CN116451882B - Method for predicting carbon emission and related equipment - Google Patents

Method for predicting carbon emission and related equipment Download PDF

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CN116451882B
CN116451882B CN202310718556.3A CN202310718556A CN116451882B CN 116451882 B CN116451882 B CN 116451882B CN 202310718556 A CN202310718556 A CN 202310718556A CN 116451882 B CN116451882 B CN 116451882B
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CN116451882A (en
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罗雅愉
蒋抱阳
孙超
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Industrial Fulian Hangzhou Data Technology Co ltd
Shenzhen Fulian Fugui Precision Industry Co Ltd
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Shenzhen Fulian Fugui Precision Industry Co Ltd
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Abstract

The application provides a method for predicting carbon emission and related equipment, wherein the method comprises the following steps: acquiring associated data of a product; when the associated data has a missing value, acquiring historical data corresponding to the missing value; inputting the historical data into a preset prediction model to obtain prediction data; according to a preset mapping rule, mapping data corresponding to the associated data are obtained from a preset carbon emission factor database; and determining a predicted result of the carbon emission of the whole life cycle of the product according to the predicted data, the associated data and the mapping data. The application can improve the accuracy of predicting the carbon emission.

Description

Method for predicting carbon emission and related equipment
Technical Field
The application relates to the technical field of energy conservation and environmental protection, in particular to a carbon emission prediction method and related equipment.
Background
The product carbon footprint (Product Carbon Footprint, PCF) refers to the carbon emissions of a product or service system over the life cycle. Currently, commonly used methods for calculating the carbon footprint include input-output analysis, process analysis, lifecycle assessment (Life Cycle Accessment, LCA) and implementation of lifecycle impact assessment (Life Cycle Impact Accessment, LCIA) methods, specifically as follows:
the input-output analysis method is mainly used for calculating the carbon emission result according to currency units and substance units. However, a plurality of different product types exist in the same production line and a single batch, the different product types relate to different technological processes, the average apportioned calculation result can cause calculation errors, and secondly, the calculation result is based on the carbon footprint of a single factory, industry-level data are obtained, the condition of a product cannot be known more accurately, and the accuracy of the product data is not evaluated. The limitation of the process analysis method is that secondary data corresponding to the original data cannot be obtained, and the credibility of the carbon emission result is affected. Therefore, input-output analysis and process analysis lead to poor reliability of model calculation in a calculation model of the carbon footprint, and secondly, the carbon flow coverage rate of the calculation mapping relation is low. The parallel development and accelerated development of LCAs and LCIAs results in inconsistencies and lack of operability, resulting in robust compromise of the carbon footprint assessment calculation model.
All the above methods lack evaluation on the integrity of data collection, so that the final calculation result is not accurate enough, and the carbon emission condition of the product cannot be known accurately.
Disclosure of Invention
The embodiment of the application discloses a carbon emission prediction method and related equipment, which solve the technical problem that the integrity of carbon emission data cannot be evaluated.
The application provides a method for predicting carbon emission, which comprises the following steps: acquiring associated data of a product; when the associated data has a missing value, acquiring historical data corresponding to the missing value; inputting the historical data into a preset prediction model to obtain prediction data; according to a preset mapping rule, mapping data corresponding to the associated data are obtained from a preset carbon emission factor database; and determining a predicted result of the carbon emission of the whole life cycle of the product according to the predicted data, the associated data and the mapping data.
In some optional embodiments, the obtaining, in a preset carbon emission factor database, mapping data corresponding to the association data includes: according to a preset splitting rule, acquiring a plurality of substance names in the associated data; and when any substance name does not exist in the original database, acquiring data corresponding to the any substance name from the carbon emission factor database as the mapping data.
In some optional embodiments, the acquiring data corresponding to the arbitrary substance name from the carbon emission factor database as the mapping data includes: acquiring a substance code from any substance name when the substance name corresponding to the any substance name does not exist in the carbon emission factor database; and when the data matched with the substance codes exists in the carbon emission factor database, the data matched with the substance codes is used as the mapping data.
