result are uploaded to a blockchain, wherein the data uploaded to the blockchain is divided into a source data layer, a carbon emission set layer, a carbon footprint data layer, and a carbon footprint authentication layer, the data of the source data layer comes from the data collection end, the data of the carbon emission set layer comes from the data stored in the carbon emission calculation model, the data of the carbon footprint data layer comes from the result value calculated by the carbon emission calculation model, the carbon footprint data layer includes authentication information of the authentication result, and each layer can trace the data of a previous layer.

11. A computing device comprising: a memory and a processor; the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, which, when executed by the processor, implement the steps of the method in any one of claims 1 to 9.

12. A computer readable storage medium storing computer executable instructions, which, when executed by a processor, implement the steps of the method in any one of claims 1 to 9.

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