In some alternative embodiments, the method for predicting carbon emissions further comprises: determining semantic similar words of the substance names when data matched with the substance codes do not exist in the carbon emission factor database; and when the semantic similar words exist in the carbon emission factor database, the data matched with the semantic similar words is used as the mapping data.
In some alternative embodiments, the method for predicting carbon emissions further comprises: and updating the mapping rule when the semantic similar words do not exist in the carbon emission factor database.
In some alternative embodiments, the method for predicting carbon emissions further comprises: when any substance name exists in the original database, acquiring a sub-interval list corresponding to the any substance name; traversing the data of the sub interval list, and acquiring corresponding mapping data from the carbon emission factor database according to the mapping rule when any data in the sub interval list does not exist in the original database.
In some alternative embodiments, the method for predicting carbon emissions includes: and when the correlation data does not have the missing value, obtaining the prediction result according to the correlation data and the mapping data.
In some alternative embodiments, the predicting method of the carbon emission amount, the obtaining the associated data of the product includes: and carrying out normalization processing on the acquired data to obtain the associated data.
The application also provides an electronic device comprising a processor and a memory, the processor being adapted to implement the method of predicting carbon emissions when executing a computer program stored in the memory.
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of predicting carbon emissions.
According to the method for predicting the carbon emission, when the lack value of the associated data is determined, the history data and the preset prediction model are utilized to obtain the predicted data, the mapping data are obtained from the preset carbon emission factor database so as to ensure the integrity of the obtained data, and the carbon emission in the whole life cycle is calculated based on the obtained and calculated data (the predicted data, the associated data and the mapping data), so that the accuracy of predicting the carbon emission can be improved to a certain extent.
Drawings
Fig. 1 is an application environment configuration diagram of a method of predicting carbon emission provided by an embodiment of the present application.
Fig. 2 is a flowchart of a method of predicting carbon emission provided in an embodiment of the present application.
Fig. 3 is a schematic flow chart of a mapping rule according to an embodiment of the present application.
Detailed Description
For ease of understanding, a description of some of the concepts related to the embodiments of the application are given by way of example for reference.
In the present application, "at least one" means one or more, and "a plurality" means two or more. "and/or", describes an association relationship of an association object, and the representation may have three relationships, for example, a and/or B may represent: a alone, a and B together, and B alone, wherein a, B may be singular or plural. The terms "first," "second," "third," "fourth" and the like in the description and in the claims and drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.
The product carbon footprint (Product Carbon Footprint, PCF) refers to the carbon emissions of a product or service system over the life cycle. Currently, commonly used methods for calculating the carbon footprint include input-output analysis, process analysis, lifecycle assessment (Life Cycle Accessment, LCA) and implementation of lifecycle impact assessment (Life Cycle Impact Accessment, LCIA) methods, specifically as follows:
the input-output analysis method is mainly used for calculating the carbon emission result according to currency units and substance units. However, a plurality of different product types exist in the same production line and a single batch, the different product types relate to different technological processes, the average apportioned calculation result can cause calculation errors, and secondly, the calculation result is based on the carbon footprint of a single factory, industry-level data are obtained, the condition of a product cannot be known more accurately, and the accuracy of the product data is not evaluated. The limitation of the process analysis method is that secondary data corresponding to the original data cannot be obtained, and the credibility of the carbon emission result is affected. Therefore, input-output analysis and process analysis lead to poor reliability of model calculation in a calculation model of the carbon footprint, and secondly, the carbon flow coverage rate of the calculation mapping relation is low. The parallel development and accelerated development of LCAs and LCIAs results in inconsistencies and lack of operability, resulting in robust compromise of the carbon footprint assessment calculation model.
All the above methods lack evaluation on the integrity of data collection, so that the final calculation result is not accurate enough, and the carbon emission condition of the product cannot be known accurately.
In order to solve the above-mentioned technical problems and to better understand the carbon emission prediction method and the related apparatus provided in the embodiments of the present application, the application scenario of the carbon emission prediction method of the present application is first described below.
Fig. 1 is an application environment configuration diagram of a method of predicting carbon emission provided by an embodiment of the present application. The method for predicting the carbon emission amount provided by the embodiment of the application is applied to the electronic equipment 1, and the electronic equipment 1 comprises, but is not limited to, a memory 12 and at least one processor 13 which are in communication connection with each other through a communication bus 11.
The electronic device 1 may be a computer, a mobile phone, a tablet computer, a personal digital assistant (Personal Digital Assistant, PDA) or the like.
The schematic diagram 1 is only an example of the electronic device 1 and does not constitute a limitation of the electronic device 1, and may comprise more or less components than shown, or may be combined with certain components, or different components, e.g. the electronic device 1 may also comprise input and output devices, network access devices, etc.
Referring to fig. 2, fig. 2 is a flowchart of a method for predicting carbon emission according to an embodiment of the present application, which is applied to an electronic device (e.g., the electronic device 1 of fig. 1). The order of the steps in the flowchart may be changed and some steps may be omitted according to various needs.
S21, acquiring the associated data of the product.
In some embodiments of the present application, the product (e.g., refrigerator) may generate a number of associated data during and after manufacture into a finished product, which may also be referred to as base data, such as the parts used by the user to generate the product, the amount of electricity and water consumed during the manufacturing process, and the gas (e.g., carbon dioxide) released by the product during actual operation.
The associated data can also comprise energy consumption data acquired in real time, the energy consumption data can be acquired in real time through the intelligent ammeter, and the associated data can also comprise data of products in the manufacturing process, which are acquired from an industry database and a literature database.
Machine overheating, abnormal manual operation and the like in the industrial production process can cause abnormal values or noise data in the collected data, so that the collected data needs to be subjected to data cleaning, namely, normalization (min-max normalization) is performed on the collected data to eliminate the influence of the abnormal values and the noise data, and is as follows:
in the method, in the process of the application,representing normalized data, i.e. associated data,/->Represents the maximum value in the acquired data, +.>Representing the minimum value in the acquired data, for example>Attribute value representing data in production process, +.>Represents the maximum value (1) of the standard deviation +.>The minimum value (0) of the standard deviation is represented.
Normalizing the acquired data to 0-1, and eliminating the data exceeding the range of 0-1 to obtain the associated data.
After the associated data of the product is obtained, an original database can be established to store the associated data.
S22, when the associated data has a missing value, historical data corresponding to the missing value is acquired.
In some embodiments of the present application, after the associated data of the product is obtained, it is determined whether a missing value exists, where the missing value refers to that a value of a certain or certain attributes in the existing data set is incomplete, for example, in a process of collecting energy consumption data of the product in real time, due to a power failure, the smart meter cannot completely collect the data of the product in a production process, for example, in 10: 00-11: power failure during 00 hours, 10 can not be obtained from smart electric meter: 00-11: 00 for this period of time, resulting in a loss of energy consumption data.
In another example, since the smart meter is not installed in the part of the workshops and the process flow for producing the product, the energy consumption data corresponding to the product cannot be obtained, that is, it is determined that the associated data has a missing value.
After determining that the associated data has a missing value, acquiring historical data corresponding to the missing value, including: the object of the missing value and the time when the missing value exists are acquired, for example, the object of the missing value is energy consumption, and the time when the missing value is 10: 00-11: 00, obtaining products corresponding to the same model from an original database, wherein the products are 10: 00-11: energy consumption data during period 00, wherein this time period may be any of the historical days.
S23, inputting the historical data into a preset prediction model to obtain prediction data.
In some embodiments of the application, the predictive model may include a combination of any one or more of Long Short-Term Memory (LSTM), recurrent neural network (Recurrent Neural Network, RNN), convolutional neural network (Convolutional Neural Networks, CNN), and the like type-built models.
Modeling training data of a Random tree according to nodes by adopting a Random Forest algorithm (Random Forest), wherein the training data can be related data of products of the same model, and inputting the related data of the same model into one or more networks of LSTM, RNN and CNN in a decision tree for training to obtain a training result. And constructing a characteristic curve (Receiver Operating Characteristic Curve, ROC curve) according to the training result, adjusting training data of the training model to obtain a plurality of characteristic curves, and determining a prediction model from the plurality of characteristic curves. And inputting the historical data into a prediction model for prediction to obtain prediction data.
According to the embodiment, the predicted data can be obtained through the historical data so as to fill the missing value, and the influence of substances and energy sources in the industrial production process on the life cycle is predicted and estimated.
S24, according to a preset mapping rule, mapping data corresponding to the associated data are obtained from a preset carbon emission factor database.
In some embodiments of the present application, a corresponding mapping rule may be set in advance for the product, and a preset splitting rule may be combined to determine whether corresponding mapping data needs to be obtained from the carbon emission factor database. The splitting rule may include splitting a substance name corresponding to a product, and the carbon emission factor database may include a life cycle impact assessment method flow database, a manifest database, and a product life cycle manifest database.
Splitting the substance names in the associated data to obtain a plurality of substance names in the associated data, and if no data matched with any substance name exists in the original database storing the associated data, obtaining corresponding data from the carbon emission factor database as mapping data.
When it is determined that there is no data matching any one of the substance names in the raw database, the data corresponding to any one of the substance names is acquired from the carbon emission factor database as mapping data.
For example, in some examples, the substance name a may be obtained in the carbon emission factor database when it is determined that there is no substance name in the raw database that matches the substance name a.
Because the names of the substances in different databases are inconsistent, errors such as inaccurate identification of the names of the substances are easy to occur, the matching degree of the two can be calculated when the data matched with the names of the substances is obtained, and when the matching degree reaches the preset matching degree, the data meeting the matching requirement is used as mapping data.
When it is determined that there is no substance name matching any substance name (e.g., substance name a) in the carbon emission factor database, a substance code corresponding to any substance name, for example, substance code corresponding to substance name a, may be acquired, and when there is data matching the substance code in the carbon emission factor database, data corresponding to substance name a is taken as map data. Wherein the substance code may be a CAS (Chemical Abstracts Service) code.
If the data matched with the substance codes does not exist in the carbon emission factor database, determining the semantic similar words of the substance names (such as the substance name A), continuously searching in the carbon emission factor database, and taking the data matched with the semantic similar words as mapping data when the semantic similar words exist in the carbon emission factor database. Wherein, the semantic similarity word can be a synonym or a paraphrasing of a substance name.
If the semantic similar words do not exist in the carbon emission factor database, updating a preset mapping rule so as to search corresponding data in the carbon emission factor database again by adopting the updated mapping rule as mapping data.
If there are substance names matching with the plurality of substance names in the original database storing the associated data, obtaining a sub-interval list corresponding to each substance name in the associated data, where the sub-interval list may include product quality, usage information of the product, extract name, production route name, processing technology, and mixing type, and the naming of the sub-interval list generally includes: substance name, separator, usage information/product quality/process instructions related to the base stream, substance stream content (e.g., 25% iron content).
And sequentially matching the list data in the sub interval list, and executing a mapping rule when any data in the sub interval list is not in the original database, and acquiring corresponding mapping data from the carbon emission factor database, wherein when the mapping data corresponding to the sub interval list is searched, the matching and the searching can be preferentially performed according to the geographic position of the carbon emission factor database.
Fig. 3 is a schematic flow chart of a mapping rule provided in the embodiment of the present application, as shown in fig. 3, for the implementation of the mapping rule in combination with the above analysis, refer to step S31 to step S37.
S31, splitting the substance names in the associated data to obtain a plurality of substance names in the associated data.
S32, if the original database storing the related data does not have any substance name matched with any substance name, executing step S33, otherwise, executing step S37.
And if the original database storing the associated data contains the substance names matched with any substance name, taking the associated data as mapping data, and stopping executing the mapping rule.
S33, acquiring the corresponding substance name in the carbon emission factor database, executing step S34 when no data matched with the substance name exists in the carbon emission factor database, otherwise executing step S37.
When there is data matching the substance name in the carbon emission factor database, the data matching the substance name is taken as mapping data, and execution of the mapping rule is stopped.
S34, acquiring a substance code corresponding to the substance name, if no data matched with the substance code exists in the carbon emission factor database, executing the step S35, otherwise, executing the step S37.
If the data matched with the substance codes exist in the carbon emission factor database, the data matched with the substance codes are used as mapping data, and the mapping rule is stopped.
And S35, acquiring semantic synonyms or paraphraseology corresponding to the material names, if no data matched with the semantic synonyms or the paraphraseology exists in the carbon emission factor database, executing the step S36, otherwise, executing the step S37.
And if the data matched with the semantic synonym or the paranym exists in the carbon emission factor database, taking the data matched with the semantic synonym or the paranym as mapping data, and stopping executing the mapping rule.
S36, updating the mapping rule.
S37, stopping executing the mapping rule.
The application can obtain complete data by presetting corresponding mapping rules and splitting rules, and can be compatible with data in different databases (such as LCA and LCIA) so as to calculate the carbon emission of the product and improve the accuracy of data acquisition.
S25, determining a prediction result of the carbon emission of the whole life cycle of the product according to the prediction data, the association data and the mapping data.
In some embodiments of the present application, after obtaining the prediction data and the mapping data, the carbon emission amount of the whole life cycle of the product is predicted according to the prediction data, the association data and the mapping data, wherein the carbon emission amount of the whole life cycle can also be expressed as the carbon footprint of the whole life cycle, and the carbon footprint refers to the collection of greenhouse gas emissions caused by enterprises, activities, products or individuals through transportation, food production and consumption, various production processes and the like. The carbon footprint includes an amount of carbon emissions at a plurality of stages within the life cycle, which may include, but are not limited to: a raw material stage, a manufacturing stage, a logistics stage, a use stage and a recovery stage.
The production data and the recovery data corresponding to the raw material stage may be the number of parts included in the target product, the number of packaging materials for the parts, the recovery rate of the corresponding material for manufacturing the parts, and the like.
The process flow data corresponding to the manufacturing stage may be the amount of waste during the manufacture of the target product, the emission factor of the waste, the greenhouse gases during the process, the number of all devices contained in the target product, etc.
The vehicle transportation data corresponding to the logistics stage can be the number of different types of transportation modes used by the target product, the number of manufacturing materials, parts and the like in the transportation process, the transportation distance and the like.
The usage data corresponding to the usage stage may be electric power in the usage mode, a usage time of the target product in the service life, electric power consumed by the target product in the maintenance process, an estimated value of direct greenhouse gas of the target product in the usage process, and the like.
The process data corresponding to the recovery stage may be the amount of waste treatment during the treatment of the target product, the amount of energy consumed during the coarse-grained treatment of the target product.
In another embodiment of the present application, if the absence of the associated data is not detected when the associated data is acquired in real time, the predicted data may not be acquired, and the carbon emission of the full life cycle of the product may be predicted based on the mapped data and the associated data.
When the correlation data has missing values and all the correlation data exist in the original database, the carbon emission of the whole life cycle of the product can be predicted according to the prediction data and the correlation data.
The method and the system can be used for determining the boundary of the full life cycle of the manufacturing industry product and improving the accuracy of the carbon emission (or carbon footprint) calculation model, determining whether the missing value exists or not by acquiring corresponding associated data in real time, such as workshop information, and solving the problem of incomplete data.
With continued reference to fig. 1, in this embodiment, the memory 12 may be an internal memory of the electronic device 1, that is, a memory built in the electronic device 1. In other embodiments, the memory 12 may also be an external memory of the electronic device 1, i.e. a memory external to the electronic device 1.
In some embodiments, the memory 12 is used to store program code and various data and to enable high-speed, automatic access to programs or data during operation of the electronic device 1.
The memory 12 may include random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid state memory device.
In one embodiment, the processor 13 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any other conventional processor or the like.
The program code and various data in the memory 12 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product. Based on such understanding, the present application may also be implemented by a computer program for instructing relevant hardware to implement all or part of the procedures in the methods of the above embodiments, such as the method for predicting carbon emission, the computer program being stored in a computer readable storage medium, and the computer program, when executed by a processor, implementing the steps of the respective method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), or the like.
It will be appreciated that the above-described division of modules into a logical function division may be implemented in other ways. In addition, each functional module in the embodiments of the present application may be integrated in the same processing unit, or each module may exist alone physically, or two or more modules may be integrated in the same unit. The integrated modules may be implemented in hardware or in hardware plus software functional modules.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present application without departing from the spirit and scope of the technical solution of the present application.

Claims (8)

1. A method for predicting carbon emissions, the method comprising:
acquiring associated data of a product;
when the associated data has a missing value, acquiring historical data corresponding to the missing value, wherein the historical data comprises an object for acquiring the missing value and the time for having the missing value;
inputting the historical data into a preset prediction model to obtain prediction data;
according to a preset mapping rule, mapping data corresponding to the associated data are obtained in a preset carbon emission factor database, wherein the mapping data comprise a plurality of substance names in the associated data according to a preset splitting rule, when any substance name does not exist in an original database, the data corresponding to the any substance name are obtained from the carbon emission factor database and serve as the mapping data, when the data corresponding to the any substance name is obtained from the carbon emission factor database, the data conforming to the matching degree are taken as the mapping data, and when the substance name corresponding to the any substance name does not exist in the carbon emission factor database, the substance code is obtained from the any substance name; when the data matched with the substance codes exist in the carbon emission factor database, the data matched with the substance codes are used as the mapping data;
and determining a predicted result of the carbon emission of the whole life cycle of the product according to the predicted data, the associated data and the mapping data.
2. The method for predicting carbon emissions of claim 1, wherein the method further comprises:
determining semantic similar words of the substance names when data matched with the substance codes do not exist in the carbon emission factor database;
and when the semantic similar words exist in the carbon emission factor database, the data matched with the semantic similar words is used as the mapping data.
3. The method for predicting carbon emissions of claim 2, wherein the method further comprises:
and updating the mapping rule when the semantic similar words do not exist in the carbon emission factor database.
4. The method for predicting carbon emissions of claim 1, wherein the method further comprises:
when any substance name exists in the original database, acquiring a sub-interval list corresponding to the any substance name;
traversing the data of the sub interval list, and acquiring corresponding mapping data from the carbon emission factor database according to the mapping rule when any data in the sub interval list does not exist in the original database.
5. The method for predicting carbon emissions of any one of claims 1 to 4, comprising:
and when the correlation data does not have the missing value, obtaining the prediction result according to the correlation data and the mapping data.
6. The method for predicting carbon emissions as claimed in any one of claims 1 to 4, wherein the acquiring the associated data of the product comprises:
and carrying out normalization processing on the acquired data to obtain the associated data.
7. An electronic device comprising a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the method of predicting carbon emissions according to any one of claims 1 to 6.
8. A computer readable storage medium storing at least one instruction that when executed by a processor implements the method of predicting carbon emissions according to any one of claims 1 to 6.
CN202310718556.3A 2023-06-16 2023-06-16 Method for predicting carbon emission and related equipment Active CN116451882B (en)

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CN113435054A (en) * 2021-07-06 2021-09-24 天津水泥工业设计研究院有限公司 Carbon emission assessment method and system based on digital twin model
CN114462133A (en) * 2022-04-12 2022-05-10 天津水泥工业设计研究院有限公司 Digital twin technology equipment product-based carbon footprint digital accounting method and system
CN115796001A (en) * 2022-11-01 2023-03-14 深圳市中融数字科技有限公司 Data missing value prediction method and system based on seq2seq

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Publication number Priority date Publication date Assignee Title
CN113435054A (en) * 2021-07-06 2021-09-24 天津水泥工业设计研究院有限公司 Carbon emission assessment method and system based on digital twin model
CN114462133A (en) * 2022-04-12 2022-05-10 天津水泥工业设计研究院有限公司 Digital twin technology equipment product-based carbon footprint digital accounting method and system
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